From 262e6bfdfb62d7507e777b1b3d293276b1211b85 Mon Sep 17 00:00:00 2001 From: bushuhui Date: Sun, 26 Jul 2020 19:55:29 +0800 Subject: [PATCH] Add English version --- 0_python/1_Basics_EN.ipynb | 1015 ++++ 0_python/2_Print_Statement_EN.ipynb | 587 +++ 0_python/3_Data_Structure_1_EN.ipynb | 1999 ++++++++ 0_python/4_Data_Structure_2_EN.ipynb | 1180 +++++ 0_python/5_Control_Flow_EN.ipynb | 693 +++ 0_python/6_Function_EN.ipynb | 1075 ++++ 0_python/7_Class_EN.ipynb | 1308 +++++ 0_python/README_EN.md | 36 + .../1-numpy_tutorial_EN.ipynb | 4887 +++++++++++++++++++ .../2-matplotlib_simple_tutorial_EN.ipynb | 467 ++ ...hon_notebook.ipynb => 3-ipython_notebook.ipynb} | 0 .../3-ipython_notebook_EN.ipynb | 338 ++ ...scipy_tutorial.ipynb => 4-scipy_tutorial.ipynb} | 0 .../4-scipy_tutorial_EN.ipynb | 2423 +++++++++ ...sympy_tutorial.ipynb => 5-sympy_tutorial.ipynb} | 0 .../5-sympy_tutorial_EN.ipynb | 2534 ++++++++++ 2_knn/knn_classification.ipynb | 171 +- 2_knn/knn_classification_EN.ipynb | 341 ++ 3_kmeans/1-k-means.ipynb | 2 +- 3_kmeans/1-k-means_EN.ipynb | 997 ++++ 3_kmeans/2-kmeans-color-vq.ipynb | 2 +- 3_kmeans/2-kmeans-color-vq_EN.ipynb | 241 + 3_kmeans/3-ClusteringAlgorithms.ipynb | 2 +- 3_kmeans/3-ClusteringAlgorithms_EN.ipynb | 224 + 4_logistic_regression/1-Least_squares.ipynb | 2 +- 4_logistic_regression/1-Least_squares_EN.ipynb | 5154 ++++++++++++++++++++ 4_logistic_regression/2-Logistic_regression.ipynb | 2 +- .../2-Logistic_regression_EN.ipynb | 705 +++ .../3-PCA_and_Logistic_Regression.ipynb | 2 +- .../3-PCA_and_Logistic_Regression_EN.ipynb | 279 ++ 5_nn/1-Perceptron_EN.ipynb | 256 + 5_nn/2-mlp_bp_EN.ipynb | 4929 +++++++++++++++++++ 5_nn/3-softmax_ce_EN.ipynb | 176 + README_EN.md | 110 + tips/InstallPython_EN.md | 89 + 35 files changed, 32097 insertions(+), 129 deletions(-) create mode 100644 0_python/1_Basics_EN.ipynb create mode 100644 0_python/2_Print_Statement_EN.ipynb create mode 100644 0_python/3_Data_Structure_1_EN.ipynb create mode 100644 0_python/4_Data_Structure_2_EN.ipynb create mode 100644 0_python/5_Control_Flow_EN.ipynb create mode 100644 0_python/6_Function_EN.ipynb create mode 100644 0_python/7_Class_EN.ipynb create mode 100644 0_python/README_EN.md create mode 100644 1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial_EN.ipynb create mode 100644 1_numpy_matplotlib_scipy_sympy/2-matplotlib_simple_tutorial_EN.ipynb rename 1_numpy_matplotlib_scipy_sympy/{ipython_notebook.ipynb => 3-ipython_notebook.ipynb} (100%) create mode 100644 1_numpy_matplotlib_scipy_sympy/3-ipython_notebook_EN.ipynb rename 1_numpy_matplotlib_scipy_sympy/{3-scipy_tutorial.ipynb => 4-scipy_tutorial.ipynb} (100%) create mode 100644 1_numpy_matplotlib_scipy_sympy/4-scipy_tutorial_EN.ipynb rename 1_numpy_matplotlib_scipy_sympy/{4-sympy_tutorial.ipynb => 5-sympy_tutorial.ipynb} (100%) create mode 100644 1_numpy_matplotlib_scipy_sympy/5-sympy_tutorial_EN.ipynb create mode 100644 2_knn/knn_classification_EN.ipynb create mode 100644 3_kmeans/1-k-means_EN.ipynb create mode 100644 3_kmeans/2-kmeans-color-vq_EN.ipynb create mode 100644 3_kmeans/3-ClusteringAlgorithms_EN.ipynb create mode 100644 4_logistic_regression/1-Least_squares_EN.ipynb create mode 100644 4_logistic_regression/2-Logistic_regression_EN.ipynb create mode 100644 4_logistic_regression/3-PCA_and_Logistic_Regression_EN.ipynb create mode 100644 5_nn/1-Perceptron_EN.ipynb create mode 100644 5_nn/2-mlp_bp_EN.ipynb create mode 100644 5_nn/3-softmax_ce_EN.ipynb create mode 100644 README_EN.md create mode 100644 tips/InstallPython_EN.md diff --git a/0_python/1_Basics_EN.ipynb b/0_python/1_Basics_EN.ipynb new file mode 100644 index 0000000..f1528ae --- /dev/null +++ b/0_python/1_Basics_EN.ipynb @@ -0,0 +1,1015 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python Basic" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "a = 10\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. import & Zen of Python\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['1_Basics.ipynb',\n", + " '2_Print_Statement.ipynb',\n", + " '3_Data_Structure_1.ipynb',\n", + " '4_Data_Structure_2.ipynb',\n", + " '5_Control_Flow.ipynb',\n", + " '6_Function.ipynb',\n", + " '7_Class.ipynb',\n", + " 'Python.pdf',\n", + " 'README.md',\n", + " '.ipynb_checkpoints']" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 导入库\n", + "import os\n", + "os.listdir('.')" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Zen of Python, by Tim Peters\n", + "\n", + "Beautiful is better than ugly.\n", + "Explicit is better than implicit.\n", + "Simple is better than complex.\n", + "Complex is better than complicated.\n", + "Flat is better than nested.\n", + "Sparse is better than dense.\n", + "Readability counts.\n", + "Special cases aren't special enough to break the rules.\n", + "Although practicality beats purity.\n", + "Errors should never pass silently.\n", + "Unless explicitly silenced.\n", + "In the face of ambiguity, refuse the temptation to guess.\n", + "There should be one-- and preferably only one --obvious way to do it.\n", + "Although that way may not be obvious at first unless you're Dutch.\n", + "Now is better than never.\n", + "Although never is often better than *right* now.\n", + "If the implementation is hard to explain, it's a bad idea.\n", + "If the implementation is easy to explain, it may be a good idea.\n", + "Namespaces are one honking great idea -- let's do more of those!\n" + ] + } + ], + "source": [ + "import this" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A name that is used to denote something or a value is called a variable. In python, variables can be declared and values can be assigned to it as follows," + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "x = 2\n", + "y = 5\n", + "xy = 'Hey'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7 Hey\n" + ] + } + ], + "source": [ + "print(x+y, xy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Multiple variables can be assigned with the same value." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "x = y = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 1\n" + ] + } + ], + "source": [ + "print(x,y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Operators" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Arithmetic Operators" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "| Symbol | Task Performed |\n", + "|----|---|\n", + "| + | Addition |\n", + "| - | Subtraction |\n", + "| / | division |\n", + "| % | mod |\n", + "| * | multiplication |\n", + "| // | floor division |\n", + "| ** | to the power of |" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1+2" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2-1" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1*2" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1/2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "0? This is because both the numerator and denominator are integers but the result is a float value hence an integer value is returned. By changing either the numerator or the denominator to float, correct answer can be obtained." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1.0/2" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.5" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1/2.0" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "15%10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Floor division is nothing but converting the result so obtained to the nearest integer." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2.8//2.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Relational Operators" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "| Symbol | Task Performed |\n", + "|----|---|\n", + "| == | True, if it is equal |\n", + "| != | True, if not equal to |\n", + "| < | less than |\n", + "| > | greater than |\n", + "| <= | less than or equal to |\n", + "| >= | greater than or equal to |" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "z = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z == 1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z > 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3 Bitwise Operators" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "| Symbol | Task Performed |\n", + "|----|---|\n", + "| & | Logical And |\n", + "| l | Logical OR |\n", + "| ^ | XOR |\n", + "| ~ | Negate |\n", + "| >> | Right shift |\n", + "| << | Left shift |" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "a = 2 #10\n", + "b = 3 #11" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + "0b10\n" + ] + } + ], + "source": [ + "print(a & b)\n", + "print(bin(a&b))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "5 >> 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "0000 0101 -> 5 \n", + "\n", + "Shifting the digits by 1 to the right and zero padding\n", + "\n", + "0000 0010 -> 2" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "5 << 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "0000 0101 -> 5 \n", + "\n", + "Shifting the digits by 1 to the left and zero padding\n", + "\n", + "0000 1010 -> 10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Built-in Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python comes loaded with pre-built functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 Conversion from one system to another" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conversion from hexadecimal to decimal is done by adding prefix **0x** to the hexadecimal value or vice versa by using built in **hex( )**, Octal to decimal by adding prefix **0** to the octal value or vice versa by using built in function **oct( )**." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'0xaa'" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hex(170)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "170" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "0xAA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'0o10'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oct(8)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**int( )** accepts two values when used for conversion, one is the value in a different number system and the other is its base. Note that input number in the different number system should be of string type." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n", + "170\n", + "10\n" + ] + } + ], + "source": [ + "print(int('010',8))\n", + "print(int('0xaa',16))\n", + "print(int('1010',2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**int( )** can also be used to get only the integer value of a float number or can be used to convert a number which is of type string to integer format. Similarly, the function **str( )** can be used to convert the integer back to string format" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7\n", + "7\n" + ] + } + ], + "source": [ + "print(int(7.7))\n", + "print(int('7'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also note that function **bin( )** is used for binary and **float( )** for decimal/float values. **chr( )** is used for converting ASCII to its alphabet equivalent, **ord( )** is used for the other way round." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'b'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chr(98)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "98" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ord('b')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 Simplifying Arithmetic Operations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**round( )** function rounds the input value to a specified number of places or to the nearest integer. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6\n", + "4.56\n" + ] + } + ], + "source": [ + "print(round(5.6231))\n", + "print(round(4.55892, 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**complex( )** is used to define a complex number and **abs( )** outputs the absolute value of the same." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5.385164807134504\n" + ] + } + ], + "source": [ + "c =complex('5+2j')\n", + "print(abs(c))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**divmod(x,y)** outputs the quotient and the remainder in a tuple(you will be learning about it in the further chapters) in the format (quotient, remainder). " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4, 1)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "divmod(9,2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**isinstance( )** returns True, if the first argument is an instance of that class. Multiple classes can also be checked at once." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n", + "True\n" + ] + } + ], + "source": [ + "print(isinstance(1, int))\n", + "print(isinstance(1.0,int))\n", + "print(isinstance(1.0,(int,float)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**cmp(x,y)**\n", + "\n", + "|x ? y|Output|\n", + "|---|---|\n", + "| x < y | -1 |\n", + "| x == y | 0 |\n", + "| x > y | 1 |" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], + "source": [ + "print(1<2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**pow(x,y,z)** can be used to find the power $x^y$ also the mod of the resulting value with the third specified number can be found i.e. : ($x^y$ % z)." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "27\n", + "2\n" + ] + } + ], + "source": [ + "print(pow(3,3))\n", + "print(pow(3,3,5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**range( )** function outputs the integers of the specified range. It can also be used to generate a series by specifying the difference between the two numbers within a particular range. The elements are returned in a list (will be discussing in detail later.)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 1, 2]\n", + "[2, 3, 4, 5, 6, 7, 8]\n", + "[2, 10, 18, 26]\n" + ] + } + ], + "source": [ + "print(list(range(3)))\n", + "print(list(range(2,9)))\n", + "print(list(range(2,27,8)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3 Accepting User Inputs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**input( )** accepts input and stores it as a string. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Type something here and it will be stored in variable abc \tHello world!\n" + ] + } + ], + "source": [ + "abc = input(\"Type something here and it will be stored in variable abc \\t\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(abc)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/0_python/2_Print_Statement_EN.ipynb b/0_python/2_Print_Statement_EN.ipynb new file mode 100644 index 0000000..650f004 --- /dev/null +++ b/0_python/2_Print_Statement_EN.ipynb @@ -0,0 +1,587 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Print Statement" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The **print** statement can be used in the following different ways :\n", + "\n", + " - print(\"Hello World\")\n", + " - print(\"Hello\", )\n", + " - print(\"Hello\" + )\n", + " - print(\"Hello %s\" % )" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello World\n" + ] + } + ], + "source": [ + "print(\"Hello World\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In Python, single, double and triple quotes are used to denote a string.\n", + "Most use single quotes when declaring a single character. \n", + "Double quotes when declaring a line and triple quotes when declaring a paragraph/multiple lines." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hey\n" + ] + } + ], + "source": [ + "print('Hey')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "My name is Rajath Kumar M.P.\n", + "\n", + "I love Python.\n" + ] + } + ], + "source": [ + "print(\"\"\"My name is Rajath Kumar M.P.\n", + "\n", + "I love Python.\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Strings can be assigned to variable say _string1_ and _string2_ which can called when using the print statement." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello World\n", + "Hello World !\n" + ] + } + ], + "source": [ + "string1 = 'World'\n", + "print('Hello', string1)\n", + "\n", + "string2 = '!'\n", + "print('Hello', string1, string2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "String concatenation is the \"addition\" of two strings. Observe that while concatenating there will be no space between the strings." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HelloWorld!\n" + ] + } + ], + "source": [ + "print('Hello' + string1 + string2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**%s** is used to refer to a variable which contains a string." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello World\n" + ] + } + ], + "source": [ + "print(\"Hello %s\" % string1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly, when using other data types\n", + "\n", + " - %s -> string\n", + " - %d -> Integer\n", + " - %f -> Float\n", + " - %o -> Octal\n", + " - %x -> Hexadecimal\n", + " - %e -> exponential\n", + " \n", + "This can be used for conversions inside the print statement itself." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Actual Number = 18\n", + "Float of the number = 18.000000\n", + "Octal equivalent of the number = 22\n", + "Hexadecimal equivalent of the number = 12\n", + "Exponential equivalent of the number = 1.800000e+01\n" + ] + } + ], + "source": [ + "print(\"Actual Number = %d\" % 18)\n", + "print(\"Float of the number = %f\" % 18)\n", + "print(\"Octal equivalent of the number = %o\" % 18)\n", + "print(\"Hexadecimal equivalent of the number = %x\" % 18)\n", + "print(\"Exponential equivalent of the number = %e\" % 18)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When referring to multiple variables parenthesis is used." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello World !\n" + ] + } + ], + "source": [ + "print(\"Hello %s %s\" % (string1,string2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Other Examples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following are other different ways the print statement can be put to use." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I want to be printed here\n" + ] + } + ], + "source": [ + "print(\"I want to be printed %s\" %'here')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_A_A_A_A_A_A_A_A_A_A\n" + ] + } + ], + "source": [ + "print('_A'*10)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Jan\n", + "Feb\n", + "Mar\n", + "Apr\n", + "May\n", + "Jun\n", + "Jul\n", + "Aug\n" + ] + } + ], + "source": [ + "print(\"Jan\\nFeb\\nMar\\nApr\\nMay\\nJun\\nJul\\nAug\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Jan\n", + "Feb\n", + "Mar\n", + "Apr\n", + "May\n", + "Jun\n", + "Jul\n", + "Aug\n" + ] + } + ], + "source": [ + "print(\"\\n\".join(\"Jan Feb Mar Apr May Jun Jul Aug\".split(\" \")))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I want \\n to be printed.\n" + ] + } + ], + "source": [ + "print(\"I want \\\\n to be printed.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Routine:\n", + "\t- Eat\n", + "\t- Sleep\n", + "\t- Repeat\n", + "\n" + ] + } + ], + "source": [ + "print(\"\"\"\n", + "Routine:\n", + "\\t- Eat\n", + "\\t- Sleep\\n\\t- Repeat\n", + "\"\"\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. PrecisionWidth and FieldWidth" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fieldwidth is the width of the entire number and precision is the width towards the right. One can alter these widths based on the requirements.\n", + "\n", + "The default Precision Width is set to 6." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'3.121312'" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"%f\" % 3.121312312312" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice upto 6 decimal points are returned. To specify the number of decimal points, '%(fieldwidth).(precisionwidth)f' is used." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'3.12131'" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"%.5f\" % 3.121312312312" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the field width is set more than the necessary than the data right aligns itself to adjust to the specified values." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "' 3.12131'" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"%9.5f\" % 3.121312312312" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Zero padding is done by adding a 0 at the start of fieldwidth." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'00000000000003.12131'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"%020.5f\" % 3.121312312312" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For proper alignment, a space can be left blank in the field width so that when a negative number is used, proper alignment is maintained." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 3.121312\n", + "-3.121312\n" + ] + } + ], + "source": [ + "print \"% 9f\" % 3.121312312312\n", + "print \"% 9f\" % -3.121312312312" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'+' sign can be returned at the beginning of a positive number by adding a + sign at the beginning of the field width." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+3.121312\n", + "-3.121312\n" + ] + } + ], + "source": [ + "print \"%+9f\" % 3.121312312312\n", + "print \"% 9f\" % -3.121312312312" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned above, the data right aligns itself when the field width mentioned is larger than the actualy field width. But left alignment can be done by specifying a negative symbol in the field width." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'3.121 '" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"%-9.3f\" % 3.121312312312" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/0_python/3_Data_Structure_1_EN.ipynb b/0_python/3_Data_Structure_1_EN.ipynb new file mode 100644 index 0000000..0d4bd2c --- /dev/null +++ b/0_python/3_Data_Structure_1_EN.ipynb @@ -0,0 +1,1999 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Structures" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In simple terms, It is the the collection or group of data in a particular structure." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Lists" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lists are the most commonly used data structure. Think of it as a sequence of data that is enclosed in square brackets and data are separated by a comma. Each of these data can be accessed by calling it's index value.\n", + "\n", + "Lists are declared by just equating a variable to '[ ]' or list." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "a = []" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(type(a))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One can directly assign the sequence of data to a list x as shown." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['apple', 'orange', 'peach']\n" + ] + } + ], + "source": [ + "x = ['apple', 'orange', 'peach']\n", + "print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Indexing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In python, Indexing starts from 0. Thus now the list x, which has two elements will have apple at 0 index and orange at 1 index." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'apple'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Indexing can also be done in reverse order. That is the last element can be accessed first. Here, indexing starts from -1. Thus index value -1 will be orange and index -2 will be apple." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'peach'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you might have already guessed, x[0] = x[-2], x[1] = x[-1]. This concept can be extended towards lists with more many elements." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "y = ['carrot','potato']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we have declared two lists x and y each containing its own data. Now, these two lists can again be put into another list say z which will have it's data as two lists. This list inside a list is called as nested lists and is how an array would be declared which we will see later." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[['apple', 'orange', 'peach'], ['carrot', 'potato']]\n" + ] + } + ], + "source": [ + "z = [x,y]\n", + "print(z)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'orange'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z[0][1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Indexing in nested lists can be quite confusing if you do not understand how indexing works in python. So let us break it down and then arrive at a conclusion.\n", + "\n", + "Let us access the data 'apple' in the above nested list.\n", + "First, at index 0 there is a list ['apple','orange'] and at index 1 there is another list ['carrot','potato']. Hence z[0] should give us the first list which contains 'apple'." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['apple', 'orange', 'peach']\n" + ] + } + ], + "source": [ + "z1 = z[0]\n", + "print(z1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now observe that z1 is not at all a nested list thus to access 'apple', z1 should be indexed at 0." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'apple'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z1[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of doing the above, In python, you can access 'apple' by just writing the index values each time side by side." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'apple'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z[0][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If there was a list inside a list inside a list then you can access the innermost value by executing z[ ][ ][ ]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 Slicing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Indexing was only limited to accessing a single element, Slicing on the other hand is accessing a sequence of data inside the list. In other words \"slicing\" the list.\n", + "\n", + "Slicing is done by defining the index values of the first element and the last element from the parent list that is required in the sliced list. It is written as parentlist[ a : b ] where a,b are the index values from the parent list. If a or b is not defined then the index value is considered to be the first value for a if a is not defined and the last value for b when b is not defined." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 1, 2, 3]\n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n" + ] + } + ], + "source": [ + "num = [0,1,2,3,4,5,6,7,8,9]\n", + "print(num[0:4])\n", + "print(num[0:])\n", + "print(num[:])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 1, 2, 3]\n", + "[4, 5, 6, 7, 8, 9]\n" + ] + } + ], + "source": [ + "print(num[0:4])\n", + "print(num[4:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also slice a parent list with a fixed length or step length." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 3, 6]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num[:9:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 Built in List Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To find the length of the list or the number of elements in a list, **len( )** is used." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(num)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the list consists of all integer elements then **min( )** and **max( )** gives the minimum and maximum value in the list." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "min(num)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max(num)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lists can be concatenated by adding, '+' them. The resultant list will contain all the elements of the lists that were added. The resultant list will not be a nested list." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 2, 3, 5, 4, 7]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[1,2,3] + [5,4,7]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There might arise a requirement where you might need to check if a particular element is there in a predefined list. Consider the below list." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "names = ['Earth','Air','Fire','Water']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To check if 'Fire' and 'Rajath' is present in the list names. A conventional approach would be to use a for loop and iterate over the list and use the if condition. But in python you can use 'a in b' concept which would return 'True' if a is present in b and 'False' if not." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'Fir' in names" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'Fire' in names" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'fire' in names" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'Rajath' in names" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In a list with elements as string, **max( )** and **min( )** is applicable. **max( )** would return a string element whose ASCII value is the highest and the lowest when **min( )** is used. Note that only the first index of each element is considered each time and if they value is the same then second index considered so on and so forth." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "mlist = ['bzaa','ds','nc','az','z','klm']" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "z\n", + "az\n" + ] + } + ], + "source": [ + "print(max(mlist))\n", + "print(min(mlist))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the first index of each element is considered and thus z has the highest ASCII value thus it is returned and minimum ASCII is a. But what if numbers are declared as strings?" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "nlist = ['1','94','93','1000']" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "94\n", + "1\n" + ] + } + ], + "source": [ + "print(max(nlist))\n", + "print(min(nlist))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Even if the numbers are declared in a string the first index of each element is considered and the maximum and minimum values are returned accordingly." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But if you want to find the **max( )** string element based on the length of the string then another parameter 'key=len' is declared inside the **max( )** and **min( )** function." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Earth\n", + "Air\n" + ] + } + ], + "source": [ + "print(max(names, key=len))\n", + "print(min(names, key=len))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But even 'Water' has length 5. **max()** or **min()** function returns the first element when there are two or more elements with the same length.\n", + "\n", + "Any other built in function can be used or lambda function (will be discussed later) in place of len.\n", + "\n", + "A string can be converted into a list by using the **list()** function." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['h', 'e', 'l', 'l', 'o']" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list('hello')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**append( )** is used to add a element at the end of the list." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "lst = [1,1,4,8,7]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 1, 4, 8, 7, 1]\n" + ] + } + ], + "source": [ + "lst.append(1)\n", + "print(lst)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**count( )** is used to count the number of a particular element that is present in the list. " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lst.count(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**append( )** function can also be used to add a entire list at the end. Observe that the resultant list becomes a nested list." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "lst1 = [5,4,2,8]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 1, 4, 8, 7, 1, [5, 4, 2, 8]]\n" + ] + } + ], + "source": [ + "lst.append(lst1)\n", + "print(lst)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But if nested list is not what is desired then **extend( )** function can be used." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 1, 4, 8, 7, 1, [5, 4, 2, 8], 5, 4, 2, 8]\n" + ] + } + ], + "source": [ + "lst.extend(lst1)\n", + "print(lst)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**index( )** is used to find the index value of a particular element. Note that if there are multiple elements of the same value then the first index value of that element is returned." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lst.index(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "999 is not in list", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: 999 is not in list" + ] + } + ], + "source": [ + "lst.index(999)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**insert(x,y)** is used to insert a element y at a specified index value x. **append( )** function made it only possible to insert at the end. " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 1, 4, 8, 7, 'name', 1, [5, 4, 2, 8], 5, 4, 2, 8]\n" + ] + } + ], + "source": [ + "lst.insert(5, 'name')\n", + "print(lst)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**insert(x,y)** inserts but does not replace element. If you want to replace the element with another element you simply assign the value to that particular index." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 1, 4, 8, 7, 'Python', 1, [5, 4, 2, 8], 5, 4, 2, 8]\n" + ] + } + ], + "source": [ + "lst[5] = 'Python'\n", + "print(lst)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**pop( )** function return the last element in the list. This is similar to the operation of a stack. Hence it wouldn't be wrong to tell that lists can be used as a stack." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 1, 4, 8, 7, 'name', 1, [5, 4, 2, 8], 5, 4]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lst.pop()\n", + "lst" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Index value can be specified to pop a ceratin element corresponding to that index value." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lst.pop(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 1, 8, 7, 'name', 1, [5, 4, 2, 8], 5, 4]\n" + ] + } + ], + "source": [ + "print(lst)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**pop( )** is used to remove element based on it's index value which can be assigned to a variable. One can also remove element by specifying the element itself using the **remove( )** function." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 1, 8, 7, 1, [5, 4, 2, 8], 5, 4]\n" + ] + } + ], + "source": [ + "lst.remove('name')\n", + "print(lst)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternative to **remove** function but with using index value is **del**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 4, 7, 1, [5, 4, 2, 8], 5, 4, 2]\n", + "[1, 7, 1, [5, 4, 2, 8], 5, 4, 2]\n" + ] + } + ], + "source": [ + "print(lst)\n", + "del lst[1]\n", + "print(lst)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The entire elements present in the list can be reversed by using the **reverse()** function." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[4, 5, [5, 4, 2, 8], 1, 7, 8, 1, 1]\n" + ] + } + ], + "source": [ + "lst.reverse()\n", + "print(lst)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the nested list [5,4,2,8] is treated as a single element of the parent list lst. Thus the elements inside the nested list is not reversed.\n", + "\n", + "Python offers built in operation **sort( )** to arrange the elements in ascending order." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 4, 8, 8, 10]\n" + ] + } + ], + "source": [ + "lst = [1, 4, 8, 8, 10]\n", + "lst.sort()\n", + "print(lst)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For descending order, By default the reverse condition will be False for reverse. Hence changing it to True would arrange the elements in descending order." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10, 8, 8, 4, 1]\n" + ] + } + ], + "source": [ + "lst.sort(reverse=True)\n", + "print(lst)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Similarly for lists containing string elements, **sort( )** would sort the elements based on it's ASCII value in ascending and by specifying reverse=True in descending." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['apple', 'orange', 'peach']\n", + "['peach', 'orange', 'apple']\n" + ] + } + ], + "source": [ + "names = ['apple', 'orange', 'peach']\n", + "names.sort()\n", + "print(names)\n", + "names.sort(reverse=True)\n", + "print(names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To sort based on length key=len should be specified as shown." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['peach', 'apple', 'orange']\n", + "['orange', 'peach', 'apple']\n" + ] + } + ], + "source": [ + "names.sort(key=len)\n", + "print(names)\n", + "names.sort(key=len,reverse=True)\n", + "print(names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4 Copying a list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Most of the new python programmers commit this mistake. Consider the following," + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "lista= [2,1,4,3]" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 1, 4, 3]\n" + ] + } + ], + "source": [ + "listb = lista\n", + "print(listb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, We have declared a list, lista = [2,1,4,3]. This list is copied to listb by assigning it's value and it get's copied as seen. Now we perform some random operations on lista." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 1, 4]\n", + "[2, 1, 4, 9]\n" + ] + } + ], + "source": [ + "lista.pop()\n", + "print(lista)\n", + "lista.append(9)\n", + "print(lista)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 1, 4, 9]\n" + ] + } + ], + "source": [ + "print(listb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "listb has also changed though no operation has been performed on it. This is because you have assigned the same memory space of lista to listb. So how do fix this?\n", + "\n", + "If you recall, in slicing we had seen that parentlist[a:b] returns a list from parent list with start index a and end index b and if a and b is not mentioned then by default it considers the first and last element. We use the same concept here. By doing so, we are assigning the data of lista to listb as a variable." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "lista = [2,1,4,3]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 1, 4, 3]\n" + ] + } + ], + "source": [ + "listb = lista[:]\n", + "print(listb)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 1, 4]\n", + "[2, 1, 4, 9]\n" + ] + } + ], + "source": [ + "lista.pop()\n", + "print(lista)\n", + "lista.append(9)\n", + "print(lista)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2, 1, 4, 3]\n" + ] + } + ], + "source": [ + "print(listb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Tuples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tuples are similar to lists but only big difference is the elements inside a list can be changed but in tuple it cannot be changed. Think of tuples as something which has to be True for a particular something and cannot be True for no other values. For better understanding, Recall **divmod()** function." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 1)\n", + "\n" + ] + } + ], + "source": [ + "xyz = divmod(10,3)\n", + "print(xyz)\n", + "print(type(xyz))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the quotient has to be 3 and the remainder has to be 1. These values cannot be changed whatsoever when 10 is divided by 3. Hence divmod returns these values in a tuple." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To define a tuple, A variable is assigned to paranthesis ( ) or tuple( )." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "tup = ()\n", + "tup2 = tuple()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you want to directly declare a tuple it can be done by using a comma at the end of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(27,)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "27," + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "27 when multiplied by 2 yields 54, But when multiplied with a tuple the data is repeated twice." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(27, 27)" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2*(27,)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Values can be assigned while declaring a tuple. It takes a list as input and converts it into a tuple or it takes a string and converts it into a tuple." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 2, 3)\n", + "('H', 'e', 'l', 'l', 'o')\n" + ] + } + ], + "source": [ + "tup3 = tuple([1,2,3])\n", + "print(tup3)\n", + "tup4 = tuple('Hello')\n", + "print(tup4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It follows the same indexing and slicing as Lists." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + "('H', 'e', 'l')\n" + ] + } + ], + "source": [ + "print(tup3[1])\n", + "tup5 = tup4[:3]\n", + "print(tup5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Mapping one tuple to another" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "(a,b,c)= ('alpha','beta','gamma')" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "alpha beta gamma\n" + ] + } + ], + "source": [ + "print(a,b,c)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('R', 'a', 'j', 'a', 't', 'h', 'K', 'u', 'm', 'a', 'r', 'M', 'P')\n" + ] + } + ], + "source": [ + "d = tuple('RajathKumarMP')\n", + "print(d)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Built In Tuple functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**count()** function counts the number of specified element that is present in the tuple." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d.count('a')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**index()** function returns the index of the specified element. If the elements are more than one then the index of the first element of that specified element is returned" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d.index('a')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Sets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sets are mainly used to eliminate repeated numbers in a sequence/list. It is also used to perform some standard set operations.\n", + "\n", + "Sets are declared as set() which will initialize a empty set. Also set([sequence]) can be executed to declare a set with elements" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "set1 = set()\n", + "print(type(set1))" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1, 2, 3, 4}\n" + ] + } + ], + "source": [ + "set0 = set([1,2,2,3,3,4])\n", + "print(set0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "elements 2,3 which are repeated twice are seen only once. Thus in a set each element is distinct." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Built-in Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "set1 = set([1,2,3])" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "set2 = set([2,3,4,5])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**union( )** function returns a set which contains all the elements of both the sets without repition." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1, 2, 3, 4, 5}" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set1.union(set2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**add( )** will add a particular element into the set. Note that the index of the newly added element is arbitrary and can be placed anywhere not neccessarily in the end." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{1, 2, 3}\n" + ] + }, + { + "data": { + "text/plain": [ + "{0, 1, 2, 3}" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(set1)\n", + "set1.add(0)\n", + "set1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**intersection( )** function outputs a set which contains all the elements that are in both sets." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{2, 3}" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set1.intersection(set2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**difference( )** function ouptuts a set which contains elements that are in set1 and not in set2." + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0, 1}" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set1.difference(set2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**symmetric_difference( )** function ouputs a function which contains elements that are in one of the sets." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0, 1, 4, 5}" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set2.symmetric_difference(set1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**issubset( ), isdisjoint( ), issuperset( )** is used to check if the set1/set2 is a subset, disjoint or superset of set2/set1 respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set1.issubset(set2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "set2.isdisjoint(set1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "set2.issuperset(set1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**pop( )** is used to remove an arbitrary element in the set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "set1.pop()\n", + "print(set1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**remove( )** function deletes the specified element from the set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "set1.remove(2)\n", + "set1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**clear( )** is used to clear all the elements and make that set an empty set." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "set()" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set1.clear()\n", + "set1" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/0_python/4_Data_Structure_2_EN.ipynb b/0_python/4_Data_Structure_2_EN.ipynb new file mode 100644 index 0000000..20e71bb --- /dev/null +++ b/0_python/4_Data_Structure_2_EN.ipynb @@ -0,0 +1,1180 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Strings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Strings are ordered text based data which are represented by enclosing the same in single/double/triple quotes." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10\n" + ] + } + ], + "source": [ + "String0 = 'Taj Mahal is beautiful'\n", + "String1 = \"Taj Mahal is beautiful\"\n", + "String2 = '''Taj Mahal\n", + "is\n", + "beautiful'''" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Taj Mahal is beautiful \n", + "Taj Mahal is beautiful \n", + "Taj Mahal\n", + "is\n", + "beautiful \n" + ] + } + ], + "source": [ + "print(String0, type(String0))\n", + "print(String1, type(String1))\n", + "print(String2, type(String2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "String Indexing and Slicing are similar to Lists which was explained in detail earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "M\n", + "Mahal is beautiful\n" + ] + } + ], + "source": [ + "print(String0[4])\n", + "print(String0[4:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Built-in Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**find( )** function returns the index value of the given data that is to found in the string. If it is not found it returns **-1**. Remember to not confuse the returned -1 for reverse indexing value." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7\n", + "-1\n" + ] + } + ], + "source": [ + "print(String0.find('al'))\n", + "print(String0.find('am'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The index value returned is the index of the first element in the input data." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a\n" + ] + } + ], + "source": [ + "print(String0[7])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One can also input **find( )** function between which index values it has to search." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n", + "2\n" + ] + } + ], + "source": [ + "print(String0.find('j',1))\n", + "print(String0.find('j',1,3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**capitalize( )** is used to capitalize the first element in the string." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Observe the first letter in this sentence. can you change this sentence\n" + ] + } + ], + "source": [ + "String3 = 'observe the first letter in this sentence. can you change this sentence'\n", + "print(String3.capitalize())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**center( )** is used to center align the string by specifying the field width." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "' Taj Mahal is beautiful '" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "String0.center(70)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One can also fill the left out spaces with any other character." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'------------------------Taj Mahal is beautiful------------------------'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "String0.center(70,'-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**zfill( )** is used for zero padding by specifying the field width." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'00000000Taj Mahal is beautiful'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "String0.zfill(30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**expandtabs( )** allows you to change the spacing of the tab character. '\\t' which is by default set to 8 spaces." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "h\te\tl\tl\to\n", + "h e l l o\n", + "h e l l o\n" + ] + } + ], + "source": [ + "s = 'h\\te\\tl\\tl\\to'\n", + "print(s)\n", + "print(s.expandtabs(1))\n", + "print(s.expandtabs())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "**index( )** works the same way as **find( )** function the only difference is find returns '-1' when the input element is not found in the string but **index( )** function throws a ValueError" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "4\n" + ] + }, + { + "ename": "ValueError", + "evalue": "substring not found", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mString0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Taj'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mString0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Mahal'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mString0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Mahal'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: substring not found" + ] + } + ], + "source": [ + "print(String0.index('Taj'))\n", + "print(String0.index('Mahal',0))\n", + "print(String0.index('Mahal',10,20))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**endswith( )** function is used to check if the given string ends with the particular char which is given as input." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n" + ] + } + ], + "source": [ + "print(String0.endswith('y'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The start and stop index values can also be specified." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "True\n" + ] + } + ], + "source": [ + "print(String0.endswith('l',0))\n", + "print(String0.endswith('M',0,5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**count( )** function counts the number of char in the given string. The start and the stop index can also be specified or left blank. (These are Implicit arguments which will be dealt in functions)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "2\n" + ] + } + ], + "source": [ + "print(String0.count('a',0))\n", + "print(String0.count('a',5,10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**join( )** function is used add a char in between the elements of the input string." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'*a_a-'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'a'.join('*_-')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1\\n2'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'\\n'.join(['1', '2'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "'*_-' is the input string and char 'a' is added in between each element" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**join( )** function can also be used to convert a list into a string." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['T', 'a', 'j', ' ', 'M', 'a', 'h', 'a', 'l', ' ', 'i', 's', ' ', 'b', 'e', 'a', 'u', 't', 'i', 'f', 'u', 'l']\n", + "Taj Mahal is beautiful\n" + ] + } + ], + "source": [ + "a = list(String0)\n", + "print(a)\n", + "b = ''.join(a)\n", + "print(b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before converting it into a string **join( )** function can be used to insert any char in between the list elements." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " /i/s/ /b/e/a/u/t/i/f/u/l\n" + ] + } + ], + "source": [ + "c = '/'.join(a)[18:]\n", + "print(c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**split( )** function is used to convert a string back to a list. Think of it as the opposite of the **join()** function." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[' ', 'i', 's', ' ', 'b', 'e', 'a', 'u', 't', 'i', 'f', 'u', 'l']\n" + ] + } + ], + "source": [ + "d = c.split('/')\n", + "print(d)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In **split( )** function one can also specify the number of times you want to split the string or the number of elements the new returned list should conatin. The number of elements is always one more than the specified number this is because it is split the number of times specified." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[' ', 'i', 's', ' /b/e/a/u/t/i/f/u/l']\n", + "4\n" + ] + } + ], + "source": [ + "e = c.split('/',3)\n", + "print(e)\n", + "print(len(e))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**lower( )** converts any capital letter to small letter." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Taj Mahal is beautiful\n", + "taj mahal is beautiful\n" + ] + } + ], + "source": [ + "print(String0)\n", + "print(String0.lower())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**upper( )** converts any small letter to capital letter." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'TAJ MAHAL IS BEAUTIFUL'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "String0.upper()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**replace( )** function replaces the element with another element." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Bengaluru is beautiful'" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "String0.replace('Taj Mahal','Bengaluru')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**strip( )** function is used to delete elements from the right end and the left end which is not required." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "f = ' hello '" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If no char is specified then it will delete all the spaces that is present in the right and left hand side of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'hello'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f.strip()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**strip( )** function, when a char is specified then it deletes that char if it is present in the two ends of the specified string." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "f = ' ***----hello---******* '" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "' ***----hello---******* '" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f.strip('*')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The asterisk had to be deleted but is not. This is because there is a space in both the right and left hand side. So in strip function. The characters need to be inputted in the specific order in which they are present." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----hello---\n", + "hello\n" + ] + } + ], + "source": [ + "print(f.strip(' *'))\n", + "print(f.strip(' *-'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**lstrip( )** and **rstrip( )** function have the same functionality as strip function but the only difference is **lstrip( )** deletes only towards the left side and **rstrip( )** towards the right." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----hello---******* \n", + " ***----hello---\n" + ] + } + ], + "source": [ + "print(f.lstrip(' *'))\n", + "print(f.rstrip(' *'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Dictionaries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dictionaries are more used like a database because here you can index a particular sequence with your user defined string." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To define a dictionary, equate a variable to { } or dict()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n" + ] + } + ], + "source": [ + "d0 = {}\n", + "d1 = dict()\n", + "print(type(d0), type(d1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dictionary works somewhat like a list but with an added capability of assigning it's own index style." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'OneTwo': 12, 'One': 1}\n" + ] + } + ], + "source": [ + "d0['One'] = 1\n", + "d0['OneTwo'] = 12 \n", + "print(d0)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'key2': [1, 2, 4], 'key1': 1, 3: (1, 4, 6)}\n" + ] + } + ], + "source": [ + "d1 = {\"key1\":1, \"key2\":[1,2,4], 3:(1, 4, 6)}\n", + "print(d1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That is how a dictionary looks like. Now you are able to access '1' by the index value set at 'One'" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], + "source": [ + "print(d0['One'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Two lists which are related can be merged to form a dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "names = ['One', 'Two', 'Three', 'Four', 'Five']\n", + "numbers = [1, 2, 3, 4, 5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**zip( )** function is used to combine two lists" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'One': 1, 'Four': 4, 'Three': 3, 'Five': 5, 'Two': 2}\n" + ] + } + ], + "source": [ + "d2 = zip(names,numbers)\n", + "print(dict(d2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The two lists are combined to form a single list and each elements are clubbed with their respective elements from the other list inside a tuple. Tuples because that is what is assigned and the value should not change.\n", + "\n", + "Further, To convert the above into a dictionary. **dict( )** function is used." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'One': 1, 'Four': 4, 'Three': 3, 'Five': 5, 'Two': 2}\n" + ] + } + ], + "source": [ + "d2 = zip(names,numbers)\n", + "\n", + "a1 = dict(d2)\n", + "print(a1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Built-in Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**clear( )** function is used to erase the entire database that was created." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{}\n" + ] + } + ], + "source": [ + "a1.clear()\n", + "print(a1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dictionary can also be built using loops." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'One': 1, 'Four': 4, 'Three': 3, 'Five': 5, 'Two': 2}\n" + ] + } + ], + "source": [ + "a1 = {names[i]:numbers[i] for i in range(len(names))}\n", + "print(a1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'One': 1, 'Four': 4, 'Three': 3, 'Five': 5, 'Two': 2}\n" + ] + } + ], + "source": [ + "for i in range(len(names)):\n", + " a1[names[i]] = numbers[i]\n", + "print(a1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**values( )** function returns a list with all the assigned values in the dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_values([1, 4, 3, 5, 2])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a1.values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**keys( )** function returns all the index or the keys to which contains the values that it was assigned to." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['One', 'Four', 'Three', 'Five', 'Two'])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a1.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**items( )** is returns a list containing both the list but each element in the dictionary is inside a tuple. This is same as the result that was obtained when zip function was used." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ One] 1\n", + "[ Four] 4\n", + "[ Three] 3\n", + "[ Five] 5\n", + "[ Two] 2\n" + ] + } + ], + "source": [ + "a1.items()\n", + "\n", + "for (k,v) in a1.items():\n", + " print(\"[%6s] %d\" % (k, v))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**pop( )** function is used to get the remove that particular element and this removed element can be assigned to a new variable. But remember only the value is stored and not the key. Because the is just a index value." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'One': 1, 'Three': 3, 'Five': 5, 'Two': 2}\n", + "4\n" + ] + } + ], + "source": [ + "a2 = a1.pop('Four')\n", + "print(a1)\n", + "print(a2)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/0_python/5_Control_Flow_EN.ipynb b/0_python/5_Control_Flow_EN.ipynb new file mode 100644 index 0000000..493ab56 --- /dev/null +++ b/0_python/5_Control_Flow_EN.ipynb @@ -0,0 +1,693 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Control Flow Statements" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. If" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "if some_condition:\n", + " \n", + " algorithm" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Welcome!\n" + ] + } + ], + "source": [ + "x = 4\n", + "if x >10:\n", + " print(\"Hello\")\n", + "else:\n", + " print(\"Welcome!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. If-else" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "if some_condition:\n", + " \n", + " algorithm\n", + " \n", + "else:\n", + " \n", + " algorithm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hello\n" + ] + } + ], + "source": [ + "x = 12\n", + "if x > 10:\n", + " print(\"hello\")\n", + "else:\n", + " print(\"world\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. if-elif" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "if some_condition:\n", + " \n", + " algorithm\n", + "\n", + "elif some_condition:\n", + " \n", + " algorithm\n", + "\n", + "else:\n", + " \n", + " algorithm" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x y:\n", + " print(\"x>y\")\n", + "elif x < y:\n", + " print(\"x y:\n", + " print(\"x>y\")\n", + "elif x < y:\n", + " print(\"x10: break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As the name says. It is used to break out of a loop when a condition becomes true when executing the loop." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n" + ] + } + ], + "source": [ + "for i in range(100):\n", + " print(i)\n", + " if i>=7:\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Continue" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This continues the rest of the loop. Sometimes when a condition is satisfied there are chances of the loop getting terminated. This can be avoided using continue statement. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "The end.\n", + "The end.\n", + "The end.\n", + "The end.\n", + "The end.\n" + ] + } + ], + "source": [ + "for i in range(10):\n", + " if i>4:\n", + " print(\"The end.\")\n", + " continue\n", + " elif i<7:\n", + " print(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. List Comprehensions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python makes it simple to generate a required list with a single line of code using list comprehensions. For example If i need to generate multiples of say 27 I write the code using for loop as," + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[27, 54, 81, 108, 135, 162, 189, 216, 243, 270]\n" + ] + } + ], + "source": [ + "res = []\n", + "for i in range(1,11):\n", + " x = 27*i\n", + " res.append(x)\n", + "print(res)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since you are generating another list altogether and that is what is required, List comprehensions is a more efficient way to solve this problem." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[27, 54, 81, 108, 135, 162, 189, 216, 243, 270]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[27*x for x in range(1,11)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it!. Only remember to enclose it in square brackets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Understanding the code, The first bit of the code is always the algorithm and then leave a space and then write the necessary loop. But you might be wondering can nested loops be extended to list comprehensions? Yes you can." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[27, 54, 81, 108, 135, 162, 189, 216, 243, 270]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[27*x for x in range(1,20) if x<=10]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'108': 108,\n", + " '135': 135,\n", + " '162': 162,\n", + " '189': 189,\n", + " '216': 216,\n", + " '243': 243,\n", + " '27': 27,\n", + " '270': 270,\n", + " '54': 54,\n", + " '81': 81}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "{str(27*x):27*x for x in range(1,20) if x<=10}" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(27, 54, 81, 108, 135, 162, 189, 216, 243, 270)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tuple((27*x for x in range(1,20) if x<=10))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let me add one more loop to make you understand better, " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1,\n", + " 2,\n", + " 3,\n", + " 4,\n", + " 5,\n", + " 6,\n", + " 7,\n", + " 8,\n", + " 9,\n", + " 10,\n", + " 28,\n", + " 29,\n", + " 30,\n", + " 31,\n", + " 32,\n", + " 33,\n", + " 34,\n", + " 35,\n", + " 36,\n", + " 37,\n", + " 55,\n", + " 56,\n", + " 57,\n", + " 58,\n", + " 59,\n", + " 60,\n", + " 61,\n", + " 62,\n", + " 63,\n", + " 64,\n", + " 82,\n", + " 83,\n", + " 84,\n", + " 85,\n", + " 86,\n", + " 87,\n", + " 88,\n", + " 89,\n", + " 90,\n", + " 91,\n", + " 109,\n", + " 110,\n", + " 111,\n", + " 112,\n", + " 113,\n", + " 114,\n", + " 115,\n", + " 116,\n", + " 117,\n", + " 118]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[27*i+z for i in range(50) if i<5 for z in range(1,11)]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/0_python/6_Function_EN.ipynb b/0_python/6_Function_EN.ipynb new file mode 100644 index 0000000..14e1e6a --- /dev/null +++ b/0_python/6_Function_EN.ipynb @@ -0,0 +1,1075 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Most of the times, In a algorithm the statements keep repeating and it will be a tedious job to execute the same statements again and again and will consume a lot of memory and is not efficient. Enter Functions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the basic syntax of a function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```\n", + "def funcname(arg1, arg2,... argN):\n", + " \n", + " ''' Document String'''\n", + "\n", + " statements\n", + "\n", + "\n", + " return \n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Read the above syntax as, A function by name \"funcname\" is defined, which accepts arguements \"arg1,arg2,....argN\". The function is documented and it is '''Document String'''. The function after executing the statements returns a \"value\"." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hey Rajath!\n", + "Rajath, How do you do?\n" + ] + } + ], + "source": [ + "print(\"Hey Rajath!\")\n", + "print(\"Rajath, How do you do?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of writing the above two statements every single time it can be replaced by defining a function which would do the job in just one line. \n", + "\n", + "Defining a function firstfunc()." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def firstfunc():\n", + " print(\"Hey Rajath!\")\n", + " print(\"Rajath, How do you do?\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hey Rajath!\n", + "Rajath, How do you do?\n", + "Hey Rajath!\n", + "Rajath, How do you do?\n" + ] + } + ], + "source": [ + "firstfunc()\n", + "funca=firstfunc\n", + "funca()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**firstfunc()** every time just prints the message to a single person. We can make our function **firstfunc()** to accept arguements which will store the name and then prints respective to that accepted name. To do so, add a argument within the function as shown." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def firstfunc(username):\n", + " print(\"Hey\", username + '!')\n", + " print(username + ',' ,\"How do you do?\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Please enter your name : Willam\n" + ] + } + ], + "source": [ + "name1 = input('Please enter your name : ')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The name \"Guido\" is actually stored in name1. So we pass this variable to the function **firstfunc()** as the variable username because that is the variable that is defined for this function. i.e name1 is passed as username." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hey Willam!\n", + "Willam, How do you do?\n" + ] + } + ], + "source": [ + "firstfunc(name1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us simplify this even further by defining another function **secondfunc()** which accepts the name and stores it inside a variable and then calls the **firstfunc()** from inside the function itself." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "def firstfunc(username):\n", + " print(\"Hey\", username + '!')\n", + " print(username + ',' ,\"How do you do?\")\n", + "def secondfunc():\n", + " name = input(\"Please enter your name : \")\n", + " firstfunc(name)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Please enter your name : Tom\n", + "Hey Tom!\n", + "Tom, How do you do?\n" + ] + } + ], + "source": [ + "secondfunc()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Return Statement" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When the function results in some value and that value has to be stored in a variable or needs to be sent back or returned for further operation to the main algorithm, return statement is used." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def times(x,y):\n", + " z = x*y\n", + " return z" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above defined **times( )** function accepts two arguements and return the variable z which contains the result of the product of the two arguements" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], + "source": [ + "c = times(4,5)\n", + "print(c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The z value is stored in variable c and can be used for further operations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of declaring another variable the entire statement itself can be used in the return statement as shown." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def times(x,y):\n", + " '''This multiplies the two input arguments'''\n", + " return x*y" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], + "source": [ + "c = times(4,5)\n", + "print(c)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the **times( )** is now defined, we can document it as shown above. This document is returned whenever **times( )** function is called under **help( )** function." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function times in module __main__:\n", + "\n", + "times(x, y)\n", + " This multiplies the two input arguments\n", + "\n" + ] + } + ], + "source": [ + "help(times)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "times?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Multiple variable can also be returned, But keep in mind the order." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "eglist = [10,50,30,12,6,8,100]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def egfunc(eglist):\n", + " highest = max(eglist)\n", + " lowest = min(eglist)\n", + " first = eglist[0]\n", + " last = eglist[-1]\n", + " return highest,lowest,first,last" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the function is just called without any variable for it to be assigned to, the result is returned inside a tuple. But if the variables are mentioned then the result is assigned to the variable in a particular order which is declared in the return statement." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100, 6, 10, 100)\n" + ] + } + ], + "source": [ + "a = egfunc(eglist)\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " a = 100 \n", + " b = 6 \n", + " c = 10 \n", + " d = 100\n" + ] + } + ], + "source": [ + "a,b,c,d = egfunc(eglist)\n", + "print(' a =',a,'\\n b =',b,'\\n c =',c,'\\n d =',d)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Implicit arguments" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When an argument of a function is common in majority of the cases or it is \"implicit\" this concept is used." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def implicitadd(x,y=3):\n", + " return x+y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**implicitadd( )** is a function accepts two arguments but most of the times the first argument needs to be added just by 3. Hence the second argument is assigned the value 3. Here the second argument is implicit." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now if the second argument is not defined when calling the **implicitadd( )** function then it considered as 3." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "implicitadd(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But if the second argument is specified then this value overrides the implicit value assigned to the argument " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "implicitadd(4,4)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "11" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "implicitadd(5, y=6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Any number of arguments" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the number of arguments that is to be accepted by a function is not known then a asterisk symbol is used before the argument." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "def add_n(*args):\n", + " res = 0\n", + " reslist = []\n", + " for i in args:\n", + " reslist.append(i)\n", + " print(reslist)\n", + " return sum(reslist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above function accepts any number of arguments, defines a list and appends all the arguments into that list and return the sum of all the arguments." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, 5]\n" + ] + }, + { + "data": { + "text/plain": [ + "15" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "add_n(1,2,3,4,5)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3]\n" + ] + }, + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "add_n(1,2,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[30, 10, 20]\n" + ] + }, + { + "data": { + "text/plain": [ + "60" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def add_nd(**kwargs):\n", + " res = 0\n", + " reslist = []\n", + " for (k,v) in kwargs.items():\n", + " reslist.append(v)\n", + " print(reslist)\n", + " return sum(reslist)\n", + "\n", + "add_nd(x=10, y=20, c=30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Global and Local Variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Whatever variable is declared inside a function is local variable and outside the function in global variable." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "eg1 = [1,2,3,4,5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the below function we are appending a element to the declared list inside the function. eg2 variable declared inside the function is a local variable." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def egfunc1():\n", + " eg1 = [1, 2, 3, 4, 5, 7]\n", + " print(\"egfunc1>> eg1: \", eg1)\n", + "\n", + "def egfunc2():\n", + " eg1.append(8)\n", + " print(\"egfunc2>> eg1: \", eg1)\n", + "\n", + "def egfunc3():\n", + " global eg1\n", + " eg1 = [5, 4, 3, 2, 1]\n", + " print(\"egfunc3>> eg1: \", eg1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "egfunc1>> eg1: [1, 2, 3, 4, 5, 7]\n", + "eg1: [1, 2, 3, 4, 5] \n", + "\n", + "egfunc2>> eg1: [1, 2, 3, 4, 5, 8]\n", + "eg1: [1, 2, 3, 4, 5, 8] \n", + "\n", + "egfunc3>> eg1: [5, 4, 3, 2, 1]\n", + "eg1: [5, 4, 3, 2, 1] \n", + "\n" + ] + } + ], + "source": [ + "egfunc1()\n", + "print(\"eg1: \", eg1, \"\\n\")\n", + "\n", + "egfunc2()\n", + "print(\"eg1: \", eg1, \"\\n\")\n", + "\n", + "egfunc3()\n", + "print(\"eg1: \", eg1, \"\\n\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Lambda Functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are small functions which are not defined with any name and carry a single expression whose result is returned. Lambda functions comes very handy when operating with lists. These function are defined by the keyword **lambda** followed by the variables, a colon and the respective expression." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "z = lambda x: x * x" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "64" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "z(8)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(6, 8)" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "zz = lambda x, y: (x*y, x**y)\n", + "zz(2, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "function" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(z)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "function" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def sqr(x):\n", + " return x*x\n", + "\n", + "type(sqr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.1 map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**map( )** function basically executes the function that is defined to each of the list's element separately." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "list1 = [1,2,3,4,5,6,7,8,9]" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3, 4, 5, 6, 7, 8, 9, 10, 11]\n" + ] + } + ], + "source": [ + "eg = map(lambda x:x+2, list1)\n", + "print(list(eg))" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 4, 9, 16, 25, 36, 49, 64, 81]\n" + ] + } + ], + "source": [ + "eg = map(sqr, list1)\n", + "print(list(eg))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also add two lists." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "list2 = [9,8,7,6,5,4,3,2,1]" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[10, 10, 10, 10, 10, 10, 10, 10, 10]\n" + ] + } + ], + "source": [ + "eg2 = map(lambda x,y:x+y, list1,list2)\n", + "print(eg2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Not only lambda function but also other built in functions can also be used." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['10', '10', '10', '10', '10', '10', '10', '10', '10']\n" + ] + } + ], + "source": [ + "eg3 = map(str,eg2)\n", + "print(eg3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2 filter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**filter( )** function is used to filter out the values in a list. Note that **filter()** function returns the result in a new list." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "list1 = [1,2,3,4,5,6,7,8,9]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get the elements which are less than 5," + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3, 4]\n" + ] + } + ], + "source": [ + "res = filter(lambda x:x<5,list1)\n", + "print(list(res))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice what happens when **map()** is used." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "map(lambda x:x<5, list1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can conclude that, whatever is returned true in **map( )** function that particular element is returned when **filter( )** function is used." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filter(lambda x:x%4==0,list1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/0_python/7_Class_EN.ipynb b/0_python/7_Class_EN.ipynb new file mode 100644 index 0000000..4665ff7 --- /dev/null +++ b/0_python/7_Class_EN.ipynb @@ -0,0 +1,1308 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Classes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Variables, Lists, Dictionaries etc in python is a object. Without getting into the theory part of Object Oriented Programming, explanation of the concepts will be done along this tutorial." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A class is declared as follows" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "class class_name:\n", + "\n", + " Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class FirstClass:\n", + " pass\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**pass** in python means do nothing. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above, a class object named \"FirstClass\" is declared now consider a \"egclass\" which has all the characteristics of \"FirstClass\". So all you have to do is, equate the \"egclass\" to \"FirstClass\". In python jargon this is called as creating an instance. \"egclass\" is the instance of \"FirstClass\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "egclass = FirstClass()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "__main__.FirstClass" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(egclass)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "type" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(FirstClass)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let us add some \"functionality\" to the class. So that our \"FirstClass\" is defined in a better way. A function inside a class is called as a \"Method\" of that class" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Most of the classes will have a function named \"\\_\\_init\\_\\_\". These are called as magic methods. In this method you basically initialize the variables of that class or any other initial algorithms which is applicable to all methods is specified in this method. A variable inside a class is called an attribute." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These helps simplify the process of initializing a instance. For example,\n", + "\n", + "Without the use of magic method or \\_\\_init\\_\\_ which is otherwise called as constructors. One had to define a **init( )** method and call the **init( )** function." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FirstClass' object has no attribute 'init'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0meg0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFirstClass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0meg0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'FirstClass' object has no attribute 'init'" + ] + } + ], + "source": [ + "eg0 = FirstClass()\n", + "eg0.init()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But when the constructor is defined the \\_\\_init\\_\\_ is called thus intializing the instance created. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will make our \"FirstClass\" to accept two variables name and symbol.\n", + "\n", + "I will be explaining about the \"self\" in a while." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "class FirstClass:\n", + " \"\"\"My first class\"\"\"\n", + " def __init__(self,name,symbol):\n", + " self.name = name\n", + " self.symbol = symbol" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have defined a function and added the \\_\\_init\\_\\_ method. We can create a instance of FirstClass which now accepts two arguments. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "eg1 = FirstClass('one',1)\n", + "eg2 = FirstClass('two',2)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "onex 11\n", + "two 2\n", + "My first class\n" + ] + } + ], + "source": [ + "print(eg1.name, eg1.symbol)\n", + "print(eg2.name, eg2.symbol)\n", + "print(eg1.__doc__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**dir( )** function comes very handy in looking into what the class contains and what all method it offers" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__']" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(FirstClass)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'My first class'" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "FirstClass.__doc__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**dir( )** of an instance also shows it's defined attributes." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " 'name',\n", + " 'symbol']" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(eg1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Changing the FirstClass function a bit," + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "class FirstClass:\n", + " def __init__(self,name,symbol):\n", + " self.n = name\n", + " self.s = symbol" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Changing self.name and self.symbol to self.n and self.s respectively will yield," + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "eg1 = FirstClass('one',1)\n", + "eg2 = FirstClass('two',2)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'FirstClass' object has no attribute 'name'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meg2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meg2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'FirstClass' object has no attribute 'name'" + ] + } + ], + "source": [ + "print(eg1.name, eg1.symbol)\n", + "print(eg2.name, eg2.symbol)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "AttributeError, Remember variables are nothing but attributes inside a class? So this means we have not given the correct attribute for the instance." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " 'n',\n", + " 's']" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(eg1)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "one 1\n", + "two 2\n" + ] + } + ], + "source": [ + "print(eg1.n, eg1.s)\n", + "print(eg2.n, eg2.s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So now we have solved the error. Now let us compare the two examples that we saw.\n", + "\n", + "When I declared self.name and self.symbol, there was no attribute error for eg1.name and eg1.symbol and when I declared self.n and self.s, there was no attribute error for eg1.n and eg1.s\n", + "\n", + "From the above we can conclude that self is nothing but the instance itself.\n", + "\n", + "Remember, self is not predefined it is userdefined. You can make use of anything you are comfortable with. But it has become a common practice to use self." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "class FirstClass:\n", + " def __init__(asdf1234,name,symbol):\n", + " asdf1234.n = name\n", + " asdf1234.s = symbol" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "eg1 = FirstClass('one',1)\n", + "eg2 = FirstClass('two',2)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "one 1\n", + "two 2\n" + ] + } + ], + "source": [ + "print(eg1.n, eg1.s)\n", + "print(eg2.n, eg2.s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since eg1 and eg2 are instances of FirstClass it need not necessarily be limited to FirstClass itself. It might extend itself by declaring other attributes without having the attribute to be declared inside the FirstClass." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "eg1.cube = 1\n", + "eg2.cube = 8" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " 'cube',\n", + " 'n',\n", + " 's']" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(eg1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Just like global and local variables as we saw earlier, even classes have it's own types of variables.\n", + "\n", + "Class Attribute : attributes defined outside the method and is applicable to all the instances.\n", + "\n", + "Instance Attribute : attributes defined inside a method and is applicable to only that method and is unique to each instance." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "class FirstClass:\n", + " test = 'test'\n", + " def __init__(self,name,symbol):\n", + " self.name = name\n", + " self.symbol = symbol" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here test is a class attribute and name is a instance attribute." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test4\n" + ] + } + ], + "source": [ + "eg3 = FirstClass('Three',3)\n", + "eg4 = FirstClass('Four', 4)\n", + "eg4.test = 'test4'\n", + "print(eg4.test)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test Three\n" + ] + } + ], + "source": [ + "print(eg3.test, eg3.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us add some more methods to FirstClass." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "class FirstClass:\n", + " def __init__(self,name,symbol):\n", + " self.name = name\n", + " self.symbol = symbol\n", + " def square(self):\n", + " return self.symbol * self.symbol\n", + " def cube(self):\n", + " return self.symbol * self.symbol * self.symbol\n", + " def multiply(self, x):\n", + " return self.symbol * x" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "eg4 = FirstClass('Five',5)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "25\n", + "125\n" + ] + } + ], + "source": [ + "print(eg4.square())\n", + "print(eg4.cube())" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eg4.multiply(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above can also be written as," + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "FirstClass.multiply(eg4,2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Inheritance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There might be cases where a new class would have all the previous characteristics of an already defined class. So the new class can \"inherit\" the previous class and add it's own methods to it. This is called as inheritance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Consider class SoftwareEngineer which has a method salary." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "class SoftwareEngineer:\n", + " def __init__(self,name,age):\n", + " self.name = name\n", + " self.age = age\n", + " def salary(self, value):\n", + " self.money = value\n", + " print(self.name,\"earns\",self.money)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "a = SoftwareEngineer('Kartik',26)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Kartik earns 40000\n" + ] + } + ], + "source": [ + "a.salary(40000)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " 'salary']" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(SoftwareEngineer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now consider another class Artist which tells us about the amount of money an artist earns and his artform." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "class Artist:\n", + " def __init__(self,name,age):\n", + " self.name = name\n", + " self.age = age\n", + " def salary(self,value):\n", + " self.money = value\n", + " print(self.name,\"earns\",self.money)\n", + " def artform(self, job):\n", + " self.job = job\n", + " print(self.name,\"is a\", self.job)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "b = Artist('Nitin',20)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nitin earns 50000\n", + "Nitin is a Musician\n" + ] + } + ], + "source": [ + "b.salary(50000)\n", + "b.artform('Musician')" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " 'artform',\n", + " 'money']" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(Artist)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "money method and salary method are the same. So we can generalize the method to salary and inherit the SoftwareEngineer class to Artist class. Now the artist class becomes," + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "class Artist(SoftwareEngineer):\n", + " def artform(self, job):\n", + " self.job = job\n", + " print(self.name,\"is a\", self.job)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "c = Artist('Nishanth',21)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__class__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__le__',\n", + " '__lt__',\n", + " '__module__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " '__weakref__',\n", + " 'artform',\n", + " 'salary']" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(Artist)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nishanth earns 60000\n", + "Nishanth is a Dancer\n" + ] + } + ], + "source": [ + "c.salary(60000)\n", + "c.artform('Dancer')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suppose say while inheriting a particular method is not suitable for the new class. One can override this method by defining again that method with the same name inside the new class." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "class Artist(SoftwareEngineer):\n", + " def artform(self, job):\n", + " self.job = job\n", + " print(self.name,\"is a\", self.job)\n", + " def salary(self, value):\n", + " self.money = value\n", + " print(self.name,\"earns\",self.money)\n", + " print(\"I am overriding the SoftwareEngineer class's salary method\")" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "c = Artist('Nishanth',21)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nishanth earns 60000\n", + "I am overriding the SoftwareEngineer class's salary method\n", + "Nishanth is a Dancer\n" + ] + } + ], + "source": [ + "c.salary(60000)\n", + "c.artform('Dancer')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If not sure how many times methods will be called it will become difficult to declare so many variables to carry each result hence it is better to declare a list and append the result." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "class emptylist:\n", + " def __init__(self):\n", + " self.data = []\n", + " def one(self,x):\n", + " self.data.append(x)\n", + " def two(self, x ):\n", + " self.data.append(x**2)\n", + " def three(self, x):\n", + " self.data.append(x**3)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "xc = emptylist()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1]\n" + ] + } + ], + "source": [ + "xc.one(1)\n", + "print(xc.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since xc.data is a list direct list operations can also be performed." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 8]\n" + ] + } + ], + "source": [ + "xc.data.append(8)\n", + "print(xc.data)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 8, 9]\n" + ] + } + ], + "source": [ + "xc.two(3)\n", + "print(xc.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the number of input arguments varies from instance to instance asterisk can be used as shown." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "class NotSure:\n", + " def __init__(self, *args):\n", + " self.data = ''.join(list(args)) " + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "yz = NotSure('I', 'Do' , 'Not', 'Know', 'What', 'To','Type')" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'IDoNotKnowWhatToType'" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yz.data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Where to go from here?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Practice alone can help you get the hang of python. Give your self problem statements and solve them. You can also sign up to any competitive coding platform for problem statements. The more you code the more you discover and the more you start appreciating the language.\n", + "\n", + "\n", + "Now that you have been introduced to python, You can try out the different python libraries in the field of your interest. I highly recommend you to check out this curated list of Python frameworks, libraries and software http://awesome-python.com\n", + "\n", + "\n", + "The official python documentation : https://docs.python.org/3/\n", + "\n", + "Pyton tutorials:\n", + "* [Python tutorial (廖雪峰）](https://www.liaoxuefeng.com/wiki/1016959663602400)\n", + "* [Python基础教程](https://www.runoob.com/python/python-tutorial.html)\n", + "* [Python官方教程（中文版)](https://docs.python.org/zh-cn/3/tutorial/index.html)\n", + "\n", + "You can also check out Python practice programs written by my friend, Kartik Kannapur. Github Repo : https://github.com/rajathkumarmp/Python-Lectures \n", + "\n", + "\n", + "Enjoy solving problem statements because life is short, you need python!\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/0_python/README_EN.md b/0_python/README_EN.md new file mode 100644 index 0000000..a91dcad --- /dev/null +++ b/0_python/README_EN.md @@ -0,0 +1,36 @@ + +# 简明Python教程 （90分钟学会Python） + +Python 是一门上手简单、功能强大、通用型的脚本编程语言。Python 类库极其丰富，这使得 Python 几乎无所不能，网站开发、软件开发、大数据分析、网络爬虫、机器学习等都不在话下。Python最主要的优点是使用人类的思考方式来完成大部分的工作，大多数时候使用封装好的库快速完成给定的任务，虽然可能执行的效率不一定很高，但是极大的缩短了程序设计、编写、调试的时间，因此非常适合快速试错。 + +本教程来自[IPython Notebooks to learn Python](https://github.com/rajathkmp/Python-Lectures)，将其中部分示例代码转化成Python3。关于Python的按照可以自行去网络上查找相关的资料，或者参考[安装Python环境](../tips/InstallPython.md)。 + +## 内容 +0. [Install Python](../tips/InstallPython.md) +1. [Basics](1_Basics.ipynb) + - Why Python, Zen of Python + - Variables, Operators, Built-in functions +2. [Print statement](2_Print_Statement.ipynb) + - Tips of print +3. [Data structure - 1](3_Data_Structure_1.ipynb) + - Lists, Tuples, Sets +4. [Data structure - 2](4_Data_Structure_2.ipynb) + - Strings, Dictionaries +5. [Control flow](5_Control_Flow.ipynb) + - if, else, elif, for, while, break, continue +6. [Functions](6_Function.ipynb) + - Function define, return, arguments + - Gloabl and local variables + - Lambda functions +7. [Class](7_Class.ipynb) + - Class define + - Inheritance + + +## References +* [安装Python环境](../tips/InstallPython.md) +* [IPython Notebooks to learn Python](https://github.com/rajathkmp/Python-Lectures) +* [廖雪峰的Python教程](https://www.liaoxuefeng.com/wiki/1016959663602400) +* [智能系统实验室入门教程-Python](https://gitee.com/pi-lab/SummerCamp/tree/master/python) +* [Python tips](../tips/python) +* [Get Started with Python](Python.pdf) diff --git a/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial_EN.ipynb b/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial_EN.ipynb new file mode 100644 index 0000000..09a6b9e --- /dev/null +++ b/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial_EN.ipynb @@ -0,0 +1,4887 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Numpy - multidimensional data arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "J.R. Johansson (jrjohansson at gmail.com)\n", + "\n", + "The latest version of this [IPython notebook](http://ipython.org/notebook.html) lecture is available at [http://github.com/jrjohansson/scientific-python-lectures](http://github.com/jrjohansson/scientific-python-lectures).\n", + "\n", + "The other notebooks in this lecture series are indexed at [http://jrjohansson.github.io](http://jrjohansson.github.io)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# what is this line all about?!? Answer in lecture 4\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `numpy` package (module) is used in almost all numerical computation using Python. It is a package that provide high-performance vector, matrix and higher-dimensional data structures for Python. It is implemented in C and Fortran so when calculations are vectorized (formulated with vectors and matrices), performance is very good. \n", + "\n", + "To use `numpy` you need to import the module, using for example:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from numpy import *\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the `numpy` package the terminology used for vectors, matrices and higher-dimensional data sets is *array*. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating `numpy` arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are a number of ways to initialize new numpy arrays, for example from\n", + "\n", + "* a Python list or tuples\n", + "* using functions that are dedicated to generating numpy arrays, such as `arange`, `linspace`, etc.\n", + "* reading data from files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### From lists" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For example, to create new vector and matrix arrays from Python lists we can use the `numpy.array` function." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 4])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "# a vector: the argument to the array function is a Python list\n", + "v = np.array([1,2,3,4])\n", + "\n", + "v" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 2]\n", + " [3 4]\n", + " [5 6]]\n", + "(3, 2)\n" + ] + } + ], + "source": [ + "# a matrix: the argument to the array function is a nested Python list\n", + "M = array([[1, 2], [3, 4], [5, 6]])\n", + "\n", + "print(M)\n", + "print(M.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `v` and `M` objects are both of the type `ndarray` that the `numpy` module provides." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(numpy.ndarray, numpy.ndarray)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(v), type(M)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The difference between the `v` and `M` arrays is only their shapes. We can get information about the shape of an array by using the `ndarray.shape` property." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4,)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 2)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The number of elements in the array is available through the `ndarray.size` property:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Equivalently, we could use the function `numpy.shape` and `numpy.size`" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 2)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.shape(M)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.size(M)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far the `numpy.ndarray` looks awefully much like a Python list (or nested list). Why not simply use Python lists for computations instead of creating a new array type? \n", + "\n", + "There are several reasons:\n", + "\n", + "* Python lists are very general. They can contain any kind of object. They are dynamically typed. They do not support mathematical functions such as matrix and dot multiplications, etc. Implementing such functions for Python lists would not be very efficient because of the dynamic typing.\n", + "* Numpy arrays are **statically typed** and **homogeneous**. The type of the elements is determined when the array is created.\n", + "* Numpy arrays are memory efficient.\n", + "* Because of the static typing, fast implementation of mathematical functions such as multiplication and addition of `numpy` arrays can be implemented in a compiled language (C and Fortran is used).\n", + "\n", + "Using the `dtype` (data type) property of an `ndarray`, we can see what type the data of an array has:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('int64')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.dtype" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We get an error if we try to assign a value of the wrong type to an element in a numpy array:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "invalid literal for int() with base 10: 'hello'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"hello\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'hello'" + ] + } + ], + "source": [ + "M[0,0] = \"hello\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want, we can explicitly define the type of the array data when we create it, using the `dtype` keyword argument: " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.+0.j, 2.+0.j],\n", + " [3.+0.j, 4.+0.j]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M = np.array([[1, 2], [3, 4]], dtype=complex)\n", + "\n", + "M" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Common data types that can be used with `dtype` are: `int`, `float`, `complex`, `bool`, `object`, etc.\n", + "\n", + "We can also explicitly define the bit size of the data types, for example: `int64`, `int16`, `float128`, `complex128`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using array-generating functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For larger arrays it is inpractical to initialize the data manually, using explicit python lists. Instead we can use one of the many functions in `numpy` that generate arrays of different forms. Some of the more common are:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### arange" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 1 2 3 4 5 6 7 8 9]\n", + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n" + ] + } + ], + "source": [ + "# create a range\n", + "\n", + "x = np.arange(0, 10, 1) # arguments: start, stop, step\n", + "y = range(0, 10, 1)\n", + "print(x)\n", + "print(list(y))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-1.00000000e+00, -9.00000000e-01, -8.00000000e-01, -7.00000000e-01,\n", + " -6.00000000e-01, -5.00000000e-01, -4.00000000e-01, -3.00000000e-01,\n", + " -2.00000000e-01, -1.00000000e-01, -2.22044605e-16, 1.00000000e-01,\n", + " 2.00000000e-01, 3.00000000e-01, 4.00000000e-01, 5.00000000e-01,\n", + " 6.00000000e-01, 7.00000000e-01, 8.00000000e-01, 9.00000000e-01])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.arange(-1, 1, 0.1)\n", + "\n", + "x" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### linspace and logspace" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 0.41666667, 0.83333333, 1.25 , 1.66666667,\n", + " 2.08333333, 2.5 , 2.91666667, 3.33333333, 3.75 ,\n", + " 4.16666667, 4.58333333, 5. , 5.41666667, 5.83333333,\n", + " 6.25 , 6.66666667, 7.08333333, 7.5 , 7.91666667,\n", + " 8.33333333, 8.75 , 9.16666667, 9.58333333, 10. ])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# using linspace, both end points ARE included\n", + "np.linspace(0, 10, 25)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.00000000e+00, 3.03773178e+00, 9.22781435e+00, 2.80316249e+01,\n", + " 8.51525577e+01, 2.58670631e+02, 7.85771994e+02, 2.38696456e+03,\n", + " 7.25095809e+03, 2.20264658e+04])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.logspace(0, 10, 10, base=e)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### mgrid" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "x, y = np.mgrid[0:5, 0:5] # similar to meshgrid in MATLAB" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 0, 0, 0, 0],\n", + " [1, 1, 1, 1, 1],\n", + " [2, 2, 2, 2, 2],\n", + " [3, 3, 3, 3, 3],\n", + " [4, 4, 4, 4, 4]])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 2, 3, 4],\n", + " [0, 1, 2, 3, 4],\n", + " [0, 1, 2, 3, 4],\n", + " [0, 1, 2, 3, 4],\n", + " [0, 1, 2, 3, 4]])" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### random data" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "from numpy import random" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.82594014, 0.31160547, 0.77827738, 0.59082014, 0.69654657],\n", + " [0.64715318, 0.05551977, 0.38057657, 0.45135262, 0.37209654],\n", + " [0.01234335, 0.12906551, 0.75598568, 0.20905719, 0.86103339],\n", + " [0.62784645, 0.87732666, 0.96543239, 0.41053462, 0.87116428],\n", + " [0.44218283, 0.70837525, 0.15065753, 0.93552422, 0.79261749]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# uniform random numbers in [0,1)\n", + "random.rand(5,5)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.69829709, 0.04679976, 0.95770162, 1.91007838, -0.41865049],\n", + " [ 0.51678337, -0.34692074, 2.19264774, -0.59725524, -1.15314406],\n", + " [ 0.03361378, -0.0054733 , -0.77389592, -0.12696594, 1.69339468],\n", + " [-0.13267375, 0.95688595, 0.28043241, 0.83043672, 0.62677072],\n", + " [-0.09168095, -0.25064829, 0.49440189, -1.18704973, -1.28781414]])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# standard normal distributed random numbers\n", + "random.randn(5,5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### diag" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 0, 0],\n", + " [0, 2, 0],\n", + " [0, 0, 3]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# a diagonal matrix\n", + "np.diag([1,2,3])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 0, 0],\n", + " [0, 0, 2, 0],\n", + " [0, 0, 0, 3],\n", + " [0, 0, 0, 0]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# diagonal with offset from the main diagonal\n", + "diag([1,2,3], k=1) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### zeros and ones" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.]])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.zeros((3,3))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 1., 1.],\n", + " [1., 1., 1.],\n", + " [1., 1., 1.]])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.ones((3,3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## File I/O" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comma-separated values (CSV)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A very common file format for data files is comma-separated values (CSV), or related formats such as TSV (tab-separated values). To read data from such files into Numpy arrays we can use the `numpy.genfromtxt` function. For example, " + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1800 1 1 -6.1 -6.1 -6.1 1\r\n", + "1800 1 2 -15.4 -15.4 -15.4 1\r\n", + "1800 1 3 -15.0 -15.0 -15.0 1\r\n", + "1800 1 4 -19.3 -19.3 -19.3 1\r\n", + "1800 1 5 -16.8 -16.8 -16.8 1\r\n", + "1800 1 6 -11.4 -11.4 -11.4 1\r\n", + "1800 1 7 -7.6 -7.6 -7.6 1\r\n", + "1800 1 8 -7.1 -7.1 -7.1 1\r\n", + "1800 1 9 -10.1 -10.1 -10.1 1\r\n", + "1800 1 10 -9.5 -9.5 -9.5 1\r\n" + ] + } + ], + "source": [ + "!head stockholm_td_adj.dat" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "data = np.genfromtxt('stockholm_td_adj.dat')" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(77431, 7)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, ax = plt.subplots(figsize=(14,4))\n", + "ax.plot(data[:,0]+data[:,1]/12.0+data[:,2]/365, data[:,5])\n", + "ax.axis('tight')\n", + "ax.set_title('tempeatures in Stockholm')\n", + "ax.set_xlabel('year')\n", + "ax.set_ylabel('temperature (C)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using `numpy.savetxt` we can store a Numpy array to a file in CSV format:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.85030715, 0.33330859, 0.64002838],\n", + " [0.52521743, 0.21572812, 0.33287991],\n", + " [0.74605429, 0.35134767, 0.45873422]])" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M = np.random.rand(3,3)\n", + "\n", + "M" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "np.savetxt(\"random-matrix.csv\", M)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8.503071542574233144e-01 3.333085915891427220e-01 6.400283846962552259e-01\r\n", + "5.252174340396357222e-01 2.157281249144539226e-01 3.328799104985459278e-01\r\n", + "7.460542870039649221e-01 3.513476662217395186e-01 4.587342216214667090e-01\r\n" + ] + } + ], + "source": [ + "!cat random-matrix.csv" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.85031 0.33331 0.64003\r\n", + "0.52522 0.21573 0.33288\r\n", + "0.74605 0.35135 0.45873\r\n" + ] + } + ], + "source": [ + "np.savetxt(\"random-matrix.csv\", M, fmt='%.5f') # fmt specifies the format\n", + "\n", + "!cat random-matrix.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Numpy's native file format" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Useful when storing and reading back numpy array data. Use the functions `numpy.save` and `numpy.load`:" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "random-matrix.npy: data\r\n" + ] + } + ], + "source": [ + "np.save(\"random-matrix.npy\", M)\n", + "\n", + "!file random-matrix.npy" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.85030715, 0.33330859, 0.64002838],\n", + " [0.52521743, 0.21572812, 0.33287991],\n", + " [0.74605429, 0.35134767, 0.45873422]])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.load(\"random-matrix.npy\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## More properties of the numpy arrays" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "float64\n", + "8\n" + ] + } + ], + "source": [ + "print(M.dtype)\n", + "print(M.itemsize) # bytes per element\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "72" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.nbytes # number of bytes" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.ndim # number of dimensions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Manipulating arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Indexing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can index elements in an array using square brackets and indices:" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v = np.array([1, 2, 3, 4, 5])\n", + "# v is a vector, and has only one dimension, taking one index\n", + "v[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.21572812491445392\n", + "0.21572812491445392\n", + "[0.52521743 0.21572812 0.33287991]\n" + ] + } + ], + "source": [ + "\n", + "# M is a matrix, or a 2 dimensional array, taking two indices \n", + "print(M[1,1])\n", + "print(M[1][1])\n", + "print(M[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we omit an index of a multidimensional array it returns the whole row (or, in general, a N-1 dimensional array) " + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.85030715, 0.33330859, 0.64002838],\n", + " [0.52521743, 0.21572812, 0.33287991],\n", + " [0.74605429, 0.35134767, 0.45873422]])" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.52521743, 0.21572812, 0.33287991])" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M[1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The same thing can be achieved with using `:` instead of an index: " + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.52521743, 0.21572812, 0.33287991])" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M[1,:] # row 1" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.33330859, 0.21572812, 0.35134767])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M[:,1] # column 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can assign new values to elements in an array using indexing:" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "M[0,0] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1. , 0.33330859, 0.64002838],\n", + " [0.52521743, 0.21572812, 0.33287991],\n", + " [0.74605429, 0.35134767, 0.45873422]])" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "# also works for rows and columns\n", + "M[1,:] = 0\n", + "M[:,2] = -1" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1. , 0.85499268, -1. ],\n", + " [ 0. , 0. , -1. ],\n", + " [ 0.55448257, 0.53279085, -1. ]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Index slicing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Index slicing is the technical name for the syntax `M[lower:upper:step]` to extract part of an array:" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3, 4, 5])" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = np.array([1,2,3,4,5])\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 3])" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[1:3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Array slices are *mutable*: if they are assigned a new value the original array from which the slice was extracted is modified:" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, -2, -3, 4, 5])" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[1:3] = [-2,-3] # auto convert type\n", + "A[1:3] = np.array([-2, -3]) \n", + "\n", + "A" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can omit any of the three parameters in `M[lower:upper:step]`:" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, -2, -3, 4, 5])" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[::] # lower, upper, step all take the default values" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, -2, -3, 4, 5])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[:]" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, -3, 5])" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[::2] # step is 2, lower and upper defaults to the beginning and end of the array" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, -2, -3])" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[:3] # first three elements" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4, 5])" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[3:] # elements from index 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Negative indices counts from the end of the array (positive index from the begining):" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "A = np.array([1,2,3,4,5])" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[-1] # the last element in the array" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3, 4, 5])" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[-3:] # the last three elements" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Index slicing works exactly the same way for multidimensional arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4],\n", + " [10, 11, 12, 13, 14],\n", + " [20, 21, 22, 23, 24],\n", + " [30, 31, 32, 33, 34],\n", + " [40, 41, 42, 43, 44]])" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = np.array([[n+m*10 for n in range(5)] for m in range(5)])\n", + "\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[11, 12, 13],\n", + " [21, 22, 23],\n", + " [31, 32, 33]])" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# a block from the original array\n", + "A[1:4, 1:4]" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 2, 4],\n", + " [20, 22, 24],\n", + " [40, 42, 44]])" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# strides\n", + "A[::2, ::2]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fancy indexing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fancy indexing is the name for when an array or list is used in-place of an index: " + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[10 11 12 13 14]\n", + " [20 21 22 23 24]\n", + " [30 31 32 33 34]]\n", + "[[ 0 1 2 3 4]\n", + " [10 11 12 13 14]\n", + " [20 21 22 23 24]\n", + " [30 31 32 33 34]\n", + " [40 41 42 43 44]]\n" + ] + } + ], + "source": [ + "row_indices = [1, 2, 3]\n", + "print(A[row_indices])\n", + "print(A)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([11, 22, 34])" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "col_indices = [1, 2, -1] # remember, index -1 means the last element\n", + "A[row_indices, col_indices]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also use index masks: If the index mask is an Numpy array of data type `bool`, then an element is selected (True) or not (False) depending on the value of the index mask at the position of each element: " + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4])" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "B = array([n for n in range(5)])\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 2])" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "row_mask = array([True, False, True, False, False])\n", + "B[row_mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 2])" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# same thing\n", + "row_mask = array([1,0,1,0,0], dtype=bool)\n", + "B[row_mask]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This feature is very useful to conditionally select elements from an array, using for example comparison operators:" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5, 6. ,\n", + " 6.5, 7. , 7.5, 8. , 8.5, 9. , 9.5])" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = np.arange(0, 10, 0.5)\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, False, False, False, False, False, False, False, False,\n", + " False, False, True, True, True, True, False, False, False,\n", + " False, False])" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mask = (5 < x) * (x < 7.5)\n", + "\n", + "mask" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5.5, 6. , 6.5, 7. ])" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3.5, 4. , 4.5, 5. , 5.5])" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x[(3\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,3) (5,) " + ] + } + ], + "source": [ + "A * v1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Matrix algebra" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What about matrix mutiplication? There are two ways. We can either use the `dot` function, which applies a matrix-matrix, matrix-vector, or inner vector multiplication to its two arguments: " + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.3767892 , 1.47079714, 0.31117826, 1.29726746, 0.51486767],\n", + " [0.25604237, 0.97247777, 0.34479677, 0.93969314, 0.3976715 ],\n", + " [0.81557228, 1.22841789, 0.86636095, 0.93499185, 0.28560187],\n", + " [0.52515694, 1.56792282, 1.1443364 , 1.84965072, 0.74141231],\n", + " [0.78004097, 1.51298694, 1.22023006, 1.42991218, 0.71648303]])" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = np.random.rand(5, 5)\n", + "v = np.random.rand(5, 1)\n", + "\n", + "np.dot(A, A)" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3.03824466, 2.65209134, 2.94637897, 6.50153897, 5.54270391])" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.dot(A, v1)" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "30" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.dot(v1, v1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, we can cast the array objects to the type `matrix`. This changes the behavior of the standard arithmetic operators `+, -, *` to use matrix algebra." + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [], + "source": [ + "M = np.matrix(A)\n", + "v = np.matrix(v1).T # make it a column vector" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[0],\n", + " [1],\n", + " [2],\n", + " [3],\n", + " [4]])" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[0.3767892 , 1.47079714, 0.31117826, 1.29726746, 0.51486767],\n", + " [0.25604237, 0.97247777, 0.34479677, 0.93969314, 0.3976715 ],\n", + " [0.81557228, 1.22841789, 0.86636095, 0.93499185, 0.28560187],\n", + " [0.52515694, 1.56792282, 1.1443364 , 1.84965072, 0.74141231],\n", + " [0.78004097, 1.51298694, 1.22023006, 1.42991218, 0.71648303]])" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M * M" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[3.03824466],\n", + " [2.65209134],\n", + " [2.94637897],\n", + " [6.50153897],\n", + " [5.54270391]])" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M * v" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[30]])" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# inner product\n", + "v.T * v" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[3.03824466],\n", + " [3.65209134],\n", + " [4.94637897],\n", + " [9.50153897],\n", + " [9.54270391]])" + ] + }, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# with matrix objects, standard matrix algebra applies\n", + "v + M*v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we try to add, subtract or multiply objects with incomplatible shapes we get an error:" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "metadata": {}, + "outputs": [], + "source": [ + "v = np.matrix([1,2,3,4,5,6]).T" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((5, 5), (5, 1))" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.shape(M), np.shape(v)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[5.06458489],\n", + " [4.08471675],\n", + " [4.990684 ],\n", + " [9.17423165],\n", + " [8.08502244]])" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M * v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See also the related functions: `inner`, `outer`, `cross`, `kron`, `tensordot`. Try for example `help(kron)`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Array/Matrix transformations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above we have used the `.T` to transpose the matrix object `v`. We could also have used the `transpose` function to accomplish the same thing. \n", + "\n", + "Other mathematical functions that transform matrix objects are:" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.04208911 0.65828119 0.21987187 0.10069326]\n", + " [0.61960112 0.52726045 0.35884175 0.51931613]\n", + " [0.66708619 0.76886997 0.06792093 0.6548313 ]]\n", + "[[0.04208911 0.61960112 0.66708619]\n", + " [0.65828119 0.52726045 0.76886997]\n", + " [0.21987187 0.35884175 0.06792093]\n", + " [0.10069326 0.51931613 0.6548313 ]]\n" + ] + } + ], + "source": [ + "A = np.random.rand(3,4)\n", + "print(A)\n", + "print(A.T)" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[0.+1.j, 0.+2.j],\n", + " [0.+3.j, 0.+4.j]])" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "C = np.matrix([[1j, 2j], [3j, 4j]])\n", + "C" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[0.-1.j, 0.-2.j],\n", + " [0.-3.j, 0.-4.j]])" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conjugate(C)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hermitian conjugate: transpose + conjugate" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[0.-1.j, 0.-3.j],\n", + " [0.-2.j, 0.-4.j]])" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "C.H" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can extract the real and imaginary parts of complex-valued arrays using `real` and `imag`:" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[0., 0.],\n", + " [0., 0.]])" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "real(C) # same as: C.real" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[1., 2.],\n", + " [3., 4.]])" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "imag(C) # same as: C.imag" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or the complex argument and absolute value" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.78539816, 1.10714872],\n", + " [ 1.24904577, 1.32581766]])" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "angle(C+1) # heads up MATLAB Users, angle is used instead of arg" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[ 1., 2.],\n", + " [ 3., 4.]])" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abs(C)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Matrix computations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Inverse" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[0.+2.j , 0.-1.j ],\n", + " [0.-1.5j, 0.+0.5j]])" + ] + }, + "execution_count": 132, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.inv(C) # equivalent to C.I " + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[1.00000000e+00+0.j, 0.00000000e+00+0.j],\n", + " [2.22044605e-16+0.j, 1.00000000e+00+0.j]])" + ] + }, + "execution_count": 133, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "C.I * C" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Determinant" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2.0000000000000004+0j)" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linalg.det(C)" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.49999999999999967+0j)" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "linalg.det(C.I)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data processing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Often it is useful to store datasets in Numpy arrays. Numpy provides a number of functions to calculate statistics of datasets in arrays. \n", + "\n", + "For example, let's calculate some properties from the Stockholm temperature dataset used above." + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(77431, 7)" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# reminder, the tempeature dataset is stored in the data variable:\n", + "np.shape(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### mean" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(77431, 7)\n" + ] + }, + { + "data": { + "text/plain": [ + "6.197109684751585" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the temperature data is in column 3\n", + "print(data.shape)\n", + "np.mean(data[:,3])" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.4764047026464162" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = np.random.rand(4, 3)\n", + "np.mean(A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The daily mean temperature in Stockholm over the last 200 years has been about 6.2 C." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### standard deviations and variance" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(8.282271621340573, 68.59602320966341)" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.std(data[:,3]), np.var(data[:,3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### min and max" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-25.8" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# lowest daily average temperature\n", + "data[:,3].min()" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "28.3" + ] + }, + "execution_count": 140, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# highest daily average temperature\n", + "data[:,3].max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### sum, prod, and trace" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 141, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d = np.arange(0, 10)\n", + "d" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "45" + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# sum up all elements\n", + "np.sum(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3628800" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# product of all elements\n", + "np.prod(d+1)" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 3, 6, 10, 15, 21, 28, 36, 45])" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# cummulative sum\n", + "np.cumsum(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 2, 6, 24, 120, 720, 5040,\n", + " 40320, 362880, 3628800])" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# cummulative product\n", + "np.cumprod(d+1)" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.04879166276667" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# same as: diag(A).sum()\n", + "np.trace(A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Computations on subsets of arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can compute with subsets of the data in an array using indexing, fancy indexing, and the other methods of extracting data from an array (described above).\n", + "\n", + "For example, let's go back to the temperature dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1800 1 1 -6.1 -6.1 -6.1 1\r\n", + "1800 1 2 -15.4 -15.4 -15.4 1\r\n", + "1800 1 3 -15.0 -15.0 -15.0 1\r\n" + ] + } + ], + "source": [ + "!head -n 3 stockholm_td_adj.dat" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataformat is: year, month, day, daily average temperature, low, high, location.\n", + "\n", + "If we are interested in the average temperature only in a particular month, say February, then we can create a index mask and use it to select only the data for that month using:" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.])" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.unique(data[:,1]) # the month column takes values from 1 to 12" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[False False False ... False False False]\n" + ] + } + ], + "source": [ + "mask_feb = data[:,1] == 2\n", + "print(mask_feb)" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-3.212109570736596\n", + "5.090390768766271\n" + ] + } + ], + "source": [ + "# the temperature data is in column 3\n", + "print(np.mean(data[mask_feb,3]))\n", + "print(np.std(data[mask_feb,3]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With these tools we have very powerful data processing capabilities at our disposal. For example, to extract the average monthly average temperatures for each month of the year only takes a few lines of code: " + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "months = np.arange(1,13)\n", + "monthly_mean = [np.mean(data[data[:,1] == month, 3]) for month in months]\n", + "\n", + "fig, ax = plt.subplots()\n", + "ax.bar(months, monthly_mean)\n", + "ax.set_xlabel(\"Month\")\n", + "ax.set_ylabel(\"Monthly avg. temp.\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculations with higher-dimensional data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When functions such as `min`, `max`, etc. are applied to a multidimensional arrays, it is sometimes useful to apply the calculation to the entire array, and sometimes only on a row or column basis. Using the `axis` argument we can specify how these functions should behave: " + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.99782852, 0.15992805, 0.31262638],\n", + " [0.51702607, 0.45658172, 0.66789036],\n", + " [0.77771351, 0.42574723, 0.14011317]])" + ] + }, + "execution_count": 157, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "m = np.random.rand(3,3)\n", + "m" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.997828517861979" + ] + }, + "execution_count": 158, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# global max\n", + "m.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.99782852, 0.45658172, 0.66789036])" + ] + }, + "execution_count": 159, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# max in each column\n", + "m.max(axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.99782852, 0.66789036, 0.77771351])" + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# max in each row\n", + "m.max(axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Many other functions and methods in the `array` and `matrix` classes accept the same (optional) `axis` keyword argument." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reshaping, resizing and stacking arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The shape of an Numpy array can be modified without copying the underlaying data, which makes it a fast operation even for large arrays." + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.97579482 0.78668761 0.61373444]\n", + " [0.58850244 0.9784108 0.08465447]\n", + " [0.57262123 0.44795615 0.75564229]\n", + " [0.36770219 0.34095592 0.16259103]]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "A = np.random.rand(4, 3)\n", + "print(A)" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 3\n" + ] + } + ], + "source": [ + "n, m = A.shape\n", + "print(n, m)" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0.97579482, 0.78668761, 0.61373444, 0.58850244, 0.9784108 ,\n", + " 0.08465447, 0.57262123, 0.44795615, 0.75564229, 0.36770219,\n", + " 0.34095592, 0.16259103]])" + ] + }, + "execution_count": 166, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "B = A.reshape((1,n*m))\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.97579482]\n", + " [0.78668761]\n", + " [0.61373444]\n", + " [0.58850244]\n", + " [0.9784108 ]\n", + " [0.08465447]\n", + " [0.57262123]\n", + " [0.44795615]\n", + " [0.75564229]\n", + " [0.36770219]\n", + " [0.34095592]\n", + " [0.16259103]]\n" + ] + } + ], + "source": [ + "B2 = A.reshape((n*m, 1))\n", + "print(B2)" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[5. , 5. , 5. , 5. , 5. ,\n", + " 0.08465447, 0.57262123, 0.44795615, 0.75564229, 0.36770219,\n", + " 0.34095592, 0.16259103]])" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "B[0,0:5] = 5 # modify the array\n", + "\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[5. , 5. , 5. ],\n", + " [5. , 5. , 0.08465447],\n", + " [0.57262123, 0.44795615, 0.75564229],\n", + " [0.36770219, 0.34095592, 0.16259103]])" + ] + }, + "execution_count": 169, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A # and the original variable is also changed. B is only a different view of the same data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also use the function `flatten` to make a higher-dimensional array into a vector. But this function create a copy of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5. , 5. , 5. , 5. , 5. ,\n", + " 0.08465447, 0.57262123, 0.44795615, 0.75564229, 0.36770219,\n", + " 0.34095592, 0.16259103])" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "B = A.flatten()\n", + "\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(12,)\n" + ] + } + ], + "source": [ + "print(B.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.0643267 0.02070895 0.01127191 0.36318507 0.26309744 0.8332378\n", + " 0.79477743 0.52745619 0.35675021 0.55907373 0.18993756 0.15919449\n", + " 0.54789401 0.23186893 0.02898541 0.43545343 0.80684175 0.44014057\n", + " 0.05129167 0.95111801 0.40743132 0.57197596 0.6692788 0.80824496\n", + " 0.40301441 0.84369196 0.95294593 0.14876807 0.58005171 0.30849079\n", + " 0.27846197 0.01062528 0.62870079 0.6416306 0.76945123 0.39443503\n", + " 0.76619764 0.42833327 0.60720341 0.16246792 0.76067082 0.27134944\n", + " 0.36268568 0.78501742 0.36935191 0.43410334 0.10594888 0.12941728\n", + " 0.51760718 0.57260509 0.09756568 0.13216908 0.32918105 0.9338644\n", + " 0.71681907 0.58218819 0.58798528 0.81665138 0.73604797 0.91730101]\n" + ] + } + ], + "source": [ + "T = np.random.rand(3, 4, 5)\n", + "T2 = T.flatten()\n", + "print(T2)" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([10. , 10. , 10. , 10. , 10. ,\n", + " 0.08465447, 0.57262123, 0.44795615, 0.75564229, 0.36770219,\n", + " 0.34095592, 0.16259103])" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "B[0:5] = 10\n", + "\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[5. , 5. , 5. ],\n", + " [5. , 5. , 0.08465447],\n", + " [0.57262123, 0.44795615, 0.75564229],\n", + " [0.36770219, 0.34095592, 0.16259103]])" + ] + }, + "execution_count": 177, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A # now A has not changed, because B's data is a copy of A's, not refering to the same data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding a new dimension: newaxis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With `newaxis`, we can insert new dimensions in an array, for example converting a vector to a column or row matrix:" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [], + "source": [ + "v = np.array([1,2,3])" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3,)" + ] + }, + "execution_count": 179, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.shape(v)" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 2 3]\n" + ] + } + ], + "source": [ + "print(v)" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 1)\n" + ] + } + ], + "source": [ + "v2 = v.reshape(3, 1)\n", + "print(v2.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3,)\n", + "(3, 1)\n" + ] + } + ], + "source": [ + "# make a column matrix of the vector v\n", + "v2 = v[:, np.newaxis]\n", + "print(v.shape)\n", + "print(v2.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 1)" + ] + }, + "execution_count": 191, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# column matrix\n", + "v[:,newaxis].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 3)" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# row matrix\n", + "v[newaxis,:].shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stacking and repeating arrays" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using function `repeat`, `tile`, `vstack`, `hstack`, and `concatenate` we can create larger vectors and matrices from smaller ones:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### tile and repeat" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": {}, + "outputs": [], + "source": [ + "a = np.array([[1, 2], [3, 4]])" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 2]\n", + " [3 4]]\n" + ] + }, + { + "data": { + "text/plain": [ + "array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4])" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(a)\n", + "\n", + "# repeat each element 3 times\n", + "np.repeat(a, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 1, 2, 1, 2],\n", + " [3, 4, 3, 4, 3, 4]])" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# tile the matrix 3 times \n", + "np.tile(a, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 1, 2, 1, 2],\n", + " [3, 4, 3, 4, 3, 4]])" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# better method\n", + "np.tile(a, (1, 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2],\n", + " [3, 4],\n", + " [1, 2],\n", + " [3, 4],\n", + " [1, 2],\n", + " [3, 4]])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.tile(a, (3, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### concatenate" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [], + "source": [ + "b = np.array([[5, 6]])" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2],\n", + " [3, 4],\n", + " [5, 6]])" + ] + }, + "execution_count": 198, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.concatenate((a, b), axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 5],\n", + " [3, 4, 6]])" + ] + }, + "execution_count": 200, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.concatenate((a, b.T), axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### hstack and vstack" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2],\n", + " [3, 4],\n", + " [5, 6]])" + ] + }, + "execution_count": 201, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.vstack((a,b))" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 5],\n", + " [3, 4, 6]])" + ] + }, + "execution_count": 202, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.hstack((a,b.T))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Copy and \"deep copy\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To achieve high performance, assignments in Python usually do not copy the underlaying objects. This is important for example when objects are passed between functions, to avoid an excessive amount of memory copying when it is not necessary (technical term: pass by reference). " + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2],\n", + " [3, 4]])" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = np.array([[1, 2], [3, 4]])\n", + "\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [], + "source": [ + "# now B is referring to the same array data as A \n", + "B = A " + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[10, 2],\n", + " [ 3, 4]])" + ] + }, + "execution_count": 205, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# changing B affects A\n", + "B[0,0] = 10\n", + "\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[10, 2],\n", + " [ 3, 4]])" + ] + }, + "execution_count": 206, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to avoid this behavior, so that when we get a new completely independent object `B` copied from `A`, then we need to do a so-called \"deep copy\" using the function `copy`:" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": {}, + "outputs": [], + "source": [ + "B = np.copy(A)" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-5, 2],\n", + " [ 3, 4]])" + ] + }, + "execution_count": 208, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now, if we modify B, A is not affected\n", + "B[0,0] = -5\n", + "\n", + "B" + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[10, 2],\n", + " [ 3, 4]])" + ] + }, + "execution_count": 209, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Iterating over array elements" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generally, we want to avoid iterating over the elements of arrays whenever we can (at all costs). The reason is that in a interpreted language like Python (or MATLAB), iterations are really slow compared to vectorized operations. \n", + "\n", + "However, sometimes iterations are unavoidable. For such cases, the Python `for` loop is the most convenient way to iterate over an array:" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "3\n", + "4\n" + ] + } + ], + "source": [ + "v = np.array([1,2,3,4])\n", + "\n", + "for element in v:\n", + " print(element)" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "row [1 2]\n", + "1\n", + "2\n", + "row [3 4]\n", + "3\n", + "4\n" + ] + } + ], + "source": [ + "M = np.array([[1,2], [3,4]])\n", + "\n", + "for row in M:\n", + " print(\"row\", row)\n", + " \n", + " for element in row:\n", + " print(element)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we need to iterate over each element of an array and modify its elements, it is convenient to use the `enumerate` function to obtain both the element and its index in the `for` loop: " + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('row_idx', 0, 'row', array([1, 2]))\n", + "('col_idx', 0, 'element', 1)\n", + "('col_idx', 1, 'element', 2)\n", + "('row_idx', 1, 'row', array([3, 4]))\n", + "('col_idx', 0, 'element', 3)\n", + "('col_idx', 1, 'element', 4)\n" + ] + } + ], + "source": [ + "for row_idx, row in enumerate(M):\n", + " print(\"row_idx\", row_idx, \"row\", row)\n", + " \n", + " for col_idx, element in enumerate(row):\n", + " print(\"col_idx\", col_idx, \"element\", element)\n", + " \n", + " # update the matrix M: square each element\n", + " M[row_idx, col_idx] = element ** 2" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1, 4],\n", + " [ 9, 16]])" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# each element in M is now squared\n", + "M" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Vectorizing functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned several times by now, to get good performance we should try to avoid looping over elements in our vectors and matrices, and instead use vectorized algorithms. The first step in converting a scalar algorithm to a vectorized algorithm is to make sure that the functions we write work with vector inputs." + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [], + "source": [ + "def Theta(x):\n", + " \"\"\"\n", + " Scalar implemenation of the Heaviside step function.\n", + " \"\"\"\n", + " if x >= 0:\n", + " return 1\n", + " else:\n", + " return 0" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mTheta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mTheta\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mScalar\u001b[0m \u001b[0mimplemenation\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mHeaviside\u001b[0m \u001b[0mstep\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \"\"\"\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" + ] + } + ], + "source": [ + "Theta(array([-3,-2,-1,0,1,2,3]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OK, that didn't work because we didn't write the `Theta` function so that it can handle a vector input... \n", + "\n", + "To get a vectorized version of Theta we can use the Numpy function `vectorize`. In many cases it can automatically vectorize a function:" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [], + "source": [ + "Theta_vec = np.vectorize(Theta)" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 1, 1, 1, 1])" + ] + }, + "execution_count": 216, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Theta_vec(np.array([-3,-2,-1,0,1,2,3]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also implement the function to accept a vector input from the beginning (requires more effort but might give better performance):" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [], + "source": [ + "def Theta(x):\n", + " \"\"\"\n", + " Vector-aware implemenation of the Heaviside step function.\n", + " \"\"\"\n", + " return 1 * (x >= 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, 1, 1, 1, 1])" + ] + }, + "execution_count": 219, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Theta(np.array([-3,-2,-1,0,1,2,3]))" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[False False False True True True True]\n" + ] + }, + { + "data": { + "text/plain": [ + "array([0, 0, 0, 1, 1, 1, 1])" + ] + }, + "execution_count": 221, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.array([-3,-2,-1,0,1,2,3])\n", + "b = a>=0\n", + "print(b)\n", + "b*1" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0, 1)" + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# still works for scalars as well\n", + "Theta(-1.2), Theta(2.6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using arrays in conditions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When using arrays in conditions,for example `if` statements and other boolean expressions, one needs to use `any` or `all`, which requires that any or all elements in the array evalutes to `True`:" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2],\n", + " [3, 4]])" + ] + }, + "execution_count": 223, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M = np.array([[1, 2], [3, 4]])\n", + "M" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(M > 2).any()" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "at least one element in M is larger than 2\n" + ] + } + ], + "source": [ + "if (M > 2).any():\n", + " print(\"at least one element in M is larger than 2\")\n", + "else:\n", + " print(\"no element in M is larger than 2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "all elements in M are not larger than 5\n" + ] + } + ], + "source": [ + "if (M > 5).all():\n", + " print(\"all elements in M are larger than 5\")\n", + "else:\n", + " print(\"all elements in M are not larger than 5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Type casting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since Numpy arrays are *statically typed*, the type of an array does not change once created. But we can explicitly cast an array of some type to another using the `astype` functions (see also the similar `asarray` function). This always create a new array of new type:" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('int64')" + ] + }, + "execution_count": 227, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 2.],\n", + " [3., 4.]])" + ] + }, + "execution_count": 228, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M2 = M.astype(float)\n", + "\n", + "M2" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 229, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M2.dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ True, True],\n", + " [ True, True]])" + ] + }, + "execution_count": 230, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "M3 = M.astype(bool)\n", + "\n", + "M3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Further reading" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* http://numpy.scipy.org\n", + "* http://scipy.org/Tentative_NumPy_Tutorial\n", + "* http://scipy.org/NumPy_for_Matlab_Users - A Numpy guide for MATLAB users." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Versions" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "data": { + "application/json": { + "Software versions": [ + { + "module": "Python", + "version": "2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]" + }, + { + "module": "IPython", + "version": "3.2.1" + }, + { + "module": "OS", + "version": "Darwin 14.1.0 x86_64 i386 64bit" + }, + { + "module": "numpy", + "version": "1.9.2" + } + ] + }, + "text/html": [ + "
SoftwareVersion
Python2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]
IPython3.2.1
OSDarwin 14.1.0 x86_64 i386 64bit
numpy1.9.2
Sat Aug 15 11:02:09 2015 JST
" + ], + "text/latex": [ + "\\begin{tabular}{|l|l|}\\hline\n", + "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n", + "Python & 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)] \\\\ \\hline\n", + "IPython & 3.2.1 \\\\ \\hline\n", + "OS & Darwin 14.1.0 x86\\_64 i386 64bit \\\\ \\hline\n", + "numpy & 1.9.2 \\\\ \\hline\n", + "\\hline \\multicolumn{2}{|l|}{Sat Aug 15 11:02:09 2015 JST} \\\\ \\hline\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "Software versions\n", + "Python 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]\n", + "IPython 3.2.1\n", + "OS Darwin 14.1.0 x86_64 i386 64bit\n", + "numpy 1.9.2\n", + "Sat Aug 15 11:02:09 2015 JST" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%reload_ext version_information\n", + "\n", + "%version_information numpy" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/1_numpy_matplotlib_scipy_sympy/2-matplotlib_simple_tutorial_EN.ipynb b/1_numpy_matplotlib_scipy_sympy/2-matplotlib_simple_tutorial_EN.ipynb new file mode 100644 index 0000000..91c18f4 --- /dev/null +++ b/1_numpy_matplotlib_scipy_sympy/2-matplotlib_simple_tutorial_EN.ipynb @@ -0,0 +1,467 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# matplotlib\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. pyplot\n", + "matplotlib.pyplot is a collection of command style functions that make matplotlib work like MATLAB. Each pyplot function makes some change to a figure: e.g., creates a figure, creates a plotting area in a figure, plots some lines in a plotting area, decorates the plot with labels, etc. In matplotlib.pyplot various states are preserved across function calls, so that it keeps track of things like the current figure and plotting area, and the plotting functions are directed to the current axes (please note that “axes” here and in most places in the documentation refers to the axes part of a figure and not the strict mathematical term for more than one axis)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# This line configures matplotlib to show figures embedded in the notebook, \n", + "# instead of opening a new window for each figure. More about that later. \n", + "# If you are using an old version of IPython, try using '%pylab inline' instead.\n", + "%matplotlib inline\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.plot([1,2,3,4], '-o')\n", + "plt.ylabel('some numbers')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot([1, 2, 3, 4], [1, 4, 9, 16])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For every x, y pair of arguments, there is an optional third argument which is the format string that indicates the color and line type of the plot. The letters and symbols of the format string are from MATLAB, and you concatenate a color string with a line style string. The default format string is ‘b-‘, which is a solid blue line. For example, to plot the above with red circles, you would issue" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.plot([1,2,3,4], [1,4,9,16], 'r.-')\n", + "plt.axis([0, 6, 0, 20])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# evenly sampled time at 200ms intervals\n", + "t = np.arange(0., 5., 0.2)\n", + "\n", + "# red dashes, blue squares and green triangles\n", + "plt.plot(t, t, 'r--', \\\n", + " t, t**2, 'bs', \\\n", + " t, t**3, 'g^')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [Controlling line properties](https://matplotlib.org/users/pyplot_tutorial.html#controlling-line-properties)\n", + "\n", + "Lines have many attributes that you can set: linewidth, dash style, antialiased, etc; see matplotlib.lines.Line2D. There are several ways to set line properties\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Working with multiple figures and axes\n", + "\n", + "MATLAB, and pyplot, have the concept of the current figure and the current axes. All plotting commands apply to the current axes. The function gca() returns the current axes (a matplotlib.axes.Axes instance), and gcf() returns the current figure (matplotlib.figure.Figure instance). Normally, you don’t have to worry about this, because it is all taken care of behind the scenes. Below is a script to create two subplots.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def f(t):\n", + " return np.exp(-t) * np.cos(2*np.pi*t)\n", + "\n", + "t1 = np.arange(0.0, 5.0, 0.1)\n", + "t2 = np.arange(0.0, 5.0, 0.02)\n", + "\n", + "plt.figure(1)\n", + "plt.subplot(2,1,1)\n", + "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n", + "\n", + "plt.subplot(2,1,2)\n", + "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Image " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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2myyHnZ0dAlMTqwLDMmg2m9hGQb1eo9nwoVDs3zdHFEd0hyH7llxMAbmGQmqEMJAywxGaZqVGp9ujOleulFIpZAKFmZHplGtrNznx8AmkUthjay1XJo7lUKs0S2Kyg71nosbi7K7mFOf2PdrTO+evMjXV4OjB/VSDBUZRjywPaTZatCdtsjRhfWtzz10LkwR3rOtJNSKVLnoY4boWwrTY2djAq/hYQpAmCY1WjVxlWGZBlBblQmRa2I5m37592LbJnVsr9PpDpMpotyq4dh09dn26w23aE3PEo5hCFGSxJIlL6/lTn/kUf/Jnf0JNFziiYHH/IeJohB9UEcIiSiKSNKPerDPTrlGtBASiuMeV28V7ScmoTbCyNaAFxFlOmuXcWr7KRHsWdywhOI5VLlhRhOPUqdfre8cL2yNLQizLIA5DbNenNVkuaO3JNoKL1B2btbUVkrFRUvV9LMdha2cHgGatxhuvvorjOPh+Bct2yJTEsi3casCbp95iGKXMBBVu3LzNhXPnOXnyEY49eIyZ+XkuXDxHrd5mdnaaTCq0yhDC4Oa1a1Ao3n3nLBXPo9MdcenyZU4+9iGEsKi6Lpmd8e47Z37Saf9j8b7I/DZMQRrbBNUmcwsHaU7M0JyYoTtIWVg6xJ2VrfKHlo/reuSGjdKgNGSZYqsTsra+xdZGhzfPvMmzz71CfWGG//Vf/zb/6n/5TX71l7/K1ESNURyT5dAfxSQyQ0lFkptk2uB/+Bf/kt/67d/h5VdP0Z5dZHJycq999Xodwy5XySxNSdIE1zYRlssoipFag3DAtJFZgswSVJbQblY59uCDzE1N0m5PoAqTW8s3yXIwhU2jVkEyFhirAa26R9X3MPKEJItZXV1hYW6Gtdt3OHnikb32mIUk1YqLl6/wu7/7+3z5C1/AtnOqlYDWRAulysdqe5DpFKdTUJDxymuvEo4kcqx56SLDdgq8Silo+6Ys+/gDAvgPfpa6bPPqyjYAwhaoPEUYDtVqqXd0O10qnodll7/NpcLzbFItmWpPYQkDvwaT7RaplPi1Gpaw2NraIolD0iQhyxSbGyvYtomUBtVaQK1eY2qyQeC5VH2P6YkmnudTYOMFLraheeCB/dhWlfWNctzUfJ/GRIMoivb6UHWr3LhzmX6/j+8Iwuze8OTs7BTVSvnMpcppLJTu+nvJKa02f2gsL0zV8QMPu+7y1qnTrG92+LOv/RmrKytoXd5f2/EIgoDCKKOFphCYQuCYCiEEg1FEogxSqZidniLP8zGxWew/sI/K2KKSaUKWl/7WZLvN/PwcX/jSFxHCxLIdTp95h0yX/YrTlEJJHMehVnFYWV5mam6OMFWcu3itlFMcg8OHjmA7No1Wm3g4RCBAm1iOx9LCHBOtOmGSsLpyh53OgJdeeIFw2GduYYnZqTkOHFj6oXvyN8F/sqjce5FKTWo3WO2kGIMfHZJY3uyTpTEyLyeIlWeYbrnC2yhCCSrSzE7PcGDpEO9evIJpC4YZdDpdDu6fRwiLWxs72L5Br19aXIHnod3SiiqcgOGgz0TNIdMFShtYNiiZ4frl5JVKYqMRts0ojHjt9PNUGy12Nldp1lz+4Vd+AUPcFbxfe/00O9trFMLjscc/RJbGvP3OOQJHMDUzR6Vap7uzhWk5mIaF44MpDMIoZnX7BnHyQR557CRRnCEMEBqkYWOZkkeOP8QjDx7F8etsfPt7LMzP0O10mZjyqRk1dtJyFQ0rgp31AfOzkxw+fIyr184ilWIUD3GESxKGQCl0k4b4rg26vLM/CKEzcByiWGM6Ds+9dIoPPXGMildG2MJkRHuixfrW5t4xtuPhN3L6wxClBEqOMB1FNaiwvdPFNFI81yHXCpVlpGUgkPWNLU5+4ASb213SNMN1HQK/xnavx3y7tAzjLCeRIRtb6/iuzfTMPJcuX2P/wXm6O0OEI1i+tUqrVUPnoHITMkV/1GfQzbDtUpe6eukitZMnkJkkSxVbW1s0J2bY2djg0P5pvvvKtzk+M78XkftRpLSLhZbPJz/2CQ7OzeBbkgNHjvP9F59jNCzddq01Os9JRyFRlDAc9JEqpz8aYJsm/cEIYRZkWcYHP/AQSZKRJSNaNY9wsE3ge2z3RzRrNeygSpplqHAA1Hn1+edYWJoH0yZKJE//zJO89PJrqDQjc11ajTo9qencGTA32ea8yojihKsXL/HgsYd49+w5VLbJrZvLZFoTJjG2bdLfTqk36vh+wAc+8AGGgwGf/dTHWd/q8sbbp3j4xCO89MILvPLaq3zh8z/Pb/7vf/V8/0nwvrCYiqJA5gbKdJC5sfcCMISNzI17SAkgyw0KLSm03FsV1tbWkLZNkoZ4ouD4kYNMt8oJg2HxpS/9AgKNkRowFsOjJKG3vU2SadJM4XiVkpSkwrItMl0QypxwFJGmGTvdAaMwZGlxifZkmyQcko4GfOD4Ub76pU/RqPkM19aYqPs0ApvHHjzE//jP/zv++3/2X/LuW69y/cZNHnv0YWzXY3VtDWHZzM3NInU5EYUwsZ2C+fY0KnX5/ksvcObMaQDM0vjAFCAMm3DUJ5U5O9vbBL6JUjHbvS5aW3uk5LoVHN9ibrZOlmSs3b6zdw+H/fK+FYbEFnovNQDYc93ea0XtRuqAvTQD27HZ2rob/rZMl63OFvsW5rFsgUAwGgzxhE0+jhDVas33nKf8bmt7E61zqoGNzDKUSlmcnyWVmu2tHfrdLkpmCNtmamKKXlhaVIKcmZl5jh89zvGHHyaJRyids7PdRQhIooSq72EaNqaweOfcOTa3u1iWj205BIFHnJV5YINBSdB5nqA19IcjFhbnuHjuCuGgyw0p6KkcvzmBO+rhjnr3jON5K777oTLFN775HX7nd/4fhv1t4jRDZhlSQZpKNje2+e73nufZl9/gzPlrXL5xmzCSSARzs7MsLu7niSeewK9OsLq6Qq0W8NnPPEO9GvD5z32aZqPB7L4De5cLanUc1wHhMjUxyfqd2zx09CCnz5wFIJSaOE1RSiE7fWyjYGVllfZEnYonkIXJRLtNv19GUuN4iON4mKZZaruORae/Q7ezTa3ic/3GDV79/itcPneG/YuLFDIjHAxoted59tkX+NvifUFMALmSP/SdzA0yqciV3COlnUyyk0lMyybNFDI3SLPyN5Vak+XLy0jbZmZuiSwf8fDxI/yHP/ojHMfjyqUL5GlEluc46FKodjyceht7d9JrSZaXyYu70Fojc00SDrFF6dqdOvUqg16PLMvwfZ9BOEJlmjt37uA0q2ilSGNJq12h392h3ary67/0Jf7pr/8y165e5dDBAwBsb6xSmHZ53pqNbRX0wz5JHDPZrLN06EEKbHSSlrlL4zalKmFyZo5vf/PbXL54lq9+5St0drrUalXePnMRI3eQmcFwJ2V7ZcDmTsh2b8Dho0fR2iIcSaanJkgisKyAOJXEuY1lecS5DTJC6OxebUlGaOHskVSeaaI45+byKm+eukCala3zXPA9gUaCDQpNkkgc26XVqtGamGC2NUOqU2xsZCaRSHqDHre3NsEGbRtcvX6Z1199EZUlHD5ylNEw4uL5CxS5JAqHxKnEFGXbhIAsTtnphmxsbIEq6HbLZMagWkEIg+89+yKPPvwwl67dRqkY17V48fnnuHntMllaJiF5lkkxthRdxyJSgj/8k6/z4P5JDtoad9SjNVrhSMPAb07gNyeYt2LmrZirK12Sm+d58ZW3uPj2m6yvrtKJ4LXX34HCAsOiyBVKZTi2ybGjR3nkgf0cP3mIoweXmGxW8C0D2zIxDMXKyiort28yMdkuxflxmGxhfprsPY6FqTJ+/b/+dRbmZ7F9n9PvXkIIi263RzTss2///nLudLpk71ncb966BZS5bbduXqPf7yOzjIePH2Nra5WJdpM4kwjLAdOhMVFGmIeDARaCMFPIQjA9NcO169ep1OtsbG7wwCMn/gYMcC/eF64cRYFplYMhV3Lv7x9EriRtx94jMaU1Vd8neQ+p9bpDsnM3EU7pemVplwMHDxMEAa+/+gqf/OTP8s1nXwK/tKR2hercqKC0BkwCs0C7DkoqZK7p9Qf0hyENz0bYgsD3WJrfjyVgkEgaUz7FIERnKRY2joat9R1MG1zDpjE9wajfZX7/IlBG4KJwxL7ZabY6HSwhKBy7DPHmGdOtBaZnl5BZznAYgWHBOL0BIIljPNtCxiFf+eo/4OrlK9xZ3Sy1gWqDFTYpzIxqtcr25gqmbYGELEoxhcWgHzPo9ZhbLNBItntDnKBCFoUoOwAZ4QQV0Nk9SZdj3nmP3iQQOttLI/D8Okk8wLYttrtbPPrQCU6dLVfsOIqw3CqDYYhMJLHMMDBIswK/fjfKZWOT6hQ9LKOZB/cfoNqosrq2SjgaEgQBKytlEqbvuhTYuB57ovlEq0mn2+PK7RUmGqWwvNPtIkyBabucPn0WI5fEMsFxAq5ev0YYJhx/6AGmptpoleDbDkmS0Jhok4ZDfuO//W+wTMWffevr1NozJBUf1xMEQRXb8/nGX36Py9euIZVmaWEeW5TPSlgCURhcuXaTVqtBFA6oVKtorVFK4VWgWgmo1io4tosfVOj3O+Rj3SnXeu++OCIfn7MMFE3Wqzh+hUKtYjkOf/Dv/gDfdYnTFN91yaRiqzcE0+bW8jKu4xBHIWmakWubas2i3x+CV+PQof3cuXWbQa+0ALv9DkkcE8cJWZLutWHQH5RtcAIUmsX5GbqDEZtbG6Sp5viJk3R7Q9458/ZfMdF/crwviElRRtmGhqDt2HsRN8f1yJVE6TI3R2kNWqOUJhmU2ae7eUpQhkoL4ZFKCDwbqTSL+w4w6nf5w//vj/mlX/wKvidIwx5V3ydU4HvlA3MdhyTNqFZ8usMIYdnE0QjHdWg26lSqdWQ0QucSy3ZQuaAwbQLPQ8YxqjBJdMaB/UtEwxjt+YSjGKvqc/XGGo5t0u9uc/jAIZ55+sN854XX2LewgFJlMuU//a9+jZXbN/f6UhQ2hZETZzG2aSIsiyRJsRyLaqWKyjW5Lk3e6ZkZbq9sMDO3xJ2120w1G4SRZO3OCrEyqbkegzAmzyXnzl9idb27d50k0Rw7Nsfa6ia5pVEAdrBHOLvIZAF2sEdSu9YU2Fhag+Pw0iuvUfOqHHtwiWajzvXr19k3O8UwVGxvbOM6sN0bMByWw06qjHrLoWpXsD2oehUyJdnodgGDuem50oLslBPC9QNa7SmGvQ6D4ZCZuRa5ysiygkwrlNYcPnSAdrfPzVsrDEYRS4sLVFwHqSRhFDI9NcG+pTm+8+0dRuGI6alZTCGJowTLzMlzxfziNGGUsrW5Rr1S5bvf+nMCz4G4y2ilRyxMPvlzn0DGIeiEL3/2Kf7iz0PUuDLAcRyiOOHnPvMJDNPGFAat1gTNmoNSCtOEyakpUCmtidKtTSWYRUzg+3Q725jCRmbl2JYyw/MCkiTCth2CaoNhv0Mcx0gjRcYFUZLgmCZZnjM7OUkubLK8zCsCcD2PRrXC+k6PfVNVsiShXnF56qmP8M7bb2FbBd3OFkeOHePNN1/n4QeP0tlcZWJ6nkwqhJFTa0ww7Jc6WbVSZXO7SxSnvHbmMo+fOM5E1UPYAp39sPfz18X7w5XTOaMwxhiN6HS6e6/bt++wubVNp9NlfW1t7/vBYEA+jnDkWpZ1XMKg2SyTLS3LYmKihcxSOt0uvX4f36+ytrnO1Wu3efDoUaBMcjOFje14SF3Wg43CmHA0YKfTxfIqSA2jOKPX69IbDtnY3MEf1xMlcYJhOeTCxhImhfCRuUFhO7xz5hyvvvEWr799kTffuczpc9dotMpI3xMfeoKq79OemiCoVDB0CjrBEaJM+NQ5UuXYVpnkh+ky6PVwhYXKFO+ev0gyKlcyyxR8/ZvfYjToceXyJQbDiFRLlHLojlK00gzDiDRVVIIW61s9stygVgtYu9NnOByhdU6SxCjDxCpyfLNMcoR7I3K77z9IWkolCJ0hpcUwGdFotLFMl1otYDiMiKMRx44fYbvXxRU2Vc9gp9/D83yqdgVLmEw0W7huhU53hEoU+xcX6exs0w1DVBHR2dkmifoIM2c0SrAthySKGA76VOt1TGHg1RoorRmEI6YnmlQ8l3B5/wSIAAAgAElEQVTYY6fTQQioVVz2Lc2hZEZ7aoJqpYqBJKi2eOCBw5hoLMfiyMEl0jjCs00G4YhcK1burNFstDCF4s7aCrZR4NgmSmtUllAgkWnExvoqW5sbpNGAVt0njPocObQPmUYIyyGoNXH8Oq5l4Ph1onGyrpYJqYQsHmAKGyEEnl+lXg0IKg1MYWM59b1cvpmZGXzfp+LUsJwySrcrRwyTDKX1niwBpUVpeRVcx2FpcR9Li9McOXYMx7ZwfcH6VpfNrS6eV2Vx3wLnLl7G9oPxHCvGz1khhIUjBInMqddKr+TQvkl2ttZ59vkXGQ36OOP2/G3wvrCYECU/FtUqxmjExEQLSwiU1lhjs1i9x6zdRVm8etcN0EoilSbr9xmOQvxKjTjJcPwqsxMNnnvuBR4/8TA//8XP83/82z9Ajn8PYFkScMi1ZHV1Ddd18T2HIKjgjs9vW4JmrcadWyvUXEHFd2nVAjrdLvVqlVPvXOLStbJ+WaoCrzlDmMZUqgGFkhimzfLKLSalQsZ9HArqns2hhSO4noPjuez0RqRpyqUrF3j0keNUKh6jQRcx00IJC1N4uH4T4bl7/f7C5z6L49i8cupdZierJNrgyo07yEJQDwxEnhG4Lisb2xw8MkEl8IjiFK0KhOUy6G+iVEKOSwYowwburnpZVEbqYlm62Fo4Y6G81KKwA+I0KotybZPvPv8SMpM8/PB+Gi0PO4Y46TDZbHFknCVfv3GLne6QW6trPPTwA2R5QiaHWLbAlTbb29tMtCeJ44hEapyg7O/y8iXa7VmSWBGHfeI0R/S6SJlj24pqxefA/iU6210OVCskacaN5WtINeLgoWOceus0Dx5/CN+vAR2qlSqPPvQglgkq11imoNZo8fnPfpLBcIjKC4qxVFBrlAuf7zkMBwMcuxy3L778fRwvYBB2mZmZ4sD+JW4u3yYIApZvvM2DDz5EozVBnCrEOM/KHhN7p59w/tINkjhhc6tDfziiHryH9HPNYBjjey6mEFiWRZamDAYDuoMBwrRRaYKwBYXwCMR75obrjOdN+aUhEzzXYXNrB9uM2Nne5LHHHqPTjalWqmxtb/KRj36cm9cu8bGPfYx2vYVGk4+LirOkJNdq08GxHAbDENNIAYNMFcw3anzyYx/BsU3+7W/95o+d8v8xvD+IaVxjYoxK90ztksWYnIaGoPajDlOS2HbwZUZsOzi72lNhElgClUaoNGKgNDJLqE0uMohShsPSNZAqpyzWlsRJmVvkOs7e/1zHIRq7eUrpkvR0SpwMCYKHUFpSrQR0uqVvrqQiTCWO69MdjXCIsGyLXphim6IsVJ2dJ0oVU+02t1dWEcLkzvJ1Mvk0/UGfWzeX0RpazQqXLl0kiVKefvopbKes5Tp9+iwLC2Ut0pVrNzh29CDLN29w6eZ1fvWXfoE//dpf4Ht1ZCGQ437LDOrTLSCk0+3RH2sJFc8l8N29YmWlFdZ4Rd5158AuSWksiDtB5T1uHHtJmZZVrpKJLt0827HZ6m7xUOs4Fa9KmIyYmatxbfkqVa/CIw89zLmLl1C6YGtlk8npJloZuMJGNASLS/vp7HSRWUK92iQalTpfxfMY9jt0uiMW5pfwKxZREqMzDbjMzM7T6/UJRzeYnZlmarrF8YeOo2TK5k6fMEp5+/RZ9i3O4wZNFhYWKMx7NU1DCKAAw6JWD0iTjKAS8Mqrb/LUR57g4tXrzExOYls+IIkSGA2HTM1MsRH3CSpNbtx6jYWDx/jwcMgLL7xEgU2SJhSmjVSKsN8lzcooaFkDmjE3M8X09AwHDu6nUfXLfDud4wkTlcbcuLVMo1rn+ZdfZWFmmsJy6HW2cVwPnUtUlpEheObDJzl7/iJaJli2h2mVuqzUGpVpstygUZ/m2vU3efPNUzTqdaIoolqpcuXKFSQmnuOhTQEIbAe0Lt25TCpefOllwlFIpVrB96dYWd3g8KGDbG1usLg4Ta97b7Tyb4L3BzFR7LlmprBLrcfy96wkX2b3iL9wl7ycOATLxhiN9tZ4y8gZhREHDh8l15JbN28gVY5UEQ995CQrK6vlpJUZUmaEwwFTswuEYYjvOew7cBBT2PQGI5r1KlIX9Ps9VJYyGo2Ym52hH8VUXYtKdZxL5fuEcUrF9cnSuGyfJchkjspNbBPqzSYqS7lw7iyHDu9HFia2kXP5/BBhmizfWKbIFZUgIDcVjYqHsBQTk5Nk2uCdC5cZxCnxzdtUNm0mm6U+sbPTwcbmzvIyjz5ygus3y5SA3WLOiudy6/YtCuHBKGRyeo5OdxOpMjy7ghpvK6KUxNm1xGREbAdYRb6X3wSla+ebZTJmnNtYRY4yTExHlDrU+P8ykwT2FOevX2Bpej+O49KLBlS9Cq5b4crVKwgDGnUbgWBtY8hE3UfpnF6vTzIRkmtFmkgMEaF0TqM+hYEEFJYtCEdDXD8o672yIY2az/KNmziOxcz8ItV6k5pn43g2O2lKo14lDC0OHVik2x/xwIMHWL21ymggsJb2UaWM7hVSk9mCar2Oysp7c+qtMxTYxEnGwvxsOWq1xvMrJNKgF6bcXr9AITy+s/4cjufy0gsv8dCDB2hNzaNyi5s3bjC17wDDzTVSXXDmrbe4dWcNxywQluDq8m3E7XXOny8ruALfwxkvlLlpo6TC3ufTqtfJdGnFaAQ6z0FqsMvKhrffOYdllbtWCO5GvLMcyDI83+Pi+YvUalXePXuRjzz1GBfPXwQzY+X2Mh/84OPjMbRFkpalUScefhjHEpx84nGEsPjTr32dXOUoYKrdpj0xwaDbYX1tE9v+2ytEf+c7WP5NMDE5XTz9c3f3klNZysHDDzAYDKgEPuJHROm0knu60O7fAKurKzhmQZamGMJiNBpRrVaxvBq2gGMPHGL/TJNnn/8+uJVxtnaK7bjYltirc4NSkJdK730fR+XkLLQiqDVwHYfDB/Zx6p3ze26fSkIsr4JlCYxcUpg2Rl4OjOOHFmnWa2RZwuz8ImfPXqA/2OHpjz7Na6+9yY3rN9BmWV6SjVfTXpTQqjept9oo08E2Cxy3dF8tOeKxk4/gCkUYhly9sc72zhbNeoXt7pAbt1ZxhGZhYYlz11bH/bCYbLeJkoTJIKfiebi+xU43ZX2ri7krVIxdNKvIMR2xl+O0aylZRY5SSRm9g70cp928p7vuHswvTDI3NUWtGhAmI6I4IRxJDByqlQC/1uD1108TJwMOLS3QmmgRxUMcy0YXNtFoQGcQ02q20DLCHk9WxwnY6XZpT7apVgIMndEbpKQyZmp6AdM28ce/bTYn2O70aVRdavUGW1ubzM1OIzPJKIyYbDcIgqDcDypLaE1OI2yPOAoxCsmFS8vYlkmSpjx64vjeQvqdbz/HVm9IlknkWG3I8pwsTXHG+zs5ZsFnPv1Jev0u5y5eRwirLM0xLOxxcpojCvKx5eaI9+wLNR77hZI4ns/25gaPPvIQZy5exXddRlFEoXSpN1UDrt1aw3E9PLfst2UJLCEwdbkdilQ58zNt5toNhv0dlM758pf/Ad/6+teQRcaJR57k8tXr91w3SiQfeOgwviugUFT8Gm+8dYbp2SmUhjNnzmK7PoNhyK989edJohFf/ZVfPlUUxRM/fub/1XhfWExa6TLCARjCwnLKVXu3jkgreQ85JWn2nmNLUrKFgdQFk606/WEIpBRaUfG9ckIKsC0TGccMhzkVz2V7NMKvVMvFJpfs+uJQiuqO62FZkjgK2dkqs3N3B+S86+KMFwbfKweB5zqo8UAIw5D25CSFlvSHGtvM2dzcoFnxSLOUy5ev8Nrpd5lsVPnei6+zvHxt3CFY3+oyNzdHI/Do3bpFd9Aj8D2kkWJXqkilCCwoANcCU3ioPOGBo4c59f++iXVsP8PBDoFrU29VmGzVWJys0+kPkAp6/T7zUxNYRoTrW3S6Izrd8v6nklKMR+DY45o5WYAs++0EZR6TLTRQum/3lKxoiFOJZQlyBLbQDDoRqysXOPmBw/huhcCnzDo3bIZhD2HbSJWNd5aQpHqTiudh+haNWgshDIJqffzcA/ICtnY6zM/4NOtNNte3SFoTeJZBmGY4js/m1iaz87Ns90KEbaHyPoFn41TqDAd9ms0GcZKSpookTRG2V9biWRY3l9dIx7VoflBluzdkc3ONAwcO0+1ss76+wfKNa5y7cB5EjQcffgiARuATBAHViovSkOqCQmbILEEXGmG55UZ6gNQ5oYxRWRlN07lES42wBcK00blEjIlK6xFaWcTxiJnpGSqNJhXbLKNlQjA7P8tgFDOMZbk5nC5+aM7kwqZSa9LrdoiGI7z5Gbrj2sU4jnj8iSfp9bpcvlpuebKbtFyM322rJCUMi34YE41Cmo2jCNtGGw460yzu24fMJBXvru77N8X7gpiKoiBTGscqdRjbEnvajtSlm+cCwirdvFzLPatm10+XlNX8luMRVEzwPdqT0+x0uhQy5j//z77I62+c4u3zl0nm2nz4ySf49vOvItXdTLVdIZwfeJcqp96aRGZ3czpkbpLlsNkdUK/Xy2heGJJmGY5ZCsg729u4nodWEg3sbA9YrQQU2Lx2+l0c18UOqiipEIbNIIqxLAvLclnd6lBm64zLW4RLgUmaKVwHpOWDYZEqaFYcTr/zOo7l8I9+5Zd45fsvMz01R6Yj0igDK2FldZVceIDCNjSNYB7Ls0kGEfsXF6nVRpy/chOjTBjAEtYe2bw343sXcSrBttmVafNMo1RSlrPYAUpGe9qUHKXYls/pM9f45DNPk8QD5icXGYR90jhhc3uFo4eWqNSbvH36LP0hNGrQaFaJok1MYXFj+Ra1ehuZSTrdHs2JSYajIWGq6PX6rG3vMDM9szcmZmemWV9dR+scpXPWLQfbFFi2xXAwZG5uht7OFisb6/jC4u0z53Ach25/i/1Lh3nrzTdJpWRpaZ611TVa7WnePPUW4bBHrRZw684t5qbnuHDtDhfPnS93qgSEMEsyKgxyVY7NLM9wTIeHTjxMmCQMwx46HZEV5Rjedbk9z0NkCboodwwYjZN8hVGQy4RIlsm+ACMtyGWCEIIbN26Vhel+ja2NNYJaE1wPz8nRUpKocseLXSsqy3P6wxEnTz5OkSv+6I//nKNHDrOwME+m13FEmWfnu6WBYNkWKjdAF/RGXW4tr2B6ASo3+Ma3vocwbUIFnW6Pt89e5NgD+37Sqf9X4n1BTO+FYwmy3EApjSkKimoVczRC6gI5dg12Sem92LVkoHzQUpWZrgBPPfk4UqZcunIFgF6YovJyMEgN5BlSOdgOe67bHkkBURxDHKOyDL/aQGYx860JsjRhe2uTOAqxHY89O84SmKKcILtE1R+FfPWLP8vhwwf4n/7V/4ZSMD09hdaaZNgnqNWJsrIOT/6I7Wws2yLPDZI0w7RsDKWwgbX1TVxrmmNHHmRxfo4wkczMzbG13WV9a0Cr4nHj2jq261IId69fjckGo+56qen0u0hlIIvynliWhUkEWGRRWArcY5F7j6R+4LMtNErddfXKeyuwKF1B8rJ85aVXXuPpp56k21vDtixco4brwXAYEY5SZmbn6WxvszMaMZM2qDoVLl+/Qm+oqQQ5WZaRqYJub8ihg/sxE0kYJZAlmLkiUgYV3yQMI4SwGI4GKASOoctJLTN8x6TXG6B0ji7Kzf0IR2hdrvSj3g6plHzhc5/muefL8gqtFSt3rvP4Y08wHAyoV32W9h3Cq9YxTJtK4DEzM02lUm7RI9OEqzdu0Wy2EEIwNzvN2Xfe4eknH+fPv/GtPVKCsrzKMct9uhzHJpWS4Xv27MqlHhd7q7KvQKvqAR69QVkOJHOTpu8h83JL3d3vMMw9d84SgqnJNq7nESUS2w8YdLY4ceJRZvctcfb0GZRUZONnmOUG5nhebWxssm/fPOFmjyjTHDlyiBe//8oeKZFL+hG0koj8hwPof228LzSmemOi+OgznyMbTxpT2NQbDer12t6+RgA7ZpUpI73n2FzLcdrA3T2QVJZQb7bZ2d6k0Ir/4le/wv/1u3+IzCGo1CCX2I7H8o1ruF5AmkRU6k0C39/TCe69SAamc89Xu8lvge/z4IPHeOvt06gswxprGrbjYwuYmmxTqVS4c+smSimiMGT/oXLju0JlrG9s7JGhbWiUkmSFvacH7RYDT7Za9IZDpmbmqDVK0duxLSwV88zPPEaWxZx5+y0+8OgTPP/yK8RxqRnNTrW4eWeVx0+eQKYJYZhQr3pUqz7dMXFbtmB9a5vtXopSkqBaJZf6rt70Y7CbGf5DkNFepA7Yy5GyhcZ2bA4f2seuzq6USb+fs7qxwWAUMTW/yK1b/z93bx5ky3Xf9326+/R+17mzz7wd7z3sAAGCJLiIpLiJIhNHiku2nHJs2VWOkygqu1IpyZVFf6RS9h+pclzlUqpUjiuSyolELdxMcAWxcBEJgAAIPOzA22bem33mbt23+/Tp7vxx+vbMALAokv8gPlVTd+beO/d2nz79O7/l+/t+17ANfTFuOXuG1y9fASDOoOUJvDCkEbawLRiO9bVoNXwC32Nv/wDHOaywplJSIDCEpcN2SxP0GcKiVDmXr6/V8+94mpTOs23MMmO216Xd7jIYHNBud7nv/nt47NvfBOAXP/4p/vhP/pwPfOABDXa1oBkGZFIiLIMPfuQTfP0b3yTPMgLf46mnn8W1fe563/v5vd/7fYB6zU43W1tobvR63pTC8zyUUiilOHPmNKP+AOE4zHQ7NdJ7HMdYlkUic3Z295ib7WEJG9ssKE27ht0kqUQIi6BKUE+qMFblOcPhkNlWo/5uWcEcjSJjEk+Ore+ZbodXr6xBLollTuD7CKNgeW6GW84s85u/9Zs/V47pnQGwhDqU00AyR3sp1TAtzXHUlgOKPEOmCUWeHfOS4LjXNBwOscucX/3MJ/mjP9ZGaWpc4knCzfXr9XtdL6jd6bcbmcpr8Frgu9WPT7sCmCWTCYsL8/VFOzr6/QNeevEFRuMI2/VYPnWOVEqi8ZA4SY59R5QoUvUWJQAANnZ2WT11Rp/nkRYcQ9jITOH6TZqdLggLWwg2dw44sTLP4sIcvZbLj559ntJWdNqCRsPn5IllujNdmk2fJNfskkplCGFro0SqE+BQM1oeHmyMKAtEWWiP6m2GE4Tai6oqdlPq3iy3iCcFz/z4ZS5f2aHfnzDoj1m7cZO82phu3txgaeWEhjyUFpu7+8QZNGeXOXnqNBkWB4MxazdvclB5L62Gj+sIbmxtk2QZ29s7dZg/NVClyhHl8Z3HEBYnT53GyBMm0RjH8Wi2u2Q5uEGL3b0Drl69zsHBiM2t6zz+yKN1TnFjswq2hSDwNO1xOjmcj0cf/gZWUeC4gtE41kybwz5f/+Lncar1Nl2zwnGwxfHbcbopTR+FEFy8cOEQSDkcoVLJ+vXr9Hd32djYZGe3YpSIJ+QqI5HHz9dzHRwKZGEgCwPfdfnMZz5JmiR1TrewbP2TJSiVM+oP+F9+57c4cfYW+oMBcZKwf9BHpTGxzFFSV7cnUpHl0J35ycINP2m8o0I57THlyEmG44e65F55Q5lMakaA6VAqf9u/s+pzfv1XPstXvvIQ48whUxOUHJBJR1Oa+iFh2yeTE2xHu75HjeHR3UFJSVYtpLh6y9RjsoXFa6+/ztz8ot51TbRn5prYFQK2N1fx58iEjfVDjvZWd1aHEUeoSFVp1mQjSqlDb0pY7Fcejut5CKA0S8pSURqC7e1NVldWQeXccetFbtzcYX9vQLfV4PSpZS4EFjLOiVLJ2VtWeOXVy4QNG8dyCFVGHucEjQZpBhOlMPICIWwcWwMsnSCsK3XYAebUU6pCuikV71GCuSltisxKClUyrNjNylxhWIKDq7ssjzLmZkI67SaJ0iIGADc3Njh75jQbG5s0210mqWJr4yZlrnCq3EdpCg4GYxphi3bDI54keLZNkmX0ej229oc1kVrLt2vPqXzTurEtuOfue5F5ybM/fo7tvQEXz5zgYDhkcXaG/YqEbTCaYK1YZNIkLUzmerOkqUKYAs9zsCyDPC9JJjEqL7l69RqrJ89VLVY5cZaSixDDtTGsIQ662ANgm2VNHxFLfc2VyilNgZQ5bjnBchs8++yP6Y8nfOITH+THP35Wn4vtkKkcw9IzHqeFDiePVOeOgpWBOkST2Hzuc58ni8ekUtLwHaZ+rkwlcTLEMU3+1//tf2d3T/Nv5YbCDsO68g0wiSfYwmT15ArtVpufd7wjDFNZlkTjsU4G2zZZltGZ8et8SKbyKkmdAm590Y4OpXIWl5ZYu3aFLE34z37ls/zhn3yeuNoxbGHRbs+TyZThoPK8JhGmZdePb/Z4lDyeaLdNyNP96kUDx3VpVWGVTBPOnlolkTlRPCEe9YknGm5gFIpMFTXfM1QLsjiCrq7DWLNOyOtc1+HvFBmDUcTMTLf+v8wQvP7qK5w/f45cSQbxCJnndNpNJsmE9ZtbLK/2mAxTCmvM/Xe9m1devUwyibGFj+M5RGOJzFKKLMfIFUauew4d7xBsOTVC09DtzeGbzEocx9IVvDeNaKLqayiM4pjxXVxexhc5gyihTCKStDL4boNE6ibteDSm2/AZDgYYlqiN9Vy7yfzCPNHwgHiSIByfqAJlJlGEH4aMJxOcNMHIPXzfr/Mfvu+TZFVyOpUozwbT5p7bz3NjY4e19TVme7OkUtWl8IsXLjLob7BT3aDd2TlcVzAaj/CCJaJxHwxBkikyWTC/uIKUCY7j0R9mhH4bBQgvrNdAmStUeegp2cLCKLTyD1CLEUhsbKV4/fIV7rv7LtavX6URBIyGI4TjkalDKItddVIUeUZeeeBCaLDyhZPL7OzvE41jbr9wBtFo8MPvPcHBZIJjJkizhWfrNitDOMTRHq2eXuMXz58nDAOGwyE5NvfeczdGxfIxDRE91+H7P/jRW9bATzveEYYJoDu3gC0sJvEE90hyO67AfzpBnWNEOtnX6s4SRyMdTpk2cRxz6dIlHLPkxPIyf/anXyBODt1kgOHBLpkqGA0OWFiYp9Vu02m3mGmGXLjlLJ12WH/vxuY2KyvLGKXi2toGllmwtKBpTvNcYdkBf/EXX2bQ36K09A7ukGMHDTKVM6l2fgNd1Qs8TSw3HWapDZBRqBp5DTrHUOYKW9h1n9PUOMk05cSJk7VRNgvNW33q9KmaAvWRRx7RFTmZkaaKZjPAd9psHlzH81xSmZGqMdMofm9nQJpJ/CBA7Y11AtyyqvzOW5dHbZCm3lP1uxOExySg8lIwGkfHjh+0R1jkGRk2YPLCSy9y/7134bsF+3sDFheW2NjVnuHO1gZL85qBwTENDEtgC4vFhQX8wGfUH7B/0CdNYlwvBDnWnjWanE6X2xWyKJFVHsr3q1J2nmJZgjzPsS1LAxZzbRB8xyQMtErP65evcObsBYajiAu338HTT26T5RbtZovRYIBlCkrDY5KkbO1LXFsyThU7G1oua1qAGUYjzXhhQtzfrKEnjrCqDcus5io/Nl9Hw7tM5TQ9k9MnV3n62ef4yMc/zBM//BFkh/8jDCrgcLXOrMP2ornZHm6jRbqxydlbzgEFzz39PIawasyVkodQnOloNgIsSrpNj4998hf5whe/TByNiSZ6LTTnVtje2iDwPKJxA9sP3/IZP+14RyS/m61u+cGPfLr2GrIsqz0ngG6nzeqpswTC4IEH3sWzl15mvtfmzOoy3XbAxtYBn/viQzRD7YT+Jx9/kNbMIv/mj/4UpRT/9L/5h/ybP/h3dGZ0E+2D992NaLTwLJMnfvAdPvj+93H58hVef+MKvutgCMEtZ05jWpDJAsdzca0Szw800ltlJJlic+sA3w+QMsFt9nj4ke/QaHeJoohra+uQJbiiJFVGfUNOh2nZ+FNBwiypc2xT17iswjtVmtjCRDi6onbqxCr94ZgwDGmEPqHvMt8NuOPiOfIkZW94gOs1+bO/+DKOMPA8H9e2uefe2+m227z+6gtIleEEFsgq7FIZUkleu7yPyjXHji8qUckjCfC3TXRPjdK0egf0R+kRT888dqMBb0n22sLC8zx8VzCKkjqELXOFVDm3nb8F3/fZ3N3V1MZVbk4Ige169VwB9Q12VI3GUGlNYdxpNmvjlOU5hnCwLeqkuChzAt9j8+ZN9gYalhB6HnbQ0Fxek30ymTHT63LH7Xfi+00e//6TzM7Osrw4i+N4xPGYTmeGsvKU43jMk89eIjc94jgmriq8b56PqREqTVHnPG37MPdkV/mxWGa4NjRbPS7ccReX37hcF3rgMCeF5ZBlkuWlpRrTZJsFicxpek6d3Aa4+sZrdRVweXkJz3YYjsfEoyGtZsiNm+t86D13s3rqFO1WR/cW5hmpsvnuD55mbXOLeJKgZEqr3eZP/uBf//8fYLkw3+O3/vHfp8gVmcz44z/7IhkWgWPzvve+h0ce/TZ7N67QPHWCF56/xInVk/zwLx/hxNzH6Q8UnufyD/7Lv0MUx3zra1/F8wMoFb/2t/4mtudzc3uPj33y4wRBg7XLr+oFi+bOXl5cYDwc0uk0CcKAa+vryDhi/foGH/zQB5FZhrAhxUDFMeM4xhEOjhdgOyYq1zeJewStawldVcvwKM0SxwIHyJR5JJyhSsZPaoN8NMSRKtfhpVGQKWi1vQoxPcT3PKJRv84f9IcTMASbW5dZmJ/lse98j7vvuoPnnn8BW+VM65iJ1L1TtgdZog1S2LAhscnKEUk6QQgbXxiYtoVMUpwjhkm3nBx6R8Dh73ag81OJNkrCcXRurmJJOGqcjsI99Ot6x56kRwyMkVG6HoaVE2U5u4MtHfZUN92hXl9yrHI5nkwwLIFh6cWtQ7/Dz+2PtMdtWyANB0dJsA6Nk+f5yBxaXZ3AtW2bg2GfWcskiSNOnDiDYUr2tvdZ31jDNmzefd+9SJlgmdU5loosjbAsE7BIU4Vl2iQyIctkZT7fWjQAACAASURBVHScw5wkbV35RacL6rnhMLVQ5gpZnU8ztPBsmxOry4wGA5LhgZ7fvIIYWE7teWM7dWuVJWyywsS28tooORTEqqwrf5Mj/Et+4BOPhszOzlKqiopIOAgT4omGY5R5Rjwe6A4KFdURws873hGGqchzhoNdLVVc2vzq3/zPAVi/epVOK+Tv/p2/jdHo8PUvfJ77HngPAI6pKSQA9vcHiGyMhc3i/AxhI2Brq49o9bCylG9/62E+8ZnPADA3M0tZKPI0QVoGzVYLqST9/ogzJ0/ywfe/j3iiOHlyie9//wmSOMYyC1zHZRiNWJhfwHdcKHI8W+cTLLtiPLTKGmRp2w5KjohlUS+SqbGZJvmnCXTTskmTWBsl4dbPFXmG4brYwN7eAYFnE0cRk719Wu02QliaDti3GQwGLC4vsXcwQFgm99x1G0889RSLczof5dkGl19/mXe/6z5efu0VbE9qg5TAOImwaHB61WFzR4ce/ZFeoFmpakM1HUe9plGsPROZpsdyJUc9gqPGyRamNipHXpdpCoGPZSgQgkmSIpwGSmqw7fBgj7mFJXa2NjDylNJ06x42jQGiOuZKTBJwXJcMGA4GCMfFNg8VkceTifas7ALH85CpxLYswkaLJInIcmh6PvOrp7h27Q0arsvqyjJPP/NjXnvtDdrNFp12gzKTTFTKeLjPG5evYrsBa+s3uHB2lUwpHM/jlVevkqQTTl24i/76DTKV45ilPuZCkSGwTalDPJmjCoXvuUySFN8DlSpcU9Qo8NC38X2fudkZgsDmwfc+yBeiIc00Za8/Jh71tUHLE7AchFHQDhw+9O67+OGTT7GwtMLC8qqGLngesdLXstubIxoPueued7F2+XW8mRls08JxPa6trxMIyHID33ExLUHgOQyGY7IkYa7TIi4i5rqr7Owd/MdjmEqo3e5JnNYuqWmbTCaR5oUZHPDge+4jzbUc+CDJ+fbjTyBswfk7TlKUIb5RcMedtyKq6kaeJiSuR2o4XH79CnkuObM0i+PayCIiKcAyNHBtY/0qr16+zrvf/X6effZpGmHAffffj+u6eK6WTerOdHEcQU5OnqsqNDOQ0YCyyPjYRz/MQ996vM4BlaagyCMybKrIqA7nsiyjKgZS5Dp0BY7kCoq6cXnq1mcqx/M8bKFDBKVyHFeHr6NRTMNv0PEChG2R54pWGBBPUhbnZ7hyVVfhAFxbkGQSqTJsp8QRDulEETY7hKOYQSVO4BgZUoFtoD2naV7pyKNSjgbrCYGoDM7UWzo6puelH4+/pkqTveGYTlMbZd9zD8ORatQtfKUFhp5bo1AEjnXsvUIIDEtTgyilakyQ7XqVxBYEjo0sShqmWR+nFA5GEmFZFmWF2FZJxGx3hu3tHV5/7TV6vR55npNmEsNsgi0RtscLLz6H7/nI8R6d5rRx2mH3YFAdk836tSsEYYidT3CESWnq45pMxjRCXcVa7HmcOrnKxQvnq+sEMlNs71QFlzKj6Qdg2WAo8sJgc+Mm585f4PkXXyZwLGLARnuBh3Nn8eJLL5PnBbdUKiau5x0rIGV5yW/8g7/HV77yDeygQaxKAmHg+z7jSK/ZwXDIa5df5yMf/gAz86tkyZDRcMh7PvQxfvef/0syx6nu3Z8fhfSOMEwGh7uZ7wfkWYzlWCzN9pAyYbi/gzDhnnfdw7/6vX9LoiBothklEheTUwtn2N28QqPVIQi6UKmY/OkX/n2do5k22p5c7CKTjNw0ybOMeDLh1IlFfumXP0PviSdxzIzPfPIXuffd9/PH/+7/4ewtt/DSiy9x5txZPNdl/eaWlo1KJwgjI47GuG5Iuj9monbpDwYkSVIn3kEbHsmhFzTNnU1viqM5Nbt63/S4Qd+EvidqkN3M3AKqSvIWSreG7Ozucmp1DgkkScZzzzzNiZUV1m7c4Nbbb+Ol156nnIx46pmn6c3Oc27lJJevXqZEYhQQ5ZI82WcQRWSlhS1AKm2cnEoBxXdtsrxA2QHRRCHTElVm9TFOz2dazZyOt0Pqv91IM3BthWWDUlQKtQ1Cxz5W4rdNNF6rktQSUIcgQRjW1dyjxqjMD5HUscwIHLsO+2xh4SjJeAIN30G4Or/SajSwrIKLFy9ycLCP6zisrd/g4sWLbNxY48RyC9e3WFg6QakUjm/SrpqatzZ3aDVD/vbf+hWUTJFZjg2kuSJLY3q9HsPRCAyByjNkxfrYaLQ42NdVP1eETEZjKDN6Xa2iO4pi8jxhtttEmCAMScc3KLJDTBwc5vYMSzCZTGjNzHD6liWub+5x7coVSvs4mZvnOnzuc58niie0fQfXDSHP8H2fMpf8wgffz+7OTfYPBoTtBbJEUwcpVeC6+vpG8QTHb6BG/5HQnhimiahoSW3XYzTqk0uwzJLA92iEJn4Q8MYbV8mzHAwNF5jEB3Q6Xb7w779KFo8JXYHjCmSqsD2fPI1QpQW5oNUN65tZkVNk2nh1LIu9gwHROGJ5SfdaPfS1b7CyusgtF29hNBzx8Pd/BN//Ed1OG1do13e+16Pt23zq058gnsR854mXGY7HDAeDYwj2o0bHtQ4T+lA1ClcVjKNeE3DMKL15NHyH8ZG/ZV5SxgkyU2DYfOSjHyLPS56/9DLnz5/jpUvP4ziA6SBTyfbmFmkSIRNJaUoczyFsOEipe+SmuKoM8NwjRHyWQ5pbJJPkLaGbrICFUyP0ZkTzTxqZKmgKgeVM22bGOrld5sjCgiMYs6mKMRQkSVIbnEzlxNEhwLHj+4yP5KWmXrntelDmWv14ei0qLm2ZSpxKzmuUSDzHYRBrFegr164x025xbX2dxdkecZJxY0MDdYUnONdZJZEJngtpnnHHxYvYZsniiRXW124wGI5o+QE4DukkQ2UGUTIm9HwcyyVKJtooGQLTNBlMUvBsel4FD8kzRsM+hu3y+pXrSCmZ6bZZWFzWoWTVLWC7unF9iqMTjsXezg77lQdnVUbpaKOvEBZJKgkDH8M6xDmBpgre3d1mtjfH/sEAszy8FkGzQ1ZhnWSR4fhN4vitQOOfdrwzDBOgCg1UyzOpG1lNCyUzsBVplBCN+/z63/0N1q5f49bbbufLX/oy933gfvI85+zZM0SjEWHD5xc/9jGuXVvn1KlV/uzzf07Da7C/t0ucZHRamm5OOBaqUECJEDaNoItMYgzTZjQa8NnPfJat7T08WyBaHf6Pf/7P+M53HmGvr1icbzKOtVlwhYXleJSp7mUzhINhiTplaVgmQadNpnIaoa5YBV5bh6zVDm+bJY5nI9MCV5S4YQdVmgwHehHZQmGbFjaaj1sppeP4NCEIDsuyU5qKKWzAdRwuXriF7//wKba2djh1souaIrmDFuNxRDSWJFlJpxXQH471Qm+HbO8PyEqLwD7eljKKS+JoUBkk85jhOdo6NB1/XaM0NcKO6+KYJpSSTGgZ+FhmqKq5uT7XQqHUYbvGdJ7gOEp6mnN6c1g4TSQ7poFd5lhAKSNcL8QQ2niDTv6Cbpwtc7jj9js4ONiHOOL6zZvce+ednD5zjvXNNxjvj5GJJGgIfDfkjotzLC7M8mdf+Cqf/cwnubZ+A88WjIcRjVbII49/FyybPFdVY7bggXfdwc7eAU8+dQlLWAS+y+zcArs7W7z73e/GtEzOntO00M2m9mK/9/jjLM7P0Ws1yCyPEydWWVtbxzF1wOy4HvXZVw3xZsXGodeLvmZJKpFpQr/ydpbm5xFu1fjs+Fy9to7rutiWSWH4JNEB7Zl54nibSFUUK45H4Bj8/P7SO8QwlYDrhLQ7TZRS5Erp3R/wPAfbcciqMMFxbJq+xX333MrrL19iYXkVmSaYRoaa6Ek+dWqV73/vL2mFHYQFi0uL3Fi/iWXbCNOkLDIEApWDVSWbG60WUiocx8MwbZJkQJFDFI2YvJzwwAPv5eHvPMoo3kVOFGOZMhPo403isdahK3WJ28wTbNclVQrDyFid7RE221rRV0nO33orUkqCIGBlZYnRaMjly1dwXZfWzBwPP/KdI4apyqEIAegks3ADsqJaVO6Ryo0hAMlo0Odr33yE22+9yM7Bfo3GFkaA7RaUhcQVTV7b3uLsqdMM4oTNnQNm2iHdTpcbe2NkmpJZgil9sw7dKqRvlS96s+F5O0M0zTe92Xs6mocq69yUh5SHUICjsADQYdpRj+hoL6GoDNk03J16RwC2oRujp68LIWpYgWVbNd1InCS0QwvD1cYpz/OaNQB0m9PqyjIyTVlZWWFnd5e9/T5CCT71sU9QlikyyzAsG9uE/u4eoecwOhjhObqqalrwwyeexg8bNNpN2o2WppJ2fcYHO8zNzvHJT30Sz3MRFkSy5OY31zRbg+3XfInjUR/f9/B9F1UowsAjyq1DUjg0hxgc5jVrcsXqOlhC9wMWhs35i2e5ub7O1cowZXmOb1pYZOTYjMYR1nQTqjym8WhIriSmPe2QSCiC8BiQ+Gcd7wzDVJaoomB7Z4BlQV6FO1ImusQtBH51A87OdnGEydLyCqqAB+67m6vX1ggbISvLS7zy4vM89PVvomTOXXffjXA9Wq0W5265yOOPPgJAp9NlONLc4gd72/hhm9HmNnmes72xwczcHJ3ODGkWESews7fF5cvXwIZmMMtWfJnF2R4HB/oCvfTiK8zNtLjjrg8wPtjRvEudNnlesLK6zPb2Jvt7A5aXFuh2OxS5AkJUAVs3r5PIhMW5LrZt4oc2u1ULhHCcenFNh2EJVBoTNLV7r/IcF0GGIJ1MsAUEjTanTy1z/wP38+SPX9N9b7Q5GEV07S5kkgKJ6Vi0ujNs7mkuqG6ny87efq2xZguLNKOuuk0xVkeHMApdAXsTMHA6psbnzUZr+vwU/Oo32iiZMBwM8DyPJEnqz7RFRdbneGSjCNt8qxcE2nDVn38sfHMRHHpYhkqRwsUylJbAroaTp2S5B2/il/dshwSJsB2iONGc6UlGLHM6rQC71QBhYZQ2rrCRMkNmGXfcfSeXr99E5qlmLy12cYTDPXffiTLBt7Xst8rBdywSC2zbpMzBQJHJEq/ybDqdWYwixipy7fEbJUahuHDhDGWeaQOaVdJnFbeYaR0qDk3R8qANVZqDmZcEjv78y29cptPR3rxlKNIkgkaDPC/AgiyZ0GwGbG9Xm0Sg1Ygs4ZBmaS1VnqvsWIj/s453hGEyMFjf2KTICrJsUlfVHEfgeAGdloffmOHb336M+flF9ocT8tKk2+rw1I9f5ezZMzz62Pfw3BcYDod85MO/gON6fOMb38ARDl7gVURWi6zd2OCFr3+bi+fPs/mDA1oNH2GZBGEDQ9gkacZMqXj+0iWkHVMMCnqzPTKl2L5xk+eHV1icbSCcnF6nzf72DvMLTU6dPIVVTDDLCd22j+3q3fnG+ho3b2ywvLLE2o3rfPPhb/LRD30EmeeYFhhVGVgViquvXuP06VPMdZoaHJeDUvGxMAb0IhOTEZGwCKub0RaCVqfLeLBN4Ac8+N73IUyLE8ttvKbPOBrRbYYc7B9gY3P+/Cr9keSFS5cIWy1ajYA47jPXm2Fta1+3S5ii7twv8hzzyILr9bo6nHSqaqJMsMMWm1u7b7m+U2/pKH3ydEwNlFEoYtxjRkmfl+62t10P13H0+9QhRAA0j9FEqtrLFKKiMnmT8ao9KMdDFiVFliPRN66FRW4J8iTCMnV7UpZrBghch0YQ6HC5rERWJxGnlhe4ub3H8nyPNy5f4dyZVYRpkFsCLMHW5ib/1X/9j3nyiSeIxhPywqAoCsJGg2h8mCUs84xCjul2O0wmMb4FmIIsHZOm8OD7HiAtUkgKDCslGmj2Aqfdw8ohlpJ333cvX3vku+zubOsNwswBPY/xJK07J97crL68tITnFGSFybg6JsuwmaSKyWRSg1Fb3Rn6/QHxJGY0HNabV244pGmGY5pEMiOKJ2TZW9HjP+14RximVGZs3tzkxMkzmLZJb3GZ8TAmTSKur93kmf0DbKPECxs4lkFRKmSq6A9GWMLiRz96hgcffJAXX70CosELL73GY489xsd+8WPs9EcMRxFJNKa0Sna2dhCW4JXXXiMrLSbJiCLLUbkiShSuKHn++ec5c26BEyuzvLq2RpIMmVueY25xFr8V0QhCxvtjxoxZv7bBTG+W0TDinnvuBLQogV0lyVVekKQSlRekk5ThKCY3zXpXlkmKyjOavkVvbh7Tcvjggw/w9e8+TRxr6pCj4QocEtrFoz7OQk9TsaoEOR5gWQLTEmxs7fKXT36VbjOEFLJUkVWtM7ecv0ieF+SqwKoWarvdIIlTdvb260qVnEQUuUakS7SB6bRbOvczSXQfl6MJ7nxh1Iv1rxpvzkUdC++OGODSFLUmmu162I5HqaQuBhR5PQe+55IkCW/mZHgzjGD6+UopFGjeKyxEqb8zr/huLNvStChQMxHkea5DdSCruLCMIuPm9p5mEw0CxoNt8ort0aq8nExKklQyiWOkkrQbAXmumMSx5sUuJJYtSFRKOs5YPrVKGg956pmnOXHyHH7gs721xerqCSQFomJ4SDKFh2BvZ4PACSkKwfzSMnMzMwwm8pCRtSj0XMnJkQ3ArOe2NAWplFhCb24nT51if3dbT1aa6nBuej1UgmXZBH5A2F7AqTB4eZZgOw6FcIgHI1S1Ofy84x1hmMqywBOCq1fegDLj5Zde48XXrwLUlaqmZ5Iqo654eUZWMTLChdvv5NIb65hugG2WfO/JZwH45mPfq7/DFSVrm5vcfdt55nsNoiRBxik39sZEiWK222Fxqcv62mVs12Uc5ZilQ2uuycn5Rfb7B4zjiIXZE2ztrtENuzzwwH1kWYElXPr9A5IUCsPHDgImaURp2AjLZjiK6HW7bNzYosAljlNc26TIIc1yLBMOhn22NrYoRIhtGVoKSZhkqaxvqCAMGQ4GuqcwSTl/8Vbe/d4HePnSC7znvjvJ5QgqXqBbbznDmZU5hpOSL3zx83S7cwz6I0Bw+Y1r7OztIVVJqxEQCpPtPc3MSMV3NIi0J6GhC9Bph3XuBqgak3N2D/o4rktZ5epWT6yyvqbFEA6boh0CxyJoLmkR0yP5piiKGAz6UKi6wibcAF+YZGnCJElpTdklnBAlUxxhMdubJZIZk0jv8pnKj/EYQdXaYR5Pkk/nshQuVOdj5Cmx5dIMPfJMI80tOyPGol15pEkmcSwbR2WUpYHd6RGNh3RaPv3+PqurJ7CwkHLag6fn6bvfeZSVxQWurd9gInUXgFSShqOLIXmFmbJ8m0svvorjCj7wgY9w88Z1ZKrodHta0CJPwPLwLKHXjWnUPaWO5+JYJeNoSOCF7EYHgMeHP/ALnDp1koe+9jAf/YX3YjseP3jiGQZxwtraut5gRgMavoNpe1y/dg3heKhoiGMax7BoiwtzGGVWYwzzI/AEw7RxTCpQcXqsRehnHe8Iw5SmkoO9DdIp+FBmfPg9Z9nZGfDilT1s22aUFDjC0ru3yklKu6a+HQ6HzM7Na54my2K22+VmmtZGzBEWqTJwRcnm9j7L8016Mx5Gp8WNPc1qMIwnDOMJWWlz4fw59ra2eO2NLbb39jjYrzwBw6VXWIxjwb13XWAwjAl8j1ylRJOUvb3LbO7sU2QpUar5mPM8xzZKJjLDdgStRsDsTJeyyGg2AgbDcZVbarG0cobv/uBphF1dFtMGEhzTwKkSv1O2Q+G4GEXGUz98kgtnTmg+5twkz1Oisa6+TUYRzXaXzkybaFIxV7abbOzqcKvVCDQHkjBYPbnElcvXiNOM0nIr5oR2DVQMHJvM9ZhE47qXTeeUTAyVI+OUVuCiVH6YbK3WdautAYRxHBPH+tiOuvs6vLAo0d4RUJe6Y5nXIVqn08UWFqV5SDs7HUeNUlZorJNRKDDFsaR33T9niTrUy0oLlNK6b4amOdYJcZ0EZtrom0Q4QUBmCKLxEM/3OHPyFFmWMhgOWB9tcP7MaaLJiCxJGAyGfOkbj/O7//NvH3q7WdWQXbEHWJYO4/PcwbEsZKo0ZMAKSdOUZrNdS6Lv7V+jNzPDuXM6rxRNJLv9AXE0Yn3tCU6trhJHY+68eA6VF3iuSzN0+OgH34WNZNg/NCatbo/hwR5CCHb2DlhYXAI0FKUf6V5D0gTyFMdxuPXW29ja3qz/Pwg8vvPdJ7j/vrtxTHVYyKgobX7e8RM/wTCMfwt8Ftguy/LO6rkZ4E+A08BV4NfKsjwwDMMA/hXwy0AM/P2yLJ/+Sd9hCwsncElHGd1ZG2G0GcuUTGacPTlLtz3Hcy+/DlAl1vKaaAtgOBzRarUYDofMdLt4rS5s6kl0Xbe24KkysF2f/khijDJc+zCkmDaNuqJk/eobBK7NIHFwww4TCQ3fZ5KM6vdP0eDxJCHwPaI4YW52ntO3341nHU/+PfSVhwjDNt2ZLicLkziJsQxLS0KVkESK8STjB088i2UJ2mGb0LaYTNdRxbcThCFBs9olXQ/L9nBdm1OnTmCgyK0Jk2FKeyZga3sXTxhsbe8Ruh2uXrtMp2lRGi79gxF33X0GIxPcuHEdmTaxhSJotBkkBxXXkyCOIo2eDnVy1wZKz2M0jup+OKNQlKYg8H0mUqLKmLnFFXzPwbRsPNepxSOmghEAosxQhl0/N1WkmSrWgJ5bxyz1tanCjtoLKk2EUSDcACrK5RrtXMhjYdz0HKZgS6UUHd8Hx6Y/GtVGwzEN8vKwUgeQVjknHAc5DfcsCzvwOLNyBsfR0u1z8/OsnDhJniWYlkPQcrj04svYrsvn//QL/Np/8Wu8cOllBsMhWVHQ394lKzVTRZ4XNaldnhe0uj1GwxFlLgkabYbjcSX3DVFWsDtO6LbbdGdmsIIuF06dJcXhxs1NRoM+eZbz2U9/gt3dba5dvka720DKjLzULJUG+rOmeKew0arn6j3vvZ+vPPQtHDNHFiUTWfD+99xGGo/pzc7TH+h7YDQccv99d1f3VUkgSoKWwz2/8FFme/N89Qt/9B+83/86469j2v5v4F8Df3jkud8BHi7L8l8YhvE71d+/DXwaOF/9vBf4P6vHv3JIKblxU8e2vtcjlSlhIyS3UsajhPFoDc/I8MIWcRRhVMZm6hHZmWRrc0OrQAzHNEIfqfIazNj0TEaJ3qkuX7+BmSfMzC5iFBFRonBcq2qDsLCEQJAjVcnc/AzKsGh6DlmRQ1/jhawjhHWmpR23ixfO4zgCKVModZl3OizLxHEEZaHwXZvRcIjruPhhm1dffoE4iTl3ywUunj/LK69d5s7bL7L9+PexhcX9730vz126xGy7RZbnCC/Ecx0+/NEP8fC3HqXTatUCAtfWNllaWmISx8z1ZjHLjFL4ZOoSJ5bbOn/iOSzPz/LGK9e0eCUgZcYb19c4eeIkrcDX3ONHesuyNMEWh8RgpSkQDrSbYaVIA0EQIESzZoucjlxpCXdRc2hpVRtZgOscZoYc1yMMDLL8sLI2N79YUyeDZqJstzvEcayNV5GBKmpPaOqF1RxGhYEnBFkBk0gDNgPHJpYZkcyOGaopZmqazD92DkWGb2lJ9+H+vqaFsQwaoUuRK4SRMRgmzHQgGo85GAxYWphjdnGecxK29/ukk4hHHtH84ZawsF1NA9xuNel227iuyyQa0R+O+OSnfolMJvzL3/u/CKQGP7qOw87WBmrr0OAaeYLlNt7iDc51muzuD9jf32N5oVcrCbvC0qoosmJ+cDydMrCoSeTeuHxDe7BZjqFSlJxQVIbfsS0mY13gcfwWeZbw3NNP86GP/xL/9L//JyTRAbmSOH6Ln3f8RMNUluXjhmGcftPTfwP4SPX7HwCPog3T3wD+sNRcKj8wDKNjGMZSWZYbf9V3FIDMLYQlWNvsM9NusbkzZDJJyEoLI08QgQ4HdLd4gS1EzQDYbobMLyzpUim6TeP8hYu89uor+iSFTaetd2F98TRh/ESVdYl5umhBI2M12M6iaTvEqqxpX2+/7SKdTovdfp9eS//vRMZYtk1SgSYzmVAWWS14cN/dtzEc7BKGTQwrY3tjg3MXzpHJBEM4vPTaCyysnGC20+FTH/8wMol51z138+3vPwlA4HlMJhNkUeBISR40eOyR7+BQsLq0iO85ZAoW5he1trwtNCTBsnnt0nPcd9/9PPv0D4iShP7+Dqapw6S5TgiEvHZlE8/1uXZzm1YFKpwaJSEEDd+nEA6O36TIM/wgrPv0wmYHz3XwXAdT2IS+p5VmS5fQSLGFIPQcTNdnbXdMx0iRmcKxRY1VA8hEiK0OMUqFyipPS+sGlkpSKonjenV/YJFnlCNNHkdp4otCe0oVWr4sIEkSbCG0Rt4RfM1RipBpIt3zPKwKUOqYJeRKV9iAcTREuLpdRaWSLPAY9w9wPJew0cDOFHv7u4zHEzqNBlIqzp4+TX9vD1O0eezRx7j14jKh5xAlEmGZzHZbeH6LbldDP37ww6tE4zEPP/xwfZxHSQGXl5fo7+6CPQU0Lrzt/ZQpSX84JAzb+EEAZUbL99nb7ZNKyaA/wnE9bLfELi1kKrEsC8v2WF1e5Nr165TkZPKQv2qaeC/KjOtrm8z2OowHB9x9331sb2/h2C4GGj7w/KWX3va4fprxswaDC0eMzSYwnaEVYO3I+9ar595imAzD+EfAPwKqhZ5VKh0WW/1I7wCVzHWZGzhV5OZ7LqLQopjRJMH1Ap2wy3OcSgp5OpZXD2VkjgpnTnfv4XBY8xxPh1lq4ULP0cTu48m4Lk0Lx+Ghr36NB+67nzRNtaZWptVr8yxD5QllaVMWGalMtXSQUhR5QSr1YrIskyAMkUmK7YZ0e/MA/MWXv4lMU377v/sNfMclb+rEqzkVt3Qc7Io7KB6NGUX7/PIvfYK0qoQNh0PCZhNKDVA1LcHLr7zIcy+8wN0m5FXzxdqNPeYWZ3W1z1R4ZhNZ2vSaHYY7uwzjCa12G8c0jzF6ykIbglarheN6jAZ9wjDEFBorM53/8uMq+wAAIABJREFUfmZhqwSEi8w0UNawXRpAeERIQr5JCuaoUQIwhU0gbPZkRu8IPimOoloINcOm0dEcW1MRCsd1D6uDpQn51MgWNVD1qHeRFWCL4xVBoDZIVq6whIWwDNqBx3A45OTKPMISHAz7nGitQKl7yvb3MlaXF7AsAykzWu0W99x7PzkWr7/6ChcvXCSo+MKnHopwPH74wyd48D33sriywtNPP0dzroKQKEWaJIRhSJ4lGpHu6rlwyCkAs0rkRfK4StDO/gF3nj+lqakdG5UmpCrHdRxuve1WHC8gSiT9/R0tkV6xqb70ymvMthrcuLnH4lyXaDTQAOeswLIEk1TSbQcMR2OySYYfSlzhMBiN8DyHwPdZmJ/j5x0/d5aqLMvSMIyfmm2uLMvfB34foN3tlaXlHdulDUtguzAThJpEyzKwhF339wRhyO7ONkrltXGRaVIrprieRxRPtCJEEBLHEa2qo1qpvKYCzdXxNopYSoIg1OIFVGX5JKkUexNII2KpSOMIV8yhigI/DCnykrbXwBICDMH+7u4RgKC2bL5rYZomzWYIpQBKZmfalJaHbRc0HZ0MlXmKMG1sC0aDAY572CYBkE0GZECcxMgkJlMa86WUwiirqkkJZ89ewKkWcpaA5/icWOmxenKZstA3yAsvvU6ZKzZ2dilNwczc/KEScZ4RBGHtDU1HoTLdnV4lhoFjfNIAw7hPK+gwjPs0gHGsPZd+ZhHabzVMRz8bqL+v5+jNZvp3EIbHgJSjQb/SGwTHP9QWnMQRgbCQaVFT2MJh2IblMBpX3Ex/hRAFaEHWZthksL9Np9VBWAKZjJjpzDEeRvRmu7q5ujeHlJoNdBQNcFxd3EiznMKwwXQYRRLLgmeefg7LMhmMB2C4/PmXHmG+52MIj8uXrzIzM0PoC6J4Qiolkzg6llgeHqlG6sdDahlhFJw7fZLRsM/As2k1XQpD6E3UcTQlC5oBITItoNA5iTxD2AKJzcrCIisnlpntdNjY2sN1BJNUi3ZOZFnTznzxiw/xyU9/mv29PRYX5xkMBscEaX/W8bMapq1piGYYxhJQgR+4AZw48r7V6rm/cti2y9LKidqjieOoTpxObAfG42NGKVdZjWiduvVK5Xiuw3A4rP/3qNy3adm8+MKLledhsby0VNG7asqHmzd1bG3bDq7j1MeiVP4WEYRxf480iUlkQpGD7PfxwyYPfeMxQOdskkwrqcqiwDFN7rh4jttvPcN+f8Dzz11ieWmJwYFGeH/yw++jUBIpFZmU3NzY4r0Pvp9et82ll1+qCfgzFXFicYGRY3HvHRfYvrHGnXfdyfbmTRbme+wfHOC4QX2crudxy7lTXL26xsLyKq+88gIA69dvkMmMKDXpdDrYYYFwvHregjA8ZgyKar6FpXmj4a2GqL6WKkKmCb7KGW2vU+YZ18fjY+/ZqxLdYRjW0IHp9XJc75jXCxw7jqN/A/RmZ5GZotPRof7m1g5FntGbnddy7Y7HJJGoNKk3vGlu6Si9ShxFlciBXk++K6DUisS5JTCFSX9vxNLCDDNNi9jx2N3fpxEE9Ga7NAKXnd0Yy3Z46eVXeenl1xGWoelQZMYvf/bTPP7YowgTcAGB5v8WPtc2dhBlQauxQuB7bG1fQ6YJKwuLXN7YPyTay9VhJbECSx7lDJ8i76NJQthocfPGNW675RyO7zIa7OE4Hr2ZGXb6IxYWQwqZkmQSzz6eF2zYOooZ9Q/4Tz/zyzz5hIbdNBp6jr/98COMRnFFVCj47vee4uTJZSzbw7QyPPf49ftZxs9qmL4E/D3gX1SPXzzy/G8ahvHH6KT34Cfll6ZjKmgp0wTH9YiiiJ3dfTI5qWSSOlx949UaoHfr7Xdq7whQyqnJ1qfik283Tp4+U7M+Tq16KiWmZbO8vAIcl4DK8rJuCZkaqEGkKU2KZEKRQ55nWJbN5SvXOXvuNMsrJ7Fcr5awGccTvvKFL5GkCdF4TJ5lrK6usDg/x+rKEuMo1mXiPMexLDw/YHlpgRtra5w/f55Lb6zR8H0imWGXOaPBPr1mh9FgnxsbO1i2jSUMFuZ7zC8sMh4Oa0aC6U1nWTbCLGk2G+zvHXDu7C0gfK7d3KEEwmpOwvA4V/PUEKg3tWiANg6FyoiiqK60vd2Ybgw7pUtbDmrjo7n2svoaJimQQlFtLNNr4TpOHTJOj8U5ajCr75nmrRYX5pCZolAZ/VTiOrqhOfWc2usQQhwP+TiiwkxVncs057lllli5YjAY0/AMxvEBbEmanS7CMvA8j8Ggz2AoCEO36oH0ueeeu3jh0iUAGk2fz3/+S9xzz12sr12jTCSOFeD4JtdurpHj4Dg5B8OIT/3Sp9jaOMdBv8/ZcxcYPfoDbm5skKm8gmMosBxKMwfKY72L0zmzbRtbQJbGICyknOD6AQUZ3W6H61u7bK5fZxAnlCpnoiaa99sLMYTAyGJ+63/4J2xu7rOze6C9e9NmkqREwyGua9FsBjTbXRYW5vnRc68wyda47daLWjG6+PlBln8duMD/i050zxqGsQ78Ltogfc4wjH8IXAN+rXr7Q2iowOtouMBv/HUOoiyL2gOaDt9zju2otmVw/sLFt9wE04qNUjlxHBFWrJa2ZdQeURxHKKXRwtObIY5jskwShE0GA+3wHZVsCny/qjQdKrJkMqU90wOgHytUYdEf9ZntdLjjrrsxy4y4Sngf7GtyL69MObEwQ2+mg+fp8GFwcMBsp4NwbKJoRHvlBLLfZ2NngxPOSTCEroQUgrlel/FEMjk4IOx2GUYjQrfLfjyi2erhhw1830U4LlLq3JJpUav5GoUi8AR0eywvr3AwjHnmxy9gBzm9uTkMyz4WVu3JjO4Rj8UUNgI0+0C1aQAQaRyR5zqkMmKSSHY2b9Q0L2/ujbtw8SJrO30ylXPnnXfywnPPHqPgnY6jDJe9uXmUyuvNowZmRpPaszs6psbJNksM32exStzKTMGgX1cMp7LzWi9Qh3nNxmFDcF5mdFsdPGEwimJEheSeW5mnKIZIlTEcbtHprBLHI+J4hOOGhI0VLKsk8H2iaMTyQo88L7AdB9MQ3Fy/wXg0odP2sQwos8PbL4kVS+eX+PKXvsz/+D/9Mx595FsIS7fEzM0v1nAKwQTDqnjSHYFQCpKUNE0xLbuepySVWsY+UzgCoijFtk16M0181yVOMtbW1ml1e3WeSqaS7sIM7//oh/nql79Md2aW/UHEU888z+233crG5ia33n4ryws9mqFTS9R/+H1385dPPo8QJnmWYLwNVc9PO/46Vblf/w+89LG3eW8J/Lc/y4FkKmcw6FclaA3CC3wt1SSAKM1rBQyAaHRY7pyOKQ1IrrIaG7O5sXGM7H3vTTJKg4pHyLCE5uKWElto2hHbcWsDNi1BqxSG4/DQkOYFmSfwHYsfP/88getqKIAfkJc5lhdw1113srN9owbVuY7FaBLT87qcPrVKFOmu8xMrJ1Eyww9dskyAoTQ/kHnIZ+27HkFHoCZNDg52+Mvvb/Krn/ksSRJj2w55oStytoAkUVhC0OrOQl/LSa3d2GJu9QyZUnQaAdsHmvArEyFW0qfnHHojbzZGU9T23t4eSqYIxwXT4fzZkzz7zNOYln1M4QYOWQRefUVXSP1Gm/39g4pwX0GhsIVZ31igjalwHI0Ir4aS8pg6c5LK2uv1XIdOp10n2/8/7t47SNL8Pu/7vDl2mu6eHHZ2Nu8eLuAOOOAOuEMgQEBHERCJAgmRLNkiYYB2qSxbtMt/yEKpXFSg7LKZZNNlUbZkASSYRIIkSBA5HS7u3YbbNLMTdlJP6Pzm4D9+3b2zeyAkEPzjrF/V1s7svN09+4bn9w3P93m0+2ZIHctE10TK5/X7iDLNYCI+EfdVniYjZUtF0ohDnzTVSUwNNU6wTIswiej1Y1xdRrctpCQmTMTQeaFgkGaiK6UrCqGmU6pUWVm5iYqCUxzDtDTe/sTjfP0rX8ItazRbLTRVJ88kJmdnmJ0Z58m3P4pp6ORJRBR4gtgZx6I4nd21IhN6VANDBjUhl50R5ytMQVF0bMvEjwM6zQ6aaWMq9qhjCaJeZ+QRwaDwnQYxi4tvhlRwsdIcvv3Nb5PJGmsrK9jFItMTVSzdwLRsdFUmkzW+9NXv4Ngmfphw0PTZ2fyPSpK+53pDML+TJGZvR5Sitjqt0Y7bb4sTpqvKyGVkyPw+Kri2sLg0imxGc2T9Lv1uZ3ScUA8Ux/fje0XZdFUZdbdE/0qYALT3d0b5+1FJ3N3dBufPnEbXdVBktCBhZfk6KhK1sYogpEsJaZSQqiFzM5O8+PyzXL9+lcmJec6cf5BWax9F04kSkGUZVRXiYOoAGGRZhkHk0PN9FmfGObixzVRlmqDro2gKhYLL+98rhOpWVzc4fnyJMAiwbZswijBNlThVUUgoVmp0+tvEkgqhT5xJNJqdUdteCVq0OqIWNCQ9ZmlMu90anVNdzkfMcxAAEiUpN2/d4uSp0ywvr4yA6GhH7x7j0Min3+8zOTE+6vCBaFwcHjbZurOObjmjjeDo8vvdgYb33fezbIv6+CQH+/ujaHoYTenacLYwGUVT+qAWNYwS0yRGtfSRnLBFStGxyeOIOAczTgjDhKKT4Wg5TtlFUlXyJKFx2EDXHY7NHyNKU24s36JWLlMolGjsNyhXqpw5e57VW6tkeUzgw0svX2RiepZ+p4Xrupw4fZpLL7/MO554jFdffokzJ4Tb8u7uHsnWLtVKkX6jOfh/ZCMRvySXIb1LJJWyBG/AT9Ithy9/9WssHT/G5voeJxYn8MNIgKbp4och2mBQPk5BtlQyP0GXchxLZ2+/xfhEjU43IM1SNM2gH/SpT4yhaDqrd9bZb+yh6ib9XodH3/ok3372O3zmM5/FdRzq9errrt33u94QwDR0kLpf3XG4+n4wUoEcjqUcPXbt9jILi0uDm3zgK6bp9xwzAihNu8caStO0EdANwWoohSteF9zV4z5yzPDh6HV61MfqjI+X2bizjTAPDjB1UxQ5gTSD+YU5/H6PcqVKPGjtDouwsnx3i5dlmTSFJOqj6w6OpXH+9BIvv3qF8WoVRVaEjIcEP/zBp8WLfDEsDKCoKrp+5LKmoivldQPWtnYBCAeRm2lZRHFCt90aMY9lRaPV6Y2iFQFC0j2gNGRii1FXca77vQ7V+jjtduue9OwoiAy/brdbOM4MN27eGpmBDq/3yVOnMUyTq1eu3uOuYpvaCCCHoJWlMb4HW1ublArOKI0H0a0zzLucpyEAD8FK11S0gk2300ZRdMqq4PO02m26/UPKhQKaAmGYYFsmjqvT9yIcWyNPEqRMx3EFU7vba2FYBWzDYHZuFs8LRjXLVquN4xr4YUazc0iaJdQn6izOH+PW9av4/R7vfOpJ1m4vc2d7k9euXcdyCrzvfe8hSTLsQpl/+iv/+u59nEESBKM6ooQgkopIaChprGM5BZYW5/E6YrDbMoA8Rh04uaiaim2aYrPw85H4y+b2DtMTVdqtLr2+R5pkdLv72JbJ3NwcipRRGauwunKbmdkSimKxvrpMreqyOL/Al7/5ZXr9Bj/oekMAE3k+AoooDO8BDhDgYQxsoYulEvsHh6OfRWE40gMaTDKgGyZbd9Zfd8zRYud3m4QfSlHcf+zR413XxTAMLNvi2ZcvMVZw0J0iX/jzv+DCmy6gaBrdvkerLXa5MEw4c+4C3W6f8fEq5XKNxl6Tdq+PpcejebpyeYz65BRf+Is/59zpM6AUiVIouC6bG2tIWcjk+AxjJZeTp08RRQmrqxvs7W1RLI3juhZpJljQaZoIXatU+BlkssXL164RoxJnyaiQfDTK8L0+7W5/5AQ7BKNcVtEHzPLh95oKuaxAlgigiiOwbcquNQK0oxHT0IL93vMZMDkxzsHBARAKfSBFY31tdXTM4tJJVFXh6pWrIw31LI3JdYdkUHfSVBmyhIODJtXaOCu3byNlCVOzCxAEhEFAPFBsrNZqaKqwc9JUFVSVat2iUCpx0NhBVyTmZ6eJk4SN9Tv0vYBKuYhjDkwhjEHXy4s5tTjNfrODqsDBYQtD61OuVkmSBEWKMQd8JcuQsWZmWL6+QpolOK5DFgU89/yLPPXOd7B6+yavvXYDWVHZ2e+xuHSCdi8k8PuUCzaabiEn0T3F+RHTPUmPRPoMHKyraDJoqY8mqzSbrdFAsakN7cgVJFWj3etjF1TwGdmsA1i2TRQnOJaGU1SIcwvTcdi4s02UhExP1HjmmQ+QZClJlPDKpeukmYRdKKOhjcZ5fpD1xgAmGIGR47r3gJJh2mRpTK/Xw7FMtnd273mdbhgYpo2mKoLDMvj7/mOG67sB0vDfhoD0l4ESDDS444AbN5fJ04QL586xtbmO6bgcHLa4fHNtBDaKomAZBlEs2M6ttk+5LATwsjhju7FFP4ywbY1Wq8nLFy+yt9ug2eoLWZI0ZXZ6kiiKOHV8jscffwuGonLp+lXOnD5HbXwSTdd47dJlzlx4GNNySOJ4BEwyKQka+7s793irgYgo4jSn1TzEOzIQO+SnIKuDh+H+mywd/a2pymBMRTQTyGJsyxqlW8MoaWhTdXS12y2q1aowdJRF3cQL4lH7e1jA3V5e401veoDG7jZbW9tC7WAQjQ1tsSRF5dz5c9y8fk1E0xls31lD1Q2mp6ZGHLhWq02axExOTo5+D1ODkmNh1qt0fY8s9On3fObmZ9nb2ebY/DxR0KXXPsQo6nhhD8d22dwVg7VxJArjhlaGNGbl2lWm5mbRFQNVV5idX6R92MSwbBhwj4LIpz5RZ/nWDWzX5NSp07x88VWmx2s0dvfp9T0Wl04Rxsno3OVpMnAKDpGlGMuw0CSYqs/Qbu1Tq9aYnZ7ErYxRKzpUKiVm5ua5fv01XLdI9F2uwZC/pcsyk7USyeA+D4IYWYrJsoxKoYJhpHheTOB5VKsVVFnl5YuvClJxJrGyus783AxSnpDFCQ8+/KbXfdb3u94QwJQfiZiOgtJwjaKpwa5xFEjgrma0GOYt4Ht95qbHWVm7S6G6/zX/sVHU/au5t8vExLgAsCRlY2d35Kjxymu30C33bicvi7FNk9/8vz8DgG11aTbb5HmCJKmcOHWCvudhaAphFPK+972H//M3/hWtw33Onz9La+8AOY949JGzzEwKcn2SpSzMHwNAlhIuXXmVE4uLYgQmjsmzhIP9fcYnauSyga6qvLZ6txg5TGn29g9eJ+ilyznIClEmjVKs4Rqe42HaZRjGPbu45/sj19dOu32PABzcNVXQdIs48omTlIODA6rVKl63TZIkA6C5K9u7unwDwzB49dVLaKrMQw8/wubmnZHaQJxkhEHAg2+6MOKoQT6ydgJY27jD8cVFdnZ2qNdE7WNv/4BSwUY3LGGHBHhRgqLolMYryEaTdqtDuVSk32ly2GxQKjikPpTLY0RDy6c0xHULpKlKGPYJ0xLu2BjdvkfBgTS6+3jNzowR+G3SPCS1VbIooJvB+OQEuw2x2c5OTzA1Pcm//8M/YWbuGO3eIUkU8hM/9sNMT88SpxlpHJAeoW+EYYwsJSjKXRE8WVEwVIlckknTTPC8KlVazQMUInRloPkdR3iJgqkKlxnTUDAMgzQJkQcdhG67jayZkMeESYZl2ZimTpqnJJlEs9VFkVUWF48DomSxvfufSPFb0AXuAsXRCKfbbn7PSEY3DCynIIYRdQPf6/P+dz7OCy9dxLFMklymWh3MGw2IkjNTE5xYnCf0WmiqSDNmp8e5c2eDmalJwjDk9LlzXL92U0h19FvMzi3yT/6XXwOg1/dGv8NeowHyDEl0iF0o4QcR7XYL3xM7lKoLLSKNlKmpKbY2r1MqCKb6+vrAYUOR+OEf/iF+77OfpVQqUHQMmns7vPmRB6hPTtFpd5AUhTCIiPwQy7JRNZ3P/+mfc+L4aQz97vkKo4hqrYasWuRpzle/+RzflWSd3gUlKUvQVIVipYaqKvhBNBpR0FTlLgDJGnEUEqfg99p3nVzSBAmQsrtUjmEql9/3HkejpzhJ8boCZEzTRM1lktBDGn6eatzDnH/t6pXB6+7aj2PaXLuxMkrp4kzUXYbgJGUpt5dvMjE1LWzbgenpGdIkRlPahH2b+QWDyek6YZAS9jrMzM1gahJT45P4/TanTp9i+dYNnrt4hYfOHkNRJXRVo2C5hFEPiFA0mzt3brKwcJper00c1RirCMMA3TQwE4O3Pv4433n2WfASvFSiXCqyv3eAqijEQczq+ibvfd8PUavW0VQZyS2iWw5BcECr2cA2zQGVJMZ0ykRBH8PQCPyEaLDJ+EGEIsv0BhtBPwgwTJPG7g5pClEnxC6UiJOEyalpdvb3MTWZ6tgYt25e4+zZs1RKDr7vAQlnzp7ihZdfw/NDTpw4iWWbQEa5VOHZ7zzLiZOnMA2dl198jjPnznL+wjkuXX71u9xw39/6wcV5/5rWEIyORjTD74cAVCmXmKwVOTYzwcLCPDPzx5ieO4amCx0gTc74ux/7MM8/9yzveurtXDi9xM/9nZ+k5Doga2xtrGKZOrqcUzRVZqcmqZVtjs2NY2gwNztFnsXomsztm9fw+210LWWyXuHatav3/L5xHI/+kMW0vLvtbk3TydKYLI3pd1ojR5Td/cbI6BAEHSIIfGEhPaAw5IkY8HzssUeYrE+RRAmuWyQLIpqH++iWQRT5/MmffY5qfUyMtwCnz5wmDj3SWDz40sDvLR7sPfkgvR12o46fOguICf6p2QXG6hMjprxl6gLEZW1U2zkaPcWRTxh49Ho9+n5A3w8IwxAviEdF9HvOVfL6msOwltXxhGKBbomHWLS/lVGacb+w/RCoVF0f1LvunekbvnYoZDdcQw6TLue09ndGZFLD0Llx9fI9n9HreEyNTzI9Vccq2rgFl7n5ed7y0Hmah23MAT8qjHrEkUQcSXS6bQzLpVBwmJqcp+/1R/NlURJh2UV2G7tIqk4SpxRdE01XURUFRdMolBxKhaKIgBSJOMlQZFmUAxwbU1PIJBU5TzAshzwT9UlFEUoFjuNgWg6KLGNaFoahjfze0iM0G1UB3bQJw2ik+zU7Pcn2ttDNTwNvoEkvluOWcU2FUsmlsb1NluakaUKSZsRRTPuwiVt0sSybO+sb6LqF4xRed72/3/WGiJgmJ8b5+E9/hKUTx1lfXWOsVmNtdZlypUav0+bO+ipvevgRCgWX1uEhn/uTL9KKRbvU60cg60iJx8f/85/hM5/+bX76pz5MECRs3Nnk9m/9FrppMFUbZ2nqYQ4Om1w4vYQkJQS+uADNplCFTPO7N3K308G2LOIoo9Vq8rWvf+ue3/ko3aDd7WNbFuWiK5jQcTTSi3IKRTQZpqbG6TQPKZdc0izFsR32BkX8kycWePmli4xPTPHwg+fodTxM1yBIREdPUVTWN9bRdJl2u8NLF5/lxPHTFNwS3/72d/iJj354oATZoTxgnEdRwp9+6RtIqkacSWRJQpIOwV6MfZw8cWLEBxq5st6TnoXYliE0owFvUNhOougvrcMduCW6g8L/0cj3/nOXjAiTGZ12gmpmlApCOsUPImJfzLHdn9iP6kr3/fswQhoC6TDK0lSRmo5VSmxvivnygmOyvXGLan2KS1deY25ulp2tPRo7O5TLRRRNZX7iGABTtXEUBfYaMfPHlhirVkgz2N5Yo1CwgBhd1ahWSnS7Hutry1TGJimXC+w09qiVyxi2i6xIFIoFLlw4w+XL14QcbZqwdOoEr77yKlEi9KeSRDDH08H/UVYUTMtFVhSyNMUwTXJp4IHH4HoOjtMQ0iSKZgpRO9NBkTW8MCGOM0zbJY5ilDTEdWx6fY+yLhEngdDGN3TqA+a8ggC9fuyTpBmWabK9u8sDPECaQqFS4s1vfgi/79M+3MUydQzDxvc9up17B7L/KusNAUxZlmEZClcuv4qpSTR2AyzLoNdpY1k2J0+fw/d72DoUXJt3PPlWNMMRs0+DOZ/f+53f5Y/+8I8olwpUK2X+6PNf5/0/9B50Xccw1JHzygsvvYTv9zE0WYxzSAKQjoJSGsc8/fRTvPjSy+i6wuTkOAddcRNUCwaPPvYI05M1yuUxTNPg9voar1xbo7G7jaPKlOpVoCoe4CyDOOLE8XmuXouoVGtsbW7wtsce4RvffoG5uWnOnT/H2u1lpqcmePniqzzy5ofxgozd3V0ae4cEYUB1rIxuGqzdXuHMGVFc9H2Phx58AK/vCfmXkkgR0yyj0w2QVI0wSkhSMbjsazpWHJEMJvl9TUcKI/pHLJGGIAQiMjrote9p/w+dhAulCmHgvQ6gjqZq361mpxuGiDLjGMMU9Z04yYh7bfxem2KpRLkyRmbqI/Kj5wcjQqeUJSOwGha+j3KehrSC4TCrMHEXy1IlZE3B1DTIQ1r7+9iOw8Fhk4PDJrNTk6zf2aLiGpTe8gjt1iEF1yaKM+bn5lhZXcNxCoRRSHmsTppGmJZK4Pt0+q1B/qFSKTpsrN9BsxxQJfpeG8cuUR2r0T5scvrUElub21iOy+TMHJcvX2OyVmJzb4eFY0scHLap1SpIsoamyAwGGJDUiDDJgRBdM9A1A1WVSY6602gQxSGubRBlkBs5127cxut0KFbGxOY4XkNBQlckdF3D63tYhs65c6cYDlakUQi6ShLFhHGEbtpYpsUXv/gV3v2uJ7AUGb/v02p3Ga+LzTBNcoIgojp2l7bxV11Snn/fwgB/7ev44mL+Ux/7aY4fP04Q9MXFjzMMTSaMQpA0DE3GtkzOnj3Fiy+JHDaMhXzo6toGt1Zv8bZH38zb3/owv/IvhabdT33sx1lZWaZULOEF3sjbS9E0FF1BQfjMtQa6OgCKpAh9ZUBVTFQFHNflsNUeEm+RJPFoqCj4UYBu2ui6SRgmZLrOd775HKqhk6YpURjx0x/9EL/+67/CwokHuLO+zs987EN85tP/jv/iE5/kW1+tWrC5AAAgAElEQVT7Kvv7+3zgmQ+ys92gUikjKSovX3yNTvMQ3bZZvX2bU0sLSCSUSkVM02Bx6QRnzl7g5o1rdPsR/W5X+PKlEKUSX/rGcyNAun/aOwoD2noJ2zvE88MRmAwL01KWMF4tcdjPKOk5xYJDrVZjvFbmpRcv0u73mZ6aIopiOv0urQFoq6YI4Yfi98PI6S9bo+bFfSMsR8eCikUhPjdMEZPBhMBQOXMISkNu1dE0bvi9JsOx4yfY3riFPvCuKzoFOv0uURCSKwalmkt7v8fxhQVKjk6lUuLs2bPYpkycDObOAD9M2GtsU3BFRHTp0kUW56ZBz0b8Jj+IOHPypLDUikJURaNUdBEOSxl7+3u0Wy16vT4zszPsbu/S7PSp1qo88vBDTE6OIw+GpG3rLtViCECW7eB7faJYnHdJ1kZF8TSDNM3RdBPX1nnptZskvR66ro825Js3b4mop2Bz89YaY1VBOn3yyXdiaEAeE4RgqjJ+FPKFL34Vx3VQFRlZUnnr44/SbrXwgpj5+Tk6rRa3b6/Q7XosLh0nTVM+8cmffTHP80e/5w3wPdYbImLSdYPzF86QJAkFt06SJchKgmqapGkmFCAVZaA1FFEq2vhhBCQkccijj1xgcnKcgmNza3mdt7zlUcIwZGVlmbFKiXKlRk0WCpIrK6skWYaWIqz8dFP4ynXb2LboNOXDDUiBMElxgPXVNQAKxQJ+36N8ZFewJAWFFNuQ6A1wvlwsEIYRvqKwtrZBtSKm3dM85tnvvEB9skbk93jksbcgk5KnKZNT44RBRI7K9s4OpWIBOY8pFTUkEiYm6lh2EU2TURSVK5cvk2YZ5AmOawkWsKTS2N56HSgdNT0cG6uwf/MWbaDfaVGuTeFqEU88/hjHFheojVUwDJVOcx9pcN6kAX/p8be++Z6u0N7eAS9fvMz6+jqS4eJFKe2uALkhwER+/x67ouEacdAG9ulwL//J8/0RlaFWreLYFhBRrVZHM2Gd5oGQMhlEUznC4FI4Q+aQJSQZQnnhCCgNjSxL5Rp+0CVo90jDHnv7u7QPck6efA87O5ucPXuGPIhGMjEWcO78BTbW1iiXCywuHMPQNLr9Fmg5JDH9IGBtY42JiSmSOKVQcIjiDDVNSHLEHJ5pEtxe4/r16xQKZTQyCq5OHMXIeYJm10iCHp7vi5KCZGLbEp1ul7jb5aDZJQgCeu0WnufR7nXI4owkFR6AcZozNzPBzo4gO/pBiELMzOwM9VqF5duiETA+ViYh500Xzo3MNNM0Zyg7ryiiW9hti6g6CIRd+sbWHgvz81y9fIU4Dtnc3GV2foadncaIdvCDrDcEMKVpgq7q6Ko+SOt0sFx8LxhFMiXXxY9C+v5A5lPVMa0yzYMG7U6LgquzfHuD9Y1NFEXhTRfOiOMMmz/648+jqAbnzyzxlW99CySDomPzxNse57f//Z8gpSEfeuYDZAOHkUHHWgx5ajmHrTaLi8dHkVSxICID21TZ2t6lWqmQDQz/5DgiTlPKRQuwuHljhWe/9S0ef/vb+dIXv8QH3/tunn3ha7ztsbexvtVAkXOSJGFhdoY8TdE1hd/9/d8hRwNJ5PkzMzOUSxU002JyalwUVKNEgBJw48ZNDNPh8qVLNFodDLd6z4S+H9yNmLx+l/F6jX6nxS/+j/8dAL7XQVVVoWwox0TtBrGmiMwkS5EVhTwNSSKI+x5hEmCoJpKmUHIN3vnkW0izR4VmuqljWi4vvPgKf/zVl4gjH91y0C1RbI58IWkzjKa+W7p31H9uqCu+f3DA/gGjSGk4jjI9NUWc5vS7LcGBShLiRDxhQ6b6uTOnOdi5g6pKRBFkScTG+haW45LFKX6So0kp5UqBmVqFKIn47O/9Pj/10R/jxvJNDNlgclKc01gREeH45DSrt1c4Nn+MZqfFzm6DXtznJ3/8o3z5i18hSiJ29w6YmJzh9so688cWUCUVVQLNkWnsNzh59iyvXb6EaegUC9N4vYDLr75Mvf40SnyHXNL4wz/9MnIWo2o6pYKLVRqjWilSKViYpkUSGsRRgGtZ7Hd3kEgFuTP0WL51g3a7i+M6VCtl4khib2ebrV0huavpGjk27U6HUrmMIsWARprLZGkOqtiAzp49wze++RzlUpl2e5tet0ep6GIYKssrK2hSimkb7O/vo6gG73jq6b8yFgzXGyKVO3XyRP7rv/zLeL4Hso6u5vT8jEzW+dJffBFd13jvu57g85//Ao7r8Na3vpVvfv3LRJmBbZk8/NA5FEnh07/z+0S5Rrlg8MRbHqVWcsHUkGSHSsHiG996njc/+Q4APvtv/i3vfuoJiuOCbLezeouiayHJGskgRFY1gzy7W35VRiMr4ntNVdjYXGPu2ALqAMxUw+X2nS0MUzyIjzxwlt/57Ge4ubLGU+98GxP1OvVadZSeDAbX0RCF3hdf+g5hkhLHMl6viWkqzEwvUigWmJmexBzUZaIoIQo8tnf3SdOMZqvFK1evsrvXI0pS6pMzaKpCWxdhejXrUXItfuQD7yPoNWl32timjaZrKDKYpkoQDPzrZDDN7164BsjjlFQR3UcATcqJc4nY74OqYesaiqoS5Tr/4tf/H+KU16WLwHdN94bp3bD+dHQdJW3erwRRKxfwgoB2t08ShWL4W1WR0pBH3/o4d9YucbjXQbcdoljc88MISpE0fL/H7GSdgjOQYM5AN00euPAAuqZweLDHmTOnAUZMakVRuXlzBV1RWF1fpTpW5mM/8zP8z//8nwBi5KdenxvdRwsL83idLmtbW9iWhef7NHb20AYjRAe7u5QqJUrlEooqsT3QwR+K3HW7PcI0pl4dKEQa0Gr4uAULVRbEzfnZMbwoolqc5Mpr15mZnqPf69Lzu0xMTtNvdzEsm2TQqdvda+NaJk8+/Q4UKSZKJOJQ1PVG10RRcMsVokDoTTX2DqnXx4njkDQZ1BRVldhPkWSV3d0G/+0/+K/+/5/KZVkuagiyjiJlIhqIM/74Tz/HXttnYrzKyvom9Yk6k3Mn6XY9nvmwED0Iek3ubG7j2DY/93Mfp+95OLbNlUuvUiu5KGhIhomsWXj9HnEwMOrLj4y8mBZxlpGmGXmWiZqWIXzfdN0U7qiWPWo/B4NdPk40/F5Is7FHlsXohsMDD55iefk2Dzx4gSiK+LVf/RXq1SqLs+Jm+tznPscnPv5zJPeRGyVNQc5Strb2cB2XIPLpdnvUxo7hOAVUVR2B0vDByGUV3VC5vbLO7u4Wu3u9kdyw1+9iOwVKtCGLeeZDP0IaBmRRF0OFyXp1NDuWpQmqrGDb+sheCQSZU5UVJEUhP5K+GZZGNuCEyVlMJmuYco7flyjqGnEuEXZ9JC3iv/n4R8hylV/85X8lzlnkj0ZUNN0SlIojad79EdQQoO4HpaOr025Tr1WxTUZjNfdL5QKM1Yt4uUm0e0CpXCPNYsySi7ffZqxURCIGWRKa2j7sbm/wlkcfpRf0KBVLLK+scPKkAKduP0JVIhYW5llbW+fUqdO8cvFF/od/8N8zfayKJsvEQYKqGSRxSLPbwQtipibrTE1N0e22RIqWRFiWQZKmlColtMEQdxrHuLaGYTi02k3STBqBkuHo7DR20foabsFFleGg2WWsaBEnCbeXd9gw93FthzSNaHVaGJqOmucoioym5GiWQZxKhPE+puMOb8IRSJaMu5pmQ588Sdbwg5SCY4/oJ+Qqvu9xZ+sO7U4XXddxnNdvKt/vekMAE0iDMQqVOJEAjTjx+NGPfgyA3/3tz1CdnGbu+CnKjs2rrzzPWLtCohrDaBNAsKgViST0Ob44TxgmmJpBGAYEisxjjz8+OvaJtzxK2/MpInzZn/328zz+tsfotRvEaU4SR+w1dun0PJI04UPPfIDf+YM/ZG5ykjgfOK0GPuPjFdotibljx7F0Da93SL/fplIuEsUhP/+J/4xuT0jg9qKIj/ytD9/D9UlzSGOf9Y19VDnHHXMxVB0MKLoWx44vQq7y6iuXWVxcAqDb80iThJ2dBhubW9y8uYxaLtIf7HLddpNuu8l73vd+Hjy7RKVgQZ4gKRKWpRPHoMoKWRIQRTGqLBHFKrZj4HmgDm7OoxGRMpj9SpOETNaQsxhFVYkRX6cZqHJOlGToqoxSsEiThDgH11L52I88zbnTi/zS//HbAjwG4KRbDk6pRr+9/5cClKZpJNH3Bqe1jTsszM1SLxXYa3dHs2TlgoGi6PT6Ka6jYEsB1vgEqZ0xljnsNQ/RVYmxqknP67Mwe5qz58/z2pUr3JIiPvsHf4CtGUxNTHDu3CmuXrnMufMXMA1Ikogwzpidm+Vwf58TJ5a4fP0aRDKSqaOb0DzYYaxW5dTSCdqdNr4XYBgGqmliaSbnz1/g8qWLJBmUXY1uu41t6Wxu7jI5MU6apeiGg9frUCtXKJcKJHHA6cVTbO3soalCh9syLWy3iBcm1Kt1kgzGyhahH2PYOpVChUSS6PV6hFGE4QyjTYNj85P0ur1RZ1qRBLdK1xQURSVJYWdnj8b29sht+MKDFwbRpsL1GzeIwgRZM9jZ2roLdD/AekMAU5ZnHDZ7hFGIosgoqo2myqRhQJgmnDx+AkNR8Txx487MLZEMnF9Vw2J9Q8wtLT2wxN5uxOr6Hfb2dzlz9ixXvvBloiwfSaoOnVA6A2a29jVhqbO4sECv3aVSKXFsQagDW7Y4wWEYEEUJP/rMM4zXK8iKShwFxAOBd0XOgZwsy2gdHnLu9AkuXnwBVVWJwgRdN9E1Gdd00XV1tJuHQYCiqthOiWPHLG4tr/HUWx7n0pVLABybmQXJ4sc/8jeZnpkmS2Mae4L71G616Ht9dvc3iJKcG6/cGJ3Pdz31Dt777qfY391hqlYiTTxM0yaJQkxNIU8VdFUmUk3cgSuIpNyVIVZl8fM4F6AEApAAMlkjiUJBwkRETMOlSBp5nIIqCznbQc7rIdIBP4j4+Md+BNux+Yf/9H/Hckuj1M4p1UQR3LTvSe+GAHW0OP6XyaqsbdxhaXYKrdtFkyU0CYpOgYi7ioqHex3Mmkmw0eADH/3b/P4f/QGyrhCmIa7tcGXlGssbV1AlG83UsTWDVvuQUqnA+p0tJiYnuH17mZnZ4yhyQpJEgEqxXKbb6/Lwg4+weus6vV4fx9XwPBhXJ9hrNOh7fWrVOr2+J4i8jky310bXbXTEJpWkfaIoIYwjwjgmjmIOmoeUi2UURR6pSBzsNygXXdI0p98XG8z2zgET9RK6Dgb6KPrueX32dvY5dfIkEhrV+hhrq+ucPHkcOVOpVeuUSuJezyURQQ+tEdNM9BEmJ6doNHZRFDjYF5LQiq6gZuD7IZquUSxYhFGRPH49yfb7XW+IGlOtPp7/+I//1Gj4tR9nHJ+d4ubKLfp+IuybVI3ZY0skYUStLryyzp05BcALF6+wt79LoVRhcXoCxxFhqGOLkNJ1bFZW72AYAhCGHl5ucYzZmTqvvPwK5WqdbqfF4rFFXFsbmDjqwhkiiNDknChOSfO7092qqo483bT7dJOH4a+iqCRRiKQo3LnTwC242LaJIsskcYSk3H2vra07XLryKpqpszC9SLlS411Pv4Ovf+2bVGtVen3Byt1rbBNGCd/81nMA3FgT5p4PvukBXnn1En//53+Whbn6qHM1/Pyj1tV5mtLudFEUCVXRMBQVDHvgQHLXymoYHR5dw5rS8GtFVQniVNSYEM4fIMDO73uYpkHPC7FtmySO0DWFJAow3RK/+KufFp9zXw2q32ndEzU5rnhw5O/SvYO7sipLs1P0+h38MKHiWkJLPYt47KEHeenF59HGqzxw+jTPf+uF0WuXFsfQJIm+F6MZ2SB9OiCIFSTTROpKbO9uYplFFFXh/e99J0kKaZ4yOy0cbNNMMLXvbG1jKDm7ew3SJGdyfIy9wybVcp1Wr4ehGxTcwXyeH9DpCiJpu9kkSxNur61y6uRJZCXjYK9NsVTG9z16/R6TExNs7Ipu2tzELCBm2dzSGBIpE5OTXLv5GvMzsxzut9B0jSiV8Po9VBlKYxX67S6mZWBbOkkaUXDLVGsTWLY9oBoMlCQ0dfT98L759refFc+PZnH2wgUMTSbPYpaXb7O9s4esymRJhlty+cf/+B/9QDUm5VOf+tRf9bV/beuf/dK/+JRbGmdl7Q6HnR5pmtJsdzBUjaeefIL2wQEf+Vt/k9npcZqHh1TrNa7fuMnunQ0qY2XOnFhgdW2DrhfQ6Xkgq5w/cxLT0EWEouRUKmV0TcLzPCRJ6E7neYaha1QqVTyvS6k0huf1qE9MocgSeS70mBRZJUcmy1LhggJkuUyWJWiqjiQpg51MQtOE4FucAFmMKuVkkth+wjBB1xSiKMH3fdrtHp1el93dA1rtLvVane29TS6cvoCiyKyurFKtjo1ALwh8trc3afc9Xn31NYI0o9fv0/NCfvZn/y7Hl+b58Afei+sIw4UkiZBlmRwBRKKVniMpGlmaEoYRtmOjaxqyrqGqMlEYoqoymqKQyRr5gDuRpymSLJOnKWHgkwFJnJLkGTkycRxh2gWCIBKRYpySJil5FpGhgKKjKhJZlmIZKpqmIeUZT771Qb7+3GUUKUdRh35xCU6hgm4aBN5AYVSSyLIMdaj3naYjrg8w+ro6Vhb23gpkhozXOsAxNXpSgEyOoeQs39jAsUr0O4fUJgpYpk6aRUSJT8/PQdJIVJ00ilEjkBQNRVaxbZN+5BN7HobtYuoGsiokedJMQlEUSsUSB80DXLeAnGfs7R1ycLBPDuztNsQG4/Vx3RJRJM61rqkUHJPl5RVKtRLVcol2q0O308MtFFAMCUMx0E2ZydoUYRRQLpdx3QLNTp8g6lKwHcKwSxbLtJtN0jwnimK6XY80zZhbmCPwApqtLptbG0xOjJMkOTOz8+imjSyBJKuosgBZBvfsUCssTVIau7tkWUaaJQRhQLlSQSanXC6RJEIrzbAs0jjia1//2vanPvWp3/irYsIbIpUzNJWTi3NcuSkE28JQaBjvxTHvNjUO2i2u37zJ7ZXrfOhHP8za+gampuH5PtagDW1qEmOTc+xubrK90+DPvvJt/sZ73gaopEkyEMJyKRaEJbNuGiiSIigJmpjCBnCLooulKGL3DkOVIBA1nYPDfeIopuAW6PY8IEVRNOQjhiGuqVGbmCIfDJOGfkw/Tmh3WhRLNWxLR5JVkiRirDrOzZvL4vc3DPYa27zzbU/TaOxw5sw55uePE/g9QGW/sUNjEEJfvviSCJ1NGGS3KInPTLmOoWWkqTza+aIoIcsEN0ojHQjghSRZijWYrE+ylCxOgYgoTjBNlZ4fo2vinEjKvQVwVTdFKphk5HGKpufksiJSRdNAH9pVmTpeF+yCRSoJ+RN5cCWiAVlQV2P+0d/7CeJM4pf+5aeJU+4pjhdKFbrt5ojzFAaiOD8sit+f1gE4lgZodHpNzNo4lpmwc2uLk6dOMz4/ya1rl+js7bF0eoGiaeFHInVUJRt38FZRr4+hGDglFV3TKJWrhH4PTXNodVr0rr3Ck+/4Ib721a/wzqeeplwuEYci6pubnWdlZZVSpcbu3gHV2jhB2GNhcYH9g0Omp8fRdA3bMpFle0D+LXD2wnlWN26TIjE+UJNQFIndOw1KYzU6gY/Xiwm9iJ3GDkW7TLO5x+z8DLmi0us18ZMIt+iKWpIX4Qc+M7OL7O8KBYPxeomCo4xkngF6rSYoYjZSV8Q1TuMI5b4sIM8SCoPCdrlUIE18UsAydE6dWqRYKpJECbdWbvODrjcEMCVpTOQfMjc9jmE6vHZd1Es0TePq1Uv80LuexLEdFHIODvcp1yp4vTb1ahVVzvnKV79GEOekHTE+oQ9cPxTVRFNlmgc7tDseYRTSOmyBAmkqocoKsiKhG6I4vHe4R8Ep0uu0SQZdB1XRgJSCW6A6VhvJhoxPTqNqGlnijyQnRlpGDHYdWSEhJYoTDN1BVeC168u0Wk2e+Rsf5M/+/MusrG9RrRTRVHFTu8UxfD9kr9GgULDJcvHeWzu7qKrKt194kVYnxDZiVjZb/MNf+C/xg5Dq2FAydsCnGqhLyoCi6feklHmaiihMgziOROdtUOjseQGmaZOmCZqUjwBEk/KRqmeSpSSeKLQrkujCJZl4zzxNR69JkwRJUwjiFEWJSVPIogAwRyJjumuNzt0vfPIn+V//r9+7ZyzGGlgGDcEJ7lIK7gelJBKOslkCjqnQb0eUlTJhGDBen0RTTb751ecxSy5JqhAHEY2uz9TkwPTUgu2dDkoaMVZyiPyEaDBP2e+J66NoCrWxGlHe5YWL32BpaZGVldscW5jDts1RWn78+DG272xyYmmRldVlTN0kCILR0HUUeMwtLLCzs0veaxGnCa5toJk63fYhkqajqgZxHjN3fJFuzyfqelRnxsjzGFU12NtvMDs/Q78bkfghTsGmWhunsbPL5Pgkm5ubnJg7xf7OLikpk/Vxej2f8ckJuq0mPS9G0XQsTWdtdY3jS0tEgYc06Lj6XoBlmyiKUDR4/O1PsrF+G8cpUK/X2NnZpl6rYlk2ewf7xPstHNdleuq7OwR/P+sNAUyWYXDmzGmuXbvOxVdXOH5smvbBARCjKjIl18CydK5d3eXWyg2eePs70JSUdz79NABOoUwaxLS6QufHkTQKFvzp57/Aow8/QJIGpJFHr9XBMC36/T61ehXfEw6yWRLRarZZOrWEIolJasctIksJWS5C+KERwrBuE0UJcZgM2v6iViQh2utxHBEGCaoMca6SDouBUoH5+Tl03eTf/L+/Ravdpe8F9PodThyrcnLpFN2ux/bOHmfPnCRNIc0iLl+6gmWbvPDCc7iOSuSJiOTv//zPousqBdfGtETr3RhQdsM4GdUHZEUjjnxU3RDFalUmSlIsU2d/94Di2ARSFhHFGb7fI4kcJEUhSjKiOMXWDcjiUcE7zqR7alUgCubAqJaVRCGZrKGrMXEOhuUAfWTVJs4lCqaOl/pkskaWIaKsLOYXfu7DpJLOP/tVwX8CAU6qrtPcE7t+FIb38JyGpEtV1znY3eXdTz3BjetXmF04zuFekyxTUUyFGzeuU3RLdPZ3mZkep+f1sTSTfhiSZxGG4WCaCoblEvoDUbcgpReFGIrBYw89whe+9GWq5RIkMF6yWNu6zYXzDxF4vdG16PYjcu7qt58+8yDLNy9BpGKaDmkmYdn2iH1u2UV8r0OxVGa8WCFNcuIsRLMtbt9c4eyF87T7TSzLptfuoVoGru2yeOI4BUdMQZQKDqamsre/z/HFRULfY2pqGt/zObW0yNb2LpWxCq5tC5UKw2GozH14cMjUzKy4XzR91NSxTRtFUQm8HsVyGVVVOXZscVQrnJ2eGvHdSgUHfRDpqvJ/MnQBUAY7/DPve4x2K2DpeJ3uYcj01BSWZVMcm+SDzzyDrgtS29ve/gQ7O9uiI5GK/F43TIpFHc/rE2c6mmoI+Y40wNR1wjjG1i3OnT3B9u4BaRaiKgaFgo1u6ORZjFVwRnWkVtvDtk3SVMMPIkzLod/v4zgOYeRh6DqGqR8pBuvcXr5FkkmjFC+OspHtV+OgTbvTY+W2GG9RFAXHNpHzGENV6Hb7fOxv/wS/+Zv/bvBzWFm5jW7o3FoRLiO9fsrE7Bg3rm6CEqNI3MM9ApEaD6O3FNDknGxoeT0oVEuKQpokOG4JRZZJMxXL1MnDAGnwwOiqPOqsRUmGToJmWEKedhAx7e/vk2UZ3d4gIm136bSE80oQ3CXpmU6Bdz35ZkDQEfJUGRzjjbqAmaxBEpPJEr/wyZ8cFcZBpHWV+gTNvd1RSicUHvSBJIuInobdVsfUkRWZg2aDamWcNz/2MC+//IpIxafHGa+PUSk6dPqiY9fzI/r9FkmeMlOushdHdHo+iZzgOgV6/S4rq8sUCi5bBw38dgDzMF5yuHzlImeWzhA0diiVTmNZKocHDabnFkjimMbOFgW3TM/3URSZKAhpd1o4bhFdU4nDATiFkZCfQUHVNRzXRddUKmMVjh1b5Nr1q5xcOoGh61y/dZtysUgUJViGTp7EBEBlrA55go9GtWzTVyV0w6Zeq1Msl4mihMpYbeDiI0C9cGyeJEnwB3STYrlMmiTCbTlJ0PW741eOWxw8E0dUKPptDKtAGgX0vJjNrTv/4Qf+P7DeEMAUxynrq1sUCi4HjUPMgTZPFEVcunKd1VWxyz784Jt45eJLnDx5nF7f5/baFrkiUrdMEt5zQRhh6MIA0/f7JElCr+ejkIjCaBTT2G/iuhbKoCeaZAq2paGisLW5gWmadLseuqGPjjF0455a0tzsvLDljkI0xyGLE7o9j8WlE2i6Neowphk0Ggdcu3aVY8eWuL26zsR4Ga/v0e0cCJ3n2Tonls6ysnwTgEceeQgpF6MkfS+gWCjQaYXEUYypa9xe3ue//nufYPPOFmptHMPUkVULJfXIYOAmM5TaygeplPBbA0apZwa4RY0kU/E6h7RbXQ66PnsvXaLf79PtekRJRJZkpFlKmmSYjkuxYFEoV3Ati3p9HN2wKI+bSANSXj4wWQiCgGBQu5sec4lDHxkBisPUUIUBEAqdH01TieOEOI74xX/0C/xP//yXR+J0pVIZ27LYXF8V90cY0u/10A1jxHXSTY3vPP8i73zyMZqHh3zg/e/n5s1bvPj8y+iGiukYjNcrWEMd78EmVClU0DSVxuEe7b4HWoZpKuiqxe5hk0rBEdQQTdQl4ygmjvq0AF2FlVvXWDr1ANevXefHPvJhvvylQ3rdNoYuvAQrlQq9rpBMtgbWR51OhzRNsAvlkQbS4cE+Y1N1Nu9soRkm1do4pcFmefz4KZBVPN+j5BqkaULBFY44kiqiWc/30A0bt2iSxD7FsQmuvnYTxzIolssoMkSp8JtTVJUkgzhMyNKE+vgEvYFwn2GKa5kmycj2vnV4iGXbNJuHtFstZudEZzBJQJESFN2kag4gYt4AACAASURBVBfZbez8AGgg1huELjCRf+xjf4dGYw+AJE0YG0h4mLaDoesctvtYlsV+Y5vF+Uk++MEP8m8/+zkAvGjgOXfEyltTFXRS0izm8YfPoGo6SRxRKBbIUgijkDCMyNIMayBsZdsGi4vHR3Wjo8qPeZag6yItCnyfLIswTZsoSkYGf8PBxzRNUI0y2UD/SFYMXnj+O1x69QXq03Wah02KExNk3Zye1+DY3HGmp2eJAo8H3vQgm5tbjNfHeP655ykUi7zwygu4tsP62jaf/MQn+d9+7TeoVWujOtsQNIZL1zVM06RYLlIuV5io1yiWK6RpgucFdDtt9jt99ra3qU9N8cD586PX9r+Hqy6Ao0j3HNM+QtS2cx9Psl73ve8FTFZMrLRHmiSoUoJqmGiajjngUQVxShxHozqVrsr8+r/+NKbu0hp8hueHUJ2Cg20am6v3/F53td+FGWSl5LIwO0tlrIjX8+h32hQKNoHX5rGH3oysIfzagE57n37Qo+xOsLUnotkoiogjiTTJSYlFUd7UKZoWa1tbuLZDpWIRxTG6pkEko5guczMLKJrG5OQUvu+xt7dLpVJlv7GDriiMjddxCyVarTah74+Y3sDIPWdyskqx4LJ/0KLV7mBZJmvr4p7odltMTUziFkuj1L3f7xNnEhIJ/Z4nZk91jf+PuzcPsiw96/Ses293z8ybe2VWVVdVV1cv1S2pW1tLAoFAYpEgGPDYMQHEYOwJzTjswdjMwtgxYXmYwIPNDA4BxmEGwhODxvIYgcCEQAstdau71Ut1V3V1LZlVlVW5VObNu5/9O+f4j+/cW1mSAAkREzJfRMW9eevmzZs3z3nP+73v731+iVDY3NomiRLe+pbHSaIxUZRSb1QpMkEYCxxLJyvkvGWz1WJubh5dA63sxok0kSJgIfAqNYpc0B8MWViYxzBNOp0eg8FA1mVziOOY5cUFfuhHPvL//5EUVVUxKi3qmULn8BBRFHQHQ+qNWaqVCqNSEZxmGV61QXNmHt0wULKENANdNzGtBkkc4bkOlm0z9kMMTcEpMpozSyTRCF0zcG2XwfCewrjT69Eus6KRP2R1ZRXdcCgA3ZAkyCQRxEmCaerTAdM8k263iqqjG3LeLMspudsQDLoEQUQUjLm5tUWn05n+zO7BcHrbmquxurKM43jktos/HtOo1ygUg2qtyYU3XwKg3x3wn/7U32YYpLz/Ax8kjnz8cUCRRgz8hLptINIE3TBl50rTGY5CXrnwBsNI4NjWVH8FpRarUDn0U048+BAV12FHOKBBMepS/zNG5XaCgrolA1Ldkv/SSAagQHHuC0a45S0QDXpUqgZuVdIY03yijyrIhMBQSucV5BZS03XWVlYYjmJ+/h99lH/yzz8uZ/8Od3Eda7qtmyzDkAO/5IIkVwhjweaNWzR78qKzMN8mBz70Ax8hHsqsoDuQHbN6rcZgOGQ46DDXnOWg18FzXfaDAYtzc2RFws7BLqauc2tnB8PS8SyT8TDEdMqLkg1pPuT29i3W1x/gwoULnD17lrnZGZJE0JqZxbF0KrW6LNCXGdKkDgjypLYsS9aolJjd3X0sy+LqtRssLy6wf9BF0wwee/wJLl28SJwkBAqMg4hqtUKBjleZdCsFruey2M5oNT2SaEwYBFOQYIbO7u4tzj38KLZtYtsuiqqztXWDLE2nuBaAuXZb/g2jAJFBnhXsbO+xsbHJo4+dIwojNE2l0ajT6fbuC7Z/2fVtEZhQVESWUak30W2PTEhEq67CoZ9SZMpU8ZvkOXOzs4zHPn4QybkmkeGZJrmuyUnzbg/PdaTpY8WZIk1MUyPPc6oVl0I1yNIUy/awLYtaTXZLer0uw6GPSGNElnLypBwD0XUptPQ8j83NK5i2i2b2sY0KBwf7zLaazC8uMPYDZlp1qnWTZ599AV2DzsE2dtUhTHRansdB1aXqNBFVwfuffj+JSHjXu97JpYsXp7ZLL7/0FWrVKmlcBhJb548/+yV2ej6ztcoUi1rxXLyKLus+io6iKVi2i6IbJFnBzPJxDq9coUgyyu4/rmNPUbkTquM4CCnKdvckKE0CzmQdDVZH70+eIwPWvedPgpLj2gwP+9TsiSjTQEWKN2WpQpkaI7jTLSicf/ghbm7d4df/t9/gRz74NP/yV3+T2YVlUpExOyNLt/7wfrfeCY0gigqE5RLsS6X8rZ19PEfn5o0bU8tvUWZ+qhKz2F7kP/pP/mPevPgKAP1RH9PRidKANM+Za87R68tieb1ex7UKPAzG4Yg4ThjHKu3ZOn3/kIw1FuaabN28xvLKKqapE4YR40CwfvIMaRKSZTuIPMcxLcajMZVqhUq1QhjFLC0usHX7DkvLS7xx+Qr7nT6zs23iFB49vcZrr72GyCQu+Oq1a8zOtOl2OozGcqbTNFSCKMC1XSqOXg57H7CyvEy306E3DFheXsL1Kmxv79x3KlY8jzAcM9tewPd9kiSajqskUUwU+WQ5rK6u8uhj50gSwdLyMjvb24z9AMfxuHT52lef4d/0+rZhfit5isgkt9s2NVzXI8lB0+9F3wRpF9Tr9UniiGajSmd/H13XKISsLfm+j2nZxInExY5CwTgMGQ2H9HoHdLo9eoMBnf0dOt0eFCkH3QNu39ll/6BL97CLqoHjuDz66KOSu3zEkDLL4ZFHH2NhYZlwGDAz0yKOY65u3ESkZXYVp3z605+h2z1kb3ubelO2vOfbdRTNwqnb3Nq6RbU2Q0LOaOTz8V/5FWzHQdUdFFWnVq3y/MvPUXE9Kq7HsJcwGEfyc0ElSHOCNGfsB/LEKA0SkqxAOfKZ1av3tjeoJu5X8Zht26biOvdlQJPt2VcHpclzJmvyvMljg68imDiuXcLrwbI1klgq14ejEUEQyBpWmk87e6pq3ic1WFxaoMDgve96O+25Fj/6ke/j8ODulAc+CU4gPQHjKJAGjiKXJpBZAlrJhMoVTNNG2AZBGhMZOkmekBQKeWFxt9Pj333y3xL69wicjZILPlmiCJht1NAV+YsmSYIQKZblUak5+GUTYufmNQxDpVJrYZkmmRA4jo3jyM9iXO4AdFXqzSYBSdV0PM9jZ3ePYV8GXE3TWF2ep9/v4toGO9s7RLFg+/YNgkCKJ/v9Hl6lQrXiYlkWlVoNq4T2aaVubGVZGnCKTGrmJtRSXVUxDIvDwy43b17n8ptXubG5xdatG0RhhFt2PxcWlllePcbpBx/hxMlTJGmGyCTnKRMR+wddOv0+rVb9PiLHX3Z9e2RMQJqrJU9ERdc1Dvd3CeIcyzRlDamc7B+NfXb39lhenKPf7TDbbHB4eMhcewH7yAmpagamZmBbJmlucHxxiWG/g2na1OsV3ty4RXumxsbGDdaPrUwLeSD314p6b9sWpzndTodj67LLksYhO6U/+1deeYVgHLC4sMAbl68ghGBm5jHGvs/8wgz+OOHO1ja6rdOedXjhxVeYX2lx8swa58+eJ4oifuiHfoit27dJk4Qo9PncZz9fgvDgYK+DV5/HcOoouoama+hHFM8gHXIB1DBEN3TiI7Wxiic992QiknB0jCnNAE1l+84WrlthEDtfN8i4RcjQXKbOPaPRodmiTvdrgtW9+/em0yerWm+hINBLQ4aKCaaukIiCquuQ5gqGqk0LrvXGLEHgoxsm4XjAyvIcv/hPf45f+Ff/h+SWZymnHjzHzY17c4KTbp2pGlJTpMnjp+ZaUytsQN4qpfFFnpDksHPnkLc+8hb80Oag18E2XPb7AzzXINV15ppzKJhs7Xc5sdTGj2Ma1Rb9URfPa3C3P6blVimshI2NK5w+fY7NzZvMtdtoSF1dr3tQeg7K42vQ73Pq1Amub9wmy6Df26dRb0p7q84h/cMDzp47R5qkZCLGsGzyTOA4VbIcVlZWMS2bwWBAa3aBLBNsbt7Ac11mZmeIwhBQMB2bJI7oD0eyg+3VyAyVuwcdBsMxaytytMYyNDRNJU5zLrx2gZpXoTU3x/7+XZJEIPKMJx5/FM+TaOt6vY4QgsNulyCM2NvZw7I0vtX1bZMxTaD4ICmLNzc36Xd25ZauvEIaqixUrx8/iePavOtd7yTJC2bKK+dXI2RBetN3D2V9x/dHjPyAQtGpV2p4Xp1Hzp0jTmJEBgX6NCiBRFsYlsPmxjWyImPr5i2ub9zixRdfwQ8CRA55mtPr9bm5dYdXLlxiPB5x6dIl/OGQfq9PHI554NRpRCRIc5vHzp8gjQW9bg9VVRmPQzqdDmk5LX/xtQucOLFGmAwQkcCPVSynSq6bpBlTKmUS+fi+T28UILIMNUtJy9a+OFK1P+j26PUHpLm0l55smdI0gVzePyybDm4Rfk1AmmzHaokMQnu9iEEMznB7+tyj3zOIvzZzAnDdCrpuYhgmIs8o0owoigmSFJFnDEYh4+GQYBQSBIEcAhY5vcMOSan2rtfr3N3v0N3eoOI5CJHx1sce4vzjT9yna1K1I+4uaYKhaziWTh74UMSorofq6KiOToYmA5RiIbKC5198kfW1VWzDJUoDRCExOnHsY+g6Fy9dw9IsUiEo8oQkSZhrtimEoG5WMHWDJEmIkzHXNq4Tpxmua5eZdMRwOCSOU9bX11laXqDeaHBra4uK56IrKUIIbt64Ru9wjzgYE4Y9Xv7KF/nys89w/eolHMdGU+Haxs1ymDxlc/MG4+GAsBwDmJ+bnW7VFc3gldcu0O3KuloSBbxy4RJf+NzneeYzn+Gll16h1ayXEhdJCti8fh2R+CwvzbK4PM/qyiJr62s89Y53cPbB0+i6jlWihFTDwbJslpZXWTtxHE3TieO/Jk68eelHNmn339y4OlX3bt28QZqmLK2uTw+2zz7zJR4+vcqHPvSDfPZL94YxkzgiTWKOejQ4tsmd3SEHBwtEmcoLn/0Mum4jFBVH03n30+9mXALSDF1lZ/eQL3z+s8TRiJ/66b8DwOnTp7hz+w4PnX+E3Z0d1tZWCYMxX/ziF6nWZ5ibm+X6tWssLq9y9coGmq7xxPmzdHoHxAIs/Z6ep7M/pln1ePj8k+zs3eXll17k8cfPT7Mzx6uwcXuDZqvJzs0dbK/K5q1bNGYXSLOCNLs/+BqaQjQeorhy2zUOAjRNI40z0lxFLTJOHV9na3d/qpI2VMm+SpMQQ024/OZ1Tjz4kHzBI0HlMFRwXJsAUMwWxN3p1uzoNg/u3wJ+vWXYkhVkGUyV5mok7ydphmVLHIuiaujIEZgoCqTfnCln70Q+QAU+9rF/yn/3sX/BytoJLr55ndmKOR1VAaaGnBOTzXrVgyJAdeXWzFUigkL+HoajkJe7N2EbdA87qJpBViQYuo5Tts2FSNm8cYBhGszOzOPHh1ScKmmekwohTS9Mk/HYJxEpFbfJ6vIxYpFRIIdgZdfNwQY++yd/wtr6GtVqhe7hAbu370y5S8sry6RxQBiOaLfabB9u0GhVMMtt4WAw5vjx4/zep34Pr+Jh6iZ+4PM93/s9CCG4+MYVtra2WF1dwjRNDN2m4ln0egMqtRYL7RHVRpNRv4cfxFNNkuN4fPcHvpdhv0ccx7TnZ8vfvRTT5gLDtCnQ+X8+9elp0R5k3fKvgsM0Wd8WgSnLBP1ed+qU+tVuvIYhPeHWjp9kMAqkAEex6B7u88jp42zsliiQUYDrTNx5S5i766HXasQpdO/uYroeeZJxfPUYD5w8Qa/Xx3I8rl69xqWLFxn7Aaoph07DMObyG6/TbLVQVINLFy9TrVgM/T4mEsxuGjnr68fYubNFo2oxrBrohia7Y+GYg4MBvZHP8ZML7HX6WIbFyRNnp8TLn/rpv0OaSHnCra0t4jih1+1h1iu0ZpqkWoMoL692cTTdwkyWH2eYZBwmGbquYaqQHDHNMFUw3AqwP30sxbz3GWkme3fvsr+zR82zCPN7AWfGKaDsstWSLnvliMKftSbZ1agjt7ndw0MMXUcxLJLQ53uffoIsy+WYjKZj2eVIzJGZrCTNyDOBpekMoi5BkpFncrDXsl3SNOFgf5//8Z/8LP/Tx3+LVrPJo299K4Mg4tLla/eso8oZStexMRwFuFcvmgSlNCzQjlig61FKqmlQInX6vi9JA77PQmuFUXiH5eYcRRbhWRbjcETFqWLoOkKoHB4M6PR7rBxbpt5oIDJoNmcYjcb4vk+lrFldvXqNarVKnhX8zu9IEemjj5zDy13efHODc2ce4Paoy7HmCpuDQxZaKywvH2PrznWSOKBSrXDnzm3e9uTb6Xbk3/XR84/f5wf35JNv5cbGJoE/Zma2yfNf/jKGZTMcDnE9l2g0IPDHRGHM7p0dzj7cYjQaMOz3UDXlHptJVZm8apEL0iRiOPSJAl+iUhSDzI7Ik4LRIKRQEsbBXxP7JpGmHNzdpdFskcTR1/y/qWukacqtGxvMzS+SYtJuz7O9vcP73vcebnzi0yRxxEyzihDZ1LjRLmfmlDzFtQ1eu3wN11J54onzPPboY4RhRJJEHHYPubohldWGllG2itjdvTvVdtiWyfWNW7RmVilyQRQFPPX2t7K7u8t4OEBTNTY3brFy7BjjQZfxeACGiVO3SWNBN0pJY4HlWji1KkII3vY2KfNQSqzE/t5dBv5Aapau74DhMo7HuF51+rlMbifb2+nKU+DItL1IyPUSqJYk0hjyyPeU8iHSDBzbJh4PsZdOs1D+/6B7wGZHRw37jMuAs7+7Q6NRv/caus5oNELVDQy1YHV5iZlmjceeOIdXv6cW9gd9xkHIzvZt5ucbqKqGmqeILJ/qlibv3bJN8kgSPbMkoxDyZ4ssJY8S0MAyVMZBQpH4QJPbN2/gehUpLP0qLzu3WopJA3+aMU3fv6MwOQUmtafZmSaHd3dlITtVSUqzqN3+QE746wZZFpBlYGqWzJiSBFO16Q5DHnzgFOgqtUaL/qBHrdGg3Z7h7m6Kpulcv3oFQcHi4iLdwy7DUdm0CBPmWnPUHq+ye7DL6sIyXqWOrplE6ZjduzdxLA/XcRmNAw4PDqg3Ren0EhFHPiM/4Mzp0wwGXXQ1x3NN8kxWa+bLoeDAH1NzDIZhwvr6GkEQ0mw0qHgWoxHMtucZ9LtopiYzvVwaXGiIsu5aUKt5JHlC5mRAhhYICqNAKUwyoeMYf/bF6xtdf2FgUhRlFfgtYB7p+PzrRVH8sqIoLeB3gHXgJvCjRVH0FEVRgF8GPoRkhP1EURQvfyNvRpo29u97bJI9VU0YJRKjWqs4NGoenmNimDZJOAJVWgClUYJpyULfxIBSiITPfP7zfPQ/+0lAdiZEBi+98ioXXr9Mo1nF1grSJCWM72UjuQjJspz9u3fY2z3k8be8hVxEkg6QwbWrG9SbTRzHJVcN0kJw4cIr/ORP/Dh/8Onfp1KVgbHieowPB3QPhjTPNCVcK7p/jOTmrS00XeFwIAkCjXoLr9pga68zzf4my0T+ArppYpoaQZTi2jYiSRBAkackaYaWp2SAqRScO7nE4uIqJ08eZ2amhedZ1OtS0zIcjSjylF5vhONYdA8PWWhUWZkBx66Tiol49AxRFGAYJuF4ODVlmFIrdUlyIOkR90ZTuJyu6zRs2MsiDMMkTROU4t7vJPIM40hBvzBMFLIpD13RdEzTQbVNYj/A8UxE3Ocf/MxH+aX/9TfpV6ucOH6cwSjgRqmeJxdTK/ZJUMoDHxRZBJ/+jSdXd8VC1xR+6MMf4u7+3SmbScHEUBQMo8BPwbRNkjzHNU36vi9zz0Tl4sY1arUaI7/P6TPnCaKUEydPAXB3dw9VU7i6cR1Ttzixssrm5hu8/tpF5udmWFlaRNMVDroH+KOAarXGnb1DUnQyUXDpjVucf+wx7FqNm7e3sUyL9sI81arHQnsOUZrC1JqzdDsdPvDd3yn53GhQNj5EDlGUTp+flLOfnldDpDG7u9vomkEYBqiajqVahMGYWq1GFAdYjslh5xCv4pAkAtewsBKDQknA0CRqxkjQdKYXsm9lfSMZkwB+piiKlxVFqQIvKYryGeAngD8piuIXFEX5OeDngP8W+CBwqvz3FPDx8vbPXUvLywRhjD/s49UaU32KYRjSirt8Xp6lpLlDY2YWEYfkWUzg+xhOlVRI3rPv+8y0mgCkWUGlIg9QTbfJREQw6rO3f8DxY8d49zvfTiYE1apLECYYuoS+JWmOrpsUuRRX3rqxRcWzuPLmFU6dOsFzX/oincMex1aX2QoCLFNHzXXsuQUs02Sm3aIgIh35rK2vIbKCinuX5bllet0DHn38flHseBxSb7SYGfTZ27+L59kcdO+iCIGJQ6vVYv3YCrOtOufOPw6AkgakWU4Y+Ig0LWmKUnCaxgGu506NIo9C6xw9JY9TdrcH2IY2DSCmmmPrJpYONUeZwuAsNZXbZ5hmOCK/Rx6IohjFczkaPsMomZIMRAmB6/dHZPean2Uwk0RNCSMriZmaQpHJgOQYJgU6qibfo8hLNpSm0et2WJht8r0ffD/PfO4ZHn7wAW7f3pqaEWSJj1Yo06AUZRZuVf+a7MlUTXQNTp06yd7OHS5fe51K7d6WNklT7u4PWGjLrCOOZTAzS3OKC29cp1Z1qVYbrC4v0R/0aNSbhMG4HKJW0DSdfq/P448+xt7eLuNgzJNPvYPr199kptWk3x+iYLOytkAYRAxGIbe3tsgzQcVxuXT5Dc6dfYgkhZWVOovzswyGY1RVJQkDwiwlDIZEaUEYjhgNh1IomcpB+All0jIMCiVl0B9TrdbYunWbRx49x2jcR2Q584tLVKs18kxOOtzdP8CxLRRVdvlSkXMkrqMUJrqeMMl4C0enWjT/vFP9G1p/YWAqimIX2C3vjxRFuQwsAx8G3lc+7V8Dn0cGpg8Dv1XIWZcvK4rSUBRlsXydr7s03SAVuawPtWZANWnMygPDH3Tue66qSafVmzdu0ah5LCwuMRyH1DQTw5S+cJ7nERomxImExVk2lUqFMMkwVQjjDF3XiSKfYV+iGmQg0tF0U57EpXZp926P5eVFWST3Y5ZX17mzc0ClPoNXrXFnaxvbtVhfP06a5wTjgBeef5EiTegGPqvLs4yDMW9cusbpE8dYWpynWm8RhSFVzyZXpJOrEIJnnnmGd7/jKSpuhfW1U9i2MZ1ZmrjO6rrJqHsXpUhl98kfYBgmSi6tn4osQ1GkjZJIJHcpL9k7hnJ/7S6PEsJMo0gzUmQdJ80VOT9VKHI4OROoivx6EhBMXcWbMLt1FWyrJH3eI2U6tlR0o6uYZfNXU+VgsqZJDIumyucomiZh92lCbroUSUxU6npUw8DIJGJZVzUs20bRpK+eZeh85EPv5WMf+xg/+mM/xp2bN3G9Kj//j/8rPvaL/zOzzRb7gwAjLKZBc5I1TY8n10MVCo6l8553voVnv/x5xuFIbtOKAn8sP7um18SzDMbxCEU1MVWbJI/o9WTQrVYreK5FpTlPy9AwTHta85lrz/Pyy6/y0NlHADh2bBXL0GjNtKjWahwedLh55y5RWmDuStebLMtYWVpg4I85/dA5xsM+o5FPe3GFq1eu4joaM3Nt7u7toZfI3STxUTCplEXoQpEW7KoG9bpH4I+J4wTV0Kk3KiR5wJkza4RRl0mDPkkESZJQq9W4fee2rPsqOjv7O8y1FlAQiAwc20FVdJJ0RKFoZELHNlTSMCEMv/Uxt2+qxqQoyjrwOPA8MH8k2Owht3ogg9btI992p3zsvsCkKMpPAz8NYDseIg4I/ZG0djazqcOqZVmY+gQulklXjWEAecyrr75Jv9/F0FXSDFxdEgYAlPEYLHNaVymylDeubHD+7DEcS0OkFvXaDDOzs2R5zqA/YjQaTO2bJu4WjuOytXWDJJItp0IxMHSVzRu3WJhp0pprMugN2D+4i+fVifwBmmvhxwnhIMJs26h6TmuuRpYrDAd9FpYkUzzNIU3G9AdDrLIettfpsbS0glruY+Tkfy6RvsgCZInhnuJoJR5FQSQJmZDOJ5OBY1knyDHUAk03ocyq0lzBrcrgkukCCxiOhSQMZGKaSU2YTI5tkimSbQ7SKSUReckAH6Bo2nRLlmUC9UhGNWEvZYVOksr3oioFYZKjmxYaJU7mCPrXKU8u27URmo4K5EhSqGPrpSRANg3+m7//Uf75L/8Gn/jtX+W5517k+IlVji0tce6JJ/j0H/zxtPhtT7ZtRUweguroKLFCXiTY9QqD/S5CqDSrTem2Y1n4Y0G/O2BuSbrcTPAoBQmd/TFhGmG6HnbVYX5hGcOwqFTlEPqkfX93dw/P0dGVlM9/4Xk810O3dByvzuFBh43Nm2QiJ40icpEQJjmdXp9gPOD7P/LDeKbC2I8J/QHdvs/qsRX6gw4b1zeke08SgyHtovJUEMc+pm1SkKBp5V9MycHMMVQVuQkyIVGJVFlSUDDRNZUiSzF0FSES6rU6KDJELM62Efm9Dp1p6ei2h+3aJPkYogSRmyiYOPc3bP9S6xsOTIqiVIBPAv9lURRDWUqSqyiKQlGUbypMFkXx68CvA6ysHCsePn2CpcU2FIJup8fDpWGlohrcvr1Fe6bF0rG1KQh97AccW1nmne98kj/4wz/CbcrWZp6l97jQZVAKfJ8g8Nm9e8Biy8N2bObabVRNn+JEZ9vzcrixbO1OCueWJV/LME2iKGHr5i1u3Njke7/rvXzpuefQdCFtd3SJiPCqNVRCKpUKhmKSYfKlF15mpt7g/U8/QRAF5FkqJ+lNh9Eo4PKbV+l0Ojz99NOMhiMqnie3NrlEmugqoJZzbro0uzRNHUMppEmnAXkucI84oExwJSIDQxMkKZh6OZ8lBFkhC79ZJqZ1PJFD4AfEAvwoI08CXNeVrfs0o8hyyWkqYXSaKoMQusFoOKDiVeR7Ug00tUBXFcl/EuBWDChkJmrqpa+brpZGDpClAaiGzNI0bQqwc21HBmd08jzBLZlQeZayuHxc2pGLnF/8H/4xe3t3aTZqhEHMAyePszTXxtbvpK5o7wAAIABJREFU1ZGStMA0yq1dKKbZU+EYtFpN4iJlcW6Rg942QTymUV+iPQPHFuY4HPdI8ohmVW5T+r6PbWvY1SYn19roaoEf+FRqMcN+Ks0GbJdarUav16XRmGXj2hucPXua7TvbGLpNrebx6os7LMzPoWg6z73wEkvtWZQ85jve/RRf+MIXuPjy85w79zDPffl5HMfCqzRQSNnZ2eag73NsYUHiacKYmJSK7ZEoMWqmY5eM+1QIUiEgUUmRdTJdM7m5s82Zh06TpSmilKGIPCcVOYZuUq1ILj2aTZwKdC2fBipV0cERhCIlK3IyyyPPA5JMIewcUfj+Jdc3FJgURTGQQen/LIri/y4fvjvZoimKssi9fvQ2sHrk21fKx/7MpalweLCFZym055pkwicRCRvXNlk/eZJq1QVdQYiETrfHmdMPYJgGIg3YvHYNw7Iw1BzQOFQr1OMBpmUjSrW463m4noehFjTnFjC1YqrBiOJEIkF0UYorVUCfWmMDxHGKpmmMRwNaMw12dm36/Q6u59KenUHXda5v3GDkJzz+2DlGo4K9nU329nwq+x1sy6Hdlm69erk10zS5hdvb22V+bpbL16R6uVq7NzIiswd9WlTWDFsWOktUR5Ck5EA4CNBUiCJBloNpHsFVlM1eVbvfWEDTdBk0NGP6WbieQRKOUTRFBj67RibEvaHbEp1y1GhBfu0iErkl03QNREqUTebRchkky2M1ThI0zZy6rxxdWSYk4jUTGIZJlsH8wgJxmvPQQw9y49plsgzcikcc+uzv3cYwJcTv03/4GQytoFproGkGH/zgB7Atk+P/4L/Gsgz+0X//z9BMjUhkuECGhuF6pGGBCuzt3+XJ8+fY3tnCMPQpW9yPY5RSXlEIAaYpT3IojQs0Lr7+KoZp8OGP/A3iMGR+cYG7uxL9MRwOSeOIm7e22TvYR+sfEg4CnnzXI/zpn3wOt+JRrVX5zOe+yNlTx0liQXu2zcH+LufPP04aBfzJ5/6UJ9/2GKE/pjNIME2D9fUH2H72WfYPdplbmsPUFTIBiRJjFha5kgAmQZKgFqWjzZHZ2tE4Jlcs3rh2hTPrJwDQy/GdyXFn6DqZJLaX54rAsiQV9tSpE9zobKMEGpprkyUFYqgy9gNG/n+A4nfZZfvfgctFUfzSkf/6FPDjwC+Ut7975PG/qyjKv0UWvQd/Xn0JJEh+OApYWpaV0TQzCMKC1WPHyNIUTTMIg4Q7t+/gupaU6vd7VCtVet0Dap5LIjJQVWnw+FVrb3cXXdekonzjqqwlqTmWaSFyDV3NELlGdkQfpKtg2hamaROFEcvL81QrLi+++ArH14+TiZgnn3gU1XD5vz7xb6jWZnjw7CmuX7/G9vZt1o6t0XyoyeHeiGMnFhiN+ohcsLK4NM2W4iRgUjS0tYKXX7rIe9/37rL+Is02MyEkKmRSn1HVKVFzgsCV9ExxpPUOiiML+EIIVNUsazs6lLUdjeI+FwyQWciktgPmkeAmYXOZEKDeC2gTvpOhFjQbVUbDIbZRm3bp0kJB0TQ5mGu4LC+toOk2tuOiFoI4FQz7Q+7s7LO3t0cUpuhaNvXd+88/+nc5trpAe3GZL33xOVqtFqqmoagGii4tqos8pVpr8Y6n3kK13qB72KXiWXz0v/gZCs2m5jqkiibtw0tjhUGvFKlGUkflxDqVlolVq5NkKqoi+UyNyjwHByOsik6SG9LEcrKVMQxMtSq3ljWHkyfOTiGCB/t3pxP2u7uyi6aRsjDXRjMNVh8/Rqc3ZBxGbO/vcxrBg6eX0RVwWw0UNaF7Z49Go8173/duLr72Gttbt7Ecl4cePEPnYIeXX3qRxx5+iE63g1mA6WqQ6nilXCJJBJO+r6YJDF3n+tVdTMPi6fe9C81w6X3hTzn/2OMMDncRmXSa1lWVIIjodns06xUUzaDIUhTNIArGWJaFohk0Z9oYlkexKKYXh8QsePm5S1Tr/wGK38C7gL8FvK4oyqvlY/8QGZA+oSjK3wZuAT9a/t8fIKUC15FygZ/8i35AnudUKk3iNOPqm1JPpBQpIjeO3EfiRRQNwzTQDYsoiUhEyvLSPHf2+qQY0+L3QWGxyv1X5ZPHFqnXahi6RmumIfUZmo5RTrTLFFburye1HbfSIk0iilww8hMEBTdu3iDLU2ZbLa5cfQHLrpKJnJdf/ArLS/PMz83gVaVbap4LVFJOnTqBEELqX47gUyRjUuM7vuM7JC3Q0LFsmyITZMU9YJeum+ilCWFQtmNVRQYVs6wZTAZiswI0RbLGdRWSSMgWsM0UtQuSYaTFk0AkA5PjucRRUiJ67z0O0lNOQ9o1GeX3mE6NKApQC0FvGLK9f0i3c0j3sEcYBuiaSlbSKsehtJ5u1Kusry1SrdU4+cAZ3vO+c1iW1J1FccL+zha6rnL71ia1aoUrV67TKnEdeZYRjHyqR3RSaZbTbs8ShCHz7Vm2bt8hFgoiTQmidPr3LVR9iuE9ulKREUX7/Nqv/SbtVgO34vGh7/8gWZaxtHaCz/zx71Or1vHHIU6lycDv0bCrNFtt6rU65x9/asqXHw6HJGmGo+m0ZmbQ8oxI5OzvbdPr9qjW6xiWzfXrL5PlgnPnzkIWo4cq42hM3Bvh2A7raw+g6CbPPfMcgoh6pU6eCa5du0YqIlozTUaDAbMN2XFO0hhDOZoRu2iqDKJpBIqtkisWfiIYhykL9QrdwwGj4YisSCgQaLlHnPjU6rOsra0SxxG+72MYFmkaU6lWpsfKxMxA1SDLIjr9PqN+jzTLUJRvfVbuG+nKfRFp+PH11vu/zvML4KPf7BtZaLfo93vMtOd58/IbjEYjrl7dZH39GJ3DA2Zmm4gkQ9F0Zlr1KSum3mhh2g02d55HZDGGaXFQWMwpMVEMFc+h1WrSblR48omHkfYAuhzuFfk0bdXVnElnQtcnymh9CnsTQnb4mtUarZkWYSDFmSdPHufKlTfY3tln7dgaup7QarsYRsba0hLD0QDdsVhZXqHIUlQyVM0hCkNu3bqFrqk888zneeItb2M87NNozBIEEaZZbt8U0A0pWwjDnCyTLiumIR2L8yydbqnSNEHR7g1AmtwrgpclJ1TNQNcVhMhRjQqObnLYG+OPhwwGY3Z2d0giv/xZsrieJgkKBgUpzVaTeqPBY4+/lfn5BeolLmYw9Dnt1Ni6fYf52VkKVcdxa3ieRZ5l+OPx1BsOwLF0wljIbmng0+sGqJpGFCXSacVxSLIx4yCesrGTNENT5QUqS6OpZVMY+Hzlxcs8+OCDpEZCo1FHzSJ0zQbDpkgj1CwiLqT0xHNsPFtnaXGRZsPjoTMPMje/QMWzqLgWYSwwtRxRFMSqzrve+Z1oho2uyZodhexMyWNlwlKKCINSt6Xe60KajsXe7R22bt3m+PF1Tp05zesX3wBgaXmV/b19XEdndnYW/84d6l6FME24vbPHe9/7XsajOv1uh727d1mYn8e2PSzTo0vEzGybTIzQFJPoSMM1SRJM8x6dM0XQ68cEQiHL4csvvMpHfnCZTM2wDY0wUFEwCeOERqGXQSea/i7jUQfXdiX7uzmLEIJPfvKTvOtd75x6Nz79jrehahq/8qu/DcnXzqx+s+vbQvmt6wa+H2FYNq1mnUfPPYjjVXn4kQfRdIt6U6aG586d5dKlyziOFFeORgPyDI4/cAbx2S9hOFUCt0U9GZDrGkq9yQioZRFPPPogFALLMckLWUMyTQhDQRiMGY9kB2Uyj6er2dTmKUtTsiyXnbpcuuDe3jmg1+tj6xlLC7Ncu7GH7sDYj7GLAMOr4weCiu1RrTfQNJ25STcuCTHNex99rVaj0aiSZNm0qzgpvhe5wA8TtrZuMxiOEULgDwckIkEvFFTDIBEJJDEpKgoplttkYWWVlfkZHjh1CkNT0cus4fr1TcZ+zGg04PKlN0iSFNM0UHUVt9KUeiy7iedNgjMYqo5qWuRJjGpa+GnOF194nSC7iKsVuKbO5vZdqtUqnm3ij4cU6Zh3v/PtFLk7zUAnFuCmqZMmMkj2ulIOMiGBGjqMRylx6GOYNtc3NlldWS2bFILBUF4gBr3DaTYphGB5eYkwDEgTud39+X/49wn8gIpjoOmlIUPJm9J0/Z7TishJ4xBNVzBNhdCXBpRhJIjDlMIw0Qy7tMoutVQZDPt9lpaXKHKByFVUwyEJR+SZwDANxqMx3cMO1apUyj/04GleuXSJnh9x7eoVPvzDH8bRVDavw607d4jSHFW1aczNsH9lg1Qk6GpBq2aTCY/xuEKQZDSrKrph0m61cR0NP9I56PdoeJ4scuc5iq6jYKIpOqZpEMU+rfosV7ZeKz/rLq2ZFifX1onDiKrnkCSp9MYz5JbtzTe3uHXzFqdPr2EaOjv+LQzVIrq+ydlzj/I3/+aP8cbFS3iuSxTHvHHpDc48eIaT68u89vqlbz0mfMuv8FewiiLno3/vo3z69z4FSPn8M196gSRP0HKNTNNJkogvPv8SGRoaGbZh8NTb3sJgPGQ2lAdTEMYQ7pI6FgOzztx4jG2Z/MD3fYAb1y5O9R6TFSc5zWZtikLRdJ22LrVCypF0PwyGkviYyYBx2Dlk6/ardA47nHqgwtb+CNdSIU2wjYy6V2dhbg2RZRx29plrNRkHAXPl67m27HJYpsWl119jfn4JRTW4eOEyd/cOaTaqLC20mW0v0J5rMb9Q4djqCmmWMx6N2dk/pOoYKIXcqokspRAphuUShAHPPHeBUyXgbqszpBj3EbqFYTv8m0/+IfWKJznpehXDzMGwyUGC5nIFUiBNSMoT2bQcbCNA00xIBIYOaZZQhBGZYzMKExbmZvCjBD9KJLDebHFn+4CVlUU543ckEE9qSJZpkucJqZD3DzuH1BtVTEM2IPzxmGPHjpMmAboOvVFArSJ1ZpZtoyAoKC3XlbKhEEfYlizOq0UKGFMBaRAEVGs1TMsiiWNGwyHVWm36vpI4luwkzyMUQop6c3UqXBVCUK3W2N7eBdUoM+4c29KJ4hBdgyCBspGLaU1MLTpoFMw2mszWHRbf/U42Ll+lNdMkSnNswyMRGUmSkiSJHAJ/29PcuLEpL1hhIlv1QYqhjzAaHpaREyc5YSiP52ntS7Xp9nu0GrZELDs1fvA7PsSzz73EU+cfZn6+zRe+8AUMTcU0TTTTkMQFPQchM6RIZDzxxBO85z1P8+yffpZROsDQVApUeZul6KU5qapRjsNk8OYV1CKlVb9/9Ocvs74tAlMUxVx+Q1oUjUY+f/hHf4TptZhZOMlcq8qpM2eAckI9kifLV55/kSxLqTgO29s7nFhZ4PahP/UkqycDDNchCHyScES7PY9r6WilfXUUS/+vLI2mV96j68Lrb5KWXblHz56mACwj5/kXnuPll1/l7OmzrCzXSJJDohTOnltlrlHHNh/g7t1deoNDlMJANXQsxyP05fvKs5Rcs7m1s4Vp6WRkvP3tT3D79h7HTxznsDegczggCSNOnToBImTQGfEbv/VJrFoL2zBp1hy+871PTcdD0jRBxAq6qRP2cryy+GiXv1dkOeiArSkYpj0F18sT92vrAZOAdPRr23DIypby1CBDl40BuwSgGQg0Qy+fpzMYD1nTVqdcaaBsPNxP27FK6sFse540CcrXl5+9pikUpQK/Wq1hGApZKN/fcDCkWm8hhMC2XXa2d2jPz1Kgc9jt06jX4Eid0S23HULkmJbFzNwcotRapbmUNuSqgR8mBIlGLFIsTb5n1XCwNBk4WqWH36RxMPYDLMvm4GBAe67OoD8CAxJf0O/voSkaUTwmjlLW1o9z+fIVWjNNbENjMBwSpgmZyCl0iUWpei7kMXkm+NMvPstMs02WCxaXFllfW6XiufRu3yAzc25ubjPbbmB4cpBYZDm1MkuzTIOF+TYXL7zM+YdP0l46xqC7z0zDxHNUkiRh42aXk+vL6CqM0nt/95dffhlzArFPVDJDx7ZcnHpFFvYLQbfbo92eYXamvOQqgmPraxw/foKP/9qvfM1x9c2sbwseU55l7O5uEwYRcRTxfd/3Ed7/ge/i3COrLK8uE5RCtUH3gKwcYl1ZbxGnyI6dP+Lt730fwJQuMDDrFCLhR37ge+6buhZCTFvutmVOD1ZdN8vHTV668DoHw4BBrNDvSyeLQXefixdf5fq1q5w8s4btFHiWLLY3qwZBMOTK1V3ubO/hepVp6zUKQ3mFMRTGwwGWoZNlGYkfMRr5hGnEXHuFzevXCaOYQjfRbY/W3BxRFDAKQsZhiloGE93QKVTpbJJnEEf39vO6qqFr0mJnsvysIMpy/CDAzwpOnjhOmhUSjZJBlmWS56QbFCIl+SozArMUekqus3wPtmWiaSa2IesYWcl/8ioummaSpUJuxVSVOI6mMPvJ7Fuc5uVWWpfw/Dxnc/OmNHww5CF54cKrGJZU6rmuzQtfeZEiF+RZimE5XHnzMtV6C01VcdwaUZwwN78gmU+l7XaW58RhSiQU0lwhzg38KKM/CjjoRfQGAb1RQneUMvITBoG8HfkJcXmcxXGEEAIll9ozBRmMkjiaNk4mLXS0FE1Vp+LY1swsjXqTLE3ZP+iytLbGhQtvkiQFG5vXGfkhVc8lSRIqVYfOQUwQSXPVRqOFpil84Lu+k8PePqZpEsUZO3v7jP2AcZ4yGMbEWVo6/ZbnUirQNZUkSvCjMdc3L/H2d7+PZqvBz/7sz/Ivful/4c7WBrniyEJ9LEhEishjLENHVVIM0+bMqVPML8yzfvwkD59/ioWFNWbaC6ytn5xmv7Zrs7O3j2YYkkZQ6Gh/BYVv+DbJmBzXQdcMVD1H0WJ0qyApwFCPoCrK4DROBxiZIA1V6q5BEo8xbZeG52KoOWkuMbIz+ZgPfOi7GQ/6jEpLmtcuXgYgSaQuSdFM0izDtExUw8ZzZCt0guqouDajNEY1HBqtFtc3LtNesGl4Bn7ss715SHtBZgu7dwWmqnLmzByR71MoKSLPGY5DFM3gxNoK6LIQnCZjHjr3EL//u5/irY+9la3bd/gbP/rD/MuP/xbVcrB2tpz1mzjmOo5DURZ7izQiFxMDThn8bMdhHMS41QaVUUaU5WRZgZbGpIDnutiawnve8w4+8Yl/D4BeZNiGA0g19lGhi6IZGGXWMglO/niIoev0wwivUqPquASaYOPKG6w/8ACGbtL0dGit0DnYo12rsb29w+JCG8etcPnyZXq9AW95y3mEJm1/qrUGpqnzwOkHyUVEkudoqsqJE6dJ4xBF1UnjkLe//WmJ3RAghM9DD0lnlyzPicutdoHsrI5G0nwyigW6BnqeE0bJtKmx3+0w11pA5CoKssboOBON2Ncen3kmyESEptvT/MtxKziORb8/wLZ0UqFTsSvEE/EpBmEkO1nnHjsPl1TW144TpzmXL17ErTTpHvaot2osLi2ycesmJ9dW8Gp1Tp44gabBzFybKIrQdZsgjFlaqXFw0KFek00E07JLsuUITRMkkTRFiJMxll0hFyqqnvPb//pf8bd+/O/xlscexrFVHnrwLJ/63X+HZ3uYpvzbpqmAwibLYGf7Fu35ZZIkJ89g6Pdo1ut45dC2bbvohkG377O23uT1i68jYkEURai6iq596/nOt0VgCsMAkaVcu3qVeqtNUsisJ839aXCyplAq+bWh6gSlrkJXwbVUXNtmEMgMQtUMPvv5Z9FLFK2qGxiVFpapS0tw5L7c+DpHomLInz8OImyj5JELQbPeolrR6Q+G+GNBo1XH88qrupEisoI8TzEdkziSw5NLC7PT182zjCyTNkWaFvHB7/8+DNPkpZcucOvGNYoyI1F0g0q1QhQJ8nKYVSjSpUCkUuQXJ3LgWCQCXTPIM1nUFRm0KiVvKAqnw8/d0ZAWkJUZUaEaCCUjShNMW36mR4NRkqVMUpzbW3c4cWKdVMitWr8/ZK7VIs0Frqmz/sADgBSrGqpO4HfRNJPO0MdQdRy3wrPPfhnTsnBdj9cvXuad73hKFo5FUrrQSANRIQRBcs8vzbZl5qPkQo7jHKn9ZWUQi4UoFckTd+AUXZNeaLZlIkRSdtESDNNlqb0kn5cLFNWcOozouqxZwb3mQxQFoN+/1c8yQZIpXLt2kRMnjsuhaZEjMlCFfI0slV3b8bCLabusr0nSwMbmJr5QSId9qp7FaNBl9+4I17DwA5/tAynMdCsuMzWPz37u85w6fYYbm7fYvHGL73jv03Q6+ziOSxgGvOPJR0mSlBQhnVoiUMx7bswWOo16jd/995+g4ji4nsba6cc488jb+OP/94945dIrtGddkswEJWdn7y6N5hyViodp1hkOfZpzDUytYH//kHZ7hq3bu9NuZK87BMUiI6OoFuSjHKH8NXFJcRwX267Q7fVZWVogHvTQSituxSojenlADvtdEhHTvbvH2B+jqjYiHiNy6PRH060ZyBPFLlW7ZqnkDdQKivj6qMV+qtEwMoo0xg8jkjhCiIz55m0yEbCyusLB3hZpKrhzd4e1pSXCuPf/cfdmz3ad55nfb631rXnP+8znYAYIgABJkKIoiqKowZIseYjbicvdXanuVCpJ5aKTTi5S+QdSuUsq1dXVqVyk3G7b3W27LU+yKMsiLZISxVEUKYogAGIGzsEZ97zmb62Vi2/tfQCQctpNVxervyrUPmtP2MPa7/e+z/u8z8PG7TFZJnjw9APkUv3wJ5OAWs3n6OGj9HZ3WT1wGN2oJF5zRYzUDZNms8P2zh7f+PqXuXjlj2buJ5bt3OPvVcocqRnopk4UJ9iWRRoriYo0jSDNMSqLpiyN0RILw3ZmuJzn1ZT5ZB4RTEZgBRiihaGrHT5JJbYFGBoZAtgv6xZXVxW4WmFKfs1jMBqRITAJMcz902gc7cu55DlkBkRpjuf5jKIEU2iAxosvv85XvvQMP/3x21AkPPLII0rcP1elG1AFKpXpTC+DyWhWqgpdudrU6i2ErsrDSRDSbNVnjzEFCNPfD8iFVHSDvETTRdVa9yqW/75gnTBNoiiZBbe0MLCERllIXNfnL7/7XZ7+3NOz88wwxOx1p6kyGMhzh431Wxx/4AyXL73HxfOXef/adZ753NNcu7KHMAS5zPB9gVN32dscYxQGptfkyo2bHPn8kzzx6UexTEEyaTMIC3767s84ffI4/d3bGKZJmiYzJxe9tDAdSZpKSivFMdVvoZA6kJADQVzwZ3/0u3Ra8/TGIw6urrG318fzaxh6QSFTPM8jrmrDWrPFm+evUmYJmZR8cHtnBrSnusPWIFDnU5aSVkq0UvZ/zi/93399IgKTBmxu3MZzdfZ2etzZ3MFxHHzPotNtMz83D2VGs9nCMUscr8Y7oxGnzz6EoRlcu3aVvb1djq4tc2u7h+vsYyHTjKmQGbow6d25xrgKerqh3HuzfF/adxNmx9Nd03NMav485DqD8QDX9pGxJEr7RJUNtuc6JHEAhUFpCDy3ThrHCMtFmAF5nmMZBaC6Su9f+IBHH32ULI0o8pw/+eafUIr9H4ZhCMo8RtcNCgyUjqChsA6UJXeey+p9aECJJSV+zWM4SZivquBpsyBP4lmgOnb8AW7cvInnKYyp1POZwUGGoMzvVSGYLbk/apDdd+pMeTRTAPyeh1Vgein35WtKmfHyD1/Gdn0yDN559wKPnTs9CzCgAlQwmSgPwWpEyK811HGpnqnd7sxuKwtJq9WsCLkCxzWhzJSqga6RJBm1us9krDS68kqTKJKCylkKsxoe1nUQupoZAyiyiLQQWJaFzDKSJMd1FT6U51IJ/W3vsbw8nWWHY8eOU6/XWL91k/FoxMraArppcu3KZZIsozM3z3Cyx0LdI5E5STxm7fBpLl++zC986fO89fa7rCy2SKMRpmVhJBlhFDMcjQijlJZhYlgmoyDFsiymGaNt1ShJ0UXBMAjxLIs4gTwPsa0aUNAb7JAXOqYl8XxVGiaZVB1p0yIYDqj5Ntt37rV3AkgzSSZ89noqAGVpBj/vnPkPXJ+IwCRljmGaPPro44yHI57+/Gd5++13EKbFZDJkPAkhj+n3+0RJwdqqrro1RYbrmzz+2MMAJLj81u/8G1yne89z3+0qMje/gGUK0kzecwkQlDZt796PpOY5zC8quYckjjl98hF2t3c5c/rgzM56PNkgTyWWZWPopWqHFylRnNLv7WI6Hq6lqen+ap09c/qjP4tMIqg6PtV1SZqS5wWmrUowzdRxvdoMoAWFg5SaUBhLFNLIJVS63Ery1mV8S00G+UuH0NfvVJiciVFkYKh5OMtQHSrLvDdA3R+ITAAZkwkHMoljKhA8Q2CgApVjQp6nLCx0uXLlGrYlSAsFzud5SgrkFYMYIXntrXd56MwZLHLKQrKzvU2n26W3t7efJVXmomDy2quv85knn1AOIZ0OP3r5FR5/4gl0wyQNhgjTYq7bIpcpaZJQc02KZIJnge56FLkSF7xbqliv3Et0vaSwlBvz1vYO43GotN4j5d22tLJEWtEepgFqOltZ5hmTMCEIxqyv75tyDoMhbk2n3V1hrjPPD374GuceroZ66xanz5zh6pWbyMLk+e//gGee/JSyuddtmp02cbbH8vIKrVYHkUecv3CJg4cOcP3GLg+cUOWpKQRZRW+IIqUmECdUuI+HqDSdgzhFGDrxpGAa0IpMEsiM5bWUPM+YBAkf3NrEspVee5rtQx9GPMAUBmEYKredKQtH3z/HP876RAQmXdc5fHANw7QwDJ0f/+Q9QOBbNu78AlGU4PlNjCzDa5ikWcHhI6pmH00yhqNddvd6zM8v8r/+z/+Ef/lv/xhDmDN1AZnflREIH7IAyxTcCSIaXot0sketYiX3K/Zuy8zRTJtmhSGpGTWPvd1dMqmm1W9s3GJlfpnezgjL73B7Y5NG3UfXTPyaoOlYmMJlbXWF0SSk3bJIZIlpuRiGMWMu64ZBb5KjCTArqnavN6Tm2wp4LSBOc9BK6kaG1Oshj1qQAAAgAElEQVR874UfITOJWWlMlDIjLTT0PCNFZzM0UGo00PbE7H2pY1WmxoYL5CBMyjxHACkgipSyyhotQ5uVdOqklxUOJWbYSyZlFbgEzl3wgmFY5HnKW2+/zyOPPca1yxdJMlXuxRn4joVuuxh5NMOtbKFhmSZJAs12l7yAxeVlLNMmqMiPUyb4uUcfJS+Ue0qa5Zx79FFklpHrAq3CJKNgjG6YFeNdJ82ZzR8qWZZUlS2lRBZKEUEWJZblYjsWVy5fp9RMDh06UP3fggsXLrC2vFRlKWBaDm/+5Cc8fOYMUZrTnevS64+4s7lHo+Fz5cpVLMui02wxHgW0mi0++OAqX/vql8iSEaZ1hCgccPPWLRa6NQbjlO3BHtdv32Gwt8P67as88/RTDM0+WRYpoF+YrKytsrmzTaveJE1TtMKaYU2mEBia+k5ykRJOUoVByanEifqOHMshDFRjyXTALj3G45D28gHefO0NpG6hVVis5fr0Q4kJ6MKcWapNl1lZhE29/D7O+kQEJoAozbAwsC2bvd42qyvKoC9LEzUEaalP0tAM1aJvN3nn3fe5fu0apu1iGDpv/fQS/+ZPvsPBQ4fJ5V0DuXdrXctAEf7MGg1PzVtZtsOdIMLNhoRhgG6YxIaGIUxGvV0MGbG2Ok8ah7Q7DYxE1d+mKbixcYvOfIPdnYC9TDmTmHqAqAwHERqeowThDNMBOdVQytENoxrmrXZamSM8QVpoDAY9bHMOQ1fMZWV/XiNOcwwzI0UDQ7X4AaRuQaEsnMaGyVpnnlFvZ/a+G/cdW7ZzVwmbUcEGyn6o0JhKqWUISjk106x89mSGjsAT6vY0L7FQfKYEG5sEvyqn665HmKp2+5kzp3ntrXeB/WyKBAwBi0vLFGmCYaiSS5j7ES6JAibjYFbCTTmy0+wpSTIldyxT4kSqYJ5Lap5DP4oRhoGwHCyhk8oCmcYYmklelYNKLlMgDMiSkEIT3NnaJItCglTjkUdO8cbrrxJEA7JMZ9TfpebXuD3fIUsjrt8esbW9xeHVFXZ7faxDB5lMAuyKYHn2oYcZDoc8971nOffIOW7cuIiuWQyGfR544ARXLr2PEAUnjq5iWSbaxoBBMOa98+/yy1/7Gg+ePsWli+8hdEUJma4szfAdh4WlRcbhHqVM8T1vhgHpokDoNrIoqNct8jIlnEjqdTHLmAy94Etf/CIvv/ojJpMAz80xhc6t69eUm3MU47stgiimZtr4WkIgfEwZkKUJWV7pxxsWQiuQ5d8NA+kTEZg0DdI4od/rqREJx+L9i5c5fuyo+vBMk3CshjHH4ZjtO3e4Liza3QUuXH+ZNNkHs8888tg97r0AY82g4bTQq0xnrBlMp7ZUNuHTECpLymRzRhcA8Ixytls2Ow2ELhGrS1x+U8mUuI7D9kaPYZBiVcC3pZdoZJysiKFJJmds4CxN78mWojCg3Wmwu7tLo91FEyY2sNcbqI7PXbSQLC8xDU05FtsJpfSQukUhMygUhjYZ9nEd/Z4g1A8lbfYBydxps3qqzfYHaiY7MwIcGpQYijZgiRltIpMS22+QJhGW7WIiCavbDBOmhjZpXkIU41QiYXmekmCTV2C4X2vwp3/xlzNBvDQvFQ1BqMwpHvZxbOVMYwp9Nj6i6aLCnCR5XlIWMBopTS5ZDUSD0ryeagVNy9z/91/+K4Rw0C2jks/ViE2BiDOkY2JJDWFGM9cUP4df/3vfIIpCrl6+QHduAVuYPPeXf4Zf91jqznP5xjW6C4rS0R+GhJM+WZJRqzv49TrvvvceR48eZWl1jf7uJgcOHlGGnCY8+ZknefW1V5nvzpOVKZcvX2BxYYntnT6thjVjastyg4dOHOHdD67x5ltvc+b0Cc6ceYjrN25hOA6OY5MlIbVmjZrTYBT0sXQH21eW5p7rMAxC6qIOKHZ4Gsd4fo1Ww2Rzp4dh6MpXT1h8/6UfYZsmrdY8ltvA8hvs3Nwh1GsIQzLIDMzqPPIrLC4TvlKMDcazDEmW+t9ZcPpEBKYSnfF4zPsXLrEwt0AQB6CZ9PsDjh07woUL51ldWSWTBVGaYtgue9tb9Pf2ZXens0/Xr1zi1INnZ9dHlTXQKBxQL1VJ1/BaxNEAU0j6YRX9hZo1CkobXyYEmUHbUzvLlOFbyJioKAANDYv+eBMhTNyGi74zQggfQ0hyQJgO89024yDCdn24qxOoGwZFnqvsybQJJyGtZoM0k/i2RZApYwY1LKpaz4ZpkCYxpueSxDFSmgix7+giZY7IczRhkcuM+xu2/VAyCu81ekjSVJU4oUkscgyhE1TMeWFICr1E6pbCFnTFAE9RtIJMSnzHgyzFrrWw7zKkmxIxp85IcQbPfue7jEdjbgcBS3Nz9Ho9vHqNeqMJUmVbwSTEslS5a99H49B0UcnVJjQaDTRdkMgS7T47ase2qpk7HSGcmcxsbIqZDZF0TIpIkrqCtHRmPnNZqfHOu+d55qlPYVoljVqNMo9wGwLHglRmmI6FK2wG8Zh+v49rOiyvLHDj9m1eePFlnnr6aTbW16k3mlimwZ3bN1g9cJDe3jaipiR44zTCMA0ss85ffe85Hjl3DkMvmAz6vPL6Dzl75gS24/LIQ2eZ9EczZ5qlpQXqvsfOzrbq/gqN2+s7LMy32OsP0fwWeSEQRoO65+J5dcCgbSsFC9PSERhI3WR1ZY2yyNB0E93QKPISzTCZTALOf3ADqKqLPMeqNvSWmRNEEoSNKQNcxyIMKkfnyjw10y01nvUx1yciME2CgJdf+wm/+svf4Fvf/g4buwOe+tRDvP7mj3nt7Z8B8LPLt/FrDZI0xfN8wlQnCgOWlxa5cePmPVkTqIBUL3PqZc5YM+haJoVUtbEuAwqUFdJoZxut2Sa8peRWup02m5Wjb2xomAas3/JIsnxmnwzQG/SRZc76zTErq3OYD65iGzamAVJaKu02NFzPVUGo2s0Nw8AybfaG29SbTVzPx6t5fPkLn+H3/+x5kiTF9xukSYlu1dGKDMdusLy8yrX17VkwgYxcZhjCJDIttCTCwSLLS4o8I70vCE3L1unlKBwQxSmuA6HMIY0qUXlm3crUgCwPcSyDDFQpCli6CgYDFNBLElFWAKkpBA7qeTxDib05JqzvqJO76ftEUYSwLMLxBMvxGSYpR7uLfOmxMzz//PMkqSTNS8osIkklwXiI59qEUUIcR+iWgWfaWJaF69osLC+xOD/P/FyXdnueLE1JM0HN94jNjz7F73ZKmWZMqSi5euUGKysLPP3UV7h2pVIB6C6wvrWB47ocWzmIzHNkXnDskWO4nstP3lEmQDvbW2zf2aBe9+h2O5SFpNcfsX7rJq6nCJyPPvYEVy9dZDgZkmdjesOAP//Wt/nH/+gfce3GJqurB3G9FuNxj/E44/FPP86bb7zJ9ZvrPPDASS4Oexw+eorDx05iWiaXykucOP0gJ4CfvvMejVaXeqPO7t7OTB310OFDbG9u02o2KPIUw9AZj8a0u/Ns37lDkGTUfJ8sL9HrcwRRjGUKhccmMZnwGYUDMuFTJvGMVNFoNGg0GkiZzyRr9np9JfX7MdcnIjBlUhLJkr/83vfx6y3MYcAbP72gbssGH/mYqXvK3QFpmjXFSQpVpiQMAwrYSzPadwXyOFHW451KuaB7YG3WnbubdJlJyYGjJ9Eru1ZD19HJQQ4ZjAeMJxtM0oRus0sSTeh2l9jc3sTyVTmUp/s7ep7F5HmOVqZqjKCycAonIbphIpMUu1GvpsQ1nn/pR+p9GRrHTjzAtfVtojjFFDlUKgRZnqIlqRJ5+xvOh2m2tC1qOKPbNLwWIVvV8xlYlVbhNCjZjoNFgW7qGIaBzPeBzrTQsKsSSqvkZ9Ikmv2dYFNmCWQpvmNx89ZNTh07yulzj+AbGkFecvXyNX7y+ivoecaB5S67G7eYnDrKr/3qN0izhGazg22bJEk202raunOb/mDABxfUudEf7pHGKTIesr0ec+uqul43BV/9+q8j8xKRZ+SahWMZRNqHJV8tVyeNinuue/HFV/jVr3+B8SSk0exS5hFZqmEaGXGWI0xlEqobBbEMMA0w8pQ4zXnx5Rf5L//+P+CDD65w6tRxPE/x0TrdLoPBkN3tTR5/4glu3LzF+u11vnT6FOPhCMex+fxTn6I3GFLzfVz3BFEUc+fOHdrzc3iex81bt/jaV7/C88+/wOXLSrbXNUt+8NIPiZJUYU71FpNmjd29HeYXVvHrLW7fXKdWd7ly+ybHDh2mBEzTVvbg3S6LS13ef+8ST3z+y1y8cp00iSmkgUFEXzNwh9tgWozCAVoQ4cic0WiE2VXk4cq6DyEMTGFgio/vyPuJCExlUTIJQvoDNTpimh9mjk6vm+pTTy/vNzgE2NhY5+iRIyDUD2pfrFbxmfoFdCtX1L00o1upDcZRRO60mJJt5s2EZ778eYjGlCiVSFCi+OtbG7SaDaJRRH3FY3N7i2MH1rhx5wbdZpeSWJkEZBl5ns86SaZlKT6MnqKX6vlGoxHXrl1BRzIYjXFtm/E4xPd9ZJ4ry/ONzdm7UN2QeAbqT4Xf4C7N88mEaOUUzug22mRCWXUdndFt9X+GA5aOnGTzmsoUUwwsmaqur2GpEkk3KLKY8+cvoxli5m8nhMC0HUxh4Nca2I6DMAyW5pvoFat+4/Yujc4cN2/dYHVliW/84pfY2e0rI8zhgIdOHuXo6hx/8uxzxBkEoz51VyeNRrz4wktM0hCz/GgZed0U5HmI73ZIq+wsSQIlwYzC9ExDJy3SyqapRJYGZmmgeerLvTsYTYOT5eoEUUFDWNS8Bg+ePsvlKxdJ04xWyyWTMTVvnv5gTLPuq5m0scRyBQuLK2xv97FNpc2+tDTPhQuXWVtbxrIEg8GQmu+R1Jv0BkNac23qjQZ5pvzn1m/dZHVthT/85p/zxLkzGIbGZBKws7fDkUOH0QybwbDHnz77bR44dpLtzQ0WuitMgojROKDQhCLRBmOahw/w4NkzfOvPv4WhK9NKz6/x2SPHWFpaJssSbt28QbNZI4xikiDk6KkH2a1Y50kcszWazDKfzPMIwxDTqtQxk5hM5pgwq0Rkns/MZqdD2R9nfSIC03TdHZAcLSMuzXuu0w0Te7or32cYCWA7Hkkckkb7FsVjzZiVc/WKNlAvcyomDg2vRREP0IWJzHM0oAwUuGotLXD1vXdIEtVOFqZDve7R74/wPQ/fqXHoaIcbV3u4DZccCxKNNCowXaXMadkCwzDI85woUfhSWWQkBcRSwwoDDqytcOTIMV594z0OHVwlmIRMwvCeLzuTkixNZicHMLvtbuvwPb2GF/TwfZ/J+jWcuklZq+FmKZFp0fBajMLBrKQLo4Rm3VNBTuboMqXUQZaGIgXYFk8/9SS3NncpZaqcfQExPfmSCZNwQiEsHNfFnIzUa8sVgx4gGo/Z2LhDa36J7fWbLC6onXY0lHTqPns7myzOdbl27QrjQR/LKvGrsQaZF8RRhCWUUkMS5ZhIpGYTRD1sp0YaK0uioBpovnL1Jl/7+v55UYQBWbWJW+wHoTQqZgHKLQVR5cY7Kgtee+sdPvPYIwDVeIyJa0McjTi4toxrl+yN+9QaLmmWIUSK49coZcxP3n6XJz/z6X2p5mpcZhKEHDpykM3NHUUHqTURBvR2d3Fdjzdee51f+fqX2Vxf58rV6ywvLHBw7RBROGE43qJR91heWCINQ5ZXllk7cIRXXlWuKzkaB9ZWEEIwP78AwFNPPUW90SSOE7I0ZjhJSPKSModWex7PV1t2rdZgMAo4f/UWXrVhdzvtfbqNzDG7c7hZBXEkKZYN6d4ut2Q+u2+cKJghyz96Q/nbrE9EYCrLkizLMM19wltcqQ1OSzYAquP7V5okWLatJs/vvn+1pLIKYawZtFHBqovSAs9kjikMhlaTuboJ4YCu7eC7Dp/91D6IXhaSOElxXY/b66/RaS1xbeN9LMPm8PElUqnz1ltvI4SDlAZrjRq6riMrKkBeKM1uAMu0yfICmQVs3Nlkbe0Aq2sHsC1Bf3ePvf6IqNBxtJzItLCigEmwL0lxtzOvbphYVbDWDZNuPmGUJuD7zNVN3CwlTlKiKmMab99GQ2ldg5oxbLuqXBOGj2VZdDsdFuY7dOe6tFoKB/s//6//e/8DNS2i0R6GZmJUXuO2bjHq75ImKZqw2NndQ8ocHSW+Z1oOVy6cZ2dHTcqblsPKsjIk77ZbfPWrXyYdbrNZZMRRwfjOLropKlF9JbLvIChJmcQpBiab4wGLbQ3NtBju9PAdB5nltOpNRuMxRZqje9xjbjkNRGlUzGzBdVd8qMy7ev0mn/nsZzl8+DjXr19Wj0kz4ixEFvs1c5IEaLpFo96kVne5s3GH2zev8yrw6LmHuHTpAx7/9GewzJLNzR0uXbrO4SNHKHJJvz+k3W6yeuAge3s9vJrDKz96mZrr8dCDD7I33MZ1NfJcY2VlHh2lyLCx1efssbOUecaBtRVs26ZebyKlpFbz2dnZZnV1hThJaFenynA0wrZtsihECMVgz/MMx7ZxWh0u/ORdRIUh7lRORNPxrmFlY+65tir7KxjB9yr7r4qyUuQZtmV9qCv+H7I+EYEJ9rOlaSZ0f+k2Xfcfw345l2UZtuNBlnFrfZMDq0tgGkSmNcsYZJ5DJc2wtKxEzIRhsCTM2dgKwKcfOT1j9sK+9s7rr/+YrNAYjVNcq81P3rnAcBTx2LmjuLZJp9tAZjnptORMVSknq+NJr8df//ULSBnRbDfZ3N7if/mn/xO/9Vu/zf/4P/z3/G//+/+huhyALDPMOCApoEwDyFOIIqx6C9816cwvsri0zPGDizSanRlvZjKekKZKuiOptItMy8So9HWFZSDzjDwvsS2LRKq2e1SBnmlWdSFzyWAw5K233mau22Zjp4drCyhDDMshTWNlQw3YRoltKkDbrKRH9DJjMBxx5vQDvPLGm3zlK1/h+LHDuJ7P9iTmT7/9XWqtLpPBHrYtWHvwLMalnEuXI/QK70vDHMd1iWVAnoc4rgcR1Go+cZ4p9c6K6ew6FjgwHkfohk3N9xhHZeUr9+F1NwAO9+JNsSn43d/7ff7zX/sqdlXqp2mGY3pkWUomJV6VNSZJgDDqpMUQjYxCM7lz+zbrG5v8yi99ld3tDZaWFlhaUrpFvb1t8lyyuLTIcDjEGPUYj4Z0Owuce/gMg2GfrEiU+qnvVrZRPrv9Hbq1Q5TbW+RZhunVaFYdSoyMIo4ZDSJWV1XmtLykMieMjLnFNjJRn0MYhrOgpOmCGxcvUGim2kSCgKUltWHElWFsp9NmEkSzES1QeFJSjWNleYkhwPN8xF2B6+OsT0Rgutuj7qMynruzpOznZE3T64tcBadoMiSXXbQkpazVZrSBsWbQ1rknCPULqCexuq2K/o7jkGcRw+GQNI0Jw4RxMOJWNWLw8NkH+eFLb3D8RIdxBLWGSyOtQwG2V9ngYACGyuQEmKbBZDS653UvttskGTzy8FmuXrnKQ2dOMje3wPx8F9e10Q2bSRihFRlJEs+0pfJcVppDSmNqd3vjLlMBOfOzt1139pi0LDG0gjQpMQwTdCXQrxvMVB7Lu6b0p8/leTWE5SKlJDYEpvDIU1UuR4nEsm1GQUi31sF1XTQDsiSm3mgyiZRdUybhBz98hZMPnsIrDH788ivqNcUhvueQxgHXr+5ye6vPeDKeaQoBJDLH1DQ0IRC6jiVUW9sxTIbjgIMHFplMqvJdt5ib87HMEkMXGEjCMXj1D5/q91uF3w+C68Ki5tdImnOM7nKENk1BmKaYgKnrpLpFkGSYJmRkzC/WkBFMxhGjUUCn0+TipQ84+cAJFuY7bG5uY5mCMIw5cvgAvf6IegM279xiYVkRiw3TJM8y0jSj21CBYmn+EDu9HVYXVzFMk16vj+d52KZOkhVYljKI3dzcYWlpnihOuHLlCqsrq5iWQxQNqfsewvPI0hjdAN/3eeWNDYTrIwxjZnkGanZ0Ws51Ou1Zcyit9LdE1RQxKkhByrx6/H8E+6b/GEvTNCzXRyskmSywjf0AAyjCXaU0OMWYxF0A26y00S1MgxkOMxhVVPsKg3Fsi8i0ZgFpaoQ5Go3JqrQ18Fx+8zd/nVde+gGf/fQ5Oh0VICaTgB+/8TqNRgPT0Lh86aKiCVgCR47Y3B1wcGGJejXC0h8FM1F63VDdJVnNUnXaLqNxymQyodtq8off/G0eOPkY+fYmDzygRm2SJCZJYqWFjZzxTGaC+AiSjJllszAgS2OKu+p7lXkpXShRCZjlpV7dR5JmeYVNxTMHXKVZrex4lKOKYG1tmRd/9OrseQ1y0Ewyzah87HN0V5BMRti1BmkcsHboCHathb57b1f1/fffQC/rjKMQv+aRhgFnTp/gpRdeYnl1kZpjkqMhhIc0C9WaLksoLUwdtNKk3vLJs4zBsE+tbjEcTDCERiozdrd3WFlbxXNdCl2gu/DzekR3B6WPWqZWohkG890WOzt32N4Z0GzVcIVeOdpCVqjzcpzHBNtDTFswCcaMdsZ0um2uXbnIqz/a4Atf+AXOv3ee1QOHmZ/vkmYl6d42m5sqexKG4MEHz9Dv9zhw6AF2e32wM2zLptlqMRwM6LQbSrEzB9ex2draxrZ1TL+JX6voKEIRPYfDITXP5rHHHtuXcGn7BJOIpEio+R6yKHj/ynUanY7yDQQ8YZJON2lb/VZUly4jltmsOzuVEiLPZ4Py002/X9z/Sf7t1yciMBmGQbPuK6wnuDdb8mwVlBqW/SHwF+7FW4APz+8INcJi2ftky2lnrm1bjEYjwiii0agr3zrH5Dt//m2W59szsp/MMvIsVmaUozFrBw4yGfdY37hGzVYA4nh7yM/e+4BzjxzDNIUaNUkSgjDAtCwsxycKA/b6fUzLpT/ewLQFozjCFg5vv/k2586dpVbzZ7t/USkm3r1kzkzrenqbCk4CYQiMWeBSq8wzXMe+K/DsBy6rIm1Oy9Tp49JUUuYlJSCQSOArzzzFD15/h8FwSFTdz7WBMsHSLcxS4Loum1t38BwHLYc7t25wYHWZ85cu87knnyCZjPAcn7iEaLRLpNmsLM3RbHVodVpkaUaql5R5guH4CKnPWMVprGQ8kiwjCBKKTFL3W0zSELs6B+Io4vDRVbTS5OLFS+R5/rc6w93yXqwpK7XK0tzCtky63Q6GXpBlKV4lJ0yaUrPrpEHAMJYsLSh1gWgY49RdDCOju9Dh/Hvv8vTnn+bmrXXieMKp0w9x4OABNjcVQz/PJf1+j9EoYGV1leW1Q1y/colGq0Wnu8D8wiKWqZooSZJhOTXm57sUeUapmexsb9FqNkhStZm5js2Ro8rI8t2fvT+TQBa6jmPWEAYkQUIc3ZvdFFXFoLpt0863g2WKe2g0g8xAl8E9ZVsXxQhf1v4T4TGhocTdi31p3DBKMI3K660KStPr718V/opp2ffeXrFRpxPP3SpjmlII4iQnkznNZovRaEy32+bcQ6exKGk0fD744Ap5XmFEUhLFIe1OG5kl1Bt17H6NF1/5GWtrLUb9lDApGA5ymk2HSTgBTeJ7PlY13hIBnl9jMFRZxEp3gdF4yHAc4Tp1BsOIs/U6umGTxhMyWZAk8SxLml5qpSq5ipz98k0zKw1vsAxm5Ztu7RvJ64YgCAL2drdJE+WRV/cb1H2PztwcMoyROcThBMerzcZhhAEnTp7muYpXJaXEJCfVJIZmkpoaep4TToaKwZ6kmAY4tRYXLrxPd26B2xubLM618ZsNOobGpfMJaTrm61/7It999q/odmu4jsXt3gBhWIxGW1iWhYZFSUqqJYzHUK97yFx14cbBAM20iGc4Wkndd5GlzR9/6y/wTJv7T3G3FORe8SG6gBHq5F6hvqRqpaKkzCXoBkmm4zoGspDkGQyGI7KyZL7VJIxiuo0OWZySyJR+r8/qwS5hOCAWBb1hwImDx/jeX/0VJ08/xMLCIr29ba5fCzh2/ChRlOC6Njev36DdbrKzs0eSxNTqNYQQjIY9gvGYzz/zOV5++UeYloNIUnq7uywvL2BZML+wSBQlakzHVnb1W5ub9IchzWaTpaVloihUUkCVHtX2zhUQytTBt9X4UUo1L1lhr4XMiJOUVNxb5glhEMqcXGYzUq/iwA3I/lMBv8uS2UDg3Wt6nEUJWXqvQL68C5ge3AeY64apaANVtw4USzlLE1rtDkmlwXQ3mNdqd/ji5x/HNgTtuotRa/PqC3/NM5//HIks8WydV195hcWFJd5448d0Wi7XLl8nSxJMW5DKhKw0uHZzi5WlNpYFrltjd/cWWnmvz1bN86h5PqPxkEmaYJs2Tz31JLc3tnnh9R/y+NknKmEyiFITtJJC05T9tiZx3QZpmjIa9JkEQ2SW4PqqK2PbgmbDJ62CljBUBqQbSkDOdWyWl5fvyZCmmZcSpoO4ktjNKnKoLeDyBx8QBwEIGwyLUisqY02FpcVZBlmGjUGn3aLXH5DlA06dOs3O7h7v/uw9BisLPHLqOJll4bkO/9U//of883/2L1hYaGOIOs1GTQVNUjQs8lxQaDGllKSRxGqYjMfhDBi3hBJzcx2LKE5xXA8hPI4ePsrb77z9kazvSJNY90ndl6HSS8ii/WwpHEs6Dni+RzwZQpmoOU67AYzQhJgxoE0h2N7dxHQsbGFh2gLLNCkdh63tIUsLi9zZ2wbgvffe5dwj5+jMzWFazsxK3HWXOHj4EIPBECkDOt2FSsq48qgz2pw/f4H5hcXZ4PLBw4eIo4A0lZjVMKZtO/R6e4zHIZ9+6nN050Ler0xktza36FaW9mEYc/7SNcZxuj89UNlrObYaa5LCnE0X3B2U8ipYgerESakggam2mfF3EFU+EYEpz3MGlb/YtLNW5Nk9QPcUEL/7+OeB4NwFnt/NDN/ZukOr3ZnxLCLTwvd9NjbW8QW8/tqPcUyLc2dP8sHrbzPf6dONM+MAACAASURBVM70d2SWcer0g6yvb/D0M0/T29tkd3SLEyeWuX5jV4GLMiKIE4ZhzoLvcvXqJQ6sHsTx20RhgKEnGHo5cy+ZpAmdpo+Gw4WL53n9jZ/wa7/2G4ASJotR2Y9hCHQZE6Wgl5LhJEIvM2zXxa/XkVLuD7FWZoyW7SjHXkuQpRmmpQwjTQTZ7DIhzzJs20ZKqTqIhobjqGA+xbSyUnDwwDL/8O//Br/zB3+CSU4pbFxb2WoV0pidjGEcY4Vqo+jv7TAY9EE3OXrsGLdu3aa8dIM8Cqi5gp+89Q6HjyjXjyiIuDrog1XMul1S6tV4SqK0qKQkiDMWak3CNCWKJY4Hnic4deoUmmHS8F0WlhZwnAZp+f8PdpilTfYRpYdXF8g4w/FbxJMh/VGA79eJ4p4q1XUdz3UIoxhTCGoNl1vru8hYcuSYAquD0uLAqg/kpOmYUrf5tV/5e3zzj7+JV2vjuR6abnL0iFIj2NrcIghDnnjiMaCEWgPLLInjFNuGNCsZDIakqWRnZxuhw8HDR4GCQUVOTlNJHMV0Om3efv0VNja3EYZgcVFy+dJl2u0mtzd36HTa7Oxu4dTa5DKjDANl5yTMWdBJ0gApczI5UcoVFc47hVOmVYw6jgmq4BWMP3pa42+zPhGBiYrHNF3ToAR/M11g+vc0Mwomk1mGdHdAWlg9DKgyceovD6BVvnNz3S6PP/EYvd0ddnp96kurcP0W84vzDIdDbt++g9BzBoMh4XTuI+vhOg5hMmH1YJf33tsmTRIyKTAHY9otnziS6Lqa6UuzBMvxaTTnQAwY9Ia0Ok1SqUM2YTKZ8LmnniIMQ4LJiDTXsIwSKZW+NLmcuerKu+yV0lRiWdPsR1ZtX6VRDoqlnRomMldjCFPxN8tS2ZTVbCqwM8rYWN+g3xsRBhOC8YCvffUr5GjohiBOUpaWV9TzCoGtSXJsLMuBMoEScqkUB6AymHB8bt26ycpym/FwgO/7JHHM0nwXUUrSNFYM7ThQzrEGZIUKSLYpyNgn0dbcOv1BgGkXKjBISXdtjVqlR/Xuzy6SyAkLc6t87eAxskIH7cOB6f4RlExLsFyVlXxUV85U1r8cPXyMrZ1tDM0ijMJ7ghKAZ1lEo4gjxw8zlOCWMcP1PTrHlujFGZZeJzUK3nj9DRzHoNBirl29pQJo2sevdViYWyYIYtJM487tG3TnuowGSgvdaTZ5440f0Wx2qDea1H2P3V6f0aBPo9WmyFVWPP1+e70+aRqzcfsmJx54gNfeeJM4DHj2u99X575h0RvHZP3bH8Jp1ZtXjaQsZ1ateK6rjqcwSyV5UmYRYapjCmUEEqYfH/3+ZASmak0DzP0B5+fdb7qmf/u1GrZtM6nsqHXDpNFsYlo23U6bvV6fvZ1trl/uc+aRxwA1DuJZBtGwx40rl/Da8/zrf/W71FyXB0+fxK21WF1dQWZKI0g3bP7o3/0+B1d9Lry/xSPnjpIkAa26wSgWlLkkiiakcYxrWez1ldzIZBxg2yY130MrJW7TIckTJr2Adt3Hty0eOnuGK9fWGY8nCNslzTWiIMCttVQWZCgsSdj3ZooFKlj1en3CcML27oAwzrCMkjyXlQxvPmOgG4aB5zrMz88xPzdHY2GJbsdjbnmFslLF/O3f/l00w5yRQqWUjIY95tptBuMxRZaTl4niNWk2aRoDGQYGQTqmiCStzhxHjhxBypxb65t4tk6z5mMUMUvLK+QywXFVRpekIZpQmUhaxMjYqiywUmpuHVMIfM9EE4JhX73G4w8f5rXXX+f0mYeY77QYjEoe/8xTrG9sz0Ds+wPR/cFnet00OE1XmduYWkIw2MLScg6sLnP9+g10o8BzHQbD0cweO6xghSPHD5PIlFGvTy9W/3+/H5HmCdR85r0ut0dbaDmYJrhNB6tmsjfcY3N7i4vn3+X4iQfo7W3P5g5zqcwnxsMhD5w8zasv/4hMpuQ5mMKk1eqwtf0+9UaDdlsptwZhSJSmXLlwCUPoCCHwXIe0Ks039+7NaO5uGM2CVJFSGb7MGkjTQDSFWKaD1Vmh1CfCTInFCfHxw8onIjAVRT7LcEzTpO7oHDl8lGany9pih/n5BeI44eSpE7z8w5cRQnDh0lU+99nHuXrtJtevXsayLH7x67/Is3/xHc4+/AWklDz7ne8SjgpWlpY4euwoC8sxRw98AYC/fuEVdMPk2MFlHj17ij/4g39Nmhv49SbHD66wduQ4l86/x9LSAnOdc8go4Pq1q8wtLLGyvMRgsM3jjx1nK0ywUp21g6sEyTajMKLZauM5HlGa0m42yfICz3PQDYMgCBiGE2yjAsTjmNjM6Wd9nv3uX7C5M8J3m/zTf/Lf8Ie//0fs9fvkVQrt+DWWl5awXZ9Ww+fQoQMksiSOY+I4YX19nb2+SumnXSPb8XBqSjbjG1/9MkFeMgn3My6jGhvZ6/VmPzSA3/j1X8HzHKJw39Y7TWLqrSaD8ZgMA1tTSpZ5lmNUIySxBCeS6K5gXJUX9VaTpm+ysLjM2WNreI7JeLhDGqdMwgknjxzh6tWrlDIlE4JSSnIkRWbNsCZTQFaW+LqOWROcPvkIr775QwaDjO2tLQaTANv2cRyL3/2d38PSTKhr94yffNT6ebdpRkJRCnrDETW/VjnQFNQdQSGpzDQhjGKCYKC4bH6dVCuwmjVqXonpWHR8n14QQJryzC8+w7/95u+hOQ6bt3ZZObqCk48IEknN8zE7FnmheG7CUFIvtuNQbzbZunOH4XDC9s4eC0vzRKMRo9GYtXCAbdkcWDswkwAeDPqYpkteSLJMfS+OEOzu7bI816Vbs9kaBGSFNgs6oAKQVkhKXVRqlPvLtAxsMyWNE2pVqX/i4EHanQZHDh5iYaFLvbOIZyrawR/+3v/zkZ/3v+/6RASmdqvJ1595knqjhuNaCNMhjUMs2yIPB2zeGGBYJi/81QcYlst4MuLogTnyOOTkkQVOrcwRlRCOdnniiUexDINzj3+KuTnFfE11nc1QkBcJ+l2guWloPHr2FBqSYycPMRlpSM3Aqzc5vNKlSBP6gdr5HNef+dAfOXyE5168yJvfv8l/8UtPcmX9Jm9feJ9PPfwgUTRht9+n07BoNBrouk4cDGjUa0SJpNFosF3haaZjMb8yz7iXzNrMXzx6nJ3eDs8/95f85j/4DZ577nkuvH+Z5aUubqPBysI8C4tzGLquJF3feZNMgpEnDAP13hqNBppQYm5pvG8EUDouZa8HKJMC03HpFTZeqe4z/dt0XBZXVrh5+TJ115l1+CxLcPv6FWRpKBfjUmFMeSlUpitzHAGWVpJGEkM3yYuMQW8XAwh6W3Q+/RBxNODRcw/zneefJ4kmXLx2TVmVS0VYxLLIYrAqFUyZF8QJWKZJkCT80tNf4bnnngMdFpfa9Ic9hCEYjiZouomwPSZFCpHCEn9eULr7tvsDmJZoxNkEoWtKd2gc4bkWGCa5HN+jvV/mNjXfoum3ef+99zhy/DCdhRZ7w00lKeIaYLZ58w1FKi3jmAMH5jDzEUFpARF+TVCzfSbJGGHAMEhoFEpnPktTjCoLWZjvkqc5lrDIREYUxshc0UvUwHiG7fmMB33arTa31teRUtJs11lZXubkiaNcvXwFv97Ecx1WVg8wvzhP3TWV07Hl4DdbBMMBb77xGo89dg7HscjSlL3ekFEw4IFjJ5SUD2r20xYVQTodMAw//pwcfEICk20KHjt1lFAmeMImM02ySEOzHdy8RiY0ZFZSr7WYThfktk1RFMgCzEoJPYwleV4gHMGBg6tcuHiVOJvgYtLWE2p2grANNN3HLlM+99RnKfOM0XhIp7XErVs/I5eCU8eO8u7P3md+bo5Ww1ciX0LQneuysb7Bnc2bHD2wgIwlozhi8dhhPmW1sCyL0rARQBjneL76Qd+4eoOz5x5FqTDmkEG73kY3CkZxRJqmDPtDnLrLmz97h+OHj7G9uUWt3uDwwVUMHeqNOhfOXyDPEj64ep5hfwh6jfEkoD23wHg0pt6oE2cGmSagIlECM4fdmm2zPZXTraQmvTIi1Fw8m1mAyuKI9Vu3efH7L/BLv/glYJ8b9ev/2a/yr7/5rf0vT6iglCYJmqGyJ9mwIQyIYhW03GpUZhSM+fa3n2VxtcH1yxd5+MxDvPPOWwgtYTLKsNy7Ol2O4i5NgxMoHOfQ4hFe+uFLpDLDEhaTSYBmWkDOZz77JP/8n/0LHL+G5f38LOn+dT/GZJY2wsp59NOfQRcOO3s9RDWTmOcJlA5ZPpnhS+2Wj27U2Rv1WDm8QhJNuBVu4zoOrbpPmOZ87au/wHe/92d0mj5b20PaCyZpqgTx1o6sMAgCJonSNO/1dnHcFrW6j+XUMM2SKIoZByGGAWkaYRiCRt3Hsi26jQZ7u3tgQhIHlFnGoDdgfr7Df/ff/te88OJLPPboOb74pS+g6SanT59m2NsmjEO6nTnmO3U2t/fw7QaTaILQJVqZ8+nHz1HkGeOhMh6t+R6+a838EU1LBSxN9xVWmCUUeT7T7fo46xNhEQ6QmSZxrhOVINMMzbBBluq4ELgalHlCVKqNMI0TRDAhjOLpxohlGLiWSRbHRGGCV81CCV2nX9iEWUEW5ehFSrfTnploNhseN69epuXXGY/7HD6wwGS4x6FDB+h0mtTrDbY2t9B0wcadjRk1/8ixJRqOS60s6O9tkIQjbBO0PMEwdFzLwjAtENpMSN+0LCxTpzu/QCJzsjhlca5NlO0T3SZV+fT+T9/EwGBl9QACgziN+MbXf5kTxx7gM08+Q6s7T73ZQRMmjU6H8WhcfQ6VF1+lx53mJTKTyLiyWk/2/4Waix33yZOYsHJVMR0Xx9DZ21PDnFPagVZKzpw9zYHVldlrNTSpmOAozlleZmo4VrMpdGeG/x1aW1Ocp2I/Y+3t7mIJkzgEyxWQ6WRFgXaXv9tkElBkkiKTGJrFwYPLJOkE0yqxHItuu4ktDFyhCIV+o6Es2P+GoHQ/npRGBWVofuj6o4dWaNZdHFNQazRIi5K80Cs/t5T+uE9WFGRFgaFFdHyfSTCmUW8irBa63mB7d8jm7oAkk1iWRZYGLC401fsUAs+yCNMUq+ow1+w6UZgShxN2d/uKz5amTIKgIuymhJGyqkrjkLTqoGmGiYxj5rr7qgG+79Pv9/jc557m9TffJBj2mIwn5LlkafUAKytreJ5Hd36e+cVlLLehfPNsgWlZ+H4d2/VptJUaRK3uowsFSSic0sWsOqhZXswCVhx8/K7cJyIwlVUwknlGVAF00V0Ca26REZWgGTZOhYm4GsSmTfO+jkKZJ2iGzbPffpY8ThAFJEnCq889y+bmVkValDz80Fn0IlWebLrFwaPHWVqc57NPfIoXXnyJM2fOcvXqVWq+h2k5nDl7ir29HisHV2i2Okz+P+7eNEaS87zz/EXEG1dG3lddfR/sJrtJNsnmTYmkRIq2JVqypLF8zKx3PN7ZMRaLBfbLYD8usNjFYo2dHWPs8dgj22N7bNmyrIMSaUmUxUu8j2aTfbHvo66sqrwj447YD29UdjdFW5YEGMQ8QCIrIysrKyMin3je5/kf3ogwilja6LDRH1BvlSlXTbbPtSiYOkmSYloW3mhEkio4Tkmidj2XpZVV2u0ZNga5drW4ihiulRyCOMQpFXj2B8+x/8BeGvUyO/fu4fFPfZqlpSt4k5Bnv/8cR4+d4Nip93jt9bc4cuRtPM8jDEJGXkQWR6TBhDiKiVRZCb3z3lkG3TUqJtMbgFWpYVVyS3LrKiDz8U/+LABKFJDE0h7q1PHjDDY6RLEEnSaZIEGTriRJSHDNEkcXGhoJpuWwuroqlQgUk9iTmkq9wQaPPPIICRFZGmIUNLI4JgjHuEGAboFTzLFpumDnjt0cOXoUYOpQMhpNCOKE/QduZW29TxxMCMUPLyeuTTqby7Zrt0VKMN0eKQF7dkrOWhLHDIZDHNvgoQfvR1NTDC3FKRRo11tUnAKVXL731OkLiFTwsQcf4s5b5HClXq1xYO8t+QBC4DhVsjRk7CWUCg1s0yHLJ6iGrpMRsrBlnjD02bZ1nkLBwrAcNEOjWa+RZjGOY1Gp1yhXqtTqTUJ/gtAgTWDkTgj9CaViiShRubzU5Xt/9yxlp0SxJs+xStnJvfZUkjTlyNun0DSNKEmxbHta8Qhxdf8UChYF20aoMQXblpU/4Pshwipim4JisUizNUt7dusP7f8fN35kYlIUxVIU5VVFUd5WFOWYoij/e759p6IoryiKckZRlL9UFMXIt5v54zP58zv+Mf+IF0YIEjaFojVNxYqC6dItSwKyJMDXTTxvRCQUwiRBM83pc5tRtCSzXBgamjBJsoRCoUAY+EwmY/r9Lp47IMkV4TQlhSSlVC5hWAZ7du9kvdvDc0ecOXuR06dO8O7Rd1hb6zDKm8txDtc3NXNq/QSyHxIrKq7v0+l0GI37aGqGECpRKPW/y6USSRROX9vbNA70pfKmP/Lyq1WRXrdPtdbm/LmL/M1Xn+C//tVfs7R4hVQV+Dm7WxcqmSpYXlsnDkOSyMcLAsJUwY9ClEBnMh4wXO8xHl6vY1Uxcytx36OQeQzy3egnKbtvvoXllQ6GbU6dSaxCET+4HuyqkRDF6XSKA0h3FxKSKGFuti0BmPn2OElxnBK1SoO/e/YZHrz/o0SBShbHRKEim9x5pZUkAlUXCE2l0WhwuXORMAlQMIiTFFUX6IrOjTcd4D//8R9+IKjygxrc12oxbYaemYReip0J7rj9EDpw7ux5hGFz/sIlnv7ed3noY49x3wMf47ZD9xFFMQN3QhTFaKLE//gb/4rPfPrTPPvC05w5+w7Nqoltqyx3zqILuPvujzAeeiiqQc3R2RissLR+ZZpkwyjCDQLZX3Usur0BcSrI0oiiVSSKE3xvTBTHDLo9olCqj2qaThRGOXRD8tk0TcE0BRfOn0XoBvVmmyCIefvttxkMXQxLVj/z8/PceJM0zdhccoM0zQCmrAWQhG3dsFjL+Y+dzjpxFE0rcd2wMc2fHvUN/7geUwB8LMuysaIoOvCCoihPAf8r8O+yLPuSoii/B/wr4D/m970sy/YoivJLwP8NfOFHvUnd1BnEKoZhYacRttDwMo3N67diFCGJqJTL2AoM/JiK0PCTEEUziUnyhpxg5A4pCJMNP6BSLqMbOgVDY+xKT66ZVpPF5RWK5QoZgjiDG264AZBoZys3R9OEwC5IlPXa6gprG2s4jsXyyipnTneBLgdvvBHDNJlpbmV1/TK2USHMsU7CLrG8skijKa9UTrlJ6MtGpetFKJmBrujMtmdY6axi6xbFokG5ZXP+wkUWFhZ49oUX+MQjj2BbKp/9hcf5z3/4p/SGYynRm/hkiTL1UIvilDBN8cNEao8HcsIzDMYoScI77xyl1N5CZzCZWj672dUTT4+vCuwB8MJLRJMezXoNPZf+MHX43/7tv+X//K1/D5AnRwuhgGlCEEEY+hgGclqnawT5csPSdSxdYdvCDKN+DzdPOMtLF6lXawiREhkxYRgy9kYYmommCTRN2ne/8voLABj5RHNTgSBSFKmOaVdI4mRaMf1D07gPikgJsDOBrqYoUUhjtk2xWiYKPI6+8xZCM/j2t7+F0Axeeu0IH73/IKHrUjAMRpMNXvjBdzl1fomtC3Lp0+muYWgmVcehUirzzLPf4/ChOzh66m06Gy66ZQAWUZoSRhGGrhNGEZowqAgDu+CQRD6gYRhSBWJmZp7JZIKmQIZOkiUYhoVp26x3e1RrTYSmU6tWeeed44RhxML27Rw//g7jYZdz505z++2H0DUVz5sQx3K6aFlXJ7JeEEvSeZwSRkFuWKBJTBcWNUul1x9SKFh01roYVpEgiBHqmMnEZ+z+sIjjjxs/smLKZIzzh3p+y4CPAX+db/8vwGfynz+dPyZ//uPKtbomHxCbzxZsa8rPinQdYegMUxVPFOgPRyx2+rz82hFeO3aMulNAM00szUAYOgINQ5M3xZAVTNEyScKAJAi497b9UrFA1wgmYy68d1JKaBgiP+iCsT9mtbOCJqScSL1e5+LFy+hCpd5sEkUee/fu5c03jzDTanDrrbdx6cpFhv0Rihoy255hcfEyxZI1HZ8ruoGi6pw4foq11SX5dxtNdKERJym9kayWYj/GKtkEbkgQJ1glmyj02NhY4S/+8s/Yd+NBThw9wi0H9nN5sUPJMcg0WXJnSUwchuhCpVopE03GZHFCloTEoUcWjIl8jzQOWVm8xPLli4xGI0q24OD2Fo8/cAs/f9+NfPqR+/iZh+7locP7ufOWfdx/581s275riqlRsnhaOV2LfdkEcybZ1eucH0OkaCRo9NY7aKpOGoc4BQc/GGNYBkkiT+D13pAt81vo9z2iVC6TinYJwzBIo1gaNIqUyTjGtiw0rUDoh4QhRIrCIw8/wu//3n+SXn3ZNaj/ayZuHxTv7ykBFCyNz33uF0iyiM7yIuP+EE0IllfXiROZYF967Qjbts8B4Jgm1VJ1qjZQrwrCKEJXVUzTQRGCtV6HNIkYByMuXTnDZ37uszhFga76hMrVpKSrKgoWmhBcWeoQ+u7U5mswdLENnX53nciPyNCxizaj0YT1bo80ibFME8dx0DTpdjO30GL3ngV0Etyxy6Urizz44Men2mC2XeCFF56n1x8yHI2YeB5JKvW8hK7T6w/x/ZAgiFFUnShJ80QJmqZgWQUsU2CagmLBQNMtojidurr8NPGPmsopiqIBbwB7gN8BzgL9LMs2yUVXgIX85wXgMkCWZbGiKAMk8Xj9fX/zXwP/GqDZaBDpOpaqSgqFMCEOGLoRb7x9HNu2r30lt950MwBaDo13gM1rvR5FRGpMahZJ0ggtkA1zTVOZm2khdJORN6ZYLsn+0uaOEIJtC9uZb88zdieomqCz1sXzXC5dXiYMQ/q9Lisri9imzntnrzAcDrHLNp4/ojvy6a4NmdlSZ/mKi+WYmMKgYGaomsLy8jJ37byLy5cuUyxVWetI7lSQBOixhA0QSP+1/mgdpygI4oRxGBAFMU899U0O3XoLvh8zHI04euI0CuBYOvVKmVZ7hj27d3HTgQOYpsivbjAcjaRaQX6o4kQSfKMwmmo1+ePeVGFgU4XA0kDTdBYW5smSCNWwp8+tLl/CtiSNRQgxBdQpWo4IzwRGluTqlgl+lGAoGUJTcArS383UBVne5M4IWZhrc/T4O+ixzgQJGxh7I5rlGfxowh233sMTTz9BxangWEZ+vFOScYrlVCnYFpm46hGX5liqD4rNhJRNdHQgVqTMTpGA+++9F1ONKM4t4LkjkjieaqoLzeDchcsIYZHkKMOCbeH6Ywq2RRRd7RVdG7rhYJgmen4FfvK7TxDGqhz5BwmGZREELpgOumKBIig5BbRNmy9gY71Do9nGKVYlAXe9g9YV7NixmyTxefP1N1GEQa1Wkc35oIumm2xfkOdznGRUm/M4pRJhqjGehDiOyd333k8QxBh6hqLqlEtSXdWbuBRLRXRNZTgakaXy+c3+k5VPS0WOjo+SdNpY73TG/LTxj0pMWZYlwCFFUarAV4H9P+0bZ1n2+8DvA8zPLWRbt2+nuyq/rEbJQWglXn/1u8zPNJmbnWUyGSOE4MCBA/zFX36FsinQLZ3Ij3CqNeIgwLJ1olAaI1q2zuLiKoah02jPsLi0wmc/91neOXYM0pj17gi7UMw92+TOPn/+HLVaA8sUCGFw/MQpdu3aiW0XWLx8iUOHDvLeSekke/uhXRi2YLUzwIt8avUaxYKT94wCqgUDXU+IUlheXmYy8TB0k1Z7hiTJCMKAQ7fexOtHXwNg7I5oFBoIQ0PJDIY9DwjwRj5bti1w8cIldmzfi1MqcfcDD/ORBx+StBU1mpJ0QWoyhX5MHIfYlkEcS66dLgyyNEakEhOkavFUUA7ILcGlRpNtmdPtlnUNjcW08iumoFUtsbzeu476ApCqFlrqgy45YpYuKyXdtJmbddCyiCAMEbmTRpYTdt94+3VuuelmrixdIfRDXCXANB2SLOT2W+7iuZeeo+JU0LQCG70BpUqdJIz4/Bc+xx/8wRdxNSC7OqbeTEpZYqJowXXLutBL0TOTWNERWUTGmIIq2LVnJ6oS0R0Mef2NV/nIRx8mBKLAo9FsE/guq2sbLLTaaLqGUE1MQ6c/6k/db1u1thSS83wKhkFv1COOI468+QpRlqHrggLwyEcf4snvfQsjygijCNN0CKOI0PMIAx9N17GsAlGSEvou7WaDN998jTQT6EJHUwWGoUMqe0vLq6vs2LWHOIHlxRXiNCGapLx74j00YaIKg717d/ONbz/DIx+5h9AbUSqVIfcDlErLAcVSEW/iMh67eF7AaDSeMgpQTQJPOgwHQYxpO/SHQ5yCjjuJCH2P4XCI+j6vv58kfiwcU5ZlfUVRvg/cC1QVRRF51bQFWMx/bRHYClxRFEUAFWDjH/q7SZry5a99l9Ggi2MKFrZuxzQFd9xzF0kitbKFmjA7v4WTJ0+yMD/LymqH4coKaZgwubxIsWRx6NbbMNOIzvIKcwttTp+5yGc/9zgvvfIWu3fu4It/9IeUnRKNeo39B29F0wRCs6bTiXq1AlmMH8BwbcCh2+4AyCVIEmabReZmF7hwcYl2s4KuC9xxzLbZFstra9MmeDhxwSkx32iz3huytrLM3n034fsTkiRj0O/SaLZJEoiCmHLDxh95JEnK4uXLCF1jZnaeQX+V0QgCN+R/+I1f5z/87hexLZtPPf4zxAmoWSTvr6FzD/pdKtU6mhoTxUyJl5syqRoxZq6hIzRQ8woKTccwxKbrFQKZ7KQe09UKYFPhst/vEwYBun1VbG0TRRxEUKkoTEbSU05oCmoao2eZ1PGOwA9g8/w1LGmTtba2Qsmxue2Bj3LlyhWGg3WazTleOfKyHLWnKWRQqtQZT8YEOaA0iVN4H90ryiV1741bkgAAIABJREFU41j2296/nJMcuYjQS3ESsAoa9955O2feO0mvP+Lxxz9NEEEc9ylWW8DbxElKq1pB2Db+ZIxp6Li+bEa7QYCh64xHPWqlGnq+hNMNB90wuLCySNVxGLgTdFVF1XRCL2amUWMcjKbE5cVLG5JMTYTvT1A1Dccp0VnfQFEFw97omu+NvHqcPHmaOMnYtXOb3I6KoqoMhuus9foYSsZwPOGJb3yL+twWXnr5FYqOQ7FSIkp1Qn+CpguSKJ4mWMu22LtvH72hz9zcPM26nNpmls3EnVAv6DzzwsuEvke1XKa/sQRIcG8cXa+m8ZPEj0xMiqK0gChPSjbwKLKh/X3g88CXgF8Dvp6/5Bv545fy5/8uy/4eH548LNumPTuLH4WMg5BXjrx73ZV48+fNqJZK1CsOrfoMhYKOm0/yVpZWiE3457/8KySRz+HDdxBGCQ/ceweqsLjz8K0MJxGd5SXac/NTrSOEQFNVTMvCD2JZMdUruLnjx/LyKgsLsoHdG3TZsWeWzsBjbWmNVqtCmGqSP+SGmI5BtVKn3ayxtLzOwpateN4KKBJcKVSJ/rYLKadPn6ZeajH0PUns1ATbd2xjNOgycHuUSxXuvOMeuuvrdNY63HDDds6eWeTJp77Df/9r/x2XLl+RJN2c8JvvsFyrXCXJTxBNk/twHF9NSvLgCob9dZY6K5CkzC9sB6DomNcluyDJKGxqMwnpaPsvfuXz/M0TT7Pe64Eqpg34LF/uTUYxhZJgpjHD2soVdmxrkxLjxy7EKpYu0C2pAGnbKlEk7a2TTPDiy8/g5goFi6tLRFlGpihoWgHHKTEauozHY/7Z536J3/1Pf4T3ARxU3Vby5PTDXnKbEXopRqywa882Dh68iZMnTxGMxzz04P2MrpFA/t7Tf4vrjjh58iz1co04SamUHJZyKZMgcNk5vxPXH4NTIIpiJmGI41QJIwmFiOMIV1FwCgXcyYTVzipRAmHqY+g6vVGPpSsjarUWiqZTNGTDeTQY0Gi2KZXLaMoiSRpjWzaGKSkymq6zc+dOslj2gpIEWs0mvV4fwzBwLAvfHfPZz3+O1cvn+MRjj3LqxLvsu/Egg8GASkX2g06eOsf3X3yNoddHCRTcMOLYifemZh1G3kO7Vjl2M1b7LlJUDUZ9lyz+J0hMwBzwX/I+kwr8VZZl31QU5TjwJUVR/g/gLeCL+e9/EfhTRVHOAF3gl37UG3S7PZ741pPXbbtW0sSwnWlzF2Sz1TKkK+5ad5VWrU5iJ2S2hh+kvHf2Io6hML8whwG8/OpR7rxDahzVKzpxUIPYIxU2KBlxAoViFXc8xDQFnbUN1tc7tOa2EHkTikUHIQx6vS6OU2KuOMfLb73Bzt2zqJlBFHoEk1BquMagKwmpGqNl0OuPKBVNvInHRneAYxsE+Vx9o9tn245dnDkr3YbHo2WCwEG3DD7+kYewrAJJErNnzx5++z/8Dg8//DCZanDmzGVOnDzFG0dP8cB9dxMHHuO8BDFNE93QsQvFqV12GIbTBL8p15slEamhYxWK3LB7j6yO8uWYNxlPe06+YqKZ0o5JaHLpF4U+W7dtnQr0TY+TqpIoUvFSTX2gSBxJAKlhCFxviK4oRECt7DB0JfRi4vkoGOhCipWVnQq9UW+6vNmcVpVz4KDQVL7wi7/Kn/zxH+EpGkqgkJkZWWISh/70y/R+E4L3a3zbmQANHnv0IVZWOhw7/i6/8oUvEAYSt2U7DqZlkcQZQlPxgoiYBJEFbFnYysaoh6FalMpF1nrrRGmKrqqSChRFEvKg6+iqye4d+/nu88+xZQbKTpVzZ05y84FbOHfxOIauY5oOrXmLcXcsK1lNMB65OMUi3W4XU2jESUytKrFMcZLiTwI2NrpUq3WG7hjHNjBtm+XOCq47IAxcXG9AmGSYmsLKyhpxnHJl8Qq7995EGGVMcpE9wxDoqkaWmEQkRLmlvRRqtICEVBjEYUgqZHJS45AwTQG5zzcvSnH2T2ARnmXZUeC2D9h+DrjrA7b7wD/7cf6JLEt/yOJ787FUG+hLkwJ0dKEyOzNDFrv4QUYaJozcEDu1OXXhHKVSjeOFU9x04z58P+TNN48wP7uVwdjDT1JefuUYtq6xPvYxNIUwyTA0ZUovcIMIRdPxPY/3rkgLoh1b5mjmWKNyZQYSl0P7pTb3em+N06eX2RgGREHATfu302q3MYVGmqj0+2u0621Kjs2Z905x3/33kgyHUre5oNNu1Tn2XsC29izbZlt85KMPs94bT/29AP7ib/6KWlPn+9//PodvO8Sv/9oX+Pe//R+5/c7DvPry89xz9z0I055OpK6tirzJcNrwBtn8NgxBGF5VxYSr9wAZm/2q6Or2XFpFyaQJwtmzFxi6IzJVoCQBlsibzqqFqfukqkXqxThzBWzdJ0rGKELQKNVY762xsTFA1QWh54FQMSzyHpFsnJbsBhkhmaJAqJL4CiPFxdR1Hvr4o1w8fwkvEiAyMlNWi4oWoNsKOoKJq6BzfaH+fo3vJEn4zKceYWWlQxjF/OxjP4vrutiWMeWmPfH1r9JoVBjFUC4VWF3b4LZb9xOnAWEUcbmzzk179xEFOZZHCDrrA9rNCn1Xtuh106RSb9Bu2rJi8kOEJln/q50BWxea9Hoe5UKV2IYLF85z3/33MR7J10eBrB7jFAxLoCQ6G91VCraUnanXKximg6LpuK7LlrlZzvkhTUPyNbvrK6ytdfB9n9XOOvfe/yCuO0KoMYEXkym6HIikCaZh4OW4pChOKViyj4WqMuxtIISgWpEJqj+Sy0ohZMVs2EWi0Cd2/4ma3//U8UGaSteGIEEzCwTDIbotS9HV9Q6FYhHDtAiTjOOnL3L8xCkiBGvjZYjPc3Gpg6rpU+XKarUinZyEoJ9bF20mJYBisYibmRSLDtt27sBQE/wgRNdiSgWbp5/7Dgsz8zRrLcJEZXnlCvVqjdAPpTGjiDEMhyQLWF/tUG/UsawC3d6QwWDAoZsPsri4yoG9t3D3YWmu6IUZ5XKZycTHNHXKpSKmMJhr3EAcXOSlV15jY7jCfffcztF3jvPLv/wFXnrlDQ7fcRtJkhEGPrZt4nsuUSwIw5gkud7MIE2khtNmhGGMuqnndM1zYbiZxOT0TYrQSVsn2zJ59KGH+NpTT5OJHAyZRlhawCgAU/fR1BLbtszTW7+C640xDIM4DVCEIIwCHE3K4aqawNTlFNS2DDb8AaVSgdFINsbDOKI9O4MhpOV6sVjkmWefyxPxB5/CBScjCgS6+cHyJ6kXs3PPdmnyKTI2uhts37IgLaDyc7BULlMqFUgila7rMvFS2q1ZhKYycCe0qhVCLybw3alw3MCdYNiy8tMVhbJTpTv2+NaTT1GumfRdN5/aWbz77pv8z7/5v/Dlr3wJkO7IO3feyEyriZ87xBhayqXLl7ENi0MHD3Ds5CkURafVqBMmGaVSge76Orcdkj5z1zi5c/DATbmkzA7WN0YUK0UUYgzdxBQKw5H0+wujBFUTKHFIECZUy0X6/bwBqEoMIMgEVLRtDNPAKpbpj0bSo840mbiuhK3Ef//S+ceJD0ViUpSrpZ9hmlN3FKdcRTdsSKX4u25YkEYMQxBCoVhvSc6OU6Sh6dOlAACxD8KC/HGEYPu2bSi6OQUXboabJ6XNqsmoN+ksL1GuNzGAzmDCDcgriKmrJKkgUQx2bL1ZiroPBoT+AA19ajmk5yC/atFB01MgRQjBa6++SqvdplKpoKkqu/fsQskiwkTNOUgJnh8zHA5ptRp4Qcw9d9zF6vo6H7l/C+VKmW9/72sstLdz++23cOH8OS6cO8Mnf/ZRhqMRniZIkgzdkPswSSw0TZCpOr7vE02G9AZjwtDHizPGvS6D4QhNU/Fzjp4iLMIwpFQsUKo2qJbLvPbmG/zqL/4C5E3xNInZv38P0RPfRgiBF8RTbSYhYpIMtsxVuXHvFl7cOEO1UpY62WlKxSngeSlZbu/tex6OZdBoFrCtOp4vFRB0C9xxhKIbuO6Ixvbd7LvxFn7n9/6IcZbCB6C8r43NpPRBYek6X/j8pzhz+hxnzp1nx/atVCql6cVQEwZPfvPrJKlKkLgYGehawv4bJHjSRvaS5BBEAg8vLl2mXVtgvTdGryuYRpHDd9zFi6+9QeDJfVt1HKI4xrZVajg89dQ3efijD+G5E954+zi7du3EHY1Ikgyh64x9abW11Fmj1+1SqdSYuC6BF5EmSQ5shBPvHmPL9p3UKkWCIKBSLtLrDZiZaeB2Btx++yG+/eR38IOY0bBLqVxHN3LeW5JgmhaZMCjaCVaxShCGxKFPwdCwC5KCYpgmbhiRDHo0ZrZQLZUYex5hEFzVYNKM6Xfup4kPRWICKFVq2Llu0KYhAUgTQ1XTKTpXsUxC00gin0zV0TSNLI6IPA9f01Fz40o38vEmHeZm24RBxMSVaOlGaxZFN9GRk6AsycFtYhM9La8o7bl5ClrGJFHIooCltQ5GHEiOWBSgGyrVapNeb4AmCqwunsOyN3WX5ZJh4of0xy6Nmo0QBWzTQKiC2dyIcDLx8bwABTl6VVSd5557nkvnztBo1/nkz32agm1TLBbZ6PfJkojVbp84MHnhpVdozbdYW1pj5659/MEffJFf/tV/wVNPfRVNmARhjNBUDMOgUilTr1VotdrMbdnO3Ba5H93xkPX+CHcgG6Wd9WUMYdLrDXj9rbeo1pv0ByPWbZtWc2baRE+neKYV2o0KI9dHy7WZ/Jj8ZygIefmOYxWIKTtVRoEkXsvjLBOgIXSSLERTDMLg6jJAwcCyhcQt+SE37LuR3/2d3yO2dIQfEVvX+A2+z9jy2n5SlpiE7zOxmGlXOXnsNK12FXbsxNQEmjAgFyF84utfZWZmjrWudDHRjYxDt+6eNrYLhoGuC9b6gyluqV1bQGgGzWqZKFP45Mcf5Q//+M+YXajQblamF0MFQ+o4BQGLl5boDSaUbR135E0rDglgNAg8l1KpwvpGl3KphGEI1jcmxEqGbtkIIbXcN3o99t1yG0EUUq5WWVpeIkt8ZlsVojAih7WxurzE7OytEix5TXnlFMvoqhQSTOMIQ5XaFIowpttRdXRDStkAuGE0HXrI746sSDP1vxGhOEVREIZsIuuGzcQLpskpjpOpBfZmbKxe4dChW7lwaZEw8imWHEyzwFrfRSRSGF1NIvYdupXu+hphEuGUa9PkE3pyqlMsFqeo5msrqO7a6lR8fdPFl9hny0wdyzSx7QKmabK8vMj62gZRGLNlxxbSOKS70SOKZU9A1QSqljIexxRLCRcvnKdULGFZB+l0VrBshyz10E2Hgm3z51/6K4q2Qr1WJIhDTKFMkbZS0lbHIqHdqgPQKMv7E6ff49f/+a9gmoLf/M3f5PylJb7+ta8RhimH77idomPy6uuvs2vXLi6cP0+r1eDIkXfwc7VKy7TYvXsnu3bdjecF7NixnY3egEmYTLs0t9x+M8vLq1Qr18ADDJ2yU8gTk46hZCAULL1ErWxNTQOEkCdskoU5utlg5EkEyeaUavNUTLIQXRdEUUiYq0AGmsrHHnyIr37lq1gFh3EaXpeUgGlS2lyyqQVnmpwMFZRrlnJWFLNz2zYc22Tlyiqt2Qa6aefHTEcTBoVilThJUJHH0DIL6Pnn0WM5VncnEw4fPMyZ8yeIcvJxqdbivnsfIYljnv7ed5mZcSgYBnEsP7dUJk6ZhCmOadKab2GqKnGSMhy5CA1md25DUXWSyEc3LFZWrjDsdRkPR1RqFSxdMAx8NjFIutBotuTUeFOvvT2zQLvdIAh8Li+usnuP5MONRiOiMCRJN6vVMYYhsByHOPRkcgauVccNc4KIQUKYphiqSmd1GbvgMBkNpr8nFPDC+Ics1H6S+FAkpizLiMOQRqvNYNCfJqdpjCa4bu4rJ1Sq1RpnTp8nVjR0DVbWewzG7tTqybYMmq02V3LPLpDuoePxmFq1CjC9vzaiOMawHYrISeC1ceOOWRr1OsXcr93QM7ZuW+C5516gVrfp5bQAgCRJiZOYYlEnjSKKDQdTT9lYHzM/v8DS0hWKpYpccpk2cRRx5N2jpPGQoW+we2EbMRovvvwadx++FU0YHD58F6dOvEuWRMzObmejP+DMhbPcc9sd1B2HZ5/5Drv27Ofw4buYbVb4n/7Nv+Tc+Us8+9wPsITg5x7/edzxkD27t9JZ66IQMZdbQQ8mHifOXuDdk2fQdMGrR97l9nvu4gcvvoGugdAN5ltt/vZbX+cTj35s2kzXVMiikFt2z6FZKlGkcuXiOXbu3EuURSTJhM7qKppioIqUQc6hKpmCjBKWnkvTBh1JZBVAZpHGkn6CkaJmBo899km++bfPcmU4wogVDJQPVBAAcPsqupljtxKTAhKztGnLZGcCy9K59bZbSYMxQewzHI2YcRyGozE/eP55bMeGLKY3GON7HuudPjce2IpjFbmwskijMksSj4jSlNfffR0Fi0FvSLloc8fh+3jhhefR1BRTF5i6PM9sRydOA9b6AwiQCgOBilNoEoc+qSonlV5eTSaRP61oBiOP4dinXC6x1ulQa8yhihB/EqBp0B9NMEyTLArxI9kXrFYchr11vMmQew8fpFhyEKYEWnbXV9i6fS++P8EuNPEmLrqmYhoCD504SXITgoQgJ/NeCwEI0QgDD6dYxtOErJRyuo6ucv0k5SeMD4XsySaVbmOtM7Vl8sabErEmBVtygFrNBo1mG03orA+GuOMhUQIrq6tYWsCO7dto1GvUazXSOGJpaYl33nlHSoiqGVeuLF4tp69hTbveJktfEHpyEtKb5CJrUUB3bZVzFy7yt08+yYXz5zFNge+HRKFPqeRgGqYcKycQJ7K/0h94uJMAoVskkUoQRjSabdZXO5w8fgy74JDGPoauSUEwVOrVGkQh/fEY2zaxdGUKbhwMBmiabM4fO3lUOg6bgjeOv83+vftoNNssLV7kiW8+ge9PZEXoFLj3nrsp1+p89atf5bnvP8vrr7/BemeF+x54iLmZGrfcvI+iKXj4gfsAMMhQkwhTE+gamEUFoQvefPsYIzckDK9WlkkKd9xxK4WiTobP6vJldu7ZgaqlOKaOkhqMXZckC6dIaF1VSbKQKE0ZBWP8aMJ8e56yU0FTDFB8kiyk4BQpFRo88rFHeeLJp7l46Qogvd7+vqQEsq9k2CoKxXzMDRP3etjAjh1bWb18hbeOSAkVU1howmDx8iWKtk3oyc/pjiZcuLjE/I4GjlXkfG5wOppsTHW+Qy8mi0Ka1TKPPfIoL70iHYuDnJ6iihRVpGyMeqz1B4SetKKKUosogclE0p/UVLD/poMSouGHhDnl5dLlJSJvzNb5JpZtsmffjWiGhmM5lMsSPtGs1wiDgFGYsb7ewXVd6YxjWszOzvPSKy8TRgpZFpPGIaZdIowCKfamqRi6JgnDXLVpEkK7zqRAEQZhmv7D1VBuTjo1A/0p4kORmEACtzZvAI1W+6r5ZaHOWmYSJdnU00ryhiQzut6aYXVtzOKiPHk3bYzn5+fZu2cPaRwRpQq7du8mjGJ58zZZ/j6ObRHl5flUlbAgMPNSeWXxMhfOnKE7cHnuhRfRNRVNlWW/N/FZWVnBMG2WV1e5eGWVC5cWCaKIyI/wvID+sEcSqbSadfrDHrPtOq+/9CylSkXSDsKQjV6PG244ALrBSmeFKIyIU4VXXnqRM2fO4DglZue38OKrL3PzgVvYv/8WiobJbFkKxZ2+dJZ9N9zIoL/KU9/+Jn/3vafobaxw880H+MxnPsUXfvEzbNm+laXVPmcuLPONbzxBrTnPyvqIAwduZPnKRR55+KPce+9hPv34Y2zfsoClGxhqGdM06G6s87OPPYJdkCaMqibQVDh0x2Ecp4SChV2x0PWEIE6Ik5RIUbhw+RKakNWRplxj666qNEoSTSxUEy+QCSyKY3Rd4Ic+H3/kZ/jzv/oal66sXueQC9cnmyy53rBiqkSZN78Ljkxkwo9Q05hPPPYojm2y54a9WLZDEPs8//1n6F5ZYhK6JGlCr9vjvXOXaM02qTgFXjl6nCiQRgmGajH2Ely3T7FsE8YhP/fJT/LWm2+T5ARZUxcEUUwaq6SxtBQ3dB2noBMaQkrcAJ21ZckACENaM7N4uTKFkYPExsMuSZIy8UNJZfKls0yxaKFpEAQxq2vrLCwsYKsJu3btxNA1TNsmQxDEYDozpEmAJGLA9h27Kdg2Wk4QDqMEb+ISh570k4sTDBVQN1U2rl82CyH1t9yJR7VaQ2y60SQSL7eJZ/pp4kORmK4VH9Bz59jNpdzECyhMujTSHFuRG1UmuR6SJnRUTWd++24aucY3gCqujv37w/HU7vj9YehimpA2p3q6EGRRgJ+/pFWvUyo6lCyFZrPJhQsXCIKITDFRNRXHcXAnPt2BSxhn8pYpeFFIhiCKFfrDHp7rUS3XKBgOpl0iCkPsgoOmaRw8cCPjyQQjLwZKjk2cpJw9d0ZWSlmIqml84bOfn/7vd95xD5Gi8M3v/C3ewOe1N15m3579CCUgiBNOvXeCr3z5T/jKl/+Egm3zsY89zO2HbsY0JFn3G1/9G+LA48UXXsS0bc6cPk0cRbz04sscP3ZMHg/LJ4sjPvmJh9ize4d845wQnKSwtrqEqupsdLpsa0vAqWPquCNp3T0cDTC0lCQ3/Yx8sE0HXYgpwBKgWpJLHgWDgTvhZz7xGP/Xb/2/kp6RXH+VnrjKFA7w90XG1SZ6OnHzc8IgVQVJ5GPaOgVDp95o8PbRY/SHPcaZPOdSYlzfp1FrM9OosdYfUCw4NBqzks+W+hginSK7Dx+6g5MnTpBkAXESSqdgIZdyqkgloj0RGKqFGymowRCnKFAyg1JZOpvohsGw32M0mmBZxlRVQNN0fC/CdopomsL62ipC0ySXzqlMlSxrlSKKIpH9r7z8+hT2cezEaVrNJqpm4rljJn6AmnnEeaM6jALpACRUkiTGtCx0NSVWriajJI6uul0LjTj0ZTWVSjNMRROyWtIM4kzFC/8bmsrphn3d4yj0IF/Gvd/3KokjQtvBjkKSOMJ1XRr1GiL/PcO0SONoOqGr16WvWalSJQx8FKeBgYQMhFE8lcpVdBPjGjhByRYEmMzNzaCrGe1GhZm5eapV2R8aDroszG/hrXdOcvLsBYauj2GaclLi+iRRgq0bFIoOYTxheW2ZguGwvL5KozlDpVJhMJCj8Vq9yfLSCjccPMS7x47w/RefZ765wMzsPOPJ5Oo4XlgIS8Igllc3qFeqrHRWWZhr0Z2MOHb6Te657SMsrqzi+z16fRd0g298/csMfY+KU+Nzv/A4xVKZIIj47nefxgsiXvjBK1QqEox3+K67sAsOulC5uLSGXdKwLUGUpIxGYwoFizCUdJ7xcMiRt49QKkmeoGXKabFTKjDojhC6zsj1CP2YTJ2wY+teuv015ud2s7Z+kSiKCcOIoTtAF4LHHvskmm7x737797GMIn70Dyzd0ghQULQPsI3PTMJUgi43p3OapqGmMSeOH6dRq3BlaZnl1WVUBJGiULIcOiurJLpCEGZUKvaUMlJqN9EUD0PXKRgGYRQRRRru2GM0djENOVkUAjTFIIpCbNPBC1x0Ibjc6VA0TAxhgFIgjFVmZupcuLCMXhOsrXVRNJ3Z2da08e04JUqlCo1WHSUJ0TSDKFG5eGmJiecTxSHtVoO5hS0EQUCWCY4eO8bGYDw1PF2Yn88pSuRMAB3XkzpLIG3FVCVmaWmJWq1O309YWR+QRR6Kbk+/U2PvGpqJqiOEhueHbHR76IbFJAeBFmxTgjJ/yvhQJKYsy6YTuc14f6LalJ7QNUVm6fGYRFMwLYvJ6gYLpoEfhAhx1W+9IGQjTwCmZU392K33Oa9O8U+5cJqjyCZ4ABQLFtEk4NGH751qGmeKyfe+9x0e/cQn+PKX/4bheEKMRrNWZb0n1f1soUDeH9I0CIMMoRlohs545NKege98+7s88NEHANns3LdvD8dPnZ/aTLdnZ7BtKS9iWQWCKMAUCu16k05nBdMQrI8jZtsz6HoCE4gDk5ffep7bD9xNEBZRWGUcjBiOBjQas6x0VvjyV76EKTRKpTp33nU3tXoL09S5dOkyzz33PE8//T38iY/tFBm7LhNN8MTXn6I/7FMtV7n7vrulYaYQaGnCPXffz8baMqau4kcTFAzOnrvE/j17CaII03KIkxShGSyvLWPqgiPvvImGTqVaBBHS2XD5l7/2G5w69R7PPvMCmqZdt3zbVAm4dgn3fsrJtbGZlN4fSRqzdWGOSRixvtalVqmz0ZXHrDvoS4ClanA5Wkf20KXW0sXli2xrz3J5cZ2tC01CLwY9JJiE9Poj4nhCnCBF7YT0mrNNZ1qBzzarhBNZ+T14/0d59tWX2Rh2abZL9LvrNOpVXHfAloW9JJGPZRW4fOkC/f563prQUIixLAvdspnECuVynXK9RRKFaLpOHMTMtWeZa8/yzLM/oFIpk6URUZSyd+9u/DjDSCNeevlVDt95mCQMKZYcXDegWjPZsW0LL7x1EtKIOFOlJEycABLTFKUqupoShT5ReBUeEIVQMFX5OI2uUzL9SeNDkZg+KDYrJpCiZIaZO/SiT+2ooyQjcr28dzMmTSIK+dXRMg3CwCfwfWIhM3wcJ5w/f56tW7dRcBwMXUz7S2HerCRHMWdRQCknOD70yMdxhz0G/W7e+0mp1So8+eQ3ANixfSuz7RKrq1127Zjl9PnL+WfwGQYOYXdIuWjR7Y6wzDHVco3T751m7/4DpEkiQW66lBTRSLjl4O28duRlTp06zoGbDqIbOi+88Dx33X0XQZzhOA6HDt3Ol7/0JerNKjfsPsjR4+9SL5TINJ8oKHDi7NsA3HnLfYxdl7VuD9cdMdOQfR03iEjHKzz57b+mUZmdfv6ZVo31bpf77znMwtZt0+OxKZt69Oi7mKbFwnyazx7GAAAgAElEQVSbYqnMubPnWLBM4tAlToYUhIU7hv379zEejEGHhx58iGf+7tugmZw+fY75+Rbbt+xkNJYJ4eMffxzLqfFb/89voQgLT4mx4hhdOD9k311wsh9qZn9QXJuUrkV925ZNlCm0W20MI2M8ko3iSrXCaORiFi1efe0tGrU2RceeCsBtbdfp9Ban6pS9kcvurTXuP3w3x0+8Sbu5lc76ZSy9QJKFlMzitArsuy6hF9MbudTqNZ599WVMYRC4IWGUohsGN9x4ADsf9Wu6he9PWFnpcO7cRVqtFhN3iO8FpFHE1p076I3PM8gpL0VHYs8s28K2TF594x2EyBh0N+j1e2iqYGOjS5LEFIsldm2fJwikYYShGyglnSCIKeS+grphEcUT2U5JQ+nKSwiawfsbIkJJMVSFMImp5hPr8P1SDz9BfCgTUxR60+kcyHVtGPjTsnLTW73f6xIl0GpKPM+mq0e9Vpu+NghDvOGYmXYTITRa7VkuL66wc/sCEzcX+8qTFIAjpDwvwHjiM+yuc/Ydg2LRIUsisjRCqDoXLl5B01TG4x5JNKZR3c3uHQscO3eSomnihSkpCcORi+qYgIWmqRiWjqanOKKMoSicOXmKfTffKgGjikGj2eTEyVMAWJbGZDKiQAnHKeP7IYauUalUmHge9WaVSrVO4Lk0ay1Goy7SRi4kClQMW/D6W2+yd+ceuS/iBMcy2OgNCOOQemULo8kGYRSzuLjIti111tak3vgbb77Ce6dPs95bo1Zp8dBDDxOFKtu2bsEuOJimzng05Kab9nPp/BkWNQ0ykzgNmJlpsb7WxbQlX7BQsEAzCXyX2VZNKgTkSennPvU5AP6/3/5dlJxvV1B8JrrFJk4Hrk80m83svy8+yH3XsFWYgB9naJrgmWefIUlVxqGLZTvSDtwYT88DQFauQkPXBQN3gh/J8y8IXHRT0HVd6pUyuhCMXQlNSbJQAkqFnDzqwGQyJEsLzLZnCLwx4zDArDQwHTkM8MbyPcvl8vusjxI8f0RnNZYASMPA9yKCQCqWFiwLd9AnKxSo1SSQMgojAn/Czh3bOHbiPVozLY6dOMOf/vmXuO/uu2k22zz/3AuYtVnUJMIoOKRBQIjCzoU5RBYRplHe0I7RbQs9u2rJpKmbKxeYmZmhVi8z156lOTNLwVSnAnJ/9ge/9Q8eox8VH4rEpKi53VB4VeTeLlamQMsoTnAchzDwMTR9mqQ2M3yaRJiGwfzcHEEYcuHiJXRD9qbK5TLNVpuJ6yKERr1eo1ouEicJlmlQrjfJooB+pFEryKa3opuEnkuBmNtv3MX+fTsZ9LpUarK57o7HuKM+lUqJu+68jfFkzFtvn2RmS10aDCwYbKyMON/zqQATNUPVGti2hjsOqNVnGW9scOHKJVrNFouXLzE726ZRs1AaVbZs2crc3ByvHXmZ05fOcuv+Q8RRwDe/8S0++7mfZ+J5mELh7nse4MzJU6QptBqSHLva61EqKghF7pswDjl9/gzFosMdBw9y4vQ7hHEoex3AynofkY4p10wGbkKxUMQQOqc3OpDLeuiKjhAqE0+K3Q0GXSqVOoPBgMXLl7h04RxCFxI8qJpkicfCwgwrufDfmdPnCHwXUxfYtornudx2+500alX+65/9Ob3eCFcDI8nxSbng24+r2b0Zm0Jwm9WWQpHQG4OSUlZUNtZzw1GzAAOXYq3AaNRFV02SKKLeKkMsMVpgE0Uxrttna7uOGwQoqoE3GDHfaGM7DrbpsDHqUTAM3Im0Orf1Ag3TYb23RqFQJoxV6pUqy3FINHIJ4pCKU8MPXO6+57DkMqpiqgYp6Uk6szPbyFJZzYZJjGkXmJlpc+je++h1u4g4YG2tg2W1idMU3/O59567iaKALAoJAotiySINE06ePMHlxSUKhsa2WoVapUij2UDoOv3cNfnf/PoXCCNpZBD6LkkqJ4RhlJDG0s/u4rmLNJo1bGeT9gS6qRJHEb3xgIJT+OGD8mPGh2YqF4fh9KYLDW88IAo9Jl7AxAtw3atC+YZpoWpyeRbFkhEdJRn9Yc5HqtWZaTcpl8skccSpkyfwg5A4TlhZXub0uUtSjdGq0ptcFcfabHqHnsvHP/YR5tsN/n/u3jxIkvu68/vknVlZd3f13dPdM9Nzz2AunCRIEAchgiJBkJIoS5RDCoUjfESs19q1YteOjeAf3j92vVortHJQXlpyaKmlKUriIZIgQZAUSBAHMQPMffZ0z/R91V1ZeR/+I6trBuAhrmg7YL+Ijs6urjszX77fe9/jwP4ZVCmmMlik3W7zza9/natXrzC7Z4RE8OnYVSzLwtAUBgoDHNl/gNGBIaZmipw+OU2n3aTretxZWuXAwYNMzexm8fYiYSxg2TaNVgvXttD1DN3eSTg2nupJ33/sJGrW5OyFs3TaDfygw6s/fLlHwLQwNJmZ2b0gC4SSzMToJAoKxcIAqqymlkiGDEpMvdngzMVXWd9oc/r4KU6fOEm322FksMiunk+cZVkEvkOjVSeragwUBhgbGKJQLGI7Djfn5wDIGEb/IlIarPDUMx/FddpoagoiBGi1ahi6xK7JCa5du8KTTzxFuVjhA088x9NPf5gv/tXX+Oyf/w1bdStVnyTFKN07+t9JSu+EA/xcx1Qm6D9uZ0K3I6mrKSJREBB4NoWCSeDZmGaObFZGEmMUFCRZZH0zZc/vYJYyht6nnxw7ehivh+m5tbRGwUzhEJqapWBmiEMxlUVRVSrFAllDYn1jBVWOU6uuwKfVbWAaRfwgwnbfLuA/P3+HO0srBIGH46Q/ge+zsHiHcrlAo14ntNqoqszUVE8gLghSdyAJms0Wu/dMMTiQZe/MGL/yiWfZtWsXlaLB7OxuHrj/BGPjI5TLZXRdRZZgoFwg8H1sq47bbRL4DnHo0LUsXKebwhpCmNo9hapn0I0somyklk9iSl1UtAyJ8P+T5vcOibfTamBmsyAWMLIqDIxCbf1t3Llut4udKVMRPMIwotVqEvgexVKZwPf621bXIe5Jwo6NjhJECY6iogwMMphOaJHcJqKspFRSWesnqJldE7z2g5d56rEHcR0LF6hurvPiyz8gdEOu3kxP0D270wRSKhmY2RG80Of1c29S327z8AMHUMSQQwemuHp9EVMf5sXvvozteERhhCTXmZ4coerXKBUKLC/dYdfkGImgosk+qiLj2D5qIuJrMq1ui9On76fT6WLZHpphkggqhYKeam8D29trzM7uptNsoGbAD9KqyLbbFMwCgizjaDUuXDuDIKo8fPIRbNdncfEaqqJQHBpgq7ZJPldAlVW80AdUZDfA6nTZP3sII2OyublBuZTyvxr1JoGbJqWd8PwATVXw/IDV1SUq5QqikuXEg4/wH//i87Q73R5ROPixidtPaljfe9uOAN3PCtUQSWzlxyR1E1sBMWBsYheNRgOv1wCXeoakaj6V8t01OczlqytIskSz202pI6UhbMel7ga0VmtY47BrYjchCqam0rFCHn7wEQxdRdU0wjDm69/+CigKra6NH9ztzliWhZmV0WWDzbUVZqZniAQJRdUREp98roSiaBiqQnV7k2J5kMBP5aArAwN8/fkXiaKI3TNT7N83m+q2ux6ICoKk4LgemYzGYHmGZrPKyPA+zFwZ37U4fGA/HcdGlkUkSUptxSKl75ISxamOtyilMBxVN9MGuu9jdT2yhkIUgyDSB4EC+EECyCSxi2X94kJxwt8jLvn/ShTLQ8n97/kgsqr2p3GKBIp6j7VQrzoKfK9/e+B7BD8BiLqDuQgi+qhxSBvijqKSSyI6wt0G3YCqvK23oBAyPDLKgZlh5Cjk/OULLK2uEQYRA0NlksDHzCoEcYzlRAwVKoShjSxnetOZmEceeZQLF85z8dIVdD3PnZU1dM3goYfv5/XXziAqEnISc+jALAIR+/btZ3AovYLtLJvWVtexXIvLV84TuD57p2aQ1dQFI4oC7jtxkoxhEIYxr732CoahoYrp/py/cx1Blmk0HExN7ZtJpr5tHRTVTCdLgOV7HN59gMpgGcsNaDXrrNe2yOsGsiSytLzJR579FYaHBnFdG0nR+5bQttXCzA+ytHCdlaV5ctkMgmQwMTbK0Og4ulniM5/5DF2ri6xlSAKfKA5/ov/b/11xr+X3TmLqJ6tMwD/61G8B8OorryBLUp+oW8qnx4mmKrz4/fMMj4xBbDE2MojnW9ieRb0Z9vWTFF2FwKdQHCaJXOQeU9aPXXJaFlVVqHUaPHj8IX7wo++SNVKk9vJWnZHBIpYT8b6HH2NxcZE9e/ZQGhgiiQPWVtdZmJ+n2XNWFiKfKIpxnQA/iti97xCiqlGvbvP+9zyA49h9p2jLaqeGl7kc5YEy+ZzO1atXsd2ARq3Dgw+dxglcOk2b/fv2osigm0U6rSayohD1LOQlSSAMArI5kziKUhCm42Fm88Q9D0dJBM8L0h4iUG+0MYwMpqlx8tTJN5MkOf0P3YfviqXcToS+j2O1cKwWtpMu41qtZvq7k04Jgoj+8i6ISKcGsU/gOwS+g2O1aNarjIyMMDidUlTeGYI50N8euOdKr8gy5YEBdEPn5NGDKGLCxvY6YeRj6jrlUpaMlpDNmmzVumysthjKl9iqrrJZa3Dj9k3WNqrIksiZs2fJ5cqcPnEcgFyuxJFDB1DkNPF2nRBdN5AlCc3Q8EOfxcV0mud4IZosMDM1hqHoTI3NAHD+2mU83yPqVYKLt5dotVoIic+jjz6KLMvEvebk7J6jiImKZXfJZs2UPCvLZLMmI+UJdNnEzCiohoyuRHSsJmfPv8nK2hy7Z2Z45PhJRgaLyJKKllFZurNAEMV85Utf6n9fge/TbNkYmsypBx5hY7tLoTzKyVOnmT18gn//v3+O/+UP/hAvSJBEmbbVIBZlwujuxVBJ/gHLNLJv+3snEe246/pOjGcpPVWBuz2qQPCQbJFLl29y+/YdZElCMTKpXZZ6t+ns+QEZQ8TtWoRBhGV1EWSZkfIE2UxKyjWzKXp7bbWK63XRVR2j9z4UUUSUYxwv9Zz76ne+xSOnH0ORZbqeR7lg0mg4aLJKGMHQ0DCmmVYmqpJO2HK5HIok4toufhgjSqnVvKIbnHnrTb71wreZnhhNMUrJXVhFNpsnl8tRrW+zeHuez33u89y+s8i1K1dAUvCDiGa1QRRFlMtl8rnUkEMQ5b4CgSQJ/W1IBfV2Elbgu6nJZRD0rbz8IIXRhGHY03f6+6emf+8+fjdUTLl8KTl8/JH+3ztCcfeGpr+9oabIb8+pO5gKACNjMDm5q68MsDPF0zU1hdObA7TtdCpU6j2NKCvs3zONZ7V5zyOnWV1eIpfNEMc+t25cZr2+1VcbBHjyfR9kZHIPrfoWlu2l4L0k5Etf/Ws0I8vE0AhGLs/ZNy/StbpYXZtCLk+tsdV/n7Kss3tqmspwmcDpMjk1TWlwhEI+g9jzhl9aWsL3Q85deKM/0Tk8ewxZEnnp1R/x3/43/xVxFKFKMSEKN+fnkKOQMIFOs0GtupWKrnkWSRhiGmUkMcYLUjY8kLp33IO+F2SZJAwRYpWms03oafz2b/8XWLZPNqP2K7odayFJ0dPxM/Cv//B/ZWQgRXFLkkhsJiTthCgTc2BiL3M3F4iit5tSwt1+0j+04b0T9z7+nc+181q6C//iX/xzvvn1rxAGIR2rgySmCO1KaZCua9FquNxeXqNUqqT7yoCsIfUF4WzHJW8WcLwuj73/ab717RfQNd4miRKHIh3Pwg8CIkfg2Wef5dt/9w0ajVSKeXZ2N2NjM0z11AQ6rSblcplz5y7QqFdpNlrYro2q62iSSK3eRjdz6JpE5EdM7d6N77tks3lMQ2Vzu4qmSIRRwMbqKpoh4YURS4vV1Dora3L8+ClOnzqKoho4PUR8t9shilNLpjhKJ3BJHNBodDAMDUkEq5sS6LO5tKrccerduX3H7MLq2siyzAeffuIXqpjeFT2mJHmHfXNPE2cnND2TYpiCAEVJ1773JiJIJ3OilPqDVYZmcD0fwfNxoS+X6nqQKFnoJSWARpwmpzgMKJULzG1vUK/VyefzCLGH3quodpqeYqIiSSHPf+ub6Ma9GlEiCX7fUVYSA/ZOT3L8+En2zs7SbDQo9mAMzUaDC+fe4j9+/vN0rA4DA1kkWcB2XOzlOxQOH0r94QOXsbEJLl+/zCMPPMLfvfoSWcCyWli2xZEDs1w49xZHj6Xql5os8OCJY7zy2ht94a6J6T20mnUyqo+bqDRbDXQjXaJpajYVa/N9giSBoJckbCdtmgseHSvB6nbxAw9VgSCK++4dOz2G0G6SzaV6UmEY4voRpqHgayG6BQEiki1y7PBhFubvvI1i8mPuJU4GeLs0671KlH9fvBMmsBP3LukAbs7Nc/i+U1y58CaGkaHRqqOrWTpOhChALpfh2NHDvP7GJYaHKwzn0wtSx67S9TyKpslaNeVm3rx5K1UG6FVIQRj2G+aKKKJlBrGw+NKX/5Yg8TGzMoMDw8zuO8TmZi3FsgGZjE69Xmd1bZV2s40kQUbPYLs2dnT3sxTzOTq2j++n5PNs1qTTbmH2ptRhDOXBIlEQEAcWsiSgqiqqZuI4XRwvJAy7yLKI4+144Um4ro8kgh90kURQVTVtaqsqipdW6YKo4Njde1yCQdWzqHqq71UsFhCl//Qq+J3xrlrK3Ru+5/XVBD3XJrineei5NnEUEEdBf3vn/5WRcXzP7f+kTOu0UZ5kswiWhWBZBLUqQa1K0mqwtV3lkYdPc+n8BU4dPUBGF5EVJRWuUzLoRp5CbwQaC+kBZ2YVYsFHkkIkKcSP052U4GP7PhvVJqvrm9idJst35li6fYvXXn6J115+iZe++22y+QLPfuzjNJtbkIREYUKnU6dSKrGxsYXjhfhxSgM5fuQYc/O3ec/97wVFpWNXMTWFwXIZRVW4cXMOy03wwoSuE3PivmNcvnGZUrmCLMtUyiVsKyQOQlbXt2l3WlhWF8+3EHqmkwQiipqkUAJDpmje7UFlTYkv/e1foSoanVaTs2+m4E1R0pi/tYDVtalWG9R61kKW4xAjo97DZYsyMZ7n0rZS7fTA+cmVesKP60W/Myn9rCndO911q0m6bL83Kbk6/OUXvojndDl8+Ai6rlEqlJEUhU6zQeCnnm2i4DBQMlldXWZzs05WF1PnXdPE9n2GyhXCMODW4jXGx3ahKHJKR4ljVFFHTFS6nkettoFlWeQL6YWs3uoyOT2F74dMz8wgKTqCqJDNZlleWabdTB1aVFnE921EQUaV1RT1LaQyKHEUYWYyqKpOEHjk82bPgEEi9Ls43bvQm0IhRzFfJJ8z6LSbfONb32NpeYVA0FOaE2nVWy4VUHUTXVdRdRNF1bG6Ht2uh9ATf7M6FqKkpViqOOVcWrZDtd5ge7vGW+ev8Z2/+8FP3T8/b7wrKqadUDWtXy39JL3vnWpJkxSMjIEQh6kYfpyKUxm6htNpkOhpv2BkfBJZkhipDCBqBtOTIzz/wkv4nsvv/PZvAPCdF7/H+uY2f/fi9zh89AiGkSERBKIoQZRDRBF2T41z8WraIFVFnQQ/Jf1yV2BOEcX+ARkEAYog0m418YZH6DoxQSyAbJLEIZXhsV5iTchkSkRhQhRHREHA/NIS09MzbG9tsWtyjBs3brJnz14OHTpMrVrjA488xosvfQWAIXWy/90s3Vlk1/QUcRRhaDK/+6n/nHPnLqOKKj4yE9N7uH1rAVmXU4Z75BIkKvSWc4p6N1HsVIeqIVPCTN2AXR9ZFtE0hQcfehBFEmk0qhw+cgDX9QmDAEEU2LdnDwuLiwRRhCZBLMhEmRDFimk0avhBgphJUOTUXglR+XuroZ2KaYe8e++UbidJ7dz2zgpsILb6nnP3JidJlBgZHuT69RvIgogfBmQ0HXVwCFmS6HS3URSZ3dOTlEtZ5leqJILP3l1j/crIdlxmd+3j5so6b16+TMGUUhxTrBIqMe1OCxSVfK6AG3Zpd1p4kUdW1TAUnWazyvjkrj5eqFHr9tUJVDmtNA0jQ6enY2V1HPKlMkHXwnVdxsfHyWQyNJstBsslNE0jjGNazSaaovQtzQkTPBwkR8B34Nd+/Vf46tdeQDVyDI+OkgipYkEYxtQaHYQ4IBEVavU6ZibDxWuXcT2f2HMQNQOVhDAK6dpu6sCiqSRhQLvdRpZEMubbe4D/kHhXJCZBEFE1jamxEeZuL6bGkdksSRRy/L5jDI5NMjubNoDn5m4zPTnCwq3bbK0ucvrUScIw5OatBeyuReDZKcAsjPouD2EsYBoqL3/nBXQtR7fb5XN/8Zf8T//DP+L7SciHnniUc+cvoSQeSRwiiDsnZ4Isi8iKnkpztB3OvHWVqZkClVKlL5EC0PU8TE2j3mzguhH1WoOByhDNZpv1tQ3WNjbx/TQZnTh1Cq/rY+Zy2HYDGEVVZaIwwciqXLtyheJAhcHBErumd9PudMhkdLTxEba3a0yNH2Z17TZnL57hoZMP07HS8fBbb53n2NHDgA64nDhxhHPnUmuoJPRpWS0mK0Mgx2iyzIHdh7i+cJUkDFFVlUazi5lR+oqLWS1H3WmQVTVQVKLARdVN1tfWGBmpUCrl8IN0epPJpKL2v/LcL/Mv//CzAIhJiKuDZItAzOb2NllNYycNpVy3nib7O6Rx7413ypfcCxkQJC/VXuqZV+5w6vrHluS97TbZ0wk1l64a8e/++M/4vX/6j6lvrfDm2bfoWB1USUE2JAzNRFMVtp0qA4ND6IbBuQvXEUSbqbHJHvnYZ7tRpWQqKJLJwf37uXPnFksbtzHVImFiIwfgySpdK2R8uIDadBBQ+6RnSJdK+D43b86RMQx2TU+xtb6O0rNxCqOYuNe6MM0M+2Z3s7m51R+CVCpDuK6H021hGGllL8kCoCKbKtVWDUNWCEIfM5dKBT37kafZ2qry+ssvUW+0+pplXdtFU1ONeEORyOdNZvfsQZUSwjDE90MatS0anTayGyEnMX63zdBQhcF8kbHxKW7ML//8J/9PCenTn/70L/wkv2j86Wc/++l//k/+MdO7Rjiwd4qjB/czNljkodPHOHvmVeZv3uDcmTdYunmT+uYqfqfJ7qkxDF2krErEoki3XWXvnhmmp6YhCpCEhIQI3/cRRZG/ef67dPwYp28WIFGr1VBzA1y8cIHH3/9edFnsOYSkAuyaqpDNmnQti9WNRQ4cvI8T953C6dpo6s6VVySOY1RZJohjfC9EFnWKhSz5nEmiKEyMjTI2OsLE5BSNRo04Fmm3WmRNnUajxa2FWzz6vg+wUd1ma3Mbz/cpFLI4jk95YIBmvUoQBBiGgWHotNsdDhw4TL22yPLKBuVSiY3NbTa3ltA1lUI+R4yIJKtMjA+xurLKxfOXePD+07h+RD6j4/gudxaXUAWNIIgZLJVpdTpohoaQKGiKzHY9pfhoRpYojlhfXWdqagpZVlBUHddxCcOQq1cuMTE5SRhGZEuD/OD7L4EoklFE5BAEUUKQJJyuhSAp+ElE4CRIyt1EtLOdRBqCeLcHpRoiUZigJBrNrS2SKGaiUmB8aBSr2SESQTVCfCdGdgNkRSXxY4RIuHvZTWRCL8TIS8RySOBkkJQAKUh49ZXXePKDv4RITKvVIp/N4vo+umHSbFdTRr4QEkU+U5NDTI5MksQCiqyi9pZBXpja0j/66PtYmJ8jCEKEntuupIiYmoQigyhoNNsBDz70MEvLaxw4uA9VVpAkGVXVEEi4fPEi9XoLTZNSlVBJwDAy2Hbq1KzJMreXVhgcLJMxjNR2KYiYn58n8H3yxQFuXb9GNpslTgLiJEqljDtdoiQmiUIunj/H1ctXuHT+PKWcjms3yBoSuibSrm2RyYhIeDjdOhlZ4vVXXmb/bCpFHEcuI8MVxkeGmJmaQFcFjhw7yvBAieGhCreX1mh3Wnz/+y+tf/rTn/73/9Cc8O6omEhQFZGO5TE2MowfxAwUs9QaDX7z48+97b6SquFHHvWGRRDExEYWv9NioDKEquoQBQSJiCLE6EpCN4KvvfgD2rYHeGQMA9txMPQhltdr1Btpz8Oxmvi+i2IrWN0Wd157nWc++D46bR9FM3jwgQ/wp3/x5xzZf4RY8AlC8W0VE4DlpCfUDgcqjMGQRDTDJPB9Wq0WJ04c55XXf8RgsYjjuDz2/vfyt19/gWvXb5LNZGg1N0kCka2NTRAUXvnhKxw8eJAEenwll0KxiO/ZDOaHWfe28b0uhZxBzpwgjuDylWscO3YMy7KINJnRkTEauxuEQUK5hy6dyA2y4s+ztrbN2PQA7W7qhZYaB4DvCwwNDLNV28QLfQLXZ7WzmjY/nQ5GxsTImFgdi9l9B3FdH1HS6NQ3gZR4HcQKihjjayGSLeIGIeWhEazaxk+tjnYqm50q5y4WyeN//Gf/HYReCg6UVb72/HdYWKniOx5GIiMqMDxcplQqUCyWOfvWeRzHoRu4ICoktoSQCVCMdGkUxCKSJPI//+s/4L///X9CGEdcvXoVVVZwXQ9DMwljD1nU8P12Ou537/bAZFFD1jQMzaTR7qbmEl4KLYC7S+KModNodgmTgA/90gfZqjY4deo+/EBAVUBIfG4vrrG9tY6ZzRE7Tn/qJUYJK2urTIwN02hb1Op1BEVla2szhRlkMjSbdaan0mV9s1nHyOWYv7NOEFrkclnyhQqmqae2Wa5LxswiEYMCG9t1CvksEQKtVpNsoYzl+NQa60yNjeHHHd773gfZ2NxisFJBFgVe+eEPWFvbplQuYOYybGxuISkKnttl4XYVL/jJ2mf/KfGuaH4nPXvh3dOpRbXvu1y5fJlczzVlB0caeR5d12Frc5Nf+/VPcPqBB+h0WqiSRKlQ4PbtBQAUIaYyXEGUDQxVIfK6ELiEvp/imwYGUmRrFJD4Xd53+mhKjO0Z9U3PzFCttfA9ry+oVatuUcqZrC4fYBwAACAASURBVK7dRkxUrlxZ5MbtNaLEIIpkBFTcjoPrpsnJ1BTiOMDxfN740Rt4XsrnA3jogQeJooBsrohluzz++PuZm5tnc30TTc6SKxRwHJvFhZtkMlkuXrlCeaBMEgfoeoZd07vJZvPM7DvBkSMnuX1nkU6nTiZz16l2bu4GoiTRslKVhPc9+h5mZvciy6lcSRL6qeyvkiAmKgLq2wwZJFlIl8YRfS6t3gO/lgdS0rQiib0rukYUJQS+i5ExU5um3vfm6qBYMYoYE8Uh3juoFz8tfhICXJPT4yR1yvWZHh+lVdvCrrZ45KEH+OR/9kkO7ptmcmIU01B55pee4MR9h8nJESoRQiZ4Ww8qyqRJMxZk/u2/+UO6ls2hQ4c4depUKo9j5DH1dMk1PpzSdmzHxdSzyKLWp+CEsYckhWQyeip10iP77kgJr1Y75HMFspkszVabzc0tVEUjZ6RTTknRWZifZ3Njgyjy0XogojAKyeVMDN2g0aNb7TmwD4CDBw9SHMgRBncNCXRDZ7gySOAG6BmNykCF4ZExVCkVV4x9NzU9aLdp2wEdW8ANwn4isSwLx/dZXN1moDSKG3ZZaXdZWVtBkFW6VpfqVpUjx06QL2cYqhSRpLDfy+p6ARPjg5w6efTn2sc/K94diSmJWNvYZP+hI9y4eYtDhw9x+MgRiAK2Oh5ip43leiS+R+gHDAwO8/orr3HxwnkA9h7YT6dVx3N6jqWCSqNeRRdFRifGee6jH+FTv/Ycn/zVj+N6fmohLQn80tNPMDUxkRoxhiH7ZmfJZTMU8zqf+NjT/fcXRwH5XI5s3sB1I9ZqW3Q9kXoz5PUzV7kyt8zicp1WO6Bea3DzxjKXr6S0lWy2yPDQCNmcSc7UkSQJ1+nS6dg4PR+3oOdWoqgq2UIWYh9VBTTY2lqnUh5kfW0NtadTHgUuIyNDtFtVspkMhVIBx2/w+o9e7/cdOu0Ob549S72nuACQzWiUy+nYOxFSbagnHn2CyZHx9PVlGcvq0mpaSIrS0yuPQPQRRJt8wSCfS5OfIolsblWJogTH8fr62o7dZdf0TF+cXnZtYlEmiEXsHWUZN/ipU7mfFD8J15TL5zl++hS/+zuf4nd/51OMjQ4Qhy7Li3dYWV5geWmBem2DXC7DfcePo0jQrf9kvWovEojiiJd+8H0URaVULnLf8eO0LQtBMshminiBiKGZ5M0CzU4T379bFTRb7XS51G6zXt8iwe3/L4pkBvJlXnnjKi/98E0c2+X+0yno1o/TyurzX/gio+MTJEjIkogkBOiGRqFQotnsYpo6pm6iyiqFbJ4HTh3H80I0UUPRTGQJjIyOLIpEocfg8BCHDh1FEGU2N2v4fkIUpZV8xszg+C06lt3f5wBe0GKgWGB9LcXZxUIqkPfRD/wSx08+jK7qxFFArlTGMDI88fgH6XRDAhfCKKbTbKAkCRvNDa4vXP659+1Pi3dFj+mP/ujfffrXnvkQS6urFLJZ6i0HQ0zwBY28EHBtZZ3B4VFizcC2barbmyiKStk0MVQFq9lkYXmNza16ryoQiRIFQ1d57MknuXLlEkkcE6shnZaL67rsmRzltVdf5cihA2iKSLW6ie10Cf2AQk7H6zbTfkUS8/y3vsne3Xvw7S6DA0PYVsjg0ChZQ2dkYheVygjHT57i6LH7ePKpp3j4kYe5fvUyuXyOfbN7GKhUerB+H9dxuXHjJlPT00CK4YqjkFZ9G93Q6XZs8sUSUSxTr7Zxug6SKqJlCqyvLqWNWCU9qMeGB7Bdn0wux2BphG63wfr6Jn7ooWkmVy7fYGRkGEkSQRBxXZfKQImh4XFK5SJB4CMgYHsh+UKRSrlCo9mlVW9jZjJESYAb+MQJeGFMIoocOXiYMIp57bXX2bt3GlWVieM0Wbqui27kOHj0MGfeOIMsKwhZFS+M8J0Oo2NTeK5L23JA0ZHk+Md6TffGTn9pJ06fOE0mk6HVbGK7PtXNDQqmgq4IXDx/JrXA3l5PLeddm62tDY4cPoSZLeA4DtVGG0kRiMK3v6YSq/h6iC8KXL+0wFvnLrBv315URWVkeJi19UUKhSKCqFAqlVElgygOsOwOcRwzVK5gZotcunKWwcIASRzSsRoEMYRxgJgI6FqGj33kGcbGx9E0lSiO0KQEVVWZn7+NZbm4to2Zy9NotogiGUlVEEVAlEiiBM1Ira5a7U5qfpnPsbG9ga6biIJAs91BkRXu3Fmi02qj6BkmJydotzpEcYAoaZimhtX2sK0uY2MVcqaEICYkQUISx3R9C01QKRRMHjjxEIkgEUYwd+sW5fIgrbaFmc0iijJT03s4d/Earu8wMbmbtt0ho2dYW29y4dxbv1CP6V1RMYkCOKLC5UuXWV5dwwhtrJ4MnpPA+soSzWaLa5evYKgKuycmCe+5YkmqxgP3n+Q3f/MTyKKIRITc8+tpNhpk9AyCKKOFCmMTE+TzeWZn9zBQLjFUKZHNZZmammLh1hKDQyO8/MqrtG2fr3z9qzR6PahXXn+VRx97Ci8SGB4dR1NlsmaqXW0oEoFj41hNrI7F9tYWQU9Sy2q3U8a27TK/sIDd7qCpGpbVTPVzPJeOZSNIMpKkYnkJZ966zNytRWqt9DOsrayRxF0yeoY78/MkgkoSB3hhQrlcRhM1DCNLoTiMrktYloUkwvBYAd93Wd/YxDRz/arGDzx0PcPx4yexHZdcNoMsyzQaLRr1BmOTg0iygBdGZLNZPNtHkzSsbofz598C4MGHHqTbw7gkcZjivnoToeGc1gd4SraIkaQOvYVins3qFpIipeP/TvizVSjfUSn90Z/+KX/wb/+Y//D5LxOEqdaW5QQomoEXhGxVlxFQiYOQQBAQULl69SoAhXwWVRQRPOHH0Oah1sOg2QpRFNMRA/7my1/nztIqlZFxhkZ2USqVcF2PjtUlk8+jG3kkQcW2QlY313jvo++l63ms17doduooqklWy1EpTdJ2Hd7z8MPUa/W+yYAfCEiKzhf/6kvMzB7kyuXLXLm5wPkLV1HVDC3bZWllk+X1Glsb22zUGgiKzlbTYr3WwnE9yoODlHJ5KpUhNE0jZ6Z+h5KU0kqS0Gd1eZHpmUnanS7tdhvf8QixUZQQRUk/fxyEIKpEiUI5V6FYMCiYBRJRRpZVzp2/RKNeRxAVDDNHvdbE9gVcz6cyNIprJ1S3a3Q7Poqcxbe7/KLxrqCk7Nm9J/k3/+pf4bseqq7tQP6wIglZCPjYJz7BX/+fX6CQT0edrXabrH4XZJcv5PF6zgyhHxH5HpGiIkkygR9w6coV9u07CEC13uLi1euUS0W0JODBRx4k8F1efuUMhw4eYH1jgz3Tk1y7co4wiMjlDMZGB3uvM0i7VaVj2ag7HDtBw+p0UdUEJA1VipHlDLIiIwsih48doev4BF2b81euoKgqVsdGUhTGhipUm03WVjdpNBvUW+kOPXLoAGury1hdm9ndM8SRT5RAvphjcnIvnU4rlUYNXERJQhAVfLfL3NwNZF3nxrXXcdxUMP7wwYd77rnp2/V8j2PHjqGoKpos4HghURRx8eJFVpeWKZdzhJGPpsj4voDndbGigMD1iYIIU9f52K/+Jp4Xks2oNJptkjhE01LWuShpbKyv8bnPf4EgkchnDIzeBPPDH36Gv/zSl8nlB/DcLqIh/9h4/954J6Uk7gE+n/vlD6EJEeXhIRQxwQtCzp97GVnUqNVaWG4XzciSBD66YXD06CkSQebLX/7bvlGmaoh4Vs85Odu7yPkmSegjZHpYIk9GUwTyZpaP/8rHuX7lPIqqUu/pOcmKzHZ1k4mxCVqtGrVOAz8IKJomWy2H+RuLzO7bjyzJnDxxnEplAEW9S/vYWF0iEWSuXZtD1DQG8yZBGKcee4Asy2QLRQK7jaIqGJks1WYKYl1bXSIOYvJ5E13TKJUKuHbqcjw3dytVcPXTcztOUnrJ7j1T5HImi2u3GcqnLISFO8t4fkIsKLhBgBSFSLLErl27EBQdSZFZvn2bRrvJvn0He3y/AFUS6DiphLUi9ooI28F2XXzP44t/8Sf/36ekCAKogKprSKpG5KcHalaKEM0iL37j+bclop0EtXO/wHGwQtBlETeMU9uZKMEwZKyOxWPvfx9r69vcf/9JPvPZz/Fbn/wY3/zWixw5foTq1gZRFPeN/Tw/pDw4yIP3n+L7L7/OwECBPbv3Iis6YeCyvb1OpTLCwsotxgaGECQNWZKwPRc/CImChDCqkcsWKfUoKKKkcHX+NgClUoVSCb76ta/y7DPPYHUdDDPL/OIKYRSiShEz0yMYSoTnRziOTRinvQDH8rg9f4OZPfu5cX2O/QdmAVDFkEiSGBoaoV6v8sDJ9/PC97+DLGi8/NrLPProB3DaNhBxa+EGU1NTlMtlnIiUY5ckPPzwe/jC2l8QcU8FI4sQqLjNNpIiIWseYaCQz+VoxXW+/o3neewDTxJHHlbX48yZN3jsA08yNb2L2f2H0DSVg3tnqFa3uHDhEgB5M4dEhB1EREgohvdjyemnUVBUIeHQ0cO0m1UWbt1ifHyYg4cP88aPXkFTZELZQ9FBDVU8xyKrm4jIaLpOu+MiyiqCB4mW4DsxWnanGd5bOKhddhymfCcGIwRPptlq89Uvf4nHn3iMWrXK3tlZrly8hGmWeO65T/Cdbz/f7zn5TkiUMWjU19i7ew+GqpLL57h2/Qrjkx9KVRnwMDQZqzcpvnL5MuNjFeavdpBkEdvxqAyUUWSF81evMlAoMj5W4dbCPLvGRpmcmU05e6U8iiym+mWu199ziqySy6eDI8PMEYUehw9kyRZLuLZFLneEQr5IIshs1Fx0USEMQko9xYcwCAkFGZAJnNR4c3hwiK5lY2YzJGHA9durJFFImIj9fuJOhOHPRx/6WfGu6TE99aGPEgGyKOAnIrEgEQsSdreLWSgSCQJ+ENFoNWl0bG7cmOP28hrb9QYr203WVta4fP062Xye+blbrK9tIsoadxaXsW2bmakxFEXlsfc/zGf+tz/DCxNE4Nqlc9RbHfwg5tLlK3SdFjeu3WR8dJTTp08gCCKFQpGwxwty7JCBwQF2je1Cy+S4fmOBruMzd2uRtbUajVaH6ZndnDp5HEWQ6Ngut27d4nt/9xL5QoWbN29AHDG/tMbl6zdxLIf5O3d4/3seYH1tmUqxgCBGZHSZwPeQZB3f8VNTTiEhiESW17dZ29hmbXOLyfFx4jg1K0gQyBgGrh8zOz2N57aQVZH1zU0q5Qr1dge/65DJ5qjV6mQzGjEycSKAAHtnZnuI7hqCIBP6IV7s47keoiSiiAp61uTA/gN0LJvdM1MgyIRhahW0e2aKOI5oWx75jM7q/BwxIUePHuKB0/fRbLUxMwZrq6tomoYqCbhegpzEINPvN8VB1O8FCV7qvCvFAo9/4DE0KWFtdZl8IU9CzO7padbXllFVBc/38MIQUYgREhnfjxibmCR0LTRNYf72Cl7go8oGsRAReiqamYBvgpQmKUkRwDeR9J5hpWAQxgK+63D95hxHjx4BUWV4dIg7S6s0att0Om1UVUEgRpBFJASQJUI/5uCBg+QKRU7f/2BfnVIQJM69eZZ8Ps/m5jZB4OA6PoqeYWJiEkOVUXQDx7U4dPAIgeewd3aGUqHI0MgQnueRy5fwfRdBkBAlIE7Yqm7jOC71ah0/iFhZWSOIfFr1Gq1WC6vVwPVtWrUGpcEh/vYb32ZpfZux0RFuzN2i3XXwwph2vUoiisiyhGkaeI5DxtCJQh9VN4iiiK5tE0VRajahyMTx3cpWFEWuXf7Fekw/d8UkCIIEnAVWkyT5ZUEQZoAvAAPAm8BvJUniC4KgAf8BOAXUgE8mSXLnZz2347qsrCxTLJZRCmYqA+KHuLbF2XOXIAlotSzuO3GaF7/zAsVSjlKhgiyJFMwclcFBoiig6AySyxUYPFbCzOXY2qoxPj7O6uoqd27PMzhQYWJinLyZuqeMDRiUc8fYrm+zf99BAt+nUChy7foNtqpbqLrG4u1l7jtxAtt2WVy4xcjkBFevXuXipSuYmRJdu8H4RIpKn9g1iudFeH7MhUvXAXDdLsvLmxQLZZLQZ7AyzLXrVzg4OwNhguW4kGgsLS+we3I8tcKxAjY20pI9l5XZqjdpdbuE93jjybJC2xqj1Wyza2qKx99zAlWRkEQDM5tle2uLqZn7mIp9rs1doNlYxPd9jhw7QhCAoiQsLCyxtrrMkx98mjfPnuXYsWOMT05z7q2LjFRM/B4qW1IkBNEGMniOxbdfeJETp+4HBMDDNDVc1ycRNMDjsUcfYHFxheXlW2gqvPD882RLWWamduNadT71G6k33s1bd3jjjbNEqHSbbp+4HYQROBISPeiFkeEjH34ax7Y4d+YiB/bvpdlsU6s2+P5LPyARQrquTyz4iIlKRIgf+mhylrXVdeIo5LHHHiXwHEC4K7kreYAIarqE7vPs1Ls9kkDqppQWT0cMQr72jW8RhzHPPvsM01MTLM7fRtWykHgYeplmcxUl6zOQLyMVNW7eukW5PMCePdP4vXN3Y2ObbreDICpsbW3SdSNKpQEuXDyPJIlsb24xPTPJ3O0VSgPDbNdqNF/rUCzkGKVCu9OhUhkmkyn336ehSwwrY7x59gyKriBJAvl8jtALcd2Q0E9Q5KQPDfjGN75J23aZnJxgfmkdJJUkCrE7qcRus1pFVHQEScEolLl16xYj45MoPfmTjK5j91RlwzBE0XSKxXSFoPziXgQ/f49JEITfA04D+V5i+iLwpSRJviAIwp8AF5Ik+YwgCP81cCxJkv9SEIRfB55LkuSTP+u5JyZ2JU8/9WH2zKai+Zlshka9zfSuCcrlErdX1liYm+M9Dz+E73a4fv0WYxPjxKFPGEVkMgaSJOLYLooious6nXabWnWLiYkJnB6MwHZ8Aj/g9uIdHn3kITpdh9WVVZbW1tm/Z0/PXMBifGIcVRTIltMdf/bMWf7p7/8z3jx7llaziSpJrG+vIwkSrmelTeJMincJo4RcLn2cmckQBh4bGxssLa/ieSFB6LNv335u3rxBuVTkzsoaD526j9XVTVRVYX1zFdvr2eJ4Hh0fJgYNhiojzM7uZnxsgtvLi/iuz+VrCwwMD1MplzAUiYcfPk0UJSiqjiyG1BttZAk8p8Mbb32frJFjYXmLg7NHMDMmqq7heTErK4tkdIMnPvgUAN12lbfOnsH1XRTVoFbb6GuEC4oKATz1zMfS+1rtvniYkTH52pe/wuj4MI8/8UH+6vN/hh8G+KHfB2vumdrLwuIah48cYGxsF7fmF7l69RLbzbTn1G2mlalZ1JHdgIyh8fjj78cwdC6eexMAwzTY2tgkl8tjOx0UNUGQZTSlgBe0+kBXKUhQNRPbavP4k0/ylW98922qmUqi9bFNO0kpcDIohv222yClstixTVZMvwdTl3j2o8+gKTIXzqewFdtOT+qOZyEnGWwnwg0TfvVXn0PpYZNq1S0WFu7g+R43b8wTxRGKlsF2XEaHK1jdLqqU0O50GRgcIJvPYjXaZEyT5ZUVBivDJEnI6OgYOTOTwjpEkTCOiYKAs2+9haGpKLJKEPoEbsBWvcnQUImenhvFQolWN+HW4jJuCMViKTXtsLskUYggyQSeS8Y0GRnf1RdR7LYbmPkSSRRQb7RYW19PHYzEBNsN+i7aAN/60v/x/3yPSRCECeDDwL8Efk9IrXMfB36jd5c/Bz4NfAZ4trcN8NfAHwuCICQ/KwMmEUcPznBz4Q679+zi0ls/4tFHHmJlbY7Iq5CVIK8lyDh0nA55U8C16nQ6bSYmJqhV18jlMpCodDsuo0NlcqbByFCZbtehUh6g1WpTs+ts1SzGRsdoWha35xYYHh3jgeEhigMDdNptSoMVrHYTMhlu3biF49jkckU++yd/yt59e7mzlEpd2F2Lra0GkRihiipQxU8EdEUhkWoIvXW3GwS43S6ddpNCPgUo1rZXuP/UcY4cPUyxVKK2uc7IyDrNWo04domDEKNvpZPubMPI0Khu06huo/Qa79NjRU4/eD/Pf+cH6Nk89ne/z4c//FRPZlVicDC9glXDkPsO3o8gKcAlNmqrUIPRgSEk2eDOwk3Gxgf7ZNJiaQjfD9FVnSiMsTo+2RxIKLiexUh5CE2T8bywb9PuOB6ipPGR5z6G54W0WnUsO2B8YpyVxUWiMMHMDHLlxg101eDyxcucPXsGBI2Pf/w5FhaWsKw2165eZbAyTKlUYHpqElWVmbt5lShMUFU5/d432nRdl9kDB1lYvIqm5uh6AdWtVYYqZcCBwCeK0+8uk82zMD/HA6eO88PXzhA7IaIhp5WTs0MEDkjsFBSb2AoIXl/xEiAUJDJiBi9KgZqxlPDDV1/jmaefwg0ipqcmmLuZ6joposjU5BS376zy5PvelzrgxBKqorFVbTA8Osb1a1d7lBaZjK7R7TQplQpkCzmkOMTM5fF9n8sXrjExNU15cAhBUnE9l0ajw8y0BgrIooiiKtjtFlnTRJZEgjAkiUNUNYNS0Jk0MkiSQNdrktWytFpdLt9cwswVObB3gvk7S+kxljHRVJVms9E7xrssLcwxODxGIZchERX8IMQ0DAYG02PQ831UEWzXRVb1lPIV/+LI7593KfeHwO8Dud7fA0AzSfrSeSvAeG97HFgGSJIkFASh1bt/9ac9uaoq7N67m8roCEnoM/L+92IYGfabBp4fkEQO9913mE67je9ZOK7PaL7E+Mgw2/UaY6ODbFSbxFHIzbk55hZSBHjgByiqQhDIZHUBVUnlGsYnxtGCgEPH7iMMQ0aGKingTJDZ3qyTy2c5f/EmI8MVVD2DIqts1FpsvHYGgGrt7kdRdY04cQhDF9uLOTg7w6nT97NnzzRCT01yY3WJKPRp1KtoqoKqqnSsLqtLCyzOh3iOi2aklzPPTxgdHsO2uiALvYRUQ5Zk2paLKklMTFeobm6iqSqXzr3B+x46xcuvvEaYnaDR6FAq5YijiMD3UVSVwUqFm7UmeTPPoYMnmZu7SKPToNbaYPfUIY7ft6eP+haSANe1UTWdMLQRUJB7tXkYROiKgu36JHHAxvp6inY2MkRRSOC7xJGA43jEhoaZzbO6soogyVi+jWkUMfQc7U6XwVIOq2shKx7feuEFHjh9moGSyb7ZGSRRxLZtavUq1+evM5Av4zkegZ8673qhwEc/+gm+873vkVFzOE5Co27jxW7fIKDTSciZ6dhclkQ2atucmJjh6OH9XLhwCceTU9FAyesv6YRMgEYKG1ANEXwVCFA9GS/yCWUVestLxVVYX93G8UKO/1/cvWmQJOd55/fLfPM+6uyqvufGDAYYYAAQII4hCEoMkhJ1blBc7UqWZa0jpF3La8eG7HDoo8Neh/3R4QjboY09ZNlh7ergiocogSQIEiAIECCAweAcYO7pu7vOvE9/eKsLgCSvQobXQTm/TE9WdnVVZr5PPsf/uO88t65fwfd8ojTB0T32h1NW1tcQKqhCah3dvLkFQJYV3LhxC6EKLMvCbbQ5alts3t5ge2ePpcUetzY2uO++B1g7ItHyzz7zLEuLPbq9RZoNnzCKWHQXGI4DfN/BmQkpHuo3Kao2y5ik4aYhDNquK/XAm0vceXeDK9dvcv36NfJCIsfjKKTIEroLfba3t+U1r2F/Z5PtbZVjR49QFTmSJA6WIdCFQTQNEJQzc8z/d7a/tpRTFOWngc/Xdf2fKIryKeC/AP4j4Pm6rk/NjlkHvl7X9TlFUV4HfqKu69uz164AD9d1vf8X3vfXgV8HaLdaH/vVX/kVXMvi2PGjvPPee3i2TxBP8Wwfx9NZXlwmSVIsy8S2HZK8mOGTdK5dv0aZ52xt70nNGk1HM012traoVI0kDAiTFENTyIoaVRdUeYnrtwjimKIoMHWo8pLiEHYw6+d0GybHjx7jjjtPc/z4SfIsZn9niyzLMAxjbr9c5iUL/QW2Nm/QaC7QXegThAGe6/H6m2/jOSa65aAp8mJrqsY0mBJFU3y/g+c6VFXFrRvXsU2DUoXpJMSxDdrtNkGUohQ5bsND0Yy5tvd4EuDZNv31U3znme9zz92n0VSVldWluSohSMXBspLyKPYsp9/dusXm3hUMYXLy2J2cOH03ZVnynaefnEvNlkWNMGx2926gKQ5FWaAaFg/c/yCOI22ovWYbx1QpyxohFCzLYHNji063ywvPP0NdpmRJxscfe4JvP/U0eVFQlBXUKWFY4LoaC50FTNskiAKW+qvcuHYN0zAI0oA8UT+EtH7gwQd59vkfUlY5WZZgGBYN16HlSUOJg8GETrdLWaQIrUCfjdpOnjrJ6to6N25u8tLLrxFGCY7fmPecDnXBgXmm9Bc3vXTnP9dmgjLK+YnPPcFLL75Ms9NA1yU6fml5Cc9vYugCVchs6eJrF3Esh5df/qG0oHcdwigjjEJcv8W9d5/h+s2blJWCmF3fVqvN3t4B7XaTNC1QdZUySVhbW5XTZ6DRcJmMJNfz3XffQ9cMJmFAt91iPJlQlBV3n72DTrfHM88+x2CSEc7OpzVLTcq8RGgFZaGRz/TwD0u8yUx7fGlpCcd10dWa8TSi13IlDabd5DvPPU93cQ3LttE1jf/6t3/z33spdwH4WUVRPo8MlQ3gfwRaiqJos6xpDdiYHb8BrAO3FUXRgCayCf6hra7r3wF+B+DkiRP15z79GcospSBHM86TJCHWSHKwqlLhuRcuoislC/0VvIaH77u8/MPXMYQgqVXiOCZPYhhPyYtsXmNHM+U9TWhkRU2SxpDO+jeTEUfXV2m3etx19ylWVtbQ1RrbMtjd2eGlV17m2PoyaZZzsHOL3e0rtPwWuuayN9xA1zVcy2MaRHRaPcIwpNNcxnQNDE3Fsm0UIeh12xS1xHkUWUKJRhTFHD92nDQrKMsKw9CkDVV/gdaMaFsXUlFgEsbMDHRRNJ3tzS06oXhpeQAAIABJREFUfYmt8lyXSpnRCmY8tMNSr8hzmMmlHkprrK4fIcsyJqMh/eV1+ouLvPXuRVbWj5LEAZbt4bo+WZwhNIVWq0FRlnz+p77IC88/QzAOGAdDgiCk25XecqamoAqTOJ5gzxxthsMxp06d+NA1f+qZb/KFL/4S3/qzr3B7U2YPhmEwnU5oeF2CYICiauxu71BkNdNwSpLXjEYDVF2QJSlFkXPrq19FmB5K+T7EYD9LKFKLaVySKwJ1PEVXaizHJMlSLEfhYG8XIXTKsmRtZYnbm9skSUiJwO0I6kgnVyR8wczcDzXBtdQiL0ty5/19dmKhufL7PvzoI7z+5hsk8ZTVlTV6M26dRMRn/PCHF1ldXeGlF34gA8c0pNVwGY6GnLrjTt5++y3yM6eIg5jltRXGk4Bb168TJSWD3W0M02RrexfHEBw/eYI4jlhdP8JgMJ7dGAU72zszS3IDTcisU1U07r//LJubt7l1c4s0rymrnLosEEpBUljUZYFtasRpjVBy1DqnrDWGB3sYtsdCy2d/NCWKQizTINd0yiKnLnL+7s9/gW88+Wf8nZ/+Sb774iU63S6O/tFx239tYKrr+reB3wY4zJjquv5lRVH+APgF5GTuV4E/mf3Kl2f///7s9af+nf0lYBqEfPnrT1KUNeUsHcyLjFo1yWfyoUkak6cpL7xxg65v0mzY0sa553FydZm11VWoc/b39ti4fQvH01hZPsmrl17msYce4b1rb3GwH80Wm83u/pjlXo8kj8iyjHi6z5V3ZKm1sbOJ6zi4nk4UlriNBjCh15P+XXEYc2TtFBsbN1leOYo1HLKxscNjjz5ENiOv2q4Les1gf5vV9SMkSUS33WISROR5hmU5xEmGZelkaYTQDIRh0V9ZkxdGN1Ap0QwTp7lAFI6xLAfTduksLM0BkmlRY2oKWaVhWg5H1uWCEEIAOclM5oW8nMmkWriuSbvto6g6wTTgx1bW524XnueRJiGaXkFtcXt3m/HumGmU4/tNghm/75nvPsl9//ifzN008iyZk3mzXGFldZXxeIxt+0RBiuN6DHd3+POv/Qkfe+B+4iJlPByT52BZDbZ2pCpBUdbEWUVZ5/MMtihytFKn6bqUQsNQarIqQ1QuumUzGo04fWKNrFS4tvkujuuylyb4rsVBENButNALwWg8pdlJGR4ccM8993LPPef42tf+FN3USUYleiuHWE7rsjLFQI7M1VAhMxMU+FDfqSwLJkOpNvmtp77BJx7/DEUacPT4oc/bDGISpyx02kxGI3b3Dmg0fLIsY2tnwEK3TZImrCwucu3KFc6dP8f3v/d9fM/nzrvu5OLFS6yvr+A6Fppasr0z4LELjwCwu3uAaWpMJiFomtTtshySNOO+8/cRxxFvXLzIlcuXMDSd3b0J06yYy+mWtUZRVBIjl84wSUj4h6bN8EhxQJ5pc439oixxVGlT/4OXL/L5n/wcWxu7tJpt1DIniBKc5kc3vPwoAMv/Cvh9RVH+W+AV4J/P9v9z4PcURXkPGAB/7697o7IoKMqa8+fvQRPQavk0Gy3GkxGX336dbruJ4zQpiog4DYmCguEoJI4iFtsmWTTgxR9c4/ixE3Tbrbn54s72LY60l9jYvMlS/whH1t4vvc6eac9NNA/tnTQhMHSVkydOsXcwwnVdiqrG0jWKsj8/xnVdNNOi2WjQ7fXoLvQ5ceIEqtCJsgnDwQTXdbENlV63A9S47RZhnGHpAt1oIoTAtQ2ysiHxILPMzmk0UIVMpQ97RAC+6MggXaQYlkdd5fMbLEUwmpkclJW01SnLEl0DaqmFnsQxpWahG8xNBFSgrgrKSmZYZVlKhnlc41gmQoNOs0UwDNjZvM3ANzi6uIqu6Hi2j6apGLqYu7h6roPnu2Q588zp7nPnefH5bwHQbTWplIRnn32Gv/uLv8i//qMvE4wDVE1lFE4pC404/oCsiNAwLJMiyPEaTQ4G+/h+m97aMqPxhPXlLpu7QyxHY5oNydOChXYL17FI8pzhOMDWFHZ2d/AaTRquw8svv8ZCt80PX3yex5/4FJ/+9Kd55dVXIYgIBwmK4c6Bl3WkQ6MiU0zgL4AGM5eyjPm1f/CrfP1rf8pnP/uT7OwccOz48fkhQrfY291le/M2o9GYH166hKEaWI6FmGjopoPXnEn/Ap1ejyef/BZn7jiJYWgkUcSdd57lYDDg4sVLlEVJo93h9sYWjqXTbLWo0SjLFHKI04zz955jfzDkynuXEULB9S1azTZRnM2gAgqebZNVlVSJLTPQrPdBkZoGwiDOMmxDhofDSd1kMkUVOrhyiKMIja3bN6jqHNuxaLUaGKLm8U88+tct+b92+xsFprqunwaenv18Ffj4X3FMAnzxb/K+hilYX2lw/drbuJbFaN/A9x1UreLY+vLsqBzNbtDrScsabxZMsjzH0HWOH5NHKZrOqTPn0HUDXakZDAKyMsWxHUyhoeiCnZ0d2u02ttcgTTKqssCyTDTDJEkiiqzANDws23k/aM2eGGGc0fA94nAK2IzHU1zX5dKlN7jn/gfxGx2abUnaLYpibpBQFBWmrpHmBaZegxAUCKIoxPNddMNgOpYLvCpL4qTAtjTSOKQsC4TQyDKpIOhr5ofcLPIso93tk5W1lAQW1uxcmFRlSpZL++ksy6irglzV0A0LU9fwfJdgGs69wcqy5Ny99+I5Fs8//wOEmPXQKNFTyIocv+ESBCGvvvRDVo+dZGNjk3vvOYsQgjzL+NpXvsYv/we/RBAE9JqeNGdQKlzXl1QUv+Jf/u7v0m52SS055jZUg0E8Ia8FS4uLpElIr92auZH4JHmO77fJs4RpEDAdDxh5NSeOLrE1VciCkMHehIX+GqPBPllRY2sKcVGjaSbDcSBLNl0nTjO67RZPfeubPProIzzy0ANsbG1z6Y13yIuMLK7nUAIl/XBJd5gt1UVGmCS4rsmnfuwTRHFBXlTYloai6tSVzFbH4zFRkksOnGbQbHoc7B/g+TaqZrB5+yZn77qLvd0tRpcvY+o6luOQFSlREnPr1hU+9rH7OXXyBC+/9BKObeE6DiurqwTTEQoFQgBC5+6zd/DOO5dxLAPb9RgPdtA0iQmcTFPirCKragxTSAlrVaEQBnlRzbmNf3FThEYUSxhOnmfs7Q+wzCU0TT48h2P5ILFti4WWy4VPffpvsvT/b7cfCa7c2TvP1P/Df/dPcWwH6gLNlIukKiEIJjiWAYqGKjSyfObjnkZ0Zk1hx3GYTkZ0FxY42N+n1W5RlyXNpo+qeySz3ovQLVqeRhQlLC6vMR1/eFA4S1qIYkmezbMM25BA/6KQut1VmUvht3DEYDyh1+0QJ9n8bx1aGxm6SZUHFOho5BwMRzTbvTk/DeS0JpiGJElEsymF4A/5b4d//9rVGwDsH+whVBgcDOl027iuPxfGMyyTrBT88deepN/vYekGeVWSZOUc7FYpUrQtjmIM0+DI+hEeOH833bY///71B8a8+3t7DCYjPMvDMDR2d7fZ3tggLlJcUyeOa6qy4NOf+0lGozGagN7iClkSzr/H7t6AyWjEdCQhFmWu0llos7Gzx6XXXuf4qWN4jsflyzfYHYxZWl5ne+sWnWaDSRCx0F9mf3fWi9IUFhd70r1GlcHSczzeu34Fz5EPD90ySIKKOBtTVx5JGCA0gaoZjEYj4qLGdj06MzzPkfVVwnyCkmt85rOf5WD/gD/75nfIS0lbMWwViwaFFqKkFlpdkls5RVGSxRWP3/8g/V6b0Tjke89+l1//h78xP4fBNOSdy++xtbWJ1/C4evkanm9z7NhRLr9zBVVTefDBh9nY2Jyfc9PUGAwO6PcX5ffzGuwN9tnZuCWlZUyHM6dPymN1lYX+ElVZECcpWxvyHKdJQpalCCUnTjI026RIBFdubc2b2rr2YQRkXqlzvlt+OFlTdfJDyld+6PgCCAPH0nG9BjeuvsfHHziPaWgMB3vcf/95jhw5TrvV4Ny9d//t58rVSMiAZZk0mwvs7Y9o+jaq0HFchyJL6XS7BFGK55iURUaBLD86Xdln8ZtNTE2h8QEt5VoxyMsKy3LmGU+aM88O0hxMXf5bFBX27AZX0gJdqHO1S0M3MXQgT3FsyUHCttneO8Br9bDzBKFbFEVFUVRyVK9W1OikccjBeCCzszjka09+kycuPMbucESSJVQlqAK2t7ZloIE5ILTXl+Xj65deZW2pj2laHFlfIklSHNsiyRIcy0HoBlk4Ig1H7O6+z1WSvQJ5iT/4RHT8Jre3dwkD2R/54s/9BEGUUZUZlmWgqDoLvR4rK2tcv36d3d1tTMvn5B13AXDl3TflEAFZNrq2gapZc03wupLC9DeuvYvr+miaQxSNcRx57vIsptNtY2qCG9dvYls2R1dssixifW2dyXBAt9tlMtjm2NF10qxgMhygCRVFzbh9c4O1I6vs7Axo+itE0z1Mx8CoAU+FqUWeV+i+Q6VoDEdTirJA00zGgwPUlothWIT5hCKGabzHc99/ngfuv5du02MUxBT1rEeXlUCJZsrmehEJ6iKjSkIa/SWoU65du8YDH/sYdZXPISKvXXqDtbV1NBXeffcqjYZPlee0Wj6nz5ykKOHJP3+STzz+CV78wfM89PFH+Pa3n+Lhhz9OkSdomkYch1x5+zKf+OQneemlFzh18hiWaaKoGq5tkMYxqlAo85zBvjTLsGwT0xCAoN9qUys212/v/aWglFeqxBupOlQ5UfY+pSQvQRcpURyjGzaObTGehjD73fE0RDMsFKFx+tQJdnc2aB47znA44eQpa76OPsr2I5ExnTt3rv6jP/hDytkC/6u2wywDmB93aD1zGFzitJjvi9OChu+TZ/LkqrVcSJViz/flWUxRVPOg9cGf47RgOh6RJBF7+wdEYYBlebi2ju04HAz2cZ0muqGjCvk3LVPjYDYlcV2XJJxgGyalKubOpnt7+6yuLLGxuc3qyhLjsTwmzlLGkzH7ewNMy4K6YDqVJcQkGqHkOq2OR42g0bAYDQMcz0IoAt+1ub2xTRJHrKyvIDSbooTpeEgYTNCERqfbZnAwJCvAciz6/UVeufgGmmHjN3xM0+DxC4/OM6jpeES37ZFV2jwLyLOMg8GY/d1thK7TaTVxvQbaTEJWqFArJp5jcPG1i3zsgfvZ3NzkpRe/hyZUfK9FMA05GO/iNWwcw+CNt2RgsmwPVdEYjoYUZc1Sv4NuZ2SFSsOymSQxw8GQxuIiSRLiCp0bV3ZYXFrh2JElbm1c+9D9YpoN9nb2qBSdSTilyktaLWnEeXN7n0azyXKnwXC8h+cKTh49g23peH6H5557jkTX5uXcoeoASCBhFIb8g//wl1DrnK98+Sv86q/9xxh6jaLq6ELl4msXEYpAE/D0d74vyzZFQwj44hd+mms3t3n9zXc5euQIdVVw7fo1XLeJ677f67n63hVO33mG0WjI/v6+LAM7XY4eOcLW7jYLrRbdhS5pHDOdjrn8jqRAdRb6KErBeDDh6PGjpDlcvrXPu1dvfogqkucZugp5BbpuvO9qDaAacm1kGbbXZLS/hdtoUcz6s91efx7gDEp6HZ8zp08TJTkXHnuQNC145JH7//ZnTMwML4VufSg4xWlBy9MI44oyT7Ash7ys5qVOUVTkZcXh12j4Pmodk+ZSYXE42GMwGAAS1BZMA/KipK5yhK5jGBYlObYuHT40VeX6lXe48+5zJElOWaRYjkez0USoNbbtYigKb7/xOnecOQ3UOI7FeDQljQImQicvSoo85dbNG5R5jqbWFJWCaWpEUYSp6xwMdimznNHwgDKXnyVLE2o0siylKEssXcrW1giSqKa/4M5kLArKUtDt9RgN9ml5LtdvyHJvcanH9vaAjQ2J5H3ooYcIgwk3b99kNBmhqpbEeQmVg90t7jx7B2+/9S63t0LuPXeOL//5t1nutTh78hhHjx2Zc7uSJJt7jS0tGXQ7TXTD4LnnfsBjj32cPMsQQjCeRLieSV5WnDl9B1Ec02g0aHeWeO+dd7CPq3ieidBl5hRGEcdPLgHw8IOP8+KLL3DtxoCTZ45iaCpVlWFoxjwoud0mIgpo2xqD8ZSjJxcp4pJgMmI0GGM3LbpOlyANqLIE01Aoipp2o0VdJLhOTVoYGEpOHAbsVAmGruA5LkU+pTJ82u0m/YU+N8eDOTJcoSbLSqIoIg6n/Oav/xpqnVOVBa7nUpUpiimzhJu3bjOdTFhe7PLU09/HckzG4ynNps/SQo/X3nibOE5JwjGD/W36/UWOHVnj+s3b5HlMv7/MOJhw/wP3y8zIcciyjE6ny8s/fJWjR46w3F8izxK2t/doNz0Mw8I0DMqyYrC/S6vdw/V80hy+/9KrWN6Mw2bMFDqqHN2YKZHygSyqMNE1Ics5+301D2f92F9astIuzaBCTjujKCCdJQP4f+nwv/H2I5Ex3X33ufp/+1f/imvXb7HY77Gzu0ccTlEF+L58ylVljevZxHGCoWskSU7xAUZzEic0Gi5VWWPOHHJd25AcoDiRzV4NsrwgjiJub2xhmAYbtzdkj2oaYZsqqtDwG01UoTIeDknSDMdx0ITAcXXiMKYoK5JY1t+m/f5odDwYc+zUMXa392i1XMIoxbQs4jCQBOGGJxUnLYM0z3EcZ+4jluQVRZZwMBygqhatphyhC1Wj0fCxTANmXvaWY7G7vUd7lt3kSc7K+grTyZiDgSzP4uSw1NIQmopQxXyfbdlYlsVwNJRmiO0O/f4y33j6aWzLRzMMbMdmabGHpSt86pOPk6YFmlrM+2Ke784D0uFU0XGs+c+Hrx9uLz3/NGWdEUYRC+0eb1y7xmJLlt15XXPuzIN89wffodvscjA+wNJLWs0uaRqizPhpCtZctnaYlmTjgKX+Irqic/6++/nei89i1DAYBhKtrpiEQYhqFOimRp4WDPYmrK6uU7sm+7e3OLIkA2NeFDz88YfRDZ2ihK//+TephUGUlUyG+9x//h62tnf52Z/6HGqd4/uNOSexLEsMy2VvZ5M4Trh6+W2CMCIrKvIkB01BVTR+5mc+y40bm2RZwmQ6JStLDM0kS2PSJKHdaWMaJps7BxxZW2FjcwNNFQjL4tbVa2RZziOPPTa/txWhS1uvGW6orCoG+/s4fovJaMRLr10mKmrKIicv31/n1Ux++a9Camua+HcjuKscQ1XJEBjIHmaS55w8ssy9997HK29fB/j/BGD5733LspTt7V0MXXCwf4BtmTQbLsFkQplnxFmOYzlkmZxOAViWzt7ePoZhMQ3G7O9JDKc2y6Zsr0FVZKSJvJEXlxaZTKfk+WzBKoLpOMH1G5RZgmZq6IYgz3KmkzFxHGHbDpZpcDAc0W54aGpNmsmLtri8SDCNiLOcMivJixzTdrh48XWWFxfZ3hkymUzxbAvTdvAbbYbhFA2VnZ0hrY5clELXZWYlBJXQOHXHGUk0zQtsy8YQAoT0BJsOR1i6SzTO6fe66IbG5u5QZk69LlVZsLa+QjgZMQ0jNKHQ73XJi4zhzCUXoKxKkiShv9Dn6o3r3Lh5gwsXfD527i6E4/PqqxfJKijLbYQQ/Os/+iq+rXP0yBHOP/gQvqozOBjgeg2KSrYeFLWmqDQMQ6AbBlmuoADjSchgvI9hetzcehvdcHn8xz7Pw49MeOZ7z/HAAw/i2gbf/O7zxOOE1JXBrNNYxnc1xof3SJ6TxgHoBoZWUScJS/1FgiBAqQ2yvEDJNSZphmV7Ukoqy1jotolT6ZjsdXyMpgdRymQzYKm3TK3n5FMpgjca7dPrL8/PU5QkTMZjHn3kITZv3uDnP/9pqHO6HZnxGaZGmRcIIdja3CTPEuI4kMRcQycIhgjdQdd08hmTwHYs3nznbRquT1pUYMqppK6bFEWB32hw8sQJoiji3nvv5erVa1y/fo3+Up/JaIJpaui6SZ6naORYZmP+ees8otfvc+mNd7jz7nvhtcsSLDl7XU6Hyw8FH2P26mGgyTIZeMpZ+S5UnbLKJTJ8VgvqQsU1VIQwMA0NQc35e89x5swJ3tsa0u9+dMPLH4mM6djRY/Vv/ZPfIgonCGHM7YjzPJUlzazUKcpq5saR4XkyK0ozyQkraymQVc3Is6owUCiwbJckDlGFnOrt7Q1wXVkqCgXKekZFyDVs2ySOUwxDllCmZRGEEYKKsoRZzMMwTIIgQQiFKHnfJbXf67K1s0Oz6RPHKUIVuK5FXhSYuk2YhGhCJUsLfM/HsnU0IciyFNv1SJOE6TRC13RqUZPNgmqWFozGU5YWe1SzDKvZaTKNcrY3N8kqhTgOWFvq0W64JHnBeDylqhLyXMP3nTl+yzAMyqJCzHppS4s9TMti88YNKlWqG5w5eRLLa1Bg8MrLLyFMl36/R5TVOIbCUm8Bw3Kos5AnnniCqkznqgbZTPsIIE1zNF0nmI4Jg5hWy+aFl54hy3M2bh7QW+mxeX2TlWMrDAdDbN3i8QsX+Pb3vyM/20KLlW6fveE+eVUxHMYsdBfZ2L1Jt9lle3eHoysrWKbHPffezZ9940lATudMzSANM/I6w/U0gtnqPJw6JXsJC902ulawvHaaOJxy9eq73Hn2bhb7Szz13ee48OgjfOfpp2g2fT75xKfkZ1rqkeWyNP/Kl7/MhQufIE0T3rv8Dp/93Gd48flnsW2HWzdv0+r2eebZF1hfXWE0GdHwXXq9HqpQQZFSxq5joAlBMcOWZXkh5U2Wl7n4ymssr63x+qVLtBotzp47RxzKUG27TTptlyguuHTpdeoi48InP8nVq1c5ceIE//bLX+OXf+nvk2fyPlXFzLG4TtENY+6enBcVcRQQJ7ICSOIE2xRYloFAgCZotRoSvzTbgskYx5WVQpYV+K40KigVg//pf/ldolLhj/+P//n/DxlTxnh4gG7ojAZ7mHYD3dAosoQUnTLJMAwTYYBWFjhegyiQizbPMizbJM9yyjIjSDLJNSsyVEUnjlPyokIXKmmW4TU9siRB1Wyoc/Ikp64UiiojCkLiPMN1XCaTKYahk2XSCsc2DcJAZlFRMCEtc5pek2ajgSYEvutgux7NJMKwLIJpjNtw5+TYdJapFWXFNIw4mI5wdJOFbpu8KDAMkyiKqOoCa1YeakKb8/HUKidJEoo0oNNdYDxrsne6TVB0poHFYDji7F13cbC7RVHEaCzgN12mYYTvmvT7PdKiIAlC6TmnaBwMR5CltBe6ZFlNq3mW6zfeQ+geJ08e48LDDzAeTumtrHPp0mtEKVydBugzmZeNL/0p/ZbHHXec5PTJo2iqhSoEWa6g6ZIWIxRJk3nhpWcIwxEAdtMiUyrsho3V6uIlGbpl8Nqbr3PHkZN0l1Z4/Y1XuXztBp1Wm2AiGe/jUDoDp0VGu9NmksRMkhi/0UFXjLmnXxAEuKZB23MZTQe0TY+dUYApTHTLYP3OddIw4szpUzz3/PdkCasL3nrrMq12n/5ClzffusTf+8UvkGYZmqbRardJkgyhwu///pf4lV/+RW7eus1bb73L6uoaX/2TL3H+/HkWFxfY3NzgxrWrrK9KJH6r0WJ3f5cjR9cpK5UiT2i3m1K6h5pgEqDPAI2723sIoXL6zOkZTEQwDaZMp2N81yEvShoNF6FZdLsGuq4yDHPKsmB7e5fjp86wtrpClgToGui6RVmmROEY37FJ45w0idB1A8fS6HSPMB2P0DWIk4yWL++/Q3gMyGzrEIen6Qaq0NHVGn3G6UOz0etCAmSNj95k+pEITHVdoc70uXXDQggosgRVaJRlzTSM6BomlEika5ZjGCamZZEmCYqqoelSpsHSJVdIZjIFWVDQ8CzyNCEME1RNRRMqVRFj6Bpur0scxBiGQqvdJE0STMtiaanHaDjGts2ZRbdUGQBodXzKMsNxG6hCpSoyWp0WGzdvYeo2QghaTRvXcxhPJviupAkUZYZWK6wsLrK7v0uaFoRhwtr6CmmSYJkGzkzuRDMEpmqhCYFpQK97ijzLyNIEy5VlapnnlEhXjMlol+NHT+I6DrfjiDOnTlFmOaUKjmdhmTaaCtlYUkearSaDgyFeo814LHtpZQnTIGFp6QhhFHLj+g2KWW9iNBlxbK2PrpusHTnGH/zxl6iFyXK/z1YcMhpNWF9ewNAlkz6OU2zbxDR1TLNJvHdAluc4psftnRFFUpCnBW63ycHBNnWZ0tIXGE33SeOAwXjEpx//FEkc872XXiAvoeG0qNSCuoYwyfA8jyAI0BWDsizp93tc3byKVmkUuTRSiFLotI4SRAErXYe0KDl+VHL4Xr11je8+s4nX8eg0GkSBgWGbvPnWJWzD4OHHHmE0nuD7Hq2ZTDLAn3/9G/yXv/WP+frXv8HFS29y19k7uXL1Kr7jcf3WBlcuX+bUieOsfeoI3/z2c+xu7tDsNHHdmUGDWlEgS+AolZlSf6lHXRUE04icAhSd/YM9drZ3WFlZ5s23LuO7Dp7vUZYSBmLbJnu7O+zv77PUX6QoChY6Lvs725KYbuoIIVDrAlWVuMBSMdANQRRGWI5AFRq6UNF06cgiVPkgrWZTRABdrckrhbwCU9cIJhN898PT87IssR0Lx9ThA/ZV/0+3H4nApCgqSSr7IK4t2NkfIlQNS4NK1YmTmGAqM45GQ0bjqi5QFY1Op810EtBdaBGnBq6hMZ1K8mKj1WA0HBDHEYblsLKyzCScopQKnmchdJ3xcDovqUbDMX5TliPC0LFtk1bToSwzNN0kDCasrq1K4KUrm/JxJC9CMBrS7DQYHEwo45illVXyPEbBRcxoJc1mm7oqKMqS/kIfYQhsQ58Hw8MpntB1yrqkyHLJoI9TBDW6KZvlQoUgivAbLmUp6Pe68lzlOcF0QL/XJk4yfNeelwhVHmA1u5SZjTB0igo8z0ZoJa5j0vJcDsYBYRSyvX2TvBSsr66SFRmiLgjjko2NHQ6Gu7z73ps88sA9LC4t8vJr77DvBTLDAAAgAElEQVR/MGSh18V2JIbJ1i0syyCNQ0DnYDBGFRqGrqOrKuurCxiqRZjm1CQYlsUwrxlN9ynzkjKHIDvgW888TT7LpO684yyra+s4tjyXli5I8pKD/X3eufwW7777Dhs7G/zYhc8ghMZXvvIlGbQsA6KAf/Qb/yl/+Id/xIkTx/nTr3117mJzuAVRwHg8pm90WFlaZLEvm+KrqyvzKXAwDVldW+eRC4/x3/83/xQMk+Xldb797adodRYou03KLGah1+Htd98hCEOGgwF+00Wh5O67zgFC9uV0FeoCw9CpSjnEKSswLYswmFIWKePhkDzJ6Zzsc/pMSV6UDA72sV2fOkrwPJcrV66ysLCA12hQlQUrq+t4foutXSlbUpYlCI2yLGdOPHJY4bgOWa1jaQaapmLNyN4gg9IhRzUKI0q0eYO9MjQMy5kTx1UhM6uyklP0//w/+0eoQvB7//u/+Ggx4Uehx7S6ulr/w9/4TfI0oSxl0DkMRIfllNDUudQIQMN3CUN58gxTkzIagGu5CCHR2kma0W75Uhq1lGRKIZgLteu6ymgkKSVlNiu1KoUkDudcL8PQ5t5maZ6jCRXbtSX7XtcpKglwMy0LXVdJ0pgoSPB9V06yDIU0ThHCoChLXNedB6IwDD8UkKIZ4TZLQ3y/Mc860qyk2/SI0xk9pCoRho6gxvd8fvDyG4hZX+7uu0/JzKjIMTQJWk1m/YPDfR/8t8xzKW2iKeimzNb2d/fIiordvS3yLMdvdEmSCW1/gWx2845mKg4P3HMG3Wly/cZtvviFnwYgDEMaM8MIVQguvnJRiqO98SKaVvHOtU10U8PwXFwlI68s8iQjiML59MxzXLqt5lxfKY0DGr68bkkRomCRFhmeLTD1JhsbG+T5+9PHn/qpn+T6jVts79/AswV5LhgPx9iWT5bIB1VWRvN+1O2bUhzj537+i3iuw3A44OTJUyRJhCoEVVmiCsEf/psv8XM//1MkcczVq9cJ45RLb7xDkud84uGPEQdDiiIiCEJarR6vXrzIyvIKyyt9HKdFUUGaSHzaYUCKkhRNFXi+w3Q8IY5THNfF0GE0CsmLjDvuOIVhWGRZgu36NJtNtjdvs7p+jD/8gz+g0eqwtLJENDrg5Om7ee3S63ziwsOYpoVCQVEUlEVBq9Wg4XsEUSqzKSFwbFuW9mU5I3/zIfcdKZksN0OvCcN0PpHM8nLev9LU97GGD378ob/9PSZmsbHZbLN/sIdhWbiKTp7EqLq0c+r1Omxs7CBUgWFq6JpBq2MxmUxQFQ1NFFi6hutZJHFIs9UkTU1s1yHPY4QmZCZS5YRhOC8Vq7JgnCWUZTUnzAIIVR4fRRFlWeP7LoYwCCcjVN3GsF2CyQhNSEZ2miREYYYQ8j2m0xDDNomCBMcyiJIU03o/KDHT2AsmAY4tkbq+75KlCULI/o1tmwhV4FiCWtPxjPcbkAeDEaZtsrN3gGtoGI5EFudZRm95kc3NDeI8ZxqMUGody3VxTIuiLEmTkLhUSbOSTrdNWYSURc1gsEOz4bHQ771/PXZ20S2dpcUe48F4rou0vrrC7v4ul966xl1nTxNnFe++d5O77jpFY/ZUPtzuufcc33jym9x33/1cfP37nDku+y660WFveAtmqI92p02eZHT7XdIZoPH2zQ1838PybdKinO1XIQ8wbI3V/gnevb5JGBY0mz6ub9DOHV54/nl00+IzT3yWhW6L//OP/428rgqkeUaS1zR8l/3tIcsry/zMz/wdAKpSct5WV1dIkgihWwwOBnzvu9+lv9Tj8ccfIZiM2TvYJ81Skiji9KmTvP32W3z/xddoORrdToPz5x9AV2E8GrO0KjUUwziFuqDZ7LC9eRthmRiaSV2GWK5NFMUIXUcvS6IwxGg30Q2N/d1NHPscw4kctCw1m3ieyySIOaap9JbWcB2Lhx98gCf/9Gs0Gi6u67C8skKZJ3MIR15U1IrONAYUk1EQY2qF7AeqMmMrK6lCsb29y3A4Zn8SoqsaqmFSZSmqYZImct/eYEAYRGi6RpGE/MIvfGHeXP+o249EYKqpqLKEXKjomoZv20CM67QosxLFVEjinEbDx/cd9vYG+K7UErRNA92UPSRVGGiGgl7qpDOkuGkacjGpAkVJ8RyHoIzQ3SaGoTEeHmDZEhyXxCGW7aKoLmke4RwimoXCdBpiux6W7RKHAWVusri0xObGlsyqygxNqKRZimkIaqGhlIeN79noVSsRqsyswmmCadkohBimzJoOJ4+mroOmImY0gixNyLMMRZWZYFbVsqRRBC3PJYlT4jgiL1V815bBZJb92KpDmeUIJSWNZTruey1KNMR0TBKG86mn2/Shrtnf3aPZalLkEjAJEoZxiJWahhEKOYsLbS5dvk6c5mTRmDffept+r4ljG+iGjUwIpBbUPffeS5YXBJMYvaViWW029jZoWC5ZLO2pJzMh/LTIyJOMt69vcvqOO3jr6g2O9hdI43Dubyd0QdPsMg5SojCawwLqSsrPhlEI05D/9Xf+GUePHMXSNbIoBdun0V7AsR3qsuCBYydY6Mj+0Xgy5uzZOwlDmWFGUYLnC773vWd54MEHKMtCKpEGAa+/9jqdhT6GobG9vcNd99zNpVdew/dWMAyTM2fP8Xv/8l9w7PhRNKGi6Q5lFRFnFWEUYbsOVVmRRBGWrhJMRiRxSm+xw+6uxKIpQzlVVnSbKE4oi5Sl5fU5OvzEiePcddedPHbhUdI04vVLb6KKAt+X2aouVHThkCQR71x+izQHdQayHOzvyVK6yCnygk89cQFDyAxhYaHNuzc20WdZ+AeD0q2NTfrdJnuDAWtHjnHl3cssrKwzPdjBbbbIo+GclvNRth+JwKQoKkurqxRFQZFJEqKGiuc6BERUWc7yap94Opbi60s9hK4zGQ44cnSdOAzAtxGagqrqckErGlkSEARj0qQgnGVFqT1TK9Rlb6dGkyWWYwIuYrYfJPmz2/OIo4QwDCmrnKIs8RtNwjBkfzBAzNJrgCSOJeI2SzEMjTQrMQ0hyy5FSLyKqxJOJxiWRxRO5kBNr9FClBKfU1YFZCUlOYZpYcxIzaUKooKiiHAMl1EQYnda5FmC67VQKBgFAS3PQ9M1kiSlqKBSIUtKCYdIMows52A8nANJ4yRCAbI4BcNEMyzSw2lilrG6topS5ARxQBxHWLpGb7HDzeu3aboumlrR6XaJYsnxsmwPoVsMD3bnJd3q+hG2Njc5c+pONnZvEI636bg+RVlhaAYHozHttk2W51iaTaoZ6McMBsWUo6ePMrixj+fbpHGG53qcPHWSdy5fAYayX5Ik5FkucXC2iTvjaz368CNEScrKUh/TdiiyhCCM+Nj996EbFvu726wfWUXoFr3kUAbHJM8ynn7qaS5c+DiPPnQfALd395hOI6JwQqvpc7CzyUMP3c+R1QWSpGK0ukKz7VPmOc888wzHTt2BJqSt1HC0g+NKPe+D3QPKkvkUznBdojBBFyqqqqOpciJblhVFWXL2rrsoKoHQTOoyxzQ9rl27SrPR5OJrb3L16lWm4YTz99zH+fsfYTwaUH8gOIwnEbc2dsgVjYZnMx5NUDSdushpthokccLR48dR8ghNU8kVC8/UaHR6XL1+A6/RYRiELC20YQN0VSNNM27fvE6cpkwPdqiLHEdXoPm+c8tH2X4kLMIBhoMhpmnQXujJ7ADZGGw1fBaXF9m4vUGUZOzu7jGZTkmTBCFUDvZ2GYdjDMuhLGrC6YQyz0nzBMOUXDLNEDRbTRrtDprukGeSklCWGUJAXlaEUUoSh1RljSYEaRwRTEYc7O2yt7cn5U9m1YlhGmiGgBIcU2AYJoZhsri8OP8+8r0Ktvd2SWOZ3tp+E0V9vx9m6jqNdoeyrAkmI7IkmGGabPnZVQF1gW0ZUBeURY1pWyj1zEHWNimznGOn7iDLItkXKyq0mauFJgSeY6KqOr7nowmBbRkIQ5clXJVS5RFaXVOU1Zzzp1CizXqPnueSpDGlKqEOraaP59mURU2j2aLfbzOeTDh+TIqjFaXsT9RVTuMD2lJlnpDGMQv9FXRVpddqMpxuoxv27O94ZHlOFhdMknje88mCkION2xiGThJLqWJNaJw8dQZV0bhx8wZZkrC7O8R1XBq+S1FW+L7DyuoiBTV1WXDh8ScYTfcJk4wf//EfpyprsqzgnvsekJ58VY5hSQkYQzf55pPfoL/UI4tTTCEXtKbW2KaKYxsYhobv28RhzGS8z+7eJlWdMx5OMS2L2ze3KSu5vOqqwDBt4iybPex6REHIdBoxnc4cfIZDmu0umm5QZDVFWVAJjXanTavh41g6rVaToqpI04QseV+9c3t7l53tA/Z2tvF9D1VoKFU+5326XoM0iYjCQIIiTYPVXpeTd5xmobfE4tKyDCogSbu6wmA45vb16yRxQjAZkCURwSRCrwvGowPqPGZtscPZE2vcf+5O7r7nHJPplOFgj93tWx85HvxIZEyqqlKVBbvbOzieRSU0LEMQTEYIYRCGpRylz/Sr/UaTwf6eBFUaYGsmWZqBomGY8mlvapKb5rgNTI1ZBjWhRmB5rpx0CYOakjIrUESN5bnUFCCkO4njNgjDkKZvU2YxmqFiKhKha8/E3+NQ3liaWrOztUNvsUMYTGl2Gni2jT026faWmU6npMkE07IwdZ0sCaiFBrmEPmiq9PyqymLebD9EhcexNEoA5lO2II6J4xRvqceNa9dIwhij36Pd8gknAVkpOXhlkON7PmmckFW17GGpMlh4vkucZKS55PCNJwGa52NYLkKtKZMM2/UoyhwoMHV9XpaSyizxYDDB1KU/WlkWvPjSK3zuM0/MG6mHulEqUFQVWV6QZZnUFXJbpPkY22sTB0MyRS6kIJzi+x5BOMUUJqZuohsaVd1iGoZAype/8lUc12F1dZ2irLjv/F3zaSpAEhfcvr1BXch+0jee/DMev/DjJFGAZWocXTtKrRhkeYqiKrK5DTz1jSdZXF7kxz/9KYqZ7IdtGVy9ehXfc5mMD+aE7O5CnyCSuCyhViwtdXjrrRsYjsPy2gqj0ZBWq83WXsCN27cp8xLDtMhmVCpBgKXr9NaOUGgOP3jlLc6dPYFl63iezf5wSlFKUKiuCZL4/TH84vKKvFfKgt39A86cOcvG1h6n77yD7zz7PJZpzRUzat3h1MnjCKHjNlvUeRfNa2AJdV6+bW5KHfvFvnRL+fzP/hx6nTCa/F/cvVmsLdl53/erWqtWjXs8873njj1PZDfZZDcHU4MpSo5jS5YjD/FDXgLDCGIEARIkz3kK4AQBDCTyAAMeEMOCp8iTJGqiSIrNVlMkm0327b7zfM+8x5pW1arKw6qzb7dlWzIZBA0X0Djn7nPuvnvvrvrq+/7ffyjwO2G8J1yqV19avYbTNOjlYknSuXqkyyVbncznRzk+EoXJwVkRJZezJb0wBgHpbAGU+GGEKySLtNta6RpXKLY2RxweTIiGdtSpjUG6gAEROAyGQ/KiQgqJ51mzfGNq/CCg1w8oq4JymREnPbQumR5N8KQkjvxVEVwsMs6eHaNzTbYs8JUglA66qhGOZW1bzZiLEC5ZWllN3LIgDC21YXp8QDIcYXROWRU4jT1BPaU4OZ5Y8qjn4QOLmeVOKT9gNp0xGA7oxSGL5amkRCE8D9HaNBJdVygVMRz0+MGVd3n9tddppYfOcxQ29uk0hUUYK7xc5jlGW78e020ahWcvhlbYImu6gnm8/5CtM+coq8JiX1XVhQ5IhGhY6/c4ni8Yj9d48OiIw6MTDg5P2NwYWxfNDgg9xR2UJ/FVwizNGMQRhfY4np8gjcQXLmUDSayoc6DO8aRiUZUYpyFdToiTmFdf+wTf//Z36SUhvU4BcLD/iCSJ2XvwgECFSCGRIfzJn/o5jk/mhL5kMply8fJlXCE4mRVId4kfxswXSzwV8Pa33uS1T30KsNydQhfcvnOP6XRO6CsynRKpGG0qslwzdCqi2LMJO8Dh/glZXrA2HpKmBWtr67zxjTfRjcP62ohWKo5PLFFUuS7gUhl453vvEvUS1ra2yJc5SQcHLBc592/dxn/2GcBiQ54KGI/XMHWBNqCNw//0P/533Lp1F601x/uPWB8OuXv/4WMbnyrj1U+9igwSIs9hvljgCZe79+6zsXlmlXlXnXbyvsdstk/teqTLOb4/xlQFoZ9QmQbl1pR1y4svPIOuLL4Zxz7LRUqp9cpS+Ec5PhKFqW1bBuM+tW5Z6pTR+ga+r8BY8yxPKfKi4pS4dZoAmhcVUdyt5d1qZb+BgPm8oMxP2NrZoqoapAtRoAjimDTV4NbovGQw7q8Im0PhfOh1LeZWMpAVdq0uhINQHvN5xzuqNeO1EUWasr4x5mgyJfBDlosMIRymJ0cdI70hK/Sq+LYI8umMMB7QmBrwV53M1vYGSnjopmUwHFiXQWNQwkMoD6MraK2kxdQt83nRjXEjXn/tdRazE9s5KknYMcj9MFhhT8cnE9Y2NhHC497du4RxgpIdON91GwcHhzzx5BMcHZ6wtbPDg3t3OHvuAmVVEIWq6+haZrOU3iCmdlpuXr/BPF0ghGC8tsmpFa0vHVrHRVclZ8+e4fDAent7rktV1VS6pio0BFbWM0gGNi7cWRKokLqBwWhAowvkoEfgSV565mmufv+7mE5mk2dLwOHo5AhfeLSm5tXXP02U9NnbO6bUKcrf5sWPfYzDoylxLIhjn8Z0tjlhzNvfeZuNrW16nTBa+T55fkpZacl0ii40tWkI/IQokpi2pNQeyk8wumL/6D6ffu3xhvzeg32COOGJM9tE/QFNWRL6PieTKf0OBzyZ2I7r+eee490rV5jO5yTJtj1XIp/ZconvuStHDJrKBjiUdqssRcv/9Yt/h8Ggz+bagMuXLwKzFScJQFclyvOhyqjax0WoMfWqKJ2OfQDLhQXfPeFSlsUKRPdUSF2ntCiOjvYIw4i2qTk4OGY+OaI/WqeXRJT1j05B+sgUppPjOf1eDykk+3v7tE3NYDAizUp6UrGcL1daNk/ZrmaxWKKUT20MDi2idTA4OMbyNarIpygtwzsvasqqYtltnA6PJgx6YTfPOzho/G69f4q1bG5vMT2Zri6ASlcIpQhCSW3sDF9VDWVVsb9/yORkgqcCkiRkuUwZDEZUVDYSSj8Or5RCIEPLUymF3ej5nrfajmXVEulZ0PvRwT5xLyKOe5i6sqigsd1h2Buw2Dvk4d6EOOnz9ttv8/EXn6c2hjy3hWFv75D1zQ3CsEdtapRvU4pPt5aW4CcQwmDyBqTL2d2zTKYLtC4xxoLfyleUVcFsllLkGX7YJ076HB6eYBpDlpe8+vGXmS2WSLfu/LLsnbUxGaEvaTyBrgzPPfci7175NtM0RTk9QhkhWpeCnBZNurSsboDDuw944bnnWSzcVUbcd97+Hg6Ko5MjhCcwmeH85cs89dQTSBdcIbl5+w5P94ecO29X9fP5nDe+8btcuHiJRV0QhCFpmnL7ziPevfIei2XKT/zYH+Ott75GUdec3V6HtibLMqRwqSuN31mbrMZh6RKEfSaTCT947ypPP/Mys/kSFcXcvnuXy089xZ3794n6A3SWcufufYwxrI9HZHnOM89/kieAk717nFkfsNeL8URCulwghMtgMEZJa9zX6w9YLlPGG2vMpierhUiWFXz6U5+k10swdYGLYT7bR/mWdX9aZOaLBXleslxMSXpDlFJMZylSuiucz1QT8rJejeGFyUjiaOUAe8rp8qXDeNTv+F2Szc01NjY3aZsKXzqY+rGrxA97fCQKk+OAMWBwcFVAkS6J4oQ8L+kNApbzKcNhzMnxxI5LmfXpPpWlmHROqdsVqCyEokhTZvMlQUeUNLVABTG+pzGtYbzWp8xLjCk7nlGN0ZUFDjEsdUF5WOF7HvNFSb/XozAlwyTizt0HDAYjSl0SRcKOQq5gNB6xWMypjY+vFLPZhCSxJ3OL7F5zvJK41Jm9q52C2MP+GotZTRKOEMpjNpuztr5JlmWIBnJTEwqJAYyu0G3DdDan6k6EjfVN7tx9uALh8zxjMLSkxLapLSCHptfvY2YTED55miGVQ6+3Tp4+QjkeVdUwGvZwnJoyLynLGuXm+FLihDYB1m2gzDM2tze5f/cBUji4vs/x3YcEQcR8sYAotjwZNyAvC+pGonXBDEEUBngisSNsN8JvbZ+hNQWOp0iihL2DPZ586mnbrToVi5ntYC5evIArJINkwHPPP0+cJJja2tPUlUZ6ildeeYXZ5ARjDEWe8/57V/iZn/7j3Lx5l+lszrU3r3GysM6Yn//CF/hXv/Lr/PbvfI3D6YJhr8dimXF2c0Av8cnSJSpQlEVKFCeI7rJ58GAfU7fcuP2IV155ldoAJbz3g3fZPbPNd7/7Nj/22deZLefEyYBXP7HO+1ev8uQTl5lMpnzn996g6pxA4yiwr7118MOIMs/Y3h6w9D0c125Y28beTA4OJ+zu7vDOO1dwPZem6oiaWcqXfurH6CcJ/UHM/QcHKM+m6MjAwWldomCMp6w9zc6OdWcAS64MlYMxLUWeE3TWQVXdoLuEHdOA20gcV5IXBZ6SVNqSN6/euEOVZ0ynJ/+f1ISPRGECuHT5HHlW0JqaQb9PrYvOG6djXdc1QrjourHZVlp/yO+n1x8wncxojCbpRyANSRKS5yV+6OMKhygKOT605ukq9PFDH0GLwUEIi9nIzt52EA8wrWFtvIlpHlDq0nZCec6F82dBKJbL2YooaRpDbRrCTtzqCkv2bIEinxElfXodc7lPD4ca1wst81pXCOVRFMUK5Da66tIsWqK4j25qi8/4AdhzmbIqGA76HB4fU+karTPbpWmN6tTg2lQUaUXdOERxhSsV+3t7tkjLlrUz2zy8d5+9vT2mJ8esb2whlWGxgFq3FvAul5h4hMLheDEnDBSVqdneGLN3eEISBmhj+Pabb9C6Pr/25d/mx77wGmBlCm4HsDptyTNPP8Xtmzdp2pBCL3AMJFGEWpeczKYsl0vOn91F5xqnVRx3uN/5i5f51GtnKGvrFPpEI8ED4drE31NdWJYV+L7HfD5HeYKrV97hwlm7MfzKb32FwWidg6NjBuN1snyfM88/yy//m3/DqD9cnUuHswUbg94KZwS7kXQ9iXAbyqqmlwxZ2xiwc26XZDgizwoq41KbmqeffIKDwwOevnyR23fvM5mc8NqnXqVtrG3P17/2dS4/8xy9JOKZT7zM9vaGzYdzfR7evYmnJEVVc/X9G7zyiVdYLufkWrMxtlmCu7s7vPnmt5jNF0SRx5mds8zmS1whMA186ztXiKOYlz9t00rKbEKaNsyWFQePrMf4o709Sl3jCWe1OdWNQ1lq6qqmagyeK6h1vnKmKLQhDj1K4+AJQRhaPWgQBgwGIxpdUiH403/ip37YMrA6PjKFCcA05coG5PGDj79aG1trgj8Y9zk+PFmRItM0RQibHmp1Zi3GNCvypBSCRw8OCJOQ4aBHni+gbtCNYbnMcYVCCIkjJEZbW9s8z7quyprYT05OiKOGMi8tEAyIUFHn9m4tqmpVoGzR7CK619eZzZeU5WNqf6kNiqqTqSSWDtAAbY1QlhR5ulkTjUE3NiV1Wi0RtOBIwiBiPl1w9swWURwjBCS9iEprlnlOEoYo4eEPehwfHmBqSd1URHGfsirQhebkyhXiQY/tjY2Oy2VlOZ7nWi904+D7MXmao5Rk+8wus+mUjbH9XNe3trh//wGmMmxtbVA3goPDLkrq3wJBy7LCcVuE9Hnq6edZzufcfvCQGzevk1cFF89dJvBju1jwPL70pS9S1zV5XpBEATdv3yGKE3bP7LCxPiRMbKF3hY9wa+pGrvzcgzCmzGeEYcTvvvVtHAxPfuIV7j884HDvES9+7BXe+MbvYkzLpXPnMR0Z9v7+EQ7gCUvenc4nqEChC43rPb5cdLm0gRBSUZYTXKmYTfY5s3OWr7/x+1y+dIEnLl/m6GTG0dER+/sHZFnKk09YAXFbFdaONlvyT/7x1/kLf/EXePOtb7G5vkbd2EI4nS2oq5JcazzXxfclga8oSs2j/UP6sUdbOxw8egTCXcm11tbXmC9yfuVXfwNjNEVeUCHRyzlFpWk7mc9s2gHxfkAYhvTHY8Zr62yf2cAXEuEH3Lv92LK4qsGY0+Rp+3kJz3pSnRwdUtU1bV2RVf+pYEzYlFQAoRTjtRGz6fSxviyKrBI/K1HSJS00UtUoFVHRYJYL4qTHQmvcrFx1W2sbm5SlXrWhQeiR5Tbg0jEaT3kY0xKEPtOTpTVvMyXG2MBNx5W0CKBGui1xZLssoAPMJXma02LN0UxVrdwtB/2EsqrY298niW0XJZWzwrD8IEYpiaNrZpPjzjlTYnCYzE4YDcYobQ3SNA5JGLFcpARxbNnasqYxcuW++e6Vqzz3TJd829YI5dFKj6rM0LMTWiEZr6+xXOYslnN8KcELEKpAuB61aZhNZ8T9IeoD0hetS4xwEEoxy1LK+YS1aEhealSomBwdWhO/sqapG15//TW+9d0fUDeSxpSriHJdGVSQUOmCrZ1t9h9Zken6cMhzT/0URVHRNhVJv4/WNZWuyLKCwXBMEEREUcDH+tbS997de5w7f2616bMhAAE6S6nx8X3J937/TWbzJYeHR8im5vmXnmOxzAiVh1QB169d5eKFJ5ln9iYohIsfRARBwGKZkmYFb1+5wYvP7n7oXM2LhjyvcGIPZM3bb19hsD5ASUk/7pGmBed3d3jq8nm++4P3Od67z87Zc/T7MWfPnuHR0TEbOzt4ruT6jZvEccQzzz3L1avXuHj+PL/3rd/nmWeeoR8nHWPcJwlBdB3wjevXGa1tEgUC15E4UqKU3WpXufVYms8trnTy6CaelHzmc59nbTxgNpshpaQ11rrEcSVvvPn7NMJbFZq00Dy494CiAq9bYNh031QT6y4AACAASURBVMelwulCDIQnUbSnDTwAdaX5G7/4t3/4YtAdH4nC1BiDEApjNE5rX9JgOGS5yGyuVcdHEt3K/PRDSvqJJbSFEbmu6Mc94lhRpHb9PT2ZggC/8xD3POtHjWm6ka0z59Itw3GC73mYxtCqx9u5Ms/oj8a40iOKIM1KPGHxLIUV+Yq6pWkqskLTS3yLOUkH0SieeeYZZtMpWxtrTKfzLu4no21qdMc8D5IY3wuYTY4Zrw+ptC18eWm9pXTT2nHP81bjqxIeurXarjRLEVKQLhf0QguiU1tDvTC03VepK+7fvdd9bkPyNCOMFT5WP7dYpFS4Fs8JPPKiwpcSc5orpzXrow2ODg47PZVA53qlNzwVWn/9q19Fuz6/8uu/xc/951+iLGuSSHUi2BandWgdn62dbfK8ZGNbEQYSXRmKPLUi586aF0BXzsroX3i2CJ07f66jaNj/f8ILkEHCe99+m+s3biI9xXw+x5iaX/j5P8VkckI2PWI2OaKoDJ5oeerZZ3jja2+gPI9Fx74vtb0Q+4MBFVA2HqXxGQoQymc6nyC9gBbNbN7w4ovPspxdJfQEWVFTN4YkksRxxNffeIteEpPrhpu37nDx/C5Xrt/ixrXrCE8QeB7PvvA8gygky5YslwtM43Bu96wNH1CKy+d3SLOMyfEhz73wMaSAxcJjMZ/xuc98hrppkK7L+NRRU0lc4fHzP/9n+Pa3v4vv+7z0sZdx2tIGcEh7vnQ0Nh4dzVZFqULaQlTbCPvAkxSVfez0eqtWoZghHjWKFiPstu/0WCyX1PrDzg0/zPGRKExgt0yzeYonS5TagLamyFNaJFEcs5jP8MPI8iqUYnt7mx9ceY9+nDAajyzBEphOF0glEI4EaqRwoCkwdUvbONYWtN+jrAqEG6CLlMZUpFkDEbQIKl3jKYkfBDhO3W3gQoSvENJhPi8IQp/StJbIWaRkhcZXgtksxVMeypFI14pqo1CtuDB5oYniLvm2ltSNYyUwpuh0eDlx3ENID5Pn5KZeaeaUa+kKWjrkue1YXGFXzkEQWj5W06Jzy3o3rbGWL8LD9yW9/pjFfM5w2KOpNU2t2d09t/I/3394n9EgYpnZE60sDLIbD3wpKcqc5XJJEkfUjYNUDsNRQlZUBGlBUzcIJTg4npEMRvzrX/0tfvbn/jQ6m6zCOe1msqXoRvQ49nFcjzqbMhyNVgZzwMopEiDP0k7F7vDo/h32D/bwlU8YD/jm733L/k6e89qnPsmTT1zgzs33uHjxEscHthjXbUOgLPhfuIL7927y5/7Sn+Ov/bX/ja2di0RhgNaayxcvcLB/wOFsQVUWvH/9Lp7T8vGXniSKE7Su2Rxv4ocBi9T2CtJT9JVHGG6yv3/A7Vu3+PQf+0l+72u/xeuvfZrZ9JjJZEqVp2zvnOHyk0/wrd97i7ffvY5y4QuvfxKAKEosNqYUG+tjwriHdK24eblMWS7nbG2ss0wza907meEKmN6ccHR4CMLKikrTUJc16xtj3nn7LV584TmEkBgDrmtlRkEguX3jOgjb+VTVBwoP3ahW12RdwdKmpa2rzm5nYNXQAFXGe9dvYnTK1vomP/fn/wJmOeFv/o3/40eqBx8Z25O/+t/8VfwgsPgCrd226ZyT2RJPKsbrI7SuKIvCjmphgtaaRhd4StG4NVXlsrmxRb6cW6sTIAwifGmZ0m1rXQCkEKxvbHB0ckg6W3xIDV2U1vlSdV2TKyRB50WDdKlNS60NUgmM1iT9IY1pUI5Bm6ojfzY0xm7qZvMlSRKyNh6xf3hsV8//DqFu3TicHB0ghbQ+Sa5AhYrFPLUSiA9Ym54yuJWSTGclB4fHCOkSeA5PPnmJvNAr3WCl7fenFiiT6QJdFpTaMBiNiKOYUpfoUmOakiKv6fVt4Srrmlobal2QDBKSMOT4pGM6C8VwPCRPl+iyYLnM0TVk2nDh8iWuvHcdGcSc21njT33px6lMQ9tULGYz4iRZuVymyznjNauvKsuaShckvWRFZ0gixWQ659q1G7x79RaYkqrM+LN/9s/QNjX/+B//MmvjMX/xL9nw59/8jV/l0rlddFWtUpRn8yVVUVC3DXVVs0hzsizj6rVr/OQX/yRvvvWt7j1JpHDZOzhmVlR4nmIY+QRKsDYIGQ8S9vYP2NhYQ7iC4doaRVHgCJuOe/vOPSrT4gmHZZrSG64hPIlrLMnX9Vw8GfLt736Pc+d3uXfXhlSe2xyxsb1JmhXEyYB0acXMZ7Y3OTzc58zOGRZphhAeWhcWUwKyZcpwnKzOqUpXeLKlqh17feQFDjVf/NIX7Xmja6689z47Ozscnyz4ztvfozT2PJeYlSNEZSAMQ37+z/8X/N9/9x8ANgC0yWvy0m7hnn7yCZI44tGjfWbTCds7Z7j74AHz2Yz/4b//q3zxJz/7n4btSa41vX6PKO5x+9YNG+QobCRTnpcs0xwh2lVck87turIW1mQOIO4lnBweWvmCromSgKP9fTxlO4syn9siJASLxQKnlR3bWa4kLOcvnGOxsJo5VzjMJgvieIjR1mepMTXJIGGZLYnDIYv5AgeDFi7LZU6ShOhCI7vxcX1rC4eK45MJ0otQHlRZSd04oFsKU3YXkHVgBJu2u7E1xtQtURStOiWwnkq00pItgSiOye4+wLiGJ3fPIFWAWaTghOTp3HZOdcvBZLJKZxkMh7hFxWK+IE8zeoM+w+GA45MDRuORjUqKY3xP4rQ1DobhYEStC2tOV1WcnCyQyrEjpmsxtpYWWWuufv9twv46ha4YDEb8yy9/hbPnzvL0hQv40RDhScqyJh4MV4VnuLENLFlmLe997yoAN65dpa5qzu5uc+PW3RVoKwHfrRBKsrO9BsC/+uV/zmDQZ/fsLrPpnDAO6fd6Vr81max8tYPAt+dAlrF74TLT6QkHB4ecu3CZMl/w6NEha2tr4MNsNkU3Hmg4nuV4fmCZ9NoQBYLaNEhPYRq7Vn/qqaeYTk94uHfEi88/y9ffeAulPHbPnuHeg4dEYcDHP/EJXnjmCba2NgnCgOO9R5wslvRHY65fuwbAYDiiNg3j0YDGtFS1odcbUOmC5TzrmPcOQWjPiSxfEIQhnmxtJhyGfDonjhS+8pkeTUj69nXfvn2HLMvZP5xgTIPzAQ8mIQRZI1AKKmO4ef0WuixsAETpWiyytkLtg/0D/PO7hKHNJbx/9y7KdXCE5GGHF/8ox0eiMLmdncfJkd0SDEdjtNYY06C1JVSe2t+Ca9neRUqWZcSRb21o44gyzahpqLKMuNenyGuSvnWNzDshrR902EWHJyg/oCxrSlMjlOL4ZEaVLzGmQQgXh4Y0TYkjn56ypDzhCEI1QAqB6veo8rRrrxVrG5sURUG6mCOks7LAtQ4DlvYQR5bp3QpJ7PtWhycddNclBKGPzvWqozotSlIIaEvrFlhb7srR/jHDQQ/lS2ZpwbYuUKEiTx/rxjyl6PWs5ksUmjTVlEWKUj7GaLIsJ8tyTNUyySdcOn+Og+OTlW9VEMYcnZxYzKnbPA76CYKWoNe39ITGQeuCtdGQpm4wxZyf/Zmf5h/+0v9D1Otz+94eN24+4OiRFefWXacopEddpHhCUH2ArVx3m8XNzQ2S/phzWym3b15HuJIwDLh54xbnz+/wyseeZTadU7cNjlQ2NMITaK3JupF099wusQp58PA+ddtQlVYbePHyJb78a1/G9TxOTk7oJyEbowFBGHB//z6elMw7wDhSHmlRsJaE9BOYaU0yHCGFB1QcHzzErG1weHjM0dERWbrk4pNP8v6Nu7x//Q6ff/2TzLrk4yeeuMSNG7dQwMVLT3Dv7i36/ZhPfvIV4iji4d4Bh4c2vj7orJZP+UGj8TrT+cIGsHqSIi9ZznOU9Kgch/t3btPrryGFy/mLz7G1tWYB76rmG1/7Op6U3LxxBxlEpF3Yhegi0Z3SwcVQA41U/M5XvwqeoqoNWZmu0pyj3oCtrQ2LS4UhN65f76gtzR+IH/9hj49EYXIcl14YYnBsQdIFUVdATq1oB6MeeWrxjTSf4iJX3ZQKJQeP9ujFEVJIwCXLM3phSBwrZtOMpJ+Qp0tc4awyvHSpOTw8ZGNjYyWOTSKPmSkZRJHdUiW9VVilEIrAc+33SkELVWX/3mA4IMsyHj6wbXaUBODaDV7o246nrEG2XWyOH3B4NGE0HqOrCl/6K1+m6WyBHPRAG3AbckBIB9oSHBvKQIc9RUkMRQZ1y/bW1sqVMozDlRj49MiL06QUiVIDtK5pjbFuCkVBulwwXhvx6NEDay+jBH48Jp/P8ZWgNFZHRWVfj9Y1SlQo1wFfEsZj8iJjNIjQdcMv/dN/gnIF/9kXv8A//Kf/knRyZN9jFw1kKsPu+fO4fkwY+Fy8cI6qbvje999lnuZs9gKyk0NuvftdiqJg58wO05MpRevy9jvXCONw9d62tzY5OjiiTG2RCleBDta+tDQ1ddswXy47rliFdOAzn/0c79/ctzYxqQ2gqMqMl194lvdu3IbO+A/PAwyb25scPXzA9hkb314bqzWcTC2zeryxwfU79yi0YW92C5qKKAi4c/MmaakpsxTH9agah2WaMb91G2MMb7z1PX7yC5/h2nvvsbG9ySHWFvfszjb7XZGKI+shXxsLSM+XdsRe29pib/8hx5OUQa/PxuYOG+vrnDl7FlMX1AacTphuSs3hZEYQ1yxS2/3UdYGnA1rz4Wj5vLt54yo8oci1ZtCLWd/YtAm+nT3xaVHSZYn0I5bzx2D4D3t8JGxPTGNwXEkShwz7PdJMr+xH94+OKfPM3qkN1rRfxvT6A4zR1DTovEC6NlYJrPNjFEZI5VBVDULZiJwwTpBCEMfxymwriiJMU3J0eEKeLjk5nmBMS5ZlJIPEgq+OXNEE7GasIs8r0qzE1AKDx8HBIVHcpyg1QjiWR9WUCGHQTUteaIpcY2p7RylL2zmZqqLXt0VYuQ6mMSRJvLI+aR174YsGcPzHxcWT9PojGmON/7Ux1l5D2q1cnj5e4i47a41TLO1UTCyk3VCddo9CuKggQqhwhS+ZykZLtUi77esIoFrXDAZ9THcGmdbg+wrH1LRoPFnz8jNPcv7sBr/yb/45iQSnKaHKUU7Lcj4jz5c8uH2NyfEB+48e8YMfvMOtG++zNva49MQWu+fP8tSTFwgTl3PnN7l8aZeXX36RTBvySrP3cJ+d7W02R2u2u2sbjJA4UjGZTLqL2MZ7BYFPUbUYI6gdgWkMh8dd8srxHvPJMXme4wUh53Z3qSvN+bNnkc7jUFXhehwdz1g/c5bbdw8wjdvlGQp+/Cd/nM2dHaSUfP61T3Lp3Davv/xCJ9aF4eY6w7UNksTKru7duY3vK4aDPkIINsY99vcPEErQ1JoiXTLs98iKjN2zOyRJn/R0KVEaJpMFWldEYcTe/iGvf/Yn+PhLL2Kammw+5/qNm/ZFOxJBTVXDzu4l9g5OyOt2lc5b1zW+B1VZWD+0ugahqOuaU2iVxvL0zp07z3h9k/ffu8L7712h1DWlrpFKkaUpdafD+9Vf+/IPUwY+dHwkOibXcaiqEuWBrsCTHkIFzNMFURzh4lIbQ9JPWM6XNKJm/+iY0FdsbW9xcjTBcSXpsqMOdNlwRS6o9RKpAoRrulHodPNgtV9BKPG8kPVtBaYhiuxdWAjXMruxF3JRNTgYTGvHTqUUWZ7hBzbJZLy+SVkVjDbWkTh29R8PyYqKJIooUksEnU0mtI1PkadIFXRiYPuadGNpCNEp6B1FyO6O7YeBtTBtus7HkSwWC5bzBcL1WKQZO4BUgVV7dy6Gldb4vsS0hjKzIZhBbFNyy7ImikOyogIBcX9IluUr+99kaMmarvRouq4KQKoQ0darIqmrilbY8dEav3moQLF3fEDsK87ubOCg7PufTXBVwNNPXwBgsbQFVBcFMoSWmrIG1cIy38OXCWmhWS73CPyQqmpQvqLINOvbZ/jtr3yFxSLnlU++ytFkynCwjhAuUgjyNGfQ71MBy3TJ7OQIXIv9KT9AespSSoAoDKhNg6Bl7/CEKE4AveoeqtqAhKu373Przh3W19b5vTe/xfbWBusbFeliwYN7dxiNx/T6Q7a3bUfVT0Iybdg/nNFWOevD81y7epXLly5w89Ydnn7yCaSn0KalKAuK0nB8NGVze4OqNhRliS4OGK+NkW6P7779XZ555gUOj454eP8uzzz1FD/9Mz/DP/i7fx9XuoRBSFVX6M5JQwo4XbaNBgmmNkgpOdx/tLr+yg9s920Hpake12M8F+IopCg1AWr1eRWlZtCLYDCidjrYQDiky/9EHCwBKl2TYlXPQjjovKCuHZo6hyBACWmdKrHyk1Px7snRhCiOqSqr2xmNR+gis7wcnbG+tcXk6JDAc/GC2HKEWoOpcubTBaPxmGVqhb7W+6imbaVNHzUO6WJC29RIIQlCn8APMWFFbUrWByHlBzgbwvMp04xoPGIwHCBkiHDpujAQxWMJjac8WqxxfxzHCCDLMvzQx/NDpK7tuFQ57B2ecPbsFkVlaJqqe7ykbSVFDWWV0Ysja91ybAu21lYTuJLcOAIV2OCEUy5UbQy6chCuRzqfWpb8ByCCvKgoixKwPJ/G1KCtd/mpbku6LUXVsL3W48b1G+BqxsMRLZrYVyjpkRcaGVrWcKFz+gryZYGuNUVxykUSVAWPE18dRdXatJSeGvHgwUO2NnZpOolSWjX866+/wZM7u/ihh5A+ly8/zb1792hqjXIdfvD99/njX1pDV5XlnPkBaWbVBWlmCJMRw/EQ/+5DlFLUeYHunr8uUhAez14+z937j8jqhqpueOHFF3l47zaj9U3S+YSPvfwKB3v3eVDe5+Mfs77cpi4ZDkZMZxMiJfCV5PyFC6yN+rz7nW/jyIC6rlkbj8iyFF9JYimp65rDg33Ga2tsjXpMTo7YObuLdF0e7R0SRxGt4/HgwQOSQY+10YgbN26yf3jEaG2dttZEcczt23dXgaZFWeO0Na0jeevb30M3zgorAlaTwwf/LKVEAo6QVGXB9tnz3Lx16zF+5Cpmi5Sq3sPUa6vtpy4LUl1QlT+67ckfaZRzHOe24zjvOI7zXcdxvtU9NnYc59cdx7nWfR11jzuO4/x1x3GuO47zPcdxPvGHPX/TNKiwI0EKlzD0CUOfdDFFBQFtXWO0TVABeHjvIfPFgtl8jgoDTk4mLBYZQlgnTLtVq4mTHk0nS6kbB8eVuFIhHEHbWv2V1aoVlEVBGEdWglLZoiREiyMlcdInTnoIpbrR0ApPs9x2I1EUopSHxCGO7UUrpMNiOccxmsFoRBj6jNds+yw8z2rVgk4hXpTdqrwLF2yq1TbN6Irx2oi6YVVQTN2iPGnB9w7gNo1hMUuZziaWkFlVhEKiwj7CtWD76ZgXxgmmNQShpOzIhV73PML1qHTdxULblftw2GOxyDrRtH1/YRxZuUyHRd2/ew8/VvR7A0q9pCrA8RSV4yClD5W1JxkkA0zdoh3AU7h9OyKXmcZzFHlVUJqSZW3JtIHbY+/ghCeefqmTXUiWuSZO+lR5y9p4yM7uLmW25OBg3yaPOBI/DNg8Y2kIfhjZBUAcPqZ+ALPJhK9+7Zt8/JWP4zkgXYMSDlVpg0e11mR5wfaODU/AaN5/7z0WacH9u3c5mpzwm7/5m6ggQnoBeweHfP+dd7h9/TZ5vsBXkjO7Z5lPT7h/6zpvffObVC1srI8pdU0Y+CzTlLITwgZ+QBDFnBwf0+KhgojpdEbZtS9FWRL7HvuPHhEqhSMV71+7wcGjRxwd7nM8mbB3sM9o1CPuDdGlxZCEF9A4Ho1r7XTqusbpHDSklEgp8Tqysec/zour6oaNrR3u3bv7YVC76dw2Kk2XGs7hwR7ZYkpVN3+g2P0wx38MxvQTbdu+/AFuwv8M/Gbbtk8Bv9n9GeBPAE91//1l4Bf/0BchBEkcWm8eXeO4ktlswu4Z64TnSYVQgQ2lHMasbYwJfMXaaESVLzlzdoszZ7fo9WKkEAxGI1uMjKE2BhHYtJTFbE5jGrLS2Au6rbqfJcRxTNFp3vK8RGvNfLogVMoayw36OO3jwiZcn+HYRmUvlnOW8ymucCmrgqpqyIqKXtIHaT21/SBgNp0SJUFnwmaz7DzPJ0l6GBySLnbZ1O1qoxTEfTylVnwkoGOVgytclC/xPY9hf8hgPCDpjW2eXONgXNBFZj8DV7BcpLYrTJeWDZ6X1iS/qlbpLqaqGK+PEK63IjcuFxm9XkRtDK6w1jK379yzyTBZRrFMbcpM29I4GiEiXE/SVprlZElaaGaTGY7j2ZigxkFULb5UqNZFeIJkbGU7o/EIX/jIRtKGkrrO8fyQUtco6ZP0x8SeIM3sCBiN13n6yYv4UdL5uxcMhwOEW7G9PiRdprR1lx4zWieIra+75/koD55/9ike3bd8oiiO0KYljmJ8LySKPHwlUaLFM92/9wG5TrZc8tnXX2Xv/h10kSGlZHN9xJlzZwBBmhX4ouaFF19Ea80zTz/BdDZHSsl8PufR/iHT2YJBLyHwA+aTE3pJwsVLl1b/hhAedV0TRxGmLrn3cI8g8vn+976PKwRr4z41DZ7yOJlMKbKSyWRBU2RMJjO0cRBC8tbvf4esK3BVA1lekHcF0fFCwtEmG1s7DIcjRmsbJMN1qkrz8NEjqtqsotXX1tYYDIZEnftApluKUtvNnTY8++wzhL3H4aA/7PGjgN8/C/y97vu/B/zcBx7/+609vgkMHcfZ+Q8+U0fy9JQE4VJVJb5SVu2PS5j4dlMX9ynLGq1LhLD5b0kcMZtMKHKbUutQMzk6XG3ZyqJgcniE1tXqMYBlmlPqBtVxOGpjqKrSgt79ZFUkVr+/yHCFA21N7TzOms+ynF7SRwi1ijYqO/HqYjlHOIJ0tiBL5zZlt27sSJNaYHbQU/iitt7hoS16vQ9sm5oqYzadEgbKUgiwhSs3NcKFwJOsj3s0bUWla3qDPkVl8H2JrmrqxkEpD4ONcDLY4tYKSRQq2sbeCKRyVmGbR/v7q5FJ+WrV2QFM53OUJxl0pvNFXq78q1Sg0NkHDMoc8HoK6ULS72FaUEEEvi2uZa0RHxR8uh/28dFLuwAxxnB0dMS1q1fJMjvOD3shVW24cfUGd+7cZTo94mjvAD8ImE6OOXfuMqbxGPQTHOnhKUXcD6m0RgphgX+lmM3nXLt5iyQJ6Mc9aCsb8ikcsjRDnHpzdRwzsEaFlS74P//6/w7Aa595nc2tNdqm5sKl8xwfPuTs7hnrL6Y6EblruUhf+PxnARt/JYR1sDw4PODWjeuoKCIMfEpdMxoNbRacqWibmtl8Do5ECoemo4rsbFtZT6NtEMPW1gajUY8L589w4fIuQTeFfPWNb1F2XL/d8xe5dOkS586d58zOjoVBdMl8vmA6nXDn3n2E9Dg82PtQQaq1ZjAYcv/efZQfkOU5tS5XEeIAnhS888470Pz/J0lpgS87jtMCf7Nt278FbLVte4qg7QGnTvxngQ+6kd/vHnvEv+f4IPk8DD2c1uJFYehbCkFeoELL9/EDawrfCkVLQVZoilLbjVxHlMzzcnUxmarCkzYPLkisCwFY3RxAkWtbcGAVPnlKrvSksm4DXaBlbYxNFGklrhJkZUlZFKyvjVkuMguuCxclBFpXOE6NqSq2drYs9yjN8JXD0I/RTUtZ2u3eeGOLPN9HyYjatUVybTy0q/9a0uvbcMoPdkwmbzCeje6WCMJej+FoADoHabdndVlT5FOcOKJtHNIUuuuMJIpwXEmp53b0dCReHFKUuXXZdGpAokv9oYKulzlzU+OHEY2pEYFLQ03Qs8B9EIbMC9tdVF2MNy3UdUnrfdghlEpjPOtUSqWpjN3KewK8IMSXCukEyAgO9g8ogDQrqIwhSfp4ck46m/CVG9d46skLvPTSx3n6hed543d/h/ffu8Zg0CcrNK2pOFqmSMdqAYNYQeUz6g9ZjjIODibUxlg2vmPpAoXUKKkwpibLC3ylGPZ6GLPkzNqAqtb8o3/4S2xtb7O1s8Obb3yT8dqIKs+4ePlJ3v7O2zZRt4Xbdx/wuc99ltu3b+C4HnEy4N7dWxjTYJqK8XjMSy++CMC1995jNp9izm4xHI6ZTq2nlJSSXi9i9/xF0jTD9TTvX73Kyx9/iTRLGRQZGCu6lsrh4cN9zpw9x/p4hCcli9yqJu4fHduPvvp3mbkpojDk5q1bH3q07iCEo+NjpFJ43fVS1Q3Hx8e4wsOTgjM7O0ynE6q6+QPP/B97/FEL0+fbtn3gOM4m8OuO47z3wR+2bdt2ReuPfDiO85exox6DwYDZxHpfO60kjhWD4ePYJKV8iqUNsVzMZ90z2A8rChRhfMpRkgjXwxWSycmJHQGF7cTieGD9tjG0CFwlEE6NUpI0tZiUCq33tq5q2qruQPUSP45XK3UHiSsclPQpMjui7R/ud3KBmqQXrbqr1lUrygLA+sYGtU5Z5jmB33VFjuTw0Z4tNrqyUUsNq6Tf02KkpEfS63PSORIOh332T5acPXMO37dMapoChGfdB1QIwlInTGMwpiHyJWXdUneq8bLMQLBKRJlNbYimEArheqySSGGlH9w8s2UpG57LQmfEoYcnJdmyBQ+MyaCqSPohWkpaCtKytlHdle7M6mw31T3x6meB6JCO7neSKCHwQ+5dub3S7GXaUGvNdL4kS1Ouzmb0BwMePNhne3ubN9/4BkKGLGZzXvjYBfb39wnCiF5it4l129hkYs+lqnO2trfoDcc8fPSAOI4ZjYboTFHVFdKXhDJACIkuU7bHCXE0Zv/RQw6nBXVjuPLuFYLQupZ6SnF0Yp0/b9y8zmc+9+MAnL94mTxdkMQhB8dL3r/yHl/66S/x6GCPjfE63/zGN1bWKiqK8EpNWZbM5ylbW5ssl3OiwGPQH5DOr4DwiH37GpP+gFqnaDQIyMspYThiNPQJw4BfDENi8wAAIABJREFU/Nt/h6LxWBuPEJ0I8YNFqaoNngrxBF0XZH8mlcJpatouWy6MwlX3dHrID0i5PE9Rak1VN1Qf/rUf6vgjjXJt2z7ovh4A/xz4NLB/OqJ1X0956A+Acx/467vdY//2c/6ttm1fbdv21TiyYQO1MQgq0lRzsLdPWRQ2b92zVhXTacp4bYQr5AqIzTpDfaX8FVHQGKy3kHA7i5FgVdBahAX+TMMys/iL5/n4SljBL5DEIdvb22RFjtO9Lqvcdiw50Q9tUGFo/8fU2rBMMzY319ClXm2sdKFJ05SysOB60XGzkmRAEAS4UqF8xTK1QlttbFx3nuYEcfyYd1RV6Lpi2nGTrL2KQec2g69unJVPTty3WI1pSmTb2qSWOEQoRdpZfNTaWKlBB1wmvdgmy4Q+yrMOhaaqKOsaV7iYplploJWFpVgI6ZAksS1KWqOdEiFqHCnxwwTVreV1Xq8A0tOCo2SDkg2e36BCufqZkA5JKEhCgZLW9+pocrjScGVFYZNflUKXBZ/9wo8Txgm4Hq0Xde9lzORkQpj0ufvwgCB8LEo+vblIIai0pihKbl6/znAQsj4aWQyttd7uBjvKtW1NL/Loxwlnds/yxZ/4HH/pv/wFPv78k0SBz9rGFsfHJwhlnSZ9XzIYjXjxpZf56le/Rp4uuH/nHi++9AKf+OSrPHX5PJ9+7VW++vVvgOtxeHLEzu4uSRgSRwGlrhn0e0hpn2t/v7MD8jwedax5rTVpqekP+7z55pssFyktGt0UtJWL73URYY4kL+v/4Gi1vra2Kkqnh1SKuisytdasbWx+qCilWb7CmDY2OxxY+dy/d58sz4n8H50e+Yd2TI7jxIDbtu2i+/5LwP8C/AvgvwL+1+7rL3d/5V8A/63jOP8IeA2YfWDk+/f9I6s7clboFYZkcJDuY/6Mp2Snbu6RpxmCirWNDYRwmc6mCM8nFIJlmhMFPuI0jhsYjEYUuSaOuyLlJWBAeoK0SAlCj8APOTyYMBwNSLMUT7hEgW9PaMFKbyWx45/WFb1eTBTZeOej6ZRGW7IowkUhrdTF8zrypn1fUWTV+Y1pqaoFo/UNMl1hKvBVTRKGK3a0aECFEXUDgSeo6xLp2zimrMhwHcnB0T0u7m6hdU1RlKvYJ7DcqGxpaQgylBhd0ev3WcwXBMrSCPJCr7SCtWkRwqNB4rr2PZ6a9cWRvyqWi4WVSLRd95X4PZblgkDG+BJ0U6A8D+V5LPMP3EIrjSMTPM9QVQGeZzjFk6tKoKuCxO+BB1Ag65perKhcywdazE6shW3TcPUH76ySQACu3bzHpYvnCAbr3L59HdpzJOEWg17MYjbFFS2+8iz7u61BJPzXf+Wv8JXf/g0ePnpEFEScZjMZ06D804TmypJ8Tcs/u3UNUxmSfo+KnNFowMHBPk88cYksXVpLYqn42te+xvPPPoUKIt678R2mc8uX+sxnP8/d968zHPR5cOcOWV7w1NOX2ds74NbNW7zw4ou4nsvxyZSd7W18v6IsSxbzOVmWW5dT7BY2XTgoqTiZLRn0QnxXoDbs+R7HPf71b3yNunWJlOUcecKh3+8xn1trZ7AUFU/5RNjiUukST/k2yxD+3/bOLUiO67zvv9On+3RPz313B1gsQJAAL6AoghQZSqQk2pJpO5GVVPQQVeJLJVWJU4kTPySVh5RUqUpVHpOHVPyQkpOSkjgpy5bjJHaisqSSTUmRSJmUeKdEQgAFEMACu9jZufV0T/fpWx5OzwC6kjJAYlSZf9XW9vTcvpme/vp8t/+frHJIG+tmJlG5HjqJ8X0f3/eNSnSWk1bkjnoWMo9prgdvJpQ7CPwvIcT88Z8uy/LzQohvAH8ghPh14HXgb1aP/xPgw8AZIAL+7hu9gSUEcn7iA2E84uDGuiHxt+Qi9zM/4YKJKSVnOWTjiWkg1Jo8FxSWmZNyrYJwOkVYRjFknkvSOmNtvcsszSE1K4BUZ9jKXEVrDeOIlLJJ88LIFimzugnGQ7PakhbKrSFFVnX+mvyUrvqUpCzRVX+OrYxj1bqk2WqQxKHhdHIM3W8aF/R3d2m0ulX53STm680WaRKRC5umlMziGUVhQsggnJnBZGEzHA05esthzp49xx133clsFhFV1UWEkUDfONAjTQvyIiHLC4b9vSphbS/6eozdik7DJ4hMwrXAJk2TxecLo5giCDnQ+8Gqiy4qNeIsRDkOZWZWT8ryqDtThG1XpHcKx6lK006OVSqKNANVUHcdMwhsF2iNuc+GIkvIK+6lcN75ZzlEOifVMZ1Ol1Br0nHI+aeex/c8WjWHul8nCCPWLJ9Epziew3q3S6JT+nsDPEfw/DNPMxoFnLzvJN96yYRJ5CmUKbbtQNVXN0kyOh7kwibOclTp4JUZrusihM1wOMFVFraw2N7e5qGH3sPZ717g2LEmnU6HrSNHGY8nfO6Ln6PV6HLw0GHWHUmWZ7z03IuceMfdhJWIZbvRAmsuT+/Q6dTNvFthaHFn8Yxup4vv+zSr1VVZZHRbHWzlVMPGHqe2v8nm5iaeq7Bsh8FgiCUdehvrJFpjSQe7agO42g7gVu+hcWz5A+FbqkcL2wCiqt0k1VdXXGHlhK8Hb+iYyrL8LnD/D9m/D/z8D9lfAr/5kxhRlkaMwPU8hM7otgxJ/UynFHlomhMl5LOERqthmCWLbCHn3fRaWLaiyExHtCV9pJS0uqqi6s0oi4xROKNZqzENTPUK14hXTqcxNc+nv7uLlBZ5XrC23uVAb52gGrzUWWLoQCrZZmywVZ1my/SCzFdtthTkOkM5NZI4ZhJENNrrFcm/Ge/Ii9R0OMe5cb7SZjAYsj+4zB3H78JVNoP9Ia4yk95ZnlfNkrbRkbMEJVD3FRe2TdLd9w1HeafRpFbL2dnZw6vV8Wp1psHVkLVEmhBUQlnadDq1xciKq2AahEwm00pOKzPjO3lOnqYIjOT5cDxASjOvFSc2COOQBd7C6QjbxqnGMcSc2M9tktmG3GyaBCjHoRAaLVLqlkuWFxSiwAGUZTqxleXRaDsMpmZlFsVmJZjmqUmS2xZ7u5dxXI96u0XdsUzIXOvQO3AAV2YMh0PWux10mhqmAWHRaNZptTu88sppanWfKErQmUZhJg/iOMaWNnGSo7Wm12kxjmJs6dDq1snzjDDKkNLBcSziKKLIbMgtDmwe5nNf+ALveMc7+drXvs7D736I5154keO33wZAu91iMNjnwjjgrjtu59GfeZS9fp+7bz9GGJr1huVYPPfCaR64/z5s22ZtrU276dLqrJEkBb7v06pGmSajEbaEVqfD7u4+1Ot8/WvfAOD47cf5yv99EgC/5uLYklEULfJMaZYvcknz/NKPwrUVuLRakbueTxJfnY1znOvv+oYl6fwWlgBpLUYhxmNNrY65WtU8mq5cEKqZxj9tGC/T1JxshelWxbaJohk1z8T7OjDJ3bLIyBHUlKHqLfIMnWhGwzGOsmlXWmJrGz2kzClxCCYBtXoDaTnklk0Rx+C7gCk362TGbDYh0zmu5xkJ62m4aFRUDouVSBiOsbHIAccpQYDIM1KdU6tK1q5T40DvEHmuCWdmrs9VJp80DUJUTaHTDLKCmqvwqsRNu9liOKqYPZ2aIS8rMxp1nxybLI0W5HdzjMdD2u0uynOIk6tXOsf1ycqU1sYGZV7gelUYLQ0tsMwxjrRCmmXVyIyHLuKFU1rcXxQ417QA6MKITRTCsCrMH3No7QD7wZCmpwgSbXJW1Q/fY95VrBdX7zTVOI4yq8IixVFGSODI0cML7qNZpXobTQcopXBcH4gIpiGtdodYa245coTLu/s88eTT3Hb0MOE0RK3XCeMQy3EN06ldQi44dGSL+Nx5RuOAgwcbC45wANevMx0Hhmo51rz04p9z6OBhfK9Go+ZxeWcHv+axt9vn1OnTlNLFVw7K9Th19gLlq6/QO3AQrTUqE8RJzn0n7yWMzHeQZRnbF85jO8ax5HnCaDQgCMacuOsOJpOQZ597DilNBTDMzAUh0jnfOX1m4XBm0x93FprHzJ2PVfF/JXGE4zikaYpbqU/PE+F+rYZjW2AdqI6Dx4HeBtuXd3/cG70pLIVjoiwpdIzbnNOKGL2zRqtBOBkR5Q5bh29hNBqblgHfp9boQpku+jOGgyHNVhMdh+SpQpBRYpPlwkgESQmlXOR2AKNNn2goM4JJQFbGWEXVk6NcXOUykzGWKJDKRlcn8bzjehqaFUUYhuRFSqPdIMtLbClwHIc4L+n1eoRhSHety2gcMJ0MabS6eHWfkojpNKbe6OA4FmXuoConl6YzZFFJNRXVCV+VYUvbYXdvn/Vej8s7++ztD9g6eJD+7i6HDx8kKxQITZZkpvcrjmExJO+y3lsjmSXMQpNUtS1NKZVZOUmgMB3gSRpjX0MznOUFUmakRYGNoZgt0aRFga/Ughs6qk6E+YrJsazF/nqV25izJTqWxX4wxLEsgsScOTpNqbsuaVEQpyHgL04uR7lmhrAKPbIMVJUEO33qDO958J2cv3CJogo3/JpPNIsYj0fUKymtQb9Prdni0uUdXGXTbraY6RKlFHtXdmnWfRzPMhfLKEVKI58OGJnwyYT1bptG21zQpuOAuq8YTQI6zRrve9/7+dJXv0wUtjhxzwm+9cqrSNslzzPuPHEP3znzGmMtcHSA43rcfcdxxoN9fCXZ2x9w9OhRXj9/njvvvIN0FrFz6SKzJEXHUxzXYzKZYEsLv97g8OEtUh3TbPpsb++SCYtZnFBaNg8+8C5Urc75c+e+Z7XjKrUI5cDMt5WVBNhoPDYFk+qv3vA5uHmIrY02jbYJ4UWREgRTLl66wsFem/PnL/Huh+5nOBxw78n7+fSnP8NnfiIH8INYCsckpbUY1/AcC89pMJ2M6A/6tBrrdCrZGh1PabTX2Nnepp4Yqe8ru5dZ761R5FlFJmdK9Jm2FwlruFqRyYqMTrtJlCQoTI5JOg6dThNhmTm7PE05dGiLjLxKdpvGwCwOyaucC3m+SMzXfZcwSjh4eKsaIYjJ8ggK0NqEqFFk5vFSaVEWGeksxfVMZaPWaDHY2yPNjF6eEJ4ZI8mNbTXHQRZGtWNz6yDBNMRz1gmTzMzQVcnZ208cZzzoLzimqD6f1glKGbFQW0qm45B6q7NIkGeFwEaz0TvAzs7OYjA5L1IzOKs1WRaZqhsKXxWkmSZKBK4taXqKOI0YjkIaLfOZdJriVM7FKhVcQ2wvuFpmznMbR2ZYskmp900IKAz9TVqWKMdBUOBI2KjVyPQMpSygrOhiS5Jc4soS1xEEwYC1doML/QmDwZiDvTrMzEogmUXEsfkO9vtXyDprxHHM/vAKx1pNpN8gzwq0TtE6xXW75HlBHI3YOnSAi9sxUtrccniLKAqRRcbZ107h2ApQSAqyQqDznN56j8lwn2Szx3q3Q1CFyxQpzbpHEMakKMgK9nYuc3hrk+Fgj2bdx5ISW9rocGJ6zbICx7GwZNs0X671kFbJfffdx9eeeIoPfuD97Fy6iFIO9959N1dGRpdxvLfLOAzZ3NhgMk1Z63Y4euutjMdD7jxxAjtLmE7DhTyTY1vceeId/Nf/9rt88NGHsC0LSwpmqWlcnvS3kRKUa8QhLm1f4Df+wd/mk5/8LwsBhL3+kJMn771un7AUjqkoDCVHw3fZvrSLW3NZb3Y4uHmY/f0hwXTCbBaQ5QU6NuIAeZoy6O/RaNTJdYrf8JiOp4RZQd13SSvxykznpNr0JBldL5M0BwjGpvs6zzW25RNFI1zHISsEZ89fqOSMBJa00LMJuhqyzaqrz7x6lRWC7lqX/cEVQ5VbKeXOK4LNZp0k0WidUqu3TfNiaUr6ANNpQKfbxrIVs5k5wPkimZgyHpsOd69e5/RrF9E64fjRLc6ceZVGe40r/SumXWHrIPVmC9uCIAgJwoiN9R55niwksbI8x6vV0VXrgiAjnM6MykwQVE42JsGsrmZxSrfTZW9vipRQogEbgcJxYqRVEswZRP2r+QXlOCY0UYq00OYYVyunNNOLFZSSlsnRiZzEtimzbCFDhNaUWYZrK44dWa9e2QfbJNrTVJJkJfG0wGtI0lijZwk5KbNqZCWcRijl4FimtcSWEtuxUbnD4a1NvnLqDO9/3/t44sknOXTwMPVWCylt9gdDkiSn3WqyPywYT6ZonWLIL0N6vXVmWlNvttAzk2KYV4gtCQ8++CBf/LOv8PJLr9LtNinKjFI45Fpzz1138MxLr0KmsYVNrHNzYguHWTzCtu2riiqd9uKkn4wGZuYt06ytr/P4l75Kq1ljFk0JpwHKtfm5Dz7Khct9yiQEbgHLwXUskiQxDBNJwbdfO83RzS6ZkviuRb3WIKl+i/V2hzSZMZ2MF/nWJM2JVIooU5oNn1rd5vN/8nmO3LLFl7/6NJd2+lza6ZOlmnb7LLff+uMHPd4MlsIxKeVgO4rhKMCWFrccPsrLL70MGGpaaSnGoxF5XmLZijyMGA32uO2OOwGYjoZkecmRo7ews7OHlJLO5gY60YaKZK1LlmWkaY4UJj9kS0lzvYdtXc2L1Opm3gpdYiu5qJ5JCwphG6UWaebOAILJGEvaOJgkeqPRZhbF+HXBbGYvSs+zKDZzdFWpt8gzat0u46FJcNuWx6XLl9k6dAhbCiQlsqYMd1CeL5xSmkQLnu1Yaw4eOgTC5p333svFs+e4dHmHbtXHpHXJxnqPwWBoKFYr4j3Ti+UwnWiUV0cpx7BrWq4ZtymEOXmtkryIUbUW+3uX8VwAmzTLFjkg5ZjmyrRyIPMk9xzCtqs8k0VaVHNamRmOvRr+aWpum1kSUmYZaVkiqufMX69A02777I3GqCq56itFhAYUqT1lqC0aYLjfZwWOvDp0qnVKMgtxa3VDHZNm6Czl+Weep95osruzS6PR5dXTZzh58uT35I/2h2PS3Cj5HNnaJEsSjh07xs6li3g1RekpfM/FkpaRgFIuTzzxDaZhxGMf/Bm66wfY27vC+XPfxXFd4iQnyzJ+9uF3caU/IopTfM8IdNpWTrdjfqvvfNcDRE8+wWgSsra2Zhg2O2tISnReksYztE65/+Q9ALTXWtTzEs9T6HCCozwoZ1WO1TLioBVV8/0P3I+UHt98+lkSrXEdwQMP3E+epviO4AMf+ADNhkuRlyRFQi01RQfluUjbZTwJ+YW//AtonaFnAb/6tz7CUAvOvPAsjvIIptffMLAURHFRNCMIQrrrm6z3DhCMB2z21mivtbh47iKjUYBfb7G23iWYBMSzkE6vx5WdXUaDEUlh0Wy02Nu7hK2q0C3RBJMxIs8M40DVYjAahYsc0yw0+4IgIggiRmNT5jRDr2bQMwpD0rSg02mbodU4rcQg40oLrml4pHS6eH4QZlhKUugc33VRnk+RF7jV/J1SLv3dXZRykcph89A6vmeGZKWQ5FnJoD8iSU1jZpKmhMGEWs1nZ3ubPE25tNM3jIRFwqWL22SipOYqgtCwAHg1h6zI2dzsEYSmGTRNE2qeg7TAr7fI09RUIi2XmmvhKIXIDWNCVojKYfE9TJFOVW2bJ6+jSoxRXdMFPE9cw9U8E7BwTsK28avHO7ZNViRkmUXd9xeOB1g8xleKcRiZEC9N0Wm6yFGVGGffKAryNDcS1rZNORdmLC2ktFFKkcxChH3VTmmbecsXXznNTBvb5hJOUkqktBlOJmR6RpqXKNelVdE+W1IwGI5RtkuaW9iOR5JoTp8+A8DxW29jf3/Iqe+c5sLF8wjbZhLMiMOAMDQDtv1+nwuvf5d+v49f8+gdOERRZrRbDX7nU59iXK36kjBgbW2NcDJhZ3ePNInY2jrEh37pFwnDCbpqtXZtM8MoLJtUx2DbkJkowXxZNrNwzP5+n9fPn+fQ5iZ/5UMfIopzQ/WTFzz5hKngzSLTJ5frHLc61mVpc277Enma8vjjjxsOtVqTZ559jj/6vU8bZwjYzlVh178olsIxCUswC6f0+9sEkwmj0YSdvQFSSLaOHKDV8gw5WDJjc7NnBmxzQwOy0etR5hmjQR+/vobreASzGXt7e2bMxGtgK4mtJFleLkK6A+trlZ5axe7oWKaDezxFkOP7NSZhQK3hVmX/wFB9WA55UamveJ7h9C5NOb3hG+fie+6ip8k43YEJB2NtZu8QrG30zPR+VvD62fM0qpVOEIRIx2Fto4NdljiyREplEvkFNNoNvHqdE3cdx7aMRLZjO9ilIAhGdFp1BEZAQDk2Wmesb3QNvYWSpEnEaBSYUFHCNNZE4YTROGIWG8K3MEqqz+oSBhMsyyFOwKmUaOc6Y75Si9XQ3OnoNMUxPW84lkWe21dzGNckw9MsI8vM/3EYoYt4sRKzSsMnFVUrseiaMvb8tcG0FOjMWnSWS0fiNzxqShHrnP5gzOXLl5G2WtgA4NR8LMtQthw5egt/6eQ72Tywzl0n3oEtLRKd0Wm3sB0zmiOlpN1sGPkpWbC9vV0VQNQilxeFwYLnSClFGIeG1TEzXF5ZJswYSSnI88yoqDTMhWpjY4P9gVH8zQpJHEV89KN/gyJJSLMZbr1paFF8l2bT5yMf+auMoj1efvE5pLQWv+Hz27ukesZXn/g6V/YGSBywbWZ5dWx0jPJcTtx5J7cePcq58+c5/dprSGlRrykubF/CqxnJMumYERjf88kK057w/AsvcuzordR8j4cffhjbsjh75hRf/vITRLOY555/niAY3xDe76VwTJ7rGp34rKgmvqfceuxW8jKn2WqhZxpX2dRqTcIoprO+zkavx/E77iDRCd21LiWSnUvbZLkpwbeaTaZVh28wneE6Hpk29LPhNKA/GjGdTE1SvJLndj0Pr1HH933TdqAUruOR55rpZITWZsC3yAukZULQ+ZhHs/ohg5nIj2YRWZEbdoP9gMHQLG/TtKBRr5nQLjM/AL/hYVsl4cTQooBhEJCOQ1k5skbTsGBKSuIw5NLlXbKCir7XnKxZJhlNQqQlkVIhRLYYUA6mE8JxQDBNzEiJ45DpnM0DB2m0OhVpXIZODVeP1qmhVfEa9Id71D31PcfMsW0mwl84JMeyFo4nrWbv0qKgEHoRks0d2Pw584re4jWr50+CMTpPUJZHWg3VlVmGzhPDj+W6aG1GMFxbkeaQ5AmiVETTmGZVPUqzGVd29ginU8JwZmTM1zuks4jB/pASBylN9VcpRZnFtFtNWk3z+v1+H9IZGxsblLmmUa8xDmb0eutYtqJRr1Gve7RaJiQejU1H9dFbj5PqOX1MSbPT5dr55WgWk+cFrufT6XR4/eJFisysPKW0aLRanLz3dg701ihSI3slLfOdPvbYB3j8q39KoybpD/okiebyzg7JLGHr0CZ//H++wG/8w7/H7bcfw3FcWvUOa60ONcej3engez5COnzzmWfxax6vf/csUto4fotHHnmEc+fO0KjXqSmBFEYOqqYENU8SzWJst0aRl4xGY/78G8/y0Lsf4hOf+C1+7Vd/hfc+8jCPPfZz1+EJrmIpdOWEEAFw6mbb8QbYAPo324g3wMrG68ey2wc/HTaeKMuy+Rd98lIkv4FT1yOO93ZACPHNlY3Xj2W3cdntg58eG6/n+UsRyq2wwgorXIuVY1phhRWWDsvimP7jzTbgTWBl443Bstu47PbB/wc2LkXye4UVVljhWizLimmFFVZYYYGb7piEEB8SQpyqdOg+9sbPeMvs+E9CiCtCiJev2XfDtPNugH23CCG+JIT4thDiW0KIf7KENnpCiKeFEC9UNv6rav8xIcRTlS2fEUKoar9b3T5T3X/bW23jNbZKIcRzQojPLqONb7WW4w2wryOE+EMhxKtCiFeEEO+9ofaVZXnT/jAkG68BxzHEHC8A99wkW34WeBB4+Zp9/wb4WLX9MeBfV9sfBj4HCOAR4Km3wb5DwIPVdhP4DnDPktkogEa17QBPVe/9B8AvV/t/G/hH1fY/Bn672v5l4DNv4/H+Z8Cngc9Wt5fKRuAcsPF9+5bpWP8O8PerbQV0bqR9b8uP4Md8uPcCX7jm9seBj99Ee277Psd0CjhUbR/C9FsB/AfgV37Y495GW/8Y+MVltRHwgWcxvO99wP7+Yw58AXhvtW1XjxNvg21HMCKtjwGfrU6YZbPxhzmmpTjWQBs4+/3fw42072aHcj9Kg25Z8JNq570tqMKJBzArkqWysQqRnseo5nwRsyIelWU5H9m/1o6FjdX9Y2Cdtx7/DvjnwHweZn0JbZxrOT5TSZ3B8hzrY8Ae8J+rcPiTlVDJDbPvZjumnxqUxtXf9BKmEKIB/A/gn5ZlObn2vmWwsSzLvCzLd2FWJe8B7r6Z9nw/hBB/DbhSluUzN9uWN8CjZVk+CPwS8JtCiJ+99s6bfKxtTNrjE2VZPgCEmNBtgeu172Y7pjelQXcTcV3aeTcaQggH45R+tyzL/7mMNs5RluUI+BImLOoIIebjT9fasbCxur8N7L/Fpr0f+OtCiHPA72PCud9aMhsp3wItxxuIi8DFsiyfqm7/IcZR3TD7brZj+gZwZ1URUZjk4v++yTZdi7l2Hvygdt7fqaoNj/BmtPOuE0IIAXwKeKUsy3+7pDb2hBCdaruGyYG9gnFQH/0RNs5t/yjweHWlfctQluXHy7I8UpblbZjf2+NlWf7aMtkohKgLIZrzbYyW48ssybEuy3IHuCCEOFHt+nng2zfUvrc6ifcmEmkfxlSYXgP+xU204/eAy0CKuSL8OiaX8GfAaeBPgbXqsQL495XNLwEPvQ32PYpZGr8IPF/9fXjJbLwPeK6y8WXgX1b7jwNPY7QG/zvgVvu96vaZ6v7jb/Mx/yBXq3JLY2NlywvV37fm58WSHet3Ad+sjvUfAd0bad+q83uFFVZYOtzsUG6FFVZY4QewckwrrLDC0mHlmFZYYYWlw8oxrbDCCkuHlWNaYYUVlg4rx7TCCissHVaOaYUVVlg6rBzTCiussHT4f0vlXItGAAAAA0lEQVR7Rq2vKv7FAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import matplotlib.image as mpimg\n", + "import numpy as np\n", + "\n", + "# load image\n", + "img=mpimg.imread('example.png')\n", + "\n", + "imgplot = plt.imshow(img)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Applying pseudocolor schemes to image plots" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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E0kp2xlUAzjZ2OVrpsxSOONfYoeZlVFVGL48JVUm65PqntTLEeBC2U5qv5ERbCd4oJ1sIncV3ZUi0OSZvhyx+PKSMwFQ1xnfBCuO7TWvxjwMq2xodWZovCJq/X6MYBDx04hqj44L6tQLjCdrH+sCBd/LL198KwIefuI+8IUh3Yjqf8xCBwXiQtwPKWsDSY381KuX1wfwWoBKHfYxPGIpOSdEpyXZjJqcLti915ofWo4zSSIpCURSKJPO5sbFAca1KZV1w9OOC0796k3RBYv/ZAp1/UHD1J2F43CfsZqhEE+8U+GODLNyO7aXwj37ix/nFn3sP/keajI54jI6o+T3TBTmfgCoDLwETa6Q0XB+2UNnBwpaJU1CqMIyOhGy/ucXk/DL5yQ4iL6l/YROVuAhGsdZAZm7hZ0sRk2VF1nL38icWeSXm649fprhWZfWt6/N7KGnoZxGTpxbg/1ji5fd76FCQtX2SJYEsXGNN4NqrQ6eExn+4gj9krpxmkalyaswVdbeoXg2Av+Z95pRj6xWnCKVnEIXAKhdNVKkg3nH40Qw8l9pZO14Cw6MeOpAUdcXoiMKfGIqqA7OrN10AxEtApQJvPcD3NTaXFE1D3rJMjhiHk7UkwxM+ZdVZYEVVokPB/v0aHQial91mlrcg7Yi5MgDIm4LOc5qdbp12JWE8PMCIAEYnoGhOAwlFQNZxz3wrziTS1y6fyekCU9XoUHH0kYLmlYLa/9vk+tVFROn6ou5lLFXHlEaSaJ9QOdC9E4yIVcGLwxX20iq7WRVzIsFYQT3KMB586+mLZG03FjJzsAG4zW5wZ4Prf6fEeM7iW3rUmyt+f+QUbRlB2lY0LsHkCIQDTedxj1gVGN+y+4YAHQpOtbpk4wBfuevHXsGbTl6ltjwmGFhqVzxq6yVrH/SIepreGZ/+mYD9u17TJX8p+S8WlbtVVOp848ZlsELwpZqlLy4QpJY0bxIDoQ9mqjuiQBAMXIh6cFLRvesoa49mWE9itnbIt1fp3WUxXo3O8yk6VFS23aLKGz5l6QBZYSDaN4xXFV7qdvDcd7hNGU8XWO4WM8pSDEKyj1UpW1C5LkjWYvixHeQ0UghDio8tE18fgDasv2sZlbZY/cgNTKvG+FSdrOVTWU8czjbFaKyCYGBoXBZsJA3ufugK2+MagdKEqmRSBEReSfvhbdKH4VyQoa8s0zsXEu9YkmVB3jLEW27h+AOoTCyD0wJx9xC+WEdlzpLSoVMCwDyid6CgXjtWxhfIAoJ+iZWCU78D1781Qs/oDAOPdLWkfk1RTsPeOhDkNYj2DCq3yKkSS1uS2rrGeIKy6hQlhdukAOpXDZtvN+ibVfxUoCNLUSsJN33ykxkDAvyRwJsoGpcTirrP8IRP50nF/gVBtCMxHrRf0EyWXcAkKd2zRXuWaLfAD1y7q1+MGDZDssyjkjoFmS46S+6/OfkoP/eB72P/jcwjcl9KKc3k+KldLn/fInfdeZO1eMCPrXycH/7c36PoO8sp0T6Z9uhnEb1RTNaLELkk6EpMAGHXzYWNDOK390iSgFEWkC4Jrk3a5HVJzUK6WkVHEm+sUYUBfBY+GjM4A2Cd6/ZdfcIPNVHFNHq5JBC7luqWZveERj4mCYaWR144T+uBLunjHWRheerSCWwmGYxifF9zXbcwTUG7knD9bTX8nuDGDxXIazHHP1oyeDhl9YMBp/4g4aX/OoDf/PLr/c8jrwuLSRjHR3HciIM/cLwUlYNK7fwzcItGmOm5012h+dIYmYM3cQD45luqcOep+Tl3/siLGCUPcYaCQUFlO8efGLwEykjOlZLxHTAd9i3BwAHytQ1N0LecP7NBa2WIN7H4ieHmt7Qo/v4e55o7bPXr3LOwyZnmHvYbepz65WuY/yth7U96LD41YvNdxzChR+35PYwn6N9RRaWG5uUC6znFNzjl+CVXP3qK5z972j3z1I1T0hCqkv1hhVEacnm7Q970kAXU1jWiZK6Uysi5a8OTjhNVXDuwGsL9qaVnXSRzZt3Aget2qxU1c+vcuLhjdSSpbBycZ31LfNOjd6dTYsaDsG8pKwLpNl9nFc6On24u1ZsGUULeZKq8oH+HRCWSeFMSdgWiEKAs2VqB3PMdxcBz7tbmW6psvSnAm7hzK5sWqxy4nrUkVjkYYPuxVV7eWML4oENJq5awP4kxHvT3q9P5aBHaEnYdrPBzF99B2LfUX/AxuyGqVmAjg40OYynHT+4eer/5Gyd5/mfv5ZnsGNk4QBYwyCO6WYVr6x3K31mi/YEqS5/2WPiCxB8KZA7jE5rJmQL1TV3alQR5JeZYs893v/cznK9vEf3AJslqTP/0gWuZN/x5xCxbK6ldg92HDHy65eZ5r8QfWWTp1pMOBOHlkNERj7whsLnkQmebcG86LhOFCA1S2Dm2e2V3gfW9JqqVs/CsZeGjMYtPWfbuCcEIwl7JZC3i1G9/9dkkrwvFBNPd8lWicvBS991MKdXWC2rrhZtoyfSYxB2Tt0M6z2bIHBeNSWHj7S3u+j83MaHlsWfPIox1UbPIrQjtS8pIoYMp0VE5pReMbiHblS4U700sZSjIFgRXPnWC/n4VL7XkNUnUtWSFx6PXThOHOYn26WYVzi7s8XxvhTd3rnDjp0H86y5LT47YfcDtnrVrE4R1CzxZ9NAB06ikw2kmZ3OEdbytGXcJoJ9F3LmyQ/03GvhPVyn+/h7VjYK0rTj2yNhhV7kDgGvrhupNS3XDsnT3DqIEfwijo85tMr7FHxb4wwITqCnIzRzwvtWNk4WdKylhLEG/pPNsztojCpm4Pi2rFl2xjtQ4PcebuJD9ZFmRLMPgpMJLXH+q1OBllnjX0H7JTQRRwuLTJcc/WiILGN+dEQwErScDsOCNJP74QLFZz7mttZs5jSuOVVjZ1vhjyOtOgfkfaLP85k0qX4iRBZQVRfbBZbIXm6gpEbFWyebAvI7BHwmaP19n542C4QWnkI50+jx89yVUrUDVCo6f3OX4yV1uPruCnCj6H1qj/aRH83JBdSPjf/ns98z7z1pBWvp4YUn3AcP2m2H3QUP3DYZk1VBWLTZw7NXB5RbXryxSLJVEqmA8BWO/+8gXUcnBgrFKsPJPX6F/VqEDWHrURdfCHYU/tPTPzdZOdsgKbr8wdU8DQe1ln0v9Diq37DwM8U2P5eU+xdifu3Nr7QGLrRG6F2B8Qdg3qMwyOWqpPheStTzqrwzZ+bqDAM1fVl4XrhwcTLAZ2etLidAwOuLPlZgqLGnVhZdnEm4OWU5LrO90rpqU9N+4CrWS5Y8FXHqf5M5fnsyPV4VB49w4VVgo3ESWpXNZVGadRbUxoFhrYJQkrweMzpaoQBPvGpJO5NjaWuH7mlqY88z2Gp401KOMc80dXhot8/ZjlwB4qeKIcIP7F6ldnaB9AVYyPOHaPDomyI4UUAhU/7VD1E8imnHKMA8J/94G6bNrTC53OB45LAUcXSBvQm3dzl1eL7Uuornvop2DUxJRQmVDzMmVM9B7poxuJV3OLKZXEzBnNAJTEciJwvgQb0oGb01of8JhN8HIsYSjrkHlAn805W9NLEX9YMDL0EVJZyHnvXsD8qYluBriD6CoQ/WysxSKqrOYy/gANB+vBVQ3cpYe75McdRZQdUNgPOc+7nx6DU+4jaeMBEtPjgkGFXYfNBxb65JrRVFzcypbcJvUyj+/xBmV8/lfv5+sBTcnK1yPF1H1At/XpP/fKouf2eFCts7ovlV0OGWqewKjJLVnQtIlR2w9Vu2RaJ+09MgqGSv1IWfqeyz4Y44FXZ4eHyfTjhaS6YN+XgjcJK9Md+jRMYcFCQMmkFz/+XNENYs/shQ1gZdCdX3mSUBZVfgbOV7iQP+s5bhfIOjdU1J/xWO3V6MFhDuOP9Yfx5Af2C6bvcZ00TjqSO+cIt62VG66Mdt5UFDdDDnyJ2Oef82s/YvJ60Ixyan/G3VLRkf8OWBXRsJZS4WlVGKuOGRh8UduAYX7B9cJ9jPQBjXOKVsRMinZv1Aj6mnO/2zCi/89hNUcOc7IFkKC/YyiEeAPcjdwI03W9qhsO9xjNnknywGq1UGlBlk4EqMoXIpB3vCnbqillwR805mXuTlpkoeK7qBC6Jc88sJ5vLCk2A85f+Eml94bcu7XxnTvqSHykoWne4z/95TJlcNhaGEE3tilvgSeZj+NibyS1fqQpPTRRqKkITg6Rr9SY3BC0Hq5ZHQixh9A44ohGGqSBY94zyCMZfPzqxydgtacCly06gFL65JEGDVXTDOFM5OZUpopqZk1pVIwgUIWcPyDkrQt2HvAknYgei6mf84S9BxDuIyhtm6IulOXNDOkiz5pSzocqumsnsY1TQn0TzkLMtybuo0xpEuGoCcJu5Aft2AcSK6020j27hOM1yI6z0rCnZTJ3TXyhkDmziVPj5bceXaD7d8+Qdg3DE/GzkqeCGKvoDCSwWkIBoLquiNiPvu7FyhqlgjHk/PHklPvucIwdxZM/Hf22PZPzsmMZQTB0LL9fS7/RqmcM50u97XWGeuQUJW8cekGAG+qu81qq2gSyYKT0R6f758g0T6j0l1/mIcM45CdpEY9yDhW6TmC5EigMoPKNMHAcYi8VNM/Hbl1o50bD1DGknQlpnE5p3tX6PCmjmT87SPqn6271KDtiP17LUcf0Wy+WVHsxERLCUkW4HmaleaQrb7jc2VNSe26ceD5bz7L/nvvRy8XGCWneNdXJ68bVy7cL7ECqhvO/K1uZLSfG9G4llLdyGheTuefx7s5Qk9zerSlqCnnCq258JINPMZrITIrqW7kxJuOkRpcC+CLdba/3kX5TOxhlWPKqtRQ1BThfknYK6hen6AD4ULb+yXxRkK0OSZ6ZZtyCtOYiTfP5zK+wBrBsHQUhv5nl2n8UZXxI8suRPvpKn7b+Qvf+bYnyRZCJquCshXN2b0iNIjQuHyxXGIDg9Duu91ejWaYkpYeFz93kn7iZlzsFSz9aozfFyx/fky0k+ElBlVYKps5orTE3RJvoskaito18FJNuuDRuGKI9jVCgzfWyFxjAkVR9+fu3K0Rudnrq5WWzF1YWqXuenQyrG/JW5ag56yPvQcstXVNGUuytkd8fUBZVaQtR9ZLVhyLurLllH/vvKC6ZYh3HGZU3TJ4E2dNB30H2quJIOwJR+BUUFYcsB51LcMTPnk7JOwbqhsuCpq2JXee3SApfZJVl75ipbOQa/d2UdIQeSXVe/ZdlLjiriU0NC45CotV0Hq5pOZlNAM3Hknpg3XWdeNKRuN6iT+xHF3sUQxDvv/OJ5gUARWVczJy1tFSMGTBH3M5c+lkExOwVTTZLWp4whCrgqVwxPn6FmuVgfvMK4iUG5fBCY+8JsmaCj31DnTkXqN9jSrsHJYA0L5AB478Ojxr6N8B+/da4jCnjKB+zVBZl5iKpneHx+pjGvxp8GI6B9PSw/M0IjT4U64hQP6Wu6jdzDn2AUXQTQ5Z1H9ZeV1YTDMR1qU0jNdCtC/myakwdbNeJTPrZf6+tIgsR21ntHf6mGYVf5CjKx790xFn/uMeN75jkeUfukrxP68gk5Jgyj0S08EV2uI/ew1RqxI0AvKmR1lVQIAqPMrVKq2XNXlLUjQsk2VFdVOTdBTtT0Q8/wVH5w8zFx4P+i5s7oh4lhdfPsL2ao2FzKBDSBYDRvfHnIi6bIYleivGSwWLT1q23qooGga/L0Ea0tLHlwYTGwLvAGPY+eGEOMzxftcyOtfCH2qWro4QSU6xWnfuZ9Oj9eKIvTfUyFo+4UAjSov1BEFXOnZ3aVClQeaH9yuVloeU1azfb8UrZviUjiRHf8ultWy+JSBbsJhAoCaC0RHF/n2u3cnCIrUNzcJzEzbfWkWljsZgfLe7VzZgvCLxxxZ/ik8BNC5KklU7Z4h7Y8d0ngG6RdPSuwDRtmDvHgeGLz49QeUhe3d7XP3UCeIHupRVx4zMmpLRWycs+SVJ6RN7BXGjoPX9W1weLJAVHsoKCuBMYwDAWjzg0rBDM3AdsPfho6gQ4j1Hwu1dgNYLcLwyYPelo9y4r8255g43kjbxVLG0fAcnPN49ya/efBNm4hHd9Am7kLVvneMuSFFWnStqplhapWscg94XqMJglIMj8oY/HyMxXUOgD7BDAAAgAElEQVRzrotwuZmVdYn2wUsEX/eWm3xhe8HlGN40tN+5S+/FNa6/29JeGVBohdYHDHBfaZe3GgVEXYPxBApQk4LJHSE33hvjhSV87Msu8z+XvK4U0yxaNtuNZ8op6pakC69tqiwtwVCT1xXB0O3GADbNoNVApAUqLfC2c9pJk2KpStS1bAwaLOJ4ICacJuoOcqwSUyUENkkpq4qgX1JWFaowyKQknJT4Q0lZi5C5JGsJqpuzdrsEZB0JKtvODTQ+xDsGHQoGqc/q8S69UYXh8ZDmy25g2xdzBllEsR/SekkitGWyLOk8ZfHHho3vzqiGJcYKdj69Bqed5bX+xRWO37+BfqlG5QnDC/+k5I5fzCjqPiLJEVnueFVAsuQmbHVTE21NE6HbIVlD4U0VzMximslM+dyqlA6zwA9kdp4/1NP0Ikl13bL3UIluOBrB6Di0vuiRN6H/pgweD1F5QO2mYXREIsspXuQJBmcg3gGTQ7Yg8IeWaM+QNwRBT1DZMgzOSIqqwB8xx6TSYynshi5v8gRM1mD/jQGUULmBc3keXUDfUaBjyei0Rhhx6FlmJF6AY80+vTRmtTrg6Y+d5/53vsjHn7mL1WNd6tOASTC0hD3D8Jiica2krAk6Tw35/uXHufyOBR79yP0IM6VlTCsyzKK8AEuew/8Gp2By1BKe73O81eN6r0WSBMg4pygUvFAjXzCc/p2S/TsDhPGpbBUuDaSwyNzgpZpL7w1Z+axbH9p3Ck1oZ9G54IUkW7AsflHziUfvJeoI/KFzz3a/uEItB1Ut5uTKMCwpS4XnaZIsoP7BmstVbEqKmqLVL9l5o8sPPHL6Jpe7C19mhf/55fWhmKZlMMBFGGZYz8xKyuuvRcNnymuujMS03AMuQdLu7ZM8eBqhLdFTV5DNKjIr2X0oQFxusZyMEUmOSnLo9tBnjswxp/SBU1gliDcSkrUYlRr8jQEiLzDbu8h7zhJ0ncU0S//QAYQDS96QqNRNAA8gneWvCZrtMVnhIZ9wyb8yd/jR4lMedVXSeNEDC0XDYWtpR2A8yfJyn3EWsP3YKtE+eJOQ3rUl9KpbjZVNQRkKwksh698YsviMUxo2nCZ2tkNaz3RBGyJgfKJCZT1FZY7YOMNGRF5iKwch6JkV9Gqsyfj+XFmZwOFSVoo5DmV8H5Ua8rpH53Me/TtctYBgz1U80LGl9kyI8WCy5CJIzcua8aqjSFR2NKMTyrmYiRtbWUDaOaB6GF/gDxzulDch2nOpLMHFGB1ZRscFRcNgWwX+1BLNOgZ/INF3jRC7MaP7MoKrIWYQsedpOs0xoSrJtaJnYo7Xe4wKh/M89ak7UQa2J3VOndp2c89I2lFCd+ICJM1nx6ANd+xU0RWff/To3+L77n+C7ru2GOuAP33pNMfWumz3awxSn9anQzrPp5SRyy1cerLE+ILi8ToD6sR1iR8BxITa9UEvkIzX/IPNRJs5DQMk2pesfXJq0eYW44mDYNE0kldWPJY+b0jbipXPGta/vaD1pBv3+iXJ+O0j/OdrlFRcxLsE/aYRcaj5F/f+Hv+T9x4qv1RFaIkEhsdDkhVBvCd57uYqvv8lQux/QXldKCZhLNG13sH7vKB4cI14Nydr+RjvMKYB0xD+FBea1TkCXP2jKEQAlRe3Mdu7iOVFSHJsHNB+WtI/P1UmLVdiQ07LScxy3awS8/fxRoKJPdITLfxBDstNhIUjf+IA8917PaKtBJU4t695uUQHjvFshXNPdSBQmaX7cgvTKbCnClaO79N/bJnKTTB/e4eND52g80KOLCyTlYMAQLyVMtnvEC5I4gJ04ExzlQmCvs/uUpXad26xP6wgvlgn2rPs3O9TXVqi81Qf67mJXHkxxYYBMitRmcX6ktHRAOM792B4MqZRfnnQcsZnupVWAI7PdGvEbnacLCydZx3Irv2A0UkYny5QAw9/5FxbO00/KquW+kc1i19I2X1DhZ03eAQ9hyMly85aivcMY89ZVToQFBVnCdSvGsZrkrTjrKaw55TZ+Aj4A0GpfQw+tlOgNgPSowWhdLWuKs2E7HxBMQhpVzIaQUZa+mRacXd7i1gVlEZSq2bcqC1TLBiuP7fKmx9+kXJa+OmF3zlPrTRkLZ8yclErL9WoScnZ/2B4igcoI8XVH7DIPZ/ywyu0psxsldl5sm0ZMVcwM7fVLQb3YizoFiw8Y9h+q2XlUShqLjAjrEuQzhuCpS8k+CNNUXPjM6/VpZxXILOS5iseg9OKcN9SVCV33XGdjSdPoXLL3ptLmo/WDlJtrLMIh4OQgZH85OPvo1FL6J73mRw1yEJw5JMl/kQS7+bEcU6S/DWhC4iiRGTTSJEQ2MBN7mTRPaAsneafySwiN/tOpQYdSbdLn1rE33BYANYilzrTLG4fE3oumbEnydsh8ZUeuhm7+jfh4a4Q2lJGCuFL/EFOcLWPTVLQ0xyye06gp3H4ouHaWVQkxncTIRxoRmsuVSLeM+hAULkpGXVAJoru00uc+dCQ0YkK6QeWWH32gPPQeCWnf65K2pHEW1DZSMnrFVTmkpRlbinqLlRcizJ8aShLhX/fkIU/kuw8UCXa1xStiHTRZ7wmqNyzQnzTWYnxZkLvzirGF5Sxc4tmeXZqnM/BeF3xb8mZc22b0wfCw1UJbgU8Z4pLZRYdSuKuofVKyc1v8tEVS1FzUa8yhKAnpriJIVmJiHcNXuIWWRlDsmAxSlDUFd4YDK7iQeN6Se+sT7IkqV/XJBPpML2BpYygetMyOuFoC1ZBXgSUNYOINFkvQi5mTMYhJvGQE0XgaYSwVPycaxePMzrqLKV2JeHGVpv4piI5X6J2PZ5cP4Z+qcbqY5oWJdsPKkCRLwiolVSbGUWhyFNXNsfmAsppzaPSOgWUWYKeS12au2LaYUWz4MI8nWf6ndfPGJ6tQ6Mga4Z4qXN7hyclYdcS7luylrNWX71mZjy/aLMkGGjKWBFvOxJxN6kw/sYRRTei+fSMUDsNLs2KCwYHm1avW2NhaBl1CqxnEKWLaO/fGZFlmkYtefUS/wvL60IxYQx2PEFUK9jRGDrtA2wndQmxZVVhPOfmCW3nVo03ni0cd5z2JbJVASqMj1eoXncExis/5erRrP3nm/gPr3H92z3O/Yo/z1UDpkm4Hszgk+mrzErK5SYyqx46VvmS2k1Fuui7aN5A4401OlbI3FDbcK6mLF0GfXUD8maIMHDyQ0N0xXOKpgDrS/zNITbysYFH6+KBohJ5CVRciZTE4TAzQuAoDTm90GX4qTV0BBs/1cP/qGV4zEMtqTmOUX1ue14NUcQBacdFw7yJi4BlrYjVR9O5UnJAtjft29fWsbgVcwLm1QiMP7WYcj1391Rm0KHk6CcKrnyf4zqNTlqCfen4NjcE2w9G5A049khKvOMwsbSj8Eeu5lL7uZK07ZRddSNnshIQ7rtk4HgjofGSdosWB+oOT0pq11y/y8IirjJlt4dE+4b+6Zh4x9K6OKGo+xSf7NCLXEmWxj2K6ifq+BND93yLo5c14xVL9Y8Dwr7hRitm6aKjM6x+Zsjy5+O5EjCejzCeY69PLXmVGHQs2XyzINwvCPYmiEIjJk7b+1OX2zRitxEODUU9IOy6QTa+RJQG2RshS0fMjXqOnW48QefZkrzm8gSrV4Yu0gsUCx6ytPhTJVfUFBHOogu7sPO2Eiwc/3cLqAc8ytMZXqooI0EwtPMyMsYHW0gMIPd8Wi+5lBlbSE7/ivs+2M+oBpLy0Rr7b/hrYjHdKqJagTRDFQabusxoq1woeoY0zZTSofP0gTtnYldCpHrdRT6ufE+NIk9Z+azLmK5s58jCDd6sWuAMCJdJOT9/JrI3QjIF1Zt1RJaTXljBSzW1Gzn+IMfE3jyhctZGb3ygqKKthJs/4fGu88/z8n933pVTOVVxE2diyBs+Xs9Zdbcqy/kz+S5s608MVkmsdNGuydUGO1FG74GCU6e32R1VGR2H6k1oXsoZr/l0niuxcQjazC3TtOPC7rKAaNfhEbO+sJGP9aYE1VcppS/3XocusncrKK4ygwnUofSV4x+E69+t8Xc9dDiNtkWOoR4MYHAqdJSRmynDE1XypmXpKad8skbNFcSbFFQ3pwX6lCLsTV1wCcFAk7U8/MG0mN++y88ro4PSxnlNTmkILr9yNm6ydM8c9tyYrP9IRud31fQ7aF2ccP1bq/g9x28a3lFSVuou2tWw6GMpjXpCmvsUhSJ8pkK2aLBKUTk5wHusxZX3+Jz/xQOlBCDSzMEPeYmtRIhxTrBXYoOp0i/NfE6EvQIQTJZdW+OdKScstyRLEpHkZKfq88/c3BGOLuALxqfqlLHLjxOhRnQDNt8qMCcmND5bcWTZqbU0L+sMRDd88nMJ/kASDA3d+wTH/8BBFsG+U6DRVsJ4pXZQsvqrkL/yX0n5y0jTX7Jff+xHsOMpI1sp7GqHbKkyr2sEEF3rkZxuHzp3Vl7k1hpIqjAki8HcWtr6HwtW/7lAJPkcVzKxh/f4i4h6DTscIZc6X7Gs6atltsBNq8bWW1usffgGNs0OFwKLA5eo21C0Xxgh8hLZGzF8YM1dw0D1YheR5fPjRV4iJunc/ZxVv0xOtYg2x4xP1eclacvITbrGuzcYZQHZZzqEb91D/v4C/sjSeGXM4GyV9he63PiOxXk0KF1wZMZ452DiNq6WVK+6NpatGFGauXL6SnIrvvRquTXCN4v46VCiI8nuvd40ZI/jHu1B65WCcCdldLJC8+m9eSnZnYeaLH3ObSqyN6JcblLWArIFjzIUxLvu/smi5zhJG5oykvPEay+xSO0Kw6ncucMzaoqw0Pzcxrz/dcWf5gg6hTo6ohxZdM8p85VvWKf3B0cAWPnea0z+7VFufJfBmRMQtVKKwkWwfu3hf8/7PvZjoAWqXtB6JKaMBaO3TLjzJ6Zl8ZVy8IByfSXiCNM8sMxFWjhLKi8RacH+GxeJuiUmkIxX1ZzpHfZchNefGKpXhoxP1TGemD/rjHbjTwzGF+T16fuRs7pU4ZKaR8cOrJ0ZzmmnTHmZG0zg5sR4VbH0p735mJhWDRt49O6s0r1P8Mo//ceft9Y+9GdOoC8jrxuC5cyVQylEFCJ7o4PvlONqFGsNhHa5bkLbQ1YSHLaa4l1nxVz8BwFL/1t4SOGo3gT/2WsH59Vr8wjWlxKR5fNFolsVdKuCadUoV1yCpD+G5NzyXCndKvFGwuInb6J2+pjQY3JhBW+sCXvFvIjc7B5yf4gYf2n/vPLiNv27mtPnvLVxMMoCGlFG3jrI5m+8Mmb/7hrDk5LkeINj/3kXLCRLTinl5xKSJUHWcomueU26HTvwXqOUZsXibhUTKIcjfRmlpCNvbkW5HwWYgrGZIeiXHHtkzMKzlmjXKaX2i44MCtC4OGB0YcFRHpKc5qUc2RuRLcYM37CKzErCm30az7j8wGTRI1n0KGNHvfCHmvrl8dyNnSkoV5fo8G5uBQzfsApKIvtjdOSRdqaE24qgtu7KHFe2NfVrlv0PHqGY6o2Xrq+46/oGVS9Q1YI8Peinv/WZ9yM8V1Ne9wJXaXPHcOzXfDfXYY5ZiihExIdLr8w2JefKO0t29+vELURKR6RtX0xdVPDlMdUrQ8BZVjNr/FYpKpIycjQQlToAPfzhTbzEsfDBzS/356zKqFvyD3/m19k/HxBvJgSDguqmRqQFsjdyG3KSI/sTvMxSLB6mkvxl5HXlys0sppmSUoWBwiXaHrhWt9QFehX1ffZ+duzVn4QTvxzh7Y6cckkzvKl1IqoVWGjNrRXgsDK81fpJM8Q00qB6ro0iy517FwZ0HtsmOd0+sHqmltksypef7MzbVXlxe36PcrmJSDNuFZtm8wkq0uLAmgoDqhvu2DKWaBz4jXWJodfWO4RnXPv7D2e0XnYuUbIcsHdPgH7jgcU0vKuk/lRM3gCtXAExWULZilHjHNVPQEls4KEr/mvAbeAQPQA4VPUSZtUImP8vSoO33Z+e7H7eo3OzS3xhheFxn8myjz8xBNMUo9rze+w/uETz5TFpx8MfNolf3plHXWfXCW/2yRY6TBade1LUFf5QMz5eoXFpPP/hh6Sj5paTeJWToAPB+jsX8VLLykdvEF0S7L3tCNWNgv6ZgNrGtJzxTkHvjnCeOH5srYuadJCBJo5zfKVdaZUkcMGIFypkZzJs5hRJ0C+R5dS1FcLNwdmv5NyyMcreyM3RvJjWjM+w1RjrK458UhNvJrz0Q1VWHz14Fpm4zAkbeqjeBBl6qEi9Jjo3Hz81GxvgF5ZcIvXY0XSKqmuvl7oKrGWk+Jmf/EHWrk6ruhYOzxJpdlCTvD9ExBG9OxS1zpCvVl4fislY9O4eslJBVGLsJMEeXZ4rGJlwCHcxsfcllVL/dET7uREyK3nhx2pc+JcjZM+xH20YoFdayKxEbO5htYbxBKsUjCfOlH6VxWPT7JCZbeMAUcxqd4CNQorVKeCaavYfXMKfuNrdXi9F9SbO5RMCm6SHLapXFZk/5MYmqfulqDA49D/gIo5rB7+KozLL8Nk2rXu7FFrR69awmWSy7BGMDK2XNP0zCn/sdsH9b0qpPxXjTZxLo6Mp2S+1iNK4dikJ2qBv4TTdmsD76hIos++/XCqCtzd2CnbaB7cq3/5pn7IqiPasS6QeZfO+9ifG5TQONJMjMfXtnlvQU2WdnG4zPOETDCzB0AG8Ya9wdddH+RxA1hWXOpTX5Dwkn9ck/thMx87O6Shb33aM9ksp7WcHjE/WXAWEyIXCtx6KifbcL/gA3LWwwTOVZXRPQpwzGLmUKD32oRDoowVk0pUP2fPJ2mLuSs7ngLWuP2bzIAzcM+bFwTHgMKkwoPqZV9h9953ULwmyliXaN9ONe3q4Pfg1FaEPAHgzJSvvPiCorLvo5e5DBusbjv6hpLLu+slLrUuOnv3EVy8lucPRILbe0qRoCMKuS7Le+vZjWOGU9MxFLCuWykeaX3Ie/EXk9aGYAO/kcTco/aFzraafzywUmFop04Eql5uo3oRibcod6aUsPbKLjUIG93W462f2oDdwmSBTheBtu5C/3tpGnb+DYrXOZDlgvKro319QWzyIhE2uHqVxuocQlsFLbayCyskB4E1ZsB6L/3eFaGviLAAgilxKhkxKZH96ralSotXA7t2ScayUi/IJ4SKRs2eMIzdZw8DtlDBXTmpSMrhvcb6IrBKUkSu/W2iFrzTHPqAYHnNAvDdxOXFFFerXrPvZo8SbF2IDV7fISyx5Tbi0lBkAHn9p1/YruW63ViGQhUHtOAvJJul8PGfK3ibOnFr9E5cmVFQF1Q3N6FyL2gtd8F252OG5hqvAUPFcX4UB4zsXpqVmSqqbLsAwY+zPNjMTe3O3TU1KKpMSjsTktQMX1XgOENehOCAsFpai5pG1fCZLiqXP9dl7oEm8C6M3pAR/Ejpqw5LPK4NFjOeSrSfjEHUjQkeWcCCoTAuO1jY0oIh2knnFC6+Xzqknolo5tGHNFPih+XDrd8sdehfg6CM5V/6m4MgfuWvONm4beAfEYcCqEKau3+ioQkeu7tj+3e6aS5/xsMK4/gVkboDDhOa07QrupUuCb37PE3z6V95IMHQ1ytz3gsa1nLzh4w89V0jxq5TXB/itFu3Xn/jbB67cJJlbTgDi+BEG93bI65Ldb8lofypkfAy4MOJkZ5+Xrq9w/t9MyDvOb7/6o5rF1ojWT09Bw5/tk/2LNSYrzgLYelcxN7/Vx1tE37nN7vOLLDwjKKouPN2/t3AIXCEgMKhKSRznLNacEuknEfs3moiKxmYSEWuO/yePtKUIB5r6UxvuWaoxYpwcWF8zUWoe4YNbMLaZJTUdl5l1YQMfkeUMH1gj3khcOknbI2sIxseg9dAOeanY32og45I7fq5EV1zZ2aIi2XybQCynhM9U5uV2503JnEm//LnhXDnp5jQh+lVY05dSTK/+3F/fny8uEUeHFpo74TDYa8MA04hdUf29ydyFxVrseMLgm/9/7t48yLbrKvP87bPPeOfMm+Ob50GTLU+SMC5ZDB6wwUyNoavKDVFRNNVURBd0R1BNRzdd/3RQQ3QXEUQ3TRVdhsaNgTIVTAZP2MYGS8iWZEtPepLe09Mbcn6Zeecz791/7HPOvfkkbIGJaoV3xIt8efPmzXPPPWfttb71re87Q9KwaF+LkJMMa2Cui5KbNlualTdY5UbjGoPI8saNVuqVzIqMjfRKOayduwYsTppGlje4OSBeaRLPGVqHUGBHChkpxqsOO2/LEbWcQ39opJhHJxTK09gji7RrWvEisbBHFkc/mRguUi+qcJk7z0cVhIqNCcwGUY5NldCA3YvI6y5x12P7fpvupbxq9MAUkwLTdR5d7FZ0htw1AHk0JytwG6Dz5O3KuWdwrkVatwh2c9xBSrjg0npun2s/vEB8KqYzP8JzMuLUZhK5tD7eYO7ZkakQkpRsqc2nH/uFbwr8fl1kTPEJj/V/WydTTeLY5uT/ZsT9s47P9ff6nP5on/rNCdF9DVqP+wzOaI79acL1M5Ibe3O4tYRb/4tgPNQc+21JEJgL98rPSBwH4psr6A9ayGaEfSVApxa4ObalGJ5QTPp1VDclaRqhORllzL3gcP17jcB+bmvyic04kYz7ZkjR81NwtHktwPWNgwiYnVh7rrm5cmUwkcI/bLacKVcVkGdKHF2Ul8L3TJa31Eb2DKiZtlwzPtOQgMC/bS66/VttOocHyD+cY/2dgkOfG5J7VgGASvKxY5jerhGRk7HRN4IiOI1igyu1A7RtIScpuX2QSAkHA9Hs/+U4weqPD7xHHUavDE5yuiPr0JQoec3GGU4bFLrmG3qDELi9jPoNI5hX3XSFX58FBzqX3l5ssBYB3BGUAPzNMWAkaWVoqBwwDU5p3XCrwgUbaBUKnRGjowHuKGfvvGNMBzYVzas2uWuz/kiOFauDOmKF2iaADE1b3R5liLDAOOEAJglmzns2U51t2Ahtgq0WkHRrpE3J/jkbr2cIx7MZE3AgYJejVcqeBuBZaSF3qKrOn8n0TRWSNCwzc3hIIpTphttehudk9EY1bDtHKfP7QAVbvBrd5W+6XheBSeeC/n7d3OQaXvynOeDgXfVJuynXfl4iBHQ/qlj/e6XRoKRZN9lLb7tJ4iiEreifcFhojLm+toDl5th2zuLHAtY/aD7keNXsZCqzCGOHvJ2hYhtr16F/McN/b5/eKOA7Tj7D2mfuR06Kqe4gx9p3sI5MaNTNTSbrqcETigswrVkVydK0/lPoDablWRFsykBUsd2lRA9HiNrU4qlqIwuBCHzk9S3otIzrSn9MttQ2F3toNHd29prMHemzv9VizgEe6iH/KCM6bVpIqpbTfM5BPrxH+NR8lW5bieEzaRv23zBP66o5p866KTtF6FaBqjq0meyoynCi2ASi8jOdDbwzwak6FzM/Nw2AJsqxsLBNcHMdRJKiPRdvZ8L4RNN0nApcpsRgSg4QlEEHcO3KDsve7qNdBx241U1b4k7ldWRHppyLWxJnbHhP0ZxkeMxh/tKEZM6lf9riyKfHLI1zwkVT5qFNRzYdWHSfNq8x9+yY7bc2kYkhKi4/NkSOYnr3zeNvFlib71XHLMJpx9fqjcw8ZruO1R+j2nVElKLrLqooA+OOQ9KwGB+2SBuau7//ea58+DzRfJvGeozdi0yHrDz3rk20HHDz/ZrVTxsxwslhxeHPqoLPZILK5FCA10vZeXiOueeTytgh8yXdSzFJ20xNBPUIx1J0GhN29pvksWR0WOL2XUbHD9G4PvnWCUxgWKTCUeiJRBcSrVpqrLEk8UyKufZuBaFEBDn+dsTkP80ZH/r7FFphAtNbY1btQho2swhDl8VxjnuphpXD5EKM7WXG+G/iImxzKzWuWyx9OeTmdy9x5PMRT8+9gfxhTe5rVC0HS6MWEwLfTF1nmUQXOjUkFnHfZ++Hxiz/P8EUmNe6wlMqHKEo5/QkPNAyLoNSVQLN4A7lDkucoFpBNfNmpVM5EN1zoTWhsTBGOR2yTJIs+XiDnP4Jh+Zli7Rp0IPcN7IhMjbZU+4brk/cFmZsYavI4Go+YhJhJe6BzGl2icgEXwJ/CmzfkRHOvq9XlHWYIOZe3yU9ZHbl8oacXSVgXGURRWdPdRoHnluWbmUpV3KCZomrWcdHTjLiea/AVECGGi1sM/RadrpSGB0NaF4bs/hVGB+dEmIR5hpVDqw8lpM0jFZ8qQmWtAT1dVUdU/u5vhEv9EynUxSlut2PSebN78RnWuyfl+RvHAJtan5CGAcka9MNS3dSoLiGMsFTa4dJ79YsP0pB0o1egQ/mnmDuKxIrU4zvLju7zh3D2Yr453t4v+mT+yZgJU3jZOPtmfPm72oGz8zzne95lLc3X6SX17g0OczpB7b52D95lyHp/h0hQ6+bwCScIh2s5ehMIGxNcixBxxbWnotyFe9969d4/ufuxh6lJG0Xf99Inaye3WFjc452a8JikUUJW3H2l4qL07M5+qmEtOVy7Ywgw1h+kwvkSNI6u8+ZH3mZp1bOoTzFSz8u+K/f/Gf8v//+uxnclTL3hE3/gglQ4UstrFRgxQLpaPyhIA904e7qEGwODQZSAO+ACU5l5y/PK+ysvHlnMTVRC0wGNVPWoXV1s4okIzraroJfyWkK1iWNswmj2MWaaNSjTfYuaOYvx/Tu13SeMMOe+efnYVmj7x6SPNdEqIJANzDsZn9rMi03JhG65lfdubTpIGOFleSm01ZkSeUxVu/n1fC017DkOCGvu+C42JHJlvJ2QDLnHbjgyxuv+uo7VbOhVC6Fg8FI6GmZY/ciso5flX0qsMl8IxIYz9nkjiDYNRpbyoatB5pGxiYQzD07YuuBJp2XUvbOO2S+EW0rPfqMtLGmcdPYfh396XVGqcco8ag5A/qxT5Q4XFwwek8AcWrkRCBhsTVifXfa1YrHZlOSyyFZbCN6hbT0alJmS4kAACAASURBVAQuOE5OuhAj8oPBqHzvWhgpGjFvsXdB4r8omX8+rzDMcqUNifV/LNLqRYSLLmmh2pE0LNA269+bYW+6+NuC0/42vdxsquPcY8UxTQ6vl5rOXvKtkjEJY90N5kRHPR+dgbA1splitRLa9YhPPH+RU3mxw6UKZyMhXA3If22RI4kmbs2z688jI5jzQKT9CoeIFwteFNKUjAqQGtVN2d9q8cQogBMmYBz/dZvPHz/L4J4U2bNZ/JUvsUjROayb3Wtyss1k0UZ+cIveqEb9jxsEuzlyu3ew9T8bdJqNKigBVVem+v9MKTcblO5c8ZyNtz/98GVkaMyjwr9efd8eOpckj3XYfpNP54mpxKosrYmipnGa0QYIT1piKpNL0W6OE/LGNHOzUo09TrEG4cGgxEG6Q/V1BuT+RkuHEXqxjZ7hrGnfecWoD1C5GIMxC63s1uMEuzfNyOKVOt7eFJcqwfASTC7LPZiSc+3IDAEnDcsMQteMNrayofuU0RHvXorpn3KNy/DVqRTx7Xsl9liQ1Y2ZxM7bMpp2zPcsPM3vbLyFm70Oh9vmJt6La8SpzWAU0GoYob/BKKiCkiU0vT0zFyeXp9eM07dAaGTRxIiWAjgS0Xl848CwugrsCixXrkVjI6e+ZV6jBMJnB32VU8gNdRyD0c18bJkvsDddspUEtj18a5qhHvd36eW1StY38+XfSVB5XQQmYWlUIrHcnDwzJZ3l5qikmN6PbPaHLa69/99x4ep/Q/DGPRofrrH+sIfIYeHibXYGNeZbE778xv/Ir/QO81OdNU598h9Rb8Nou44cSVQ3RaCx3Kx6bcvNCdohceSA0KR9j7UfT1AbS8h6inIU7S92eeZPzlPbMhPrpc145sOSzNG6GHS0mBLnMP+n0zJYUteUKaLTOrDDa881wHhBolN1MzPFpvHREYFfdWZklCKitKrjk3Zr5iSaLyVtoOHH7L5hTOcTNVovx+ze5U/1rXyB25+644aLFsGOwo40k8M1GtcKC/NO4wC25O5Oqo6ShoOBpwxEs+s1BqUyCOc1u7qwrdgMM9u9CBGl2P6MC64Q1YiGagXT88gUCNe+U2FOd5aFJZCc14obuFB/zOrScHciDeiKWmBlpkTZeEeH2rbCBeafnbD2zjq9CxbNq5ZxDImMaWte0+y8TTF3pM9Xf+1edv5Bg+euHEbWU17or1Bvh8S/vwRAJwM79A07/TtCWAs4+ukUbRvMa3zIor7usPmIgRO4aMiL3bYh0/Y+scrkmCY53jUa93eZDtlsoCjLVaAagC/Z4yXPyRnl2FFenbPh2ZaZr/MMcN+5DLu+g3IhUg4baYf7a9e5Fi9yJTIM+NyxSFoSf/M1fexfd70uApPWYPsph+YHRJlNkknC2CUFgiAhc4zrLhjBsdXWgOcf7jD3jGB0VDCOXaQ09kkAP9VZ4z2X30d7boznZNSPJmy9PI9lK6SdF7WLCXxWoVY43x4zjl0yzwSofOQaofuBzeOT05x45xq9/3QYt2eyDq+vmCxb5ngnbuFDZ4BZLAsdeGa8RFqM714m6sjK0bd/b4qIW9DIuHB8g41Bi/Fzi+S+Rs8nHP3dgHoRmAyfKa3ATBHFWL5RqDygQ4VhgAP0+zWO/aZEPuDSvBFPnU8cgfLMjZn50LkSc/s+H39X0bo6ZnK4xnjZovlsgohikzHVim5MUbodIIbeGXheJRBVeNMd2dMBHKooA3PHqrIj7doHaAFgyjR7KttlZglnxjW0a1cBS0Rp9Xtl56t8vi7AcQAlrUpuxB2khIuuIQ1G+hXSIf6eon/aQsYWo5MWwbrx71MOJB/cx9WCPHaQAnw3ZX+3QbMluNVvm0bJ0AEL7E93yOqQtDXJvMJdmuA4OaofYB0Ouf5jErcW4zg546FP+yUHMoGoqUqEbWfQoF0PyeqgEknckXhMS3s7yist8DIbLMv/rPgclG2oMULBzgMurWua+SLjlLEJzMouSvz9HF2cizJjej5aZZK7eIV8qEyVgQbcbxXNbyXIM8mN9S7CVuhiylvHFqOxg3AUXt2AntlKQscNqR0f0EvbPPyOr/H5l87QaoR855EX+IWdu/nd//gwVgrigR5tO+N06zY/fuJL/MvPvQ+FgzMfoXZ93KUJ8e2ArJYz6bUQOTTWLKIlF7oZVmhkN2obNr1LhwFIOgY/GB6zqiHY1hMe40MQvnXI+vuXsNddw2PJOyyf2GNrDZwdgTox4VC3T6CKDktq88LGEvnYQR4LcZ2cdj2kdqMoTXwPNZspgMkWorQaRpaFS7CVaEahi+dl1Boxu3d3WHnHGvaftqqsp76dM14qJHBzM1YSz0Hrurlgx8sWrZtZNSajPRc5Tqqu251s9fIY9QwV4s5VBZ87glb5eAXyt5umPN8colqBKReL17QKrlPu1HCKLtadWRAUoHZ5XOWcWRGgyoFo1QoqcFw5lmG5l7/vywN27+VK64YlbiSFDcXCHlk4I2NQoOahAUbPyU8IY5cwdvjhNzzBnz73EGHsstAZsZ20sb2M4dtTVCLx6gl1OydNJXO1kDA0duiJEggBcWzjBSkU16xlaRyZVxwiITT6viGuBmWb62G2BNNSTJUTCtUMMIHKHhuDhqRlfqF7KWeyaMaQlGPcfenKSrJYRgrRydEFEH/S22ErbVOTCTtJc0ahQR/gUf1t1+sjMAlwXvIRuXEiVcW9mPsa5WlUV9Guhzz41A9z7NAul3eXyDKJWkz4/F/cw8LF2yR/sMgnag/h72nkB3rUvQT1O4sM/DaP1w7xlew+rFOK5lWL1S9kbD1o0b7mES5YKEeaaWth9ImiJeg+Zuj5IjcKiTLRNG+krH5uwOhsh9yxmSwLou028rDGP9fHkTlCKtKFDOEqdGqx9fI8jZdtRicyvCsB/LbL1g8YUqYh4RQjA4mEyzVuX5C4J1zqAGFyYOevTlecYMUOXi8lbpkySLmClbkhO4MGncYEvnuLwE65fVeDtGbMCcdLkvp2TuYJdu8TBDsOK48mxB2beNE3XndHberPMG3Ll5nbHRlPfnwZK85IC+0fc+F3cS6vvfLzLX+3LPVmeUxlgBICuxcdCEpANW2vvMIUQghTTs50LVUrwOpPcDZNmWOC0MGuYPk4FCNNEzO/V1EfpFXIoBjNcuVaB7pMccea0aQ33KTBuZz6dcn4eM7o2Xkad+3hOVmllf3FrVN8+J/8W/7x0/+QvUENnQmUK2g1wmp8BUApQaosDnX77E+Cyvk2TV2ySHLrPQoRFZuVgMHIPKfhJdh2zmTksf4InPv1jMatpAL87eJzKacn7C3T1Ji9mvKLXdKaea9+ryD1OhbOMMEd2VU5Gy7YcNvGGcGlyWH2EtPtLI1dM1/i7scma3uNCh1fb70uApMVCRo3YHDGWM7Eyxl23zjpNm4IGn8uyd0uad1iUEiQtiKobadoO4c/m2P7vYqFL5uTqB7v0P2tNV76kPEG8/eMqD9a0rxl5DeWH+0jwgRnWDd6N0mOtT9E1wOWPxPTf/MK+2clS08kOBOL4WGb4VGHtDlH1DGYgteHzlWH8bIg63XgocJfORNQ8YQs7LH5KkOBvxOjs5lLI7GwEgvdUUQrGY7Q3Hyf5sxHfGyoyhPgAMgLBQP4nE9eUAZGhf+XYylu3lhAfsIiXSqkP2JNVujz7N1rdKCtwiUFjPa2M1a0bubV6IeIYnQB0OvxxHQUDy+hXTMoajIoH2dzSN4OkJPXsFPeiUXNBqnZEmBmWLcEc83Qsj5wDlS7bkq+O7K5O2kE5euLJMMuOrVWnKF1oeVUdIWVNLIsQk2VCIzYnDGWkLGhKSDAvi6NYF8jg4FbBaTSuTZKHG5m84zGPlls0+hOyDLJaOxXJZkjc0LlMoo83nHkJb4UnSD//DzDMxmilmOvu3ByQhbbVYMoHztQh7XtDrVGjM4t6oeHjI43CXaSVyiylgPsGrMBVOdWm8ypLFf3z1nUN2aG5GNtUkFMuadtoz562t9mwTEZ2kS57LhNLucryN4Ee/ubVxaA10lgQhuro87lYubsK4L6p74KMO1ULXXNaEdJTmw2KkXG/YcOs/iYIULmLhz/mJngP/Xhm9M/UQ9oX4KNRxYYHm1Wg6vtywOs/SHJiQXCM01az+6jAw9vL0NbknDBpnfOSNz6PcXgmKR1w5RE0SND0tTgVOmeTzrx0LllGOETiVAC5Sr8PRgvh8gbRRAc2aggNxhWaKRfrR2X+i2BUGbWKHcUVnnzFDdU1vFxNk0b3eqP2X/wEL1HQoIna9Qf2SbJilELS/Ed9z7H7rk6k36Hxq+1mSzJwnkV5i5B82aMnKTEiz5JS9C8kVZONNlSG3urAHMK6oI+vFRhN+YDMMO07stmPrHMLKJ7j+I/bc77LBdLdRpkHd/IFc8okLr7cfW3yg6b9h3ydmC4Wv0xKmia7lmrIF36HuGJDu5+bJQQMDffrI6R+dB1VfZVIHmJQ82ObwiB3ctIujWsXBltI2nhDhThYjE8PVZkvmW6mxpyB9yhKeWsXQdOTkyQKTaHrPgsfvYvPsiF4xs8d+Uwk7GHW3DoPN8c92ww+5Mn7kV4iiPv3mB4fQEdm9EWEduVgqQdJKAgn9jIWpEZeRl1L8Hby0haDu7LCVDjpR/wWDw3hI8cpv/9Y3w3RX96nuC2ovNsH+3auP2EeM74K869YAaCZWpm52bn5obHLbQ1PW+TmYE4C00eyCmp+O9gzO11EZjsUFPbVthhQQWIFJs//kaaaxn1Tz1jgtP2LtRr1e6th1OJklLgypRegvBEh9rlrWkQq9dMUKsHtF9O6Z12mKwItBC0L4P2PZzNIc6mucD3HjpEYz1m6amU2rU+9Y1m9besVUM42/h2gdrzkc0UpSycgURuNujcMAaJ3iCvgMPc1aRjB9eFeNFHrkyQWtDtjNjZb1bYUu94QOvjjaqULVdes6FWdqgM01m7pq3b+WzA3htyylsyyyS3R+a73iRgtTVge3G+GrgMFyzahRNvvOhXGkj751wWno5welG1o6YrzYqoaPhBNrIfTuVYSjKoEMjtmHypYwDWkkRaYlUrRvbF7kXVBTeb7leSH1q/Yi7M0npaoq0acilaV7Kz5TogrlayqUtJkRnQe3Z+Tsw8H8AZJijHMpSFAhAvMScz6Ftoy8cadyjIasCFETqxSfc9RtdqNO7ZYzAKyGOJ3HO4+MsbtH8zrHh6ZRMnLkozaStUIklzifAUOramlIFQojsp3rXC8XdLEy57eHcPTfk39Ag3fOyRhb5SY/duI4kbPnLEaIbXco619nn6+wN8N6W/X6fs48aLNbydCdq1aVyfMDxlzp9xok5msl/D1UreOEKu1apzfNLb4V98/gN86MG/pOuOpp2/gvj6za5vGJiEEP838H5gW2t9T/HYPPDbwAngZeBHtNb7QggB/BLwPcAE+HGt9RPf6G/krjBciRAmS5LcsYv2qyJ58CLjVYf5T7wIzGAS9elJ8nYmRAsO/u2U8apbadAAiEZ9mv6PQzJ/jmBHoa2pgBgwHRqtB3SeG5B2fPztCFX3cIYJ8bxXyMYW5UXBBs+HjvGwHwjCQznphZggSJhqIkD7N5t4rZho0WbvgkM60ghbs7PfRCWSaOwQ9T1WP2WjbE3UtUg6tnFlKZYVmuBAp3TVMBroWSA4cnYbS2iSTBKPXYL5EdfXFnBrCS9sLFFrCbpfGxlWsgZvc8jGOxdAQOdKgoxMppm0bdwN071C66pVXw70qsBG6AC505+y2YvSQHUaWP0JInHJTx0ibbloaXSmS/OI0jACQLlNrGRqImFHuVFlmPH6k71JBayXZUeVBZUB5s7mQPl5zpRx5XuoyJZJRrxSBzz8zXH1GnnNRhTmkVbBl7PHeVHSWYX9kWGHh55gciGm7aUkE5fG6oj6iYQkk1hC4zQSgr9y0YHHCx++wHt+8kn+5Ml7cfZs7AT82x5WYuRDmhnYoUMTUzZG8wH+vgKtSZouwW5WzfV5fYtko0G4KMgXzXyec1+PcdihfTXH382wcsXVn7BwNl2+fOUEnfkRYeyiM4G/Z679PJBViRx3pudw/bsU537NUCnkJMMdKXrvjsgiB7Eco2+ba+HS5DAfevAvAdhJmiQtSdJqsfn2NnJlAh981Y/lNa/XkjF9GPhl4DdmHvvnwGe01r8ohPjnxfc/B7wXOFv8ewD4P4uvX/8gQkXnBXMrJ42AYGLM9KxU4+6FuHuGnKhaNawe1c1QZUSeS/OlMcmcR207NanpeDIlMy51TcYFtL+8DpZFcmweLYTBlXxvOpfkFMqMk5TRcaOBXIrgmz92hwqiZYDr/I1D2l5KGJsPeTycylU0bUHgpQyVIK2D7NvkgUIBja95uEPN/t2a2/cbB97orSMat4zQ3M3vbnL4iyHjY1OXlLRmMfi+EXMfs4kWoFPk14MX5/COjRmNfSNiZiljVx7X2L+rgbLNJjA8P8fiU+PKvNKOFN2v7NO7d550pVmNNZSYjxVnFWCsBQYU9z2y5U5VhmUdn3yxVsmPVKcnMxbula14EYhkmB94buZL8sLho3rs5FwllQyGZ5SumnnBMngJZsqzMgsrNYyi2Aynhkb8TvtOQTmIcPfjaaAqGgwlI/zOZaXGlWa06hLslYEV40irLCw3Z7Rbw1nKGYwC9LaPd3zAZBV239yleSPldlznxMc0YDhKmW+RNC2iriBa1OQ+yInAv21x1w9dZpD46H/WNufVscjqRgamPoubSQvtyOL496v3MT7RxN6U+NuC6HhW0UhkzXgCuqPiPQQ2uWMZVYXimm5fclBBgWmKwnW5+H3Xy3DGhi6w4IyYKJffeOJB/vdv/23u+xc32Ug7THKXBWfEz7/iLP7N1jcMTFrrPxdCnLjj4Q8A7yz+/+vA5zCB6QPAb2ijpfKoEKIjhFjVWm98vb8hlBGYV65k7tKA8HCd9rUEu28kO42aYhHVS9G1wK9kQpLVFoNjftUqFTmM3n6axl9cNe/BteGwIbRRfHhWmCH7IarTqJ5TXpRKmgtBC4jmJO5QVZyhwVsiogUPb9OA9AB6Ikltw2cCiBMzjCwK5YGNhxViv45uZWBJamsWw7sydGJ4TUt/scvoaJd4JWP4o2OIHDbfoTn9u+aQjc5NPiXMZZrWHzTIfEhPRSzVhgwSH+vwBNvOCbyEtKAkrD+zjPWOEbW/bOAONLWdrDIHGB4zx7vwV7vkDY/OU7cr4bsyKGnXJp73jFNN8TVtLVW2Q8ncMmnDSKvoUg0z0Xh9M3unXEHSNG3szouKyaJVOHFQaSBBafUkKDENkVPJwlqZEZETygSwbKVePEebAeSiLFPt2sFWtdYGtypHV2bA9QpjKiVWMN09VdAHMr8g4BakVG8vI3ecyh3aSmBQKE20GiGh47K/1UIOJLqbEkcO8uKI+HYTLR22//g8+i3GtMAZGKPTeCXFqmUsLRi7sfGnl3EHmku/fwGAI+wzXp3idINzLWrb0yy65CnduWSk8HZt0ibVwPlKZ8DNnTnsUBPsJGYj8IwGux1plG04W8NTivlnDfhvwZRkWgDvIoffWnsbDy5c48XREh9606N8sncP884YKRQ1mfCrX/124E9e9dhe6/rbYkzLM8FmE1gu/n8YuDnzvFvFY68ITEKInwR+EsC3W4gkq8StaleSindSlVhlh6ZdR7gOJClqZxfRbBSAnSaT4oAednr3ser/pXEmUO3eQWBX3nXl0pYRrE9rRi7E208qaQzlWpz8sObmu0y7GKkhN3pNOrMqdQS0wQdEboT2UaDCgpslIW0AiQHJo0Vz4Z/8xa+iJhOu/dYbaNQj+rllyH5FsFWuReyb3c0d5ASZZv0fxugCq1jrt5lvFVlnJnEsxcZTKxz6y5yNb6sjivu1+bUtopMLlcNuVhOISURytEVtu4+zOSRdaZpS0Z1RFAiNznq04JD5An8/J25JtDRkxPL8l3boUASeSKOF0RmP29NM075Tosm7Q9FTQtK0aKynjA5NSw13KCojVBkZhUsAkbsmsypKkAOvVbqNJBkSDmBN5VzgnSVhGZCUI9C2IHctwgULf0+xf06iHNOw8M6YrLxdD4l2AoJTgwoEX+kMWHvIYphbBM8EqDcOWWiYz8i3C7leO+XaZ09w/juv8vTRRdovaeLCbEJEKXaoiFtmM3Jm9JNerYlQLhXYNG4pth+AAPCcjCizySdGF33zbQG5a8D7YEcdmENc+IpgdMSl88KEwek6fi8383iFwYIz1tw3t8ZzwxUGic8kcFl0hzzdP8RKMOS4v8vZQ9u8zDe3vmnwW2uthfibzxRrrX8V+FWAtreijW5RwdQtwEk8m7TVrij0peKgsoW5aG8tItOpiHopkWqMHC28XkGTb9u4/YxowSm4KhpnZJixd7Kn7XFeSDxMHy9NA8q5KntozAbTWlYR5fJc0JiPcQsOyvZ2G5VYqMRCF2aBspEihSbu2BXwmq0YzSZRC7CXF5G2IoxdpG34Rl6vmFWauShlrCCGdORCYjFIfOpeUpHuAJJc0ry4x5o3B2hkAmlNMLxvmd4ZY0UOsPrFIWhttMi1Jjrari52GZpzkdYsdHcGt8uN7rhMdeW9l98h8h/sZoRdm2A3I267uH3DtQp2FHFbvCIwzb42TImCo0NmaLX8PmlaJM2y3JL4+3nlN5hJc7PmbRdnkJAXKgKlGwpM5+WAqcLm1zGiABCZJpk3M3PholXQBiBazhn3A5aXe8Spjb9oRpucupn37HsJ6b6HDE0GArDbayBthfvFJto2E/tNNBv//hT+EYG2cha+Zmy3sm7dyDSPc5xBUjm7AARFQ6ASGpyRlrF9j/ANR3H6xpyyPTcmUxbkheJpXGCKTWM8OruUYyYbeudqDE5BvALyRp08UIxGDp4Na2Gnsk7/xEce4p4ffI4XdxeZWw356uAIvSjgm11/28C0VZZoQohVoFTYXwOOzjzvSPHY113KtxldmK8yGrefVcCpOyyGJGeCkpXpGaGracqd1iz822n1u6WlExgWbOezLxmmsucyutit8AwZKdPFK4S7snqzOpY7tcXBTOLboWY4dkBBFPtY9Qz/I8X4RqQ4OcxRjsaOTNq8+aBL8OYB+9tNuo85DE6Cv2v+xpWfWAS1iIwEaTrBejngLQ9f5pmVi6x+YWgGbYuB0/27Gvi7grV3unjrMPfWba5sLnJudZtr4y6BN031W35M++4NXn5xmdFRm6UnTGDtXMmQkcLdjwlXA7wCayjPW9K0DgQDkZugX5Yx8MpAVC7TvdIkTYm/l4HSzF8+SLhrrBugO5nzql2/xKEy/2DWaz676XHMfg8wWpXYEUwWTXbXvpYics34aA2ZaGThpPxqapaz8iolabPMm9LmTDs8Nfw6fztjcMIlWs6wh5JgwzbM6WVYaQ65MjYby+TJLsf/KiV3OzgNCztSTP7BHt4fzR08b6kJBPOXJihXEs37JE2L5tUhduTRO1ej+2Svoo3ATCexNNGYVXUouqRqZ5ekeZTWNU12/4iGl7DdbyA8RbhsuH3jI6Y0dsaqMh8oV9wRZHVwe/DPfvjj/Ku/eg8Cg6lBm6/94UXcnsm2AZ785EWSsyGBTPFkRsf//8+J9w+A/wr4xeLr7888/k+FEB/FgN79b4Qvlas0tCxxFHc/JrhmxPyNTZJH8JVrFUFv8PAZw7fAqrIkmerKfPLVVvjGY1XHruwUldT8yYXCimfGAkpGqqrjywBlb/Vwhw1zkyhMKSc1/rMBe3drkhMxlq041DW78eagwdJvONihYeySCwanQR0LiU5q0oFbtYkzT1EPEsIT8OUbx8jujeELGEH+/RgV2Pj7OaPDLm5P0LmasWkvGWmgVbh3dZ2bw04FdkZZcR6kRjkGxK9vpNwunFLnnzM7edmViVsHgesyEMj0lQmxllS0iFnA+pXPM8cSXNsnXW0ZINeXUJQi5WdYfh7+THlSOjCXJWN5LFn182lJaEdm/q9/0sGOCoxqlJLVJVk9OJh1FLNysyVfJdpHsQkUXTnlmIBZ2zGSKG4fwCbpmHOa13N29pvs0DRYU2ysyNffYbP8mHmNuGPR/tUm6++A1lVteFCeMYLoPhtjpYrct6ltK6If7XH5bS28HRsuDvH3mzSe2zVzi0udAwqe5sDv2CAKlQrlaWSsyTDE25qfoHKLeNGm84KgftNkgEIbzzgtIPMttAsI+LOf/tf83vAcn9s/X730ZOzR6iuyQJJ0BEnHmHzOfd7HngR07jFl6lW98NdeD691vRa6wG9hgO4FIcQt4BcwAel3hBD/CLgO/Ejx9I9jqAJXMHSBn3gtByGUfoW0RdpyD+youW8xevvpV9wElWVTqnH7ZheGg61pt2884q0wq7CIUukv79SqztKsZVNJCCwJZ1BYhRdecrXtFJFK3F1pQMy39nAtRTY2ZUbJRQmChP1zNuGyIggSRj0Xb08wXpHg5siBjXNiRBT7uDdcwjNFhlgEl9HxGt6+AeqTuQ7+TkzcqlHbMrrNeU2j6jkNJ6YfBziWQlqKMC1HGIx0TArsvStld9tn9Qs5uSsYrZbT9NPz2VhPGS9P8RYtIUdQ206rTQNK0oTR8bFzsyHIl9YPaErNrsG3n6T19G3cOGHnkSMsfm7tVWWGZxUu8+PLyNSuNo/yWvCKv5s0D+70ZXDKXYMV9k861eM+kBWSNaWdvCVAFxK0+WJ7Ko+SKiarvpE82cvI3VLM3wRyGYO3L4i6GnsoYWgCAY0Q286JGjkMbPqnJCID5YGSNo2XwespJotG54mZeG+PEgYPeMx/uM0v/KuP8DOf+zEaTo5QEJ6cq+gUpVywKHTSReIYG7HR2JzzYuO2JwKZGFiglFRxnBy5PCFtNIxky7P9ylTWnCfN/pJN510bfNsXfppD3T5rOx0WPudy+y2K2g3Jzps1HB/T7Rge4SjyUN87QX6qS1NGTJTLbR5ElwAAIABJREFU3wLZecV6LV25H/trfvSdr/JcDfz03+ZArDDD3uqRLXcINkyXovRmy7EqW55yufvTdme5knbR3s50xY1xLq8dEHuX1w/aKNlb0xbzrLWQ2NzF8paxe0UbutiZZJQS7AZFIC1AysRirhay/egqNBR5oCrBu9hSWA8M0ev1ilSX+SB6DtZyRuO8IeRhQXYmrDCrkohnR4ZVW+pZp02XpCmQKdS2c479ieL2T03YCRs03Zg4t8mVReCk9EMf1zbGDLdpkA8d2lctRofBSjRRV9BYKybPY1Mml0CzyHlFMCoBV/9GzzCwXYcA2H1gie5TL5tz3Gwc+FzLc9r64jVzjbSb1DcKv7TSPy/wpzfWzGdRsc8xm8asO7MzyqssK23IottngtAdmmkkLVHIgGD0rWd4btWGpafKltox5ZcZRdEGj2lYyNCoSsRty5g5WBo5sbAyCDtZJTkjPIV2NFEX5i+ZbD5pGUJm+u4h3h+3SdqCYEeTexZC2/RP+kxOpZx+z02O2nugDQkz9wQyPHijGz5ZMbTr2mZ8qGgQiThBj8aoonkQj12ifR/hKsMan1lZxyerGx8+ACvNGV7QdDB6UEkuWfiEj1Ca1gvSNG2OG2npOT+k44ZkTYvn/uA8qgk3o3me3D3M+s0u3+x6XTC/RZKb3RawntmFYseVez0k4NZrlXRGxfyehFiYNyDfer7KbMrgJXsT2OtNnVZqQWWTpHZ2D4qy1WsHLJTKndze6k138xlJXP/5KyTvfgvaK4TnXMXac8tIR8NqhCi0nsgFSlm85eh1nvv8RZynagyPCez7e0S367gF0G0JjXIUltBQtGUtoY3rdKLw9mJuv6FBcHPA5EwdZ6KNnc6cRH5wl2xU4+UXlzl3fp1B5LFYH9OPfdpBRJg6uNIEp+29bsXHkgk01nTVtle2yYpgmm2KXONsDHBLgLXUjSoDuzCmh93Htg0949GXD8jrVud31jg0Tgy2dXap6vCBCcD1jRjn0o2DbjEzy+qP8Td3D7we7SacnKNRbEYwzaayAiO3El1lU5lfmjkC2Aa3XHQJdsx7TJsu4aJjKCyJJq1L7ElO2LVIW5C2rApMr62ZABWejyG2SL/WYbSS4rRjmBiFidtvknQuF2XnBNQXOoyOGpwyaQtu3GOz/KjN8H0jmo82WbzXZCL+LQcrdRitwnyv8FcscCYJ0+xuZkSosiur1zj5kVv03rpqWOP3DInHrtHp8hLSUavqgspIoW2ByDR5YOE0Y25tzHPs0C6bvSZzhb2VO9BMDhmm+uD5ebLNLjcccAaQvnNI45MNHv2/3kTSFshDf31p/1rX6yIwlTNvd6o7Vj8uAkmpky3qtQPPtR9/Ht56/oB6nhW7MPOc2QB1wBqqFlSBrgpWpRRu8XuVHnfxfwuKVrrh0cSr4B4ek19toBKjkCnrKeXHE+U2g7MKORGk3RQnNkdZZkXWTOprCU2eWeSZhe1lxJ2AzQddjnwuZHKyjbLNMGnuCJofnPYVrKRwa7Fz2t70fYc4Rg1xFNB6QQJGXREgrRdlzr4xUACDCQUb4bS8LYPATFAqKRzVuQ1cI6t6fBl7q3egPDsQlEp/v60eydwyc19aq3hp5ec9evtpssAyjYpZd5VOC+KDBEryHPpDapcNDlmW8UAlu1wGvjIAl8HKlHwW/r5C2YLJkhlpCjZD3N0J0YpxUrEnecFqFuYmbFF1x9ImiMxkvzrIyRqK4yd32B3XCE+ESMDatUlaAntsxq6sDIZHJP2LGXNPS+REsP19IdaLDRavZPzZ1y5y5IF93v/9X2Kcexz3d/nEz/y96n2JQnFiFmM6oKRQnEfVrrN7r5Fmwc6hkA0KbFPWK8fw46xEIeO84m/xch37+IT1vRbpwMPKjEV40pLI04YnFy8muM+7DE6BkgJ1pcFkVZBcnLDyex5u/9X5VX+T9foITOgqUKjJBDkTOKAIJg1DqsuXOljX1qufqckEq1YzmdJMyeGsbVclfPkcNZlUgmtqYgLP7CnMbxt2ePncclkzz7eXFhCNOknD4tAnNeMVEH7O/O/U2HpQI2xlyrRdGwvDd7rrrk2e7Z8lPmRMNcdrTby9gsQpQeVAN+XMyg7rv3eCwZsLwfjcMmaCLxkuUP+EQ7isce7tE0cOvReXCdZs0nmFauXEuY1vZ0S5g2dnxJn5PlUW/hebWImhDZRAcmNjClw7g8RkiN7sjJl3gENWfV90g8rHRJhAxydcDWgWAe1VzRRmlh3lhGeXTFnIFCNpfrVwTgb6b1416omffWmqoZ7n007UjBebvL5FevSkYfYLweS8IdTaYdHE8C1GqxLlGgKoKswNxquSuGOMP3NfsHehiYxh7vkEr5cyXvFIWgX/rXhLzgB6b0jxNh20C/6WRR4I4oWcKLONukBRNk1qPuOaZu6SsWGP26bjeeRTgrUPJATPe8iv1NASGi/2uPf8gKf7h9iL6tw1t0lbTtBSYA2mMMYsx0+PxlNnmkmIOLKKKhRPlQvBtmZYDBS3AxPkc0+gBfhbIWnHvKl43mayYO6GRj0yGuTNhLhdq2yt8qsNdCzgeMzS379OM3OIMpvel5YRGTTrEZnnVxpO38x6nQQmqixGLnQPBqVmA/IctX0ba7GLuHR1FjM0tuLNhqm7c03uWAc6a+VzyjUbcO58rAxIdz7nwPfFDTn/pXXQmvSRI7gve6R1CLYErUfdYnjX7MRpA/bSOjICZ9eGBbBigcgFjesmRU6agvS2x/YXjzN3M6G27Rh8I9P0zpj2+/abferftcWyF3H18WOsvnGT2nLC884h5p6w2b8f5v0xk8wlzmw8O0NaCpUJNjbn6CQHz4m/b4KSvznG6k0Hoitrag52ql5tldIoFFpKUMiNFOVWmSW92us4GwPyYx0z+hK4CM81bitl0MkNhlR7fpvb7zpN60aEc+lGpXYAU6AcIeg9coq5R9erjK72/DbadRhd7FYcuNqOwY0Gx6eXfVaDZF6R1wRuz7TQ3YFm/7xLYy1n/4KFjMHtG0E+Z2iypvo1g8VZxVvLi2T79nMLOMfGBF6C52Q4p/bp7TXIgmmgdiaa4RFJ/VlJ2tCk902of7HO8PwcW+sB6cDl4rk1bsd1+r65dqc8rBRsaWb/Epfo/BL+1oTx8Qa9MzZJWxOvZDQWx7zryBM88eT9LLZGlR787Cq715kvGRyXGGFKzWjiIaUZRQkXBVlg4w41ciKIVjKkm3PlS8exMkMgXngmZ/+8RAiNlcPWwxn8h6976XzD9foITGqaMR0o54pV/awor2YDCVDtmt7OhHixhjNIiO87gfvoc9M/ccfvzGZRdz7n663s+k3k+TOmrBlPmHt2RLAbEHYlhz87JK/ZBzp5ScvhqX/zRtookqYg2elQ04Awbr9yKMmDHBlaPPyDT/H0//oGalsJGw/5BLcNf+b2OxMuHDesizBz8M8ZKoIrc7qP2ezfY1rQk8xFa8G17S4XDm2htKDhxrSe8ChbQGVJU395+ApBr8rrrAg2r2ZVXVlbN+oHAo7VG+EUrq+Nzd0pNnens3BhBCriBP9Gj+hYB7dfqGSWgQZASkMPadTpfvIqIvC5/d5zdJ4fYW/PWI8PRwzed6/JqsosbWbCvfnUBv23HKJ5ZcD4hBm3aawZzCj3TcsbwB4KlIR0XqMtgb+rmSxaOENMB7RrQO5owXTlwHxNWqack6FAhhZ5Kyfv+dCZSpoADE8p7AIotzKjguHtayaHNOKWuSZ7ZyVvPHqLl379HKMTHjd7hsgY/rc93n7oeXppjTB3CPNp13QvruHInBW5xQrQtGM8mbHoDsm1IRCPYpcz87e5sreAa+dkPlXG6PRisoaLPTa8pNzXCGUhC00pr6erESY7AlEzDtZpFmClAn/H4J2TCzEuRtMpuP4tUspppQ6WWzMZTr61PQ0gr5LJWLUaql03LG7PTOS/+KEahz4L/mK3ajtXvwP0zjfYu5fCFcQ8bp0aoV5qwIkxWeTwPXc/w8cv3QMjG3tkYZ0acfxHnjZP3t2vsjb50jo+h7ASj6RgHDsbE+gbZq7re6hOA+XZ9M/W6VyJCRfNhTV/GSAndy1u/5djvvQf3oRc0IRdj2BHs/9wxLGVPbJeC9tS9KKAUewyVwsJ7JSbv32KwT3KaDsVqx/7nFzaxRY5mbC4+fETOHdkS69YhTBcutIkLwiJZrVRgX2gG2rFdTPG0R8edHIR4gBgLWaCROVKzMEsTMQJbn86pyaSokSZeZ3KJBNY+MJa9RhMg2X7z69NO3yFG+zsMbW/skF4ZrGybZ9cWMbKCkfa0KJ/VhAeS7FCiYwE45MpWc029mETyei+nPqzHkf/6Dbr37VghqE9iDtUeJ2S0LxqMTgHzkAQpQH+ovmh7WVm8Po7E1qfqQGFPMmiINgsBOgSTfdSjP0BxXhVsOAkWHVNxw3ZjwK+1j/MkVoxMK0tlrwRe0mNeW/CblRnEJv3v00TT2a8JExnzNvPsP2YpzcOkWcWWWxTbxpPwf5Jn/a1iLQpCZcFC1/LGb8pqZQ0Afbvz1j5rLG937tXVhLX8ULO0U/A7ftssppk/osWu29qM/eWXaLHvkW6cjDNZO7MaGaDlbXYRdcD8oZX2UVXEhmpQgU2w58bcvjf1Vj/oYSkeYTb3xFz9Hdt/K0Q65mrxN92kdyHbC5DBRk6s6i3QzPfdHpUgc6fvHIBPZHgKdR8jPNk88Dx3pnZ+ZtjomK4VAcuYq8AIXd2sYRALbVpXQvRM/SGuCWpbySITFdyqhSOK4N3hJxd2aEf+yy2RiS5ZH23zaFun1HsMvndFcJj2hggxhYP3XOFW6OOEYvzwLYU5NPdXSgOcJZ27++w8JfbhCcL95ai/NVSkLbcKjiVk/yzciQiTlDD0QFNLFELIIywx1NPtOpnr1LKac+IisltQxFRgfEjm51ZE3FyAKsCDrj5VpSDWSC+dEspsrJylUoG2jdSJ3v3dbAj4xXX/apia4ovY/dNUDp3bJMXbhhXk9Fpi5vvX6B5Q9E7bfhMdjgt5ZzIlHO6k5J0wNl0STuSIEiIxy6ykaJu1aCY2A8XLJRrPmtlQzwnCBeNTK2WMEldAjslkCmLtTGL3og4t/FkhiczFIJAmrK77sR0/TFRbjNOPbr+tMO8BcQzmZvl5uSuyYRK3a/eGZvmdU3cttCZVQ2AAzjNhLhTI/dsamuC6LAZtLYSCxlleLsQLoPIBc2rNo3TCcPWt4hQXHIq4PL/cDf3X3iZp146xtJSn50XFmAhRg0cmldt0geHLLeHrO8GHP7w1Jqn1DOWfcHNX3RZ+KUO/PebtEOfuWczuk/l5DWH/tk6yZveQGMjZ/dtGQiNmpi3P9qpG43ubLpTy74NjRxSgXUj4MRH15jF9GbpBvZWD9VpMFlycPeNIkKlFzXfQQUu/bN1gtsZkyWDH8VtQeumecWd+12sL7qMj2jOPHSdzWGTo0FELwpIMom2c25ePQyOZlO0qH+hzv49Ct1JWf6Ew9GffpH1cZvNvVbFOO/HAbsfPYoUBvAW+ZTBXY597D6whDNROKN8Onozq6rRm5B3alPN6AKL0lH8yrK3+N5vN8m3zITSbOY7u0QtgEK2FykrG++SGe4MprrVdwa1WVzpwCof6xkOHGXpWeBg42VJowhySbdG++qE8WGf1c/v07urTXDDobFmshgtQZw1G8/Zo1t4dsbTGw1GZ1LCRRsr1zSum9ENMNlTuKxxewL/RY94UZF2M5wbAeMVie2nSKmJOhm7b4PuX9nGrUZBdteY4NG6EZ8b5ESZQ9ZQJLnRdfJkxqI3wpMZcW5XJRrAXiEPOOeGeNKcr9g1oyF7SY15d8KGI+iNaqRpESRjG22bY/d6msmSi5UUVIqa4MjqHqPYUAscmTNIA6wU0rqgfS0nehvkmYXupNz6Dhd7DP6u6fDmgWZ/EuD2/jMIxf3nWDoXyEbKV584ja7lbK3NQT2HgYOo5YzuzWHsEjck3c6Ilz/QBadhVAEtEyDO/nLK3IcDJkuSv9e9wZ9+9CFe/FCO9hRWkKEzc8fZn3OxxtqUP9IItulMHAhK5IJ//K7P8CuPPYzwFI0jfbJr183vnzzO2vsPMzqhUN0Ut5bA5QaH/jyhdSMimfNQy+aYrERVjPa9ewVLj0vCJUH7JcXWu1OCXZu98w75m4ckLzZQJ0KufOk4F95+je1xg61bc/hrDmkIclmRuxnWizV69xfRYyLZfIfCGbewhGZl3tyUcW6z2WtSF0VXqhhcdoc5SVPiFgHKHebGyWpmMr0MQmCCgrw+rIDssiumJyFyeQk9HL0iQM0GklfD7KzalOpRkjF1GEEY4fSH2CtdopV6xdAvxfRLQmcp6Wt+8ZUlZOnjV3LRqtIOyNsB2ja21wC17YSs49NYj2msw/45n/nLGeNlyVKnx81eh0OtAYPY5+jpHdaeWyZvZehQEi1YiAyywJxjb7/oZAJiKcK7ElQwQTr0oBkztzygt9dg9/6cxss2aR3uObzOFXmW0TGYe0HzXxx5nH+9/S7etHgLC03HmVAO8ElXsZOYzH3eGTPvjGnKiGHpZgrgmGbL2cY2u0kDocF6skljBPFcjdq+kQPW0swlSt/CHWrSumDylgm1Irsq8TGVSOzQcJmShsXixwJ2fjAyJd3Yw78N4yPmerIyCEMXvfzN85iE/jvQ5/1ml3fiiL7wvf8dvbsz5FiSt4x1kgpyrLA4QUGObKb8/bsf5zce/TbASIuoWk7zeYfFJ2Ouv8/hBx55jMf/p7eY1/3ZDV54/hDOXEw6cqfeXkVAEoXPXLrnQ6EAIGxt5EugMt5sNUL2t5ugihug/OIoU+65CsvPUaENCg5/yiJ3psPG9Z+9RfY/LrJ3d425FyKGPzck+KUOq//zVZ74zAVqG3DkR6/x7NoKh7p9LKHZ+otDePvGQbd7KWX7TUZKN5lXqFrOvedv8gvH/oB/ufZebgzm2BvUKl++cexS+712FZBKhnS57Cg3XbEiG6om1EssSAiiU4t4t3qkqy3CBZfRIcnksOb/4+7Ng2zb7vq+z9prz2c+PY/33n53ePOo9yQhgYQEAj0kRJCZ7NgGYohLMRgLU6EgSbliqHISuxgMcSAxUUjJDImCxSQCEpr1pPee3qA33vl29709D2fe8175Y+2zu/tKCErP5XrOqurq6ZzTp8/Z+7d/v+/6DgufTHC3R3Qv1DHDHHc3wto41Pe3j8ISVBiVndNftcrNi9skLMdlQVFhPjfmWekE5t5JHKn4myWVYVy8iu+VZ3Nwf5PG1RGZayLDlHDKwd3VvucoRTRTwdkesv9QszRvm31oi9lKT9t7JLrKHI48BptVZDPGWPOYfSJj/+5ixC34TdYQug9FOmgikOR2jtWKkGauuWu3PJwDHTraP6Oo3BT4OzqNR31zh8fm1soO6JS7X74u4wK0aB9wM25zkOiOyUCVoHiYmYSZRd0OOVvZ5cNfehMiMfQFeqjPqeZXLESmiJuCqWe1kwFA7/EBvquPhcHIoepHDIYuM7/vEjW0q0IuIXjngPDARQ4ktbMduocV/Fcd7I6ie6e2lr7+wX/6ZaXUG77uAfB11uuiY3KchO6jESQGLASIWJIb+sTPC5GscHThGGU2bjskGtqawJgKzLftc2OphWomfPTi/WTfrkHM5OI87lTAcvsQMaNQSnCpM6/fKDtHxQbKNhB+huhY5NXCvO1Yrc5GJlShckUfmHFTYQ4F0YRmfQtA+RlS5shqTNLTtwumDMxAkQ8Ua1cWmF8wdNZdkjP6+DTpksKLPU69ZR1p5KS5wd0LW3RCj1wJaqtaMqIMnWCiBISLCbKa4FoZrkz4uWvfS5Tpt7BdH5Wi3eGtGrXbitLx0MPhnEPrRoy5HZPv7sPiHOlklRvf7eNc6HLP9BZn7G0u96aQYoeKElSFom6H1N4eEWQWrcJi64WdOcTnFmm/mqAMgd1LdOFwbMwx/2k4Kjlix9cYOxzHp8NJ/pPRGeAV42N8elKLjYdomkGhnXN2R9q5seimxta7x5cIEyqb8YmiNHaPCGd8rH6COUgQSUZtLcbfNVi7L2d9fYJ7H9gkzY1SGI0P9z2wyZOrp0gmUvbvtsg8dWJ8sXsK/5LDaClFJALRzEgjk1wV6dJTMaOKxByZTD6niJo68TZuKvJI40g1M2SYOqyGE5xy9zlIK5xxdnlxuEgn8Xl2f4Fe4DLsejAwsQ8MHX+WaFzRiOGpO85TWy+Y7kPtHtFfgeGCYuIFfTz0ly2MBHbfnFItkluSTOI4BcvczOmckziH+vbWEBIzo3rVYnAuIfxyGz+GxtWMzllJZV1wLEH8G16vi47JOzuvFn7xA4AWr46jbaKhXY5Yrek+g6HLg0s3yx0GQ+ZEex7C17evvOzQupiSm4KtbxJkno5pNv+fNrkJ+4+mrPyeLnDBlMXWuxMu/JKOIXr1A4WZvVGYvwEISuzpeCc1pnTLWoJxw4Mzo1J0mw0tlv9QsP2o7gAmn9ceyxvvyjn9fytuvF+w8OcGt74jB6HKx73r7BGL++B/W0bkMJzXsorcUUSTGcLPuPfMLVyZ0I28sihtPDNH5iqmvwS11YDUN08o9I97h8vOiP03TtP+yPOIP9GC5IPAxzVTNg7qtOsjmm5wgo1uFoVz/P50I5eGE5a3yXKDKDPJlaDpBkw5Az79+Xs5/xu7X4URqeEIpPyqbqrsngremn6y8oSvuL6BOOI6NWqlfY19GJUbIkdhl5qpvvMtU1Q3tBjX2w6JWzaVl3d0Xp0tte2uZxO3PQ7P25ghTDzX5erPmqShhekmPLh4C1PkDNICp8okFy/PI5wcY9emeVGn6LR/YpUbf7hSOAjAaCmlsmYyPBuXqSaWlZFdrpJMJzSfsUmr+rYy0ljg6e++hisT0txg7f88Sy61YV3UhrSiyGYjpid7SCNnv1ch6rgYI4m3peUyytTyF7un8Pa0tm80qzMbEdC4ltFdkWS27vLcfcXwXYPSMieIbLJM4DgpaSrJUoPWn/oEUxpnuvW+FHPLJlsMmf+ITeaIkmGfmxB9V5eXv+e/f00d0+uiMPnn5tR9v/b36Qz0wek5Md2ej8oFix8xSV2Dw781ZPLf+UQNg/1vC5n6mFP4/hjsvjVBmIo7/8c+YhSSzLe48d2+NmEDMBTTkz3Cj00TfYvexl/8NZOr32djtDW+Yqx5JK1Uj2ljvMlUJ7onZPFNMdIJO8e96hDeEZXWo6aZwatVxmP/I2+5yLX/9QKTT+5z7QcmiRZiZuY7NJyQbuRiF3wR34oZJTZ7n5nDDPUVz+4rbe52RpE1U06f3mHK0x1EN/LoxQ5ba23IBM6uycKnI9yrO7pDWZkn90zdvQDhcpNgysL4+zv0Ald7VFdjHCfFkhkNL6QbFHwwK6X1dTx1ciWIMpOarV+7qhkxSB36sT5p5/wenkw4jD2Cn5jUmwG3j4vwNce9EwXqtnWctHm7E8RouVZ2ayJOjlwphWD9O5s0r2T4G2HZNQHl18oyMLsRnbvqhK2xzY0idQX9NwbYTkq46/Ho/dqqOSx4RK5MeOqlFYST4110CGdynnj/v+LbfulnAI0/BQtpeRydP7vJ7rBC73KLrJohB9pmeYxF+Vu6Sw4nNH+tulocZ8U56h4qzCCnv3A06Pi7GVFTQwfWSNE5D1ZfEM7kTD0FvRUDqwdOVzFY0rKazKPsampr+v7R493SeTNJpIY8xq+7k1NpaBM8aebEtypY80OS2CxTs4HSKtq9aXHpn33wP/1RLs8NBmGhozKzItHBYOVDCuf6JuEd0xy8UqW/KBguKVTHRv5dLTkJAhd5rU5Wy1j/RZNhf5JKLcR6SpDOoiUihkIaOXZfMSj0aeKYAZxlZagYyAQiH8sxMsiFVoqPpHYLKPRoxqjYeh6HWe7aOiDRUTz46CWef+lOzj16g0HssPfzpxFLcPBQG4Bzv5Ug/nlIcDz0Eq2Rk0ZO/Ya+wlkjhXuYMZwzyeopwsrLojQ+MZTSz89/1aG6nuNe3SnthjWI7ZPM6cCetR/NyFPFdG5QdSNaSwGDyNaiztzAMxPsSsbu8CgGKUgtPDM50TEBtJ0RabEzZIqcVBlMOQN2RxXO1A8YpA4Hka/TQn71kDiTyB8pcLuxwRmAYyOz7MSYd7tU6PiId7woHV9iax91uqa90Tf5mla5AKN5F7uXIUcJ4YwOmkirFlYnIliooG7jBdbXUrJ3RAy7HlYr4qlXVnjz3VdwZcJar4Vrppw/u8mlK3Mk9w+pPVHhe37mp8nOaH6cEaOLUqovHFcHi1jLQ6zlIVnH1cUpMkgrulCEE6J0RhAZxA290+XuCUQKZpDRXzDJPKivaofTcVGqbmYMZw1kJJh4OSW/LAibGqD39jQtIrdUYROsHRhkrH3VjxvFubauWFb7CJcsc/KEIhra0EzI86PRVY0ktasm7r4WhSd1XvN6XRQm0HloppkRFQJXFRvc+PEEmGDl11KiGYP0bMJMu8f+l2bZn66U9xmvYd/FdhOSRJLcHaACE9uPiUNLz+PviMoD/sZ3+9gHkLa1mPaOPx6w+p4qdle3vEZiUF9PcXZjjDjj1Q9UuOuXu3TvaSHjI+fL/rJB5ghGd8RYlYStYR2rDw+31jlIKuz9Qo9Br8Uwsom7Hjd+AtrR0S5KnEniVLJ+dQpMRbNhFPIGQdg2CS5ECKD5RRvznP67O8MqcSo5vNmgdsVk+pmQ3DY0XoQmpbK9w+4/fDOdb9Jt/yQghGLKH9KPHS3mzA0GkY1jpQxih9lKj91hpfSjPt4RjTkzQWaRKgNT5HgyoZ86mCInyCxcM6UTezTtAM9JCDKLQeqwUtvnYz93H+944BVu/uSZI00eICo+st2Eg85fWaCE731Vp3R7cao9t0n/wTmCMy2864elliyZb5FLHSYQtbXjZFyrYaSK4ZykthaT+RZzdE6fAAAgAElEQVSDORO3k7N7Pueeh27w0rOnQUmm/7VH3DDpnnHI3zDiiefP8eYHLtN0A8LUohu5nDqzy829Jt17E8wni+PX0BsX1p5JMpVi3d8h2vfJhzammyCsHKcS0300p/Gkg0wUwaTewk9qUL8Gg2WFSLWhnNVXDOYl4aQuYjsPG9Sv69Qba6iIqwZJTWANoL9gIhPFaFpbGKeelpYYiZbWyECUEpqoLuncCWnXK2GT8caQ7aSYpvb7Ttcr+LeMUgfXfywnDyXCVEw8K5Gh7vIa15MT8Wnf6HpdFCaVCcJdr0ylVaYCOydPDVQg2XnEQnghWd9imzr5mRCjECZaVoZ/WQAmnXsM8g2H5qtQW4vZfsxi7okUOUpQQmJEQZmEYm3t6r9dAKX7Dzawu3o3xrnQJQNyPyAA+qGDE4W8+oEWlYU+ppETxpZueUfFG4kecTb2G+RvGPHhp96kd+0iA+HkOi21EVBx4vLE74UOtpkxVRnSOBey9tIc7nu3OXxyBhvoX0gQwLV3/Vu+c/m7SJXBC5vzAIQHLlZPpwLLUYL81Aulm8H+j70Z5/3bdLZizi9ua/6LN2CQOEw5A9LcoGkHDGKHVk2PtqaRYxbG1J6Z6N+nDlVTF4CxDCJVBoPEYc7TI+L4PoAm/hVY22ZQL7vCmzQRTq5/9s8HzFZGDN6vMaLxaKfaTYwCBD8+3o2F11+rcxqv8de15zbpPjqPW4hYjdgmnHKQx2AufyMkrVqYg4Sdv5NT+19slCEwA0XYNJh6SrHx0hnqliiz9rztkGDSJ77sky3EfPHyCnee2sSRabnhMNkcsN1pc/BWLcy1u9p9wOoL0vmcwWYVqydJZ2OSnlNgUAaiY5G5kLkCI9NFR4YCM8iRgYGMwd/KCSYNlHk0gvlbgmASjKyweg5z6tehvyz143mi7L7dTk5tPWPnYRsEBLOK5kXF/v0CJQ3S2Yhmu+CoKUGaG2XMeZJJjfkuDeFWjdyEymZOH33cG3aGNdB0grAtMAMTkb92eOh1gTHV6ovqgXf+49LP2+kk7N7vMf3lIeb+sIhvMuncVUcm+sqBolThT37KprYWE06YHNwtSer6ZMlqRT58PUK+UiXzCrDW1YLEtJ4zvbJP74lpookcq2uQXxgw0RiilGC6MsCVCTujGr4VM4gd4kxqBX+h3B+79Y27i/FyZUKYWbgyYZA4mEbOi9cXqDQCJiojHJkSpNqSZPxYq6tTTHzJJLMFvfM5TEb8/lt+gx/60j9gZXqf7X6NNDf0dnVgcPqP9BknP6UzRcP3Pob7R09y+UOP8IazN8oxa/z3x88JIM0N1jtNLJnhWCkNR1PCb3UbTFaHzPm68IzB3uNrjCmNv/Zkwm5ULTEmzyw4Y0bO7qhCyw3Y6teYqgwJUouqHRGkli6WP6oB+NsxqHx3/wQPSk4WMoeCVvBVjPCiWHUfncc5SLH6MaN5T5sGBjnr77JZ+GRC5hpsvVmy9OdH1Wr3QYfc1q4BuaPJht6ewhwpEOjE5qsjkpqFMgU3/06i7W1Swfkz2g1hzMq+dH0W6aeIdQ+RQbYUYmy4ZDMxxr5F5uXIpv7bWd/COtSFzTnQpNeJF2N2HrZRBvjbiqgtMAfaoK6/bNC6rI/pw3OFk2hHETe0W8BoKWXiGUn3HHg7urDKSGOVmSWIJvT/mPqQ1BRGIkgaOXImoFoJSTJZRpt7Tlx+Px7lKn+uLw6pJ+g9HGG6CSiB+XKF2g1V2i1HTcELv/r/A4xJKEXtuU3S1XUtPZmeZK5TIW26XPmRKRY+mXDrR2KyNKLxOZdwUrHwqZjKlsnWWwSd7xzi/o6LvxNjhhbdMybinQekuXH0wt7bJx3amFu2PmB83SZ3hx7ZPQPY8onmEjhwaUzvo5QgSk36sYMjU7ICYxl3O6A9tccndC9ycWRaeiH1YhdDKEwz1/IQwHQTLJmxP/RJi9RclQpk19Rg56kemd2k+1iESgwqL3r8z2ffwcq0HnHS3GC0WsfpGsw+mWDEGWYnIAOu/PKbyL2MpX90hmWhbz9IHVx5hBGFWFTNSBerwsOj4YUljuTKhB15BDqnx0CXNDfKx9kcaRAhyw36hkPNjujHDlPegPV+E89MGMRHhWMQO2V6DEDT1u6HANXf6jD40eZXJZWIxTnMKCZd1WlgX0XK/KvM6ITewUpqNmaQYw4SwimH9ouKpCZJHcHSn8c6CnszpHemsNuNNOCt8/CM0u98TCsIZlxSV+BvJ1S+5DM4k0MzYWdQZabWJ1cCIRQXVja5eHUeMaN1ds51F3dPMMpsvG1B7+4MtaNj4jMgqRedSR3mPmPQX7JIagp/S2APdGHKPAgsA2UUI9w1jT2lHnh74O4phvMax0oq6CIhQAa6KAH07iiY6QcK/9WEW2/T6TNyJsBxjy4kllMA4AXBcgyVZJkgswUyVjqb7qJDeE+OlDnxXSP6+HpXMT2SQb2W9booTKlvcPimBZqFYZsaDBGDIeblgPT77sNb72E9P8nsyymD/+KA9HKLpCaxexl5RSCBxDcYzrjUVxMa11Pi322i/u4eOEXOWtGahi2jzIITpgbzTCctD0Sjrt8kx0ypmhEHkV9KQw6365AIZDMm6+gTaVuqE6ZOTiPk/Myu5r0IxUHkszOoljE6U5UhQijC1MKfjHn1JR0qk/sZg80qM+/ZQd1q8Y77XuHWHQ12I30iXtueJN/wEMDcF2Iy1yCccqh2CucFU7F0eo+mGxClZtkZdSOPOJcoJfCtmE6sT8QgtWj5Qfm1LrweQWTT8EJuDppUiy7QNPITAPh41OvEmnNVNSPdySVOabkKMOf1uDVqsOB3S7xJFkV6/DyadsDcb2/TTx36/9V0mfMGunuSM9Nk2ztHnKdCn/dXFSiAqKkPa283Ia1axFWD+rWAnUcqjBYUYOHtpew+WCFqgVU0ZpklyIrsOxlpm5O4LsgcCCckMoDMsfH2cpyuoPd4jPzjFtvvgTOtfYZFpNH5lSNir1yvMJrV3LfehRRnyyI+FeE4KXnNwBCqJP/uPOLRvKgZ1MMFBRjkEhprOcGEgXMI1kCPedU1QdQW+DspnbPat9zqovGmhsDu6gJiD3I6d5h4RSTIYFEQtWzkMZ/3YfeYvKrgL2WpgTRvY3ArrekDiCZ1lmKaGjiVGHV/n8mmJvjuvjjNa12vi8JkJHpGju4/TVqR+J94EdCg5+TTBlf+3gRJPUWZJsF2HTGRYHcV/SUHYcVMfNTHGuUYqSC3j1wLbZnhWzGr+23CAxcjkHj7xe5QqgWUSkLu2Nqmd9MiaUgu9hZKR8h8nAnXjGnN9Kg6MYPIZnp2F9+MSZXELYDhcdYWFK09elwLIg14OlbK1ecWcfYM/rMf+Cwf+YNvZv6ljMGcUYZL7tZrGEPJszsLLDa6xGMB5qoPluLUnyQ4W33Spof4wvOs/8HdjIYPsTy9DVCqzMcFQBo5NTM9MVKmuVGOnmMgfNwx7faqTHkDwkx3V+MCcvzrILVK/MiRKYPUIUgtarYuUOXtMj2q7kZVXJkQpSZxLsvHCFOzLGKDxKH26zt0Pzh/QhZDo4aEsjjBEaXg9qKkwojcFIhc+3w7HcFo2sIKcvqntETk9B+OSKuFc2OsqN6C/qmjHabaamHjMWMgQ8oT2O4VAZgmDOe0R5P3qRqH9+TIV1pcvmAwURmVY/n5CxtcXp8hv2+E/ZJPWlHIoSRpFMkpocVjp1Z5dmORREEWSWikZLaF3QEdRa93zjrnDOyOjgwLZkEozcKu3MrpnDWxe3osi2taUFu5BYNlqF8T7N1v4m8q7Zy5rNnmwwXdPdldCM0cacZkl6s4d2kDwrH5UzS0cSoxppmRZSbpt3cIrjTI6imtuR6HNxtU5wa0/ICbm23Wkxb1aoB1+kjc/Y2u10VhSnzYeUQy/WXwPvok2Tc9gLWpLR5yC5J2iqwm+M9IJr8iWH/cIHMU3fcOsIGoYWOkAn8nLvPKwrZJ+Duz3Hx7rImRmcDqCVJPYfcEway2uh0n5bp7gv7dMcJUtNoDpqsDbJkRZ5IFv0u/wFRMkZNWDLqRxzBxihPUwxAKIRSemdCPHXqhg2NJwtgiO8b1qJ3t0HUa/NmvvZWp3RSnk+AcGOw+5DC4L4KOTXXNYPGhLlFqEmUmG0/PkfmKhU/mRG0TWQQKXv7QI0w7XebrPSbcIakymHS0uHkvqpQY15gYWLWiEqzuxB5zXo9L21PMNvsIoegpl2holwWsE3vlbh1QAt791DmBVcFJXGlcBFNllCD6+HmNn8tKbZ9bowapMuinTlmgFn/1OkFmcfiB2SO/qEYN03XKsS4fjZDHeU4F6VK4DtWNiKvfZ9N+XnB43qaylSEyyD3B9JfDUorSOe/jdnLiqvZcEkpjS4mvRydZ0LiskR7vUk/QeUfA/O/ZjGYKhv2ion7JoPtYRB7YhE7Mcv2QtV6LXImS26Ye7GO8XAMUSSWHVFBth+VOp6wmZAMLtxUyXDS1Z1MkyB1F+2XFziMCZ1+QVHQxyS2IG4r+nRluc8hgaNNsD5jxQta22hh3jSC02Vu2USOJuj8guVEln4owGwHpboXckYQzYAPhroexHJKmspTNZCMTq6qLUhDYTDYHuGZK70JWYoXOmf2S79Zs6/w60Dy417peF4UJ9JUIYPOnvwl3X5E8WME9zOmd0W/cZHNA44f2aDgB6y+tsPZuE7FuYvXG2es6CSOZtHVcU6xOxk57GTIwyWuK7JE+Yq2CkUJuKZJmRu4YoARO5ShN9/pBm4nKiCCz2BnVaLtDdsIa85VuyX5uusEJMPiFi0uaoDke8RJRdl97Gx7OvsHiSymgE2SjpoUyRJmc8sT7/xVv/fA/BfQ4eeniPNJRtF7U/4tzkNI74zHxhU1gElue5B4BTDrDsnsLsfROXIEXjYFq09Bb/K3aqFSuT/t9VtVEiYk17aDsjDqxBzZlAenFujO7tj1JrgRZx0YGBs6ecUK+MF43K+C8T++2uWZaFrTdoFqOhqkySjpC7dd3SmAc9FhnnloqcUjVHxx5sY/9mVwHa6sPTBDXtUtA5VbIcMFl520J6nMOMlKE530GC4JoSmAX4lu7pz9kouifAX9T4zFGojEnp5tjv+QTthT1G7kekTAZLioaTzp0HkrYHrZYaezT9kZc35vg/sVbjFKbK1tTqEaO1SuOi9igc1DlVrWhQebEQ1YTjTlOpSc0mrtOm3wqRlwYED3XxrqvS9WNCJ+fpjo5JAotPUopQTdwmZ4sNi2GLjMzHQ77vv4bCwHTzQHDyKY6NaTlB8SZdp10Gn3C1KQ71K/nZHNAXJXU3YgwNakcC1Gdrg7K0Xy8Nkd16m5ElEn2uxW4cfJ4/EbW62JXzp9eUne/558A2qsm9UVJGpNxTtDWkd1b36yY+bxg/wGB1dVEMpEXNh75UWxT5hqknnbvu/UdObJ3FJg4TpjNKxnG8IhvkXtZKS/IKhlWR5I7qrxf7mUnsKTzK5s0nIBB4nC6esBeVGGt12K6Mih3qsaiype3ZpHP1gguRLQ/Z5N62ijMPcyoXu5w8FCbg/cEyFcqfPrH/if+4fXvwTRydkY1Np6ZI2mlLP6ZNsbPXEPf51/mbN9oc/7CBo6ZYooMV6b6pDajEwfOGASvFVv//dt22gaJw1a/xmDk6IMq19HRdkeV4ZFjq9+kYhC2ta4rqSus+SGWlVH3QuRYolJQBnqBSxBovOj+xVsME40xmUZ+YjQcfz9INZ1hvMP32OQqz/2X92FEaZkBaEQp+fOvnHj+hu8fCYGbdQZ3tll/b461Z5G0UppfsbB7Sm+jW4LhoiLzC+3g6OjilVtQ2TiK0Zah5iLV1zKG05LM0zwgt5Nj9TM6Z20d41Ss/t0xZIIvPf7LvPPpH6PixDScUF9cmjHmFa+UFplOiuMmpKnEtZOyUA92KswsHbJ9o60jx4tuyDYzukOv3CELD13cVkizOmIQOiWGOs6Rs82MONXFZeOZOZJ6xszpA+JUftVtxhSBcxO7bAy0n9btlBaAg26l9AMPDzR/C/QmkC0zDKFwzYQrzy69ZhHv66Iw1RqL6p7H/wm16/oSa8QZwYKuunFVknqCymZCUpNU1kbsP1Bl8ofWCH5pAQC7dzJzDrSfcepKjESx+m4bZSlEIsiaKeSUboXklAdpXk156PxqiRuNuwLQ/I6GE2CKnP2wQpxLprwB3cgrKQN1W1+5w8zCN+MSH7JlxjOfvcDsE5q5W9nJCNrabMzt5BzcJQlOxRAbvOPhl/nC+hnum9vguc+cJ2lmLPylZvG2Xx5h/MIe4oMNhqeq2N2U/pJTFo3xSl2DpKKBymgyx1gYMdvqE2eSzsAj6rg4Wyb+BozmYeKx7fK+B/2vf7Vr14YnbhMeHL1GspackDKMvzeGEntpSKMSEKcSx0pxzZSaHTHlaDxiXIzGOFXTDtj+b1dIahJvU3dpx7Gn7NLVE89r7P0uPBfl2EQLDfbvcQinFNZAYPW0mt4cQfatHd0xRkUC8aGL7JlkkzHuDV1pxkJYIy12mmLteBm1oH0xI2waBFPaCXJcnJQJ4R0Rwsy5sLDNQeCzvdGkOTWgs1VDODnT013mq12uH04wDOxSLAuU5OIHF29xV22LL+6dYe2gRaMSsHtpEnthSNhxWVreY7HaKUf3G4M2o0QHTe4NNPHYc2KtoHiligwFE2/bZBDZDEYO8+0euRIcjryyczr4yhTJZMLMfAfXTEt3gyC1ShLwdHWAUoK1gxYPzd+kaQc8s7vI7kGNvGcV5nECTg+5+gP/3X/6dAEl9VZkOOPhr/URYYJ3Syu/w5bAPTwKZYxbDsGUwDdjRsZRjHfqOphhRtS0SD0D5zAlczUbNp+KUZEBtsCqxiSHR5c5f1Mw0vUNq2szWrE1qJ0b+GaMaeR0I68Y3YJyJMpyg17sIoTCN2NcmRJmZum7vTOs0hl4xCMb76JDc/OocPgbIeDib4SM5l3COyLcSkyWCW4Omyy3D0lzg6SZMfN5HRPl72bE/6zLfrdB+LcbOtygb+ritg/B5FFUulkEJDqHisVPDDF3uuSNCr5t0opTYICyTUScMjxdY/euClP1AeurRbSzArd9bNvm2NpYncBth4QHLm47xG2HpbYq61snilEGZVcaBRZ2fcB0ZcCUMzjRtQUF0D7uGpqF2dn+PQ7uQc4HP/wH/Mo/+kHMQvuXNf1yrBsv4XslEC7CCKsfM/WVnOGs9jvvn9IXgrt/4BUOIg2e34jarEztE7QsNg/rZIcu4XyCu2ER17UdyeCUtvGoX9fdU/uiloLEdY33jDWRYwmKe9UhuhBw5UunmH1oi5n5jh6fpvX4tFjt4MmEy8X/ely9MBYMr/VapLnB5fUZTDfh4CtTyNND4lsVhKn4pfO/x89d+166kcuWrLPbqzLT6KOUYLKqL+5hajJVGXJ52WZ6WrueDoZuaSQYZ5LRap1zD+8y6/W59pgWbr9ydR4yUdq1AFTn9MWjE3pEiUmWCZ5eX0K+XKXxph1tuCiVpkBs+fjOa8eYXhcdU7W9pO79zp8qhIg6ycIcZmSejrAek4vHxmtXv89memWf2v9wZHcbNa0ySnpcoMxhRjBlsfNG3Q2NmaoAKheoVDNvcz/DbR2diOGhC6nGhpbu3jrxXOcrXZ54/lxJN7D9mGSjgpwdce/8JtcPJ7hrcpumHfDxTzxEbinaLwgSX1DZydi7TzL9TMpoWuLvZGz9YEQamfxfb/s3/Ny17y1jl64/tUTSSln4i6P5cTRp0LwSM1i0S1vUqHl01ba7OoKIIuxAhgpvP6f+ueuaMV1sw49HItCWINd/QY9U4+5nXJRuF3MeL0jH1/H7jb8+MSZXtK9WY+WQqhOzWNUbG8fJm8cFwKCxsM9evwPjUgV3D8S3HTD/k0Pi05MYQUrumdg39koZzniUG7sR3K6XE2FCOlEhbtll5PdYWpSbgu5pk7/9D/6C//2VNxEdulj7Ju6eIKnpgpPb4O5qMDyc0Ok3oHEpACPSYLi7Jxjcr0XdWcdm5vQBQInf/ON7/pJu5vOhV99EHJn4Fa3FqzR0VzgaOvzQvU/zJ6v3MFPrc+XZJao3DHqPRJhbNiuPrREWO5oNJ+TKC4vI2RF5bpB17HJMTAa2FmkXHKXRap3GyiG9gYfacamf6dDZrWK4J726bCclGto0WkN6fa+UnahUQGwghxIjA3nHgEYlYFh40JcXNQH+dYtXf/G1ESxfe5zBf6ClhAYeM1uQ+AZxw0QGWUlwA21wJkcpzq5kGNkMZx3sG3s6sinXvs72YaSDL4faLtY9SLF6msRo7Nqw6aJ2XMSmi7mlT1Rn0yK7WiW+VSHc9cBQCD/jjY9eLD2WxyvMTN7x8MssLe2jRpKVqX1kKLC+UmWU2gSR9m3+3O8+jLsjqKwZhBMF5rKshZBx1aD90oiwJUkjE9Gx+JFf+ykm3CGmyBBClUUpbOoPdy/B38uJGyZmqLD7+sPpaL6K0ym6ylBxPMQtmDDK8QZ0UTq+8rrHVH1wogMaF5e/rijdXsiOj3WgC1JeKbzPvYK4lxtc77XZHNW52W3Qi9yyU7KN7ATV4MGlm4gc6u/Z5OGZm7zyM/PYq/tl7FB8erL8W9nePqo/0E6bQXgyBBLdRWWuqZ08uynWMEMGOTLQF8L6WsaHP/TtWqQ6/v8mT160ZaIYzonyojAe91JXC27tXgGkrzpYVoZRT2g4IXEqaVQCGhVdfC4NdTiGHDOqGwGjod4JrdcCPr5xge5h4R8voX9HjrGvLXcvrc3SDVy2b7TZHVYQGSQHLvVqoHEsN6HdGGJ4hcVKgT01VrSZXx5rc8Ve3yufg2WniG0H56KH8UwN/1WH/pUmebEzB7C0tM/M6QPm79+ictchcWQSJSajYaGGuGXhbJk0p/snHTm+wfW6KUwy1jsgMlYIBZX1Ee5aByNV2L1EhwkEKXK3S21VMex6+Nsx8elJfbs4P1HEtHWoJKlKjEhgLg3J6in5REJj5RAzgGQyobJmEM0mzD+8ydLdW5w6u8P5M1ucX94qx7Zu5LK2MVHmtt0cNssrxLXPnsI5EATzGVeeXSK/VsUQCruriBsad2heSansZAgFy3+mcZLdByscvCtAJQa//75f5a0/qGUlB2GF7Y+cYuoJffLV1hMtKXCMktWcHfsASr6NjPW2txnoYiWUjqEeuzgqz8aI0qNuKYgRccr6jUlkLTlRWG7HjsLD24pS8f3txWhc3MYF6fiaafRxTW2p4pkJs7U+8xU9WqzU9mk4QUlJ8GTCI401rIGmImwGdaZX9nno318HdBZaZhmE33y3Tkcec5v6A23/6zqlL5PRGZBNN8sobKD8DCCDHLub0riW0ZoYIGsJMtK7eu6ufk2NGIbzWlBbXc/JHIUR6zincddUXS9cUHNQL9fwKxGXLs6XLGpbZjzVO0OQWeX4Fh643DW1TT4y6R5W6F9psrPTQMUG13YmcPYN/FM9jIURRjvCqWiYQfgZSSbxVnrMnD6g2/OZn+jSro/Ye2WSfGRS8SMGI4coMrFNfXtj38IYSVQudIT9LR/5XI18IiFu5oyWUoZnEpSEqS+YuE9WMbdstr48S/eL0+x/ag7HSqnXAhwrZardJ0pMvG2tv5N/1NLpv69xvW4KkzXISrdFI1Hw5Auom5t6pCuSRXLPJG9U6NwJTiXmxntt5CglXG6Wj3H7MlKFv1N0Ez3dnSgliNs5Tj0ifGyAEUjCVBudOfJI/7bWa1GxIvZemUSlgueuLXPp5UXWPr2M7OukCJEJvB1F/aJk/jM5Vldw6ZllnG6Ou6f5MHv36yu1EcGtt1UwI0VlR+9iyJ7k44N7yk5h4+k5Ovek2INcX90PI1JPuzWOi7c1yjHDHKeX4e9kyETvHsqo8F4+5iBYvZmj1jd0ESo+gBOZcs52kTRzW3GStaQcydyWLkLxekXbqlaPbnt7Qbu9WI0fyzUTaoVOLleCw9Bjc6TFvpe7U6z3m9waNdgc1dkM6vQzF29H0SvGvJXGPp/ZPou9dkDUMpFJzs13Wuy/+/xJ/yYpy1CCMZM8qdllMVKmKD+M7IjdLOOc7BMTvG3lCqmvMEcCmSjiut6dzBzF3BdiUk8gI4FQumsaLuiLQDhhlIZvZgDJCw1koImXw8imHzpc7U1yEPm878wLvGFpHbcd8vSV01h1Pf4ZicC76GDtFSZ1QxCfbVL/eAXnBZ9GRSf6VF/QHVYUmboQ9awycslYGCHGFj2Gwv98lZ0dvdtmRIK5zyrmP2px6g9h8S8zwukclWgB/cSzkvrLFiIR9FZguJwj7xggzw048603yB/WF5eaGyGNXBN43YjBaehcEOQmJcb5WtbrAvwe275mroE1yPC+fB1VXAHd526gRgH5vXeUaa93/E6HzW9p8cAPvkrndxfKxzHDDCNKsQ+PHjup27QuJRzeU8EKYPkPAnLbZyoOSWoOq++VZQvuWzGX1mZZ+KiJOcqY/3kdQLB43xar16d4/IEX+PzGGWrnIg5HHs6fNoha+ko69XxCZ8Vi+plYCz3fbuJv6cKUekdvVGVTMZyWdB+LMFd9lj6Z8PybFo9AdV/RfNFkOA3NqzFp1Wbi2Q7hbAUZ5txuKJG5BtYwRwl9f6ejO0cZ6S5UGRB+8914z60d6cm8o4gj5dnMPJWxc1ehieJofFNbru58+haZ0D8fd0LHxzzga3ZPx5dlZYwSG1sGJdPcMvJSV9d0A/qxg1GQVE0jZzNskFSgYWpbliC1kEbOmd/fQv3nFr17J5j5Us5gXp6IkhoHco7x07EnlSo66riu5UwAmSPLgmUkisp2ri9OmS5ESVUAujtqv6TIXIPRnE7kjeu6k5KRlq2kHthd3b3GDUjPBmQjfcFLU1k6juLB7//pW5HnBsw0+qzuTmOs2fiF71L/jMJItM/RaCGnds0gnNTdWpxKOgdV1J0Ji+gP8jsAACAASURBVB9qYDW0y6nTNQh/MCdKTNynqrRfTTi40EY6Gh+rNAKdBlTP6Z2SxE2F3ZHYPVWef3klw/9be2wd1shDi9l5jd+NHRRyJXDtRP8/vzVTcg8zoFqDpK5fq/8Q63XRMRlJXsYxwVdntgnfw3jxKmnTPXHVv7g3zca3HAHg1mavzD7LPZPcM4kbJsM5BxkKvG1B5urf793vs/peibMryW04+MoUw3+9yMqHNAZhJIq9oMqLXzjL6tokCPiT5+4njC1ubraJQotcatA5vDsgqmuvnGDS1B7dlkIGULuVMv1Mzv7dJk43Jzehc29agvCtn18tQeCnr5xGhhokd3o5wzmL0YxV/k/jxBWRqfLD6SQYcU51I8LbTTBDhbeb4O0m2N0UM9Qn04nXMzhS8gNULh0Q36oQBXo3bfwhZkNkTbdfbis8AWh/zfexuJ97y8K9ZdF61qTxkkX9ZQvvE1V8KyYqDnJXJjTdoDSjG69B7HAYesSZ5HJ3So+xuc46G8tont5d4uHfu4jd1UGSgzeP6Lzvfh0VP9J6y7EVb9b0yRxZFiWgLEoyyk6MdLklyq4TtBogdfXnwbL+eecOHTiR1PQIl9u6gClDO1A2r6ZkHkRtRR5LmlMDdrpVwq6O3Qa4+cIsSTMjywT5r0+z9P/q4yiu6YtRNhljDgXOZIA5MBgsK9KH++SWIohsKo0A2ZPceqeWmQTTgu3v0FyoMV63/u0Sc6Tw9hSpD+bHm9RetZh8WnvPmyPNpbOGiuqqoUmdQ8nWYQ0pVenNdBxfVUqUx789yMgtQW4JENp+xTnU0he389pTUl4XHRNRDJdXkbN3lyff8SUq2rDefOoi6twpRBAzWlBk15tceM8NghfnMcOMcLmJTDR9QElRGlYpAWk1Z+6Th6RNj5vvrFB/8w7O0COJKnjbBtNP64M+c45O4vW1SSi4HU034NLLi5yZ3CefEHRCj+47c5K1CnnPIjcFEy9qQ3a7q+0lAOKqgRkp/G2FWQhDha9N6R/75lcBSFVOmFmYGzbu/hFnCcDcG+jt8eJ1GX/OrK9/TRmb9RvFzpPw3DLFBI51TUFM3vCRoUB6CSzpIhEeuHgv6fBFDQNZTNzMGE0dHTK5rWOulYTMrpKehWw24oE33uCUf1DebnXUZr3f5NLaLKcW9vAK76exZu+4O2bTDcpMtTiT5Y5slJglL6zhhFwZTpW/M694xDUgy74qyy5pFhSCVJ0oTvq9PpbMUhSowbzk2b2FsoseK+Wrq4LU0wXEyICs+DoGI9bdTGUrZ+cRCSiyVoLct6AFd89u8WI+h2lmDF9qYSYCa3lIuOvh7kbF3/EJFlLW2ybOmkFwNmK2NmLfqmKOBMlqFeUrJqoj9jtVvG2DqK1wDsEMFWbo4Bza7D+UMbWvyC2DpE4pTh8ujSO7IGqBcwidOxXmwCCaSqk2AkYdi7Mze6x3mghTlRbKoPExIRRZJmi0hsigUnLncvNoJ9hI9TH/WtdfW5iEEEvAbwMz6D//m0qpXxFCtIHfA04DN4DvV0odCp3v/CvA48AI+GGl1DN/kydjH0Y6PPLYz8bdkzEzRb69q21UZyd0oGAtom6HJMXJqqTA6KUlp2k0beH0MmSsOPvvIg7+ZQ4McZKIKDFxPlPjzCf2iGZrJDWJDHOs/jGAJrchM9i+1aK7Ps3iWzbJcgMhFFFiwgs1VDvXKSu5gYwVC58e0vtvhjgfniQqlOphU5t1+Rshw+mK3koenByFXri0hCm1KRhom42oYdAuEoaPr9QtIq1sg8yT2L2EuK6FqUacI3KQSUYuDUSuyDyD3bcv0D8lUPf0WZna50z1FvdXNA/oxeEiKwhePJhj0htweX+KmaVD3DMp036fXuyW5NHdoErNjkobXeDItbJguwPsRdXy64qMubO5w8b6RGmRMpa9AKUAuHzPC2cGW2YkaNvhqhNjy4xu5FLzI0apzbt+5TP86c99K9GeSefuHG//LI0vb5aPMx7hxkXpOMY0Xsc7psw2ePMPP8MXN0+V3kxK6MMgt4GeLkAy1H5G7p4oyZVzX4gJJk3sjqYLiIHJ5F17+vXdmENKRfKVJjgK7uqjXqly+vMJvTMe3TsMclPh3TJ14WiCd81hK27jpDD7xZhbb7NIKjk7F6fIvYxsXgeeDpb1Rc6wMyrNAWKngfH9B4gitBKgYqVEiclg5NBZNshjSRgV0EEtg1QwWq2T2zkHga/dJNyEw5HHQqNLN9Dyq2s7E7TrGi+LGyapJ8rJzUi1rCw3/+N1TCnw00qpZ4QQNeDLQoi/AH4Y+IRS6l8IIX4W+FngvwbeDZwrPt4I/Jvi89dd+b13IDsjHak9NXGCn6JGwdF4l2WIIKY6OSRJJHEmyxhre8xvOYwYLukrpwxzopYJONiyR5xJ+kNXhw/cleK+OydOByw2uuwMq2BpgLYXuVwwE61BilyGl6dZrHZ44sWzPHrPNbb//BSTGxmHFyTWZYfUUyhDkvgeDWefzVkdA2XuKDoXBKM5Sdj06Z9RqB2XNz968cT/L3uStJ0ymrWor2ZEDYPaWqy7PVcynLO0BepszPc98DQAlsjoJD4bQZ1RahMWujbLTBglNrOVHoNEi3fDzOJ0Ia6d83pEmcmf7t7HlDMoC0jVjpj1+my69dK7G7TH93iNO5woMUvA/jD0MI28FKWCdrAs/ZtSXSCsfbMc5eCISJnmhnYfMMzSMTPNC0uQqsBSonRLiBKz9Ib6cneZ7mmT899/kRc/doHtNyrqL1hlGIFIcoQ0yqJkDmKSpvNV3VPmGWS2we6Dkic2ThM+08Y4lnQuIy1L6Z0qPIpCgKOitPDpIeGUQ9Q06J9NkfsW+URSSkik1BHwcg9Gbxlqm9quYP1dJu0XBOFsirMnwYD+ihaXu/uC6lXtmRS1TGa/lLH1Rq1WiFYSppcP2D2sYQhFFhlkI8n2oIUxkmyP2lgdSZKCDASBpXWLlq19nFACd18XwNalnO03gdU1MGKDaMFkrt5jPW9ScWIub0zjVyKEUKxM72uc0A9QFBCKoNgdLnY7c8pAh9ey/trCpJTaBB0ippTqCyFeARaA9wFvL272fwCfQhem9wG/rTTy+EUhRFMIMVc8ztdeloURpTqAkTndFS7O6d8ddE7eVkpEFBNdnCaZSLEXMsKtfYzZCW2nWrgL2P3CgKsqSV1B2JQkI4+KE+tuxdI2FHudKvVqQC9ySyp+lJrlbH15fYY7T23S+I41bg6aLJ3e48s3lnEbcFCXNK9kJBWDzp26rbcGBt1PLWMq8LdzDs/p0W72iSE7b6hgndZR5/thhTvqe0SZyc1hEyMRzP+xYP27EuKGSX5+SOpH1N0I6OMqwXyhRbrcnyqtcDdHdWp2hCEUdScsT1pbah3fkdcS5Uk/XnEm2Qzq5EowSjTHaJTM6qtrYat7/PP4sZt2QF7VJ/ZY1OvKpCxGrkyY83plwRvfZl3O45gpDtqGZdxhmYYeZYVQpErjJIehh2Xk5FJvSnQjF89MqLtR+TcmnSGTf+8Zrv/YCuEHQ5yrLlnT59HffI4nP/AwgyWP2vWhxhvl2O7mJDirTEFu6pTZt3zHV/jsX96H1zsa08Z2J8NpSVzX4LYSlJ2Tt6t/H7b07416gjWVntC/3TWzxYtfOEv6SISJdpjoeHXcqYDdhoO1Y9F6RWGNMlLXwEg1t6pzVhv87z0osLomTgdG8znVrzh0qjNksynOuom0KPPkEJoiov85nUuoJIQTCqsvkIH+WTihn//uQwJ5zGUmiCwGscNCo8vFa3PMzOvz79LaLItzB6XjqqpqrygzBJTGlhJfG8lZg//Iu3JCiNPAQ8CXgJljxWYLPeqBLlrrx+52s/jZicIkhPhx4McBXFlDhAlmd1hGO5exz9UKolJwVIYjyLKim1pk6vMmz+xf4Jx3C4KYvOmWY844nzAvuD4ih+zpJrxlR9tMpAKrFXFmel9bixzUSbrOUXxTkW4h/EzT9IvtV5ELlJ0z8VJK75TJcFbi7eX4twySGphDnVZq97RJV+Zq58HRvItINf9nfv4moEegQeqwdtAic/Wb6a1bBKdi3AIoHSv/x5a+SolyDBp3NGN7lDA1iVNJ1YnLDkMVOIFvxXgyoYPucvqpw4KvOURBZtF2RlzvtZn2tRvjuJMai22PFxrQXVQn9rQjQKqLy3gkCzMLU+Qn/JvC1MTItF+Ub8WYpvYAr1pRac2SSqO0U5ny9WZIxxeEqYVl5GS5QZiaTPv69jdHTVyZ0Pr1TS58YJaPfey3ePztj/OTE1/k8bvfyv6jKbV/e4QlpdUj8uS4a1KCQpwMl7tTGIkgmNLWOGOA29/NysiksT2KyPUOq36PTRJfMFpKceyUqbreIRxv37+4oUW00s6Y+KhP1KjQsAQ938basZh4QZUCdCNVWIMU+8YedneKaz8KlVrIsOuhRhJ326R/VndYzRdN0iqlp3nq6eclAz1yilzz6I7ZspdRUeNlFiJmZRQja64NBcPUojpxVLHOLW2X7yPowpxbgtAfe1YpZKLJvUn1JJb3jay/cWESQlSBjwA/pZTqiWN58UopJcbkn7/hUkr9JvCbAN7cktr61ikGp/Xv3B1B+GixSycUXKuQzsecX97i2o72fk56KZ3zJt/2zmdZ/Y2ZktEsMlWGPY6Lkt0fE+jg+qkWhp9SnRtgGjmi6ELOzuwxatvkShBnkmbhMzMeY5p2wFZQ47lry1Reddj6oQHNP6uQm1qnltsF8FfXb3LcEGS29tY5/Qd7BEt1hu8PYWCTKqM0YrvZbWA8U2NiU7H+HoXsKNxGpNMpMsnusFL624wPim7k0XC03UqcSe0blUumK4PS+G1sVxKmJp6lt+SDAvzWrgfF7zOrlINEicnWsE4/dLhpNolzyZzf097dUbXsmMZmdI5My8e52W0wW+uX1r2eTEgto/R/mvN77DONI9OygxqPcuPnBCcz6jwzIakeK87F/wja9vfxqRfoZj79zGXxdy7x74dVHm6ts55aHN6rmD+1T1ptlV2SDFMy1yxHO5HqEAmlIJgWqNAhWo6wNm2svu4yhguC7jmJt61PwGCqyHjb0zKjxJfs3SfJLYXVkyR1k63DmrbQcf8/7t40yrb0rO/7vfvd45nrnJrrzvPt27PUaGgNSAg1YhBgLwYTgwwkeCXOhzgLrzgJy068SFZWBlZWbGMH44RgMJhBIIEQCDRLtFoterxz37731h1qrlNn3vN+8+Hde5+q7gYLSR968X45VafqnDq1936f/Tz/5/n//zErzT6ru21oB8jLNbbfmlG/KchsNIj8ZZvhUT08e/RjewzPzmAKwe3/5DDHfuM+zWdW6D8hWPgTi7gm8uvLpHFLUb8T0j3vkLo6Y3J8PaYgQ30NJtWCY6rnrkBngTqoKNpXFNuPaa1xI84NWxPJJLapWBHLjUF5HfXziqJYud4f1khnS0YCIlOYgcIavr6B9dddX1dgEkJY6KD060qpj+ZPbxYlmhBiCSisLe4Dh/e9/FD+3F+6MhNq6ylxw2SyrJUok9CketlhdCaCVooAgsQi3ahw+uG73HeajJIan3rlHGe8SQkQu3d6xEuNUlkAIKobRHWb1IbaXJ9qDqSCJiYWSpfT4UobQ6jS8aMbVvBkzNqoyfx8n97qPHHXJaoLJiuKzMpovyxw91LW3mNg9wwaqyn1G0OitkdacxgesciU1toBXfrsBlX27jXhcMLiMynbTxqkraScJCqE4Qt5CttMc4VEvZnXJw3SzOB+38OSKX3fJU4lVScqf79Y0sgOcNOKLMWVMa6n32+pMqAXadG7puOX8ikl6VZC3QwPGC2U3ydmiTP5qVVKAieZccBDrx+6pWzxa1fxnqCxrDAxCQ7F9EOXf3z6j/mle+8hTEwOVXrshFU+0z1H3QwZJg6/90fvJnUgbmb8e/Od/ML3/BqHzS7P/ovjLFp9/vnf/xFtbJnjTEaakToSGaakpqR+R9H+9iGDWy0ya5pZWENQRmE8mR/LfJMXxgWF1PHwpwaMfZsHl9e5uKahiPv9JnEssa5WqN/RumH2KGP9O1I6fzhDVNcSMqd+dZetJzvIAIaHXaprivWnlpGR4shvG9z9TpATqKxr/8LeOWhdT6jfk9przgWZ622nzjRLsoai9Mvb75vn9AQizVh4VrH1eD7AbOX7JZWADk7FjQ+g77vU3ZCKFXHt4ZTmFVNnWqYOTEas9cLs3X214Te4vp6unAD+LXBFKfUL+370ceAjwP+SP35s3/P/pRDiN9Ggd/+vxJcApMLdDhk9pb81IgEjk9HJWOtzS4WaSFZvzUEt4eZWh7jrIlsRasslmjeQE31BF52Y/at5KyC1DGScsXqiiW/pFnfmpYjYQFkZIjYQyTQLzOwM7AzDTckmJsExiyONPV74whk4O0JmBun7R1SNjNYv1gjaJltvkbRfhtb1Md0LFcbvbFFbT+med/TgYyR58Pj9Mlvq7/OXi+uS+S9IBh8eYZoplkzLAFNIhTgyxZFJqahZSOAW6pn7W+9FZhMkJraR4uTyukgOZCn7g0GipthO8fPid/drPBUBrdB3qpshD8xscnPYYc4ZlV26UaK7bwUxl2NjbJky54xwZMJOWOXy3gIbtzpU75iYk1xONu9z/NN/+Kv0Vip8d/UWH375I1xob+DIBAOFIRSz9pgMwZLbZ/ABl1P1HV7qLnOo1uOXvv+7Ic2IF+sYfoKlNP0GwNnUjyZ6XEI2K5gzJm1nghHqjezPKdLZCNYdUleRBjrLKYJS6ujMw4j0udt5SBIPPRp1nyubi6WcyWS1QealmKZieESgTEVyKkCuuzi9jNYrMVtvcdl6Z4fU0vK4CKiux0zmLQYfHhE+W6d2SzvoFmTelc/G3H9vlep6rjPlFNdRvqVCHZRAl3Opo5h9KXcX/p4RmCnxVpPtJxPMrgGxxqKkmWpVjK0K5qyPYSiyTGAYimhiM3ZtDEPhzPr0H3ZBaUdqFRmI0GD58yZBpwEv/JU7/j+6vp6M6Ungx4GXhRDFn/vv0AHpt4QQPw2sAj+c/+yP0KMCN9DjAj/5H/0LqSCa0QqInReKmlchYqP82ogMskqKMBWOkxCbinRsYYaC3mmHmesKw09K8Nu7tUf80OyBP7P9iEfcjhG21sWBqS44UKawQWKV5cNidcAgcsmU4M5gRqe812qYCYyWbDrPS5KKxgYOfSaid9phcNwjaggaqyki00Fw92GBig3CxDwgn1Ksez+YomLFjBPRcMOypNTgN7hmTMWMmLF97k+a5Wd3ZQz5RTkI3fJ1tkxLM8tB5GAlFi1XZ0oFsN+2x3Sjavl9mJrMVcb0Aq+Uwi2eBw6oYJbvYY1ZD5o4MuH+Tovbt+extk28TW3EmFmUJolzvQw5sLk4v8jeeYhbKWfP3ecj3/4JFq0+h80ud5M2n+o9SFWG/PvNt3G+vsH/MDzBhfbUJun+pMmp+k75+XpxhffNXWc16PCehRt8YvUCy+MxKgixegOEl98AlCplePcvuRXRGvis/7OTNI9AVBc88cMv4acWu0eqbP3uEW1aMISkCs6u5siFcwnWTEjy5Jj5/Dq6328ShSammXK6s81tmTKaOIj7Ft62ImwJhJVSf1lbla2/Q3+2zNIWTda6JnnvXtAecNVP1TBirWggMqi97Gje3pKF09OkYtCBaD9+lEkdkEAHT2XoDMnpK7b7DocP7yLXK3R7uvOn8ShFGlg0Z8bMn95iGDgMhh6WnRBHJtWmX+KbhZYVhh7GdDb0qIQMk9c5Gn8j6+vpyn2JA3z1A+s73uD3FfAP/rofpH/MwtmByRIsfDVg77zD3PNaRK26rpgsCowNXYtPFizItWKSdsLeBcnsCwkiSsgcE+/WHv7xGaxRSjijJ7+Hhw2q79uiket4t1yfSWyXaev+6eOiVCpSWdBlZMv12ZxLcOd8grENocHeBYXxgkHr+oTuhYq+6y1oT6/uWYmzpy8688QIqXRJVYjNbV+fBTvj2Mcy7r/PxuoL4lnJ7rgyLcdkipePLez4tTJDKnCaRBll9jKMHKRQePnFsx8ELzpzpsioypBx6uAYCYe8PZ7ZPVY6udirDjKAPx8vIfKAIqNcsUCBPycI24qH33aDD85e5gnvFjThWf847fNjPrF6AfeQ/rxtb8KxWrfsPB6qTjush7w97vkznKluci9q8+zgOI5M2PDrLHpDZq0RXbPKK6P5MmvbizxcmVAxI/zUKoPjmt/gk19+jLOP3KFnexyf2SU0KlOjAqXAMFCjMWri63GUmTr9sw0m81pS5NBSl0O1V3lPbYt7/gxte8w41Vbn4rt2aZhp6ScIQGziMtW3HgZOCXbLvIR2ZULNidi712T2ekb3AYH1UA/xTAtQ9E9IaveUhgSWNNDuzxpYI0X7WsLqD+gxEmfHoH5H+8plnm75Z1sCf0HlmOoUxIZpOZc6OdUmEnhbeuLdiBVLf2rBT2kKTlpNMbd1SWaOBVFnX9DJ/5ew72LVolL7O4xNDv2ixe3vk6V345MffBlHJlz5uQf/Otv+L11vjslvK9PSp44gmFesvdslrWRsvl2gZEbUBlA88virvPjcSbJqilUPdRctg8VzW4jIIXNMZG9CvNTQ/B9D4HYT/I6J+S49DLh/srhmh3T9CnsTj3v9tv4so9zi2cpKmydSzcTG1Jlb0HWpvWrhbSniimBwHGa/6pNaVZx+RhILAjvnUzUhailcM+WheV3RjhKn9J8DTWOJOwlGaFHP9ZUL8F0pwdaoxvBGC2fXYC8Ba6DYCXTAK1u2aBKvMrQh4egIxCcCPnT2Mi1rQj23+/i1G0/wlf4JRM9i9i8EZpCRuAamhEZdC/ErQ9MjQOMZmU2pi6UM3Xa++ieneXV0mv+rBkldMXNJK2ZGdVBDQEH3Q1tkSpQZ6L2xJls3HZ8rw0VMoWeRgFIJtGEH3Bx22Amr1M2Qp6+c5OyJdX2+rJCb/Q4t1+d2r11mk0Fi0jjeo+tXmMS29vf7dyM2xo1SmM2RSd4ldPDkHm1bd0bHqUMv8vBkTNsec8+fAeC+36IbVlBKYJsptkzLoBTGWgngzJENlBIlh28S2iSZgeMkjPseT2+dwmrqz7j1BCx/IWWw3WL+L8bc+BkTpxoRXanRupZhTQTKEPizgvqdGBlmCEug5hOixCXsG1gjCOYUmaU086GWIgcmlXVBMKuQoba3V4JyTCBztHWUv6BY+Go+ZR4knGmv8YVHl5B+RtzMMEINggszI5rYyOfrtK+m+I8ZmA6Yty0yRzGaVEgeH7L7DydYf+GS1lOMieQzL57nHQ/eYOcRm+XPDb/pkPDmCEyZ4Cf/24/zC7//YQD8QzFHP67lKDJLaDmUIKH/8SOcSEMyaRDXXe59p8DuGkwWbRpMpVcNP8Fan+Afn9GyJx/ZYnB1nszO2I6m5Z0MBPFsjOGmpa6yu5zgmjHGviZj16+Ud8uW63Nzq8PMNUF1dcT2E01a1xVJS98tk4og6AjGRxKMyMDdNMiWdbuX3G6rAG8zL6XzrMnosACRsPTnEcO7c/hzgs1jMa3FIY8v3OP989cYHnXpxRVujjpcvbtIsznRQvL5VK9SAmnH9EcV6p+oYT3UxwI+ffsMSmkg3bJSjvxvECxIElfpqXA7B3bTXJ1gX7dVBgW5WpBUpp2YzFHISLPrjRTsnmB4RNMd7KEOakldMbjZgRO7jEP7gKB9ga01nYAok/ixVU4WL7cHNHIT0XvjFudPrjGJbT2/tTvH4VaPMDFpuHroTylRdovsvAHQ9AKC1CqzxQIbW580OFHfLUvYm8MOJ+q70/McVfFTixWvx32/xSTWtVEYTwPSUmPA1S0NbBcZd8v16QUejpXgjzwqbq7g4Oi/7+xIMlPTXSZLips/WKF6WeDP2bgTfWxloC3MZKh5fXfe51C5Kgg6qjTjNCaaBpTZ2k3a8GWZKZXYlwveJvhuflOppHzkRz/N//3597P63XWCQzFHfx9a1oTUodS0L8ovaabEE5P6u7eY+1CfnT87je3nZaLSlUCWCRwrIQAwlNYj61s8zSmqBkxWDtKCvpH1pghM0hf8y2vvJa0orL7B6f8vInVNRis24xVB8PAEEFhWRBxLIKX1WS1jETcU8a0W1mOCmcujMjjFSw1S18DuJ+yFNvbKmFZtgrOvw+BYSSnY/tp145mjGPlemn/bBpkSNJ2AO589yrFPj9l4u0nvVEtfUBPFxturTJZ0aly5a+JsmyB0BmO7SSlAliiDMDX52o1j2vwggbnvuM/45jy7D1pUNjKq6yBDi9MXtNj7i4NDrP3Pp4grBnHVoDovOP3h1ZIeMoyc0s8sHNs4DX2RNrzcJdh3S8OAzGu+YZfmwPkI1Ou+TypCc8QAY1/ZYE605TRQXujF79ldA/t0ip3rSgMH9KSLVbgZn1rYYRLbB6bDXanZ7EoJlhoDamZY/nyj12Ahd/houT7X7yxyZHmXTAle2ZrjSHvvwN8pQPhx6tC2x7Q7ulwDPddVWKqvTtrl2ETdDXGsZCrvETklPllgeZvDOnU35N56myPLu6x1czWD0CDbrYCpyyTTV8jTI6zn6hpcr6a412XectfSKW5X4bfzmakMTvzehPGKi5FA/7hkeDbGaoSoHQ+RCNpXMkbL2s7MiHXGFOqkj8xLWTzc5d+8/CTf9bYX+WDrIs9PjvIHCw9yyt1EhjBzSbB3QaEshdk3SjrY8IvzXHbnSwxHJJBWFWlTldK53pYiWBEki3kgBsanIibnFBS9+29wvSkCk0h190JOBKYPr/ykDRmIKEPZGeRa0kHXRXgphpkxOKkznqSWYQ4Muh/0mbk8VWi01gdEjQ7b/8CHfSTRIh13rISW65cDiK4Z0ws8XSZ86QiVHT18J5S+ALv9KupOhaWXtINr4um7SG2g8OcN7KEm6k4WTYJOXtsnWpcnUmDYJ2Ic4QAAIABJREFUKfdGLR5sr9OLPFRoYPgSe5Tx5NxNun+4ggwVItMlWjCv2PZrbFPT+JBZzGYBSjubpJlBL/DKzMAzYww7JapTyqN0h1XiWJIl2i59/IjHzLWIuGJiTXKCr8qdZtTrg1LqCmSgMCc6EzJSSCoKI9R3epHmRFGp+WNGKJBRPuTnKYaBU94ACt+xfujScn0adsCV7QU61QmrN+Y5e+4+DScgTEyufOU4D73jBgArlT5Pf+JhjrxvlUQZVK2QV587zKFH1nFkQsWK6PoVDi11S3zQcyLC1CzLsabjM4jc8nxnSpTzXqWp6L5VZEnDQAeuuhuWQ662TEsh/6JxUjw6MkFKRQLU5seMHQ/zjk3tvmJwXGA/rWvk9iXonZGEMwJvJ8OfNei8OILDFUYrkrgTY/UtXvm7Dqd/LWB41MOcKCq3LSbHwBpo4X/TT/AXtGdiZueDxPlUuhxoV9z//Qf+Hb20wi9+5G8T1y36HzIJTlm4Xa1nXnQiU1dhSkViZ8SPjLRDcCXB8yJGW1WQqpwEB10dVG5bBBf8UoJXmN8a2ZM3RWBKaorMzpCB1MHAylChoYNSvkr9n75FameYEUTtrDQGXGgPyLxmqQcdN1rc/zsRWd9D9PRrxdNVBDplzkzBSORW0K4gkoK4IdgywLV1UIqa4PQ0YL3c6bN1scrwkEkwqzs0rYsJw0P6EDZfjUg9g51HdXBF6buX21XEAj54WisJhKmme7z/kSt87Tce5v77Ez6xeoEf+KnP89l/8iTBTD6lnN+FCnLrVs0oZ1NENgWzpZGV5cvGsE69GjBqekS+XQrdx7GkWg+0xdL3jEhX5wA9gBpXczLnayaC9SRwUR4Ug6o6K3J3NJYUtRTmUDB7MaF73iQDwrk8SG4YBCsJg1stKkcHzFR8Np5fxN0WyPd0CUyLILFYbgxoOj6V85E2eAhdHJlQP99lHDsIoRjHDsfef5tMCQaRS5BYnHz8bnk8g1BniwW1Zqtfw3Ni+r5LYOmfbU3qZdC6s9bh0FIXP9GNhP7Yo1n1NbH7Dcwak8wos6Nitm2m4jPrjbi116HpBUxiLUdSlKnCzJiMHapNn+pb+/SYIzszYhRYNJ5ziOoCbxPCjqJ/XDL3Qsj24zXihmD8QIhhZgQLEjmWZLbEGaQMjltU1xRhR+bnRU+Li1SiTB2MMlvfDIspcGXAz/3bn+Dnf/pXufuBKklV8dBjN/k/f+fDuA2RmymoUlwwTQzsVYdwWbf/00ww7jpYsz7tRj5s7PpUzIi1zVl6ZwX1ZzwtYDjOcS1Lt+m/mfWmCEzmSAv/d15U+LOCSc58FtE0OAkvVxDIX5PZYI4MUlcHtYXKkN3GLG5u46SkYPF3nVKKVktz6BNWOKkakSo33/5V0Fnsvi5TZM4PCucyopYmWNoDxWRO86MArVGeM/tTZ3qRDI6LMh0OUxM/n7R2ZcyFH75Cy/b5ky8/yq9ffw8rWVr+/WrTp++7pDmZVUaFmJl+t37o4ll6sNGxEtLMwDZT7WIxH2mvzVjmpa/2K2NeY01z5HKxscIaa/AbDgaj/ZnTzLWInYdtPVGc020my7nedV3RPZ/rRU30XdscCjIJzoZJZutN3PvUEpYDcQ3SZ9pUvvN2CYxPXWgCgsRkd1xhuTEoM6swNXGF7vTtJxiEqeY0DoKDPnkFBgJ6EwX7qBQVSzMIgPL9FpqahlMEt+J1oAdwD767zqDHoc3axQVmz+/g5qTpOJaEholSoBIDFRqMBhYjO8M6o8cvqpcd7IHSxpszBnZP0LyVEjVNnL5i5nrEFg5xTREuJJz+rYitt1SZfTmgc0lw7wdTzA2btKKQE8GdD5kY4XRPFOC3PoeC1FUEHcU/+uiPo5oZaS3l7y1/mcM//gf80Of/c9pfspks60wXQKxWiDopTjPQwX2vSnVuTNWJ2Lw/w8LKHtdeXUZYGfOAu51PlCcglNIOK6/bUX/99aYITElNg3TV9ZT+KRfDlyhTIVJRHmQV6g1m7ZkYob7bOP0MZQhkbPBidJIzd/okrenQojVKS02mRBZ3fUX4lxw5bzvDnzMQSm8+M9BStvcOzSMSQfPEHnvrDaLYpHUjo3tWYk2geTNBBhkbb7NLNUC7rzMuzg8Jtj3Cw3oTHfL2DlBSHq7e5Ytrj/PuH3qO5154tBS6rzrRATwmyQOJkgpzrHGZQeRgGRmj0CZET4g7VoKKDDJDYZgZlqUzJlmPS/NJpxcDWkOq0Ekv7rKgAzZMg9PgqFXqEpkTiBoCZ3fallb7KiG7Nw30xkSQhZozltS0BlAxCHj3j45x4Qeu8txXdVfu1NtWGUUOYWzSqeo7sw5UFq4Zl49boxrzNU1Lcc2Y1d02c40RnhkjhGJzWGe5PShf07ADKmZUUmcyJabfC8UocqjZYRmQilUxI3Z8Pb1eqGsWvzfrRdz85GlmP6TJDgV7oFXTwcwPLapNnySRxLdrnDl/jytXDtF5XjL/5R1u/dAs7ctZOeMVNg3iijZ41TdS6LycsXsu4M531Ugdhek7VLZTWl+x2Xs8wdoxNZ4XCsxJ3o3L+W4y1NlSUpky/gFEIpADk3/00R8nmY8wt2z6p8HdNIjrublrBtQSokkuItj0MT7XIlAwEymCl+eZya8PM8ho3Eny6wnkJNeSj/4GWYR7dy2ipqCyoajf1j7tcUPgzwlY0Sd8bmbIdrWO40UkT7foPa4tmSpXHdxN2H2sxczlEXFDH9S4JsuMqXDg9dZj3K4oOXWpayCDfNbHNbBWs/L7IqhRS6jk9jrWrklaKbS3KZnUUUOWchhC6cxABmBYKaGpNFHWzj2/UotLzx/j299+kX5aQaTw9K88jptNS9eibJA5eTWTujupcjGutUGDJJGkqSiVESFivjai250jWc7VFfKMKUuMMlBtP1KnfTkkapol6FoYGxiROkD6/MuWfE2/wMxZCK8NVMVnEABq3+sUvPipc+Bp2dobzxzlwXfeKAMMaGzp3rhFxYxKitB8bVT6/QEc7XTLnyklOD6zi2lkjGKHtquPtysTXJnQDSucqO9yc9ghSCzCHFvq+pUyQyp0p0yhZX8L7CjNjDKITRIb01e0vQmjSLuERKlk8/4Mh49MBz//9ukX+FLzJFdXl7B6ksFxyGSH9uUUa5LhLxi4u4LRisAMwJyk7J2xmXsp4e4PJTS+WGd0TMMVqa1vIvZQYXVNrKE2M1US5N5UggUOlnFOVxDX9ciAkU5vPmzZiFSXXXG9uBEJTUsxFAwsrKbP+H6dqUZsfq4DDYFU7+Zd8PCbD0SvXW+KwCQSLWKueWaCB5+6xnNPn0FZ2u897OlAs7U1h5wIwuMKS6KJktWQ+ntzn7LAofO85roVy4hVuekARodsElcf3OmjvkCdvmIyfzCdippwaGkLQygGgUPnsS02NlpsvM0ms3Rt7u4JjCTflKbC8HX5Zo2hv11F2BkrXo+KnLbM3/LWV97wWBixvlgOcJRCFzPRrX2RKwXOVPwSmAWNg6gcY7F7gnhGkuYQuDHWgcHq6f8tqk9NRmWkSu1mmVsRFY/7A1SRRRWrLPnyrKkAwmWovy66dUYK5xY3uHKpTurletg5iC5D9tGAFJc/d4rOt21St0OUEjy/tcK5zhZXd+fLLKkwFwX42ufP8db3XuVmv8OF9gZf/MxDPPiuG5giYxQ5pKbB22dvMUltulGVE3WtJLFS6ZeT/cVjsfY/nyhtavrK2jyqZ3Po9BZdv6ID0hGo5OTnIkAVOEOWCcZ9j5vjWVZvz5Xv7e4KkirszUn8JcWxj8Xcf69F4yaktmDz22w6F1OMKOPwb5vc+bA2gzBiQdgBa2IwPCqIOzGZJZl/FvbOGLSvJGw/mrP+HVWODpSl9Vg/Ek35cAVYbk4H/BG5Nfz4iFbWGPc9Zl4ycuqNNmSI8q2VmYLMMzF7wQFji2/VelMEJmVAdmaMZWaEssoLXzqjMZJGSljVGyurJWRSkdUUIjRJzoaa2LvnstF1cTYswqWY5i++ytr/ekofuMLeaF9w0prOWsu5dTPWw5cBBK3ckGArn32Z01PmUTufRZEJc9WEW1udUgKldR0Gx7WzbuqaNG8IgrZ2m4jqWvNG2BkPHr/PteECj7Xush3VqZnhAVqHklDdTLXOj6M/x9ZWk2rTJ84MokTi5Q4wQdvEiGHyO4tY8RS0dpTe9IGERpBRX52mLZN5I/+/svJ7M0ixJtMgLBSk6Dt3Zk2DUtGVAx2M3giX2589JftGWLJ8dODin59i7h0bbFydRwYCuyfKLp+SOpi7O4LJsqLpBLRsn25Y4VirS5CavH1plbY15uZYz6AVk+An3n6HIDU50dxlL/I48fY7WjAvn44HuOfPlN6AVRmWOJ+fWgeE7oAS/A5jk5oT0XJ9rl1dQWSCQ6d12WbLlGsvHoETAbW8MdGwA25+8SiH37bB3sTjxPwut3faPHf/EO5MgLpcJ3MU/rzC7gnCTsbMS5LVv5OiooSepXmCresZw8MSb1tQX/WpXa/ibSta18fc/MEKcUWXbMLKIDPpHxfU72isU4YaWzIiMdUhzy8BkWreXMH1K0o+0AoEBacuy4XkVM/GmA+Y+aynTREMfc5DR++P1NGYreEfzJQKueZvxXpTBCaAeGyRuimZl+JuWkyO522CWGiyram0gJypUJFBbW4MT7foXIpJXIPMzKh+JmG4WoNHKPWIYSp/Anrjm4HuxPmdnAPmClo3Y6K6xO4nKCmwJgaZKXC7BvfMeebzQcGV2R4bZoMAl55l0rqutZaqdybU0gwZ2TrttgyK03ao0tOaSMb0zlJQKppyUpJWhcpJrAEYuxahG2PJFNtMS3dha5KRmZI8mSoZ70VXRYYKt5uwd9rG7U5TnqBtHPg+ceW0hG1MlRgSKZDBQbxJKCCbYk6mr8gkxPXcjSVQ5RCm9DXZtJgcLzp3UujJ/cufO6X/Tp5NZWhO1/iQLiFdGdMNK1TM6bHaCfUwZFHC7eUBpcieumElFzCz6PvaQDNJJHONEZf3FnDNBM+Madk+vcjD38eF3L8cKyGILJLM4O6dWe7GAmsgOfW2VW5+4Rj2QIPLc92MsOlxc/EoRihoX005/mqfV8/USTcq9M5EhH23NJ2ce2KL7W6dld+2WHuXpHFda3QZuxaHHtrg7uVFMkvzKTNHO95UNk2WvjzmlZ+02XmrS/tFSC1KDBJ0Zhs1BOMVhd3LR1sa04xJGaAshYi1gkERoKKWKjMmJeGR773Cc392HruvtZSUnWHe8FBC64lHDQN7oAibgrApcPqapWGEibbHygNSYTv/rVhvjsAklLYf3rGwQt3CnHnOpPdgpqkhUmmqiJ1h7JpU7htwrUUwp/A+e4lsMpVZmDz11gPGl0BJS0lzANztJgQtfYaLbMLvmPhzBsacQWZPQfe4Rnm3bLd61KwQz4y5dfFY+fP6nRRzs4dyHaBO4kqUYbLzqH6TnbBakmIL+kORLd2L2gRzitqaDh5K6KDgbRpUz08OaODIQAvPFY7FKB2QCsF5JcHuZUR1eSAIVbayAyWqkUDvlE39nn5vGSniigb9iwys6NAZkTZQKIJP6oA11HfQ/ViSDlqizJiMVAcpI9XHYLnW58avnMX1pl2/YgwhQuSln3amqVgR4zg3ERCqzH6C1CJLBPf7msQcxxLP0RF5f5u/KHPd/7GBbUuUIZh4BkPbwBqnKEsQxVoLPbMEXu6aEjYl9f90i72JR/2Khb+gSF3F2u8dw6jD+HDGzEXBZFEfS3dbYz5mkBG1PdqNPpOv1rDPpywf3mVjp8kTx1f56ivHAbj7FBz+Ey06JyNtHb+20qCyZhB0VDmpbcQGa+9yWP4SLHxOsv0EWvTumoXKbemz2CZq6gFje0+XW6mrvfDimsLp6gCk0CVdoVMezmZU7xgoE2Kpr50X//A8eIpgVk+LCzelsmZqhxpL4G3rLKmylZU69jJUmgK2r4wTUfItC05vjsCEwOpJFp7VqWwhZ+psS9SFCfL5OpPjMSIyMMcCZUJlXeFtTd+hcMfw/uIW8XtPlc8XErvebkLQNkktnSlZY8qDnTratseIFE5fETaneJM9nOI9aWawNdGpgDLA29Lpb1yXmiTqWmTSwIiVztJWfFTPPsDUBw7erS2wRoLJnKFr+IYeRbDyY1CoUs5IXX6lroHpZxhxXqrmmVLxN5Whs8X0NeVWZSvD2y0umLy9P05RUpDZRp6JCZxcoSS1BGmaz7cEitTWwUQG+n83IkVW138j8TTHrlgFdaWIW+YErv7mOWp7Kd71iP5xl+p6TNSQumWeBzRrKKjZYTk6sH8ZOY8uSiUrzb6ePwpd5Gv0CVuujyEUFSsisSuEM7kO+jjFiKeGqiJRmH4G/tRnTkaKra8s8eRTL/GM3SKZSUBBXDVIqqoEoeMquDtQ3UqJaga9ExadSyHRx+dQT/W4uzqL0wqwnYTn7h7i3NF1bu10iG1JMONhTTSul7iw8B881t+lQWxrIFj6pGL9nTrI3H+vg90HZn0MYHxYoloxYq1KEYfrNw3GKwpvUxBpZhPKUkQtSHKCbTFbhqUQVoYf23BsTDFBnElFlmppk7jv0vyaC6gD1QVoeKPYm6kjiBs2sjc5gDEJP3qdDf03st4UgcnuKY7//ojr/5nLmX8zwbj4Kts/9ghHPjlE/BHAkLRiErYszHFE1DS1//wgQl04Cc++XGZNRV5gD1OCtknQNnG7CaNlq+zMpVJ36cxA4e7EYAisnsYLxocrNG/lvu8Tg9QWrN1uI32D1MuzN6C1qbGr1o2Y3kmb5J1LeoPaosS0TDMlq6QHSgZPxrStMc8OjnKyscMhu0tcU5z/8HXu/svTelK8oTMUaWRIoWi5Pjsn23ReHGHm1lS4BkaSC48NNT4V12Q+cJdvun2rKFuLR29XH7+4YWMPdHQrLKGKbmUaiDKbkiFlJprmExnu7rSDJ9QUfyqyprhWTIpD81W9Sfw5G3uUkdkG9kBrXHu7KbsXJI88dZVLHzuH9CEMlDZz8BVuLyVsSCaDVAdTQ2hddFcQ1gSjQ4JwJWJhuceD7V16kUcv8ghnLKzxG6spvpHPnOlndC6mfPboWc49dZsr17WZ6uRYTPWWRVJR2hMwMjBig96DClGJaH9Jp5mNuwl37tcxWhEn5jQ15vZOm6urSzjViCySbL8rpn7Fxt3Vyo+V+xPO/osBV/7pLJWrNr1TNklFY1FOD4JvH9L4XJ3OpYCttxi4L9j0zmlM1nESgpdadB7ahocg/so8UVu7p4gNu7zZd89pFQN/Lu9Om5D1agRzWhLaHiiipsavHEOPyiTu1NlZhgpvN8EPzZIlABDMWgSzcxixvkasSUb17uRb0qV7UwQmI0iQfZ9Tv2ZqLaWKx/zvXwem9k0m0w/rMHVP2V/GFVnTfqvwAvSurcWMF6YFujXRBOHxUl4fL1sk+YbLVqY1ihEplk9sluJpjtS8qSusYO2auHsSp58xWTSQPviLitodvXmFoDQOBJhkNn5qIUXGE51V1oNm/nkFpsiQscL3DIxIISMIPqpl1AeuYHgho/MiWIPogJ1T3pxEpOoNLdKLVWRL9VWfpGbprLEXlu+XuPLACEXiGSSuQEkd/GQ8zUz2Y1Al+TNQ06/9KfYV1WHmesr2WwwWH9tgudZnbdTk3pUFlr6oy8HeaS1tc3fY4vt/7It04yoPV++yaPXZiJulVtPHB4/xfO8wL186AiicbanxuAicNYvhq/N8nnmUAe//gb/gYrRCZmkZ3bgmy+x5/0o843VBfOVjJut/r4HZM8usyYhA2AI5lihLUb+ToQwT0zc1sdUSmOOU4x/NGP/skKuXDvPowzdp1XzGltZmurXXobdRx3zvLt1XZqjfNHj1R6rYvTp2ZQjfPaK3VcdthjSqPv2xR3ynipqHjbrLzLWUzR/xmftdj/Tlii79qxB+Yh5rrGiFutTy+452fF4WhE2D1o2UsGXo6fJHdfcwtbU9+GhJMDosWPxqzN3vlHRe0FmSSPV4QnVTY6/FNaQEGDWJtxOVWupBzu3LLFEazX6z600RmMhS2N3DvLumP1DFe92viPy5IlCVfnOV16eNlaub9N+6DOispjhwoGvq6mbMaDnXmF6bfm2NC9Bc76rRIYPG9+gp4UyJEutIM4PqLSt3nkgJZrTl0s5DktY1pTEIpcf7ScUBJciW7bMb1ejYoxJncrqKr7xygkWp6/m4pmV6w4bUAWGg2G+abPgJJgcznOn/N9U8t4YxSc0qOX8ASU3/r95uwuBklcarusw0g7QMTjCdHhapYvbp+yBE6dyrXO0OnHk6i008g9QSWqExj+mNW4qgLZi5ntI/KfmvP/wxPrd3tjTCXH6iz9r5JtYvz2NOdPl+tL5HN67ymU8+xpf6j7+OJlMsz9CZWNTQzHbQ3LyoNZ3HaVkTpJ+VmZGMFKljlMFzfzAqglPiGTj9FL9jslAf0nrc586lJYxwKiuSNvVwY9DJu1x7+iY0PGJRvxOTVCRhbNI83OeFl04wf2KXqhNxa6/DQn3I2LfZ26ojOjG9ppbTaZzfYfN2m9Yxn/O/MODu984ylHXcPszdT9h9QGOP3mbA4V+y2HqrpHY3Y7KisPoCt6sZB1Fd6BGVdob7lh6zv9IsZW+jumD1eyzMw0PiyMR61WMyp3Ek0xfsPGThbQDobLt+0y8zHxm6pUWavla0G1HqyLISkbHKR3MMkupf7dj89aw3R2DKl9gXkES9hhqODjyHlIi6nsY94FX/mteo8TSLcrtJWc4VGNP+QOV3zLLEk/kAY7GJUxvuX1koL/7MUvmFaUFDkTYSug/YtC9HxHVZKjXKQM/2ZEognKkOdtG6zhBsR3U2/Dptq83gFLz99E1u/ulZemcF1lDg9ER5sjNLl0lGmJQXB1C6Du8PKu6dHmmrQjTjYO6MtBi/Qncch3rTebtJWdLJ3oTMaxwIctowU5Ul6Z0fPszM9QSRUTr7ZvY0UDp7CSJVxFV7Ot8UK0Qe3cwxfHzzER5qrfH01nHes6DJubf7bYJFSXU9ZXhE8pVXtE6U4VBSfYxIT6Wnjs7OzECfFyPWMzdJPhulDI3RAMxeTLSJWHFdJAqZj0rsD0Kmn5UBqjg+oIP2nc8e5cj7NOMraSUYvtSDtROJcWrEsFLF3TRK8f/U0s7PIoPky21mvmOdQXIQ59sc1vnAyes8vXaM3lYdpxlgWSlbW01EJWX8uXm2fyajehfmX4zon7DYO6vxUHc3I5hz6B8zkQEMjhkYJ4Z4n6oRNvUc3eCUvkatZX2zufehDKcVEE1sVJRhdU2iwIJMEHVSurmbSdqIsXZN5p/NiOr6vI4PV8puthErbEeWmZM1SsGVmEHKzOWw/F1rpDHLotv7zaw3R2DKFGri6yAkJaSpDjD5c6XZJQeDV/nyyURnTmn6ut8Hyla4200YL1gl5tS8FZSkX2t9QHBM60WMli2ihuDYB27v+4jaFrztTVi9eYxgPqNx1SJ1YPeCjQzg0KfHZLZERha9U1qMLs3lgYPULLOmtjWmF1eYJDZ/tnYW48SIH55/lp/3zuFua2qCNUzL8iPxDJp705O9P1VWUpBIWX4dHGnhbE8Ah+DYDFFdYo3SctO5XZ2OF9y7tFVhvOhoF1VLz3f5C4JgJaa1MOT4zC7L3oBrP/vAgWMqhjHKMkq/tqQq8XYzzLykq94eYsQNLfsy1MD1x155iHitynO2dlD+wPI1/pB5xkuSB/7WVXqhxytynmRiYt+2SnE6yAc3PUDl3m4mNFZTBkckCI13RQ09HT+Zk1weLGpZY8QBPKkIRKafHXDmfW2Z17mYwvtg/uw2W9f0kGRB/4jjWsl/NAOdXYYzEDUljdsZrVdS9lii8WSX7ktzPP7ua7TtCU+vHeOPL13g207f4lJmMNquYs2NOXd0nZvbHeKazZFPJoQzJutPWngbGmgXiQ5ESmpuaOMWDL8tQGaCwSkNlKtWjIoNnGZAvFbFO97DmEisuZQIsLqm/r1QapK8k6ESSVZJkdWYuU8ZZSCq3tZCbwW9y1rXHRGrVSnLfoCwZUHLKkdzRKpIqvJ1XfFvZL05AhP7SrU8E3pt6Vas134P03JOTfwyo2q8vMMg1/y2h2l5R9yPlfSPu5iBzgrUcbfMnADS9/X0NG++is7crc8dw4zB3jOIK3Dos2Os9R5rH1ohrluMlywtbBfmDbNYl3KTRNcla6Mmye/NYcQQdLQu+D/++d/jn/yrn+Cf/Te/yi/87I+Vd5yi/DDH6YEp7MSVhC2TyYLB+JCi8+A2x5pdZnPKy81RhzQKGAYO44If5ySlZXQxRBinktQNGQXa+GA49vCcCEKbmpmSZAa39jpc/PNTtA/pQb+4ntdX0kAGCeSBKa4YJJ7AHsSkrkHmaLnWylbE+js8Xvj0Wb7/+57GOpNyyO7yme45Pvbr7yZtaEnZWXvMj85/lV+RT/LipaNTvCoPSKbPAUpF1ARzYuSeZnrFVQCB09P24uGMhdudYiGvXeo1G2g/3mSNU4L/Y5nw7w/IcgK5EZpa8iXWg4qaypEPpdrTzE1kisatFPFqE35ij0tbi3zb0h3esXybpznGtZ15kkSycGiP7W6dDVkn7LmwkHD/vRbujsAI9f8YNzNkYBDOZVqxYVFfpCoxMOyEsJ2AyLWvMwj3XFrHezhWQuXoYPq/LUao/CbJyMRIBFklBQHWtQoiS/W4yTBjeCp3Ts65puOlOZy9ZErRIsfU8mMlg4ysJnVDwhIlOP7NrDdHYDL2YSRvkPHsz5LKzOo1q3w+TXVw6g8xkk5Zmu3PGMYL1oEgVN2My3KvAMgbnm5Nb3frZIHEGJlYfYPWDX0ytt6hOPoHKduPVXGOe0RNLZELun0OkKZ5NqEMGnZA3Qy52F9mv4/L4IhkM24Sv2PIv777XjbeIUkWUw4tdZn1Rtgy5e7nTdJKAAAgAElEQVSwhRSKbuCQZCkwIUkknhPhphrTuLS1WPLHkszQlkFWStWLSucUP7QxzbR8BEoDzSi3ilKvIbMmmUFSy8gsAxElmCOdsYk438DDiLRi4m3HjFZs4rqWrjHChGDGwNnTjq1GKPjon76DpUc2WKwOeOFLZ3DQQ4JxHbpRhf9n7V1cvLWC1ZPlRgfKja9EPp2cFcoPAnc3o3fGIO3vazIsQduecNeETBpYvZC49VqNAF5nFf5aEDwzBYv1IZPAJtybksMzS8vepE7+uUIdXFMbzFDpOaVY4fQy+ntV5uf7fObiOd7/4FUeX7jHV9eP4DkRvZHHt596hUvdRZxWQHJX+ycG2Kh8xsgIBcGC/lzjIwnefZPBCaUlcLcqiFqC6cYkgYVwMkw3pn+3SfNwn8nYwb3koY5rAw5jrMcNsDKyyABD4TRCOn9ilJxSY9+NO3WnWdR4adocKqScU0tjoAXLwsihh29FUHmTBCZDu+0KoZ1467VpgAGE56J8fTTK59zphaYcG9HWetLKs0scxlvXAa5IP4vSqAhIhRGmsz1BFiaOAwP+i232/myJmQ+sszLXY9YbsTZqMvrCPEFbm1u2Lip6J2XOExPU7+gNEjfz7t22QJopGdqVthtWGOVDg5N5gbMHdl8xWRD8+v/7nYQPh9xMZmk/rFUrh4HDMHDwQwshIM3nTAq1AIBJYCMExEoHoSCySNPpRgtDkxBtYijNVAuY5cRfkEShSdWJyklp0IHItWOCyMI0U0wj0wDuR5vl+2bSwECD8EUGpUyB5WfEnoEZZPTPN0k8cQCYB9h4fpF1YxGnpzMOGQiMJ3q88Mfn8Y/EOM0AkpyHGB0kp2Y2IPSMjki1LlTYNHB3tbeZDKG2ltE/bnDU3eWlTH+uNwpKxWf+q1Zq66B9bmGLl9eOU70vCDp6ELSgdBTD/M6ewhlkJI7A6Wd4OwnjJYvKVQf+dBbr+yZ85sXzHD62w0PzWizwVtjhq+tHStnjY4/dZXW3TXoiJtuo5NduitsOtM78/Bi/ZZNFkmo1JLznErsCx0lKrSRbpkSNCdvdOs3GBPvdQw4XEi7zHt1BhSSwsBohaSIxn6vjt1Xp5hvVDT1Gk9+klSTv0unmUFFxFFJCxJRBrXhNdXNq7PGNrjdFYFKmSbLQKrGe/SttVTToO998HfgLvK41+Vr+TubpDgLuFNwsOnPjBQtvJ8PojUjnKrgv38VqNdj5jUWSo5TDfpPE1v5uLZ26j0/EyL5J/VYubYLA3ctY+vOI+++1tEmAo/QFMJC0bJ+2PWHNb5BuehiOorqVX8R7ejix/WWb7pOU7XTQQUK8Zu/EsSwdiIqfCUGZIZnmVFEAcsvnalgGnv2By3YSxuFBQTnQsh1ZJlBKggXEJps/anDkD+p4Gz7WUF/FRVBKPYPENYhqBs1bAVHDQhmK5u2MvbM2i8+ErH6vVSqOyomBOdbk0tGxjDOtHtc6DYgFcWhiqXxMYV9XTgY6IMhAYA009hS1FHZfTO/kPnTPGyAUv3L17cztoyV9PWs/AA66lC4szTMvZbKYc8RiiGvT946auhPo7SomR3OT0pGWMskkTBYNnOdqHH/qDtdfXeL+ZIGzj9zhfcde4em1Y2BqnazV3TbBnsvhozssH77HV185TmtmzNnZLaIFyYzt46cW3bBCy/Ex5tdJlEGSGVzZXORIe49+6DIObSrVkB85/hwAv/Tiu8gmJkYlQZopdiXCslLGgVWqQhSrYBEU3TbQc0uJWxC39fXjbWek8mDZNlq2kKGid8Lim11visBUIImGn5RTo7I3QXl2GYzK1mXv4JGUvSl5sHBJee3PQesNjA9XSpwpaGtpWcNPSBY0YJweXeD+++ukDkQzGVcvHUYkgs3cQtkcC4K5DBJB2kpIPYsT/2GHwQMzuDsxVi/A3W0RdPSGidBE5LalsZ81v0FWSzB29ecdHDNw9jRwG1cFxq7FA49uYMuUXugxie1SmnY/wbQQ4S8su4EDX5tOdsD4Un+Rm2AOPcSmA6HAiAWTph7Ia8736cUecSyJJnZ58YLGpN524VW2f1NTKwqbLBkkKMsAn3zQM2Myb2vcztbqmAtfGTI+XNETyocUNGLkfIz7YhUzUJz4oZvc/J3TyEU9EmHd9MgspQO2M3VnkaHWeopaOpNSRq79JHRAgnwKv5khYsHRf2UTNV+fEUV1iZGo140LZKY4wK8EXdoleWlbdOVErIcU3V09kDhZUlgjgb+okJFE+noivHdy2oqvbGbsPizY+t0j8GiMvTLm1k6HK6+s8Ni52+z4NWa9ES/cPEJtbsy99TbDGa1+6VgJt/ttuoMKv/G2X+bvfu2ncW0tA7211eT04U2ajs/5hQ12/BphrLPgMDb5zNZZVndnmGsPaa9MSnmXQo/q6v0piyFqiFJBIJGixF5Fqmf+zEBgxPsaMJY+30aiyqHeUkYo+huCMYlMlYTAA8/n30s/QoQHf6aCfZT2exowlwX2JCVqOJp26wDjHtTDowSLVUxfay7tB/OCxSq3/5ZAeAHzswOt6/zpJQ5/6Db90GWhMuTyZ09jrExofK6KPy/oXIwQfkjiCOQkRvgRnYsRvZM2qQtONSLdqCFfI3AUNRRBSwclp5+ReALx1C7q5gy/9cfv4vF3X2MYOdTtkL2JVlBMMoN6Dl4XMhsbe3VGAwcSgaikqNjA8BLm2kOS0MY0MhxLZ0WmkRFEls6ejiRlhmQxzbwsSzsAFyJhYagvj5ob8szlk5wZTZFmoUDk4wpGmmHljj1JVTJelFQ3UmSUsvn2OrX7KUuf36P3QJPNJy3CUBLVBW/9r57n0s89hHkkZ8J3QuSmhwxyvfUEjIxci1xjOnZvanmdOrq8iKv6UeuwZxw+v038pbk3nPouupwHrz+dHclwep6sXshkpcJSdcB2LhhXyDhniFwJovgciuo97SCSepA4OqjGStC4kzI4KqnpyQM6z5p0n7SZn+/j2nFpJT7rjXj0xB1u7XVQscHZ2S3WRs1SD8qWKf/9rR/k/MJGSVxePtGnG1Tphx5VK3dFdkPub7dQPZsPPnmVJzqr/MbFt0IbNu/NEC7oG1xv5DH7NQN3LznAHgDNIDCS3NwgZxfsx56MZDrMK1KFjDMSV5baZlntb8ocU5wg7mviW9lZS9MDQHeWA+L7u3V/GQi+fx2YDH9lFRYfKLte9jAlmnGoXN0kmllg+c8M4qrJ5pMzNF+2CJdVqb8zSWy8R7sMbrVQH9oj2KozXLfYfmyF9hU9ciD7YO+FeLsmo0MG6kqN7JTPktXjXtTGlVrYLq1q9X6nnzFZMEBA9EKbk3884cbP5Lo6udFA1Yl0h8nI2Jt4mEbG3bCFaWRUvQinMXmdVnVx1+yNcjmP0AQnIQq17Gsc7XtMDEw3JoxNwtAkkRK7ojdDgWkFkcXZk2vc/tk2x/8nnS0JpUs5GSSIJCOr6WBmD+Iyva+s+Xi5OXwh4idUFSMxCFrwR888Su2C1B22MRiX9ecthMuMWOunyxAyRwcoa6CVCKyhHiZMKvr308dGCAHHWgPeM3+DT1UWXgdmv9FKHeNAQCovyZaDjDLmnRHbfg1n2yBuaDnbzMqzs5rOllJHETVh5hVtKLn7gD6Hdl+xd1pvUhkACk585DrGL58hqM8T18ASEDzgszZqsnlvBjmUfOg9ueF1TYP4G36dtjPRuNReBz+0iNeqOgif1Jjkrb0OkJfhExNnfsJvPfsEldsWtgWbh0yaFy2SOYP+zQ72/IT6nWL+TkGmSPNW///P3bsHWVre952f572/596n793Tc4O5wMAYkEAgBAgJLCSFSLIcS/HaZXttJ1vl7Ca1tUntll3Z3Le8lZRTrmTXiq2VnVg2sZUoWJawJQQSSObqAQEDAwzDTM/0/Xbu570/+8fvPW/3DOhiyX9Qeaqmus/p09PnvO/z/t7f5XsZBR0rDzqj9sioahm1U/Y+tpC9D+Bcbk7zQ613RmBCXx5Q8qAE3xsuMPp+lBmlm1tFhrQ3IJlHr5L/tlHC2QmJxuRWN+KXRQcnuPRBWzBElzK8RgDYJHMhG9tV1lvjIlq3ZeF1FR0aohdVEVPL1lUWs98aooIQK4wo+xaDKV+EupTmUtRkOy7TdAbUx/q0KVPaQHR08iSk0obzf7MEvYzlXr3wYgtji34oF/2oLItjk5jdfpDvxoWrx6ikGz127UQ2cmxiOwlZlvcI3Fimdm5EP3Rot0s4513cnDHvtjPWPiXysJaT0Qp8bphfohM0MYC07GCkGam3u4WMNCtwTVHFIN1fovbiJr1rxvF25CZgDTM6ByyRlQ1VMX1LPciUNJONWLSq1Z6kOKqBtyEBahQYutMZWV0ardWnqphDWNpXoXT/ywVO68p1JQXFDLMig3q7qVzDln0UnRigLvpCtu2py4ISSHC0uylb1wkPLq4o/K2UwbRFaU2IsFagef6xo5RLUiKNv5zkPESfVs2HuRi7Y7AdlTh1cZ/oOrWb1N2AGb/Ls48fJ24muI0A3YixVx1Wd6rMjHWL8z46v9F6CTNU1N7M2PoxxezDJk4vJlmqIm3REu6lTbwwKhD9Vy7tO6g91UrWqKCGEWYudTKSPFGaAhk+Eo/7Udc7JDDJGgWYKwPOd3vdaI2+NyfGUZUy2fom5sS4lHQz46SuRX+hRPniAPPCGs7T60QfejcA/mZEVLOx+orxl2PCmsmB/xvCZkTn3VAqhxzZf4lB4lC6RrKXc799jNZRmH6qz9JdZawAhrM+1fU2aI3VDrECj6SsSNd8uAbOdcdpugOmq12U0kSVsZygmtGfMolqikM3L/L6K/OstyvSnAwdBn2XUjkkjs2idzTq/YzWCB4QrZewegbmkgQX5UGSVJjYyXZT8vxrVPWI5hTrcylGM2R2qoU5rUnzfN77v+oYhi5gBWFscb7dxD/YwFvto5IMI87I8ga4GYxg70LrUIlmMG3TuklInrUXN0kbJYJpn8wSCRG1xwrKGu7CAYT/pormd1STcimuSQnlbst73H9ihfVH5slu6jKYs3E3Da5731m+vHxd0cS+MhC9XRY1QoJfvsmkT3auP0HdCZg9cIHnzlyDNiVT8rYUUS1Xg8yF1rauczCH4G9nlNfkZ/6GLsiw/XlF/axGG/J/RxVDFC5WM2oXNMYpxeZJeHVzCsPIJZsTE1x4oyMT28HDU5ihTSkVwnR73GV5qURaT2hM5i2Nrin7+cUsH+WLDbmVMxiqZy8fMO1tk+wNUqNWinYdVBjtBqL8+bRRKtQFtC9KA0booJ3/TrhyOknJUrkzqZIPU+Ns39gkGDfoHs6w5/pEA4ePnHiJr379JjJbM3EKNj4c4r3s03wlJfEUwadblH6/ztqtcxix4shnllGtHsHxabZOmHT3Ven8ogcc4NAfZ2hTsfFjPsH7uuz/twbmICZo1Fh/V4XuYah8R9FfcHCmVtkObF59fY7GTJfhIYW3CRfvLVO5KHfD1tUWbmsce7VLMF2SjdAHNRXQikuFnOtyv05ru0Izxzo5vQx7IFO6rT9c4NC5iHCsxP3/5BE+/7l7mVrJMHJwZlw2CA6IX1s8nnDN1Uu0Q4/O0CMaOFTeNCmtZoBsRMjtwkuSXR3/1BmWe3W2OpXi2JfzwLOy3qBc3b3Tbf1KQqMiRFIsisxKNS28VUnj07K8LyPNpAkOWL2IpOKgLRkfg5A849kanf0eG+/JoBIK9SRUOG1F/4Yh3su+OLdEef8qBZWjqlUCuPKzuCpBavzGdTa+Po+/qdlZKuNtGqS+ZsbvsvEbh0n8kahd9rZE3dH6rj8zhJpzZmuKmWqXcTPGiCEqi6LCyDvQ7imcDritjLBuFDIhSUOUKYbT4K/Jubjhvlc49/8cAwX1c5EoCZQV1YsZQf56lcd3205F6sULuaq2yZMrB2htV5i9mNJdMPG2wd5K6R42Sf2MY4dXCglgf1MgLUYCZpQBBklJnKM7R6oM91Uond1B5dnSKDBp12E08r1SjVL7DmnZwRzEJBOyf9bfVSaYrMDxHtfOrHKkusEhV0rLr13ztof7B17viMAUzZZZ+6n3EjYgLYk3uzQa87vqxTLKhIcfugkscFoG2yc0OjFIb+iyeSOEfQej79G6J0O5Gb/8rsf4zMydAOhQ4S/KidrrLZN6BsH7uigFGzeUcduZyHxUDSrHtulnY7gb8gtNr89yXaCR2bEetS/4lL/wFBd/9b1MvJTgP/g0/U++B6vt4J9v4c1OEjSF+LseVrimusql4RhX1TZZXJZ+QOoouvMW3k5GJx8zn/tJE3/J5He+cg+/9Atf47NfvofJv9R0DpnEZYgOhOyf28I1k0LS1QgVriXTPRDBOdSujZQcBKHVjDKiEbygt17GrEo5NPretlNOzi3z7LkD1OuDYsJXdiMar3RQQYz27HxKk2DEGWlJYBlJxSH1DSHQ5qC70lpMZhq4nRRnakg0cHj/rS/xzB+flCnW835B2N2LDxrJqxiRQAtEpA7e++EXefLBkyigt6BwN6Tv424pDCQb8XZ2s8rv1WvaS1HZG6S0EuSzayfM+h2WBvWi91WYfI6WFqeTcBwaT8dsXecwnNL4awK8jHII2DPfOs44Iueyfdwlc6UPBTIVi+oCHHXthM52layu6AQurcjHyW8g3QXhZIosicIcKOy2BcfFMkolcuNyWor+tEHzTIgRuwTj0D5WY/NGxdhpg+DdE0RVRe8gJHMh9fqA2VqHmhNwoLTNhUGTU48f47r3nmXG79KKfF7ZnKa9U+a+E6cJU4sFcslpp1scihf6C9/1WP9V1jsiMOFnhHd0CXoOXiUii03iUPA6bllswXVkEjXksQZIDAytCnAa5KaYqcJwUn6h8TyfK91KNHAwygnhpElcV6KXbGiSssXFj2jsTBG3XfQ0NM/EGGnGxrs8sqeaJHMp8XjGq5tTuHbC4aktXlucwVl02DqhMOJbcHegs98i++R7pOmrNdqzcbpZQXp89uxBjt4gzf1hbiM0nJTpkruT60JtaeKSYv4RxeZJTXlJccxbwT3WZs2skzZiGs/ZGInL9otzeFuaHWBqJ2UwZeLtZARjBnZ/t3QocBha/sah8hYvrojqwuiYmdWYNHc6prrbwzq1uMDcgzbtn5GgNOpZnfmVMtf+y10CtYFFWrIwB7kkRpqh4111AyPOCryTtxEy83se7QM2T756kuzWLjxRJbPlgky93UnX3t7TaMVVzeBoxLf//CROboLgtHc/pvnBLU7/2vUYFf09s6Qr15U9ptSV7GXlDsW8kfHc+jyunYi6YzJScNztLwWToE2Nv6poXSXlXP2s6FENJ6RRf9dPnOKx/3ITg2mZ1LUnpak/mFa0rzbwNvPPgui9O6WIw9UtGu6QhiOZq2rZaCvPJE1F0JSbdzwVc25dbnZmoEAr/A1Nf15h/eM1vD89gL6jxQ0fOYeB5oUb51jdrhH3HMamO9w4tcTTK/s5WNnmXHecslnBQPPuO86QaIM3OhPiv1ftMlXpFbrpI6liA03DHrAdlwn3cEJ/lPXX4U3317Li2IShSdgXZOtohD167JYjtJbHYd8hCS0sKyXt2oR92fjKzVCllDQ0uZjYhc+XaaW4GwZWT6EjA2VoerNmYaLpNQMar2UMpm3sjT7usTZOWyR1G1NdZmsd1i6NYSiNc8EtLp6tay0hb9agtBJgD7SUN0q0bJIymJZs9pGQfsMZYrkJwXQmNjiRprNfrMJHy8npFb9+9kPYZop9sIeypeT7iU8+Tu/6kK17AoaTimBMKCDDpoG3M7KhyrE3kXjjiT8e9BN548G2V/xLuzbKT8kSo3A7tu0U348Ke54R7EApzUdveIHe9TPFe832EHmNMEHFu+RYlUgmBbB1wkXbkkmNVrDhk7qicLk3O9J7dqXTlnG+ynJ96sOXsIY5NcWD4bSImsVl+NShU4QN6y04pSvXlf0kayiyIVc+P3/NGkfqG9T9gIVqCyPctQEzQ/A3BMtkRII8H04L/CMcy5HRBpQvCStgMyxLBhsKDWlkTClN812Ee1QHPRAs2amNfbRCEb0L2m4OLtU4XZ1bYe0aVRqGRscGyUxEUskVE2qaC1tNGh9awXykwflek3O9caLU5Lr5ZQ4c2GCy3OeOxmtcP7VC0+6jtWKfv0PDGXK4vMmE2+dITcqzw9UtTCPDNRN8M+aAt0XDyVHlcakIWOthhR91vSMCk84UWWRiRAZ6kHO2BrtYCLccEfaFfjEaZbvliGjg4DUvnwCMENE/+dVfIUpMDCclCWwO/OkO1UWNigyyoUXr1ghl6MKTrXXUoHvA4MLHJvAfrNO+OWTplWmmq11qTsCHb3yRcxvjRAdCkmaC05FNVjufUVrTDOY8huMG2ydKxA0PI9EkvgRSnSgOlzcZpjabYRkulHDm+3k/SJDEo1KuPyUgvagKwZem+V+OPsqRqQ1uPXKOsZ+9yMPLx9ADk5kHHea/0WXyG0tMP/gGM19dwu6mWIHG3REDASMlJ5zKsfnyqZME2x5eMyj+Abh+jOvLXW5vY/21X87trpUmSky0Vnzl9HV4a0NphgaSERlptuv+2t9tpGa+RWaKPk9tMSkCmBmLAoG7YXHrx15AJblLSw4JsIZSsolscX5eDagc3+H0k4eB3czKaeWebDe2+ObmEczo7YPS3qAzKtv2PmeGWfG8GWZsnsynnqnNyk6NA6VtPnbfk2KB7WqimqY/pwmb8g9g6lRCZituu/8FgrulvBlOw8bNsNgZQyVyE1O5QkLU0ALaHMnsuBKApw9uo0ODTyx8h/lSm6YzQFmaZCbKdajEzilsKuKJBKJ8IJJR9O7CMSVZ3RsVel+cIRyDw5UtAA7Xt/By55gwtfhXz9+Hb8a04hLjXr8wzajuYUjPl9oc8Lao2CEHvK0i818dVilbIQveNkfLa9w59hofaJ55y/H/q67vG5iUUp5S6mml1HeUUqeVUv80f/6QUuoppdRZpdR/Vko5+fNu/vhs/vODP8gb0QOTzNmzoUwRXXfLeWNOy79o4KAHYn2tQwPLSoufjVa5PsSuRbh2gmFk6ESRNERJwOoZ2FsWeiAMbaCYPEUNTerB9vUaa9XBHChee3kfT52+iodOiWSHaskJGXGkEl9dhjA2YjCiFHcnoXJJo1o2ytJUzYBe4jLh9onHEtLEKH63vJ6XVfmI2x6IyHzqw9Pdw5ysL/Hk64fp/M4+yv+6TuW8JdpInRzb5XugNaVX1zGiLAfAiT633c9y6ZEMe8fCal9evXvNoLASN6sxwbYEo+HQ4eZrzjG4UKPiRsS5OaRTijB7l7tdZqbxFoBsZit5Ps1oH7Kw8ymZkD0hrmnCyYQnvnQS7/41jJCi+T1qckOuYZ3bDV0zsUb9rARa0R2Xr6kDv3Lsm4T/fPZtQZVvV9bt1WIardSV10VVkwPvW6RkRzzx2mFsO+VP37iOL33pNn76vsf4mbsf5+rbLmDEMiE0Ysmirvu1F5j5hTf5i4dOwktVBjOapKQpLxr4dszCBxZFskVJ+eevKSoXjCLImqEE5H7ooNyMp3YO0k8dMhTl+hAdGVgDsYt3t5Bj5ojccxhaKHd0oxOfxtTXNM7IMQ+nUjajMmefOsC59jhNZ4BpZHx49jS/eOIJABa87eJYjKytRqwFgItBk6oV8s3NIwA8unGUQeIUmXjdHDBjt99y/H+Y9YP0mELgA1rrnlLKBr6llHoI+F+B39BaP6CU+i3gF4H/N/+6o7W+Win1aeDXgU99vz/iTQ4Jtj2Um0kwKku2MQpMo9Ku0ewxLDvFnX84lExKxwY632e9lo9XiVhba9Bo9ojdhKhWwd1JyGwLY36Afb5MWosFaBib2CeFuxKGFrVSDhSz0gJlfWmlibNsE9cy/EWbyefkhK28T1L0zn6T2qKI05uDkYa2S3lREUzLqGV/aYftSKANcdeRBrUj2VLtgvxuVIdwTDH2iqZzWPPIQzfyqY89hluOqP3SGuk/n8Jf1wKKM43LSHN6GBR+cSPtcQB/K0UbMPNESjBmEi5V8yAg5gujNfK0A0nFz3OUqVCzOlPFdeUzlLyI2m9v0vkfm4AERxux7knLDmY/yqEDVoFrsoYjrI9JXDJoHZVMx+5YZA6sLjZxp0XkzAzFEsrp5Ox9U7SXQAwua7y1D2VG8I2dY4Rjl5dxf5U+E0jWFFVNmY4pzXvGz3O4KhriTz1zDNPW/Of/dheZrTn4pwOyT0gwiavyeR7+2o1Mncow5LqlvCwlWjChuaaxxiNfuQne38V5skp5adcwwoh2jUPNEGwzpdHsMed3isyk7Eb0jBK9BUOySUsCuU5EjLDsR3RWSzAREjo24SRMPWFiBRmdwwYTzxqc6hyjeVoz99629ISGC3TzGnrW2w0oF4Mm22GJbuqxHZcx0LhmkmO6Stw0dpFTOwvMl9qcWttHwx2yGZUpmxFLgzpr3SrwzR/4uL/d+r4Zk5Y16nba+T8NfAD4Qv787wEfz7//WP6Y/OcfVOpKKurlS+WYDbMaYzi71taGkxLseDJx27Jx33TJHh7He7RKZaqPZUkvxHBSlC3TOOVmRRAr14fEqUmSmCx+WODzqZ+RBDaNM9J78t0Y342xjIx+2ydbKuFYKXFqcqK5yitn5ynZEVNTbYwI5q5bY9/X+3QO+SzdVWbs1QR3G1ASYBqvDYiafjE+RwFK89nTt/HsxgJBalGZ6osMRSw4FyAXdFeYQ5GViEsKI1T4q4o/+cxd/PzxJ3nj+X0s3+Ex9nKPYMwU/Q85SeggRPkegylHuEu5QrARa1QmfCYj0dTfDKifT3B3NFFDsf4eMH5uHfvn15j4uQvYn15j+LE2mx8Iad3Xp3XUKDA1Sukic9qLfRnZ9ezlUlm9CGOYYKQZpfVUMqVEvMmsvlh0GXly467axIeHokMUSW9EsEsUVteZLb2YuCLEWGoL/ZAAACAASURBVDPYDUrHP/Eqa//sMEai0XskdPZO3N5uvQW7hLDr6798kTC1eGz9as51x/HNmPKigZE39Q/+6YDta/MbTBXi8aSYJvanxXwyc2RyphWUlxRhauG0ITtb4UM/8wRRTZG5MpQYBSVRT5AbYvvcGNtRqZBf3tiuYpdjvE3JmJSWvprZtrBWHZLMICul1KpDMDV2x6R7ULF1vUAH3HZG4zXYvn9QaIM1/QEPfP12Tu0s8HJnhgvBOEFqca4nHn6ndhZYHVbZjCQ4teJSESg9M2bWa1P3AyacPleXN/DNmEHscGhs622P919l/UBTOaWUCfwlcDXw74E3gJbWesSDuATM59/PAxcBtNaJUqoNjAObV/yffwf4OwDmeF08wnzZ7COOVrbtMv8NiCqjtykXyMZ7EypQCJ/ZlSGdnH4xkgVJU0Wa2JhWJs1xU9M+7ICVoFo2YV0V/SWQEe0NhxcZ7HdY61axjIxTa/sw+iavvjGHCg2am5qLF8c5WIXxv1jB35wkrprYfU15LaO0HNA55FN/vZ+PzWUyY5qaeLHMTVe9xqPnjzBZ69FbkazEGuoCNgAQ18DbFDVGK5ANZYWaz33xHhZuW6Y99FjfHmf20U0wDbKxKsP5Mp0F8eG75cbXmXD6BWL55c5MoVYA0I4tkiwkDC3KfoTKDDpDjyQx2bH8QoXA9WMsK6V/KMTKRP5jFJS+szzPwXpSwAZGgDqtICs7BXxg1FOyuympb5A6BnEtd4r1NCpHKasMrt+/zIUnriJzdl1jnQ4MZsQr7drbz3Hp9w8TjCvimi5kdFUCU26PpZopvZvc8eRKraW9axSQVCblmxEJCTUcs2jd1+dqd8CRyjqXhmOFMSlIcGye1mTOroxyXNGYHYu4ogvT0b1SLaPHTadfBK+v/PFtOPnEUcQA5Vwn7PH1a8T4Zlw47Kg1l3Q6FLJtAtWLKZkFK8cS0kThPd6gqiCZNMDM4TYWZEcGxB0HM4LhpEmzNqAbuZztT3Kwss3hO7fYjISVYKC5trZKKy6xPKxxuLJFwx7wcmeGDIFijCZuM7700DwrpmEPaMWlorH+8uoMP+r6gQKT1joFblBKNYAvAsd/1D+stf4PwH8AKE0v6FsOXODM1hQA5VqEZyX0Hp2lfVjR359i9QwyW3P0pkWcf7dA0KxLuRLJRVyJc3fXCNxk1zIo8QwGs4r6Gyn6l9dwnxbnkcpySloeEiVmgRF57rUDNCZ71P0Az4o5+9wCk9ds0vQHnLkwy/btUH3BBSKWPzxP6kFtMcXpCXo7aJSkZ7TiMJi2RZs6gnCxjNUzaNp9rpleJUhtzKHBxu0J09/IA0HutJLZiFTstgRNp5fRutqi8WrG0tE6zdqA/j0ttj+aFbSVfiiTszrQiTxaoU+Q2EyVugwSB8dMKdkRWitCyxK3WlMXiHEgpzKIRtMIaQ7SUxrRHMpuVAjK9Q9WqZzZRgWyUUfBSSUiKjdaIy5W4hl0DppkpjS3R46yo4zo5W8fJr1tiH3Ol95YVzIOlcLM+5Y4+9BVMC6lnb+WO8smivt/4XG+/uu347evsKseBaVMg6EuK+usYVYEpMwRo8+obrF1nYlpas5sTfHMt47z8XufZDsqi3vvtEiu1N4c0DpaIrNA25rMT7G3rML9tj+vRUiuJ3pTo8ndF555N9VIPndc1dz6sRd55o9OknhSugouSbLAfuigrIxZr00rLrEdlTDmhpQfL6MyQXyLL10+DfVS6m+mbF9jksYm3qIjQ48IeKaMa0FmJfSvD6h9bpytvzVgEDrM1nYR4J1ISrrDFVHBWO7V2RxWWG9XCkYBiOlD6uviq7ulOF/dj90VU01vO6NufM8C6QdafyUck9a6pZR6FLgNaCilrDxr2gcs5S9bAhaAS0opC6gD3zO3Uwmc/W0xRAxrJu3Dim1fk94VCGjMyohti+mFHV79zn5KVxlUL2T4GxEq09itgKjps/R+B3xF5ZKmd0Az+VyK/ctrDB6eY+s6k4V/VsWcTOnNmmInZKZ41u50YmxK7gLtocfizji33CoWUq3QR0cGMwvbrO+fZPy0EEkzG5yOQfsIVC5QNMGlx2IzOBjjrtqUlg1614esBHWC1OZiq4GeDlGJgRUadMbAHgjqt3ZObKV6Cwp3C7wdsUP60D96nEf+yfuIKmXGf3aFILEKTlwhbQJcbDVYaLRwzYRO5DGI5RbcCnwa3hDXTKh60kNz7YTQsC4DUIYjOoGdc/Psy/WdRgqX/opwA/dSGEbaV2Y/Ipwui3Jk1Sd1jKJJrQ0gE8DkSHQhzcGCetkjrmfc/qGX+PbiIZGJnekK7cTdHTiMdJhG1upXypUAmGFK6ppi2OCabynnRhw5a5gR1k3issHJD77KMy8fJt2s8Ks/8V9Yi+v0U5ej5TWe4ThGBL39PpkN1kCCktmRoDRStHQ6iuGkFsWBJekxpS5UX7UJJsQhN3NyG7BAAKJOm0Kmt/FGwmoo5OqVoI5rJhwub/L0siDG/a3dzzH63OXnfMwoJrg2wiYHEuc66NXFiNQ3cDcCrvqsRX9WUflqhaiuOFcfwwgVZkShmLkcHgTkxh6cHKDXPcoHOpyY2nULWu3XONlc5uGHbsIMIBzfbUkETeMyJYIfdn3fwKSUmgTiPCj5wL1IQ/tR4CeBB4CfAx7Mf+VP8sdP5D9/RGv9Pd9pUoLevMLuK7xWythXhkXf4nLLYZsx2gQzZfqzNt39DlFV4bYld64sSmp88/98Smrh22En8nE/tIhpZGR3KXbadfpLVcrz3ULrCGSj1DwJSnU/wLWTogS6uDjBgYOC5XA3DLZOOJQvQXUpERnVUOcTNRF9G0579BYMSucNBodi7H4OqkxtKnbIfL3NoOSw/NI0/WmRP4lLcqG2jikxO9wSgfvg7iHBhs9j61ezcaPB+IsZgz+Y5cf/wbf48oUThaxJ2c1hFFBolUejSZqZEsYWa3G1CEqj1d4p41yQ168fkJ+V68PLgl0U7GLCXDuB2GL5/4CJ35rGPy9Dg8LuKT/TIznbzn6L6iW5m6t0VztJJEok2Ix0tK2BIk3h8Ueux+koLCB+s4mZ01QyU6Z5TkvhtDU/9ksv8o1//l7ct/GLS12zCE7fbY3gAVvXmTRuXueZF67CHBp87L4nOT2YL173O1+5B7ujmH42yjXdIRiX8wvSE+wcjzE7ohhqxFKORjXJgkbTRqcj/Dqno3hy5YBIrQQSvPwNRf1cwmDKxDC0aDGZiXDkxvuk9YTMcsSKqSKW4EYMysroHo9B22RDiBID5lLcDZPEg3DMwu6lvP73bKxLLr94/8P87plb+fnjT/Kdzj5+rHYJgM+9citjXywX3nHOTkj2hIUx7JP5LmuewDQyx0ABLzLJNHLca4u7x9TbAfWDzxu++7n5AV4zC/xe3mcygD/SWv+pUupl4AGl1L8AngM+m7/+s8B/UkqdBbaBT3+/P+AtDznwG89f9txI0kQBqlwqmrsAfhATV8bJLEV1MaK738FIpGdhDzK+8soJytWAO/adY8wZ8vKjRzh853m6kcuR8Q1ejndlZF3Ehnum3GG5V6fqhSwuj0tDcS5EhyZuXUq7C1tNkppmeCBh/qsGW9eK4L6QUEdnQ5G6AsTDBHvTImxo9MDklc1ppio92rmMr79m0DmSMfZSTpxtZVhDg9SBxieWmPR7BKnNjUcv8ug/vp3oExFb2mXihZQ/fOndjH3To/+RNnFsst6WIGp5Ma6bMFYaFnbZvWhXbG4k15tlIjTnlCLskxKIGnmQbvd9EkTVcjh0MKzsMqG6ILK5++DrvOSdvOycJZ6JijO5A1t5mRELgDT15IIcBaNwMsPZyXtQPQEjpq5M5aKxDHNDFeXNaFoVTkvUy2y45e8+x6l/ewOlbrJruZXtynRkvvWWoHRl32mEzP/Ux7/Jo6tHmXza5N2/8hzbUZlhajPvt5h0uqiUvPcVY0yI4H73cIa3JgEiami8ZVuAls7oc+xCHjIH9IkuE39UonW1STimCV9pkrwnofqKOO0kHnTnLdx2JkqkRsa57jj7yi2e3d6PWUowYof+tFHg0+x+hlpzUeMx3rYmqIaU/Yjh4hh2VwKmu5NgBgmOl1FadOmmHsm5Ct0jHq3I50IgiHHfjWUqm2msOCskTYwwKVRiVaqLfhwI+twKcqOGYVLclHYTiR9+fd/ApLV+AbjxbZ4/B9zyNs8HwN/6q7wJnWWXyZQAMLL8ztUGVMkXPXDfo3+0icrA6aWoTIukRsVg8skt4skytWd9Ou8W8NdLf3E1yYGQi60Gw6FD5+szqBI4K5rUU8SBfD3tSn/LaWsaRq5v/LyDEWu2j1d5bTZ3YhmTg77yPgWIc8XkcxH+xQ5qGLJx1xy9eUP6I5noUg/mM8xGRP/0GPs+cIkwnSBILOIyOPN9rGfKtI4atI8YfPzeJ3lm8wCzJan/PTPmTz5zF+aUZt8XYenujGP/8DTe/36USx8A75EG1j3bxWYGLsuKtoclwni3XItjE9+Nd6VQ9qhU7p5D+Rrm6PrRGqln+m7En796DVdthAVcIcn1mFSSkZYdVCII8LgmFkRmKMoAwbQcM29NyrsR+HMv9QQgbOSI7/y9GImM5FNf896PvMDDbxxlJkfLj16DoUhdk9Q1sVvhWwLTlc1wI9H0frHNo6tH6YUOlb+9yvlek1m/UzR5v/DAXaTTGUZbEUy61N4ccunuMioHro69nrJ2s1GUmSPRuP4+jbcpfy9zYHasw/ZClShv3GcOpHZGbTFl54iJv6EJmwozVoSv1rnjg89xritBY1SOm7EQhDMHGsuihJG5mqmpNpE3iWFoOl0fDgeYL/v0ZxVB06W0ZhMvy55+bO1q7v3Ac5zrT1CxQzbDsmi+ZwZeKPZL9kquxxUmhYUTgLsxQDsWwxkfI8rwVgUyox1LhPNyvqQR/HdESdm7jFLpbeVORit1VM7EFnsjgNqbQ5KGT+KZmIGm9qzHuS8coXIeaqdczEcb7P//TCZeiBh/KcXfyjCiXVtrf0Njd+XxCFMWNOTvxHXN7NUbHL32EtP7djh4aJ333/oS3ppB/1DM4octXv+5cZY/PE/nkCgZFk4SLsKvWvGIJxNmvTZBYrGxXWXmtmWCHY+Nm+H++5/g4/c+ydKwwVW1TbZD+fzXVFdJfWhfDcGYyf6HEp588CTnP+oy+VzGzCcuED3VZKwkMqxJZjDh9zCUZhA79EOHILIJQ6sIRElmXAaTiGNxWhlpP41+Zhga35VN5toJrp2gtSJOTUrlkLM/PfJB34U8JBXBMmlLHDZ6V8c5PkkuUBVLgDJDwSdled8p9TSlVWkUSyaiC9qGGUpTOZjOSMqao+U1xh8sXQYNuHLFDRcz3A2qb1GtTHYF3UwjY2etRsUJmfXlhtB0+pwoLUmTPUUIue2E7gFfekTbisGsFqR+IMEibOb0krxnljlCmbF70P/8HFrJjWpU6tSfdrn//3ykMAJwtzWt4xoO9lkdVov3cfGNSbLIZPv2qPjdzoIlmXkjYn29Tvt9gei0J0YBHDZuaRHc0mP93hgzUIQNIZU37T5HKsLdrFpyZ7AMAeJa/ZThrC/aSrkgXFSzyRwD7ViETZfEU4Rj+STWEa6kESaYg6TItH7U9Y4g8SrDGCEBMEqlwgnFmhwvmqtpbrYHoqGU2gbhmJVrCyl6syUyR3SL324ZkWb7WrdI+/e+zunoPWLrELhQvZQSNE0GU4rSEnBS7lx1Nyg4QcNjIaaToodmcaGNUPyjsW84kRWuLMrO+Pxf3EZltsdks4trJtx4/DyWkbEdlQsO0sqwxlK7TnNqwKXhGOV71uheatL9aEC50cH/ozkGhxKW7zLJXp9j3yspd/7UWV7uzLBpVghSu5BkjVITp5SSakVn6IlK5aYv5WdfZEdqW5rMArufp+hGjq8ZMwjHFNm4pvxVh/7/JiA8yxBdqBtOniMMfbRnY3ejXccUx8KIM3aOOnzg5As8/sj1BOM618lWhE2BAIwuMmsoMInBjFA0zMFupmF3ASUuu8GRiA+feIkH/8UH8Xe+/wXwvfpLcdXkH/7cF/jMm3eweHqWmWMbnKivsB2VASiZEf/ywU+K/XVg5ARjg42bFKBlAhzvDkEAGq9Bbx+UV2Rylvhw8/vOcOrrxzGH+d7KrcaTkiaYVHzui/dw9G+eY2NQpvPYNBPXbLDdKck5tCLe6E0IC+KCi78OwTjYPTlmKoVsaKHsjPIpn97hFGtiSBLYhOMZ6UaZ6X07rHXHuO69Zzn3hSO0hx6v9aY4WlmnaoW4ZoKXD0TiTHpScdnA6juYcUZUs4kqBkaiSUsWzk6IETv05h2CmTLudigE7r8GDaa96x0RmADM6Smoy11iZEgAENcctLkboUEIkkYiILPMUoUmtOrrXT+udobTEzqEGQjx0Ugy+rNmLkgmQUxlXBaUjBxX0t1nivRpTwLZ8oVxlJuhIwMSxQV7kspEn95GGWVpKhcVcf62U39Xq8fdNAimMzInwytHxI7JLbPSLVwayEjWUJqJsT4Gmm//+Ukar2WoGYPGp89xwNviaHmNz6zdidZwYWmCiRgWvqzozhtUlzK2rjX5+q/fzoG/9xrBH84wsAQfNXLW7TYVwZToWt20cElmpoj55qW1MXodG+1muMs2mavxNhT7HtrEnakSV02iikF3v4OXN9FHZeFLy7MsHLZxtgYFRMDqRcX3cc7lFGCiJhyTxrXdy6VXKjpn6VOQY0ekVJAAmfgUuKX7TpzmpX9yEi9OCh+zYv9c0ei+rJ+UvZU/19lv8Zuv3c2NU0twDOpuQMmM2KZM0+nzhQfuIlkQHzeQILl0l100tuOqBKTSyi4Bt7dPsE79WZl03f9TT/D4v3kP5n4p7UaQAm3I3rC70HgjZXHjMOEYeJ1dFQfPjJnxu2yGZex6CKs2YVMyy/KKBMXUlZudjgzKKxm9G8Rfrj7WZ3ihCZlCLWiIFV6OxWot1ZiYO38ZWBLEneecOyPXVQqpLxNN6f3tGjVkviD6QRrke20Ii2zpe8+6fqD1zghMhhKfuFy4ymwNdt1S4qywwB6tylLE0vsdxl7RWEFG2DDBh8pSWjiBpqZi+UYTb0OCVlhXZE6OxQg1XksTNHaF7fdmUOW1rNj0IxdfsOkeztClFFVKsbyYwYUa5VXpL3SuEt1kf00VLhHazDdhRxHXDNLXK6SNlFmvzbfWr6LpCZu7nBMj//A/3ItRg8G0gTmESafLIEfljVxUnVJEb94FTIYzGrCYeSrE+tU1Jpw+7/9Hf8J/WnwP+nNTWEHG5vsTyvUh1Uca1K7p8fTrh9g3u83GX8xiDaGMXPz6xIBrj4ljx08vPM3vrt6P28mKrGbjtgQWJ6hM7HKnXDdhOFnC2QJtG6S+AYjp5WDKLI5tZu/qGI1KN7cFsDulGi2VSnPbiHezTzMQYuwTv3sTfiXD20neMpIeBaURBEBbqghOqS/vZRSc4rJJ+5qEY5UeT6/s5/qplYIl75oJJTMirmohlZtyHhN7NzPKHCnVnA6Yd20TnB7DzFU4h3XNHR94kWFq86Uv3Ya1IFAAI5a9ZsRqF8VelYZ36svn9bYztJ1w29x5DDTD1KZqhWQXy/jb4La1AExLgnMbFafKyejP2cX+AAjnYqbnd+gFLtWzFtvH5XqyW6aoFeTAza2gTN0dMuX2OJ+bVwKYwz16VoEc68QzsYKUxDOpLQbENQenvUf90rEw2oO3GIf8MOudEZgyoVSkB6ax1lpFcAKxX7IBZ6dUWDkNZ30mXpDgkTqK+vkEb21Y/DyuOfT2OVTPwahGtAKwWhmDKTmdo6971yhbChoUvStybe3uuwJmp1osVGU83nQGhActvv3nJ0kmM7wNo7iIVSIbLapLAzeuCebFW7PJDkY8vHyMuUqbILUp2yGDxOEzf3kn9VT0mVrXJRiRwe8/egc/cedTlMyI/+nWb/C7Z25FaxjujymtWUy8kLH04xnDaRfny/t57ZpZFm7d5u6Z16n+2os8cO5dNL80RlJyOf5pUa+8+/irnFrbhzagv182n7NtYJ2q8npaRZvw7/gY/TtjFr4iPbbMVswd2CJ+YBrjp6Iia7JNGT4sv38Mbcm0bey1iK1rLcxIKCen1vYVAXokiRs1BBSblEbStHJXHukbqWxXrlYbMjX7wn++i+nXo0IV4LvRTNxWXAQpqxflvaY90Iecr/fhm58jTC3aocebnSbvmz7HK90ZXvzqMZKyRiHQEGsoJprr71KktYTqqzbDackqjEiRfrOJl2OGgqbiQ7c/z8Nfu1HKd08XTf24JLZSpRUFSJZvtKWENWIwU9mTUd+HSYGWjDIad1vJ1K2pqCzJHtZmPpWzMtjMpXgyRTSQHmFlos9mq0Las5m5d43D1S2W7QNgiCrnR/a/zEpQZ87vsDys0bAHYvHe05jx7nTTsndR8qNlBSnmICGc87E7XNZXulL58odd74zmd97ENC+s7doytQXsmDZKpI0S0ZhL/2CV/kKJzFKUFwe4rRgz0pRf2yapOGyfbNBfKNGfdVApNM5GzDyyjhlJz6f+SrvIjPamoE4nT1MdVYisldZ3Jz7ltYzSyx7xA9M8/fohJhxpTnYTl7ieCf/O3w1IRgKljRSnI3d/lYovWTCdwYrH9vOTzPkd0sxgzBlyuLqF5SYMBZSOu2mSlVOyUko/rxO+09mHZaUoJRZAqaNIXMX0N038G7YZTmv88za/90f3shLUea0vki29H+8RjsHZzx2j8+VZvvnt62itVrnhnjM09re4+92niWua6+4XqYrCy81P5W/4gkbfeXwGbyctjBEA4tRk+W4tJZuG2mLC1nVO7lMmx7jT8cWbLEdCZ05+PKJcsmSg6B7URGN7enGpZBlRQ/P+j5/iCw/cxdiruXPH26gC7F2pa5L4hpTreU/Sbl2O3dq5Bp5cOcA3nrwO2C3jTi0uENezHIGtcLowfjqifcggrSXUztiiMtlSReAcuZ/0ZxXXffIVvvbIjfnzu4FVG9LQL62oguNnhIK5s7sUWVn3xhDLSlkdVtnJ2f1PvXkQM4L2EcH7bZ4U1HdUUwzH8wpgZiB4qZaNterQ6fqEoYVtp8wsbNP/2rSQxzWQQc0L2Y4FEtGwB4w5Q/Y52xgJRSaa2sZlZrLSd0zfYij7tueg8d0HVz/oemdkTHC55TeQHpguHputAX5rwPDQGOTeVf2FUlH3Bgt1/NfXsXoSmMxYzPpaVzvYs1PSwIzEQmg0LQNpUDodXZj9wW7vaTBlkPoiYlZ7o4819DGDDOdLFo0bBoVKgDlQ+KsWUUNTfzPD3ZEpVlI2MSMDu68wYoNwIsNa6GM+VyW4dshD376RD93+PE27z7n+BOmaj3uyBV9rUFmEcEI29le/cSP2wR6fPPI8/mzMd37nerbeI5uj8ZxcKK7SjL+gaX+8h/dolWf++KQQSG/o8g+uewSuE8nTP//2DVTfMACD1547xtGffZWnV/bzYze9wamL+7jp42eIUpP3jJ3nuc4C58rHxCvNU5TWNev/w5Cp0lCAmzl04KM3f4c/796EFeTi+vlEMkGOuXvGJ67pIjsarcwRU1Fnx0Dbuw1vMxQHEqel+LlPf43/8m/uYaybXuaQC7sATvnPdHFzAwle4vsmkW70uiwXb/vF+x/m0Y2j1E8E+FbMTuDz+YfvyHs/hkzhNjQT3xGybtjUzH/NoD+1q6rpryG2W7kc7od+5gm++Oh7CmJy6uXgSW+0r3Y/t7++Ow2uXkpoXWWhUtg3u83SRgPGKVx3s46NSij6ctZQpn1JWTKpbGihOi69Qynazagd6hJENp4jkJBe4JJ5Odg2P0Sf3vcM7bREmMo1thP5XIqaGLEurqlRjwl4ixloahsYSrLT4axPafkKQPSP3mJ6p2RMu29jNIXbLeWk3xTsb8jj3KiyaMZZCm0qBsen6S/sRmpJdeX70np8uUbznpV4UsJljiqCUuZIqj2yT+4dKBGMmQRNi96cyYNvXs92WCJDgUGBTSktDTAHsfwbZti9XGkwAnfTEPXNiYxSJSTzU1qRzz5nG9+Mede7X6ff9ovU32xEGJFB87ToRZlKlANv+bvPFe89uLuLGUHpPzZwehneo1VaN8RCmQhAPV/lNz//MX7z8x/jgLfFP/zxLxHc0ZWeRgKv/adjxLHJKw8foexHPPXyVQwSh996/G6efk60O1JXgYapn77ATx69HAQbpybPbiygDSitZrSOGkW25ORcN3dHhNVGx9+IRNc9dXUBsARh6MNuyffRTz7BV//+naLSeAXlZBSU9sIBrlx7Lw61Z6+oDAaZQ9MdMFvqcHPzAq0npnE3DexOXrakkkX35z16C1J+BQ1D3HbD3eA0QnYP39/lv56+AZWIu/Gon5R6EoCsnG6UeuKLp03JeFACAQGxpVrdqaJbDjN+tyAOY2rR5yoL8r20Ir2uzJLghM4BvhNDUJqqF+J/vVJAQrJnGgznUhwzxepLb8sz4kLuZDsuM0gcqmYgWuK+SOYY0eXUl5FES+ZbmHFWZFOZpS6rPlSUYLSvwCT+EOsdkzFd6W0lDbTSZTCB0Rpp7ozsnp2dUEo8O3cG8XYlUFUK/Vkbb0e0iKxAo5W8prBEzoOB3GVVASeIGsLHah8yyWwY7EtpzLc4MrZFkNqcbzfhYJ/StyvMPNnFXG+hPVc0v8MEI/WIKx5RVYia7qJLUskYXKhhTw/5sdqlQrz9XfVFnnf30Tsp+JaZ/+rSPmzQW4Cg7XNpOMY+X5wElZ03cRfLJFOa2gXxtiutZYw/Y9G7t0v6ZgVroPCEScPvPXAv7g4Y4/AzP/s1jnkrrMZ1/vVD91Pqg3pojFJTcW5jPze+7yxzfocvOyepvG6TlGDW79CKS6y3KzQqQ4ahjW2nbLfLzH0rLS6wpCyN3qgqDdrMVthtQ6amGvrH5IldCgAAIABJREFUQqx1h+zgkGzJl9I3VDg7Nqmr+dTHv0nJiPjyr90N1dye+vtpKr0Nnil1DWngGqqYzmWWBIzPn76Zhckdzr85xfMXrgFTbh5RQ/htKhGxvuGEUTgA9/ZLMEhdKTPNnGPmdDTdlofhJ3njXibDRkwBfUhdTfOsJqwb+Y1H5Tw5GHtFboTlJU1saOoL7aLxfbi8ybP1BQYzNihp5JshjJ1JcTopZpgxmHNI9wfowAatWH9qhqmVlCg3PA0PxahQAm5SkmB4NphmOxZYxLnuOI6Z8tDKCQZTBqWNjNobfdF6UpDacl26V8AzUtvA7kSUL+Zmr3kikTZKZA0PXv3ep+z7rXdGYMqywkpmtK4MVNqUzZV6RhGlRb7WwGwNiI9VsAdCgB35rUdVQ8o6cnZ57seen5PdP78H/+S2RTModSUoRXXZtHd94lSBX8pQnH3wCNf+xBm8fz+Gu9FFRQnRwQmc86LuktZ3XYJH+KbRprU7CjWf8dkv3cM990oGNMgc/va1z/L7L95S2EwP5jKycgqxwazXZiOqMul02T+3xeLyOKmf4a9YdA6YBW7KiKHytQo7dwYEQwtt2OK8sQPDGU1lET7/u/eS5FSKu+54kZtrbzJjt/mPK7fx6kNHOPvfjnB+oKmWhZNmd+Ev/+Ak/kaGN2kQflAAmK6dYJspFz/k4q1K2WIN5OKfeClh/V0iEyxgSQGeuosuqaepfquEtgSXk6EpL2n+1t8XHlftTyrYVnZ5+ZaXa3v7Rd8LpzQKSlcuI4FbDlxgZVDDXbUIJ4VaAuCv7+4N50xCXBKph8yBxqua1lGDsdcFqT2aGFpDjb1pYcRCSdGWoL/trpLAlJsWdPcbBcrdu3+N4EvT+KuK/pzC29QMZhVhx+X2695kmNrMem2+sngt8baHEwO5LXlclqDrdFKCCRvdjCAnuuvYINofcWk/NP5kErOpKOUZe+eEhzWQ4c4ffuN2Pnz7cxL8qluc7zW5dmyVPzs2zdhruWxMlKBdyY7ocxnVxxgmGEMuo62kjdLu4x+gD/X91jsjML3NGmVMkH9QzxSf9D3e6GaQYQYZw0Njebmmieq5rlFJApE1zPIRreBeGqc26Fw/QVQV2siov7Tbe8r/vhZaBMC1f+NVzveaXGw1CEOLNDExJzVnHjhOg5itkxV6+6FyEZKTC0z9Zb943952hhkYBE2Fv6ZISopwIsN+sYp5fZcwtWREbUQMMgfTzIhv7RI+WmX8ec3GLQpszQNfv50fv/N5NqIqc+U2H37XaT7/u/cynNLokz3sJ6sMpkXCwwihckoCo/H+bTodH2PZw+4oejmGyemA2lY880cneWz6+uKiCRcS/BWLxodW+ejcS8XxGMmm/tOX/wZVL+Sjcy9xzFvhty7ehXMwZSOaQMW5Q21Xsf4uszBV+On7HuMP/kystMZf0HQOGnSOZthtCQh/+6OPMWu3+Pyv/g3GDXC6MXHZfFv77rjhvqWZ/bZrT1DaO8WLKga9xOXOqbP8gTuH3ZHMJxjXuDnlZf+fDejPe4R1o+gNta82qFzatfwur6dsXm+i7tvB/MtxgvkYb8kmKUnZGjWkVE1doaaYgfxOf8ok+NI0qS+qEWYgTIbBySGVsnwu34xZCersXKrTOG3Rn88t0Qcy6excDeU1A38jglx2t7dRxigllMoh5iMNsbPaFp2wzIJzG+NUEojrmvlr1tjMgaRNu49R0fK4IrIEmZ8juVuDYio+mrZdieweIb/NQUIwI//niD/3o6x3ZGBSYbQ7nUPqWitISe0R6TN3N1nto4aRNMUBb7EF+xv0Z3ezLaufYnciuofLZLZieGiM2oubtG6aFLcJKIIUQFTb7Xs4bSlHnnnlMG5dIP9ohWmloqVsaZydiGqiGUw77JzImHxGE4052L0ElWT4mxF6ygFMuZt6UmLGVU3NjXj0zDE+cuIlBplDqg0OTW3xxvP78BDFAatrklRT0qpMa8YcKQEvBOMMpzRJMyELLOIZaRiP2PtGmNshfbNJdn2IgfSdhrUcaxXAcEp+xwyhdg5aR8Ffsohrms3HZvntxgylVUUwqfmp+75FOy3x0QOn2edsM2O3eTWY5V8e+iKf3byTh843MTBQsSLYF+OuWgVfcGSCaAaK7v5c6C0PSr/wE18D4D/+0/t33WdrZiH4Vqw9gaZoen+X9XbuuyNKij3I8MyYP/izO6WX1FYkJcCA1L5c5C31JfuRi1xhDURBwgoko/XX4Pj4Os+6Tcz2iOiaY5UQSk3qUriadA5IBum2Mwa+DFdkj8rnnq+332J9ZPeFexfXpA/FQDSRjEhQ2XQU/UxRmewThpZIQw81rWs0U09Dd5/J7OMtav8Kzn9MkcyEtL8yy7Y/izbgee8YKpU9wPFQsH5BWiC5R7bfI9G/Ubsk9Qw6+y2CSU20P2J2ps90abUQkHvk3d/zFH3f9c4ITMZb5VqpV4vGtzFMxPc+SElMswhSScODhodKhXzYu2Ycq5/SfKFVHNDhhGCanK6Uef1Zm8HUFGasiUuSySgN/obgQ0a1tRlq4qpi/c6ED1z/Cq+3JzlSlwvsUr/BoO0znDBYvK+E01bse1TkdhNf0a7bVFYMaqc2SaYbOB0TvSCGlU5HmPVux/z/uXvvKMuuu873s08+N9e9laururo6u1utnCxLsiUkW7KNsTFjg7GBgQE/mDW88RrSzFvEGQa/x3swvFkEA4MxBhtjHMDGSLItycqpW6G71bG6u2JXunXzuSfu98e+91S3LIdBLJbe7LVqddetqhtO2Pu3v79voHGyjDbh8fDCTm4Ym+PW0in0wYSlmQLdKR3nwTyVFyUrtwCR4Pxf7WLgR1/iQrfCkNXktttf4sETe5GxhpjwMC5mKMzFdAc0YnOrnZ1/yVZb0tuqGIfLl+W15efUcemWBc4GBEVJYksK5/vosSS2NPJ6lwvdCmWzrXCxrKIwfHnpIPNnh8CQECjzNCSEO7ro8+pF/ujcrWmHKsooMHjsjYtcV5nj0394F5nVGKcefdOE8j/r2d0ffSO4frUlxRa50qsYvLymulF9wmTQs1Lpg/Sd8T7o2Ls8Q3Xe6ruU37YUysCvMa0x4dZ4KiNxVjTCvCKLStHzM+oJloM86UKQuyAwfInuKR8jowPenS30WOnY+m6Qth6BLmlOiRTI130VftHdFhJ/ZIOljaKygV7KEmYC4kgn6Ri07m4hAwORWBieJCi7iEQy8mxI94xJkJc0dycYgx4zwxtkjIBzmxWKwIFfPk81yDBotakGGbqxwYDlsRm4RFLH0UOePTPNyMgGUxm1M/AjI+XjHa2OMZq9PIL8nzJeJ105gez6W1+2BfUmwg965WQHa3OrgoocHakL1bb0ItWa7ya4y6pc6I5mac5k8QaVHcrg46uYrRgtVJ7XladWFfgdSTKrSYov9UFv3Zfk3n2R9vaYO654mbLV5sah85xtDPLM3xzi7OFJ1q7SQZJ6A4V5k86oxsYNEa3tkuo+naW3T2KsNbA2fSrHAoyrazT3hBROGmihUBfxqoPnWYw5dc501Q1z67ZZ9Tne0iS2VQimWdMxusqraMhq8nJzlG3uJjftVr+bBDrNnRGRLfAGlZBUyK0JyF2B5KEy+QsS781N9NurmA1Bc0qFA4ByVtB8gb2m4Rc1OqMajWkNvyy50K3wj8cOALDd2aAeq232wfIyv3PXX6F1dBI3RvR8sdmwiTMSbVeLjSPDvPOdTxANB/ybt32V933fw0S/P8pjH72R/EKEXd/iKJFstdPSSSm5vCv33QxlSLd1XmFLyJtz/F5lo7Zw/dSWsKAwwMhWuYDFcz3r5uaWhKZfUS2/UU+3/wNHlf5P6opF75eV1MlZUQkonTFJUITcBXVe+lFd9obq7AW+Qdi6HFO97+R+8idN9ACMtvrSfBh8sUtpuMnSRpEkUE4RQ3sUrikjDbPgK8eIdZuNgzqdMcHa1TaLPxNS3WfSntDYPBTzQ296nOsm57muPKe6gGbE/sEVaoHLxXaBVT9HK7KJpM5Cu0S1m8WPDBqBw3W7zlOwfIbsFoaIqThtnF4ydMYMUsnSaxmvj4mplx4Qr6xC3Cvhi/nLBLz9YW36uOdUd0oPE4yVGtnzqnzU/Ah7TXUH7M0Iqx5htGNa+5V9hNVUeqrutNr6JYZI/ZZhS5ZS3yfZ/OoYN119ijU/x8nmCH9/5iDRx0YonYmY+XyHqfvaaQvVGxJsHFAJrBP3a+z4YocwL+kOwdrt4xhragXJfabA9r+D8omA4cOhCk28qE7BP8y9AV0kxFJjyGri2oGKeHIUkdLZEKy/OSDOSE63hhm028RS48rCQipXcRcNNg71jqW9RSK1mpKw0NMEmmA/kSd+uEzxTSsk1zTT32/MKJAceq6SvWAEoyOYbVa498BRtllVntqcBuDuyjEAanGGxN3aemmenn4fn8sRjgXYWsSHr/sGn/7Du7j/t27tEfZepeP2ao4Blzz23eBLkdtj4fcsdfujz16+e/wE8WSXcDBKvbtTD/FYZQw61Qg9SHDWFfjdnpCYLUFmRVI6G1E6Cd0hSTu2CQrK3G/3DRe45a0v8u47n+SuO46g+z2r56pIMTxQC0DsKCPA3LxEN2KEISlYXXSRcDC7gGlFRFnl/97HuXJLMc1Jm+wnioz/tYV7SnlrtX2LoNPDgDRJp22T5CKSvS28sQjj9g22V6rKtvcNPlqgKmBXDxVNAEnJ6S3qsYFjhBgiIWf4TGU22VlYZzxbx4tMyraKjBdCpiRQUHFPrdBGSnGZx/w/dYjvYC75LzKKzpi8eeA9CMdOJyPpWqntAmzZtvZlJ8A3ZZn1Rx+oE16QssYBwpyO1Yzplg2c6haI1xo3U4wJVJeuvU0ycdUyjhEy++QUpVOKFdsZVU4IYb7HXq4rhXlfA6WFyrpj192zvPzkDsYejwkzGgMvVIlzNhfekWP7l1rKFsTSuXiTo7oth9rMjKxzXXku3TY9sjBDu+5SfNpGDySbB6XK3tMlxIJ7rn+R7c4GzdjhUw/dQpKN0Rz1udxjLlIoomBQEGmY5GVRQb0J2a4nrF0P5ngbv+5gVA1yFwT+gCIElk4lWB9a4baRMyx3i7h6mEZCL3RKTGU2+dr5PUSzOaJSBAKmd6xy2/AZxswa/+P//F7seqKsahKJFvGq+W//XOPSyO/+drA/WUkN3vLrjwHwyQdvJbGSVKjrD/UIhW7Mzk/FNKbVddPYIdIQzuzKln9Sf2HyK0CyRaLUuwr8Tmy1xRu+Y5HqP0wQFNTPi2cSmlOa6pa+Y5W1U4NMvuEiBweWSRA8sjCD/3Ix9XNCkFIY9C5sHuixydcEk/eep+plWFkYAAF6Q0fzBWEpxhnyGC01WDg8jtkSOGsS786WCo+tmew+NE/B6jJstzjTHCRjBHQvSUHpRBYz+Q382GAzcFn3cozn6qkzqqNHVP0MExnlOnGsOkrZ7TCdq/JH133yOSnlPxlpen1UTL0hu76SotSbaLUWeq2DsVJDr3UwlxvotQ7C29reXTopCT9QGFW9iVhcpbmrQHd64DLSZfo6lyzKrfEthXViCbxhBYZee8tJMmbA6cVhxcspCDojGmFOUQiyi5L8XEJ7mwInc/OS4cMJhXNqhTv18A6iUsTiW3pK+6Esy7fm04vX2GgTZRU/KnYlkW9w8sw4oHK9hqwm75l5ATsb0NijbpixRyW6p0GsPsBXz+7hhcY2dJHwk3d+TSnNY/V63UMddfHWEoJiTzzbYyq3pmRPta8qoyijuEbuQ3kysyb5/VV4axVvSnkpRa5g/uwQtTDD4U9suVbWApdz1TKTTpX/etXnyc0JnHKXD934OD87/TXu/61b+YtfeYdyOjDAXQsRCeiXkPe2NIn/E9fJK4qq/kR0qY7OaMff5Cqg+wmJIfiLIzfxudkr1SRv9rhs2a0FWvN0gqKB2VZ6zH5IZWtK0i0pLCkoqCCM0tkAo6P+vs9u7wuV9Y6S4XgfHyNz90qPRqBE2u6aCjT1QwNros14tk6CoGwqx4qwFJNYyltc75LKdWIbJu8P2P2na7T2hKmNcno8CzFhKcZdNohP5+C3hxh4GUae7tng+AbahomI4bryHPvzF/FiE03I1IHA0cP0/6B0e/0JqxE4dCKLTmSlQuDNwMWLTfzQoGB1U1XEaxmvj4pJH5Q3yLek37+aSVzfoyn93r3c8lB6l/T7i3kaVwymzgD9Ll6Y05UsQYC7oQ58e0QdcKnDxlVKRnD3nYd5eGEn44UGQaIzd2yM7JyWug0C3PSuF3ln5XmOdLZzujWMq4fYesQTf3YNsQvNnREiEzP4kIVdV1IVb8gku7j1PhNLZ/2QQ2dcdXCiXR7jlTq7i2vYesR2Z4PPz1+pUjMeLaUdnbXrIbESZj4bs/ejx/Bjg7LVph3b/OOxA2hWTBLoiJqJs6Kp1nVNgahBD0fRu5d08IKt1R5IPaukAHddYTB3/++PcqY9xK7sWlrR9aOFXD1M28/1nx6huUuVBokh0GIFhmuRZPVancEXkm8KpQQu+/6fAnj3x6V//2pgOigqyX//zd/jvQ/8DEQKv5O62rJ2x5V3t10VVI4rD25QVjv9CT7MqW1dMJCgdwTvetuTfPlvbybKysssUURC2vXUInjjBw/z8OeuwV2TGJ5k45BAbG/zPTtPoSE50xzkuvIcf/n0TZjrBs66wGoq18rYVazvMKsWEi2Cxp4YzRfEhRgz75MsZojdBC3QyM6rTrPRhfKJkNhSuOHaLREfvukhinqHhUCFls62B+nGBqOuoq+4ekiC4Gh1jEG3haNHLLRKZMyAmbzKFekn9fYf74ddrDTz2GbEM/f81muqmF4XE1NBlOWN4s7LHrt0chL5HMRxarGL/irEujhWj8cx3rU7LvuRtemnvk6vpuPpT04b93g4L2Yov2WZjBmoLonl8dQze3FWFLmuule1/c2mAjr7I7EuD2cMKxF3XPkye7Ir/ELlNM/5Adf2tqnP+QH//tT70P+fCp1hk8396jn6soz+3/UpBH/1/A1sG6vS/OIYoIiS/Ta3NxZxxzXHKVttMnrADnuNX3vkXSoAtGaSZGOMqtJiGW01GfWDAEC95z4W0h+Xdu0KczF2NWLvRxWe1Gcl23qUuiz2S34NyewPT+JNl/BLBkY3IcxoafS59pOrRP9j5NWxpd5I/bsvGd8pVOC7GZdu6cKMxtj/dpZRt8lXjlyB1tGx1zSirEydEPp41Pav+DSmbRrKix+rtmX45q71IpuuV2kil+r9+sczsUDqW6ku/bixKAuFm1dZWRjgjkMvp0k9i50i8w9sx96UqsHToxtoYU/cnFUsdLsm8EZ6cVSTbfza1kKtddR2TsQKuC+fiJXrZFFQ3ye555YjlMwOeb2rUneDDK4eshm4OHqkcCY9oha4lCyPkuWlNr+78usseYU0Jfh0fYjxnNrKLbWKDLotLD3mb974sf91tnKXjqTTSX3AZbOF7Hjpz2SzpSaiOE7/3/95PDOO0Y3TL2vTV2SxMEmrAEUHiJXWKpEU5rps3OORezJD7rZVRrMNMkaQ2qwmmRi/vGXJAQpjEoli+l7qXNm37HDnTB6f38GFboWPN4b50/XbuPfkvdx78l7e98RPsi1X4+JP+GRWFW9Fi8CoGQyMNXh6eYr5bpmNIEctzPBT136DpaMjiLep1cqqqQQObywCU/L1o/s42xpkLchzpjvCh254nOLTNmK4izATkvEuZlPpxEpnI+xNhY0ZHqmYFLYit2On57TY+0x+2eCJP7uGstnmTHOQhx9TqnxLjzlyYpqVZp7Da9s4sqGSReyqjxbLNPdMfT5Js2vjrqnP+610bq+2cHzTpPRtunSvtNB1zis0/5VcpuU/2Mm6n+Weq19S53dIuRtYNYEWKn2f1KA1YVE61SE33+vc5dWxMZuCdq/StV/KUNhRIzF7ZndBj6+mqQXMvag6t77quZBZSeju9Gn7Fvt3L+LqIRrKMvjk7Bj2Zk9I6/QUA7pqXIRZBTc4G2rSifMxiS0JAwNnoIuwEvUVKnfS/vAGNbwh5UZq1jUe/sy1fPnCAapRlkmnCqiq98aB85StDqNuk7LVoWB1WWiVON8qo/VOzGxLSVjWgyzt2CKIdeabJY6tjrKwXObo47s49oXXHDv5OuEx9YaWyZB0Oum/rxz9aknkc8rtUghEuQRCoOWyJMUset1DSFXKNHZmiU2H5pRQpMZ9HSb/XJE1/f+ofJXafztKcTZg9HMGKzdIxl3VdejGJrauxJRX7p3j6NNqyYydfoKr7KV3bNnA9i/Ivs2Kt+mwPpbljDmSlr6JFOwcWSeSGnEsCAoq1VVEAmFIWsfL2HvrPLs2ybsnX+DjJ27i+3c/z66r55ldrTD6rmU6nx0FBP741g36/OwUV83M4ccG29xNfvbffZbfPHIPmh0R+QadmZD8CZPEVGS9fpXUvwAu3cr1W+KxA+1hHbueoAeSvN6lbHd4221qxT1aHeOeq1/iopenE1loQrJ+83YqR2roviTpRVL1RaC1tRxD3VCZt/XildTrfftqqF8xpY4Cl3bu+pOU1lffX16J9cXf/Z/1J6fEENw7+BK/d+otYCZovkGUj0lsjcRKMGsK/6seEHRGsgw974O02DyYpNiR2RLUDoWUXjCJv1FGq2zxmLRgq8PpDyhXAHtTSVj8ooadDWitZ5nZdjblCx3dHEvxw9hWVVeU23oeu5bgVZQxodmG1g5NsbXXbeJRZV4YRzp2VTHY+5Hm6rMrEzjdhw/82AN84lN38ag9w5tGZkkQlMwOzdjhyMYEupDEUrC8WiKb77L61GhqhawcLXspLU1lItd1BbqE4WpCYkKQ/7an87sar4uJSWgampshun4v2sNH0IYHVSknJSt3b6M9JuAK1dbmpTzhvg7WsQy5Rcn6mwNkqFF80cRqKg7SyltDZKAhAgm5EBlqmHmfypezhLkEa9PH+L/K/Ls/+Gt+1f4Q5z8kKT+ikViSRKo47FIvTTGr+2SMAGlKzGbM1H1dqgcytMe3jM1AnaQwr/hCZkeSXQ6ZGzE4Ux3k+fltcCGTltdDtyxT9x3KhQ5WwwJ65vuRWpXjwyXWKgkXhircOX2K441RJjJ1ypMdXlodw9sD+XMw8pDO6l2h2rLlIo4+vouZG7ZCvv7j1V/hN4/co76Rysysj5FFDsRXN9GP5BXfyQZnTQHifcfFoAhuF/xiz1wvsShbHR5b2sHN4+c5WF6mGmToRBYTmTqbgcvMvzlJ48dUaSA1VZ30XRHtRYtgYMsS99IJ6dtt115pX3KZ5YmmJoLY0bZ4UJdOXJq47LG+NMluxPzBf/l+fvmX/4LHmrv57JPXq8mod07jTC9ld0kZwy3fbLPtwTaIDLU9PXM3H5wlk+6QWqgqV6+yenKIwmlNOQ/07HeMrhL7NmbU+0TASLHJSu8tliyPWuCy8NIo5GIauyWZRUHS88bqb+MAwoKSr+gLDiISSFQ3NehYyI6OyKhfTAx10hMTsssQZtWk1O8MfugHH+DBtT189uGbcFY1joX7VIJwUzHekWBnoDNgkbmirsJQQwPPN/HXXew1Q3UfTaUiaG9Ter9k2sN9/n8R8NvesU2O/dq/Rfoa5qahMsiqGt2JkN1/HmDU1CThTRaQhqC616R1yEdbszCnVfvTPWnTmQ4R9taKKf1eB8uQ7P3j7mXdnLBgsXSbhYgE5ZdjGj/UxPcNxssNHCNkJr/BkNVkh73G/RsHePrxfcSlCDPvY7yUu6yL06+a+vQBUNu75kyCLIVct+s8oHLsXnxmJ0lFTZwj2zbxvzxM6UxA/WeadJ+pYKvKmsbuBAZ97tl7nGfXJnGMiJ2FdbzY5ImjuxgYaxA/WMFsKhqBs6I6PRs3Rly/f5a84bMnu8KIWefXn3wn5Uctand4aOdd4h5T2ar3RMo+tKcSCqc0gmJPyuIoljKQ6rq8Uckd9xzhopdPnQ+7scmpw1PcfdvzXPTy7M6v8eKP7icqObTG1eTR3/5GrsDwJM5m9K0noldMLH1sKLY1MvMt4oxJbU8Gb1gwcDK6jBKg/Ja2LDvSbWEvb84vKSxR7ybEjsKaAH7h1z/JxxZu4+QLUyTFEK1ukmRizHUjZYebLbUQhZUIzespFUKReoBLDX7+g5/lN/7xPbgXtTTdBdSCpVjbkFuQ+PfW6VwopH5c/fEPc28g/lpFqQ4Kve2a1TMrXFQ20u0xHashqV4hifO92UpA/oRJmAV/xmfwIUthYr0L3qor2ojU1QSF7PHZ6up57HXVaZQ65M9Dt9yvPqEzkTD8DMgfWWPA8bjYzDOab6bv+eTCCFOj1fT7xefGySwKXvq9j7wmjOl1UjFJDDsirrmwvYP0DeKRCG3FZfnnQrbeZgdTjwl9C7mmLDPiWCB9Zfjfn5RkoCGsBC0TkTRN9v5xF31Vbd2SUg6t1iIsDFM8DdlldfV02jZJV2fDztCuu8yf3M69P/AExzoTlCyPu998hBc/eiUXb8z2AOPLKybYmpT6GigRCqxswKDdpha4zLUGuOGmkxz+6n780ZB622Xoe5fpfnyEzpEKUUFlwQspySxpdLC5f+EaRq++SCJFqqNyyl083yIaleR8geYrtnFQAhJ47tnd3Hj9SU61R+i4FrsnVzi7d5Ik0ImHFMDemYlh1qR4LqG+Q8Pa1FI+FvQmqx4Vog/aFmYV+L3u5Rh3G4y7DWZbFcYPrnDRy2PpMaebQ4ASMOuBRWwJjK6qmsy2pD2q42x+m+1bf1LqTVCXTjw7/lix3LO6T0YP+Oynb2fwaJRa4EgNmlM63SE1gYw8pGO1EuyaOm7pVqSHfemB2t589Jd/mF/49U/y36I7WTg8TmJLtI5OnJEqbsrcCk3QG5e4OppS6esyEntN45w/hN7Z8ifqb4nDnAp40EKovH+eM8tDvPWW56nd5Y4FAAAgAElEQVQGGcpmG10kfG72SlrLOdwCiJo6p6DUWqXTMbXdOu6qRnY5RmqCzKJGZ0LhTOaGQXNvD7vbMAmzgspLMXogez5iiu7ST78J8gLZ+xjZOQ2/oqp1a0NVyVYDcsuq0aP5gpW3d0nmy/ijDWwz4sL90xTPKQmXnYfF+fG00zt0NMbsvPYo3tcF+C0TdSYn9qviVvoazrNZZEkdbL+rlq0o0mm0XILFLE+983c4+KYzhHUbYSeI4S6ZE70V2krYNlZF6BKRiRGhWllk16e5q0AwPYjUFe4kEjj/fQZD5SZaS52tG3afI7ssqQbZ1Nr2yPoE7WGd/Dl1gkefDBg+nPTwof62RZl6gSqZRQJ+2+L+b1xF1c9QtBWa/Ia3nIZYMFRosdbIUXtPm6HnY7ILqi3slwRGCwqnNKJcwupToxwqL5EgGHPq3Dl9iqFCi9y+Teo3+Ay8rEDbqLeCJlbCE8d3YesRL9XH2VlY5wN3f4Obds+qbp2ZpKumFsnU/lW/JKEkMVQloF8S0hBm1M8PlZcAKJkdHD1ksJcY3Agcxt0GYnE1VaGbnSTNAdQihXV8V+NVGOBDllqpq0GWTmzh7fVxVjzMms/i2xKKPz1P59oO+s4WZt6HD6yzdJtGt2wpGZN2OQalRaoKEQn811//IOutLNuuWeKttzxPYiUkmZi40KOV7FDXotlS3t/SlKkER4TqRt9hrymrk15Hrm8lnD+nyKpBUTJXHUBfcCibbfbmVqiFGTJagP9ykcy8oQzlegubFoJfkgQ5DXdVHbeNK9Qt277GI6mEEIn0mtcyEdpEBz2QhFmN5oRBa1JNqFGWVIbjVCV2VQXFmh2ZRktZdYnRhsoxn9aYjuFB8QyYsy4IaLRcLl4sod1QwytrtCek8nrvLWZWA2q7dBbueG0dVHidTEwAXMjwkZkHsF7IcseVL9O9TpW47cU8mp7QrrtIqTRh+ojHe4//MC8+sxOAt+w7SVxTkhBQiSJLG0VcN2D35AonfibL8f9jlJO/NKM0c15E7GiY719h44CNtFUE9q4rFhgvNLi6MM/0T5xK35ofG+woVAmKavIpnFc4VWapy/YvtRh7wmfgREJmLSK7HDL8XJexx9XVmS16aBMdZvIb7Cys4+oh1W4Ws6az2VFXYBhuuRgGxa2ODIC7qOOPRTy2tCMt+73Y5C2jp6hvZskWPboVZYkxeR8peKrXDO577CpObwyln2N3bpXh4f5eE6QhWf3+Lt4utTzHtiITOhuqu6Qmrt4fCwXiHsyqrPuS2eEbK7voxibrXo6CpSbdJa9A96rp9DUTU/RIlRKrHqWPfTv3yVeOV6MWHMgs8lPXfoP6r3rUf9Vj+/Y14kTDOOMSzeZgNsvqapG4GLH8JkHsaGnl9MphdhS3yv1ikbzlc0N+lnuufxFj0wCB2t55qoIKBhLMDeOymClnQy1WxzoTZOe2MhJBLVreqGT68+vMfHoDv21x3a3KX30jUNy8T33sLuKproojM3tduIxilDvrqtoJCiqKKygnLN8Zk3gGhhOCKUlMiZ0N0I2YJNFojwnWr0tA9CQvPumEF+SFcjvdVMe0z17XPUFnRFCa7VWXsar4xn7kHKM3L6FnQ2QsyJU8FT//7kXVwQy2/NtjS4nCBw9/16f2W5/z1/4U/zxD39niPzzzXpJ9Pg+e2Eu26BH5BvZIB/25POFBjzAwkB0dliyWpMCdUe38p5enyM4Z5JZiWter5Igk0DFzMfft/xI7zv0EJAKpSzrDJkY3ZnOPwcAfDNK4K0aYCY1zJWrZPGYuYH0gRxAbYPj4scEznznEwe9/mc50SGdSUDpmsHJjHsOTRBm1IrWv6JLNd7l921kAnv3dq9F92D+0wqDdxo8NmpFN3Vcgp7u/ftnnb49qJDrYG0KlcEhBdjnBagpix6RuZ/jLZ2/k7Ve+xDZ3k40gxwevfpL7FvezeoWaWDTfZuhpFfHTHZIMPS1ZLrkcz44ynnWJpMZbJ14mHtdY7hZ58MReNCnA0/HHQ+xil7iZV4kcg70oJUNNKoYnsTdFKt7t+0MBnG+Vqfkuta7LdLFK9HNV7J93MFtxCkqLSFLfaWF0JGZri1X87UDvVxIkN4Ic250NHl7fzT9EVzDfLDFTVBSKZx7dRzQQka+poEmRgPOiTeWeRWodl82VMqNPqZvu0u4cqA6Y2UkwO7D++9P8vruD6Z84hbOnzmSpxskXpqDik3gGdqmL37bQ6ibmhrp92jtCEPClL95MMKV8zJ31rQo0QrB+Q4Xm21tcNTJHyfKohlmGrCYZLSDMgXXaRQsUAdZZVxWYogeo96hFEGUF2Tkd0BExtK5QWYBhMUllJrIU4q5K7JpO7EBzRmGF/YkmKEG8LnCqEZt7rBQMF4n66vO8YhcKt63Q8B26kYFxOkOyp01rNUt2MsARksm7LlD9xBSJAZsHJdl5JfweOvJd+GV9h/G6qJg0XR3Y7FMZnLM2djagXVfVhN+2KJxXETXZwy4iE2Pvr5MEWxezqceU37LMxM+eQTdidD1Bs9SK/JwfYOYCECCdhMYOgTdo0b6iS2tMJzvRJFv0GNqzTv5lk5mRdb7wwE3M1Usc/sQhjm8qxf/hr+7nV27/Irqn0Z5UcoKg2CuTMyB9nU7bZrZV4dm1SYxe2sp8s0QtcFnsFHnm5RnW2lliV1WA/djuuGYhhboY7bpk6n6foSMBuSV1ggvnJCQCMxdw/9m9xFIjQbAW5DlQvojhhNjZAL+itltWXZLoCm+RvsaFuUFmsutpHHQ1zDLm1Pmpa79B3DTRSwHCTPDXXdw1SWOHSuIwuuozGp6Spdj1hD96Thm+ve22I5xvlTnfKpNIQcYIKLuK4nHbyJktPx9D9HL5oFsRFM55JLqWtv+/HVXglZXS8f90BV/5xTfT+e0JGoFDq2uz0CpRsjz0rsBZNJWuLFGUDQQsHFYyH78s1XZOfDPbvN8l7FMbnM2Y83+yB/9kkdsHT2NNtBkebKB1dPyag5MLSDKxciNtCrLnTH7lti8qLd2cmpRiWzUWOmNSRXK9rU13zWWgl19XDdQW7i//7C68/V1GnwwYe7TJxMMhsQPORsLAqZjS6Zj8fEzhQoTUIDefUDin/LuHh+v4QxHmeBvDCZGlUP2rq/OHhPysRn2fMix0qj3P8lAqJ9hLSLagTOjaIzqdIZ1uRSKlwDFCmo8O464CQqJlI1ZXi2y0M9S6Lu1xhR26FzWshiS2QO++dgfL101XbvK3PkzkGxh2hO2ola1dd9GsmDNv/jgz9/84pXILgFo1R7a4RbicKNap+2rv44cGYawTRTqGEeP7Bs7TOVqHehntFy3GHotpj+pErkDcWaUbmOS/nGP9uoTsnE77DT6FwzZaKPFLgs60ej/2QBd/08GoGWlbGZQyvQ90JnZPaGtIMBPee+VhLnTKNAKH2SenSEywGr0Inu0+xkWL3PleRNSiurGXb81TnI2xN0NWr3EUNhCBX4buTh9RM1NrVFuP0JBUgwxPHN+FMBPyL9iYLcUdql/vK/eB/sXn6dx4/UlKlseQ1WTBG8CLTZ56Zi+5cxreiALAY6d3EXsCq66qJi1Sjp9v+uBzrAdZdmXXOLw5SSIFZbtDNzaw9JjnFyfY8ZsxwgsIR/OEOTVJXfyRLlO/q+FXbIx2rLy4X9nev2S8smLqhwosfjgg4wTsq6xSsjzW/SyHn9iDNLe6jf2Wd+SCc+MGUgqKH8unk1DkakpPh0q0SV8j2SKdRo6m/I/Kgu/90CN8+vi1mGZMd63v8Caxl03CGQ82bJwVZZ/bHVRNg6Hn26xem1XEy1trabBmX/bx+PI0Ugq6h8tIXeKPRohAQ/aaOMJMyBY9fN/AtiMGMh4LK4qKYZ23EbGSxSSZmNxQG8+ziJsmpRdNouwlpN8YMqsxGwd1wqLqvra3qeNQPqZwUZEoOkxiKo/06j5TYY898D2z3GX1uqxiotclsSOwN2WKIYLyqLIaIXon4oGnf+X//8xvoUlsJyRb9Mhlt7Rk2aJHPtvlmmffd9lEVCq3MPUtjKITWmzUs/ihQatjK/vbWJC1A6Kuyfg7LgDw8zf8I6UTkP3IgqoobvCoXczTXXNTUFb3YHi4jrizqvx6RhKu3DfHzQfPsG9kVVl6jHdxVjQSV2W/+YPqvei+Em7aawZaR0e3ez5DIuHMEeVpmwwFdHf6TN2n8CKzLoiyKmFFC2KkodHcF7J+hc7KDQ56lxRQN9qQOWEjSyH/cOxgyhiuWC1cXSWvAuhv2VBuixLGv6Sy7YkFxIKBo4LZutK5LXgDlK02JcvjB9/8WJqJ98rRf32VkCs5mF2gbHX4/F/fqjx4jICFVomTX1HJKt+z4xRr1xdZeuswZ39MY+nWLcSgO2QrIDxOFM7U5xldMr4V/hS7GotvtvA3HeKvVXjqmb0sdEq8+LW9aisSKouSvmVLbKsbq+D4eL55WaKH4SVqQnrFpNiflAwv6UlnJO6a5It/dSvXTM1jGDH79y+gexqaG/GL7/ssbNgp5qR31THMrsasXZ0lykJ3WHHTylaHstnG1iMmnSr1RoZG02X0yQB3RTD+gMbIY4LtX4CB5wwKh20m/1PExJ9ZGF8t4fxagfI3bIwlm8QCf0hdf8JK6LS3xLyxrUDzzqikOZPQnElYfnuEeaiGGO5Sv8End7BK9sAmQV7gDWp0B3TqOy2aUyadISPlaRkdVUU2drhYjR5eJWHkySYDJ1rkzzQoHa9TOl4nM9fEqHXRGh6vdbwuKiZn54Sc+uhPkSSCXMan1dk6yFHXZHi4Ttu31NanbqN5Otk5DaMj0+QHLVQulKs3qnK6L3LMndPpjCfsvGqB6ysXuC57jt/+xQ+AgI0DOgMnEyJHYDUTsvMdopxF7GrM/UDCvu3LzK5VuGZigU6k6t4XZrcxOlrDMSLqnkP4aAWpwcDJGKMTE2V0lm5XiayzzQqd0GL+7BC7P+lz8eYs2aWE+i6N6b9V5l7eZAF3vsGZH64w/aUOrSmX2k6NsOeqqMWolaklCXOCRCf1j+qMCg6+8wRZPWDMqXO8MUo7tFlqFDD1mNbRcuo3XbsyxL5o4q5A7RoFzF+xe4Fcb3vXJ/kdWdqGPJ4nMZWg2WwrYmZiKKA0duEDP/oAT21O4+gRCYJG4KSK9G5ssNLJs3xxgPJjFq1tgtGblxjONHnu/BT6gsP4I9EW6bIWEjk6Qm7hTZfiTn3yJMDCu2Okr5E5b6a8m7vuOMIDX7+axFaJJGZTpPYuWgT1fRFaPiST8yl+Mo+92TMW9BUZ89Jq6ZXYE6jPLCSErkq/HfjgPEJILD3m+NM7kKNdtCXnstfv587poaR6S4AwEu7Zezx1pwT48uFD5IbbeLMFSidE6iDQ3C4wm4DoRUiNCwqzkvVrJfaGRlBQgQ9BRQl4E1siHeXnZFy0VKDlOcUZyy6rnYGIe8B3sa9WAPOKOgMfz5E932T59gHGHt5MU6z1MKEzbNEd0PDLgtx8QpBXRNGgoLb2A6e66B0V1ZQUM2muXH/c//xv/MuIeIUQOvAssCilfIcQYgfwaaACPAd8UEoZCCFs4BPAtcAG8D4p5flv99zu6KSc+Mi/J6xE5AbbGFqC55sEHYvBh9SEkFmLWLrVYNcnNvBH87THlLyiPS7wxmNEJDAbgmAqwHBCyoUOK4sDCF8jd06R0tpjAn/Gp/yYhRbC2u0BeDruooG8qkkY6gwNNNk4Mqze2I4O2ukMv/iv/pZz/hB/d/4KShmPhcPjjD0e4xd07EZMbadB8VxMc5uO4UkauyAs9Kqotk7hrNqq1Xeo0MHK0YDmVC/+uZZgNWLaYyaRu9UlcarqvPgDgoGTAc5K57KTLy2DzmSO9qhOfTf8xD1f5VR7BC82cfWQI6sTeL3UXO1wXkkomgJvr5/yvPo3+U3vepGHnjzIjdefxNVDnv3UIdpTKizAbKvSHUFq19veJtl18wWcHq9q1G2mPKYg1vncrgf4w9oEv/epdxHmJMXTCqtq7gtx5k2mblfs9DMvbWPygRgRSeyqT5wxUuzpUp9pb8gk+ZF16m2X7H05Nq6Jsdd1nHVBd1AJILVgaxumxSq/LXa3Iry2veM8nd+eQPeSNM4Jvllb961GYgoSXajtfwylH51nrZ2lcXogtSQBtSiGeSXcTQzVVesOS77nzq08wCeWpmmeGiAuRuRPmGns9/jX1ll9Y4X8fMjGAYuJr9eYu7fE2ONdYlujM2zSnBbYVbXQCGtrm2tnAwLfYOBhRy0gNj2PciVfiR3wBrc+d/lEjLvSpbndJbfkY9S6KS4IEGcM2mM23ZLqqg4dadHYmcUvqIk6ezEmM9dEeEHqneaNqS1ubAme/MzP/Ytt5X4WePmS7z8K/I6UchewCfx47/EfBzZ7j/9O7/e+/RAw8WBI7rQJT5TwXhjAeSQPLYPGvS0aMxDkdKLRgJd/tsjmHovqFYLanp5up69XM0BbswirKoa7+JKJiATdiqSxoxdt9JJNfi5k7YYYc9miclhn24NtrEfzZJ7LEP6tmpQSWxK2LPyxiD/8jfdwdeYC3cDk/Oww8UjA6rU6m/th/aBqKa9drdEd6tHyzZ5vkiGJszFBSb2/yrGA4ee6bBy0KJ32MVuS4rFN1q62MNsJZgtGnmkz8cA6lec2GHx0ibE/eR6zGVLbX+DMhyqc+WWH2fcNMndPCasRYrYUee/jn72LzcClFri0Y4urhxcpZj1Gik3smzdw19Q2Z/xLBma1193UJd5EzDOfOYRV1ShbHVw9ZPt7ZlP5TFBUWFWQV5VA7Kr2eMHqKjGnl0v9dwatNme+sJudn/mwOq0RuCuKw9StSEpHTLqjEaufm+L0/Ai7D80zd49Ge8wkzFvEto616fckMjqxq9EZMdl8b48d/VKe9rgKaHDWBX6JNIBBJMrXSiRqUgJ1vkHdoK3AJsjphPmtWaTvBXWZZ1M3+abH+sPZjDA8iR5INj49ya7yOnff1uM8WQmarzqqWqC0j866CjC4+87DlM02ZbPN2cYgtcUCaJLhRwyyywmRK8gtxSzcMwhAbZdFdjlh8Y4SsQ2r1zhcvFHZRBud3jXf6bHPjQTDCQl8Axlp6WcGFXmmxaRCcd1XX2FeUp/RiR2Dwtk2fsnEH8qk21wpwKh1yS77GF012a9em1MCeI30X+EpDzTRDdEvrJB/fpn888uUnl76jrf8dxrfFV1ACLENeDvwX4CPCCEEcAfwQ71f+XPgV4E/AN7V+z/AZ4H/LoQQ8juUZsu3mAwdUQDd2GMh59+lkT9r0I6yUJB0ywLNipE1E39AoHfArinZR99zCFSXxNjuEZV0mpOCpGmSFGP0qol5UZCfj6nttLDXJeXjKu9r7m0Z/MEIs6bTHVK4Twhkj5qYbQWA/+r/+yG6B0PyJ1WlYzUl+bkQLZTEvVVX9xLCvH4JgCowmzFGy8NYaxCOKUFp5mLC3N0249ctMz1wkc31mKWpCvY6iMRFi12CXE8OYinFfpRTWjbWcmmG3PoBF+/NTUb+wiXMapxp7eZN7z/MRS9PVodrhhTn6PDaNmo3NxECNsiTnVOnvrVdIg1J5VhAbaeVikkPFRc53Z1R4ZWR8oDy0ZBG7yafUpPQepBNLS/WvRyWHnPbDypg/MX2JFYdmjt6jPFI6fCGntYJMyrUs/rQFIPAtn99hiMnp9EbOkPPGrTH1SRv71UareilIapxDhxV9WUXVbR7/YoI82VVaVoNpQlrTVxy3fYKzDAvWHx5hOTOmMkvC0TUD4+83FNcJCBimXapLgXCtVASFHTMTtKrygRH79vLD7z3YVUV763SOlpW/C8Lwr0dxMsZdn3fmV4WYVa5MywPUZpo4D1fJnZU5mGQF9h1ZdUbFFX6TlBQQPrY4wGbeyz84ZiaUKRHd1XS3BcjAN2Ise2IVsvCKfokpqVIsbK3kNiCzb0WiaG2hkFRuROMPdIkGLBZuSnP0AsKEwoLFlFWT73zjVqX8rGI1lQGr6KCOvrbudaYDqjwj9jVsRr5NAQTgPPf7m7/zuO75TH9LvDzQF83XAFqUsr+3mIB6F8SE8A8gJQyEkLUe7+//q2ePHEkYn+LhTEHZML5SYGeC2hfFZJ4BkiovzEiqVkYvsI9WtsTujtCzGWLznSIO2ciEhg+HBC/oEpKpYlSVHl/ACJHYnRimjs0Ejdh9Xol6EymPKj1toxLaiWeeDikvsMktlVZXLgQU7gggIjshVb63uOMiUh0tCDGqHnUd1bYvL3LW/e+jNZj2j2+PE0YZ2mtWWhuhGm3CWsOF84PceHMsKquXHU3mB1JfYeeRktFOXUhJqbAqSqv6MZOcJcV3mM9lWfue0Mmv5SghRpHq2McLC/jx0bqp3PN0AL/uHqA4kAbce0GraNl3DWVc9fcH7F4u5nqugwtYblbVKLiUJkj9SN7tFBhemYLEgTPL05QynmU3Q5BrNMIHIJYZ93LEbg6YUGxnqVQcp2goLRaTjWhPa5h19Vzz/7NbnJ31DBHYuwrfAp6xFo7y+ZKAeNICX1UsZP7jGrDk5Q/NEfw+e2EObVVcVclppdgdHu2r5sJ3QHFDUtMyM5ryDc1uHhzkfFHFJcqNjUFfvcAcKmpDl2fz9MfkaMpEma4tbZGtiB/XrLgDXDNzad47twUSSnGaOqEOQnLDt72AEePUu+qr59TzQHPNxk4oWQ6YVZp6ar7dHLnJcVz6rorn+iyeLvD5h6VdLL972PqOzS8EUG3ItCbOtqET7TuEpd6lBh6BMqm2nrrvuqmxpZAOsqb3mxDmCOdkCpH/TTI0mwE6KFBezJD/owqO0UQkZvrkD8TUT1USsXEoAz3YktgNWISXVPhmP9M4ztOTEKIdwCrUsrnhBBv/ud6YSHETwI/CWAUByh+IYtbENT3SirPC/yimabihjnwtoMWaEQDEa3JkLhtYjoRzITYJ3OgQW5OUt9hkliCxIT8XKw4LUB+tk2cMdE7ITOfDxFRQjBgY1d9RBARZyUiStCCrSRSd16B0xsHLZbeGXHT7llakc2plSFC38C0I0JfAyRJZDA6GrMxF2FbEedb5VTw2DhZJi5GyitHlwQdCz0XEtcslRtXijALAVFJsIZLlE3wImVwH+YlrV0Ret2gPdUTbgqIJ3prwoYNRgI/XYUvjbG4VuLiZp7rJudT61OAm3bP0o0Nnp+dwt7dItgNwWKWwnGT2AbvgIerh0ok/PeHIKsYxVqk8uayCwo70ELV8fr6ud2Uch61louuJYzn6qn96sHyMo8szLDn3tMcf2iXyuLrwujNS9S/OE6YEwpQNxU3CjTaT5fouJKoLmhPqpZ23lU3VvH05SGKy7dD/qNjVEIfvRsROwbekEl7RGGJuaWI1pih5BbBFm5XW84xcmiNeWeQiQcFdi0kKJjofnKZL/grh9FNLqEQ9LpvgURIyYn/+wCNH2qSe9bFrygJSlSIGJncZDy3lRP3hsJFjpzajpkLcB/OE+ak8kdqSEqnEvyioHZTgD9go8WwfLODFkBrOiGzpLG4y0T3FEk4zAqSSkjSNSEXUch71DezhL6G46vJyalKOqMCfUNl09UOKA9w68s5Bk6GaepQkrOIchZanKT/Zuc7KWZktOM04CNNt+5K3I2E1oROlIHONtjxuYjGlJN6RvHUa5sfvpuK6Rbge4UQ9wIOUAD+G1ASQhi9qmkbsNj7/UVgElgQQhhAEQWCXzaklB8DPgZgz0zI4H2bhLGOGeqsWS56W1cWorEqqbd9RUXhtMZNgqKJLCYMfCND7IDRSbBaSYoP6P5WrphZUy2sxNLROyF6S50Q4fkYa9C4apT2iMbmtRHbt6+RMZVB3OGVbcQPl2nuDdG8BH3F4tmlfcpl0kowly0iE5JCpHhNwwGbzQy54TY5x6dkeXiuiaElMOEhAh3Z0ZE1Ey0Go2Wj7W2ReBmIBbYTknN8Lk7qDFRavWMkGM54XKwVcEZ6UgEh2VwoUhjutWQLXhqX06c82HZPqNvrJPqxkXbd9m1fphXYXNzMY020CSYUOH5gapk1P8eQ3SIsqLZwYoA/GKMFGjvef4bjD+3CqqtJpV53KA9tsFbNU7S38CbHVe+ztZblxl3PcJxd6Tmvf2GcW37sOb7299dSPNuzv3UEzqYKiLDqqsOaWVLR7k6PJ+Mue0hDQ++EiCCieAykqafpOgC5+Qg9cHCqgaoADJfYEoRZDbOdqErtosGKPoAeQ22XQekMmM0YLVbOA0qc/c3dOujLaORl2sEgp6XVpH7nBtozFYwOMO1z3dA8oORDF7089z12FaUdNeKHy8pipirxBgXZlYT1Qzojz4ZsXqOluwF7Q6N0Oqbd0sleVF3K/AXVHavvS5AdnZHpKqurW4kk7oKpKiRbpBYmiQ4bN4a4FyyMM/leMrXsyV80jFaguo55C7MZkJga0tDQwoTMkkecMehM5cnMNbHqEWHGQg8UhQAJ//ZDX+S3v/JOFj8cUPx7S3nm5157p/87TkxSyl8CfgmgVzH9BynlB4QQfwO8F9WZ+xHgi70/+bve90/0fv7174QvmTWN7J+V0IOkR6JL0P0IIWWag663fITnkz93AWPHdsKxkopxLulsHpAUZxTWsXmxQOacowSU0x75R0vw1irtY0UyFwWJkU8JcK3tqvWq+6C1deaPj5K4MXPnpgkKEj0Pekvv2UvomONtbBQbXdvVQp7LMTpVZTVTwD7ncM2ds6kh3ISr3AxObAxzzdQ8a16O6ypznGyO0AxshtwWq508jYyP51uYeoytx0yO9xwFjRBdS8iZPmOZBsudAkNuSzkVVNyUILkW5BmymmwEOR51x7hxx3n1972Vum9/C6TZX9O5KgfLy8oKt1Wh9I6FNO1iT3aFx7qHSEx1yvJnDZwNyfH6bsJiksZ+T35JcM2vzadpGo3AScW81SDD5PZ1XmhsI8pKjJIksqUAACAASURBVJaq/Nw1ePBz11K6bYV2ewRnQ/lnhRktzXDTg0SlGIdJWsGKIEJaBt2RTIrp6T2bk9jRcJc9Vq/Po/uSwuF1kpJqgweVDNnFiM6YQ2wLnHUIyjrOmkZwY5PVG2H0zx18x0jlM8Blrgb9bly/aroMd4rAXQ/xAONzZZJ7mwSRzt07lc7S61WQ614OfbRDfTPL2HyMV1Hx9YXzCa1xhRvVdprkTyhu3eB9Dv4ArF2rMf5ISHWvSVhQFWbxXEj4VlXBrCwOoLkR9U3lt25XVbVkdBRNQXZ0hp7WyR+3iG3Iz8c4GxFmU237tDBBBCp1yGwGiCBCw0iPtwgidMDxtpKJ9FASOAK/oDPxd/M89P695C5oNCqWcgCtK/jhtY7XopX7BeDTQoj/DBwB/rT3+J8CfyGEOANUgfd/pyfSggQ9SFi61SAxJUklpFRuU6vmyL2YxxuRRHkXLdDQO+PKunZNYrWksurwBdFDFZr7QkojTRqdEokbY8y7ytfoXAkmu3R3RYS++sjO1Q28ZgYJ5PMd8oBjRBStLuyDEyvDlPId/NCg6HbpRr2/MyK6eQPHiGCgwXWVOdqDNv5uA1uPWO4UWFwvMZ2rMuHWcIfVBLG9UuVCp8yQ3SJn+Kr7NVilGmTxYpOqrzpbE5m6SmGFFCMCNdE0I8XvKlsdEgQLnmIBrwV5jm+OAqR+zV5spsJaQyRsdLN0e4/1QwRsPSKRIt3yebHJqfZIikMkRi9GvK7EoPGKRmNP0jO2N8jrXQYsj3Obyg96JN9kJr+hklxdVfXtuWaO2W9MA9AZUa1n7ysjvPvHH+aLf3I7dqxa+pmVEC1OMOqXRMNbOnHGxAgigrKLO98gHMqyOWmRWUuo7dYpnFNbELuWYPiSYHoQv2RiNmPsxTpx0SV/tklQdvGGTHIPS1rjYD6cx377KnM/YFF+tHc8NqMUZzK8JN3WGd1Xx060SOL+f9y9aZBl53nf93vPfu7et/fp7tkwA8xgABAAAYKLuIirKC4WtdiOEqVsOrSdSLFkV6riOJXKZpdTiWPLH+JIrsRiuSJLshaKkihYIimBIiGABAiAWAfA7D3Te9+++9nPmw/POecOJLoiCvkA61Shuud24/a555z3eZ/lv/z9LexfOYHxl/eJbs5x/uyt6ue+mfD0/ga7m3PYhxanvhKT+TmjDVNwYa7IjIC0G8IlzcYvWey+vUDeTxW7D9n4u5pjX09RqSZYsAiutMgaGV43RGuIi8zYnmhG7wlg26P+sktuSl+vlAIu5Uiirrham9MZ/ORP4pBKfBLMjCnc/SnarFOFDaV4ZnOdeiZwhWDRJXPghz/yBP/ku16xP/vxPQUmrfVjwGPF91eAd3yX3wmBH/te3jf1DXp32XQuyjQiOXAZdRwMg0pnBkR72z4WEkYWTisgAMzIxncTOAcdpNQ5fmGbphPRsCIuHi4RRA6dxpS2G2IozWvbS9w9t8t+rUE/9ElygzkvoGFH7AcNwtSi5sXMeQEnFySDqRfd4evTLuebO1VQeGmwyslGjz945m4++ODLnG4ecr6z+4bFH2UWk8xlwZ1wENVZcAvlhMzl1rTN6eYhHSfg8lDGxVFmsR20WPWHHEQSUDwzYRD5DCKfO1oHb3Cz6Mc+98xt82S4SpjZOAUq3lCaODMZZy5Kycg814qh8mg5Ynh4unnIldF85Q0WZDbpwyOWWmOOvrpKbgJIrylzBJoRd0Rc/+e//T7OndhmeLVTYaD6sc+r/+Ycf/Vvf5nXJsu8beEml+wTIlLWEtCmNuDRf/Y+siXIAsk8Mt/AuRWggpjJnV2sScbouFi7G4su9igjWaxjBCnekcY7TPDmDHoXFM1rDmakqW2FjDd8arsx5jQha/uYgwDt2bi3BhhZU4LWpOi/fGER96Njhh9OGF6rs/KEODqXwnTlaPwNz3eRLalcAtnJRo/ep2v0xz4qNlj1hxhochQ7QZP9XhNzbAoHzjeYLgpEIOpIY751NWf/IahvyWab1AxxMI5kyNB9NeHmB0369xisfk0yoqyZsnHigP1hA6UEMgCw/46MxnM1koa4qXiH0vg3w4I+Mk4xpylZzaq+WrF4Nd6OYXrD51Vg9qdo30EFMf7VmKSxUJWw6YFUCO16wMFKnR/54JPfy9L/9x5vCeR39/yiXvyH/yWdhqSoniXRO8sN9oeN6nXbyBkXoMEgcji1cMjOqMlifcLWsMX5xV1e2V/mVLdHmhtcaG/jGinboWxLvpnwUPMqV6NFPt16lhei9Tecx24iv3c9lAygH/tVSTbJXPqxMPRL4beLh0s8sHSL7aBV/a3S2qhrT4hyCUh1M+Lpw+Oc7+xW/DSQoHVlNE8/9DndPqTrTCv+W/n3n3xdtMatHYfcBH9fESxqslZaCeNZha73nf9HzOhkjaRuYEZv5DGpXJrAzliQ7kd3msy9d4cH5mc7fM5sFT6zv87eXpt6O6DuxuzemsPbtLEnsgvbEwEunvlLr3P1aB7XTnlocVPEz4rP8czuOoOjOqovJY3KQC9F2Fc9Vh9POLzHIW5rFp/NaVwdMTrTpHlpRLBWx90PmRyvUb8h9z6r2QxPuqKuWJxm3NYsPJ8TdiRaZI7CnmqcsehN2WPh4+WWwt8OMAcBWdtnuiaZwNFdpmCeFCz/yHXRVP8lDzMU44qSaW8mWhDojsKKNEYi9lPXPi0k8PFBnfXfNXjbP3iuuoZXRvO8/vwG9RuiCjr/YkbUMeifk+GONuHgAzHu1duoJL7G21NM1wpJklaGu23R2ES4j67i8P6CHuRndFZGpLnBdOJi3hCuqBVIayI35R4JjQgWvxNUbZHcf2MQKqdy5ffV64WmVunfWBrJph2PqGPTfG6bW5/eIPNlKrr/fQnn79jiwblN/vHbvvAfvoJlrhW+KxnKhfY239w/WXmrHdTrjBOXh7vXeX28xNmVPaaZUwm4Pdy9zn7c5I7WAYvOiPPNnep9M23QT2qsegOaBaNxN2lzyt2vvl+2B+wmbUaZx7oj2ZFBl4495YR3WAUZtyCRnfAOi6/w7NY6d9Z3WfePqBkxo8xjlHkyqqdGjuIgqvPsaI3FmmRL3/jlBzn2ietc2lkkm9hCrjVgd3NupsBZgOcaq1IOtb/lMrojJ/Nzxm1RVzSbxWSyEWNaOcnQwJhENK/N0nIVJmiv8M27bUeM2w6dy4rJaIVvsMInP/t1Lk0WiTOTFX+EgebBxZt0j73KF6/eW/UysrsjMsB8uYFVYB49M2WpMcY0coLMrsrMy8MFRq/NoVsp2sll+tjMMJCsa7Iq7sedV0UIrXdvGyvMObrQwj9ImWzU8A4SDu9rCF/tIBWZWwWdSyn9M7JgpwuGeK/5qtCwUoAE5qxroXKo70TCQ/RsrN0+vmWQeRb20Cya7DmbXz5B9717jFfr1PZnAnll+ZPb8pqRCM5JpRqjK1l086LN1vtz7kVVEJEr3zqOfXrMyPGZe94UE4EM8vmEw/ttjERx4pcU1z+VsfZVuPUhOP1rCTd+wJ25Ok9M5l8QntvCYw69ezV5TaaydjNiEjiYpkanBv5ukcHUqFylJ2vS5O5c5E8FpdsDEAhmqTxKNLfRH6Ndh6xTw9rtVz+3g5jMFhOQwX0J7pZNdA7sA5vW+bBaR2/meEtkTOsX2von/+17mOYOtdsNzm47NsNu9X1JuyitZ8rgshl2q9c2wy731G8yyGq0zSleAYkNc7t6bZDVGGVeFbRu/34z7HJptEA/9Nnd6mCMLSklmzGNesjRbgu7GeG6qUzegLYfsrkn2VCrGTCZujTqoUwbi/LqaLvFxskDNq8tsHHygJ1ei0Y9ZDzxSI5c/G2L1C+ma4WHmdsTXEqwIHSAaE7jHSqShtAekk5G87KFNdWMTooAnJEI3MAeyq4ZLGv8XZkspTXZlY/9kTSQwzlh0dd/cJZBXRot8Mj8NQ7jRpUF9GOfVw6W6e80wdTMLY1YaoypWXLPSu7cmfo+v/j0I/ztR77Go9sX2PvaMQEdtnPsocLfkd5K0tSsfDMjbhikNeEB1nel3zg4aVc25tGcOIXU9zKCroE91UQtg/mXQ4YnXfrnoHXpjc9L6iuaNzNUrvH2I1SaV5SJ5nd2SJfaDM7WqW8nRF2Lg3tN0poma6esP2pgT7KqnIOZNIgzTLD6ITf/BzGtaHy+zdp/9TpdZ4qBpmNP+cWnH0FZGsPJWP6CW5Vt2lR8+G88we9cvgfrqSbjs4lIk1y0SZqQtPJKDXLuJTFgdQ8M6tua1IOoqwjujHBuOEQrKZ3lEZPAIRm4dL4jG1CwDCiRaB7elWIGBvPPKea/uVdlPDALPrd/vf1QUSzO2O0m+uY2xuK8/BvITixXAS71TEYbNr37c6yxwUc/9AwHcf1N+8q9NTKm4sGvGfEbgtNm2OWh5lUuhcIBW/UG9JNaVeqMMq8iRYKoK3pGwm7SpmNPeWp4ipd60hSeRI6oYMaFwqCpMbwMnRoVz8i0MozX6tTu7zGeuuS5ge/HNOanTCwfrx7juzHT57o07pWezHx9ylavRRraDI7q8v6pIrjsY6UwtWsYiSLypQHp+pq93RW8VLFzuIKRwtiqiTOuLmyVEkVaE00lpaWpOd4wKhCkyiBYznEPDNKFnPZFuY3jDWjcUHQuSRDe/LCNPVR0L8YEBxbamImP+bsGe29XLH07Y+61kFvf51P7/BKPnVwhfXDEh0+9Viks7gTNymvsXceu0V+QpvxXvvoA7/zQdfqxj28mlflhP6nxwXsucj2c547WAZuLK8y9qOjXlEyXiqfOGSoO75Z/LH3wFruPrbHwfMj+/fWZTZY3C0pRyyC3FNMlRW035/BuDzPR2ANFbT8lbohQmTPQYg5ZE1DmdNUTmeO2gRVodM3DHAS0rkrmFHbEEkl50FicMNro0L2YVchwrQRHZfVDjMGEG/+sgWXkpLm428aZWWVKX7p+AXNgYR+f0PmNOkld4R/kBAsGw+OaX/v2QxgTE28C9r5FeixmdJcwCozYIFjLcHoGe+9NQcG0mWFGNuGSZu0PY67fCfHxGGKDwWYbayFAuTmZX1KAIFwoLLoCgxNfSioaTjlZA6D83rUkyHS8qqSTbOo2p5POHfzJ9r8RpCQtB5VpavsZw7GJGShROK3Dr34vAeC7HG+JjGn1wpz+gV/4IR67dJazx/Z4fWuJfGKBAV5HMpgsU3RbUwYTH9+NGU9dsnSGNcmnYvqXZYq6L4FtqTFmEHkMJj6ek9D0IsaRw3jiwdUauatpXhHSpdPXpHWp/aOOBgPcnsKaCDI3t0VU3poIyNAqbO9KfWYQFn7/nKZ2yyBcEI+x1Jda34ggmhcvsqQhSOakqSuZEWsq71vbydGGIlhUtK9mMhmbl4xCfk8ynuZmVtlXm7FwAd2+orYt99MZFyWIJWJtuaWq1+KGQVKX7CRYMAgWFMFaxpl/E5I0bXLHIG4YjE4YpDXNhz8uu2DdjKu+2OnmYRWQyqniWm1QfV/+vDye+sY5VCbBKFjJWXwKxhuSjhgxhA9P6P6Oz3TFoLaTk9YUwYIw2qsGtKKSra3t5VihZnjCJHPAfdch+t/Nk3liKFk2Z92BGFmmrvSHalsh/TtrqBwaN2N6593ZNfzQBNdNSRKTpV/0xeJ7mGDvjNj62BLtqym9z46xjJzV1pCWE1I346qEfXp/g8HER7/SEOfdkKokzE0485+8xtOXTlZuQKU6gBEJ3y9clHK9cc1ifCahdtUWLSerBJnmbH20YAg0IwxD02kElZZ8lFncPOjQrIcMjuosPFYMD1JdYfxAaDfAd0VqZ/b/N4I79UysMCP1TJGoHmXs3+8Sv3OE9w2Bp7zwz/8CuKT0I59vbR/HcVOu7M1Tq0fUuyN6gzpZapAUvZRJ5GBZUhI1ahFH2y0xIujb1HcMwEbZEAFpXXM1n8MKFArorydMjuZEDgNZsE5f/L+MTHoImSPKh25fzADShoi413Y0wZKA1krJkcm6BB5rIlMlI5Y+x+o3cganoHED/ENpeKa+0Fy8fXmPxiYEMoAT4nFK5XXfu1dhRPJQxw2j0v42I1EiSGtCORhtmOSOTHVquzHBggjKDc4onCH4PRnrjzZMzEiEwsrDSDX2RDHaMFl4PqT7Usa1T9fY/HAdbWnWHosxgwwjtcgtxZObDxLNKcZnE/7yQ09xpnnA871jHGsMmGQOdVPs1CeZ+M51nIBeXBOc1GCevb02ytW0XheQ7N/80Fc5eF+D3/zyO3ngPa9xotbjt3/7XTjjlCgoPOyWZPpHb+baUmq6Zx6gYXjCrJQPJpGDpwS4mNYUWdETGh8zsSeSpUVtg9STYOkfpgxOO6BnInhJz8NdndGNyqB04zNLtK7mDP+zIRZwfkFMM8omv28mPL51ijC2iSYObirPkjOQv5s7CqMIUG49xv12U0wwQ0hQs4lnosjaOZO7I9TYwnlnj8krc3Quwnhd4R4ZGH6M7aQksQVWRsebaR+VZqpXn9pg9f5dEpbf4LSsMrGxuj34pJ5sbmWgKb+W9JvSh8/I8krtIfMMoo5FbknJnK+aJO8a8dfPPcnnX/wI4dp311b/Xo63RMbkHt/QG//F3xV/Lmu2Q5oxUtJYMvI0EgkiRkSF/7BC+beRCuu65PJoE1DSDLSmVJ709VviHQ+zoFA+6GldxNgyl2IqI4AxbRVOKEUYFxW/IriNyn6Q4FPaVzOCBQN7LNZAcUsWSOoL8VTGtyJnktaKcW4kJZYVSOaWO+L9ZYXFzhYKQ3xwSlDjIDK1Tl+4VVaYYQ0i+udbTJekB+Mf5Khcdsqwa1XvlXoKI9WVHtLohEHqi3yvyqB9ecruw3XSmlyb9S/30bbJ6GQNZ5gRt0yGJ83K7+zeT1wkzswKI9WPfLqupJO9qEbNitkat+kNa6zND9j7gzXMSKzKR2sWnctCIK7vSa9p9Ikx7d8UwODouMH0ZIK3ZWPE4o02XVW0ruRMVwxa18ViKK1r1h7eYvQrIqNb6kaVtlNxS+H2i2tZNrUnErQyVzz1zKmi+5Lm4EEw1qYsfKHGzscT1r5oESwYZJ8UEb53HbsmOC1nwh/90tvpfGybUegyeWmOv/Gpr/BzT3wAs5FgXfKJFjJOfjHn6C4Hfz8nLJxFSk0Pb1+s3nP7NtusEKKFHPv4BPuJJpPjOUvfhGDRYPhgVA1GVC1jaWlAf+zjfLMJGjof2+bWK8usnd8l/VfLHP87r1VaWRWEBE3HCSr35GnicDT1K6G5fGphNhJ8P676ome6B1i3cXVujjus1IVLdzt8Jchs9v/BSax++Kb1mN4SGZMZipRG5oB3IIs4dwq300BhF0Lq2oLUBt2QkggkKKU1CWIqFc3spK4glWBkTWThZy6YSWGNFEr/xkBVTGwz0TgjGctGbYV/mJN6BlaYk9QNcTIdynvbIy2p97wQKnMbojmTpC4+8amncPvCfyoXQkkXMRLweymNWzlx22J8zKxoBPZIAm+hIiJZXCHspU0Z1ZuJZrJs4B3K+01WbcDGO7Kpb8fsP+Tg7xoYiVGcl4V7pAnnRE7VChTWVHZzI5VsECBYkixt55115l+K0ZZi/z6bq5/p4B3C9Jhm5QmNM8xYeH6Gkr5x5U5GGwaTe0N+8PxLVbnXiyUoTVMHxxQG/N4frOEUkiRxQ9DPSdMUlceOEELtJ5sc3qfJlmPa33JxvmMTLM88+9xDyXzMoLAvPwL3SHH6g4c846xVpbUzkIAULgriO1gSwbPUFxmW/lnZCOK3TZj/vZrwz2xYfFqzO28zXjdofdul9lM3SCKPhpVy99wOO0ETz0x58ucf5NM/9XW+dP0C4TNd0lMRn//Vj7Dyrl2+b/kKX7z2TlqvWhzdJecTLBo0NzOGZ1VhoySGEeTyvTOYGQ/UbhlMzDrRfSK9nFsiFqj6NnTELLU9N8ExM04vHnLdaeLvC2atvmmQnVMM7jDoRz4tJ6RpRQSZzfa0xenmIQdRnaOoRtOJaDlD7utucWm0QMsJ2Zs2uXtOJtslPAaoYDIHUR3fSrBUTscJKh15ELHBo9sAsm/meEsEJrQEkZJwKV5V8lqp4DhdKfA4GkgLEwBfFppWknnYY2l4Zu4skzFDcRY1I5F9qGyJMkhckXCwJxB7IjpmBQaprxkfN/AOFGndxO1Jnylz5RzCBYVKZaokdaH0jxpXpWzLLZguCrveOxSypjURdn5uF/SDTVG8dIYGgzPSY0jrItEBco6pL7tp5sN0tQjUoQDwgpzC006chL39iIP7amTNFOuyweH9kgGpVFDcqa/FuDEURns4L1pJcVuAeNZEUn33CAanhEDdvZhhFq62/r5J77xF5ojb610/OwatGZ1t0b6a4e+7bJ1sMVfsxgdBgwV/TNed0nWnHE39QgsIOpcyuRaRImoZ1LcFdxQuKLwD6b9ley6dz9xiEHj4/24eM9ZEXVnU6JmEhzMQ9nyQ2UzWNd2XdFV+xG0BKQbLAggdnjSwQhidk1Kj9bhFY9MnaouOUjpWZK6i9W1XSvgP7XGjN8dye1QtVoAXf/08v/Df/iw/+uWfZPGPLQYPZdRfdok6mr1XF/nCy4s0Lxzx4R94ld/+7XfRuCnONWK1Ls8gidxba1yUrms55ApnKKJ3INi1+qZieApWvhmjO1rcg1IT105Z8Me8srtCc1szOq7wEovpima8M0c9ha4r+lpuoXBwc9whyGyaVsRO3mLRHeOaKR17Ss2KsVReDZZKAG+Q2RXPsh/7LLgTNkcd7mi9USwkyGzW/SNe63j/v4SEt0Rg0kahZ72XEbUN/Bu5jMHrhjRMxzluAaMI5ouGaSYNxWBJdJmmK9I3SVpFOWRKueQfaOyJZCTDUwr3CFAUujeyKMuSyjuQvoYZKrSlSesQzeeoVLIPeyj6QkYi7qsgGt8ghgTRfDGST2FyXATDAjVzPQ3nC9nXhIKWINmCFUgQkl6TrkpMI1EYaQGYs3RlUJmb4A7kXJUl/aakXsPIEBWCdaEzJB0h4IJQQbJaRlJQQFQiQUlbkllECzn+roE7yGlfTTGjnN45Fys0ZMce5LQvZ9RvhYSLLtc/1SFYT1j+I0XzZsT4mM8xf0iO4pg/ZMUfcRBJSfbKwTKWkRO7khUcnRVrIWcIaAn4/r7GO5Aszkg17kDT/8JaIdsBhw9mLJ/ssVSXHtCiO2Y/avD63iLquSZPvHyG+Ssw+KEJlpUx9/kG6UCyIwfFL/zUz/JXfuPv0Dp/QO0XFwoXm4KiARU5ebymGJ/MMdakHH33xtVqsV4ZzfNDK8+x9eE2//n/+NMsIlK4p38tEbpNahB3IFpJCZ7r8hvD+2ntyXOolSCzQVQySi5i7uqZUUQmG4g9lADs9iTjjpZy9h500HHCeK+OUU/pa59jjQH65SaTVYFiWLmBfXLMYmvM3g2ZRpd8vSCzMZSugs1KfchR7LPij2iaISv+qGIrlFZjADuTFley+arBnmqDlhO9gdnQj33CQkP+xP/yGq6Z8od/7iJOjrdEj8lf3dB3/Md/T4TOMy1ZUr9szhmVxbQV6kp2IuyKXQxIj6hU7otbqhpHW1NNuDhDM1sTKQejzkxc3ztUxK3ZdMxIpNRJZE2JLlHxMzOQdD+tz9j3EjzkgcptCTL2WBF1JJsTNctZFhi35PXUn03tyoBUlqdmCFFHpnlJXfpowXKONSmyxlQCZ4lhWv/y7DPvvsNA27qa+OS1rFI7vH0KVH4tHVhyS64HQG1HyrrmzbTqUdnTnOmSWTlv1PYSzCjn5gdFGL9zEc58Towctybtakd1zZRHn7qvEkfLbc3SMzmpqwqumNhhm7HG6+fV9CzsGEyXVTVsMAPBM4FcY5S8FrcFMd26Amakq+lj/z8dEb3apn5DArAZS/aa1AXLlbnF16If1blUQFA+O2S5OeL6YZcfOfsc22G7ohW5ZsqT//cDnPuJixyGdV579Rj20GTliQx7lHHt0zbmVFWlWbigWftaQv8Oh/EJLQDT2MAMi2eyCEj2SJ6rpJ3j9A2ZBDfk2fMOZGPq35egXJFDNuopi90Ru5tzbJw8QP/cEsG8wfi4PHuTuyM6Tzrojx/R9CIMpQlTizg1OdM94Hxzh9fHS1U2dcI7fIMsc3nfSved2xkBXWfKtXG3mkgexX7Vv6qbMyzU5x/5/H/4PaZyBBzOiwph6iloG0VvRYLTZE3RvpyRW6UynwhfeT1d8LmkjEta0uwO54sAUJfMpRybG6mMrMtSUWUSnFQqTrjVKRUL3x6J9ErU0VDs8tqUhW0PVLWYrUChRrMGuduXkrKEB9hjcVItg1J5OAN5CEH+hhmq2eepy3kkDSAXDFB5wbxdA3xNbdMiagsAzygsoPO1EHXNF3H8gQ1Kk/q8YWFYE1n0wWKZpUhpF82Lu4bcD4v6tize0Qnpa5WE1qO7HJqbGauPJ+w84uCMM554+Qwfv/+Falcujw8++DLf/M37qL37kOiJefYelKw3t6G2PbvmkyXpt01XjGoC17mUEs6ZJDU5X3M2hCLzYHoqof2CjTuQoUPcUgRLJtZXOigXln7kBu9cuMpv/fz75W9a4PYliw67Bo2tjMEpk6O/VkzjcoNp4vDujatsh218M+H53jH6v7fKdC2n9YM9bo473NzuYgYG5lSxf7/F8tOa44+mTJdspquQvGtE3Uk4POwy2ShG/EO5sUk3xd+05Zl0BZ2d1aSskymetCUyT75vbOWMmgl6z0MBi4XWU+9okdrZmJ11k6QJd3/gdS7/+lnacxOSlsv7j10lyOwKwjFNHNLc4NWxeCVe7C/RdkN6cY26GRNmVkUC/9b2ccb7ddwdi9zhDcoKZijnVduaDXSMBE5/7tWquf5mj7dEYBJJ01mPKe4AyMTCSAuA21TKuLijqN/S6Lmif1K/vUFc9GQchVX4sWeexijE3FCauCWBKXOKHelQpmMgfyOtFcTFeowRcAAAIABJREFUQJE3ZxgUty+9mbRWiLuniulGSuOaJVO8YuRvhUIJKBnZQOUNry1NVmQm9kiyJVPNsrJy8pgVgavMguR1TV7KbUSCgQJIF3KsqYE9kSwu6WSY214VMLNaLhw1UwB3IDuzykBb0tsqp55J4cpa2xGNbsnwZj0vwUqZuEfCHRseNzn2tRH2xMEKcua+bfHM2jpLdVFQKFULOk6AeqRPEDl4AxmfpzVN86ooi5qhKA+4R+X1kgxq+emEvQcdVr4ZcXjBrV4vM7xoXmH3LJzRDBaAlkzZHcj10f/dPF+6sEFekyln1FaEXYOkIfdo6/0Kc2WMC4wPa3z8bS9ybSwsg5Jg3f+9VfR7+9ipSZKZbB22aX/LJViWe9e4ATuPWKw+nhDNyfP41849ya/8iw8zuSuvfAZVZmJNRH0yrUsJZ07lWtgDydSnxzSNm+XCUNXzmI1szAysjQlRIte1fv6If3TqC7z9v3HYyyb8zI1Pok1YbQ3ZLGhVHRu2wzavP7+BGSq2b5sIOgNNpOEggeAviYwxiCRz8kIbu2jG3x6UWpdgsi5BaXQ6Z+4lxWRV4fbhRK3HKPUqsOmbOd4SgUmbMrI1EkmDzUgWQtyWjEJlIp5lTVTRa5HSxe3B8Gxe9XlKSEBulY1vcU6xAjCGkhWZ5cSryF7QsxILCgfaMuAb4itnToumZCq/G3WkDPO3LXKTmSztVHStrVAyujJIaUuT3IZXEosfQYILUFORtLU8vFqh0qKkS+XBz7xCk7oo4YxEkTYE+R3WdDUSR4F7YIrPnSWcOpVIf8mIZFc2p5Lt+btGBSS1JtIDkc9R4LWKYGoG0lfD0NgDA6sYMEyPCegvXK6R2zBelQDhWSmLrvjcvXi0WpV071+/zONbp+jfL8BBf6QIlgVcKaBITbCoMCMJ2Fao6DsOtV3N4QWX+o5gwqxAE7UN+vekzD8rGYhYC4m2U31bbK6itpz/4Sdr2CMxLc18GyMB91ATvW+M5yRMdpp84OQVfDOhtyQPx8lGj37ss/U7J5h+zKH+EcEt7Vydx+6buCNFuCATzen3j5megXjqcHTgEs7Lff6XX/0Q3CXmp2ZgYI0N0ob0Tv1dQzayYuHHHnhjqswks+UeqOJ5238IVCL2UXmuaHoRz752gsb8lL/72l/h1ivL2AODpXduk7xrxGa/w+2x4fXBIs3Lwh+MutJL00oytXBensEfPPkytspomiG9tE7S0uhuTO1lj6SV4+4bBMcTQDIoKxBwsj3OhTqlEQ7qbVXHmzneEoaXAN6+QeZpwkWNEQFK0txwQTSX5CIo6jfFvsYKpGzydg28Q1lsKitKr1QWVubpqncSzmuiboEbigt2fIFNMmLJoqypBMHclgVpDxTerkH9lioClxy5q6uMJGmKV1fmUrmbAtV7NW/oN/SmMHTVD8t8OScjk79lhhKU07oEI10E2LLRLv5geTGaLFxBMhjcpTHDWV+M4v/LnZysLRpDSSeTf9dkKlSWcOQljmYm6SFTTnmPuC3XWqWSzYUL8prKhLs1Om4L4v18Uf6l0ovJUdzROqgax0FmMwkc5laG5I5YZ/t7VI7GcVs+uxkKBaXs+ZiRprGdSa9xKkEpt+Guc7fITei+NMUKxRiizIaMBOKOYnxSeoBKw3/93t/FO9A4Q03ymSOyTBFENp948HlWvQE5iq4z5cpISNsvfPE80zVRs2i7IadaPXG9qWm5556YVEQTMeC0tpyiLSD9xuYVY6aPncvzaU0UGNIvdEYapy//gUyRw/licprI/VC5oMHz+QQaKSxEZKnJKHQhni3d+qZBY1Nzc7vLcnsk3E1Fxfs81hhgBRpnJG2P1FeMTsPRBU2wkjNZ1xWxvW1OOeXu4+0ZuJc9rKkMdswYrIFF5ircngSi0WmRbAm/b8Tw4ZAXJ+s8NTzFH/TOfa/L/08db42MqUgVa1sGSUPY26kji1VbYAyLUXpDspqoI9OvEjGb1GcWTpk3ay7bIyl5yp6OGRbAyVpR+lmgdcF0KMCYxZondyXoOENV7YK5Lb0aI5HeFVA1pHNbVDGnx3Q1oUtamqRuEK6IA0sJRch86SWVfytzqZrkKpuBRMvpHNNZ89EoHsjS8y1paVqvCahy4kGykIpQfaSqHlrSycQ5NlLVxBEKfaSpkvG8L8TP3C56GxbYWXFtE+GCZL6uytJSe6SxlZLUDGq3TIw0Z/TYEtEP71SN1HLs7JopWWoSRI6UnA2RTzEDVQFby8a6O8gJ50zcgeCOSuyRysVkAKD3r4+TNxX9O2sYCWKoUHy2ANkYWpcBLf2kf/Lop/A/MSAIHNb9kE+ffIFMG/SSOqbKq+b29hdP8Mvrx3nw068yTmT7X/WHPPb6WdxOSHbUqHzkwuUcY1CoN5gwPq5Zejon80yRxz0wiBaEvzj/UiS62gW6GiA3DZKmyfSYIK1P/F7E1rt9kpoE//p2Ydde+ADmU1muQ3w6a0PSXHh6jZs5Bw8o3Ksud1/Y4Q+efADlUylmJNrkqXuKja4VEubiOFTz46p8+63dtzGOXd63fIl1p8cnf+yP6VoTnh1usOCIlMTthp0Av/vSPXz8wotcGc9zuiGqGzcnHT649Cq//mdd/P+e4y0RmFAzoKQzKHtMM3+wzJfg5fTLqZUsutG6prY967eUchEgD0rUFenc3NYVdkmlMpovMVBmIOWXGYF7UHiotcogKNCD0anCvbRoYN8+lTMjOXdtSRC1x6rixJXByz0wSYrJX4m7yjw5J39fehK5Bfgatw9mXVX9r3Bek3Qy7P5tvEALsAQeYUQyDAgXFCvfTNn8mIJcAlDmyQ5vBgaZn6NNwUDZQykXzemsvMytIhPSVCYEmSvlynhj1rQ3keuoLem9jY9ZNLZSwiWDzuWc+i14ZnedB5dvVhgYoOo7+G7M1JdReNQVoKq/UwTEAq0dtUUDCSQgeUf5zEaqbXDwAQFfxh0qz776zWIx35BSM7elJDr746/yyv4yx2oBW4dt3n/6Eq6Z8lTvBA07YsGd8MpohaYV8ehT9zH3oUNaQNMS0cBrry9z5cBE1TWMfXRD/OPskSJa0GSNDKsvy0iawRnBssIeGoTLOccfTbHCjNG6K/y8Lan7SyqIGeasPp4Tt0zGx1zB1LWlDeD2c1qXDY48syLRKidnbbFPnJmkkcMkcvif/+H/w7+8+T7GscuzB2tEKymNS3YlP2KrjI+/51nqVsQpd58XJ+t07Clfun6BhxY36dgCjSiDzoo94PnJBpPUZWvcZqE7IchsNryQflJj3hmzHzf58fu/RS+p0wtqnGz0uDKaZxB5TPOZisGf93hrBKYCoGgkCrPw15K+ijEbw09VdbJ2MUCxpuKgUo7ly6AEggY2AymvjAS0rQugoZR7FFSQaF6jCsDm7U6lUDS8W/L3VCoLUVsa96gc82uCRVlc0UqKu2MVHmcyWXMPjAKRrrDHqgq+hkYwV/VZdlTu9uMNyF15+MN5LWP92BAraHP2+0knw0hN+ZyhZJSbH6MKYGVQAin/yt6Tt2MRrqRgahqXbZI6VQ9LWxoN1G8q+vek+NsW4w1oXYHhaQnktxOP/UNFNKcwEou5l8Dbj8gtj5MLe9U1XHRGVWbytuM3eWVX8DUlst+ISnS8wh4IEBGk3E5qgsgPFoyixDZIaoqfeOBJvvD8+yvfOLvQhqpvS0artCb/wIC5+pTnbq6RhjZ1N+Zz9z7O1w7OcrLR42SjV2FxFtwJjz51H43VMettgZh3nQnPTEVIMHPBGhuYYeHUU5dyTqUKIzCrNkLrRs7mR2dLqnnZIGmYHLzNkUogUyQNj/pORjBviq3UjtzQvYcNlp7K8fdz4raBEQmOzz/IOfTzShEDoOFEbA9bWEaOa6f8T//7TxB1FdP1lDvv2mKH2XMC0EvE0w5gkEnw6Sc10tyoglJZ9gG8Gq4CkiGNQrdqorfNKSPbI9MGz+yv0/Wn5Fqxe2uO3+83WOiMOd46Yj+e6cz/eY+3RGBSuhhVFz2g+qYCrYjmZZGLlOuMy5Y70qwuR/JGAlopafgWU7xyqmQFBR2lKFmcgfysfqso0ZICOqCkZ2OPVdVrmR4Tt4pyAZixZEZpTRfZ2Yw249208feLjKsN7gCieSCeYaxKOEJuU5RMGissoAr+jBNY4loyT9O4bhA3pewqzwNmC8TpKzqXY/ZbDsuPG+y+p4AEFOVffdNgulLCJqSPJ2VlUYIWAD9tAsX7j05p3H1LcGWpYnhaAiSBmCVaU6ENJS3hHhqpxh1mbH6kjtsTPItvJqz78pBGmcW6f0SUWcSRRfuBQ4Jvz+MdyP1L6kW2OeUNqG6A5q2MnUdM3L4iNuW6/OtvvZumkkBUlrv9O6FxTw/XTrGNnFuvLHPs3iHvPXkFgMvDBX7u69/PiTN7XB4uMO9N2Jq0ufHaMgtPG2wcZdz8jM+NPzyNNdVcPH0KgNpodp+zqiVQ4Mgo5J4PLFa+mbF/v4V7KPdt7mJO74Ji/qWUpCEQjblXM4xUM1oT38DD++HogoG3Z2CeHDHZbApUYCgZaTivSD3pc7idkGjgsbTUZ7PfqUqw/tiHD45Zao9pZiamkWMdyaR4xR5UQebl4QoHQYP9YYPF1piGEzE+qNM8E9I2JThNc4ebwRxXsgV8M2E7a7PcHFUKsCWma9EZcaG7U+G7+ms+Dy1ukqNYdEZMs78gGZNWAqxUqZRo5lTLpGUCcUdGqeGC9JW0JbY0Kp/RUuxUMC4lEVIXkzW3ggIUWZEnD5eRCk3BDASjVOKM8lRVOs/OYNZ78XsSNNVUkOCt1wyi+RmkoJSmCBZlbCoPsfC6SrIxupCWdakoLk5xvpkv55cspNA3SdqSHTk9QxDtI1kI1kQCjMoKYGQkiOlSN2iyZtB61aia8OZUsi4AclXtuGW2BYJnym1N1MmwJlZhdAnRYgpKNHasQBG70qvTSgHFfQqE9d65JEoG2tQ0tnNWvQEvDyUzKh1cbgZzTDKHPDTppw2MhgwNtCnXyhkoxhtKQIdKEbdlDH9wnzVDuxeN4uRcgjZtwnnF9O1TWo0ANzNZrE8IUhvfSvjAO1/k9cEiQWZzGNa5+Z1V/vtP/Tr/6vp7uNGb48YLx/H3NKuHOdufiLnjF3LWv2BTv3ZEuFLHOzIZ3GESdXQxRaWSqimHC60rME4t5l/I2XqfUTDyReOqf8Zi7bGIqz9s4fRkY9n6fug+Z9K/J8fdN1n9elbgwjK2Wo2CsjQjIE+O59gDQ5gKU6eatE1uNamfPmTwzSUw5XnYp4k9htM//ArpXEo6B7+89w66zpRFZ0TdjLCMnJX6sOLObRw/4LWJYJqCzGbN71fXa96TDGuaOBzFPp6ZEmYWjplhoNkOWrSckGHsESUWv/vKBXRkYh+WIeXNKTK9JZDf3rENvfr3f6Yay0OxuJ03MvutKVXZYhQj+qRFxVvzDmTBllQLlRY8uLaAitKG0C5g1nAud9zS6TW3JTCVJUu0nOLdst84+i8mdPZQVVlOec7lYWSzKZcz0CRNVSHOyz6TNmc4LW2VjfXZpApmTX0owKbNrMIjlSjhpWdC9h70BDE+X6DQi/cwohLmIEBPpWWokHny+ZMl8RwzEiFQT1dVdV1uR67HHWmeNq6LlpMZS0ZZvyGNdzOE5maE0pqbH/B598efr4CW5c5aNsS/duUMatPHHsqUKm7JwMPfE2zN4KyU2Q2xZiNzFMOzOWvndxmFLm0/5MaW6LJbXsLppcOKF3Zr2qbrTtmatKnZMa+8vsZdZ7a49O3jZPUMey4i3/LRJtRvGIRLmpO/NWW6Khyv5qUhKogJTs2x/R67AtmW97I0VEjaMkIPz0aoviC+zUiy79yWTDLqSjuhtpez/eEMNMw9Y9G+mnBwn4N7pOndn9PeGNDfa2IfWjSuSd/MO5RruvdeGWZYE0W0mrK8JmCv6VeX8A5lAx+flMzeSOHsJ19n+/+8g6it+MjfegKAUepxbdxls99hckvKrPoNEzMoIApF+9IMBQZgJFToeCPRlTKFPc6IOsICyFxF3BBAa1qTfq7KZcL9Mz/6W/zk+a/9BUB+F0fZiC1xQX/yyJwZWjua1/g7qgJFOkPJpsIFXS1ElVKBJ3N7tqiihRm9wwylvNMmleSKUGJEk8kMRGokqYO/J4DDMpOCIrgkEsgkiBbyLMVnyF0h4LqHM2wQzLBOMwwVlaRJ+RnLyZq2ZvQR98CsgJdpXeP2FP2zLklDzj9uyYNlDwySdk7uapJOjrdjVaz2pFk0/kOFv+uQtGByPCVumYCeDRGCQiPIl2wt82C8IX9zekyytGBVY1+WftvwpIuRaOq3CsWC29DfIDIohtIYRk733n16gzrGa3W6LwkfsndeTE6FWgKNH9smTC0mE5+11phbryyTN1KWTo1ZX+1VFlGOmeGbCZPMqdxeut6Eo6iG2Ui4+fsncBW07jlk5+o8zS2D8OEx8486HKUOB/fVqr5ZK0xAiV9dWhdpW7GZ5w3aRkYkSHzLTSF0CmFBcc499VsR+/f5TO4WdYDGlsa7aWOPoX9PBoi5ZilH2/kXDdyfPsL4ygLjdYE7GInQfkgNyWodMPyUjhfQD31a1zLCrjgH127NNluAyaqB19P89q+9W8xVp6IX5ofQmuTFNDjD35dyMPVMkqZJ0DWYLimC4wnKzzCsHOuSTzmBNSKzeEZnUIiKPbEvcA80XI0Wv/sC/h6Ot0RgUlrwSCCLK1iUh/9P8sucIQX5s0h5PYnQdkpB3gXHUFW2FS7nFcXDyArU9hjMSNLjzJm97h/oqvmt0iI4qtvgA7aAGMuAJKWhLFiZZFEBMK2pBE4zUDRv5AWrfMaH04ZkX5mnMZX0bUrlTCMFa98gWswrPptZLAK74NaVaG1yKQlVX9jnu++Y1fba0pCXQdIUt9XFFHNo4vSNCkIhLiLSsxLe4BsztrLPpC2h7pgBBCtFoHILTaFmAcXIYO8jCQuPOUwypzI3ADiKfTpuwDD2uOfYNi9uSe8jWklIHxjSn7qgFd32hEnkEEUWh5MaG50+HS9grTZg/eE+HSfgD6+d5ftPvl5N+vLCBOAo9gGHBWfC1566B/fQoLslQ5HDh1KSfgNVy8htC+ulBocXRFgOmImetXzM/QFuP2H1Gw477yyvRaEgOimy8GKP8Z6oE87LNYvmwB4aHJ73GNybMP+4Q2MrpX/aJp7LiU4lODccpscgdzSdFw2SlmLv7Q7J84uY5xTHHx2x+0iTsKvIbRusVMCOlkYBr15cw10IaDULHXGj6KHGRRabOtVnqu1Jz5OP9ji3sMuVwTy2JV6CvpWglGbzKycq5gWAMwLjqo01tatAV8oGzRZs8YyZxWBgOvuRkWge/0ePAL/2Z1n6/97jrRGYsttKtuJDR13BEGlFhUeyC3Gz0pKolOzIfGmKR3MU2UDBiTostGSKRZg5VJIS+W2buRnJRK7s9ajbqlszgKgLGDNcU1ZMlLIiuGhLcFglebdEphsWHNwvgNDpRop7YM4a6poKy5TW5By9Q0WwJABTlVH1lECypdy6vZFeZFG5aFBpS4wH4o4WLh9UKggqAwJF7Yp86KQ961elvmRI9kB0r420FNcTeECJQlepKDjUdlSFQC9pQCqjIlqvfsnCCjMu/so5vu8nvs1BXOdMfb/CCZWea/cc2+YgaNBYiVj1heneC+scqw8qaV6gsoMy0JWP3veffL2S9QWx5apbEc89f5rOiwYXbcVCL8dIc1p/a5Prh10YOyRHLuZERO64d4Tz+w1SD7yjssQuaD5LbYwgxZqamKFJPC82UO6BgDvR0r+cPDIlH9SEUjKWTSBpa5KWYuNLinAO7HHK4vMpw7scWs+4LH4nqPBLe283ibsZ1tjAHki/8+juBo2tjNRT9C4IfcXbN0gfHGHbGZO+Q9T36P/AhCw1Ma2MU4sCC2i7AZbKqX32Ei/+8RkyT/Pww8JfG6cuvp2Q5QZmYZ7x2s1laiUd7Lbgo7KZwGIJHi7XSXmUr98+kAG5lqX65Zs53hKBSStZRN5hwfYuJF2sYkqTNApSrC8Ti8yX0mPxWyZhVxEu5lUvxitAgiUtpYQQGGmB0XFmGVhuASGYmZBzYwpZklh+L/UBVVJApCEdmQJFSGu3ATnHovuTejPBu7KHU5Z57oHwo8ypqjBSZWllBWAhtbo1kexIm5KpWJMZZ66EDOhIVRmLNqC+HZPWRf+pBFFWqggFBSX1Z3ioZL6UEwBOTckL/XNrUxEuUvX5rGBGUhbcVyHA1p5hxsJSx7yQqMktRfPamLDT4Gv/9u384I//Mb2kXgWVEtdUWpefbPQw0NxK29w9t1MJzAGVUiTAVtCqWOzPbK6T36qR+bkoOX5ZTnJ9nHPzIzl33n2TVy8d4/zZW1zZl14UiQF+RgZYgUl2pcE7/uazXPrpuxie8gtaCxzc36B5I8G/eoQXpSw9IwJ2O++R+2iG4jCT+Tn0JUPVtiae0+T1DO+mzfxLCZsfMdn4csr1j3u4PYW7L+XU4JTH0d2w/tWEY3+UkfkmNz6dApbQjHoiNDhZM8hqwrMLFyEbeKRDE2ttSjJ0cd2U8dAlN0xe6x3D37bYZCZB3Eg001WDp566k7c/9DqemRAZFhgZg8jD9VL8i7LQqh7TbYGnzKDskSiqmqH0J91+TjgnUjrlsfKtAJXkDE/5WJ/dJc5M+M3vIQB8l+Mt0fz2Vzf0qb/+94TUGhZKlcX0zN+VBRguzUTby1LNLDKLvJDUNWKYrumqfwQzeoc9FAiCFUi2FKwKTscevlFVwJoUKWoRHLUh8r7lUcILSi5T0pZxe1V2hTPEtbYETR23IVxJqW1af2r0XAYdI1H4uzOOoLZkauX0JVv8kw3xMvh4B2JMkFuKpK44ukeCX8kbLFUUSgkU+8ASdYFQstKkNUOFG6lkSnFHPpMVqKrfEbel6X97yR3NC4+w7NOZkdAe+ncplp/KCqVIkx/6q1+nn9Qqv7n1er9Sudwat7mvuwXAQVxnGHucbhxyEAu/5kx9n2eONrj40gYLT8vfNiNN+3ObaK0Y/9wak1WT/+hzXwbg//rdD9M4d0QQ2cw1pcbYP2qSRaYEp1Rh903skWLpmZhrP6pY/YpZ3YvcVrSvhtjbQ7TvEK7USRomkxWTYFkmhZNjkjFGCxnmxARDkzWzwulENjVnIAKBFX/TBm1qcgfW/jDh6C6HuVclAB/d5VSMgbJkB5ieTHC3beIT0mAve41lT8kZSfuhgq3EVLr15fpAwbnPvAqIDO7l59axj0+IdmusfH1mG545IrkMAvpMmibG5/Yw/6kEdqVBpRp7FKPilP13dIg6qhAJjBmc8ui+NMbc69P/eZsnP/a//gVofmsqaEDa0HQuKuKm9DWirvysoqcUpZgZzUB6pfli3AJvTxqFKhcYgL+tyF3p65jBLAjZfROtZmjnksIyPCsj2pKR7x1SqRzY43LqJ4qIcUsCh1aQF4szbheyEEWmEawKE9/bsSpskhmrCtpgTWfN73LM7+8qpscKOkkRlMpdrcwMdYHZShrgDEURcv8Bl9zJsIeCFJaGtZRyQveRxn3UlXLP6SusiUXcyUnmU9xdi3Axx9s3iFuSZZlKPl+0IPiouCPv5+8qcntW1uWuQulCnvbZnLhhYE9zoq7mN3/5vUxOJTxyz2XmPJmeHcR1TtR6VeC5tyPBaXPU4dHvPADA3EuKK8ldDE/D6gs5SheLyFbVFO5bp44D8Plf/QhxN8c5Pabfq+PWY061elwddtF7Hji5AFVrWXVvju50sA81zatjju4WY83262MmGzWghb09xAplh6rvQOaalT5W0pC+nLYLpdDYYHxvhHFo07hmcPiOlI0vCQ5pcIdB+1JO3FT03x2x806PcD0hrTnUdjS13ZxozmDxO1KaThctjEQTLpnyHMZGJalrDK1KXSEpnHPssWT3JXzIiMGaSEmZenCpt8BKc8Qg8uhcVIzHTbqbGiPNC9J7qYGvcAYpmW9iRpqbryxzOpwh1bWtULHYiTdvJKS+Q9wwCOc85l4LxXpcKbauz7+pcABvkcBULjpvTzAywULZdAWTQp9mMstikoY8HPaIQsydqikso0553ZqqKvBYEwlKpUtpOeIvuXVWIebm7RqVfng5pXOGQuItVQVEXbNoiLsFCXigyF3JjMyJ7Mhlg9pMZ/pOViDvVfaXSpG6vJC9hRmXD+QBLK9P7ohtr4i7FQDKbc10ySbzpBSebIgTiTWZceJyWxrrWW2mMVVOBVUqqGZrLONmb18xPRdhbTmVblVaozLiLOV8o/mCIO0Xip6FOeV0RaEyQ4CEnx1y8p/6xC2b7iuKiy+do7GVcRNpkm4hi8GMNY+7G5iRxgLWyTHiHLcXieV5SzKv7itiZ5U0FE+8dpq3nb6J9Z4e/V5dsiEFWWpgWDlJZLE9FR2X9ukjlhpjXr2+AolRZazBXTEnfkmhLYP6dkKwYDE+IRbrrRdHaN/B3hlheTZWx8M9MhivOYRdhVsMJPIi4Hm7JlGqqG2JxpMzsuidVyw9HeH2Fdc/ZeIUji/6wgjvpSaZS2HWqYjncm5+0CRrZtSuKepbADP0vnFY9AcXUvIDG2ekRaNqKj3GyarAXUr9qtyG8ZmE5fUjvKLhHfzeEoYjduWZo3CPCndeW1UyPdpUGHGOyjSnvyCB0ghSvH5YuTnHbYfhCRujUBddfHZM1HWxwgztvnlwJbxFAhMK4s5sGiagx+JHhSxJOE/FIxKxtlk2lbnQuCmpc7kb2mN5z6QpE764Lf+/Nqk8vIxIUb+lmKzNXCrSlvx+Ml+43bZm2ZS2pKwrv4dZBhfOy+uNa/KDso8EUk5qU4Jh2fPKPEGf54UJAL6u+kLOgSIsfNEMNBSlWSkGUQUqSyQ/nGI6NTquqsa5ADHfeJlLNLhIqRQ6TwU3zgqkeR4sa5wbTqXG6QFqAAAgAElEQVTKUPri3S5yZzBDqeduLgGfEpmvCmMDRfef10k9xe5PBJz838DtW2+wBjKynKM7PWG81xWjuxJUbLD0hJhEls4s7Ys29kQzOGXi72usqWb+jx0u1peqz7ay0WNnp0MSWpAYmA1ZVJ4li28QeZAYWEezR95wMm583GP+ObugPWlST+RBtj66xMqToz/1qI7XFfVCY1vuv0Hu5Hj7CmsiuKjFZ1PssZQ5ALFv03rNxBlqJoEHyhP5lb7G64nvW/PRnGs/qmi/YDM9pmGr6POcnGDeEsxL1sywe1bRyhCRxKijmK7I9LdxKyZYtJkcUwTHMjZOHIiDTWqhlCZ3JfNvXJ+SNhycwynasVBxSu5a1dCnzIqMwWzcpn0HYzAlWW0xXneqchGogpI5TcWGffDmw8pbQvZEZQWosS0yJ/ZQV5KqjU2NGRTN67TQx65JBlDq1ZiRpPfl9ECUCG6T87AKMGGZ5bRmgShpynvXtjX2REi1RiGfErdnygS394TMWAKiU5BhVSpyLElTS2ZmUknuarPoCU3lv3LKJZmT/O24lPp1ZQoWt2e9MbQ0r8vspwouriC4VSbOLqVyQln23d5nK5vZZdCuyMSW7HhlpqYLRcUyszMS+d2kXrz3bc+bGSribl59HqPQjtIiREDmwq0POPTOOSz9ok/UdQvUOGS+gdMLsAYRndcj6rs5zRsZ809ZtF8xma4YHN7j0D9jsv+AQ1pXDM7C6HzC3veluMMce6xJN+v8v+2daYxk13Xff/fttXR19TrTs3GGi0SKFkXRijnarDiyFEuxJUNxDCsBHBhOhCR24iRAAgmJAzif4nwwYgOJFTuOLRnxIjumLNGbZEtCvEiUKHHft+Fs3dNrVdfy6q03H86971UPKZHUDDntpA/Q6KpX26167513lv/5/+84ep7jR7bZGTRNDUkiJ70esbrTYZJ7TFKfuSgWfqrCYtwUXBBJleZGTrRdEgxLiWJuEhT39q3tKkqQ30+c0mhFBCBUAY01U+/5wAbjoyWlrznzAZ+t7wh4/n1RNaw7WZTJgGxWJgXmnszJG4rxkqgLD477ROf9Ksr2R0J3kg0DGtfvUnQK3IHRgIs1zXVBjWdtmHm+ZP09GRe+OzIlB+g+XJ/agVsQZz6jEwWzZzLcflyp86o0p2gFOEmOSuXPbp+WFM+7EbtvXGS0ErL4V+ss/tW6NG1iKAMHrzepXnvTr2x885P9Zdq+iJi0MgU7cxUvAzm5wx2JCCyIsSKOKwUVnLVgdFxLCqjqjp2Noqy6bemD49XRijUvNlLcAYxXTMTUNrLQLlUR0haFtaIC4gltbq1kEh+SdDBelvvS+pe6lHQBpXUfbkstxhvLurJGHdk4Bjmczei66G0jrIZhQcy8WgCh5xL2hee6sZ0zOOkLMdnYnWJcqBsJFsmdN8DxzFDulBRW2jFpnRFHsGBNTPFWAKLmd/R0tQ43kcaCO6ZK64pIRD/TDvRu8LAaf+GW/LbrdwiFRGBEJryJpvCF58eNDV6tb8ZVds2sZEOihTwqpWi7kvLA3bcQ9jT5O2PCNU9I8lxJsZJRQDS3yyT1WRvMEGzXJ2sRCfeRQEoK0hlxRqUnHFrpjCJH19FDnOMBi1/usdAIGF3X5vjnCvqnfOLDDtu7TWaedYiXhSHU0unGix7hbikodq2JD2sWHtBs3eqx8EjOxu0epe/iTeQY8cYCyRgcd9FpiTN2GaZtoqWYxA/o/mXA9u0ljYsunedKsjcN+c4PPcHXfuEOtKtJ2w5OKhf2tHCJvJxJLqe5txij8gAd+bTO1NGgO6q5um0EpeKpbY2ApOvjDwuythkS7zbxxyXxgsMEF8c0K4rIIexl8PjLOPG/he0LxwRUZG2Wn9tNpA7hFJBj+LNNkTvpWri81KWytimEp3WKFvYkrYlXNNGGgmbNTWydS7gtemN+3wga5CbS0KqK0BpGyaP0Ld6oZhYcr+jqZAUq9stJWzNZsHUlRWND7RHMBCrBSFu/srWfwrApSBdHfofGJYfhKVAjwS3Z7WiFPyrkAJn3DHWLwBJsl61S+vWgMFPwFgtlo83Sk/Q4r6l2AMPoMEXfrEp5fh6pPcBTb6wYncjpPixfMD4kz0079X6zXUx/rJnMq4p/yR9PR6I1bS5G8y/sa5KuQ/fplOHxoHLiYS/jxK95bNwuwEjHKVG3DPGfaVdd0vmvuvQ/FBEnPo0wo4hMjdA3UuVNTbJQkjUd8giCjCpSt+nmpbd2WXhsgteb4CQ5l961RPfplPGyS7Dr0Dud4l0M4EyLwdvHwstdQLmQ4Wz54uAaLv2boDic0Lk3QjtC4zJccYXbuyGyXU4OnXM5wxWP4XGFv+lRnJjgewXZ2RZ6RsZa2s+5pLOa0YqD+2ibP77wZprLqoLWSC1OoYF+HKGURmtF+0stvMlkTxRoo5zp+zrw5E+Bk+QMbuwwe+9FdBhgRd+9Sz1mkhwnnyHp1Gq+wU6Ck1wGbvo27GWlckqpM0qph5RS9yul7jXb5pVSn1dKPWX+z5ntSin1C0qpp5VSDyql7njJ9y9rEFcZSCqWt6Sol0eGGraowVyd58SpRFvSUm+sCxOg9oQJM+xJ9yzrAKWhGskUOFqu/h6gpQPn5PLeoutmBnvzqeK3I++Tdaj17jwhTRPebi2UqaGu0kRKifKCngNKk8yLFFS8VJ+ERWRwUphitJnVq36PyOrCieaZMjAFkNTXzvrlRnjSyTXhjsAHrFPKW7pKzaQeJGle0dQGSFkTv9l6QWk6n1agIO0IVinoyW9t+czzlil+m1rUzDMeRUNArl5sam8mEq7qfiOZmBd+c9lmxzy8WDBswbDEizVh36jkNBWzZzI23yQLLCJNuJOTdH3cpCA+VDI6UZJPfNILLSxxUdEoGR6V79aMUqIgI+sWe6AfwbbDqU+nrL1dUQbSZcwjgSM4hTipYCDYI2vL9/QItsbMPTmhfS7mxO841b53zjZYvge6jyv02JU1nITGVsnMs7D4hRAn1YyOithC3pLZQFuqyBuQtl3aqznKOFc2Q/KJ/IDO2CXtKGbOFgK8VXDo6xnNCw6tiyWt1ZKZs5rxsksyqxglAaGfE3gFeemIvNdY0jULZLZOqAzFWdn/IE5pdHKGzkObL1rUVnFagZ0bz+1UzltNrlwi/JXUmL5Ha337FDbho8Cfaa1vAv7M3Ad4H3CT+fsI8Isv9cbalfqSk8oBrZWE/P0bpwZuDfx9sqgNjYd0gNwUhiflT1DXgs8RZ2SAgB6GIsSR/H2gKlyQ7bClhs3R1oncRJxf3jIHTLeUnWkcW+nJzF0Zyvv6fRlItbUZf6hIu3KWeCNxYOG2pE5OTnUwF4F8d5WbYWOoKIJBnIiN9GzR287P4YiDy5oO8ZLDZEHqU1m3qOookh5KPcnqldlal6X8daaiOScXzFjp1Yh5oZ6ZUpbJRa7Ji6l4y5VhE1Ul1agEWvA8wa42PNNy3zr9oiHRV+lRje2Mll0ZDfHNBSkTlRyBemjyTkna9SRdAIp2wY1vPI8XZYbfXQmA1NXEJzJ2hw20VkxSn/bKUFJvj0qMYu10ROusfLYFWSazwpop0Y44K3vC5lOCjl4v5vm/o2idFRxY6WuGxx12hTEFf9eliDSX7hShza3bNc0N4f2Otks6ZwqaGwXJvBxj4Q5M5hRbt9ZjCdrT6MyhmJGa1vwTGVnL4dBX5CIzPGKK4aGitZrij0qa6wVOAcONFqNElJDdL3YrEVgVp7i9MU5/XDmprBMwOjlDvNJgfERqgipOaT+2hUpSVCJXzcmJLtlKh7LbNseGROwqSXF6Qy69tUsx2+BK7UqK3x8EPmFufwL4wantn9RiXwG6SqmVb/lONi0wTtlN61qRcGWbTp0dPk3k4PfGcjKH26oC+mklLW8bXXixDN/a+o01y7FdTeFn4uSk6D1FV1I936mxRNkUDmnoiNPyaoyRHdYNeiLH4+/WYzVguo9GfyydLykiXXGHW624ykpheyyauoIQ2HGV0hUsy/CIWwFM026JO3IFg5QYx2yK6lbDDiTqyWZ01Sa2RHulB41VVdP7hnUDACDaFGdoCd28ce0wi2gvetidSPeo8EUcQFgx67TXjfemt5ebm1jUvqZ9sWDhgSnl2uUAJ86ZfcTnyadXyLYjmqumg7rlcfL6dSgUS3MDlNJEQcZie2RqjtJMEF1BzfI3JqSzU7p1E5PeDnRV8B+calXrcnpDnDjnI7//h7LOd2+RH0mhVAxvymiuKQ6d3Cabyw3Ew6Tm3YyLP2C6hDdK0dudlLQuaOYfLSSKbsk8YrJUX1zQ4G/LQoqgHqAdnSjwh9qMlMgQ9XjZZedml97rwGnKZ+3+wUo1brNzc5veHUvsvnGR4S0L5N0IJ8kJN8Y0VmNm7l+V8sNzO3sckp4kZCsdoofOSWe1N0SlGaqojw0dBhz+Qk0SeCX2ch2TBj6nlPq6UuojZtshrbVVBVsDDpnbR4FzU689b7Z9U5ueTctbJq3Q5nZYz/FYYcmsrc3Ih0Qm3sggU3XNxij0ufKcIpQaiq0t2KFbqLtc9jXaq+H4RUhV8Lbv643qk9YWiot2UZ/c3lSaYyhqR8e0zKeZMZhksawlm8aKsmU4oI3IgZMKLas94dOuEak0J0nlIAtobJaVVNFkcW9x38lk8NlJ5bexURhIhGidsu242RGcae5zG3FZ8yaacNvUjMr698qb8jn2yh/uSM2oaMhFRmnZp5cr+0zPWgmTpTizrKkYHxaSu/Gyiz8siLZzIcZPJIUtGx7Brubk72laZz16d6Z873vuQ7tw7sEVaBT0hk3Gk4C1c/OcOb+4h0q4XEoZH1YUobMnInUTbaADsu+jHSHGmxxuUboO45sPUTR9PvbrPwqe5vTK88zcF8qFL1P0b87Z/fIynUd9uo94nPjjhN7NmsYTIY0nQ7KOrgj2nEwTLysuvrdkeFOGk8k+VblEfu5E5uVKX1PM5vRuEpBn1nKYv99h7Z0loxOa/o0Ow+OSSZS+ZuaMIghzbl1eqzBmXlwy/2CP2cf6dB7apP3YFv6q0LyoOMVJcspum9l7L1YOCcQpAQRnNlFRSGHKBzqeEJ3t4Q8FvzS8ZYGi27wqNaaXW/x+h9b6glJqGfi8UmpPzV1rrZVS+pu89kXNOLiPAPidOYkKwpoLKZ3XFT+TnWCeLMjM3LRlbVFX9UdmBMOT+k5j3TgWb+98nDaMAdqVqAtTELV1LqnHgEqlkOim0jmrWuqqHvFwxxKFNEyb1/L0VNGVVhVkAWQMxkmdiiUgN2dpuOpRmhNclaASVRWlK8xSqClnM7x1CSuTxYLmOY/d61U1x2atYh/AtPpzQwFjIk4nk8/24pr1AExk1pBaXXnZkWFHLYbHlIFs2M6hFNWt07PULems7dZJV83WIqxNMblWj+VNtcdRCVwDuk+VlIERYRiCk5Y010u83oTFLw/IDs8w+0zB6ITL5770ZvAkvT55xypPri7TaKTQneD7BcPMwRm7lCh06hAfyzjXNTxIHV1hsJxUhrHzlmlqTKB/yifrGMzQ2RgnE36lP2neQjArDq+xKk2IxQdSzr5PfsTdmwIpE8xqmhcUy1/PeP7DghdLVhyOfK6giDwzZiRRpTuBYMdncizD3XUp2gXthTH+rlxR046s0elkFKlTRevhljCCxsvQbcVs/PQpjgwHjI43K4T3dMdNJSk6DASn1BuiJwkaUFEISoEdWZudgSlnhXmOPel1I8AbFVJjivc+79uxlxUxaa0vmP/rwF3AdwGXbIpm/tsY7gJwfOrlx8y2y9/zl7TWb9Fav8VttOq5Lk8O8uZFp3IktsAabSriJV1RPeSRREyWON9KdKtc2tRgZt0atUObFqIUZLMM3eZRfYJms5rRiRx/aAeMZbRDZuh05ZQqVG4mre3J0Uwm/s3ecidGFsogy92RUWLtaIpWIfWqUBvUeM25JDWpOqqz3EzuZlDfT0Wp2KZr9oQuZsQzWDR5NlsKwNOr8UxOVmOX7Ho8wzE1HSV6sdTNnLxOs61TsuIF1ilVqjNKIqSiQtjzAqckrAyCxC/qkk31nuksFWNkc62e4Qp2M6G8CRy8ScGZD85V9YxKcWY2l2n8luaxZ47QaKSM+vKcxBSRy6CU+uPYpfOoT7mQMV4RbJt26k5mMNCghbhtMi9yUG/84GOc/OdPcOFdLbIZgYmoS6Gp+Unam8xrVt8WcOrTKe5YMfOsw5G3rPIdb3ua/hszzr3X48hnxWmFqx67p0SsIutIIXyyIBe0oiE8TvJDasbPy0HtTcTZJ3OKhT+NpHZYmguqNkBd4xv8wbd2EunJxcopWVNRKA4qnqAnCcV1h/ZEUGEvq2pM8SnJf8vQI3roHE5vSNG9rL37bdhLRkxKqRbgaK0H5vZ7gf8IfAb4h8B/Mv9/37zkM8BPKqV+C7gT6E+lfN/kQ+orsj+Uq0XpgXJrZwPyuOUAtyMX8SFxGOGWU6VRfl8Z4rZajjuZl7Sp4go3H136msDgmfKGSFZPFqVwWQb1RDm+BWzKQWOBk9lsSd6WtC5c8yuwKMjJZaMXC94sXUPh0hfitiBVTJZ0NRKSN8SZWOCizKGByhRFq6iQxm7sVDph7XOanVvMYO/YrWWfoFL0sDAElRsO9Z6qHIw7ruWunMxES0UNDrWWdmo9PatYo8qagkawRuLk3UlNjRH0X7i/p4dNK1qN1IwTTdX3Sh8mczK7FexCtFPgpFK3Wb4vJ+vU3aK5B136N2vypqb7OPRu9slnXbrzQ/r9JsrVOI0c52LtDX/mJz/Jv/rSh+k8B9Mc+k5ejy95Y8PsmCue/NWbxWnPgmv2XfOCw+ANKc7QE3pkBcfvVqydjijDkkNfGbK7cZhVQP3tjM5THuMlRedZCAYlm29yaJ1zWHg4Ze10gHZFkHR0oqCIZL/S94QeZVTX3YJdKcw3Lgl/fdHQjCNBuGYdjfr1RVTap+hG+MOCInJIlpqEG3UR3+tNpLDfbVOGHk6SU4Re9bu6mfzW6clFgEp+Ku9G0I3wJgVOnFfpmx6N8S5xxfZyUrlDwF1KULse8Bta6z9WSn0N+JRS6seB54EfNs//Q+D9wNPAGPixl/oAoZal6j4Fu5rh8bpYbYu21ZWsZ6/8EvEIb5DMpKlCrt6WIkUriXbsxL07kajLG9eFaicFx1c4nrT1K9qQtB7DwKkHdkEinNKtkb+WGQEMrMDcto7VnUDStVc7VbFxOqkUm9PZOnXz+45Q+2byfbJugT92UQNR5PB7MgtVutC6VLLzepf5R0s2bxMnM11oj7YU48O6wm85GfgbqkoRLa7HrjtZMEPMGiioIAxW/UWVGPbKF0/NrPiDjUyLCDJz2wpaWmhCYZRnlWnxpx3T1TNzg9oBDF+QjZpsN07WnuHEOfFKg6CX4yYuC49lpB2feMEl6xRkvYhoOaOMPVRQcuhQj7XYI1zzyJoFP/3wB/C3PLbuzJi7dy/jZunXhfpou2S8JBcPf1SSRw65K8cJSuFv+nLbL4meC7nwPSWzj2vGN2fEKw12TzmE25pTnxTYyOCEI0rEmcvKX6asf2dAsJMAAem8zETKPtFkCwXKL8lN2BoMS0aHHLIZGbsq/RJKRbmU4gQFvl8w24pR9y0yuLFD1pTSQms1Q7uK0ckZvFGBdhXFkkQ3brbXFfi7KWVDgKXTFlx23+3J2IpK0iqtKze2uFJ7ScektX4WeNOLbN8C3v0i2zXwE69kEUKpAHlXptnjZSPVPTI1F9Ne9sysnK0TWUBj2dVk5iAGGUQsPYzqSj0wG27KjpSoS57sZIqgL6RpjVVVEdbFEYyPlgIxQCIHSwdSiR742hwYJoLKHUpfOmCliZaC7anUJDUCkoYbyo1rsGNjXRRnN2+TYdnGhqpYLp1UTojCRDCibKLIOorw8YzSd0kNQC9ZLCiacgXOmzXtsKWKscR08gVErMCmeHlDIAXhdi2nZcd3LDd5EUG44Ui7P9xLMWMjIfPWVfpnI8h0FpyGiXz7xoEZ0GY2Y4r0ZY0ct/LhSVfhrcl7eD3xgCpO0Y2AMvRonRlQhh5ptykI5UEBCy7tlSHtKGF9s0P30IA48Vnf7IBfCoXL3ITiG11oaZyhV/G6l4FCjaxsmKCoh0ddoq2S0lfEC7I/gl3N0I71jJU5/nwmRzNu+kTK2ukWK3/gcf49cPjPRTUFApJ5RWNdM/ekKKs8/wMujYtw6fQM/gDARbuaw3+uWHunRvkly8t90nmX9bkZ1MSFdsrs3Agf6O+0cIKC5e6QS+fnoA35XUsEaLZudTn1WxLCFN0mZcMjWhtVdSCVpFUtyRa5rblcZkXdLdZjk8bMtNGDYeWUVPPKoQKwT5DfNchOVUyOVu66dCHr6opQbRr8aJV4KetxE2/oCIjOqVOwyvG1xBGoUk7waFPGXyaGpcGmhUpLdBQfrsUKpgdiS99wQ41MIblhJKDHdT2lcKeAhQPTxQLU1MnqpLKmaEu6PoNjHioXR2L13lQhozG2OwmQe0iNasMjXvKJNgwmK6h15dJZXfGPW/I7a+GWiBaU4d6iuUSsogiDtkV1O2enwVN73sdNVA3xmIqErNn0vHq+medTE3FE9jmjEyXRJadKMW3NCipFKbyJrq7e1inZdKRsyGHcu9E13Ece3hhaYUpv2MQPc6JArgBJLyKamzCJXb7n5FN87uztnPxsytatEWFfpMiD3ZpmpvRlNGV4XYk3VjQ3CnZPeFUEB5JCBUbgwR0rDn3FpX9Dk6wNSdehdVaRzmiaF+HQV/qgNXlX5uiWv5GjSs3ucadyjN4YeqdTBoMQKNCZw6Uz8zKSFEiar7Z8dntdbrrtHP2dFp3/0yBxm9zwyAR/JwESnN6QuS/XXTW3P3iBs3lBx8o6H1eeqQdDVLOBHseoGTNZPzuD02pSdtsUoYdtyJcNj90TEd0nhnDP5W/8ymxfOCYrbZQ3alqRMhRtsWAXGCjGJzPKLcNF06AqPNsibrQhB7Y7ASdXaGXYKA3AsmzUeCXrZMZHa+bLoFcDCMEU0xsF5VgKwBiSLjCwgFAcX9oxa85l6NfWoMpA0rDRUSMzviSS0ZazqWhqlDYcTh1VRTNSFJY0UntGqilXMEUUR6lonvOID5V0npMxht4NPo1VGJ4qURZjldXYL2tFAPlhQXx7I1XBGtC1Qg3YaHTvfKGsyTgck4JZZYxsRuMmak+HzjqlMqiL5JZ7yk3q50SXHImirNLyVARlHaetK5WhR9mNKHxDGpeV1aDs0n0p597r0XlKVd3BbntMb9hkY2eGTlvebLLRQDUL/vz89RSNknhJ2AvySNE5lxPPe1Xdy4v1FE2zZjLnSlp32KkAsZabK9oUmbG178s4dpeLP/TYenPB0lfrut/G35hl6as94XoyEd+l0zNEW8LX1DmXs32zT+PJkNEbJ+jEJTrn44/qul20LdFcOqO4dGoGnTqkXcXsMwVOWuD0R6A1q+87RhEq5h9P9+CN8pZbpXIg8202nW6sxSTzIdmMW6mgjI5p3JNDFjoyE+aqkvV+l/LZNsWxCcFTDa571/Oc35rnp279DP/tVz74/4Zj0p5EKyAdn7wpAMjWqsxVJYvyq9l2fOuskLjnDU3rvEQ2qqzJ5GyRGupO27TEUrIo6hQSMcjJOVmQKMuOc6QnUsPdoypHZFOaajbOr/mVgl1FfCzH3/Kqlrz93LwhkZxN52wEI4V5SSOjdYkSVAmZ6URalRTpqmlyD7zjI4pexLil8Hdd+jc4eGMXL9bs3KoJtp2KYwqoAKnC0WSwX/2a2UB+E/mO8UpB66xXDSY7FpRpEPTViI5xQpZOOO1KzS7aoBKqrIj8UnmNSz3wO602okzHT7viqG0K6CYmlQvl5y4ih9GJJk5Wj+HYk90fm3m3hpDfxcsCMbi0Pst1RzflGCsU40kgUIGgxL0UMMwc3JFL68KYzdukne7kDt6klOil4ZqakmZ4EmafESfVv9HBH8rnzz7mVk5MRpYUOnEYHPWItjXjWLBYQa92DOlCk2BLIj8HaF8o6N/g0ljXxPOewFF80BMXlNQlbU3SjWW+s3QhOL2N+qM5Dn1gg91zy+SRw+o7WrTPCVVwc7Os2CUbWwWjwzKzF24rJreleF5B0o9QlnwwKLnz1guc/7mbWP3BFNcrcF1NMQrwlDCBul5JI0zptmPSM22+8aO/yo2X/gkX+tKx+NLO60nvfCFdzCu1feGYpOAprd7mcz5FQzM5VDI+LrJDQc+hHFnFD0k1LO1uOiudpqwt4bS7aySWUlNcziT9yNqmIOvVNaKgJ8A6ZUB3/sARQcxM0Xw8rK7uOKZdP6F6HOrulZOZiOiSN0WVW8/CZbOi1uIkqqIQgdoxWEFPnJquxD5WIgXsdNY0AR6aoZ3A8JaE9kMu6SzMnCsIdzKGxyLpRvrSeQx3hKLDEu5V62rWxWqlpEmQdYRy1jpZYonivLEiWcppXPAq0QUQZ2N1/2ykk3XqfboHGJsKLsxGTtORleVGtyed0nWXDkxNLIKtN7zwUHVTeU3eEECmm+qKYDAYykI3hy0aoczKDUaRILE9udCcPLnO9t1HOfOBJic/M6Z/Q5NkVrq77dUCLxbJ8uaaTBd4E0n1/KE0ALyROHg3wYioGlCsA6N3jjj0qYhDX4HxssF3aXne2umQ4583iOrAwx8WhIb5IBiWDD2X5FRCeDYgWckJjepJY6tElQLMHK54OHfNMZlXxKNGpSB00/c/xf1njkOp2DEHrxeN2Jz46LxETVxmvwajGz1Kz8VvpzCjySc+Criuuc3apKTc9Sldj6xQuLFDMvTQjqbspsy2CvRvLjG+UfH2Bz9E+4wDZ7r4meah+ZvJv2N8+a56xbYvHBNhifY1/qbQz+Y3xrTvaQIO8YpRYd024bnB1USbmv7rZYf5Bo80uCGndVZOoPFiaTjCjU7+L1gAAAtgSURBVFhBVjMX2LmmbFZXIwMARdOpqE4sSrwwFCxOabTjXCqhg7CnqhPUyYTR0IoNTBeF3bGdo5P7qjRDyttS4Na+DCbvnqrR45W2XKYqoKGT1TzbxC7D4wAyi9V5JqTznAAEQZzoaEWKrJZiFWpwaCV6GWqSKUCllT6vivihJlrzjFqLRDy2BiRwAI2Tqj34MGu2jmYjJ/t6iYZUnf415XezkaSNmuz7qVLmH5urNedWNqPBpIxuCq31gknXmVL8qBcTJwHpxCOIcgpPg5khXP3yEZyOSNKncwGLf3GRS+8+Ul0UAJprJV4itb7ejTKXNrg5IzrnSznBjPbgmN+2UXDsDx3CHZdnflgTLsZkF1t0njLMomNB/D/7d9s0LyiCgVCsRNtSWB8dksL633vT1/md4Z34Wx7xsuzHeMGpwJ5uAt6kZONtJRHSrMg6isONAXrioqaOa/tDKk9DO2ft7T7K0yx+KcCLffKGYuMdGRSKU+EGn/pBiLoxRaHIJz5ls4ChJ/TETsnG9gydH9qBxGecBHz4I5/n6/0TfO3R61FBie5dOYvlviCKcwaOtMgXcyaHc4pewOhESbKg6TwjEUM2o4mXpDjqjWGyKCDMcMsxSrEljQteTYZmqE9kds6prupWrRfYw2vk99xK7slieYrAII0zyBZyYXkcqiqF046kMUUkKaB9fdCrBQks8wBlHUEVoUAEBJmumRzPpKtm6mFOLqq0AswUoKE/kBO4dU4O3PYZrxrAbZ8xa2xJlFSEVEPMwxPItqbGTQ040q2Vhhur8pl2WBhVSzppTxt2yalCb6gNvkvu+wNlIqF6f07Py00Xv61zsuh++37KwD7Sjt7zPvY5loXUpnjyZxyPWdqk61TEfHaAGCDPXTyvwA9z0om3B+VQelJoXvniJv5QvJGdKSuN3l5rNRP6HZNWJnOgEmm/R+viuF1zoXMniu6DcgBu3hYRrXm4988w84yDdqQ25I/A3xUqnvbFgrlHh7QvFqQzitERufgm85ov/vxbK/4odyLpW9gvmX0uk7m4U4rdvz/AHbikiRkwbmhua50DhfCE2/3h1U5Kj12iSw6NJ0MG1ykGP7JLMChNy1Xxs3/xfgCSUUA2DNC5wouyasc5T7bQuYN/1xxZ6tEMUz5+z7t49tdeVztD7xUNgbyo7QvHpF0BIIZrnjiITZfWWbk67J6CZE5XHaTRibwqOgrfUm6gAA75jJxUQQ9aF8QBFVHNbyQy2SY9OJLiZKrqYlUYqr44s7xdEu4YuthY4e941QycY6hr8wYCH5iiCLFKtxWuZ+gI5MCRA0wrA0c4VA/Htp/yK1Bh2JMTIl6u2Qe0Z7pZU9FTfPsYbaIroewQaZ140ciPm9a+OxHqVRnSle8YbakqKgj7klKFW45gu0wR3H5XK4/tjWq8UxHWDsNGQ5eLRID8r8RD2bvdNTN4bqJkCHtCjSB35DX+QL1gxm/a0VmFXNsJFT5wwaL5wwJvLSA728J3C7JkKjnwZXpAuzC4Ief89y3SPxWx9bYVSl/hxVqYJX1hOSg926mU795+zsyuhVS0Md7QqeqZeaQIdrVc1HJ7bGlTYjD0yX1tZJBgeMSlvVrIBS6TC+b1//gJVKEqyIQwiTpM5j3CH1sj2oTyq11xxCZC6T4J/aLJ8bsV0QUfZRxSMjIyU4kDgZDLxa9LmHu8JHtoltJT+DMJM097MvirNMorZQSmnVLkLrRzlv7Sg5uHhK2Ure+d4HoFa48vc91dEvnN/0WA6vl7o7Vv0/aFYyobZcWRXfoiJrj7+lx2RrcwgEkZrfB3XZLFgnglZ/cNGW7sMFkS2ePWWQO4bAmGKTDDrUHPsDia1rm/C95aIJJDtiheGNRy03Syho6hPBHwpt9X1cgIhtqjDGsMUNqVdBRMqmQiLS+GaMOwbGKwTLOmnmQK71lbnFCwyx7NudI6pNzO+tXYLedMQyKNvE7D3FTT2JRCufYAVbe9g56DvyuOzyK+nUx4wrNZc0JMaufpJJJ+FpEwKmadvVfBItSEOzV8oAxqp2G3WVySTcmsA7OP245edRyY14c7Jh2Lpp6rZZtEW1NgzYZ8by/WoCzdjK4+r3nBYXfYoBz4lLnD4WPbkBnyPg24opOXR7KeyYJiMi+t+/ZFCa2HR1xJ2WYFyjI+UoJjxS1L0jmJisNNAT32Xie84ZNliTjTrmizWQsGhg+roYhXGiw8kuDk4uRLT2qSv3Dis0yOZqhC5g1Lsx97HxrR//QR0lkRonAnitZZFzdW7J5SfPI338Nt//4B9K0D/CBndm7E8nKfsJUSzU/w2ymOI2lcOqPoPmG6jmFO83vXCZ9oEM0mNFsJvlvI9lZC2EoJBiW+X1AUCjZDOn/a4n1vv48v/fIvc+JfPMnwvUP+zXs/+9In/MuwfaErp5QaAE9c63W8hC0Cm9d6ES9hB2u8ctvv64O/Hmt8vdZ65qWf9uK2P4rf8MSViOO9FqaUuvdgjVdu+32N+3198NdnjVfy+n2Ryh3YgR3YgU3bgWM6sAM7sH1n+8Ux/dK1XsDLsIM1Xh3b72vc7+uD/w/WuC+K3wd2YAd2YNO2XyKmAzuwAzuwyq65Y1JKfZ9S6gmjQ/fRl37Fq7aO/6mUWldKPTy17app512F9R1XSn1RKfWoUuoRpdRP7cM1RkqpryqlHjBr/Bmz/ZRS6h6zlt9WSshflFKhuf+0efzkq73GqbW6Sqn7lFJ378c1vtpajldhfV2l1O8qpR5XSj2mlHrrVV2f1vqa/SGzpc8A1wMB8ADwhmu0lu8G7gAentr2n4GPmtsfBX7W3H4/8EfIgMNp4J7XYH0rwB3m9gzwJPCGfbZGBbTNbR8hvzgNfAr4EbP948A/Nbf/GfBxc/tHgN9+Dff3vwZ+A7jb3N9XawTOAIuXbdtP+/oTwD8ytwOgezXX95ocBN/iy70V+JOp+x8DPnYN13PyMsf0BLBibq8geCuA/w58+MWe9xqu9feB9+zXNQJN4BsI7/sm4F2+z4E/Ad5qbnvmeeo1WNsxRKT1bwF3mxNmv63xxRzTvtjXwCzw3OW/w9Vc37VO5V6xBt1rbFdNO+9qmkkn3oxEJPtqjSZFuh9Rzfk8EhH3tNZ2Zn96HdUazeN9YOHVXiPwX4B/S0XGzMI+XKPmVdRyvEI7BWwAv2rS4f9hhEqu2vqutWP6a2NaXP01b2EqpdrA/wb+pdZ6d/qx/bBGrXWhtb4diUq+C7j5Wq7nclNKfT+wrrX++rVey0vYO7TWdwDvA35CKfXd0w9e433tIWWPX9RavxkYIalbZVe6vmvtmF6WBt01tCvSzrvappTyEaf0v7TWv7cf12hNa90DvoikRV2llB1/ml5HtUbz+Cxw5RIb39reDnxAKXUG+C0knfv5fbZG9Kug5XgV7TxwXmttCXR/F3FUV21919oxfQ24yXREAqS4+JlrvKZps9p58ELtvB813YbTvBztvCs0pZQCfgV4TGv9c/t0jUtKqa653UBqYI8hDuqHvska7dp/CPiCudK+aqa1/pjW+pjW+iRyvH1Ba/0P9tMalVItpdSMvY1oOT7MPtnXWus14JxS6vVm07uBR6/q+l7tIt7LKKS9H+kwPQP8u2u4jt8EVoEMuSL8OFJL+DPgKeBPgXnzXAX8V7Pmh4C3vAbrewcSGj8I3G/+3r/P1ngbcJ9Z48PAfzDbrwe+imgN/g4Qmu2Ruf+0efz613if/03qrty+WaNZywPm7xF7XuyzfX07cK/Z158G5q7m+g6Q3wd2YAe27+xap3IHdmAHdmAvsAPHdGAHdmD7zg4c04Ed2IHtOztwTAd2YAe27+zAMR3YgR3YvrMDx3RgB3Zg+84OHNOBHdiB7Ts7cEwHdmAHtu/s/wLhVUlI827PnQAAAABJRU5ErkJggg==\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(lum_img.ravel(), bins=256, range=(0.0, 1.0), fc='k', ec='k')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "\n", + "* [Pyplot tutorial](https://matplotlib.org/users/pyplot_tutorial.html)\n", + "* [Image tutorial](https://matplotlib.org/users/image_tutorial.html)\n", + "* [手把手教你用Python做数据可视化](https://mp.weixin.qq.com/s/3Gwdjw8trwTR5uyr4G7EOg)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/1_numpy_matplotlib_scipy_sympy/ipython_notebook.ipynb b/1_numpy_matplotlib_scipy_sympy/3-ipython_notebook.ipynb similarity index 100% rename from 1_numpy_matplotlib_scipy_sympy/ipython_notebook.ipynb rename to 1_numpy_matplotlib_scipy_sympy/3-ipython_notebook.ipynb diff --git a/1_numpy_matplotlib_scipy_sympy/3-ipython_notebook_EN.ipynb b/1_numpy_matplotlib_scipy_sympy/3-ipython_notebook_EN.ipynb new file mode 100644 index 0000000..1016538 --- /dev/null +++ b/1_numpy_matplotlib_scipy_sympy/3-ipython_notebook_EN.ipynb @@ -0,0 +1,338 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1.1. Introducing IPython and the Jupyter Notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "podoc": { + "output_text": "Screenshot of a Jupyter notebook" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello world!\n" + ] + }, + { + "data": { + "image/png": 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NeZer243DUL5lQ01uydq8Sz2TaxVT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ILXjfV1YnItLlMBKcMEQhiFelUxg6bBbafsPWDtoDovUC213nPf0+3naGcflpiGazNx3SQQxl2S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\nLOu9733vnXfeWVFxl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NDZs2crKyvNXSuIRSGTyU6dOmUw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LC2v07sbnesIu9VCSnf/0MC0zSUr2j04O6Z2yPrno7/X8o7EJ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\nMC4urub2DRs2fPLJJxKJpOlLMhpm/w7rlAm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LLjh88uFd\n53/43A+fevep3hSZVOXOyrtX3t3xeEH5gksXXXpCxQmTSya3R9vX167/w5t/+Mnyn+xs3Blb39Le\ncs2T19x1zl2pTnjm7DP3HbNv1/k3tr1x+V8vjw1njZl18wdvTl/bvLJ5PftiQqFQKLRsWadw2OGH\nJ3kx91rb/vrXyu9/P2Fy1AEHTPzYx0qPOmrYhAmhUCja1tZQWbl92bKNv/pVy/b386xVDz88+tBD\nJ557br9WnFNVDz9c9cgjCZPjTjpp4rnnjj7ssI6QTXN19fbHH9905531azvF9dZec82ogw4aNmlS\n/5UbZ/TBB9e98kpsWLdiRdE++/TmhHUrViScv+uaPW+//eYNNyRMluy33+SPf3zM4sWRceNCoVBr\nXV3t8uVb7r13+xNPxC/bfM89pcccU/bBD/amyKqHH65ZujQ2LBg1qvSoo0YtWlQ4dmz+yJFtu3Y1\nbd6884UXal94IdZKKofevuWW+tWr42fyi4unXnxx+RlnFE2fHsrLa9uzp/b55zfdddeOp54KhUKh\naHTDL3+Zm1oHtnA4fNhhh/3hD3+IzSxbtuzQQw/NYUkAAAAAAGRCLAmGjo5MUmFh3o1XHnfy8RWx\n+eHDC8aOmXjIgRNPPbHiK5f/ubGpLfbU75e+KZbEIBXLJH3mwM/c/MGbI/nvtzOZWDLxcwd/7oIF\nF3zq4U/9qfJPsfk/Vf7p92t/f+bsM7O7Ymt767VPXtvxeFzRuG8d/a3/dfD/KswrTFj2f476P1vr\nt/7nC/95y/Jb0pxt3zH7Jo31/Pq1X2dXXnpPvvNkx4NvHPmNaz5wTUHe+28ASoeXLpqw6JP7f/LU\nu099c8f7LT2WrFuys3Fn6fDSpCecNmratFHTus6XRErih6OGjTpl5ikBfAFdJPTJWLgwsM3yimbO\nnPfDH8aGXTdUmvGVr7Ts/GeEa+Dtd9a0Zcvaq66Kn8mLRGZde+2Ec86Jnwzn5xfPnl08e/akCy5Y\n9Y1vvBeJCIVCoVDl978/5uijh02e3E8V51TL9u0JEa5wYeHc732v/PTT4ycj5eUTzztvwtlnV/7f\n/7vprvfjem319euuv37Brbf2U7mdjTrkkFBchGXXihW93FQuvltSuKBg5AEHJK6IRtdec01757jP\nlIsv3ufrXw8XvH9XKRg5ctyJJ4478cStDz209tvfjra9/8aj8oYbSo84omuXoAxFW1vfvuW9W2th\naem0z39+8sc+Fi5MvA9PC4Waa2o2/frXG+7ItEVcX6hfs2bzb34TP1NUUbHwv/5reFxrt/yiorGL\nF49dvHjjnXdW3nRTKBSKtrT0d6E9FH+HLBw7NuHZ8WeeWbLf++8ti2bMCOq6CxcujI8l6ZYEAAAA\nADAoiCXBUPPdby8+6bjknwAddtCkz11y0I//64XYzMrVNZu31k+aUNxf1UHATtv3tJ+c8pO8cJJo\nyOhho+88+87jfnXcqm2rYpNXP3l11rGkJ995sqMH0qwxs5Z8bMn0USm3DJtQPOH6xddfsOCCWHZq\ngPjCIV/4zuLkmyhNGTnl9rNuP+5Xx0VD7/VUa25rfvTNRy9ccGE/FtgDL3Xe5yjAWFKkrKz8Qx9K\ns6D06KN7f5W2urrm6upMF+/Z0/2if1p33XXvp6ZCoXBh4f7/7/+NSt1TpGDkyAX/8R8rLr5418sv\nv3e5+vqNv/rVzMsvT3XIULLh9ttbduyIn5l97bUJmaSYcCSy75VXNm/fXvPHP8Ymtz/xxK4XX0y1\n31mfGnXQQfHDus5bsPVUc01N06ZNseHI+fPzhifuibnjmWdqly+Pnxl/9tkz/+3fUp1zwjnntO/Z\nE793W1NV1abf/Gb65z6XXZE7ly/v6IFUNGPG/rfdliY8Fykrq/ja18afdVZ77lI+62+5JT6SlV9S\nsuBnPxueYrvJKZ/4RMu2be/+/Of9VV320t8hi+fOLZ47ty+uu//++8cPX3jhhVQrAQAAAAAYOMSS\nYEg56bgZqTJJHT76kf1+fscre/a0xmZWrdkmlsQgVZhXeMsptyTNJHUYGRn5vRO/d8697zeJeaPm\njZe2vHTwxCQ7E3WrI5M0rmjcny7806SS7vds2q9sYLUimzZqWqpMUodDJx26eMbix9c/Hpt5ecvL\nAzOWtGHDhu1x+46FQqE5c+bkqpjsrO6b0E/96tUJ22bte/nlaTJJHcKFhXNuuumFM84Itbd3zGx9\n8MEZX/lKx/5lQ1i0ra364YfjZ0qPOmrCRz6S7phweNbVV+94+um23btjc1seeCAnsaTC0tIR++7b\n8OZ7Tc7qV69ub2zsmiXKUN1rr8UPRybbwW3L/ffHDwtGj973iivSn3bSBRdULVmyKy5HuPWBB6Z/\n9rOhcDiLIjsySYWlpfvfccew8eO7Xd+121m/adm+ffuTT8bPTPv0p4sqKtIcMuMLX6h65JH4cBjx\n5nZOO23cuHHbtm3jxo3LVT0AAAAAAGRiwO08AvTGRR/rpl/I8GH5Rx7aqbXAurd2pFoMA9wFCy5I\nuo9YvFNnnrr/+E79FX77+m97c9EfnfyjTDJJA9BnD/7siMJuUiZnzurUSmpl9cq+rCh769evT5iZ\nvHfsONatDbffHj8snj170kc/msmBRTNmjDvhhNiwta5u+7JlwdY2AO145pmmqqr4mRlf/GK3RxWW\nlk6+sFNcr2bp0raGhoCLy8youPBQtK1t9+uvZ32quhUrUp25Q2tt7bbOPxVTL7ook+3Ypn/hC/HD\nxg0banvX52bfq67KJJOUW1WPPhptfT8Fnl9UNOnjH09/SLiwcNqllwZYQ/zOeqFQKC8SSbVyUJgy\nZUrCTNd/CwAAAAAAGGjEkmDomDyxZP/55d0um73PmPjh5q27U62EAe4jc9M2Nfmns2afFT/8U+Wf\nsr7i7LGzz9vvvKwPz61MKl84vlO08Z1d7/RZOb2S8FH02LFji4qKclXMwBFtbq75wx/iZ6ZcfHEo\nL9M3e+Wnnho/3PXKK4FVNlDtfO65+OGwCRMStkVLpey00+KHbQ0NCZmefjO6c5emVGWsueKK17/0\npY7/bf6f/0m6JjGW1OWlqH3ppY5mRTEJr0MqpUccUTim03uPnX/7WyYHJlVUUZF+E7EBorbz11h6\n5JGZRLjGnXxygDUkXDGTAgaycePGDe/cDEwsCQAAAABg4LOJGwwdC/cry2TZxAkl8cP6hpa+KYcc\n+PcbTmpuaUuYLMjPZqOcgS+SH1k8Y3EmKz+4zwdveuam2HDd9nV7WvcUFWSTYrlk0SVZHDUQjC8e\nP33U9G6XJXSf2t08QGOLmzpvcjR+wPdN6R91//hHe1xqJJyfP+7EEzM/fOSBB8YP94ZYUkJvoTEf\n+ECGO4uVzJsXmTCheevW2Ez9G2+UHnlkwPVlIKGn0a5XX03sJxMK7Vm/futDD8WGjRs3Tjr//MRF\n0ejuf/wjNiqqqIh02Rur/o034ofDp09PvyVZTDg/v/SYY6p///vYzO7Op+qRieeem/Wx/Snhp6v0\n6KMzOSpSVlayYMHulcF0qsvvnEPKH+SxpFAoNH78+HfeeT8vu8mGdwAAAAAAA55YEgwd+3Zug5TK\niKJOv/gNDa2pVjLozJs9Ntcl9J+ZpTMzjBbtN26/+GFbtG1l9cpDJx2axUVPnNGDkMeAMr9sfibL\niguL44d1zXV9U05v7d7dKS81YkQ3m9MNQONOOGH41KkZLt75/PP1q1d3u6z2xRfjh8Vz5hSMHp15\nScMnTw7l5YXa2zuGDevWZX7sIJWQsxkxZ07mxxbPmRMfS6oLKEfSU8OnTh02fnxsK7q6117rumbH\n00/HD+tXr26qqkrYBK2hsrK17v3f9647uIW65GxK5s7NvM6SuXM7xZJ6sdlc6VFHZX1sv2netq0p\n7scjFAoVz56d4bEjZs0KKpaU2C2ppCTVysEi4W5fX1+fq0oAAAAAAMiQWBIMHaNKIpksixTmxw9b\nWhOb68CgMHtsph/xlg4vLRtRVtNQE5tZt2NdFrGk4QXDF5Qv6OlRA8SY4RnFFoflD4sfNrc1p1qZ\nWw0NDfHDhG19BoUJ55477oQTMlz85g03ZBJLSuhAUzRzZrS1NRQKhaLR2P9HO56Ln4m+NxeKRgtH\nj27ZsaNj1N7U1N7cnBfJ6F+Wwai1tjY+iBMKhUbMnJn54UX77LPjqadiw6YNGwKrrIdGHnRQ09Kl\n75WxaVNzTU2krFP3xJ3PPptwyM5nn51wzjnxMwk7uI1OFktq3Lgxfli0zz6ZF5mwuLm6ur2pKW/Y\nsFTrU8mLRDLP9+RQY5efh+EzZmR47IievLDp5XfOIeUP/lhSwn6dYkkAAAAAAAOfWBIMHcUjCrM4\nKvZ5NAwuE0smZr54/Ijx8bGk2sbaLK64T+k+BXmD9d/Nkkg2n0ZHB+oNorm5U16qsDCbu9/Q07pz\nZ/ywesmS6iVLenXC2tpIeXnvihq4Wncn7lGYkOZJb1jnV6alLmetxUYdfHDNP2NJoVCobsWK+M37\noi0tO//+94RDdjz1VPpYUtJuSQkprh79bBR2eW1b6+oiPY8lDZ82LVwwCO7Dbb346erRz2F6id2S\nBv8mbpHOQcmmpqZcVQIAAAAAQIbycl0AEJi8vHCuS4D+M6KgB/t2FUc67U22q3lXFlccFRmVxVED\nRH44v/tFg8ewzmkGn0x3aK3NJm+XRtuQ7kTS2iVIlN+T3QDzOvfoas/da5XQ2SghYLTr5Zfb/tld\nrPif267t/NvfYrv1vXfUq6/GHkfKyooqKrpeqK3zK5ZflNE2mqkWdw3uZHSeQdLvJ+GnKxyJhPMz\nvQn36Oewm1MNuU3cEu72g7FVHgAAAADA3kYsCYBBaURhDz64LSro9Il4XdP/Z+/Ow6uq7v0Bn8y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+ "text/plain": [ + "Screenshot of a Jupyter notebook" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Hello world!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2 + 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "12" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "_ * 3" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "my_notebook.ipynb\n" + ] + } + ], + "source": [ + "!ls" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Available line magics:\n", + "%alias %alias_magic %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %popd %pprint %precision %profile %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n", + "\n", + "Available cell magics:\n", + "%%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%js %%latex %%markdown %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n", + "\n", + "Automagic is ON, % prefix IS NOT needed for line magics." + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%lsmagic" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing test.txt\n" + ] + } + ], + "source": [ + "%%writefile test.txt\n", + "Hello world!" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello world!\n" + ] + } + ], + "source": [ + "# Let's check what this file contains.\n", + "with open('test.txt', 'r') as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "podoc": { + "output_text": "Screenshot of the pager" + } + }, + "outputs": [ + { + "data": { + "image/png": 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wcXEZM2aMl5dXZGTk7NmzxWiezZs3T5kyJavdGrhZ0yO9H+FM5b4dNtEjnJaWJgZU2dnZdejQ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\nDw9p8uHDh9lucv/+fX1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+ "text/plain": [ + "Screenshot of the pager" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%run?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import HTML, SVG, YouTubeVideo" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "podoc": { + "output_text": "" + } + }, + "outputs": [ + { + "data": { + "image/png": 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G8bha6VsNyjyPj3cFZrM6aY/H4TAfcX9v2RgvL8Wrj8fHoz7WRdiVJU6htzdJ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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "HTML('''\n", + "\n", + "''' +\n", + " ''.join(['' +\n", + " ''.join([f''\n", + " for col in range(5)]) +\n", + " '' for row in range(5)]) +\n", + " '''\n", + "
{row},{col}
\n", + "''')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "podoc": { + "output_text": "" + } + }, + "outputs": [ + { + "data": { + "image/png": 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zP9NN+jz9rTq2F3+XoM/H7n5l5mf7JYL88xfrtb9qnslgvzzF+u1v2qwPczP9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\n6V+g20X1ZY8i8irarWuXqWvY17g1w1G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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "YouTubeVideo('VQBZ2MqWBZI')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "```json\n", + "{\n", + " \"cells\": [\n", + " {\n", + " \"cell_type\": \"code\",\n", + " \"execution_count\": 1,\n", + " \"metadata\": {},\n", + " \"outputs\": [\n", + " {\n", + " \"name\": \"stdout\",\n", + " \"output_type\": \"stream\",\n", + " \"text\": [\n", + " \"Hello world!\\n\"\n", + " ]\n", + " }\n", + " ],\n", + " \"source\": [\n", + " \"print(\\\"Hello world!\\\")\"\n", + " ]\n", + " }\n", + " ],\n", + " \"metadata\": {},\n", + " \"nbformat\": 4,\n", + " \"nbformat_minor\": 2\n", + "}\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/1_numpy_matplotlib_scipy_sympy/3-scipy_tutorial.ipynb b/1_numpy_matplotlib_scipy_sympy/4-scipy_tutorial.ipynb similarity index 100% rename from 1_numpy_matplotlib_scipy_sympy/3-scipy_tutorial.ipynb rename to 1_numpy_matplotlib_scipy_sympy/4-scipy_tutorial.ipynb diff --git a/1_numpy_matplotlib_scipy_sympy/4-scipy_tutorial_EN.ipynb b/1_numpy_matplotlib_scipy_sympy/4-scipy_tutorial_EN.ipynb new file mode 100644 index 0000000..bfbf932 --- /dev/null +++ b/1_numpy_matplotlib_scipy_sympy/4-scipy_tutorial_EN.ipynb @@ -0,0 +1,2423 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SciPy - Library of scientific algorithms for Python" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "J.R. Johansson (jrjohansson at gmail.com)\n", + "\n", + "The latest version of this [IPython notebook](http://ipython.org/notebook.html) lecture is available at [http://github.com/jrjohansson/scientific-python-lectures](http://github.com/jrjohansson/scientific-python-lectures).\n", + "\n", + "The other notebooks in this lecture series are indexed at [http://jrjohansson.github.io](http://jrjohansson.github.io)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# what is this line all about? Answer in lecture 4\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from IPython.display import Image" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The SciPy framework builds on top of the low-level NumPy framework for multidimensional arrays, and provides a large number of higher-level scientific algorithms. Some of the topics that SciPy covers are:\n", + "\n", + "* Special functions ([scipy.special](http://docs.scipy.org/doc/scipy/reference/special.html))\n", + "* Integration ([scipy.integrate](http://docs.scipy.org/doc/scipy/reference/integrate.html))\n", + "* Optimization ([scipy.optimize](http://docs.scipy.org/doc/scipy/reference/optimize.html))\n", + "* Interpolation ([scipy.interpolate](http://docs.scipy.org/doc/scipy/reference/interpolate.html))\n", + "* Fourier Transforms ([scipy.fftpack](http://docs.scipy.org/doc/scipy/reference/fftpack.html))\n", + "* Signal Processing ([scipy.signal](http://docs.scipy.org/doc/scipy/reference/signal.html))\n", + "* Linear Algebra ([scipy.linalg](http://docs.scipy.org/doc/scipy/reference/linalg.html))\n", + "* Sparse Eigenvalue Problems ([scipy.sparse](http://docs.scipy.org/doc/scipy/reference/sparse.html))\n", + "* Statistics ([scipy.stats](http://docs.scipy.org/doc/scipy/reference/stats.html))\n", + "* Multi-dimensional image processing ([scipy.ndimage](http://docs.scipy.org/doc/scipy/reference/ndimage.html))\n", + "* File IO ([scipy.io](http://docs.scipy.org/doc/scipy/reference/io.html))\n", + "\n", + "Each of these submodules provides a number of functions and classes that can be used to solve problems in their respective topics.\n", + "\n", + "In this lecture we will look at how to use some of these subpackages.\n", + "\n", + "To access the SciPy package in a Python program, we start by importing everything from the `scipy` module." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we only need to use part of the SciPy framework we can selectively include only those modules we are interested in. For example, to include the linear algebra package under the name `la`, we can do:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import scipy.linalg as la" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Special functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A large number of mathematical special functions are important for many computional physics problems. SciPy provides implementations of a very extensive set of special functions. For details, see the list of functions in the reference documention at http://docs.scipy.org/doc/scipy/reference/special.html#module-scipy.special. \n", + "\n", + "To demonstrate the typical usage of special functions we will look in more detail at the Bessel functions:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# The scipy.special module includes a large number of Bessel-functions\n", + "# Here we will use the functions jn and yn, which are the Bessel functions \n", + "# of the first and second kind and real-valued order. We also include the \n", + "# function jn_zeros and yn_zeros that gives the zeroes of the functions jn\n", + "# and yn.\n", + "#\n", + "from scipy.special import jn, yn, jn_zeros, yn_zeros" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "J_0(0.000000) = 1.000000\n", + "Y_0(1.000000) = 0.088257\n" + ] + } + ], + "source": [ + "n = 0 # order\n", + "x = 0.0\n", + "\n", + "# Bessel function of first kind\n", + "print \"J_%d(%f) = %f\" % (n, x, jn(n, x))\n", + "\n", + "x = 1.0\n", + "# Bessel function of second kind\n", + "print \"Y_%d(%f) = %f\" % (n, x, yn(n, x))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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rBas9V+Ny3GWdCqmJwa+/sqKUpTZZuX0bePdd4KOPWLiPDtE52vAo/xG6OXXD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\n4LffWDPZ5yAiXMjIgOODBwjNzcVES0tMtrJCz3r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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = linspace(0, 10, 100)\n", + "\n", + "fig, ax = plt.subplots()\n", + "for n in range(4):\n", + " ax.plot(x, jn(n, x), label=r\"$J_%d(x)$\" % n)\n", + "ax.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.40482556, 5.52007811, 8.65372791, 11.79153444])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# zeros of Bessel functions\n", + "n = 0 # order\n", + "m = 4 # number of roots to compute\n", + "jn_zeros(n, m)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Integration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Numerical integration: quadrature" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Numerical evaluation of a function of the type\n", + "\n", + "$\\displaystyle \\int_a^b f(x) dx$\n", + "\n", + "is called *numerical quadrature*, or simply *quadature*. SciPy provides a series of functions for different kind of quadrature, for example the `quad`, `dblquad` and `tplquad` for single, double and triple integrals, respectively.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.integrate import quad, dblquad, tplquad" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `quad` function takes a large number of optional arguments, which can be used to fine-tune the behaviour of the function (try `help(quad)` for details).\n", + "\n", + "The basic usage is as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# define a simple function for the integrand\n", + "def f(x):\n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "integral value = 0.5 , absolute error = 5.55111512313e-15\n" + ] + } + ], + "source": [ + "x_lower = 0 # the lower limit of x\n", + "x_upper = 1 # the upper limit of x\n", + "\n", + "val, abserr = quad(f, x_lower, x_upper)\n", + "\n", + "print \"integral value =\", val, \", absolute error =\", abserr " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we need to pass extra arguments to integrand function we can use the `args` keyword argument:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.736675137081 9.3891268825e-13\n" + ] + } + ], + "source": [ + "def integrand(x, n):\n", + " \"\"\"\n", + " Bessel function of first kind and order n. \n", + " \"\"\"\n", + " return jn(n, x)\n", + "\n", + "\n", + "x_lower = 0 # the lower limit of x\n", + "x_upper = 10 # the upper limit of x\n", + "\n", + "val, abserr = quad(integrand, x_lower, x_upper, args=(3,))\n", + "\n", + "print val, abserr " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For simple functions we can use a lambda function (name-less function) instead of explicitly defining a function for the integrand:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "numerical = 1.77245385091 1.42026367809e-08\n", + "analytical = 1.77245385091\n" + ] + } + ], + "source": [ + "val, abserr = quad(lambda x: exp(-x ** 2), -Inf, Inf)\n", + "\n", + "print \"numerical =\", val, abserr\n", + "\n", + "analytical = sqrt(pi)\n", + "print \"analytical =\", analytical" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As show in the example above, we can also use 'Inf' or '-Inf' as integral limits.\n", + "\n", + "Higher-dimensional integration works in the same way:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.785398163397 1.63822994214e-13\n" + ] + } + ], + "source": [ + "def integrand(x, y):\n", + " return exp(-x**2-y**2)\n", + "\n", + "x_lower = 0 \n", + "x_upper = 10\n", + "y_lower = 0\n", + "y_upper = 10\n", + "\n", + "val, abserr = dblquad(integrand, x_lower, x_upper, lambda x : y_lower, lambda x: y_upper)\n", + "\n", + "print val, abserr " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note how we had to pass lambda functions for the limits for the y integration, since these in general can be functions of x." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ordinary differential equations (ODEs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "SciPy provides two different ways to solve ODEs: An API based on the function `odeint`, and object-oriented API based on the class `ode`. Usually `odeint` is easier to get started with, but the `ode` class offers some finer level of control.\n", + "\n", + "Here we will use the `odeint` functions. For more information about the class `ode`, try `help(ode)`. It does pretty much the same thing as `odeint`, but in an object-oriented fashion.\n", + "\n", + "To use `odeint`, first import it from the `scipy.integrate` module" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.integrate import odeint, ode" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A system of ODEs are usually formulated on standard form before it is attacked numerically. The standard form is:\n", + "\n", + "$y' = f(y, t)$\n", + "\n", + "where \n", + "\n", + "$y = [y_1(t), y_2(t), ..., y_n(t)]$ \n", + "\n", + "and $f$ is some function that gives the derivatives of the function $y_i(t)$. To solve an ODE we need to know the function $f$ and an initial condition, $y(0)$.\n", + "\n", + "Note that higher-order ODEs can always be written in this form by introducing new variables for the intermediate derivatives.\n", + "\n", + "Once we have defined the Python function `f` and array `y_0` (that is $f$ and $y(0)$ in the mathematical formulation), we can use the `odeint` function as:\n", + "\n", + " y_t = odeint(f, y_0, t)\n", + "\n", + "where `t` is and array with time-coordinates for which to solve the ODE problem. `y_t` is an array with one row for each point in time in `t`, where each column corresponds to a solution `y_i(t)` at that point in time. \n", + "\n", + "We will see how we can implement `f` and `y_0` in Python code in the examples below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Example: double pendulum" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's consider a physical example: The double compound pendulum, described in some detail here: http://en.wikipedia.org/wiki/Double_pendulum" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(url='http://upload.wikimedia.org/wikipedia/commons/c/c9/Double-compound-pendulum-dimensioned.svg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The equations of motion of the pendulum are given on the wiki page:\n", + "\n", + "${\\dot \\theta_1} = \\frac{6}{m\\ell^2} \\frac{ 2 p_{\\theta_1} - 3 \\cos(\\theta_1-\\theta_2) p_{\\theta_2}}{16 - 9 \\cos^2(\\theta_1-\\theta_2)}$\n", + "\n", + "${\\dot \\theta_2} = \\frac{6}{m\\ell^2} \\frac{ 8 p_{\\theta_2} - 3 \\cos(\\theta_1-\\theta_2) p_{\\theta_1}}{16 - 9 \\cos^2(\\theta_1-\\theta_2)}.$\n", + "\n", + "${\\dot p_{\\theta_1}} = -\\frac{1}{2} m \\ell^2 \\left [ {\\dot \\theta_1} {\\dot \\theta_2} \\sin (\\theta_1-\\theta_2) + 3 \\frac{g}{\\ell} \\sin \\theta_1 \\right ]$\n", + "\n", + "${\\dot p_{\\theta_2}} = -\\frac{1}{2} m \\ell^2 \\left [ -{\\dot \\theta_1} {\\dot \\theta_2} \\sin (\\theta_1-\\theta_2) + \\frac{g}{\\ell} \\sin \\theta_2 \\right]$\n", + "\n", + "To make the Python code simpler to follow, let's introduce new variable names and the vector notation: $x = [\\theta_1, \\theta_2, p_{\\theta_1}, p_{\\theta_2}]$\n", + "\n", + "${\\dot x_1} = \\frac{6}{m\\ell^2} \\frac{ 2 x_3 - 3 \\cos(x_1-x_2) x_4}{16 - 9 \\cos^2(x_1-x_2)}$\n", + "\n", + "${\\dot x_2} = \\frac{6}{m\\ell^2} \\frac{ 8 x_4 - 3 \\cos(x_1-x_2) x_3}{16 - 9 \\cos^2(x_1-x_2)}$\n", + "\n", + "${\\dot x_3} = -\\frac{1}{2} m \\ell^2 \\left [ {\\dot x_1} {\\dot x_2} \\sin (x_1-x_2) + 3 \\frac{g}{\\ell} \\sin x_1 \\right ]$\n", + "\n", + "${\\dot x_4} = -\\frac{1}{2} m \\ell^2 \\left [ -{\\dot x_1} {\\dot x_2} \\sin (x_1-x_2) + \\frac{g}{\\ell} \\sin x_2 \\right]$" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "g = 9.82\n", + "L = 0.5\n", + "m = 0.1\n", + "\n", + "def dx(x, t):\n", + " \"\"\"\n", + " The right-hand side of the pendulum ODE\n", + " \"\"\"\n", + " x1, x2, x3, x4 = x[0], x[1], x[2], x[3]\n", + " \n", + " dx1 = 6.0/(m*L**2) * (2 * x3 - 3 * cos(x1-x2) * x4)/(16 - 9 * cos(x1-x2)**2)\n", + " dx2 = 6.0/(m*L**2) * (8 * x4 - 3 * cos(x1-x2) * x3)/(16 - 9 * cos(x1-x2)**2)\n", + " dx3 = -0.5 * m * L**2 * ( dx1 * dx2 * sin(x1-x2) + 3 * (g/L) * sin(x1))\n", + " dx4 = -0.5 * m * L**2 * (-dx1 * dx2 * sin(x1-x2) + (g/L) * sin(x2))\n", + " \n", + " return [dx1, dx2, dx3, dx4]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# choose an initial state\n", + "x0 = [pi/4, pi/2, 0, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# time coodinate to solve the ODE for: from 0 to 10 seconds\n", + "t = linspace(0, 10, 250)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# solve the ODE problem\n", + "x = odeint(dx, x0, t)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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FFcDTT5OI/vxzW8ZpBLF4op0kohVPfUIMn/TUVOr6d+qU8eOKlWgi0YmJtIqwZ4+5Y7Ia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\n6CNyrXz5pS8mp2b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/kV8wGs4+2zuNTrvHU51j5EgycVVXW1Y0P6iI/vlnmsSlpUV8rF69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\nI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HbTplo0hxNBF9FKqfcA3AMgHsBeAE+KyHmL7VoB+BBABICJImLhatInZI1Gk3nJDCJa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\n/B/NCqfN4Rm/ZwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the angles as a function of time\n", + "\n", + "fig, axes = plt.subplots(1,2, figsize=(12,4))\n", + "axes[0].plot(t, x[:, 0], 'r', label=\"theta1\")\n", + "axes[0].plot(t, x[:, 1], 'b', label=\"theta2\")\n", + "\n", + "\n", + "x1 = + L * sin(x[:, 0])\n", + "y1 = - L * cos(x[:, 0])\n", + "\n", + "x2 = x1 + L * sin(x[:, 1])\n", + "y2 = y1 - L * cos(x[:, 1])\n", + " \n", + "axes[1].plot(x1, y1, 'r', label=\"pendulum1\")\n", + "axes[1].plot(x2, y2, 'b', label=\"pendulum2\")\n", + "axes[1].set_ylim([-1, 0])\n", + "axes[1].set_xlim([1, -1]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simple annimation of the pendulum motion. We will see how to make better animation in Lecture 4." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import display, clear_output\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(4,4))\n", + "\n", + "for t_idx, tt in enumerate(t[:200]):\n", + "\n", + " x1 = + L * sin(x[t_idx, 0])\n", + " y1 = - L * cos(x[t_idx, 0])\n", + "\n", + " x2 = x1 + L * sin(x[t_idx, 1])\n", + " y2 = y1 - L * cos(x[t_idx, 1])\n", + " \n", + " ax.cla() \n", + " ax.plot([0, x1], [0, y1], 'r.-')\n", + " ax.plot([x1, x2], [y1, y2], 'b.-')\n", + " ax.set_ylim([-1.5, 0.5])\n", + " ax.set_xlim([1, -1])\n", + "\n", + " clear_output() \n", + " display(fig)\n", + "\n", + " time.sleep(0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Example: Damped harmonic oscillator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ODE problems are important in computational physics, so we will look at one more example: the damped harmonic oscillation. This problem is well described on the wiki page: http://en.wikipedia.org/wiki/Damping\n", + "\n", + "The equation of motion for the damped oscillator is:\n", + "\n", + "$\\displaystyle \\frac{\\mathrm{d}^2x}{\\mathrm{d}t^2} + 2\\zeta\\omega_0\\frac{\\mathrm{d}x}{\\mathrm{d}t} + \\omega^2_0 x = 0$\n", + "\n", + "where $x$ is the position of the oscillator, $\\omega_0$ is the frequency, and $\\zeta$ is the damping ratio. To write this second-order ODE on standard form we introduce $p = \\frac{\\mathrm{d}x}{\\mathrm{d}t}$:\n", + "\n", + "$\\displaystyle \\frac{\\mathrm{d}p}{\\mathrm{d}t} = - 2\\zeta\\omega_0 p - \\omega^2_0 x$\n", + "\n", + "$\\displaystyle \\frac{\\mathrm{d}x}{\\mathrm{d}t} = p$\n", + "\n", + "In the implementation of this example we will add extra arguments to the RHS function for the ODE, rather than using global variables as we did in the previous example. As a consequence of the extra arguments to the RHS, we need to pass an keyword argument `args` to the `odeint` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def dy(y, t, zeta, w0):\n", + " \"\"\"\n", + " The right-hand side of the damped oscillator ODE\n", + " \"\"\"\n", + " x, p = y[0], y[1]\n", + " \n", + " dx = p\n", + " dp = -2 * zeta * w0 * p - w0**2 * x\n", + "\n", + " return [dx, dp]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# initial state: \n", + "y0 = [1.0, 0.0]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# time coodinate to solve the ODE for\n", + "t = linspace(0, 10, 1000)\n", + "w0 = 2*pi*1.0" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# solve the ODE problem for three different values of the damping ratio\n", + "\n", + "y1 = odeint(dy, y0, t, args=(0.0, w0)) # undamped\n", + "y2 = odeint(dy, y0, t, args=(0.2, w0)) # under damped\n", + "y3 = odeint(dy, y0, t, args=(1.0, w0)) # critial damping\n", + "y4 = odeint(dy, y0, t, args=(5.0, w0)) # over damped" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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SJUvon//8Z0optddfUFBgPx4ZGUm3bNli/zxz5kz66quvUkopfe+992hCQoJT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\npNcknSzz74P3+HwnjrtNmzbR09PjTcEAbNiwgbGxMW7cuOH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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.plot(t, y1[:,0], 'k', label=\"undamped\", linewidth=0.25)\n", + "ax.plot(t, y2[:,0], 'r', label=\"under damped\")\n", + "ax.plot(t, y3[:,0], 'b', label=r\"critical damping\")\n", + "ax.plot(t, y4[:,0], 'g', label=\"over damped\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fourier transform" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fourier transforms are one of the universal tools in computational physics, which appear over and over again in different contexts. SciPy provides functions for accessing the classic [FFTPACK](http://www.netlib.org/fftpack/) library from NetLib, which is an efficient and well tested FFT library written in FORTRAN. The SciPy API has a few additional convenience functions, but overall the API is closely related to the original FORTRAN library.\n", + "\n", + "To use the `fftpack` module in a python program, include it using:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "from numpy.fft import fftfreq\n", + "from scipy.fftpack import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To demonstrate how to do a fast Fourier transform with SciPy, let's look at the FFT of the solution to the damped oscillator from the previous section:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "N = len(t)\n", + "dt = t[1]-t[0]\n", + "\n", + "# calculate the fast fourier transform\n", + "# y2 is the solution to the under-damped oscillator from the previous section\n", + "F = fft(y2[:,0]) \n", + "\n", + "# calculate the frequencies for the components in F\n", + "w = fftfreq(N, dt)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(9,3))\n", + "ax.plot(w, abs(F));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the signal is real, the spectrum is symmetric. We therefore only need to plot the part that corresponds to the postive frequencies. To extract that part of the `w` and `F` we can use some of the indexing tricks for NumPy arrays that we saw in Lecture 2:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "indices = where(w > 0) # select only indices for elements that corresponds to positive frequencies\n", + "w_pos = w[indices]\n", + "F_pos = F[indices]" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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RTIZCtHWKiIiIREbBQkRERCKjYCEiIiKRUbAQERGRyChYiIiISGQULERERCQy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\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(9,3))\n", + "ax.plot(w_pos, abs(F_pos))\n", + "ax.set_xlim(0, 5);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, we now see a peak in the spectrum that is centered around 1, which is the frequency we used in the damped oscillator example." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear algebra" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The linear algebra module contains a lot of matrix related functions, including linear equation solving, eigenvalue solvers, matrix functions (for example matrix-exponentiation), a number of different decompositions (SVD, LU, cholesky), etc. \n", + "\n", + "Detailed documetation is available at: http://docs.scipy.org/doc/scipy/reference/linalg.html\n", + "\n", + "Here we will look at how to use some of these functions:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Linear equation systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Linear equation systems on the matrix form\n", + "\n", + "$A x = b$\n", + "\n", + "where $A$ is a matrix and $x,b$ are vectors can be solved like:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.linalg import *\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "A = np.array([[1,2], [4,5]])\n", + "b = np.array([1,2])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.33333333, 0.66666667])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = solve(A, b)\n", + "\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0.])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check\n", + "np.dot(A, x) - b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also do the same with\n", + "\n", + "$A X = B$\n", + "\n", + "where $A, B, X$ are matrices:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "A = rand(3,3)\n", + "B = rand(3,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "X = solve(A, B)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.19168749, 1.34543171, 0.38437594],\n", + " [-0.88153715, -3.22735597, 0.66370273],\n", + " [ 0.10044006, 1.0465058 , 0.39801748]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.0014830212433605e-16" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check\n", + "norm(dot(A, X) - B)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Eigenvalues and eigenvectors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The eigenvalue problem for a matrix $A$:\n", + "\n", + "$\\displaystyle A v_n = \\lambda_n v_n$\n", + "\n", + "where $v_n$ is the $n$th eigenvector and $\\lambda_n$ is the $n$th eigenvalue.\n", + "\n", + "To calculate eigenvalues of a matrix, use the `eigvals` and for calculating both eigenvalues and eigenvectors, use the function `eig`:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "evals = eigvals(A)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.46410162+0.j, 6.46410162+0.j])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evals" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "evals, evecs = eig(A)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.08466629+0.j, 0.33612878+0.j, -0.28229973+0.j])" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evals" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.20946865, -0.48428024, -0.14392087],\n", + " [-0.79978578, 0.8616452 , -0.79527482],\n", + " [-0.56255275, 0.15178997, 0.58891829]])" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evecs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The eigenvectors corresponding to the $n$th eigenvalue (stored in `evals[n]`) is the $n$th *column* in `evecs`, i.e., `evecs[:,n]`. To verify this, let's try mutiplying eigenvectors with the matrix and compare to the product of the eigenvector and the eigenvalue:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.243515426387745e-16" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n = 1\n", + "\n", + "norm(dot(A, evecs[:,n]) - evals[n] * evecs[:,n])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are also more specialized eigensolvers, like the `eigh` for Hermitian matrices. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Matrix operations" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 2.0031935 , -0.63411453, 0.49891784],\n", + " [-4.63643938, -0.2212669 , 3.35170585],\n", + " [ 1.06421936, 1.37366073, -1.42726809]])" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the matrix inverse\n", + "inv(A)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.10292296739753022" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# determinant\n", + "det(A)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.3060382297688262, 1.591998214728641)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# norms of various orders\n", + "norm(A, ord=2), norm(A, ord=Inf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sparse matrices" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sparse matrices are often useful in numerical simulations dealing with large systems, if the problem can be described in matrix form where the matrices or vectors mostly contains zeros. Scipy has a good support for sparse matrices, with basic linear algebra operations (such as equation solving, eigenvalue calculations, etc).\n", + "\n", + "There are many possible strategies for storing sparse matrices in an efficient way. Some of the most common are the so-called coordinate form (COO), list of list (LIL) form, and compressed-sparse column CSC (and row, CSR). Each format has some advantanges and disadvantages. Most computational algorithms (equation solving, matrix-matrix multiplication, etc) can be efficiently implemented using CSR or CSC formats, but they are not so intuitive and not so easy to initialize. So often a sparse matrix is initially created in COO or LIL format (where we can efficiently add elements to the sparse matrix data), and then converted to CSC or CSR before used in real calcalations.\n", + "\n", + "For more information about these sparse formats, see e.g. http://en.wikipedia.org/wiki/Sparse_matrix\n", + "\n", + "When we create a sparse matrix we have to choose which format it should be stored in. For example, " + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.sparse import *" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 0, 0, 0],\n", + " [0, 3, 0, 0],\n", + " [0, 1, 1, 0],\n", + " [1, 0, 0, 1]])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# dense matrix\n", + "M = array([[1,0,0,0], [0,3,0,0], [0,1,1,0], [1,0,0,1]]); M" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<4x4 sparse matrix of type ''\n", + "\twith 6 stored elements in Compressed Sparse Row format>" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# convert from dense to sparse\n", + "A = csr_matrix(M); A" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[1, 0, 0, 0],\n", + " [0, 3, 0, 0],\n", + " [0, 1, 1, 0],\n", + " [1, 0, 0, 1]])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# convert from sparse to dense\n", + "A.todense()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "More efficient way to create sparse matrices: create an empty matrix and populate with using matrix indexing (avoids creating a potentially large dense matrix)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<4x4 sparse matrix of type ''\n", + "\twith 6 stored elements in LInked List format>" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = lil_matrix((4,4)) # empty 4x4 sparse matrix\n", + "A[0,0] = 1\n", + "A[1,1] = 3\n", + "A[2,2] = A[2,1] = 1\n", + "A[3,3] = A[3,0] = 1\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[ 1., 0., 0., 0.],\n", + " [ 0., 3., 0., 0.],\n", + " [ 0., 1., 1., 0.],\n", + " [ 1., 0., 0., 1.]])" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.todense()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Converting between different sparse matrix formats:" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<4x4 sparse matrix of type ''\n", + "\twith 6 stored elements in LInked List format>" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<4x4 sparse matrix of type ''\n", + "\twith 6 stored elements in Compressed Sparse Row format>" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = csr_matrix(A); A" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<4x4 sparse matrix of type ''\n", + "\twith 6 stored elements in Compressed Sparse Column format>" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = csc_matrix(A); A" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can compute with sparse matrices like with dense matrices:" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[ 1., 0., 0., 0.],\n", + " [ 0., 3., 0., 0.],\n", + " [ 0., 1., 1., 0.],\n", + " [ 1., 0., 0., 1.]])" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.todense()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[ 1., 0., 0., 0.],\n", + " [ 0., 9., 0., 0.],\n", + " [ 0., 4., 1., 0.],\n", + " [ 2., 0., 0., 1.]])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(A * A).todense()" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[ 1., 0., 0., 0.],\n", + " [ 0., 3., 0., 0.],\n", + " [ 0., 1., 1., 0.],\n", + " [ 1., 0., 0., 1.]])" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.todense()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[ 1., 0., 0., 0.],\n", + " [ 0., 9., 0., 0.],\n", + " [ 0., 4., 1., 0.],\n", + " [ 2., 0., 0., 1.]])" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.dot(A).todense()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1],\n", + " [2],\n", + " [3],\n", + " [4]])" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v = array([1,2,3,4])[:,newaxis]; v" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.],\n", + " [ 6.],\n", + " [ 5.],\n", + " [ 5.]])" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# sparse matrix - dense vector multiplication\n", + "A * v" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "matrix([[ 1.],\n", + " [ 6.],\n", + " [ 5.],\n", + " [ 5.]])" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# same result with dense matrix - dense vector multiplcation\n", + "A.todense() * v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Optimization (finding minima or maxima of a function) is a large field in mathematics, and optimization of complicated functions or in many variables can be rather involved. Here we will only look at a few very simple cases. For a more detailed introduction to optimization with SciPy see: http://scipy-lectures.github.com/advanced/mathematical_optimization/index.html\n", + "\n", + "To use the optimization module in scipy first include the `optimize` module:" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import optimize" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Finding a minima" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's first look at how to find the minima of a simple function of a single variable:" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "def f(x):\n", + " return 4*x**3 + (x-2)**2 + x**4" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "x = linspace(-5, 3, 100)\n", + "ax.plot(x, f(x));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use the `fmin_bfgs` function to find the minima of a function:" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: -3.506641\n", + " Iterations: 6\n", + " Function evaluations: 30\n", + " Gradient evaluations: 10\n" + ] + }, + { + "data": { + "text/plain": [ + "array([-2.67298164])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_min = optimize.fmin_bfgs(f, -2)\n", + "x_min " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 2.804988\n", + " Iterations: 3\n", + " Function evaluations: 15\n", + " Gradient evaluations: 5\n" + ] + }, + { + "data": { + "text/plain": [ + "array([ 0.46961745])" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimize.fmin_bfgs(f, 0.5) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also use the `brent` or `fminbound` functions. They have a bit different syntax and use different algorithms. " + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.46961743402759754" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimize.brent(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-2.6729822917513886" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimize.fminbound(f, -4, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Finding a solution to a function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To find the root for a function of the form $f(x) = 0$ we can use the `fsolve` function. It requires an initial guess: " + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "omega_c = 3.0\n", + "def f(omega):\n", + " # a transcendental equation: resonance frequencies of a low-Q SQUID terminated microwave resonator\n", + " return tan(2*pi*omega) - omega_c/omega" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/rob/miniconda/envs/py27-spl/lib/python2.7/site-packages/IPython/kernel/__main__.py:4: RuntimeWarning: divide by zero encountered in divide\n" + ] + }, + { + "data": { + "image/png": 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oACdaa08BsBjAk8mH5A4rVMEqLJTmYI5MaLhHHgGuuQY4+2zXkaS+JUuAX/5S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\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,4))\n", + "x = linspace(0, 3, 1000)\n", + "y = f(x)\n", + "mask = where(abs(y) > 50)\n", + "x[mask] = y[mask] = NaN # get rid of vertical line when the function flip sign\n", + "ax.plot(x, y)\n", + "ax.plot([0, 3], [0, 0], 'k')\n", + "ax.set_ylim(-5,5);" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.23743014])" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimize.fsolve(f, 0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.71286972])" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimize.fsolve(f, 0.6)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.18990285])" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "optimize.fsolve(f, 1.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interpolation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Interpolation is simple and convenient in scipy: The `interp1d` function, when given arrays describing X and Y data, returns and object that behaves like a function that can be called for an arbitrary value of x (in the range covered by X), and it returns the corresponding interpolated y value:" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.interpolate import *" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "def f(x):\n", + " return sin(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "n = arange(0, 10) \n", + "x = linspace(0, 9, 100)\n", + "\n", + "y_meas = f(n) + 0.1 * randn(len(n)) # simulate measurement with noise\n", + "y_real = f(x)\n", + "\n", + "linear_interpolation = interp1d(n, y_meas)\n", + "y_interp1 = linear_interpolation(x)\n", + "\n", + "cubic_interpolation = interp1d(n, y_meas, kind='cubic')\n", + "y_interp2 = cubic_interpolation(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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jmVKv2apVKyxYsAAAMHLkSCQlJSn1eoyx4ik0NBRLly6FIAjw8/NDqVI6qGh4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10,4))\n", + "ax.plot(n, y_meas, 'bs', label='noisy data')\n", + "ax.plot(x, y_real, 'k', lw=2, label='true function')\n", + "ax.plot(x, y_interp1, 'r', label='linear interp')\n", + "ax.plot(x, y_interp2, 'g', label='cubic interp')\n", + "ax.legend(loc=3);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Statistics" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `scipy.stats` module contains a large number of statistical distributions, statistical functions and tests. For a complete documentation of its features, see http://docs.scipy.org/doc/scipy/reference/stats.html.\n", + "\n", + "There is also a very powerful python package for statistical modelling called statsmodels. See http://statsmodels.sourceforge.net for more details." + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy import stats" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "# create a (discreet) random variable with poissionian distribution\n", + "\n", + "X = stats.poisson(3.5) # photon distribution for a coherent state with n=3.5 photons" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n = arange(0,15)\n", + "\n", + "fig, axes = plt.subplots(3,1, sharex=True)\n", + "\n", + "# plot the probability mass function (PMF)\n", + "axes[0].step(n, X.pmf(n))\n", + "\n", + "# plot the commulative distribution function (CDF)\n", + "axes[1].step(n, X.cdf(n))\n", + "\n", + "# plot histogram of 1000 random realizations of the stochastic variable X\n", + "axes[2].hist(X.rvs(size=1000));" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "# create a (continous) random variable with normal distribution\n", + "Y = stats.norm()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = linspace(-5,5,100)\n", + "\n", + "fig, axes = plt.subplots(3,1, sharex=True)\n", + "\n", + "# plot the probability distribution function (PDF)\n", + "axes[0].plot(x, Y.pdf(x))\n", + "\n", + "# plot the commulative distributin function (CDF)\n", + "axes[1].plot(x, Y.cdf(x));\n", + "\n", + "# plot histogram of 1000 random realizations of the stochastic variable Y\n", + "axes[2].hist(Y.rvs(size=1000), bins=50);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Statistics:" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3.5, 1.8708286933869707, 3.5)" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.mean(), X.std(), X.var() # poission distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 1.0, 1.0)" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y.mean(), Y.std(), Y.var() # normal distribution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Statistical tests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Test if two sets of (independent) random data comes from the same distribution:" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "t-statistic = -0.901953297251\n", + "p-value = 0.367190391714\n" + ] + } + ], + "source": [ + "t_statistic, p_value = stats.ttest_ind(X.rvs(size=1000), X.rvs(size=1000))\n", + "\n", + "print \"t-statistic =\", t_statistic\n", + "print \"p-value =\", p_value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the p value is very large we cannot reject the hypothesis that the two sets of random data have *different* means." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To test if the mean of a single sample of data has mean 0.1 (the true mean is 0.0):" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Ttest_1sampResult(statistic=-3.1644288210071765, pvalue=0.0016008455559249511)" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.ttest_1samp(Y.rvs(size=1000), 0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Low p-value means that we can reject the hypothesis that the mean of Y is 0.1." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Y.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Ttest_1sampResult(statistic=2.2098772438652992, pvalue=0.027339807364469011)" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats.ttest_1samp(Y.rvs(size=1000), Y.mean())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Further reading" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* http://www.scipy.org - The official web page for the SciPy project.\n", + "* http://docs.scipy.org/doc/scipy/reference/tutorial/index.html - A tutorial on how to get started using SciPy. \n", + "* https://github.com/scipy/scipy/ - The SciPy source code. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Versions" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "data": { + "application/json": { + "Software versions": [ + { + "module": "Python", + "version": "2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]" + }, + { + "module": "IPython", + "version": "3.2.1" + }, + { + "module": "OS", + "version": "Darwin 14.1.0 x86_64 i386 64bit" + }, + { + "module": "numpy", + "version": "1.9.2" + }, + { + "module": "matplotlib", + "version": "1.4.3" + }, + { + "module": "scipy", + "version": "0.16.0" + } + ] + }, + "text/html": [ + "
SoftwareVersion
Python2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]
IPython3.2.1
OSDarwin 14.1.0 x86_64 i386 64bit
numpy1.9.2
matplotlib1.4.3
scipy0.16.0
Sat Aug 15 11:13:18 2015 JST
" + ], + "text/latex": [ + "\\begin{tabular}{|l|l|}\\hline\n", + "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n", + "Python & 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)] \\\\ \\hline\n", + "IPython & 3.2.1 \\\\ \\hline\n", + "OS & Darwin 14.1.0 x86\\_64 i386 64bit \\\\ \\hline\n", + "numpy & 1.9.2 \\\\ \\hline\n", + "matplotlib & 1.4.3 \\\\ \\hline\n", + "scipy & 0.16.0 \\\\ \\hline\n", + "\\hline \\multicolumn{2}{|l|}{Sat Aug 15 11:13:18 2015 JST} \\\\ \\hline\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "Software versions\n", + "Python 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]\n", + "IPython 3.2.1\n", + "OS Darwin 14.1.0 x86_64 i386 64bit\n", + "numpy 1.9.2\n", + "matplotlib 1.4.3\n", + "scipy 0.16.0\n", + "Sat Aug 15 11:13:18 2015 JST" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%reload_ext version_information\n", + "\n", + "%version_information numpy, matplotlib, scipy" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/1_numpy_matplotlib_scipy_sympy/4-sympy_tutorial.ipynb b/1_numpy_matplotlib_scipy_sympy/5-sympy_tutorial.ipynb similarity index 100% rename from 1_numpy_matplotlib_scipy_sympy/4-sympy_tutorial.ipynb rename to 1_numpy_matplotlib_scipy_sympy/5-sympy_tutorial.ipynb diff --git a/1_numpy_matplotlib_scipy_sympy/5-sympy_tutorial_EN.ipynb b/1_numpy_matplotlib_scipy_sympy/5-sympy_tutorial_EN.ipynb new file mode 100644 index 0000000..c4de80e --- /dev/null +++ b/1_numpy_matplotlib_scipy_sympy/5-sympy_tutorial_EN.ipynb @@ -0,0 +1,2534 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sympy - Symbolic algebra in Python" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "J.R. Johansson (jrjohansson at gmail.com)\n", + "\n", + "The latest version of this [IPython notebook](http://ipython.org/notebook.html) lecture is available at [http://github.com/jrjohansson/scientific-python-lectures](http://github.com/jrjohansson/scientific-python-lectures).\n", + "\n", + "The other notebooks in this lecture series are indexed at [http://jrjohansson.github.io](http://jrjohansson.github.io)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are two notable Computer Algebra Systems (CAS) for Python:\n", + "\n", + "* [SymPy](http://sympy.org/en/index.html) - A python module that can be used in any Python program, or in an IPython session, that provides powerful CAS features. \n", + "* [Sage](http://www.sagemath.org/) - Sage is a full-featured and very powerful CAS enviroment that aims to provide an open source system that competes with Mathematica and Maple. Sage is not a regular Python module, but rather a CAS environment that uses Python as its programming language.\n", + "\n", + "Sage is in some aspects more powerful than SymPy, but both offer very comprehensive CAS functionality. The advantage of SymPy is that it is a regular Python module and integrates well with the IPython notebook. \n", + "\n", + "In this lecture we will therefore look at how to use SymPy with IPython notebooks. If you are interested in an open source CAS environment I also recommend to read more about Sage.\n", + "\n", + "To get started using SymPy in a Python program or notebook, import the module `sympy`:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from sympy import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get nice-looking $\\LaTeX$ formatted output run:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "init_printing()\n", + "\n", + "# or with older versions of sympy/ipython, load the IPython extension\n", + "#%load_ext sympy.interactive.ipythonprinting\n", + "# or\n", + "#%load_ext sympyprinting" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Symbolic variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In SymPy we need to create symbols for the variables we want to work with. We can create a new symbol using the `Symbol` class:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "x = Symbol('x')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$\\left(x + \\pi\\right)^{2}$$" + ], + "text/plain": [ + " 2\n", + "(x + π) " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(pi + x)**2" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# alternative way of defining symbols\n", + "a, b, c = symbols(\"a, b, c\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "sympy.core.symbol.Symbol" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can add assumptions to symbols when we create them:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "x = Symbol('x', real=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x.is_imaginary" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "x = Symbol('x', positive=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAACoAAAAPBAMAAABgjEDtAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMA782r3SJ2ZjIQmUS7\nVIlAnjihAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVQYGWNg/GQs72z0hQEV8Acw5BcwNKIK\nMsxiAIkyo4mGg0XZJqAKR4BFOUCi0Q2c3QFwWaAJ3Iq5j0LXH+A9n8DAuvwxWAooysC4dn4B0wEG\n/gSGLRO4JUEaQKJMDgwMPGBROQYGMaAgRNQAKsrxq7zcHC66ACrK/hckBARgExbA1H4DiyFEmQ8w\nxCcwODEwTIOpZQGqZRdguHiSob+AYSUDA/caeZkV3Of/XGBgeJc2RWQCp1XeBKghaBQAM0c287zN\nvm0AAAAASUVORK5CYII=\n", + "text/latex": [ + "$$\\mathrm{True}$$" + ], + "text/plain": [ + "True" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x > 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Complex numbers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The imaginary unit is denoted `I` in Sympy. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAACoAAAAQBAMAAACSDPCjAAAALVBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAOrOgAAAADnRSTlMAVO8Qq5l2zWaJ3SK7RPx7\nN2kAAAAJcEhZcwAADsQAAA7EAZUrDhsAAABySURBVBgZY2DABCwHGBgYldHFWRoYTEIeQ0UZE5Ck\n2SgRNQsDmoRhwoRLWES5BcLhopzl5VXq5eUODAzMDM/gokAGzGWMIEdhmMvuYIBF1E5gAhbRe9wN\nDKxOzz2A5gABzFyWDggfQsJEkcWATtoA5wMA/Fcc5MixWvAAAAAASUVORK5CYII=\n", + "text/latex": [ + "$$1 + i$$" + ], + "text/plain": [ + "1 + ⅈ" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1+1*I" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAABgAAAAPBAMAAAAMihLoAAAAJFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAADHJj5lAAAAC3RSTlMAEM3dMlTvq5l2ZtVdCTcAAAAJcEhZcwAA\nDsQAAA7EAZUrDhsAAAAqSURBVAgdY2DAClgTEcLi7RsRHAZOMjlCxiCgwkC2ATA3cJRtqoKxwTQA\nC0AL2ft3JesAAAAASUVORK5CYII=\n", + "text/latex": [ + "$$-1$$" + ], + "text/plain": [ + "-1" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "I**2" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEwAAAAbBAMAAAAkMnRXAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIma7zZnddlTvRImr\nEDIioekeAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABXElEQVQ4EY2SPUvEQBCG3z0xauKeQUEsg4i9\ngqVwYgqRIDZWWlhYaJcmWB1i40chXm2jlRaClYeVyJ2IpRHs/BHGykPQczYTwq0mXgayzLzzZIbZ\nHaC71WfC7hAse7BSADPs8nsR7Ex8FsCA/q9CmOEXwupECYeOPCvZlCkt0zFC330ehgZlLnEFzJHT\nzMQO6VZfgPLCwSxX1Kh9juTDE2GWj4F2O4KhumuWYMAOYbLGuVENUYGGYZHzAf0R+JDbjYsbVnRs\nhcVp6m/W8IpJfyMLm2BxExjrreAWd+FuFrbH4gkQDvkIMR/H16675bpe7KsR8JZiOFbxB8e/R0gw\nagpP0iV+Z2PnLNMIsmXhqC9ClRV90mQEen0zGjZbPZFwsrBVFlXRpiMfq8E4C+n1rp16DrDE6j+P\npQCxzli8TOwm53NnRE/PNtWp/vFpkdjUWuZbWkQ4+RDiJaf8DywZUcb5dpcnAAAAAElFTkSuQmCC\n", + "text/latex": [ + "$$\\left(i x + 1\\right)^{2}$$" + ], + "text/plain": [ + " 2\n", + "(ⅈ⋅x + 1) " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(x * I + 1)**2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rational numbers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are three different numerical types in SymPy: `Real`, `Rational`, `Integer`: " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "r1 = Rational(4,5)\n", + "r2 = Rational(5,4)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAsAAAAqBAMAAACXcryGAAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMpndu3bvImbNiRBUq0Qb3U6NAAAAkklEQVQYGWNgYGAQAmIgMAGTrClgqmIKmFoApjgKwNRWBjC1AEzxCICpXQxg6uzdu9+ugnVAtDN8AXOW/L8Bpski/oPAB6K0Tt0g9ACoMP//V5DyWVcLQNQFEAGnbrQqgnjeDPUPQDQDiwGYYvrOwMA7gYHrHwMD2wQGpt8MDEwMDMwTGBjYFRjaQMYUrdVmYAAAOF8pKUDr98cAAAAASUVORK5CYII=\n", + "text/latex": [ + "$$\\frac{4}{5}$$" + ], + "text/plain": [ + "4/5" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r1" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$\\frac{41}{20}$$" + ], + "text/plain": [ + "41\n", + "──\n", + "20" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r1+r2" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAABUAAAAqBAMAAACuFQ3dAAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAzRAiu5mrdu/dZokyRFRd1xrCAAABAklEQVQYGWNgYGBUYGBg2F19AEgy+wPZihdYBRgYmFzzgWxpBhYHoDhDvQID12cQAwiAbJYGIA0CQDa/zPZYGFtfioHjAVRc/xMDaxOUzT+BgesvlM0hwMD1C8pmaUCI8wHVN0DFGbwZNB4A2WH90xYwcIfmgIRpDP7DwQfKbDIN8WFgEFc4egEYhgkM+y8w9P//DjSRTYGBo4FBOt0AyOaYwMD2hyEBbA/jJwQbKMD5iSFreQxYhuH+BgYnBnugOUAwBUSwTACRrA5gEhwdZUCDBRi4QWHLF8BgDAxb1k9A9lEGhmJWoGECwCidvTtsAnMAwwqgxYxAP05gMN0WxMAAAJ3jRrgsItfHAAAAAElFTkSuQmCC\n", + "text/latex": [ + "$$\\frac{16}{25}$$" + ], + "text/plain": [ + "16\n", + "──\n", + "25" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r1/r2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Numerical evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "SymPy uses a library for artitrary precision as numerical backend, and has predefined SymPy expressions for a number of mathematical constants, such as: `pi`, `e`, `oo` for infinity.\n", + "\n", + "To evaluate an expression numerically we can use the `evalf` function (or `N`). It takes an argument `n` which specifies the number of significant digits." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$3.1415926535897932384626433832795028841971693993751$$" + ], + "text/plain": [ + "3.1415926535897932384626433832795028841971693993751" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pi.evalf(n=50)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "y = (x + pi)**2" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$\\left(x + 3.1416\\right)^{2}$$" + ], + "text/plain": [ + " 2\n", + "(x + 3.1416) " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "N(y, 5) # same as evalf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When we numerically evaluate algebraic expressions we often want to substitute a symbol with a numerical value. In SymPy we do that using the `subs` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFYAAAAbBAMAAAAUvmV2AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIma7zZnddlTvRIkQ\nqzLsm4+cAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABlElEQVQ4EY2TO0jDUBSG/1QSLUkfKLqaQRA3\nLbh1qBBQlEoXJx901EGog651sro5u7STiyAKdlQiuFsFcdRFcFNBFxXiOffePBpEcyDn8Z+vNyfJ\nKZDQ2pNuQhJmPlNKyhr57FtitqV9JmWBvu/krFFLzrYJ1Wxyf1oqT+1UhdxAyE27T9thFWY3lB7h\nGJiiZM8Vjab3JWLcXQHZmd0in23dPUh27p7vxtaQQXmzhrTnvcLgdt0Vakf14qy1LxtDHP5hMSvZ\nzQh7O7oixWCGuud5H8CClAsRtojhllTVvOZFo3+DhhyR6mqEBXqqXAbzPuO095HKHbrIDtipeQGd\nl+nMcdYcp8wNjGdc8i+cdrG0oaZaJv+dZTsGQ4qNzJAuQVfL5LO6nXOJPeQfAJFn0wGjJMTgPVy7\nuRYp6tl4fXhe6x1aBWP8Zcj8c7eQPqFyUYji+OVm2cY6MHi+JMWALSDD7LyUxTdWRBj8c6WiVWUU\nexlCKrvsUmh3pE10yb8WtJPSIrvuS/EYHKfZ8Va8Fv8hEn8A9AZYsVBvIRkAAAAASUVORK5CYII=\n", + "text/latex": [ + "$$\\left(1.5 + \\pi\\right)^{2}$$" + ], + "text/plain": [ + " 2\n", + "(1.5 + π) " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y.subs(x, 1.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKAAAAAPBAMAAACRq9klAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nQmCC\n", + "text/latex": [ + "$$21.5443823618587$$" + ], + "text/plain": [ + "21.5443823618587" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "N(y.subs(x, 1.5))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `subs` function can of course also be used to substitute Symbols and expressions:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFIAAAAbBAMAAAAdVcUMAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nTkSuQmCC\n", + "text/latex": [ + "$$\\left(a + 2 \\pi\\right)^{2}$$" + ], + "text/plain": [ + " 2\n", + "(a + 2⋅π) " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y.subs(x, a+pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also combine numerical evolution of expressions with NumPy arrays:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "x_vec = numpy.arange(0, 10, 0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "y_vec = numpy.array([N(((x + pi)**2).subs(x, xx)) for xx in x_vec])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.plot(x_vec, y_vec);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, this kind of numerical evolution can be very slow, and there is a much more efficient way to do it: Use the function `lambdify` to \"compile\" a Sympy expression into a function that is much more efficient to evaluate numerically:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "f = lambdify([x], (x + pi)**2, 'numpy') # the first argument is a list of variables that\n", + " # f will be a function of: in this case only x -> f(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "y_vec = f(x_vec) # now we can directly pass a numpy array and f(x) is efficiently evaluated" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The speedup when using \"lambdified\" functions instead of direct numerical evaluation can be significant, often several orders of magnitude. Even in this simple example we get a significant speed up:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 loops, best of 3: 28.2 ms per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "\n", + "y_vec = numpy.array([N(((x + pi)**2).subs(x, xx)) for xx in x_vec])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The slowest run took 8.86 times longer than the fastest. This could mean that an intermediate result is being cached \n", + "100000 loops, best of 3: 2.93 µs per loop\n" + ] + } + ], + "source": [ + "%%timeit\n", + "\n", + "y_vec = f(x_vec)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Algebraic manipulations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the main uses of an CAS is to perform algebraic manipulations of expressions. For example, we might want to expand a product, factor an expression, or simply an expression. The functions for doing these basic operations in SymPy are demonstrated in this section." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Expand and factor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first steps in an algebraic manipulation " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$\\left(x + 1\\right) \\left(x + 2\\right) \\left(x + 3\\right)$$" + ], + "text/plain": [ + "(x + 1)⋅(x + 2)⋅(x + 3)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(x+1)*(x+2)*(x+3)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$x^{3} + 6 x^{2} + 11 x + 6$$" + ], + "text/plain": [ + " 3 2 \n", + "x + 6⋅x + 11⋅x + 6" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expand((x+1)*(x+2)*(x+3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `expand` function takes a number of keywords arguments which we can tell the functions what kind of expansions we want to have performed. For example, to expand trigonometric expressions, use the `trig=True` keyword argument:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$\\sin{\\left (a + b \\right )}$$" + ], + "text/plain": [ + "sin(a + b)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sin(a+b)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$\\sin{\\left (a \\right )} \\cos{\\left (b \\right )} + \\sin{\\left (b \\right )} \\cos{\\left (a \\right )}$$" + ], + "text/plain": [ + "sin(a)⋅cos(b) + sin(b)⋅cos(a)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expand(sin(a+b), trig=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See `help(expand)` for a detailed explanation of the various types of expansions the `expand` functions can perform." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The opposite a product expansion is of course factoring. The factor an expression in SymPy use the `factor` function: " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$\\left(x + 1\\right) \\left(x + 2\\right) \\left(x + 3\\right)$$" + ], + "text/plain": [ + "(x + 1)⋅(x + 2)⋅(x + 3)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "factor(x**3 + 6 * x**2 + 11*x + 6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Simplify" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `simplify` tries to simplify an expression into a nice looking expression, using various techniques. More specific alternatives to the `simplify` functions also exists: `trigsimp`, `powsimp`, `logcombine`, etc. \n", + "\n", + "The basic usages of these functions are as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMEAAAAVBAMAAAANw5eWAAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMmYiu80QdonvRN2ZVKvu110NAAACwElEQVRIDbWVu4sTURTGvzxm8py4WFkIxhUrLYJuo2wR9Q9ICsfCBwyorDYSEHZT7QbxsYW4EbXQKtiIWGxARQXFFFsuS0DQwmbVQuyiYCwWiefcRzJnnbiVt7j3nnO+8/vmztwQ4P+PWHGcx25T2D5OEJ/QlVhxnEIhpqKrJ4CCBiTKUYrplSPAJ12JRCQWr3Y1glARY6pPyaoqOEFE3V3HUguOrkQipuH9UIhUI9zvlnR0bo0d6irQs1Wd1ZtCgEwTulkiYBQLXWwoRMYwdaNXM6QUO1xUwR2T0ssHvWTaKHyH2+RIImAUj1vuT4V4ojvMLB2cLqWzbaEw/U6fHXCGaxJhHaDeEiFukmRm/nBdP7x0SHeoyJM79+XRDdrTMA60y9ExuXsTYqRwOqr7KPU34i9yVdZCOsQ5mSwCF7Cv9pzrYYflBrCXUxIxdNjxmmqEeEM+QbrvlVi7ycFrUyrXBa5jKXinBMN+4BklPnJSIkaK9G0CtvGAzgCnzEoa8gyxHqUyLSDAca7yGL6lfJmiXZySiJEC9wMQgsrAthrPad8/dc/3O7xXd2noAPziJK75/ivfP6328zxbB4sIKQ4ClS470BFJOEGPyUOeQV0jfkuI0c1UguETeh0QQ10jibCKQYBKkW8ifSYvWEAqUADpMPzS71M9zEqHncDl0ZcOIazDXeBtib/0LWC5W8EB3S8d8h3KpteR/Z3sJYpaYb5D4uXiWhtY5aREWIdJePRqCUHnvDQ3c8X0W4f8t43PgNOC+sW5X2frk7TlYRycwWBADic5JRFWkV1daSlErsQiO6yDjg+p5aktqtU46FyiyatEWAetIES8o7d6zhbDkf4Z7w+n8DAcpWscSYRUMOJYuEXs3aoKk+KUQnFeR1sgIv8+wuhEWVDDgUFvgbCXJNyp93tMytyzvwVxc7p/Iv4AakC5V6GQHjwAAAAASUVORK5CYII=\n", + "text/latex": [ + "$$\\left(x + 1\\right) \\left(x + 2\\right) \\left(x + 3\\right)$$" + ], + "text/plain": [ + "(x + 1)⋅(x + 2)⋅(x + 3)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# simplify expands a product\n", + "simplify((x+1)*(x+2)*(x+3))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAgAAAAPBAMAAAArJJMAAAAAJFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADHJj5lAAAAC3RSTlMAzRAiu5mrdu/dZmiL4QAAAAAjSURBVAgdY2BgEGJgYDDZxMCgEgYkGNhJJVgzdmYB9TEwAACPpQrvlUCHcAAAAABJRU5ErkJggg==\n", + "text/latex": [ + "$$1$$" + ], + "text/plain": [ + "1" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# simplify uses trigonometric identities\n", + "simplify(sin(a)**2 + cos(a)**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$\\frac{1}{\\tan{\\left (x \\right )}}$$" + ], + "text/plain": [ + " 1 \n", + "──────\n", + "tan(x)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "simplify(cos(x)/sin(x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### apart and together" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To manipulate symbolic expressions of fractions, we can use the `apart` and `together` functions:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "f1 = 1/((a+1)*(a+2))" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIIAAAAvBAMAAADdrw/+AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAVO8Qq5l2zWYiu91E\niTJVJ+QZAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAB/klEQVRIDe2UP0gjQRjF37pu/phEjgMLK0EL\nz1NwCekT7lAEC1OohSCGAzvlUohBLJJWbLbQ5mzs7k91gtgqtjY2sVPS2ikWFnca38xs3Gwcs0UQ\nhGSamfm+37z55u3MAm/ePtqtbWFOzLSoAHzvKMiP0PFB3cV34MPs+FqptYfRWd1mDlRbbW3mV7Pj\nRkuvZbdUIhD48ZoAuh2Z0gLRuT+VGrChVTCGADMvU1oghcR/F4hk6xWMnJqlZu84uBATPfAVGHSB\nmCpVLURNASGhkBRRPTAKzFQUsOeuVZ1fIXzCqB4o2kJBAsuEoovzPLZofgVLnFAA2C1/s0XvAcCx\nDQn8YvjUCd/IvAfIU0TyjAogPoJiA4D4Az0SwAE9/wRrn0M2fw3GX4YIIFnCgkjXAYhlOBPAGGDd\nIZbjMFEobA4XCsxAOdl7wxEBLNk4YlcPYI6BGtC1j3SFczZ/DTUAn2E+NgJZBiTAItPnGFD5BoW4\newrzAYYoh+15iz5EHUiARqU/4Kfh+AGfk+YtevL9PqA3i5CjnCwD4VzkMCHz3hZSwTpnlADWsZ0R\nY6+GvcvyNB0UwTN+i8mrnRWZf1awvtxPUbrCKAGkVq8nT9h7CsVq9Z8L6C+tpNV1DAT8D8dUdigB\n3cvSAPjt4i8793UHAtofiJTrUQUFAoE/sebAE+USwojXIqQHAAAAAElFTkSuQmCC\n", + "text/latex": [ + "$$\\frac{1}{\\left(a + 1\\right) \\left(a + 2\\right)}$$" + ], + "text/plain": [ + " 1 \n", + "───────────────\n", + "(a + 1)⋅(a + 2)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f1" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAI4AAAAsBAMAAABBB53eAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nUyuKBLIyAOpdmg617KB4AAAAAElFTkSuQmCC\n", + "text/latex": [ + "$$- \\frac{1}{a + 2} + \\frac{1}{a + 1}$$" + ], + "text/plain": [ + " 1 1 \n", + "- ───── + ─────\n", + " a + 2 a + 1" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "apart(f1)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "f2 = 1/(a+2) + 1/(a+3)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAHwAAAAsBAMAAABVvsF6AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nnOfh0q8AAAAASUVORK5CYII=\n", + "text/latex": [ + "$$\\frac{1}{a + 3} + \\frac{1}{a + 2}$$" + ], + "text/plain": [ + " 1 1 \n", + "───── + ─────\n", + "a + 3 a + 2" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f2" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIIAAAAvBAMAAADdrw/+AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIpm7MhCriUTv3c12\nVGZoascqAAAACXBIWXMAAA7EAAAOxAGVKw4bAAACs0lEQVRIDe2WzWsTQRjGn02y+dpuSCMo9LQk\nYC9KK3pS0OhfsKdCvSQIXrRgiGIugrnWi5YiWAsSPNSDivVqhKQFUU9G/wHr1UPpwdDi1/rOzs42\n231NFkoPgnPYd/eZ3zz77uzMvguEW6F0KiwCxpvEcU4Pa3oVt5thGRnHqTEyIyVtZO4N6GZXXhg3\n1wbUYaeZFpLfBgDlkBzQhp8a/f06kH+2D23y2H15Jz+HzfWyVCIc79Ywnze2JKkc4ra2E2GsRFag\nP0JsOuhAV6ejOsQsxPrIdolPtduvltvtqhw6lZdx5HEdyEyjUpageoqr4NcJY2daGK/U8NnrUg7L\nwJTN4Ix0AVir9HDGlDkrBwt4ytCMpD1pHG0Z3dxKSnYqhwJyCwzOSIbjOC197sbhd0EHbfJSxIfY\na6py2KtHv9ajvsLolv/JA5oBWj77aweU179oq238LesvsmMkcIgz0Iony0jKPcUDjes9BbzkHGaQ\n2oG+6naxQAHmbw/IWYMO6mNwGVgCbokuHvjQw3cPyAa2v3J4CFwrY1Y48MB8U/8BCUwIym/KoWML\nB6NJHTwA8RQSoOLAFEkSX9ig0gMIAIX6e1tEqFsAF2uQwFmSw0USSG/TFIipFEB6AR0K1HyHI4/J\n0gWegy+S2SrhLRpDAGY3UKJAzXdA6oEHUGSLZJHwxBYdCBDF6hmFYBVdsn2AKZIYs2iE77AI3fut\nUTmMA+d6EqAkmSKJT9DySLfIhgB9G6ZIh5pycGzh4AI0UUyRTFgYy/szqf9CfPVjwGGRXpYH1Omt\nhovkRKN+hSaoRqMIwGt8rYrz3Rw2Yf70gDuUZLhIdhyHFq1RpjEEYObt+bkmxV2HdLFEggvwi9al\n5XIcCQQ3TrBIMjuLAXBC3o85ert7JMB+QFy/eN4NI4GRH7HhwB+u8vuFvy0h3AAAAABJRU5ErkJg\ngg==\n", + "text/latex": [ + "$$\\frac{2 a + 5}{\\left(a + 2\\right) \\left(a + 3\\right)}$$" + ], + "text/plain": [ + " 2⋅a + 5 \n", + "───────────────\n", + "(a + 2)⋅(a + 3)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "together(f2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simplify usually combines fractions but does not factor: " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIIAAAAvBAMAAADdrw/+AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\ngg==\n", + "text/latex": [ + "$$\\frac{2 a + 5}{\\left(a + 2\\right) \\left(a + 3\\right)}$$" + ], + "text/plain": [ + " 2⋅a + 5 \n", + "───────────────\n", + "(a + 2)⋅(a + 3)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "simplify(f2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculus" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to algebraic manipulations, the other main use of CAS is to do calculus, like derivatives and integrals of algebraic expressions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Differentiation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Differentiation is usually simple. Use the `diff` function. The first argument is the expression to take the derivative of, and the second argument is the symbol by which to take the derivative:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$\\left(x + \\pi\\right)^{2}$$" + ], + "text/plain": [ + " 2\n", + "(x + π) " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$4 \\left(x + \\pi\\right)^{3}$$" + ], + "text/plain": [ + " 3\n", + "4⋅(x + π) " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "diff(y**2, x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For higher order derivatives we can do:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGEAAAAbBAMAAACekfw3AAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAzRAiu5mrdu/dZjKJRFRer8KoAAAB1UlEQVQ4EZWTP0gjQRTGv2xi1phdTcTGyilELIRLccVpYworqyBqHbXwtDFoIYJwiI1XHCoWlgbEQm20tdCgiIWC6WzFP9h5QRAR/6zvzcwqExJhXzHz3ve9376ZZRcIEk5/X5B26j3CUEBiBQepYMg/dBeDEcB/EZRYDQo4mXJir1z4qrc53ZB1IxAe6NnhPJyWSsUlSh1uwaX15wPQBueN20KClmqRAHbvbwRaB4kYBha4cdbotlJGSeai55FkE/EXmKBxmDda6nNG2e5XTLQISbhLvij3MiJ0qV0mKNYEwO/Omh65OJEKfOKP53mPypWGItxXKiJ5oAObuWWDcE7vuqZSQF1CyfIeQCxNZewSOMS5uFaWnrGPI3uHlPolJWuin6vaIiDQpI3PUyEdFaTFS9qQp7KzXDEBPPECJ5lsnksmM5TGszFWTOIW4ZQ6FeIvsLiBjpFTe02+QVD2+SZ5RjwDmwi++ZVdwqTq9IkfoqFIinHzzrPjMdKcLNz3SClMIIdPzKC2QGVNhkXafz+Po8Xznimnudbo5OyWNL6IXkQLJIV4UHmsGII/Q4u/DFMX6vP3HTfvZ3LXX4KhIULXrxZWopLz3R9V5WldlZ6jtHVtfQDvsmc+fdUw/AAAAABJRU5ErkJggg==\n", + "text/latex": [ + "$$12 \\left(x + \\pi\\right)^{2}$$" + ], + "text/plain": [ + " 2\n", + "12⋅(x + π) " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "diff(y**2, x, x)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$12 \\left(x + \\pi\\right)^{2}$$" + ], + "text/plain": [ + " 2\n", + "12⋅(x + π) " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "diff(y**2, x, 2) # same as above" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To calculate the derivative of a multivariate expression, we can do:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "x, y, z = symbols(\"x,y,z\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "f = sin(x*y) + cos(y*z)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$\\frac{d^3f}{dxdy^2}$" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$- x \\left(x y \\cos{\\left (x y \\right )} + 2 \\sin{\\left (x y \\right )}\\right)$$" + ], + "text/plain": [ + "-x⋅(x⋅y⋅cos(x⋅y) + 2⋅sin(x⋅y))" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "diff(f, x, 1, y, 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Integration" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Integration is done in a similar fashion:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$\\sin{\\left (x y \\right )} + \\cos{\\left (y z \\right )}$$" + ], + "text/plain": [ + "sin(x⋅y) + cos(y⋅z)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$x \\cos{\\left (y z \\right )} + \\begin{cases} - \\frac{\\cos{\\left (x y \\right )}}{y} & \\text{for}\\: y \\neq 0 \\\\0 & \\text{otherwise} \\end{cases}$$" + ], + "text/plain": [ + " ⎛⎧-cos(x⋅y) ⎞\n", + " ⎜⎪────────── for y ≠ 0⎟\n", + "x⋅cos(y⋅z) + ⎜⎨ y ⎟\n", + " ⎜⎪ ⎟\n", + " ⎝⎩ 0 otherwise⎠" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "integrate(f, x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By providing limits for the integration variable we can evaluate definite integrals:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFAAAAAUBAMAAADo9qfkAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nQRf3RTl5VjX764CLv8JfPcJj8HGPsdwAAAAASUVORK5CYII=\n", + "text/latex": [ + "$$2 \\cos{\\left (y z \\right )}$$" + ], + "text/plain": [ + "2⋅cos(y⋅z)" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "integrate(f, (x, -1, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and also improper integrals" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAB0AAAAVBAMAAABI7vhRAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAInarRM2ZVBDdiWbv\nuzJCz3LGAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVQYGWNggADG/2DwAcplYHaAsSC0KCqX\nIR2VzzEBlc9WgMrvROUyrEblg7TrAx3wDSrMtIGBa12RtKIAkO8CxJwMDM8ZFjI9ADKZjgIJJSBm\nuMDcACQl9B0YGEC28xkAVQFB/wUG7gVAmm0DfwOQYmD7yMAJYvQ38DsAKQbGbwy7QLQmA88CEM1g\n3zADRN1mYF4AohneL08A0zCC9WgDjAmm2WGuhIkGwhhAGgDwdic2xV4k0wAAAABJRU5ErkJggg==\n", + "text/latex": [ + "$$\\sqrt{\\pi}$$" + ], + "text/plain": [ + " ___\n", + "╲╱ π " + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "integrate(exp(-x**2), (x, -oo, oo))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember, `oo` is the SymPy notation for inifinity." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sums and products" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can evaluate sums and products using the functions: 'Sum'" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "n = Symbol(\"n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAADsAAAA9BAMAAADhUgydAAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMruZq91U7yJ2iWYQRM2bSrnQAAACDklEQVQ4Ec2VO0gcQRjH/8ednne7eyexsvHAyspXUiWCNiKH4KPSgHqr+CgvKRMidyQQCETu4DQBqyuTJllQDKRa0thY2EggjdeKonfgG3Uy+5idzT6mzlfs/r//j+9jZ2ZnBjDiEZD4WjVlwKNtGPiJxQBiWUvAJNJ6GF+CfI24Go4jd2jSxLgswLS5AMsNpPfDqzEr+PIVoOIZ9wlv9S6XRfLpADeAt3fuzKt/PRFiJP4HfEZ43DgjcD4tT1pNc29tu3DlxzEywUwppzLpVCN3zjxEa0xynCEaM9HHFMcSuWYmEraKjlyMMrPngqnA9y7pDfRtUyGXIozvV7qIp0hNhOXCrQgjT/ZFPEreiDByru5pvoJ1u6g0I6z+XBRhpeGnyf4BZmZUpvj7C8bsRH7FXUdNI1O0ktSQY3KxipI9Gd3cdKu8bmZJPqpmN35uJT+KjrnjKCBp/QWuUSkvXHjL0iWVefKgWfCn68N6GUpVaaVAfs3o+3FrR/R2lukx17G8oFOSejg1Y4quhPlPRtQ5tDRQIMSoG+QLRMw9Jsv3kOoGCo7mOmK1YGS4dCulNT2Ux6vI69VQTOf6m2QMyhv0VqBxQI+1T15Ec+NWEAW9FUThx9Kz9opTEYAPtXgxZkzrMeDHHzdcB7kfI4vfguZ4iccRs/lRUPOWOmbnWTm9FTyhDGFTsz3jVvg3/gK+T7KE+MaJIAAAAABJRU5ErkJggg==\n", + "text/latex": [ + "$$\\sum_{n=1}^{10} \\frac{1}{n^{2}}$$" + ], + "text/plain": [ + " 10 \n", + " ____ \n", + " ╲ \n", + " ╲ 1 \n", + " ╲ ──\n", + " ╱ 2\n", + " ╱ n \n", + " ╱ \n", + " ‾‾‾‾ \n", + "n = 1 " + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Sum(1/n**2, (n, 1, 10))" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJ8AAAAPBAMAAAAIUwCQAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nt0ciqgAAAABJRU5ErkJggg==\n", + "text/latex": [ + "$$1.54976773116654$$" + ], + "text/plain": [ + "1.54976773116654" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Sum(1/n**2, (n,1, 10)).evalf()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJ8AAAAPBAMAAAAIUwCQAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n", + "text/latex": [ + "$$1.64493406684823$$" + ], + "text/plain": [ + "1.64493406684823" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Sum(1/n**2, (n, 1, oo)).evalf()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Products work much the same way:" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAC8AAAA9BAMAAADPFy0PAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMs2Zq91U7yJ2iWZE\nELuNX9C8AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABXElEQVQ4EWNgAAEhBgbOlQvATBRC2JWBYRdD\nCooYhJPKwBDDwH8BUyaVgfcjA3sCNgnGHwysG7BK/GVgnYBV4gcDO1YJoB38B7DpwOGqdAaGydj8\n0RzvycBlZYtp0iAUYf+PAB+R3cdx5P+/DhDY/19NAFmCgeH9FzA///8FVHGG9VCJr2jiQ1zilEfn\nFEiCQfOgwbsJvD/AfkWVYEwIYuCAhCaqBC/vHwbuD1h0MLB9YGBRwCbBrMDAvwEc0KhGMbAvYMi/\nsACkBU2i/wDDIm4BLBJHGRhatoHE0XWAxQZe4j0k3axHS1ccwv//v2lg4JX6/1+tAeFUBgZw2v3A\nwAdKwShpF1kRPdjAkg4rAJV02AGwpMMOkCW4rSUnw1WhSJzfwN7AogQE2gwMyBK9U5GKT2QJBk+G\n41iNYihkMGYEG6WJahTHB4aYRJgWYEkHBzwODLM2QHmgkg4GAOSmp5CzUioBAAAAAElFTkSuQmCC\n", + "text/latex": [ + "$$\\prod_{n=1}^{10} n$$" + ], + "text/plain": [ + " 10 \n", + "┬───┬ \n", + "│ │ n\n", + "│ │ \n", + "n = 1 " + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Product(n, (n, 1, 10)) # 10!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Limits" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Limits can be evaluated using the `limit` function. For example, " + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAAgAAAAPBAMAAAArJJMAAAAAJFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADHJj5lAAAAC3RSTlMAzRAiu5mrdu/dZmiL4QAAAAAjSURBVAgdY2BgEGJgYDDZxMCgEgYkGNhJJVgzdmYB9TEwAACPpQrvlUCHcAAAAABJRU5ErkJggg==\n", + "text/latex": [ + "$$1$$" + ], + "text/plain": [ + "1" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "limit(sin(x)/x, x, 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use 'limit' to check the result of derivation using the `diff` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKAAAAAUBAMAAAD4uit9AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMnZUZs0Qu91E7yKJ\nmaurDqYVAAAACXBIWXMAAA7EAAAOxAGVKw4bAAACz0lEQVQ4EXVVX0hTURz+rtvu3bybWRD1pBcD\nH6rV6KHCoIYUBCqsh1ERxCWIIKRECqFBDCoifBk+SQSaYCARDJIMIRiDgihpBeKL0YoKwgKtSFaa\nfffsnvtnzQN35/t9v+/3nfu75547oHZMeokub1AX60kPHcxXA2XMJfU+FwNNtsLL+fGUL1yoRsqK\ny4Z8FmrKzdRHp3z0WV8kgh4/VbTDgOHnZRQsSSTmqOELrWDQz5y3w/UMw76GoI4Kvb7RcdGzDhQg\nlKvG6xk+9stxEsq2ztTUX2x+sGmXwWQDW1DbFoaKOPMG+j3E7JakoTo/k0Bv23NRR32c8riBaRPD\nE0oSeItzQBangZuG/ocCjWQvjho3UOhAwyKCKZIc0nA6odxXBqEZog64BEQDo9gNtT8+R90R3DZh\n4CpwBfhJIpwAnuGVeUBPzELrQyBLkkMaphFYChegLIk64BrwMJbCAFSIF24/wss7YBleBr6zsDEH\nmDjORjCGDQaURZLRdPrEeDpdJrvKsJWLrog6YILyRkOUbjGtHNSnayXLcMA1rKLIL7Q2Vw0ptO8w\nYi16iIsuizphiBFTWQIiZaZo+AjBisfQapkN8YeNbKcsS8hhGzp3uCrqRMuYRcMocFDohrEH+Ogx\ntDZlLzejHVrZoms3JU3vxhIiFVEnNoUyagMlNMHalA4TGatfu+UoxT+0RR75WF+EuxQrUMUhN2U6\niRfKXYQKog6YYW4OI0ls7e68RTyPO53duZa1zy1r7/f9LosW1Q/t8cN8/l/jfI9CzVRxSEP12848\nnrz8IurID/O6UHyXRyk4zkeLY7z8w3P0+BQhT4I09Iv5muUtZlbStQeNfI/MfcJFLlm0Q1UUypw7\n8+PQlFIrkogaEjmzJiuvo599p5zEOiCDcFIzZLLO58v5wA7Fm3kSpb2s+G+eQqTttcNmHOSCSRcC\nXd6gLq79C/gHf4qzfkFAyyAAAAAASUVORK5CYII=\n", + "text/latex": [ + "$$\\sin{\\left (x y \\right )} + \\cos{\\left (y z \\right )}$$" + ], + "text/plain": [ + "sin(x⋅y) + cos(y⋅z)" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFMAAAAUBAMAAAADwRznAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHaZIu+JVM27RDKr\nZt2dj8xZAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABlUlEQVQoFXWTPUvDUBSG31jbfDUxFBQEh9BK\nXYRmEkXEiBRRUDo46RJaEXEKgrMZHDo6uAiKXXS0/gMFN0EIRZyLCK5qQWg76IlJ9V6jBxLOc86T\n+8G9Af6Lfp/t3LPwO1/kCmmbQx72ORQ8DjnQLA5R5pEl3WYJGOSRpRUWKJdqEHImigYgTFRcDGW3\noY41PGrl6MmML+XNSEhYSIklFKhcdNVV9QmyOQw0iU/oa0d71L1I0DwsJDwcUasKsas7ULtnBkzi\nZyBlpF5FOxLEJoykiTcaoUXtKxd419ujlGKNapAO0BPUF6BuqF1AIR17NaAtbH1YoQoMmJSFQqBO\noq/EjNqag9YhgRZA01zSKxSUJnAM+ZoKVUBMWlA6O8AdMW1LNAroMyKBtoUp1H1qFX1sqOuQnF0D\nh8QVmtk9xXJPSDjASHnWppZwk7cxv3mL88ZDjfiCOtnMDI0SClKwFlrMH8EebCDQ6aU9IdhFLL6v\nSyTQddF92Yx5QSFYcBChEFxCJTv9VYm9elc7FGQ7JvwUYj/MJ3CTXM0O35peAAAAAElFTkSuQmCC\n", + "text/latex": [ + "$$y \\cos{\\left (x y \\right )}$$" + ], + "text/plain": [ + "y⋅cos(x⋅y)" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "diff(f, x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$\\displaystyle \\frac{\\mathrm{d}f(x,y)}{\\mathrm{d}x} = \\frac{f(x+h,y)-f(x,y)}{h}$" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "h = Symbol(\"h\")" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAFMAAAAUBAMAAAADwRznAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n", + "text/latex": [ + "$$y \\cos{\\left (x y \\right )}$$" + ], + "text/plain": [ + "y⋅cos(x⋅y)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "limit((f.subs(x, x+h) - f)/h, h, 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OK!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can change the direction from which we approach the limiting point using the `dir` keywork argument:" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAABMAAAALBAMAAABv+6sJAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEKvvZom7mXYyzVQi\n3UQ6SGZXAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAaklEQVQIHWNgYBBgAAIQwaj82YGBIayogYGB\nbQLHLwapDQxTGRg8GRj2J6xkYGA5wACUYP0LJBgcQEyGfBDRAGYm/wNqd2BwZGDgiDE+wMBxgIGd\ngSGcYb4dgytQolxtAwNjvXEAUDncNgBJUBUwaYAbUgAAAABJRU5ErkJggg==\n", + "text/latex": [ + "$$\\infty$$" + ], + "text/plain": [ + "∞" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "limit(1/x, x, 0, dir=\"+\")" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAACMAAAALBAMAAAAHCCkxAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEM3dMqvvZom7mXZU\nIkRJD0iWAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAfklEQVQIHWNggAMBEAtMQIUYw74VMDB0Lt0A\nV8LA6cD9iUHoAIMHQqiEgeH8BBUGBvYLDELGIKDCANTA8RmkC6gdCkC8+SAChCEAxJr2j4GBsQAm\nwlDIwMDdm3aBgfsCXIiLgaGLwT+PoQIuwsC4KvIAA+P6tAaEENThAgwMAMSLGqu/gFQwAAAAAElF\nTkSuQmCC\n", + "text/latex": [ + "$$-\\infty$$" + ], + "text/plain": [ + "-∞" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "limit(1/x, x, 0, dir=\"-\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Series" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Series expansion is also one of the most useful features of a CAS. In SymPy we can perform a series expansion of an expression using the `series` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$1 + x + \\frac{x^{2}}{2} + \\frac{x^{3}}{6} + \\frac{x^{4}}{24} + \\frac{x^{5}}{120} + O\\left(x^{6}\\right)$$" + ], + "text/plain": [ + " 2 3 4 5 \n", + " x x x x ⎛ 6⎞\n", + "1 + x + ── + ── + ── + ─── + O⎝x ⎠\n", + " 2 6 24 120 " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "series(exp(x), x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default it expands the expression around $x=0$, but we can expand around any value of $x$ by explicitly include a value in the function call:" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/latex": [ + "$$e + e \\left(x - 1\\right) + \\frac{e}{2} \\left(x - 1\\right)^{2} + \\frac{e}{6} \\left(x - 1\\right)^{3} + \\frac{e}{24} \\left(x - 1\\right)^{4} + \\frac{e}{120} \\left(x - 1\\right)^{5} + \\mathcal{O}\\left(\\left(x - 1\\right)^{6}; x\\rightarrow1\\right)$$" + ], + "text/plain": [ + " 2 3 4 5 \n", + " ℯ⋅(x - 1) ℯ⋅(x - 1) ℯ⋅(x - 1) ℯ⋅(x - 1) ⎛ 6\n", + "ℯ + ℯ⋅(x - 1) + ────────── + ────────── + ────────── + ────────── + O⎝(x - 1) \n", + " 2 6 24 120 \n", + "\n", + " \n", + " ⎞\n", + "; x → 1⎠\n", + " " + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "series(exp(x), x, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can explicitly define to which order the series expansion should be carried out:" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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2R1/8g1/EK8Me1mh+EX/2nm6NjnGI22FzPdUaRfTfhG8KIK2KKq1RKeCUBnJuh7RU+Iyo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\nYII=\n", + "text/latex": [ + "$$e + e \\left(x - 1\\right) + \\frac{e}{2} \\left(x - 1\\right)^{2} + \\frac{e}{6} \\left(x - 1\\right)^{3} + \\frac{e}{24} \\left(x - 1\\right)^{4} + \\frac{e}{120} \\left(x - 1\\right)^{5} + \\frac{e}{720} \\left(x - 1\\right)^{6} + \\frac{e}{5040} \\left(x - 1\\right)^{7} + \\frac{e}{40320} \\left(x - 1\\right)^{8} + \\frac{e}{362880} \\left(x - 1\\right)^{9} + \\mathcal{O}\\left(\\left(x - 1\\right)^{10}; x\\rightarrow1\\right)$$" + ], + "text/plain": [ + " 2 3 4 5 6\n", + " ℯ⋅(x - 1) ℯ⋅(x - 1) ℯ⋅(x - 1) ℯ⋅(x - 1) ℯ⋅(x - 1) \n", + "ℯ + ℯ⋅(x - 1) + ────────── + ────────── + ────────── + ────────── + ──────────\n", + " 2 6 24 120 720 \n", + "\n", + " 7 8 9 \n", + " ℯ⋅(x - 1) ℯ⋅(x - 1) ℯ⋅(x - 1) ⎛ 10 ⎞\n", + " + ────────── + ────────── + ────────── + O⎝(x - 1) ; x → 1⎠\n", + " 5040 40320 362880 " + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "series(exp(x), x, 1, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The series expansion includes the order of the approximation, which is very useful for keeping track of the order of validity when we do calculations with series expansions of different order:" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/latex": [ + "$$1 - \\frac{x^{2}}{2} + \\frac{x^{4}}{24} + \\mathcal{O}\\left(x^{5}\\right)$$" + ], + "text/plain": [ + " 2 4 \n", + " x x ⎛ 5⎞\n", + "1 - ── + ── + O⎝x ⎠\n", + " 2 24 " + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s1 = cos(x).series(x, 0, 5)\n", + "s1" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAF8AAAAcBAMAAAD1rn4EAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nYII=\n", + "text/latex": [ + "$$x + \\mathcal{O}\\left(x^{2}\\right)$$" + ], + "text/plain": [ + " ⎛ 2⎞\n", + "x + O⎝x ⎠" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s2 = sin(x).series(x, 0, 2)\n", + "s2" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAF8AAAAcBAMAAAD1rn4EAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nYII=\n", + "text/latex": [ + "$$x + \\mathcal{O}\\left(x^{2}\\right)$$" + ], + "text/plain": [ + " ⎛ 2⎞\n", + "x + O⎝x ⎠" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expand(s1 * s2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we want to get rid of the order information we can use the `removeO` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAG8AAAAwBAMAAADtMzlxAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\nme8Q6PJIAAAACXBIWXMAAA7EAAAOxAGVKw4bAAACRUlEQVRIDc2Vv2vUYBjHv8klubt4XsMVOqdV\nCg6HtS3oIJhBORfx8B/o6XCLSG/TSQ8VsTjo2MOhFR0VuiuYyU0aFNqhFBwVRe78hagYn8vv5p6n\noA76Dnmf5/t9PnnfhDxvgGBUjjY6YZS/GvVDeSmb6/7HbJqJN7GVyUZC/aE1ooXCBq4ITiDrTdnd\nfcUXdWlJ5Z18T0BrFtcE/4AtGJFc+ST5u261YCmfJXB9VXJIrzragLXpCU93WCcUDcvwePs6Lguv\nrbbvxLSN9sYIp0zNnzmHZ9PzI04gKK3i1dIdzpvAJXuJM0KNNjkwHc4/i0XrAWeEmoLyGu9auMsb\nsTpmx1F+/pAXdubr7s48ySo/kpAJTOsgVPaF31P7mGSISFpYfYoGZ+vf9vS1DueE2vhUbZO1ldnJ\n+kWZ+78d0/7D/f07MOyn39h2tFW5n6R7RWDUT346CFCWb9JYcikcS40+pUavt/ym15uhGrGfqIwd\nyVuV+4nlkIBiP/FcDMr9JHAxKPaTxMVg3E+ntp4MS59z9dr2YzfVk2cMJGUGi02KbqUFaTQOI/Nn\nUJzUAQoWqq8A9W1WjGP6i7+P4/xc7aJAh1KDPQpfA8fcPBHl5UEAtlhwxZJB4ksDFB0WJPO2JaxI\n8kILNenU1r/IHK4BLQks0Vctjb0eTFcCtyWK9DowAQFUPRk0PRy/Pzf3c5YreQTN4fShRt/afprY\nD6DiQXXI5IZ2o/2yS8Z3zjzZvnCE04damU6ILnDeP8xUrPj+V0b+K+kXPYuKl+Zu0xMAAAAASUVO\nRK5CYII=\n", + "text/latex": [ + "$$\\frac{x^{5}}{24} - \\frac{x^{3}}{2} + x$$" + ], + "text/plain": [ + " 5 3 \n", + "x x \n", + "── - ── + x\n", + "24 2 " + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "expand(s1.removeO() * s2.removeO())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But note that this is not the correct expansion of $\\cos(x)\\sin(x)$ to $5$th order:" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAANsAAAAwBAMAAABqLhIyAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\nme8Q6PJIAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAEdklEQVRYCb1XXWgcVRQ+s5n9/xsSKPSl2aRF\nn2K3IqgVdR6M+lKzCIIodbcpRF9i9skKggSFah5KgiI2+NAI+uCDNC8FtUJHqo8lGxcqWkIjtGB9\nkF1pqwZlPHdmzp17Z++d7G7ECzP3nO875zs7s3PvmQHYZcy2rZiIy9+eimEHp4rN4npMVt2txrCD\nU8VGuhOT9cI3MeRQVOzVPTeUZFzSASeGPT65GcP2Sx279jWFJt4ji8/m1kUncKbhDQ4PbRhVWKhR\ndu/NHIPUHWJhw+bmsMaIBaVtSi7skEXzEYDfAvsIjPPfRfTAc2kRRv72sw445h/R/F8AHnd88AbM\nWFF6YD/b5eUydvrnaP45i5c7AfNRdig/04XRg9P3VAqzJ22FwLJlTD7w7Cyk5hsKdnCo3jAa6bcy\nK+rM5F3YB69XltTsEOjbkLJS3ZytTs1U4QQsWJ+o2cHRYgsMyK7rErcALPhIx6rwuXtVKGFTzChX\nyI3MiRYDfo+gcW629kwMnWvBEwAbjibkczBtKARLRRPjw98F7EEYhYQ28grAoZx1GBLKRVVoQcL+\nONGBCa1AQBTo7/gVgWRVE26enmsv1tcuwVPKgKfn5h9K7uQ7ZlNJC2CWfu4O28pfFhjRzLquuzg2\nOfqDWu+c6/5p3D8xpWvkyc19yx94et8HquY/8DxAhoqLtfZuTyUvXHFrqGN2AzFjB8o18J+wvevL\nCiOVlyw4vIJgqkXMHSg3weyQ28+cq8RGcbpt3MRFxKRLa5TxIbs6eIfcfmaupw4mOtllHRJ3eVxJ\nFoW+CJ+heY3cfmbS08QSnWnlMCLPru5NHpq+ik8m3IeHt/FzPMYgPU0I0XWHBeS38fQjHn7nQANH\n3YK4jd8PojPpkR+Zib7u3cFSC+mLeIidA7epuI1fFiQ9GeUe0Zc8ZKOG0/t4iJ1jpsY3fuPsuziW\nHBaMq1waCKVWV8/eWl2tolmWONftyDT2ehxfstMZPMTOMbOGgHbjR04c9PNFTLCJ9l5jCrcZw65O\n7Bx4dTEbvxccnkgvRCSL6AV2B/YzYb+c0Dk2bNBv/JIYOqQXxQOf6HF8czMveOAjeBY7Bz6Z+o0/\nKkt6UTzwiU67R6fbtgfiOpM6B65D/cYflSW9KB74nL68fMbxMbbOxM4Ru6s8OfeVpMz1CM1OFH4i\nW3mvS94/GIb0fm6EnNmC883Qxf3BFrwcvuyWXLcRQhLtw0WBRsSgfhQmhVYCe9Ri6MrWWBtTsycP\nyWjUK2xLSJo9s7qRvAnjKzoSElhuRMsScZQMb844ktvjyDdTovsrt1/KeU3yehzjdA/EAa/cqSmH\nA0oj1xJgc11wek3zlc1ekBBWLm+Z4WclEfL8qeAWLcFRma/WVKiHsXI4vvDO/9Gp1PNpx4WDctdt\njuzRwPfyfM93Mtdk5R4G4ROeM0Ma5e5u5W4B+K17yApSWmkdMl7XklBy2NXhY7dE/p7ndBPOV7Qq\nrNwxSN/QBgxMHN/yu5YqsfjYXw+CefXR3Z5tVe7/iP0Lu0QQYvWiWkQAAAAASUVORK5CYII=\n", + "text/latex": [ + "$$x - \\frac{2 x^{3}}{3} + \\frac{2 x^{5}}{15} + \\mathcal{O}\\left(x^{6}\\right)$$" + ], + "text/plain": [ + " 3 5 \n", + " 2⋅x 2⋅x ⎛ 6⎞\n", + "x - ──── + ──── + O⎝x ⎠\n", + " 3 15 " + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(cos(x)*sin(x)).series(x, 0, 6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear algebra" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Matrices" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Matrices are defined using the `Matrix` class:" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "m11, m12, m21, m22 = symbols(\"m11, m12, m21, m22\")\n", + "b1, b2 = symbols(\"b1, b2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$\\left[\\begin{matrix}m_{11} & m_{12}\\\\m_{21} & m_{22}\\end{matrix}\\right]$$" + ], + "text/plain": [ + "⎡m₁₁ m₁₂⎤\n", + "⎢ ⎥\n", + "⎣m₂₁ m₂₂⎦" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = Matrix([[m11, m12],[m21, m22]])\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAB4AAAAyBAMAAAC5cHbcAAAAMFBMVEX///8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMA74lUMhC73c2rRHaZImaqCQggAAAA9klEQVQoFWOQ//+JAQb0/39kEHZxhXEZQlwMGUTgPBDDEcRnTSyACYL5DBwTUPn8Bqj89QGofHUYF2IeQ+fuw1ARiPk/AvgvMLiBhMB85q8M/Ao+0+F8ngaGeAUGczif4wDDegMkPv8DhvMByPwFrHIMSHzOB2wHkPmsp+cBzUKYB+Rg4RuCBCH+BbHu1M9G4YPEkOXpyKdLfDAdOwrzHzg+dBlmwPjg+Khl8L8A9T84PqQY7i+A8iHxwWAPTAXg8OMHxwdDNUw/JD6YDsD4kPh4A+QihTeXApcDMt/D2AxmHkghg/z//0ASET9gQSL46OkdLT8AABDBTMmz6EtEAAAAAElFTkSuQmCC\n", + "text/latex": [ + "$$\\left[\\begin{matrix}b_{1}\\\\b_{2}\\end{matrix}\\right]$$" + ], + "text/plain": [ + "⎡b₁⎤\n", + "⎢ ⎥\n", + "⎣b₂⎦" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b = Matrix([[b1], [b2]])\n", + "b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With `Matrix` class instances we can do the usual matrix algebra operations:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$\\left[\\begin{matrix}m_{11}^{2} + m_{12} m_{21} & m_{11} m_{12} + m_{12} m_{22}\\\\m_{11} m_{21} + m_{21} m_{22} & m_{12} m_{21} + m_{22}^{2}\\end{matrix}\\right]$$" + ], + "text/plain": [ + "⎡ 2 ⎤\n", + "⎢ m₁₁ + m₁₂⋅m₂₁ m₁₁⋅m₁₂ + m₁₂⋅m₂₂⎥\n", + "⎢ ⎥\n", + "⎢ 2 ⎥\n", + "⎣m₁₁⋅m₂₁ + m₂₁⋅m₂₂ m₁₂⋅m₂₁ + m₂₂ ⎦" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A**2" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJEAAAAyBAMAAACufiRQAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nDbOYk3PHxJxH96vsyH4p/gHeHTH4ApAfnQAAAABJRU5ErkJggg==\n", + "text/latex": [ + "$$\\left[\\begin{matrix}b_{1} m_{11} + b_{2} m_{12}\\\\b_{1} m_{21} + b_{2} m_{22}\\end{matrix}\\right]$$" + ], + "text/plain": [ + "⎡b₁⋅m₁₁ + b₂⋅m₁₂⎤\n", + "⎢ ⎥\n", + "⎣b₁⋅m₂₁ + b₂⋅m₂₂⎦" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A * b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And calculate determinants and inverses, and the like:" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKYAAAAMBAMAAAAaIdvMAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n9AyNtKqM6qgiUQAAAABJRU5ErkJggg==\n", + "text/latex": [ + "$$m_{11} m_{22} - m_{12} m_{21}$$" + ], + "text/plain": [ + "m₁₁⋅m₂₂ - m₁₂⋅m₂₁" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.det()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/latex": [ + "$$\\left[\\begin{matrix}\\frac{m_{22}}{m_{11} m_{22} - m_{12} m_{21}} & - \\frac{m_{12}}{m_{11} m_{22} - m_{12} m_{21}}\\\\- \\frac{m_{21}}{m_{11} m_{22} - m_{12} m_{21}} & \\frac{m_{11}}{m_{11} m_{22} - m_{12} m_{21}}\\end{matrix}\\right]$$" + ], + "text/plain": [ + "⎡ m₂₂ -m₁₂ ⎤\n", + "⎢───────────────── ─────────────────⎥\n", + "⎢m₁₁⋅m₂₂ - m₁₂⋅m₂₁ m₁₁⋅m₂₂ - m₁₂⋅m₂₁⎥\n", + "⎢ ⎥\n", + "⎢ -m₂₁ m₁₁ ⎥\n", + "⎢───────────────── ─────────────────⎥\n", + "⎣m₁₁⋅m₂₂ - m₁₂⋅m₂₁ m₁₁⋅m₂₂ - m₁₂⋅m₂₁⎦" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.inv()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solving equations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For solving equations and systems of equations we can use the `solve` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEoAAAAUBAMAAADYerbFAAAALVBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAOrOgAAAADnRSTlMAdt3NMolEEFTvq5lmIsfa\npuIAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAB3SURBVCgVYxAyYSAEDqsxhBFSA5QPQ1bFMR2Ljs4C\nVFVVu55jqGJfvQ5NFQMjpioGBrlBrqpYCQSAgTuYXV9ngBb+6KHKvfLxGgYmdVRVu+ZZHUCNbbB8\nL6oqMA8lTYBFAohRxS5AjCoeLIqA7hJSwSaOIiakBgD0ZimSClhTrgAAAABJRU5ErkJggg==\n", + "text/latex": [ + "$$\\left [ -1, \\quad 1\\right ]$$" + ], + "text/plain": [ + "[-1, 1]" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solve(x**2 - 1, x)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/latex": [ + "$$\\left [ - i \\sqrt{- \\frac{1}{2} + \\frac{\\sqrt{5}}{2}}, \\quad i \\sqrt{- \\frac{1}{2} + \\frac{\\sqrt{5}}{2}}, \\quad - \\sqrt{\\frac{1}{2} + \\frac{\\sqrt{5}}{2}}, \\quad \\sqrt{\\frac{1}{2} + \\frac{\\sqrt{5}}{2}}\\right ]$$" + ], + "text/plain": [ + "⎡ _____________ _____________ ___________ ________\n", + "⎢ ╱ ___ ╱ ___ ╱ ___ ╱ _\n", + "⎢ ╱ 1 ╲╱ 5 ╱ 1 ╲╱ 5 ╱ 1 ╲╱ 5 ╱ 1 ╲╱ \n", + "⎢-ⅈ⋅ ╱ - ─ + ───── , ⅈ⋅ ╱ - ─ + ───── , - ╱ ─ + ───── , ╱ ─ + ───\n", + "⎣ ╲╱ 2 2 ╲╱ 2 2 ╲╱ 2 2 ╲╱ 2 2\n", + "\n", + "___⎤\n", + "__ ⎥\n", + "5 ⎥\n", + "── ⎥\n", + " ⎦" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solve(x**4 - x**2 - 1, x)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "System of equations:" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAH4AAAAVBAMAAAByPkciAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n/1sDfgDNr2XH9LJoSQAAAABJRU5ErkJggg==\n", + "text/latex": [ + "$$\\left \\{ x : 1, \\quad y : 0\\right \\}$$" + ], + "text/plain": [ + "{x: 1, y: 0}" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solve([x + y - 1, x - y - 1], [x,y])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In terms of other symbolic expressions:" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/latex": [ + "$$\\left \\{ x : \\frac{a}{2} + \\frac{c}{2}, \\quad y : \\frac{a}{2} - \\frac{c}{2}\\right \\}$$" + ], + "text/plain": [ + "⎧ a c a c⎫\n", + "⎨x: ─ + ─, y: ─ - ─⎬\n", + "⎩ 2 2 2 2⎭" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solve([x + y - a, x - y - c], [x,y])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Further reading" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* http://sympy.org/en/index.html - The SymPy projects web page.\n", + "* https://github.com/sympy/sympy - The source code of SymPy.\n", + "* http://live.sympy.org - Online version of SymPy for testing and demonstrations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Versions" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "application/json": { + "Software versions": [ + { + "module": "Python", + "version": "2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]" + }, + { + "module": "IPython", + "version": "3.2.1" + }, + { + "module": "OS", + "version": "Darwin 14.1.0 x86_64 i386 64bit" + }, + { + "module": "numpy", + "version": "1.9.2" + }, + { + "module": "matplotlib", + "version": "1.4.3" + }, + { + "module": "sympy", + "version": "0.7.6" + } + ] + }, + "text/html": [ + "
SoftwareVersion
Python2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]
IPython3.2.1
OSDarwin 14.1.0 x86_64 i386 64bit
numpy1.9.2
matplotlib1.4.3
sympy0.7.6
Sat Aug 15 11:37:37 2015 JST
" + ], + "text/latex": [ + "\\begin{tabular}{|l|l|}\\hline\n", + "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n", + "Python & 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)] \\\\ \\hline\n", + "IPython & 3.2.1 \\\\ \\hline\n", + "OS & Darwin 14.1.0 x86\\_64 i386 64bit \\\\ \\hline\n", + "numpy & 1.9.2 \\\\ \\hline\n", + "matplotlib & 1.4.3 \\\\ \\hline\n", + "sympy & 0.7.6 \\\\ \\hline\n", + "\\hline \\multicolumn{2}{|l|}{Sat Aug 15 11:37:37 2015 JST} \\\\ \\hline\n", + "\\end{tabular}\n" + ], + "text/plain": [ + "Software versions\n", + "Python 2.7.10 64bit [GCC 4.2.1 (Apple Inc. build 5577)]\n", + "IPython 3.2.1\n", + "OS Darwin 14.1.0 x86_64 i386 64bit\n", + "numpy 1.9.2\n", + "matplotlib 1.4.3\n", + "sympy 0.7.6\n", + "Sat Aug 15 11:37:37 2015 JST" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%reload_ext version_information\n", + "\n", + "%version_information numpy, matplotlib, sympy" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/2_knn/knn_classification.ipynb b/2_knn/knn_classification.ipynb index ec8f132..6bb79d1 100644 --- a/2_knn/knn_classification.ipynb +++ b/2_knn/knn_classification.ipynb @@ -23,7 +23,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 算法步骤：(FIXME)\n", + "## 算法步骤：(FIXME: 把流程再细化一下，循环需要体现的更好)\n", "\n", "* step.1---初始化距离为最大值\n", "* step.2---计算未知样本和每个训练样本的距离dist\n", @@ -38,7 +38,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Generate sample data (FIXME)" + "## 生成数据" ] }, { @@ -48,7 +48,19 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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" ] @@ -61,58 +73,45 @@ ], "source": [ "%matplotlib inline\n", + "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", - "# generate sample data\n", - "n = 100\n", - "x_1_1 = 10 + (np.random.rand(n, 1)*2 -1)*4\n", - "x_1_2 = 15 + (np.random.rand(n, 1)*2 -1)*4\n", - "x1 = np.concatenate((x_1_1, x_1_2), axis=1)\n", - "y1 = np.zeros([n, 1])\n", + "# data generation\n", + "np.random.seed(314)\n", + "data_size_1 = 300\n", + "x1_1 = np.random.normal(loc=5.0, scale=1.0, size=data_size_1)\n", + "x2_1 = np.random.normal(loc=4.0, scale=1.0, size=data_size_1)\n", + "y_1 = [0 for _ in range(data_size_1)]\n", "\n", - "x_2_1 = 20 + (np.random.rand(n, 1)*2 -1)*4\n", - "x_2_2 = 5 + (np.random.rand(n, 1)*2 -1)*4\n", - "x2 = np.concatenate((x_2_1, x_2_2), axis=1)\n", - "y2 = np.ones([n, 1])\n", + "data_size_2 = 400\n", + "x1_2 = np.random.normal(loc=10.0, scale=2.0, size=data_size_2)\n", + "x2_2 = np.random.normal(loc=8.0, scale=2.0, size=data_size_2)\n", + "y_2 = [1 for _ in range(data_size_2)]\n", "\n", - "x = np.concatenate((x1, x2), axis=0)\n", - "y = np.concatenate((y1, y2), axis=0)\n", - "y = y.flatten()\n", + "x1 = np.concatenate((x1_1, x1_2), axis=0)\n", + "x2 = np.concatenate((x2_1, x2_2), axis=0)\n", + "x = np.hstack((x1.reshape(-1,1), x2.reshape(-1,1)))\n", + "y = np.concatenate((y_1, y_2), axis=0)\n", "\n", - "# draw sample data\n", - "plt.scatter(x[:,0], x[:,1], c=y)\n", - "plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.0, 0.0, 0.0, 0.0, 0.0]\n", - "[1.0, 1.0, 1.0, 1.0, 1.0]\n" - ] - } - ], - "source": [ - "# generate test data\n", - "x_test = np.array([[12.5, 10.0], [15.4, 8.0]])\n", + "data_size_all = data_size_1+data_size_2\n", + "shuffled_index = np.random.permutation(data_size_all)\n", + "x = x[shuffled_index]\n", + "y = y[shuffled_index]\n", "\n", - "k = 5\n", - "# do knn\n", - "for s in x_test:\n", - " d = np.sum((s - x)**2, axis=1)\n", - " idx = np.argsort(d)\n", - " ys_5 = list(y[idx[:5]]) \n", - " print(ys_5)\n", + "split_index = int(data_size_all*0.7)\n", + "x_train = x[:split_index]\n", + "y_train = y[:split_index]\n", + "x_test = x[split_index:]\n", + "y_test = y[split_index:]\n", "\n", - " # TODO: you need to implement the vote algorithm" + "# visualize data\n", + "plt.scatter(x_train[:,0], x_train[:,1], c=y_train, marker='.')\n", + "plt.title(\"train data\")\n", + "plt.show()\n", + "plt.scatter(x_test[:,0], x_test[:,1], c=y_test, marker='.')\n", + "plt.title(\"test data\")\n", + "plt.show()\n" ] }, { @@ -124,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -181,80 +180,6 @@ "metadata": {}, "outputs": [ { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib inline\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# data generation\n", - "np.random.seed(314)\n", - "data_size_1 = 300\n", - "x1_1 = np.random.normal(loc=5.0, scale=1.0, size=data_size_1)\n", - "x2_1 = np.random.normal(loc=4.0, scale=1.0, size=data_size_1)\n", - "y_1 = [0 for _ in range(data_size_1)]\n", - "\n", - "data_size_2 = 400\n", - "x1_2 = np.random.normal(loc=10.0, scale=2.0, size=data_size_2)\n", - "x2_2 = np.random.normal(loc=8.0, scale=2.0, size=data_size_2)\n", - "y_2 = [1 for _ in range(data_size_2)]\n", - "\n", - "x1 = np.concatenate((x1_1, x1_2), axis=0)\n", - "x2 = np.concatenate((x2_1, x2_2), axis=0)\n", - "x = np.hstack((x1.reshape(-1,1), x2.reshape(-1,1)))\n", - "y = np.concatenate((y_1, y_2), axis=0)\n", - "\n", - "data_size_all = data_size_1+data_size_2\n", - "shuffled_index = np.random.permutation(data_size_all)\n", - "x = x[shuffled_index]\n", - "y = y[shuffled_index]\n", - "\n", - "split_index = int(data_size_all*0.7)\n", - "x_train = x[:split_index]\n", - "y_train = y[:split_index]\n", - "x_test = x[split_index:]\n", - "y_test = y[split_index:]\n", - "\n", - "# visualize data\n", - "plt.scatter(x_train[:,0], x_train[:,1], c=y_train, marker='.')\n", - "plt.title(\"train data\")\n", - "plt.show()\n", - "plt.scatter(x_test[:,0], x_test[:,1], c=y_test, marker='.')\n", - "plt.title(\"test data\")\n", - "plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { "name": "stdout", "output_type": "stream", "text": [ @@ -408,7 +333,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/2_knn/knn_classification_EN.ipynb b/2_knn/knn_classification_EN.ipynb new file mode 100644 index 0000000..6bb79d1 --- /dev/null +++ b/2_knn/knn_classification_EN.ipynb @@ -0,0 +1,341 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# kNN Classification\n", + "\n", + "\n", + "K最近邻(k-Nearest Neighbor，kNN)分类算法，是一个理论上比较成熟的方法，也是最简单的机器学习算法之一。该方法的思路是：***如果一个样本在特征空间中的k个最相似(即特征空间中最邻近)的样本中的大多数属于某一个类别，则该样本也属于这个类别***。KNN方法虽然从原理上也依赖于极限定理，但在类别决策时，只与极少量的相邻样本有关。由于KNN方法主要靠周围有限的邻近的样本，而不是靠判别类域的方法来确定所属类别的，因此对于类域的交叉或重叠较多的待分样本集来说，KNN方法较其他方法更为适合。\n", + "\n", + "kNN算法不仅可以用于分类，还可以用于回归。通过找出一个样本的k个最近邻居，将这些邻居的属性的平均值赋给该样本，就可以得到该样本的属性。更有用的方法是将不同距离的邻居对该样本产生的影响给予不同的权值(weight)，如权值与距离成正比（组合函数）。\n", + "\n", + "该算法在分类时有个主要的不足是，当样本不平衡时，如一个类的样本容量很大，而其他类样本容量很小时，有可能导致当输入一个新样本时，该样本的K个邻居中大容量类的样本占多数。 该算法只计算“最近的”邻居样本，某一类的样本数量很大，那么或者这类样本并不接近目标样本，或者这类样本很靠近目标样本。无论怎样，数量并不能影响运行结果。可以采用权值的方法（和该样本距离小的邻居权值大）来改进。该方法的另一个不足之处是计算量较大，因为对每一个待分类的文本都要计算它到全体已知样本的距离，才能求得它的K个最近邻点。目前常用的解决方法是事先对已知样本点进行剪辑，事先去除对分类作用不大的样本。该算法比较适用于样本容量比较大的类域的自动分类，而那些样本容量较小的类域采用这种算法比较容易产生误分。\n", + "\n", + "k-NN可以说是一种最直接的用来分类未知数据的方法。基本通过下面这张图跟文字说明就可以明白K-NN是干什么的\n", + "![knn](images/knn.png)\n", + "\n", + "简单来说，k-NN可以看成：**有那么一堆你已经知道分类的数据，然后当一个新数据进入的时候，就开始跟训练数据里的每个点求距离，然后挑离这个训练数据最近的K个点看看这几个点属于什么类型，然后用少数服从多数的原则，给新数据归类**。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 算法步骤：(FIXME: 把流程再细化一下，循环需要体现的更好)\n", + "\n", + "* step.1---初始化距离为最大值\n", + "* step.2---计算未知样本和每个训练样本的距离dist\n", + "* step.3---得到目前K个最临近样本中的最大距离maxdist\n", + "* step.4---如果dist小于maxdist，则将该训练样本作为K-最近邻样本\n", + "* step.5---重复步骤2、3、4，直到未知样本和所有训练样本的距离都算完\n", + "* step.6---统计K-最近邻样本中每个类标号出现的次数\n", + "* step.7---选择出现频率最大的类标号作为未知样本的类标号" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 生成数据" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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CKg4vzlySGLHkgGNq4w5KJznqt6CLtkcXboEu2gb1GybapHFBKo61wz6pgeRBFgcdBzT3Jv7CHfAXbIFm/jIua04XtPApfuvv0Y7bSyJT4DDcTn2yk7oXCh8DWSh8Bh13QfmhE21VyRGvBqm7adzX1aajTMoA0MYfw6y5iESHf72mwG8Dr76oh8ZLO6oZdPF3wW9CCUPuDaTqjIk2a0riduqTHa+S7n+mEHgVE2nN1Edbe7woWPrkcC/NzkUX/B+6cEu06ahJ1yZOVfHbr8Nf/D2rLxhPCh9btTJ5IA3Z58d3/WmEc+qTnfhOkNgRvHqIf9MqAh2lo+I0rBtSGMp+NKJDTG05O3BcWUtVzL85/Guz/8BfsBH+Z1/HTz0++Nj8fPymY/CbTkQLC4a9BplHoe1iU3tsvQBNPz38a8dKaDmQaiAGJCA2tQTdJhPTLvyiqtY/06tGvOqJNmdIREJI1TkTbUZRUb/RUtDCqyKhWeO0ZosVEoVXRiQ+4DgvsS0a2xTII97wtVE091/I/7fHd/IjeqqyytBF9qL5WDT+Sr+H4qqKNuwXqG0Kmv8vUn//8BbJvwfaoydr4d1h2zdWRKJQf7dV0obqIbbduK093ZhWTl3VRxsP7VYjrPk9EttkYo2aZmjhU3TRt7HHcB9qb0ciI9OaGfGa+XfQxbvbelIJ9ff1uaH7Lb+CjhtM76X2+pHnO2eW2PXGth6RVgua7/mC7qKiJSmA/1n3+/n3hr9GfAdovzp44UFsfJuUiFcLZfuPeR5VhcxfrB1ffDskVD9246YQ0yv8kn8rqOZLA2m09aKJtmj6kfkzaNoqRbUDLVZ5/yBox23Beu0mA7CECqXm3oCOmzFJ2o/QllNHvkjky1jYBiCBJPcc2fVV52AqjxGoPA2R/vdbImGI72gNuUlAcq9hLyHhzyMznjRBtBmPT2g7vbGgHdeiTUejrb9CF38b9dsn2qRJxbTaqeNVg3ZqiIRhjEp6jlEQWgkr7QdIjI/2Rmh5LJabtqUHbQyhkH0JP/UYXmL4O1mJbY5WnQfZZyC6FRIbuly9J158K3TWvwAdundr1QWQmwuEA8nf4SNeLYzQtklH6mFM8wfQiOkARUdfmTvVGNKpi8i1wLeABaq6ZvC9WuB2YEXgPWAPVW0snZnFQUKz0Krzoe0SCC2HVJ090SZNOyS2AVp5OqQfhOjGEP8OEDQ9SN2HhL4Aie+MqYeqqg8I5P6NNh9rceTYJva4nvhO38rU8OoQ3w7SnU8Nvv2OjMCpA3iJHSCxw6jt7vmZLcTwBBQ+hfgOvUIMIh6MQ1PuyYRm/oK2XQuRVU3nJ/82kMG05r8w0eZNKoaUCRCRzYA24IYeTv3XQIOqniciJwE1qvrToRZzMgGO/tDCYnTR1kHmSBzKf4hXPrqKS7/jHmg5FVN1LNAdm44js/414M1C/UZ0wWaYowhDdFO82itHZcNAaOY5i4HHtxlSb91vuwzar7Jerl6VhU2maeWo5ueji76FhU1jkNgTiayOFj5AEt9FxtqQZCmgqNK7qvossGQZ487AH4O//xH4zogsdCw1aOYF/MYf4rf8BtXs0BeMhsI8uhoekILMX0c1jWoBWn6OqRX2aHQNdB3MDoB4NUjN5RD+CsS+gVT3L06m6qMdd+O3/g7Nzx+2bX77rWjjkWjrr9FF3+5qLNJpt+betBZ2naQfA03ZZ9EOyI9fpsqko/AJdIWkMlB4C0nuhldxzLRw6CNltDH1War6SfD3T4EB89JE5FDgUIDZs52C2tKEFj5CG3+I7ZD+hpJHKod8IBs54dWxQ8YY4EH8W0Pblvu3ZTFFvo50xVOF/vcpAhUnDHj42DUqtsmQ2VDadgm0Xwdk0I4bYMbTyHBSF9M94sBEIPcGxDZENY82fA/yr4Eq1Fxu4aHopkFmS87Gh6bx/53oWuAtY7rxWkDKDp5oiyY1Yz4oVVUVkQFjOKp6FXAVWPhlrOs5xpH8fNshKVgX++H1gRwuFvv2LR+8/n6LIYdWHLolWe7f1nSYPBCG2j8i0XWsLV/VBdD8M7MXH4hD7dV40WE9udr8hYXgL4Twan0PLTPP0u2cC5br7fXttKOq4H8MXq2FTWKbQu7f2A1SIbyKDcy/bl9qc2rb75DYxkjFcRBeCS18jCR2QbzyYds/1RCJQ/299vPzll1qs3bGi9E69c9EZFlV/URElgVGUNbmmAi0sMBS+kIrDl+TJLo2eLXmG7WAJA8omj1++mloOhrIoxUn45XtB8l9h3dx5iXMoQchlewLEHSF9xLbdh1wqt8UNO4YgXZL5lm08UhM63wNqL2x9w4/vh20zQMKIDEIrdx3Ds2hDQeaE5KIzVF2CIRmovl3kcROIGH85rOtiKhL3CoSZOoEh6HJXZnq6jGqBUjdB9psh9heTb/jRGLT7nB4tIzWqd8PHACcF/x5X9EschQNLSyA3D/R/KfQ9htAIL41Un3hsK4XSUDd/ZCbA6EVkPBKxTOu5RS6GgC3noMm9xi+841+HfvV9YEoRNfvd9hoKoa17dJuu/Jv2FfkK91zlh0C4ZWDwpft+5cRyM6xcAoZ0Aza9ju8mivMaQVD/MX7Qe6fwWcog/BKdsOtPG3ENi/NaMvpkHoAKEDHTVD/2JBhMsfgDCel8VZgC6BeRD4ETsec+R0icjDwPrBHKY10jBwtfIwu2gnwg6ySIPKVfhwtfDbs8nzxyiC2eQks7NlsubMt3fCQ6NpQex1kX7RuNyMIrQxJaHbQ5zJnNQ1e72pFEYH4VoPP4dX0qIeIgDej75j8m3T31uxAam+eniGWzDN0hbMKC0wqwdWPjIkhnbqq7j3AW0P8ZjsmlMwzgcJgeok3vKABxcQi1ReZzK1moPIsRCJDX9Tz+ui6EF23+HZVnobiQ/49pPwIZNBCpQHmiKyOVpxgB6rhVZCKE/oOSuwGqVuwMM86Qzp01QLa/NMe3aDKkeoLkdimI7ZvUhHb1PRgyJsmjOdK/seKa2c3RdHsPy2uSxqI2w5UQkjFT0dc7egYO6pqT0xBc25VNckK7YDoJkOGHDT9JNp0HN2HtIBU4M2aW1rDR4imn0Czc5H4VsgwYuCqeesZoM2Q2KVXn1VHN66dnQOJfg2t+g1kHoLIhkhyz2ndtEFV0dbzIHUHhFaEilOh5UR73C87Aq/8kNKt7behDftC/n+2dt1t1lO0x1mA334DtF0EUoPUXNGPfEKBvnn2k6t7kKYfR5uOB9Joxy32OSNrDHqNSBiSxZWT1uwraMd1EPo8Un64HbJOI5xTLzF2un8XWvgESeyKhFcYt7W9xDaQcLrVAOReho7bgJTFs5uPBH8xoNB2CRrfflSpcla+fhmEZiOVp/cv15u6F/LvYCmQ8yF1J5T9oHuOwkJo/TVWaNRuYZbEt4LUyjYrjVcfy1nvJAqVI5dk9lOP24FkZC2k4icjDnsNhmb/Tq9wX+7flkHU31hNQ+pByw6K71i0w1H1G9DGA4JzpBjqNyFVZxZl7qUF59RLjLaeDx23AFnbvcx4ekodiPm5d6HpSJODLf8xXlnx0h7HiqpvremkxvRfRILzYj9II+wZehz5rlcLC9DGnwBpyL2OolB1rmmjZ1+C6KZI9QVBY+jOoigPU2PsyRJr5+ehrRfS5cTTnecgnTv1GNTdjxcZmeaJ5t6E5uMDe/+FenGk/IgRzTEYEtsG7bgjeOGZRstAtjQcFBxIA+mnkJpLimNE4SO6D90zkPtPceZdiphe0rsTQfZ5bPcS7LQKH0ywQcVBNY2/eB9YvC0U3gJtsW46+Q8n2jSgUzv/B+iiXa29nKYguhngWTZK1TlBJx4PkvuNrtzcX2TOC4Ac5N+HjjtM5kDbTPM79QAkvg3xLUEqILYZJHsni0loGSg7GAjZGNL03pX7waF35646D40H4rf+dmQt8wrv9yi3T0Pb5Rb2KRIS2wCpuwWpPAmpuxcJr9jvONVcoDKZsq/MM0WzgfBq4M0EKcOkiceu37604XbqpSa+PbTPt8dnKZ86inKp+617US8E012ZBOTfhmyn4wDaLsGrvwfVLJp7F9ovs+47FUfjhboP51R9yP8Xzb8JbZfawWbVhUhklb5rhFeD8Jcsl119pPzIQMemc0dtWjYiEaT64kHN9SqOQct/DISss1HuZbpTHjHZgNBMK7QqzAf/E2j/A+ovhMhadsg4VAgjuhFIlenZUwCy0Ho+Gtu835ua+q1o8wmQexPK9scrO2jw+QGJrAmRNQcfIxE0/MVABkGsyK1IiESh7h77OYWWHTKmPxVxTr3ESNlh9p/f/9SKVQZppbZUIWF655YLJHaBUOlvWqp5yDwFhCG2Zf/Ki1413eGVMIQ+1/1e475Yg+kw+B9B7R+CedW0brLP09OhatOPkRmP9llCJAS1N1kbO68OCS2D+l+Djrst7BNeAQ2tCJoe1r97l1Ou/YM19mi7ArTRdu+J7ZD4jtYHNdUp7pWB1J/Q1P2Qe3nItofilUP9Q+jivaDQ2T/VC+LPfdHWiyDzHJCD1ovR6PrmtIuA1N6Mtt8MEkWGW0k83Lm95NC1BFMY59RLzLCKVZZG4jtB+gkLNUTWhppL8AYo8S422nS4iXkpEN8Kqf5NnzESmgnVF6FtF0No+W7tfL8x2KkqFjJ5q/si/2PIPgtLtpLzF6N+s2WsLLmOhIKuR8Frr8ocZ/ZFaPoRNB2JerVQdx/ileO3Xwttv7e2eTVX9HtwLhJHyg7Ej+8Ki7YxeYfmU6xorOxANP0QXQ2uKdhX5tlh/ezEK4PqX1q6q6YhvnUgqNYP/iK6wkASsp9dkRCvGqkoXjzf0Y1z6o5RIRIxqdpxRtUPYrDdFbIDIfGtkCVvqN5MuwnlX7OQWHK/HhdU0cehA2gzumBDtPZ2vGhf8S6zK4u2/8GKlsr2h9TtXSJd+ED2BTS8GrRehMnHtqItpyO11w5sf+EdlKyNB0g/hJQfDDOfRQufQcOBQQNqz2L1w0QiX4WZL4GmBm2uLeWHo9nn7ZA5vBpENxj2Go6Jwzl1x6jR3GtQ+BCiG49bRo+Ih3qzAmcm5mxGdL0EEgN/B68S6anr4pWj4a9AvvOsIAl09r8sQPPJMOPBfue1LKfbgTSaeQwS+2BZLmm7eYSWo/d5g3Y7/YEIfwHLZfCAWJfjFokh4dlo/Z8gdY/djBIja2kgErF0wsHGRFaHmc+D32DqiNO4zmFpwjl1x6jwO+6DltMs+8OrhfqH+o0b+6kHIXW3abSUHTq2NnX5eWjH3dY8GgEiMMQBZH+IRGAged/a62DhNpa90kdiYRCyL/cYLxD7BpC3XO3EPkhkDfy2P9Kd1RJBKn8+uJ1eJdTdjabuQULL25lFr/drYBiHl2NBJN77PMIx6XFO3TE6UjcDKYtW+FjTh0D+thPN/guaT7Fx2bmoVCJl+/SZym//o8WZQ8sgNZcj/TgRv+0qy0YhS1d2iSQQ/zOgiPra+flBzL3T+caw8Ed0wG5IACT3gta3ADEN9eia4JVD5KvdjZ7bL++2nQKEhk6jlPBspOKo0X4axzTEOXXH6IisBbn/0tX0IdSPYy280+NFUMm5BJqfD60XYE04mtHm05Daa7rfV0Xbr7US+l5FOkEoYAg5YNUC2nEr5N9FknsgkYHDNeq3BcU5nWGRGCR2RSqOtRvIIOEKL7kbGlkVCh9DdBPI/t102SUIncx4BELLQr4J8EGSMAGZUKo5tPkkO1iNboRUnz8ivXnH5Mc5dceokIoTUK8K8u8gyQOQUD/ystHNsbhy0IUoNxf1OyzlrBNN050a6VumR08yj0H7JfR26GHwZkPFEQM2Veiavu130H4tkEbTd0P9k0iorv/B6QfNKdsnNPXEypMRiaKFz9D82xBZs98sGAgOICN2kOp33IXF07FK1uwcpOYytPl00Dak8mcToxueuh/ST2JFP3+G1J8gOZAQq2NpxFWUOkaFSBSv/Ei86gt79AhdYkyoDsoPpOvXLP++OZGehFeBxLforKaUypN7v5+fH1RTAkhQcemB/w40H4//6VfxW/umNHaRfZFese5eTw9LGlxpDhiAKBLf2hx67g100bZo04/RhduaJstQRNcJ5AEAzaGpRyD3Bl7t1UjtH6QVnDcAACAASURBVEHKTf9kvNF2eoWABshR7/fSwmfWILtLK94xGXFOfQqhfiua+4+VxJdqjcJi/EW74X+2Dn7LeUOWqYuUA52l6RIULfV8X/CqzkFmzkFm/r1XNgoA8R2CUEW5OfTQF+jOIlEgDe03WCZOfyR2ARL2JQmrAB2I+HYQ38VkBOJbW5wc0NTd5vy0zTJWMn8e9DMD1vqv/DiIbmx2Zh5Cm47FTz2KLvwmuvi76MIt8dNP4Lf8Ck0/NuScQ6F+I1pYNPigxC4Qno0VZC1vuu7DwE89ji7cGm3YA208dGTyBI5xxYVfpgiafw9dvBvWO7MM6u8viTa1tv0maECdh45bIb7N4M0qkrubE8z+w9rQJb7b77B+28KBKSfOeNKKhMKroJm/QfNJ2EFmEJIRsVzqfvCSe6LhlWzHH99y0NRLEQ+pOgM4o/cboVXoSk+EYUk9iHhI2fdQsmj2peC7GUtB9Bfb3zUNTT8BCmhHAi3/BApvmPxu+ZGD2qqq1jCj8B7Ed0QzL0DrL+29skPxKn7Sv11eBdQ9aBW1UjH8NMX2SwKbsXTQwrtDnmc4Jgbn1KcImrorKH1XUyBMP1aaWKl2ipMRONPMoMNF4oMW2AwH8aq7mg5LYgc0vBKanWvZMNoIsW9aQdFA10e/3m/TYtVCkINda5WhA12f3A3VBgvlxL89ZPMHzf3P+o9G1zPNFvkdaHDIGv16EBKC7ieYApCCtguwm1UYzb+D1F418Bod10Hrb4EMtP8BVOl6gmm/HC0/bMCDXRGxUNNI8JYD3qFLPmGcqocdI8c59SmChJZHiQFpc7Ylyi2W8qNNN9tfZFkeg8irlgqJrG4t45L7APlRaYKr34wu3t0ORkOzoO7OAZ9sRDyk/EeoHoJ23IjffDKS2LPfswTNvYou7kzbFKTuNqTuHsj+04SrQiujfjNkHrUbUfoJzLkXMIfeKV/w+uAfIHU/XVk6mgmEuoJDZolT7P/aUn0u2nwqFD5BKo4e8oDaMXE4pz5VSOxuIYbs30yXJTr8svGRIOHZMOM5IDtoRxn1WyDztFUixoZXXq6qkHkMzb2FJLZHwl8c3BYJCpBGgBYWWiOKzF+CTJcsFD61AqkejSv6vbb9cmi7GkiZ/kr9o317mGbsZ2NPMyHIPIeUH9orVCGVxwHH2Zz599H0o0EOfmecOgSJ3S39sPU8C10lvtNbJbHXYW0OKk+D9itAs0jV2UWv/hSvFqm5bFhjVXN209GOoEXd1OkfsDTgnPoUQSSEVJ44TmsJVpTTP6oZdPF3oNAAKFpxDF7ZgUPOqx23BB2AMmjHtYHTnFUss/FTD1ipPxIcmHaGP0IMqxl3TylfQmjmGbTjZlNhrDzTer9G1gKiWPw9MqSsrIQ/D9ENULmiO5QV+gJexVH47ddBx502V+tv0fDq3f1lw8tDrtOxR5DY+khiu2H/LEqJNch+CvBNA6fuAScxMI647BdH8cnPCxT9OoCU7YKHQ+YZGx90JtK2K1G/oXh2tV6I5cynTd8l9g3LdIltihaarbnzYFkdid2wA9OkZdK0X20FVYX30cbDUM0jsY2Qmt9B8gCo/i3acTf+gs3wW84aOBUwsoaFy6TM5i8PDjnzH9El5gX2RBEgVb+yA1yvDirP6hU6Ui2g+bctzDMRZJ7F/h0z9ruwZO2Bo6S4nbqj+ISWp7ugKG4HhsMhvq21get0vKk70czjMOMJpDPnewBUM5CdA6FZA4dtQrOsuUQQGpGqXwJpdOE2kPkzSgQqjjeVxa55s6YEKSEkvh2EV7DmDrFN0IXb95g8i+bfh8zjEFoBqTgZUrei6YeDz/IniHwtyMnvjTV2uDuwf1kkCNVI2d5o+h6z16u2NMvOa8IrIjMe6nrtpx6B9L0QWdcOyQtvAwI11yNFbEIxFKpq5yyZZzEphBWCm5VjvHBO3VF0TIjqDmuCEFoeKfvesK7zkruioRlo8ynW85SMxWXz8wbtpqOatXTOwoe2w686By9wnqoZKCyA0OeQ6ovQ5p+B34hUnIR4STTzMhbLzttX5kno6dQbf2SHnAJEH8Wr+W1X1ahWnAwtp9j1yQOgYS9U24CodSdS6MoWUT8QIhvgZyaxPiJjEl4ZZvzFWiCGVx7wDEOzLwdpninIPB981zJhtO1ypPbKAdcdDup3mM68NwPpkb5qcsPXQeFTpGw/y0hqOQsIQ2J3JLys/TnOoRfN/A1t+YWpcFb92s6BphFjcuoicgzwA+zX9z/A93VCyuQckw0JfxGpOn3k18U2Q+M7Wcd7MoA3tPBV/n/m+DqrI9uvhcS3gtz9PSwNMzwbqb0Dr/b63teGV8c8dgiIQnhN05qJrGPOO/s8liaKHfz2wEvujMa3orM/qaZupSs9MXUf1FwHqdssFBVaBhI7jfzn4VWAN0RLtvy8Hi+ydJ8VRMecBaWaRxt2Dxo6K1p+VNeBrbacZZ+TLJoODkY72+SlH0ZmvTCmtUdnb8ZuxKShIGjTT5D6e8fdjolk1E5dRJYDfgKsoaopEbkD2Au4vki2OUaJph9Ds68g8W0HLOGfzEjFcRCejRY+RBK7mWMbDG9ZujNHYhCxTj7afj1os72X/wBt/CEaXhWpOLIrJU9CddbTMv2wpYS2XYRSAEJIzdUQ/iLk38W0YL7cZ+nOzA5f3+wdOy58AM0nmNaMLrZd7ii1XlTVnkK8CsvZX5LYZiAxulIjk9+3kFFkNftZjoXC+8ETUHBA3HFHt9xvdi7dMf8C3fIDgDb21fkZD7r6rwJoUOg1vRhr+CUMJEQkh3UU+HiI8Y4S46ce7JK71Y6boP7uIVMDR0PngWIpHq1FQpDci+HOLKE6qLkGbbvGduQVx9oboXosEyWI0ef+bqJi+deQutu6rw/PhvIfmdyA6QgDOTT7HFJ7o90cCCGDaZe3X7PEN0xLXXRx37THJVDNWqPs0DJ9cuVVFW0+JsgmAa2+ONCtV1NZFM9a9814FLKvQGRVkASa+xdkX0ZTDyBlYyhC82Zh/80FiEJkra7UU0IrBDt4sQNnzVgvXrCxhfeGfsooMuJVoYk9IHUXoFB+wriuPxkYtVNX1Y9E5AJgPnbU/biq9uktJiKHAocCzJ49vWJbE0L2b3Sl3YkHuVdtt1lE/PZbrSRdYlB9KRL7v6LOPyoiX0UqjrEYfnCoKmU/QPPvWAML/6NgYN7CNf0RXRdLCLOUTYluaPnZnTeJwQjVYf+d8j2+GUYbDkELH0JsB7yavo2hVVPool2tPypA7Q2m9thJ4cPAoQc74uYTgycJrOl29UWdxqO5VyD7gjna7N/NltZz0dgGXYevI0W8cqi9zVJMvWWQ8h+i7VcEOfF5+x2oOAmJ74C2XwXtQSMQSQ5LL74UeFWno+WHgsRKIpUx2RlL+KUG2Bn4AtAE3Cki+6nqTT3HqepVwFUA6623nlMBKjES3xZNPUig+QqRYWaeDBPVNLSeDeRBs2jTsTDz6SGzU0qJ+i2WF+83Ah5aewdeZGWTKKi+EAC/4RDI/cMuWKKDUCcS+TLU3oBmnkOiX0MG6o7U37UVJ1lhU+5tyyH36qDwCeT/bQMyd+G3r4FXtl/vCzMvWkZOcB6gbdcgNZd0v98r9OSZqFhXf9ZHUL0QEUGbjrQiJXx63VzEA79l2J+j388WWQWp6m4Qouknu8MxxJDwaub8y4+2DUThE0jsPKCez3gw1NPRVGYs4ZetgXdVdSGAiNwNbATcNOhVjpIisc2h9o9WZh7dxASxirsC9AyMaAO6aEeou2/o2HepSD8JhcV0PaEs3hWd8XCvDkpSc7lVe0ocLSzC/2wdIIrU/A6Jrt89Lrr2qFIAxavpo3HjL9im96Dcv/teGJplWj0AxCDU+2lWvGrLd289H7wqKDSCPz+4doXu8FfuP3R3a1IsGlqwFMcllS8HQf0mtO1SK6gqP7zfLlTEt4S2t+k6lO1MwRQPEt/uPV/mRbTlTJAkUv1ry+hxlJSxFB/NBzYUkaTYb9ZWwBvFMcsxFiS6DpLc16oViz23xKDyLLozLNREsTLPFXUdVR8tfIzvp4aWeQ3NoncTjQ60rXcan0gYiW9pOfMtP7NDTW1Em8Z4kNhpr9+M33Qs/uK90M60wl6HlB4ku1v5qSq+3w5+K5QdbhWuiW8jFUf2nTy2CdZk5J/gvwfhdSHxXaT2hu4x8R0wR56EyNeQWS8gM55Aaq4ZVKysz+do/BF03AKpu9DFe/f7s5eyI5CqXyDlRyH19w54M1fNoU0/hMI8yL9qnaAcJWcsMfWXROQu4J/Ys97LBGEWx9TGS+6K7y+Atsux7j6+hRyKhGoWbdgXcq8BeZQYWn0JXnzLfsdLbGM0vjOkg8MxwjDgo7/SnSkDvW8GY7C5+ZRAZz2HNh4GM/+Cl9gOP/KI5Y5HN8KL2NmG5ueji/cGXYjiAR5UnIVX1r+2uWb+ZlK3nfbnX8Gr7/1ALJVnQmxLC4vEt7GCptAoQmL5N+kK3XTWCtC77Z6IDC89U3N0SyJrEB5zlJoxyQSo6umqurqqrqmq+6sOocPqGDWa+y+afnTiSr+XQMp+YNK+kbWh8vTeh3tjwE89gS7awQ54uw4dM9D808HtqfoFJPc3tcLo15Gyw/ofJ1GoPAOIWEig6ldFsZvCe3SHPyQIB4EXXhmv7HtdDh1A2y4C7dRt8bEDzTMGlhHoL41xCUQ8JP4NJLHj2HqOxr+DNRRJWvhuDH1UxUtaeiVR+6oYH22i6Y6MZweT9dZbT+fMmTNu600V/NQT0HwcSAikDKl/ZOLi1yVECx+jC7eju/1cD6QWb9aLfb8/2rXUB6RoKZl+6lFoPtEOJr16KLRAeHmk5vdIaJneY5tPgtS99MrrxkNmPD9g/1S/4ceQfQwQKD8GiW+HhFcsiu09UVXLoNIsxDYtSh9VLSwAiTi53jEgInNVdVhZD07Qa2kgdRsW5mi3LIncvybaotJQWGhOsYtQ8FWJVA/Sh3QUiHhFzbH3EtshMx6Fql8HwltNkH/dqi6XXLv8uEAlssdn9WYNuiP3an+HzHodYttB2+/QRdviL9qTYj8ciwgS2xiJb9nl0FV9tONO/NaLTN9mpHOGZjqHPo447Zelgeg6Qbpa2jIlQitOtEWlIfJlK9vPvQEoUnP5iNIKxwM/9QS0/Qa8WksvLHwCyX2RsgMsU8RvQiUUhO39IAWxNxKagdTfY0VHHbejfiuS3HPoA01tM8GwznOA/Mto26VjrxodAm27GNqvB7ImNTzjadP3cUxKnFNfCpCyw0xBMP8/JLkXEl5hXNbVwmeQex0iX7aqxRIjEobam+2wzpuBhGaUfM2BUM1aGmHuP5DcHy+xI1pYDM3HAhkovNM9uP33aPtVaHIvpOJUiG0D6YcsZl8x8FmASNTEw7QA2RfRQgIi6wz8BCFJrClIj8Pd/Hw0Pw9N3W8FRvFvF7/KN/M83SEx32QTlkL5iemCc+qTHNUM2nAQ5OZAeBX7Go918++ii79LV0563T0lSZFcEpGQ6YsPA828gKbuhsjaSHKfojozbbscOm4H0tD8ujWvlgQMKF6Qh4470OSBeNUXoP4ZIPFhxaS16Uf2JKYKyf2Qyv5L20WiaO210HAQliMegcR3TaFSO1DiUFiAlB8yug89EIkdLC9dfWuVVwLZCUfxcDH1yU7qoSATRCH/Lto+TrVd6UdNHEnb7M90HwWIEaGaQ3NvWZu7MaLqWxOIxh9C+j5o/bXp3BST/Dt07U4lhKafQBftju2SB2qhl4VF2+A3/th26f04dNU02nEXmrrXfiZ+O2T+GlSUpqxT0CBIZN1AojcC+JD5G3ajUbs+8+zoPm8/aPop/KbjgCqo+i1SeRJSd/+EVoo6hsbt1EuE7/v88fTbmfPYK2yx50bsduxOo9tJ9nIMAqNosjwqwquARC3vWSLD2p1p/j209TcgCaTihK7wiWo6aPI8H/Cg9iYryR8hqoq2nm3FMVJO9645Ba3no5HVelWHjgZVNR2WxK6Q/YvZ69UFmiatwaiwfR8fiFq2i9+A3QQUss/Zk1U/tmjD94P8eyD9JFJ9iR2QBhIHSz6JaeETtOU8II9UnGhaK119UIHUH4FyrL2gB/Edx/T5u9bN/tMkIEiZnVXnIMl9izK3o7Q4p14iHr/+L/zpogfJdGSZ/8aHzF59OTbYcd2hL1yS+A7WcT7zDETXRpL7DX1NEZD41qh/solJxbexasxBUFW0YZ9A6tRD8/OQ+j/Zm5kXesm3avs1PYSoesyRf9ccVmQNpEe3JEu7zZmSYcdd2AFkC71/fdNo4xHIrH+M+jObIuJx9vMGqDzbzi8iX0YXbNi7ZokQXTnm4dXAq7Q4OnlsYN+GFr6fg9zc7m9knkYbvw/Rb1hmk1dhomQ9bWo4qKvwSHP/QeofobchPiR3Q0LLQXhFJLbpwJ8v9xZoUxC3H+K/fv6/dKdcptDcq0hibDcMzf0HbToeNINU/XLSHYJPFZxTLxGfvPsZ2bQVo/i+8tn7i0Y1j0jYel5OAF5yT0juOczR2WC3qkChRwUkEJph8VjAdrZ99UQ0/wG6eBdT/iME1Rcj8S0tZNOwvzmj2DfodmgeRNY3Od2u7kJj7M9S+DBw6EGaYPuVyIxHbOrKcywPHYXyYyDzl2Bt3/K6EztDeA373Ml9+9Wxl/yraFeoBLM7+wIQh+QBeJX9ZLEUPqTLufqfWVy//DBo+x3dNw9F234LmkErT8NL7t5nGr/9JmvqLZ41x665vs+To/ptaP4DaL+kKwMJKQP1kcT2feYcKdp4RJc0rzYdDjNfNr0YR1FxP9ES8c0DtqCsMkmyMkFZZZJNd91gok0qKdaO7ZuWoSEJSHQ/qktkTag4CUJfhPiO/eubdO1gs0AKzTwJgLaeB9qIOc/ng/CCCV9J1Vnd1aFEoPK0XlOq5vAbj8D/dE38xXujft/0wu6xCkHDC8ODHhk/XmIHSHwHCEHbb60RR1fvzQzk38Grvwtv1ly8JaR6NT8ff8E30Ia96f1frjOFMQ2FAeSAk/vaz1MSkNjV8uvLjoDkQaZnntgF0o8ETy4ZaDmj/9z19j/YOtph7fn8T3u9rdlX0IWbQsN3rcOT/zEgUHY4Un9fcSqGOztTgUkIFEmiwdEbt1MvEct9cVlumHcpH/7vE1b88vIkyidOmna8kOqLA+cc7bNT9cr2hsGaNUS+YtkfACQgulEwaY8whmYguTde9Xnda4Zno4md7e9L9vBMP2qHkGQh9x+04wak/PBeQ1TzJlub+bOdG1SdbZo2oZm9JATU77Dm0Z2OKHUfeDPBXwBaQMq+P+BH09ZfBXrugS4NSVNj1BbwF2H649Woap/ds1T8FBI7WtpjZK3gcwpUHGPpkN4y6MLBQ2MAhFeG7IJgrUifQidtu4RenZsAJIRE1ixe5WrFqUFPVx8qjkPG63xomuGcegmpqCnnSxuMTwriZEDEg+jXu16r5qwdmrdMV9u3Aa8Nrwx1N6Lpp5DIV5D41vZGYl/IPBWM8qHlPLTull7Ob6CGzJDvTgxB6Za47UHmKci+SGd2Ebk38WY81o+BMdstaxsg1qWo7g7IzrGm1kNKygaGSASpvRGJfCXoUnWSfT/9qGW19JCuVb8d0g+bpnrsm12fWfPvB71XO8xZV/4Cmo+x0v7K0/r9eUj1BXZzKSxCKo7qq38fmoE98XTq10QgtLqpWhYJ6+m6DeAP+fvgGD3OqTtKgvrt6OJdg8f8MNTdNmRbPYl8tc9jvohY79DO9ML8y2jqLqSfuHEf4jvajjr7AoRXRsq+18+gJSKQA8R4RUJQez3a8ktLV6w8yxzjIAeTXddWnITm34TCx5DYDcJr2vf9hT2OPLNQ+MgKvvDBm4U27AX5982m+AtI1ZkA1hhbmwCFwnuItkL1BWjHHeA3oOr3iVWLV92r0UVfG3+GFhrtMNqrhvxrgWTuvK6er8Vg3HuWTkOcU3eUhsyfzaFrByBo+w0WAx8p0fWs12jhw+AbvlW5DgORKFJ7fb9OrovYNyC2hTXaCK+KlA1cuCORryJ1g+eR93tdeAVkxpN934hvD21XYCqGIVQ7YOFW9l5y/yBXPmdPGpnHAXPqeLXYrjoLKKrt0HgSkIbMX1EJD95PtT8bvSqk9ko095plMeGDNqOtv0JqrxvxZ15a0ezLaNPRQBYqf4kX/8ZEmzRinFN3jAhVtRxsLUB0/YGdZWhmjxh5FJZQKhwuIlG05mZY/C3M0fhI4jsjnGPgfACRkJ0FTAASWgZmPGW74/BK6IJN6Mo/77jORL78BUC4V1tCKT8Uzc+D3CuWTy/lpuCpYA22XxuDUbEe/25eIE0wfdCmnwQ68kDTUeisf42oychkwDn1CebFB+dy67l3s8Lqy3H4xd8nWVH6A1Xf93n1r/8lHAnxpQ1XHVFRlLacYVWcALGtBlRPlOj6aPlPIHUnRL9m+uujxAsvi854whpIh1cds/aNZl9Bm08EfCuq6XEOMN6IVw7RtVEtmAPtTMuUKqi9A1K3gFQhZZZNpOpbMVB0Xag6E/FqUL8R7dQ9Vx9JDCM0NZA94S+i5YdD+zXWxLvy54OONwnj/Ng03CcTvTKHClg66dLl1J2e+gSyYP5CDvrS0WRSWSKxMFvtuxnHXdN/c4dicu5+v+Vv988BVXb4wVYcdtHAmRtL4n/6FbryuBFk1hslyzVWzaCNP7aG0dFNkOoLi5Ix4S/YGPygSYVUIDPnFF8Ea6Q2NR4CmZeADEg9UnulpYIuOa7lF3aj1IJl6NQ/hkgE9ZsgO9fODkqgs94fmv0H2nio3YjKfoRXcdS4rFtK/NTjgWibDxU/wyvbf6JNApye+qRAtYCmHkQ7bhkwP3rRx414IfsnyGXyfPjmxyW3661/vsPTt/6VdFuadHuGh67qJ9Y7GOHPYzsXD0LLlbZ4pON2O+TUdtM0Sd1fnHl7pu5piiVKRccd9Vt7KCFa9Wx/Dh2wCl9NYQeriwLt9uAgNL7VuDl0AG0+PfhZFqD9arSwcMhrJjte4pvIrJeRWf+aNA59pDinXiK05Qy05RS05Vx08e79tipbdd2VWGH15UiUx4kloux98ndLatOzd73AMZue2vXaC3kst8qyI5pDaq616sn4TkjtjUWzTbN/x1+0kxUJdTZi0AzdpepK9xPCGKn4GV0FSxUnTHxVoyRBKrG0RwGvauBm27FNsJ6hYfCqgqbbE8SSIZcidEmaDIhElupwkgu/lAh/wUZBYQlAFJnxFyRU32dcPpfn7Zffpe5ztcxYvv9WZsXihK3P5F9PvwpAKBxi7S2/zPHXHUH952pLuu5QqGbRBV8PdqAC4VXx6h9A/RarwszPg/AaSO1NRUuJs6cnnTRtAf30Y9B0FHYTi0PlmXjJXfqMU81D6h6TZEh8d2I153P/Q5sOA78JKk40WQlHSRhJ+GVq3FpHSaotxVUn3Mgn7y5g75/twlqbj1w5cECiG1qaHHlT8RugnVc4Emb19cenQGn19b/IGy/8ryuGf+w1hxXVoWv+A7TjFmtwUbbf8Hc7mg3KxgE0EAUD8SqR+odQTY+pAXK/5F5DW89FvSo7LA0tV9z5R4j4bajEghtb2jJb6OvURcIwnBz9cUAiqyIznhp6oGNcmdZO/ZLDr+GZO18gl8nx2l//yw3vXEbNzKqizC1V50FkBK3KxoEDztyTeFmc9179gJ2P2JaZK/R9chgtJq+7G2gzEEELbyNV5wzrWvHK0eRe0HEnJpjVW9iq2A5dNY02HRo4UA9tOgqpu6uoa4yY2MbQ0iNEV6SbjGoWbbsU8vOQsgMnNNPHMT5Ma6f+3usfkMsEO0RPWPxxQ/GcetCqbGJzKnoTjoTZ95RdSzN54dMgHc8HMpD9+4gu9ypPQ8sOBmL9hqmKiqYCNUgAHwoLSrvecPDq6SVw1X4NFKGDkbaeDx23ARk0+1eofxyZyDh8EdD8PNOYlyhSeYr1hnV0Ma0PSvc8YWdiiSiJijgrrPY5vrDm7Ik2aekltFxwaJewr/hOI55CQsuVzKGratfho3g1gcZKHIiZlG4R1/Hbb8dv/jmaHcn5kdDrv2Oxnuxyr9J9wOz1qMxdOjHd/v0h+yxknkIbity6bwowpp26iFQD1wBrYukJB6nqC8UwbDzYYs+N+eLXVmLxxw2s8X+rEgpPfIikJ4s+WsyVJ9xILp3l4HP3ZYXVRv9I3tGa4s4L7ifdkWG3Y3eibtn+Y/yjRSQCdXdD+jEI1UF086LOPxY08wzadBRoAa38BV5yZ6TyHCj7kXVpKmJTbe24GVrPB1Jo6n6ov9caQg+BSAitOh9afg5E+q1yVVXQVpCy4YfzkvtD82t2k/DqYRQdpyYX+R66/Qr+0n2TKgVjyn4RkT8Cz6nqNWKnYklVbRpo/HTIfvF9HxEpSjHLj9Y5gXdfnY/6SvXMKm7/+KpRz/vTb57Nf557Hb/gU79cHTe+c9mEF9yMF/5nGwSa7AARZNa/S3bG4TcdbcqKYM636jwkvu2Y51XNo42HQPYlkEqk7rY+OelaWATpR9HQTAithoSXNZmF3Fu2Q49uMCUEtfzmk613Lwpl38erKN6T1mRlXLJfRKQK2Aw4EEBVs3QJV0xPnrzpGS485Eo8Tzj5lqPZaOexHUp9PO9T/IIdnjV+1sQTNz7DN7+3xajmevMfb5PLWBx50UeLSbenp4XGOwAiPeqL+r+RaeYvaOpxk9bN/QeiayFV5/aVqB1qqcR30PTTWCglDJFRtDDstEkVCh+ZamLuZfsiD9qEtl2KVF/QPdZvRxfvDH4zJp8bQr1qqLsbiawCkakjAS2Vv4Tk3kAEKaKC5FRhLDH1LwALgetE5GURuUZE+rQZF5FDRWSOiMxZuHDprzgbCN/3ufCQK8hlcmRSWc4/6LIxz/mdH2/fVXEKcMlhV/PR25/w1j/f4c1/vD1wgUo/bL7HbMbOiwAAIABJREFURsTLYsTLYnxpw1UHdejvv/4B7/z7/RHNP5mRqt9YcY8koerXfXbpftNxVu6evgsyj1pDi/STaNvVI18rtgVSdytSdQZS/9CozwhUC2jjweii7dGFm6D5D3sIbYVAlsivL8wLFDFNudHCFI0mx9s5Z+7f+Iv3xm/4AVoYe/WyZv+Bph9Dx9pGcKh1Ms+jbVebiBkmxyyRrziHPgCjDr+IyHrAi8DGqvqSiPwWaFHVUwe6ZiqHX1SVb5XvRzZlDyuVdRWcff9PefS6P7PyWp9np8O2xfNGfg89coOTePMf9sucKI+z4bfW5W/3z0EEvrHPphxz5Q+7xv7372/x7F0vsOq6K7P5Hhv1Cq/4vs+LD84l05Fl413WJxrrX0Pl+tNu467fPAAibHfQlhx5ycHdc3TcDx3XmqhW5RlT41G+5QLouKqfdwQSe+KNRi64CGj2FbTxgO4WcJENILqOSSeEV0Vqfof06F6kfqt1QNJWuh9L4kjlSUhyH1Rz6IINgiYfHoS/hFd/z6jt89uuhLbfY8Vin0fq7i5JSMtPPRw0Eslbtkvdg0h4+aKvM9kZr+KjD4EPVfWl4PVdwEljmG9CaGlo5YKDfs+H//uEfU7eha33G90Bn4hw8s1Hcf73LyMUDnHYhQfw023OJt2RIZaMkW7PsOeJI5OMBf6/vfMOj6Lq4vB7Z2t6o/ceeu9IEUFQqopiQWlKFVFBVBAQlKLAR1FERBFQQQGpiggiTQHpHSHU0FMIqZut9/tjwoaQQgKBFOd9Hh+zu3funFmSM3fOPed36DflFUZ1mIiUUK5Waf5etdvd0Pq3bzYxdM5rKIrCpZCrDG89DmvS+awWG+16Jbc5UxSFpp0zDgfZbXaWT1uLNenGtOaL3xk4vRc6nU5dJcV8ACSC4zRSeCP8xmQ4X04hpU3ts4kJTC0zlgGwrErjTaPaCCOLmuTZiuJPcrNuPegKqr1P7+h/eguh+EDQCmTCz+A4qnZxMjUFj+fUAdKarACJC5xX788+y1LAov7sOKc2ALlP9cw0sW7C3SAFk1qU9R906lnhnp26lPKaEOKiECJYSnkSeAzIXPeCXMQXQ79lz28HcNidTO//FVWbBFOsfNra3y6XC5fThd6Q9tfWrGtDmnVtCMA/v+5D0avOxJpg5ehf/9J9RNbtq9miKj9cmEPU9WhKVCrKa9Xf5nLIVSRQoFige/Ufsu8sik64z3dg05EUTj0jbFY7I5+YwOGtx9HpFRBq5Nm/kB86XdLqyxl2m2a3DZwXs34xDwl541WwH1YvwtwJ4fdR+oMNNcB2W1Wk71SEsS7oCmbQJu/BI/Slkb5jIf4L0JW5qwSuekwphG/am4ZC8UaaO6tNqnHBHb1as4yhBjivo/Y8NSa1w3sA3GpgghWQkB0NsPM591t8NAT4ISnz5SyQeQ3XLGK1WFkycQWRV6LoNqwTpatmz6og/FIkDrta9KHoFG6Gx6Tp1I9sP8GojhOxWmxUblgBl1PSvs+jdHitbZrzVmlSCYPJgNnbhXRJHu/ZCoCwixEsm7oGT18Puo/omin9dN9AH3wD1RjqpPUf8OWwhTgdTvpNSW7PVv2RygghMHoYEQJaPNsk09/B9uW73DF6KaFoucIUK1eYgTNu++c01gNdaXCeByTCu39602UL0nEGGb8AdIURXq9l2sFKaQH7btSGGkDir2oz6XQQ/p8iYyaqq03vISjmZtlif3ageD4DntlXLCb8JoFXHxBmhP7+ajKE3ySkrjS4whFefbJfxiEJxaMTUvEB+wlVv/9BPA3kM/KMoNekHjPZvuIfHFY7Xn5eLL74JR5e9/+LdGT7CUY+OQEpJZUbVeSTDaOTV6e30afqm1z893KK90yeJib//gHVm6W9YRMVFs2+DYcoWbk4wfXL43Q66VFmEDeu3USnV6jVqjqTfht139dwi6vnrrN3/UHK1SpDtabBmT5u67KdTO0zm8R4KwaTnhdHPkOP0d1SjZPSDo4Qtemy8uBEwKQrARneIik+bATzEyj+n2buWCmR4a2TeqMqYKiLEpQ9apLSlQBC/9AU/KQrARk9XM3G8XgG4T30P5OGqpGSfCno9e/u09iTYsl2m4MbV6MoXkGVjd2+4h9O7TtD86cbUane3bq6p6RG8yosuTg3aYVeON3NTKM59caiEHDtXFi6Tj2gkB9terRwv06IsRAVFo3LqYZxTu09kyVb70bRsoXpNDDrOdGPPN2QHat3s2P1HoIbVODpNzukOU4IAxiq3q+Zd8d1LUngK0lu134w04cKISDoR2T8PLWwyKtf9pgU+xnEzwF0EPAZwtQqW+bNCBk/V9WRxwbx3yD1lREe7R/4eTXyNnnGqT/5Whu+H78MIQRFyhaiSBm1CnDjd1uZOXAe1gQrq2atY87+KZTIoka4t78X3v6psjFTMGLB64zt+gnRkbHYbQ4MBj2evh7UbVMjS+ep3LACpw+cR7pctOreNEt2Pih0Oh3vf599XWukK1rVgtGXu7dORbpSquyA64qaxueR+qkhI4SucKZi0JlFumKTHLoDcCCjP0QU2pJt86eLK4rk0g8rRL+DNAQj9GUf2CmllOA4BcL0UBtuaGQfeUb7pfs7XZi4bhTvfDuYWTsnukv69288jDVB1bYQiiBk39kHcv5yNUvz3dkvWBP9HYsvzKFczdJER8TSu/JQQvanf84Ni7bwVGAvXiw9gJD9Z+k2rDNOhxrDP7bjJHabPd1j8yLSfhgZ3goZ2R0Z+QxSZr2xhRB6RNByhO8EROA8FO/sWW3fM0JPSl2Wh5PKqfZ1vX3PxY60/PpAzyljRiEjn0NGdMJ1D3n6GjlPnnHqoIZKmj/TGLNn8qZZ82caY/I0YTAZEAiqNa2U4piEWAujO0/m+RL9+W78skydx+VyER0Rg9PpTPPzq2euc+bwBRw2BwkxFuaPWpLmOEuchRn95xJ3M57wi5FMfvkzlk1ZjcPmwJZo58zB8wxu+B426/07drvNzukD57gZHs1v8zcxvf+XHN728JORZNy8pBZnCWqGjG3XPc0jFE+ERweEsWH2GpgJpDMcaT+mNqQAtarUbyooBUFXFuH/v4dih9CXAv+pqMJjqKtnQ9bCi1lBykS1AQcWwArxcx/YuTQeHHkm/JIeTbs0YNJvozhz8DwNn6xDoVIpU6u+G7+MfRsOYbc5WDplNbUfrU6N5lXSnS8h1sLQZqO4dOoqQUUD+OyfSankeL38PJFJ5fs6vYJvkHeac7mcLm7fh7Zb7ZSsXJx/d592l/9fCbnGsimrCSwaQIP2tSlQPOvdj6wWK4Mbvk/YhXAcdgcgsFvtbPrhL2bvnpRtmUJSShaM+ZG/Vu6m4RN1eO2THqn3IHTFARNgVZsjKznXmedekNadyKgB6oaJrhwE/YgQRhSP9pAD8WzF3BaXz0i10tXUCkwP0gYjCC+QMag9aLMWxtTIHeSplXp61Gheha5DnkgzFTEmItadsiiEIDYq7SbQt/hz8V9cPXMdh81BxJUb/PLlhlRjSlctSZ9JLxJULIDqj1RhwP96pTmXl58XvcZ3R6dXMHuZeHveAAbP7E3ZGqW4lcTgdLr4YcLPzB76La/VHEbU9ZR6aFt++ptXKr7O263GEHE5Ms3zHNx8jLDQcCxxiditDrdGvKITnD0cmuH1ZoXtP+9ixYxfCT1+ibVzNrBh4dZUY4TPUDA/CfpK4Dsa8TA2VrMRGfclYFErOZ3n1Hz3HEbxeh4lcAGKV68Hmv0ihIIIXATGRmBsgQj48oGdS+PBkedX6nfjhfefYtcv+7DEJ1K2Rinqt6ud4XhPXw+Eov7h6HRKuhuoT7/RgaffSDtL5Ha6j+hKt7c7IRThXtX+b+t4Pnp2Gif3ncHbz5OrZ8MABzq9wvGdp9wFTDeuRTGl92xsiXaunw9nat85TF6fegOwQPFAnLduXIqqEGk0G9DpdNRsmexUd/2yj7OHL9DsqYaUrpJ2VZ7NamfHqt2Yvcz4BHoz7dU5KDqFEQsGE3klCqdDfcJw2OxEXrmR6nghPBD+n9z1e8m16EuCfR9gS3rSyD5Z3ryAMFTN1obiGg+ffO/US1Qqxo+X5xITGUdgEf+7rnRaPteEg5uPsnPNHmq2qEqH/mkXF91J3M149EZ9inj/Le7Uaff08WBSknP+8JkpSU5dDc+Uq1naPS4hxsKtJb3L6eJmWDQJsRaunQujRKWiGM1qvnS5mqUxmAzYEu1IKSldrQTPDetCncequ3XTNyzawqxBX2O32vlx8kq+PjY9VTs7KSXvth3P6QPnkBJcDid2mxpXHt35E748MIWfPl1FQowFo4eRtq/kHs307EL4vKfKDDhOg1e/+y7S0dB42OR7pw5gMBoy3RRCp9Px9lcD4KsBmZ7/m5E/sHzaWoROodGTdbl06gpNOtWn98cvZHgTiY2KZcfqPe7XeqOeouWSW40Vr1iUpp3r8/fqPSiK4OmhT9KjzEAcdic+gd7M2f8pvoE+WC02LHFJ+hgSwkMjUzncXWv3pcgSOrX3TCqnnhBr4cSuU+7V+O3ERycQUMiPhSGfsWTSSn6e/gtvNB7JuNXvElz/wW3ePWyE4o3IZKGThkZuJF/E1B8Wh7Ye49XqbzG44XtcOK5qn8TdjGf5tLU47E7siXb+WvkP549eZOWsdWz+8e9057LEJzKo/ntIV/JOqpdfylQ5IVRd9oWnPuOnK/O4cPwScdEJWOISiY6I4a+fVS01s6eJBu1r4+Ftxuxlon2f1qnO17RLfUyeJnR6HdIlCW5QIdUYD28zQcUC0ekUDCY9xSsWwWDUozPoKFW5OIsnriAx3srST1eTGG8l8moUU3p9fk/fpYaGxoPhP7FSzw6cTicfdJxEYry62h1QdwStujel/7SequZ5Ukz7luqpw+Yg4lLyxmZMZCyHth6nVOVilK5akpO7TxMTEev+3Gg2MHb58FTnFUJQsISaEVOoVAGMJgNWiw0hBAVKJGfKjFs1gv0bD2P0MFKzRerNyTY9WuIb5Mu5I6E07VLfPeftKIrCzB0T2Dh/Ph2eW4qX7xGuh79Iv8bHCNl/ltATl4iOjOF2YYlbOfcaGhq5g/+UU7dZ7Ux8YQYHNx+lXtuavP/D0DQVF50OJ0smryRk71k6DmhLg/Z1cNqdbslbUJ321qU7CSjsR4f+bVk5ax0iSd7Q7GnC5Gmi9UvNAVXet2+1t7BarLicLsb+/A6lKhfHmZTWaPQw0mVweyo3zLg7TYd+bbl06ioHNx+l9YvNadA+edNXp9PRoH2dDI9v+EQdGj6R8ZgCxQLpPvAg2KMBSaGgHyherhpnj4HVYuPc4VD6THiBb95fjNnbxLBv7lPtTyPLSOsONTPH9BhCl7aiqMZ/l3zv1KWURF6Nwtvfiw0LtrD394NYLTb+WXeAP77blmao4qdPV/Pj5JVYE2zs++MwX+z9hFKVi/PcO51ZNnWNO+Zst9q5ei6M6+fCQIJEYvY08ebc/jTpXN8tOHZk2wlsFhuWWDXu/du8PxizfDgfrXmXFTN+pUyNUrwy9tkUNmxavJ0Z/eeiN+gZvfRtzh+7yLwR32HyNDF+9btprsazDxe3HjmEouDpZ8bDR+JyuujYvy0tujXhmbc6ZlsvVo3M40pYDrEfqVrrYgYU3IBQsreJuEbeJl87dZfLxfhnp7F73QF0eh2P92rlLvqRLpc7lHInIfvPYk1QNTd0OoVLp65QqnJx+k58iSdfa8voTpO4dj4Mh83J3yt3YzQZ0Bt0alzd5mDF9LX8teIfhsx+lYBCfpSqkrwqN3maCG6kxrPrtK5Bndaqdsz5YxcJC42gZsuqGIx6pvb5AofNAViZ1GMmcTcTcNidOKITmNrnCxadfnCxbOE7FhnVG1xRCM+efPzrQPZvOkqx8oUpX6sMwD11cdLIBhJ/AZnUnAID2I+D6f7kgqXrBtgOgSEYoSt2/zZq5Cj52qmfPXRBrSa12rFb7YTsO0OxikW5+O9lSgQX4/FerdI8rtOAx9mz/iA6nYKHjznFqrho2UJ8eWAKO9fs4ZNXPsdqcWJLtOHl50VCrAWn3cnJvWcJOXCe2Kg4pvwxlpLBxRm/+l1++/oPKtYrh9FsYFKPmbR5uSUN2tVm69IdTOkzG0Wno0DxQGbvnpTCnjvVkW/vW5o8RnLm4Hn0Rj1lqmWtglRKSeiJS3j5eVKgeBDCEIwotAMpJUIIvIDmTzfK0pwaDwhjc7AdQC3ll6C/v4bS0nkdGdERcKr/BS5GGKplg6EaOUW+dupe/p64krJLdHodgUUDmPn3cBLjEzF7mVOEDk7tO8N345cRWDSA7iO60uX19ljjrbw8tluqAiS9QU+p24t3hMBhd7ifAkDNK79y+pr7dd3HahDcoDyzBs1j+8+7sFsd/L1qN7N2TGTFzF/dTwYRlyM5dySUt+cNcIdf3l00hIhLkXzx5gI8vM28u2hIqmud3n8um5f8hZSS7iO68vKYlOEc6YoG2z+qdsltneWllHz8/HT++XUf0iV5a94A2rzUIumytNBKZpHSAYm/AxLM7e5NnTITCK8+oAtCOk6DoT7Y9yGpj7jXzkPWLWqru6SWcdKyOlNOXTouqm3zDLW01X0uI1879aJlC/P6rD58/9FyipYrzNAvXkMIgYd3ym5DljgLw1uPwxJrQWfQsen77TgdTnR6hfiYBN5dmNqJlq5akm7DO7F82loKlgiifK0y7Fy7172ZajAbUjSacDgcvNFkJJdOXXU7f0VROH80lITY5G7s9kQ7hcsUomqTYLcW+y3n+kTfxwA1rLTu602EnrjI4z0fpWTlYvw+/0/3DWzZtDUpnLp0xSIjOqhCW9IJ/jMRZrXVXeSVG+xcs9ctLbBwzE+0eakFdpsdS1yiu+NSZrAlqnsVvoHe1GxZ9YHfFKS0IWPGgm232rYuB5tIyJtDwZqUwmpZjQh8MAqHQgjw6Aq2/cgbvZBCB+igwC/3tmmqL4/a9w/AA6FPuzfA7Uj7SeSN53BnRAeteKBywBpZI187dVAd4S1nmB43w2PcqXlOu9Ndcu+wwb4Nh1KNd7lcbF7yN57eHiwM+ZygogHYbXZWzlpH+MVIaj1ajfI1y7gLiQ5uPsrozpNTxvCFWmxUs1U1Jr08K3lup4urZ65lWP360yer+GHCCqwJVtbN28T8f2fiG+RDdESMGi7x9aRPlaEULV+YEQtex9dnv6plIuMBkJYf3U7dy8/THc5RdAqFShXg390hjGg7HpvFziNPN2LUkjfv6ixdLhdvtxxD6InLSCnpNqwzPT98LsWYqLBobBYbhUtnj8iXjP8eLL8AVkj4Fgy1wJy5vqzZjnULkJQdZdvuDl09KKRlNZCo7mcLD/WGcg+t74SxPtLvY7CsBVNj8Hjq7udO3JDUxFoCBrBuBs2p5xr+E7tdUddvsv+Pw8TciE3z88KlC1KwRKCqm6IT+BX0xWAyYPI0pZkm+M3IxcwYMJdvP1jCwLojsFqsGIwGnhvehcEz+/BI10YpKkP/99qcFA5db9BRrWkw3xybToFigSnCOy6XZESb8cx9Z5H7vcQEK5dOXUlSYIQDm46kqA4NPXGZKX9+SL3Ha1HnsRpER8Zy8eQV9m04zKxB81S1wSQZWfBI0bzXw9uDj9e+R6X65anfrjbv/zCUL978FktsIk6Hk61Ld7D/D1XUSrrikLZDSFcc/+4O4Z024/i4+/+Iun6TG1ejOHckFEtcIonxVtbP/zPFd7Zh0RZ6lBlInypD+ez1rzPzz5Yh0rpVdSa3mkhIwJVai+ahoa8CGAA96Cs9+CcGQ03cWutSgqFShsMzQvHohBL4FYpXn0zZrYq03WolqYdMrO41Hh75fqV+4cQl3mg8EoQaV597cGqqwpvTB84RcfkG0iUxmPS069WKoKKBePp50vblFqnm3Llmr9tJJ8YncvVsWJqbk1JKju885c58AXU13O3tTrR+8RFWzlpHfHQCTTrWY+N329xj7DYHqz9fz4CpPbl8+ipDGo/EnminQIkgZu+ZzGM9WnB8VwggMZgMVKxbFp8Ab/pOeomTu0+rzTdQ8+0jLt9Q9UsCvkJafgR9FYRX3xR21n60OrN3T3a/tsSlzAra9es+6rYuiIzoCjiwJRoY0bYillgrOr2OqLBoxv48HL1Bj81qx2DUE1y/XIo5vnn/B3doat28P+gz4QW8/DLuNpUervglEDf5thuVGXRFwJz1Vn7ZhQicj4z/FnAhvB5Y//Xk83k8rWrU2HcjzJ0Rhsx34Lrvc5sfQ/qOA9sWMD2OMOWODl4aKnnCqR/aeowrp6/RuFP9VNrmd+PPxdtJiLOAVKs2/165m65DnkgxJvxipDsEYbc62LpsJ11ff4K2r7RIswl1k871CQ+NwGF3YPYyU7Rc2kp+MwZ8xZ+Lt+NyujB7qSX61ZoF8/PMX/np09UInQAJ3gFeGMx67IkO97GupHDQDx//rMoFS3UT9e9Vu2nX61GKlCnE5ZCrNO5UD58Ab5ZNW8PCsT8hhMBgMqjyA0LQeVA7Vn3+G+VqlqZmixmZ+s46D27HZ4O/RrokOr1Cqcol1MdzGQc4iIs247CqK2Snw8nl09d4o8konE4niqJQ+9HqqTZzAwr5E3UtGikleqMeg/k+mjdb1yen9Qlv8BmN8Oj4wDYnM4NQfFXZ4Yd1PiEQXi8ALzy0c96O4tkVPLvmyLk1MibXO/XfvtnE7KHfAjB/1BIWnJqFl2/m24mVqlwCs4eJxAQrik6hRHDqnfq6bWoQWCSASNcNEuOtXD8fzoIxP2G32nnh/ac5c+g8y/+3lkKlCvDiyKfpO/FFytUoTeSVGzzWowUmj9TKjADr52/C5UzOvlkVtZAnPV5MzpV3qJ8lRCcwdfOHvN/+Y3WVLKB09VLs3XCIPxdvd0sPSAkBhf0BqNWqGrVaJWcprJiRnEHjsDv5cMVwLhy/xIyB83A5nAhF4d2Fr9P8mcZ3/c469mvL1TPX2P7zP9RtU5MnX30M7L+hhhccBBQU1H+8JAc2X8flkrR9uQWrZ693n//GtZupNqPHLB/GlN6zibsZz+CZfTCa7sMBm1qA7SBqxoZEmJrnqEPX0MhN5HqnvvG7re74sU6vcPrAOWq1zHwebesXH2HT4u0c2HSEAiWCqFSvXKoxeqOezoPasXfjIY5sPU5ivBVrgpUjf/1Ly7PXeLvlGFVu1mwg8koUPcd1p9aj1ShQLDDVXOeOhvLh01OIDo9xZ6MAFCwZhKJT8PQxE3czIcUxxSoWoWqTYL7YN4VvRy1GbzLQoH1tFn34k7t6VQioVK8cFeumvSFVumoJboZF47A7MZoNfNpzNgmxlqQCJpWty3ZmyqkLIXhp1DOUqFQc/4K+6lOMrgM4z0PinwhzG8au7M/p/efw8vfCN8ib1bPXA2D0MFClcer4brHyRZi+7aO7njszCM8+oBRGOs6qLe90Be5+kMZdkY5zyKhB4IoE7zdRvF7MaZM07gEh76xsyeoEQuiAvcBlKWXHjMbWr19f7t27N0vzLxq3lKVT1mBNsOLhbWZhyGfu1WpmCNl/lrdajMaaYENn0NH25ZYM+3pgijETXpzBjtV7cNqdSCkxeRpxWB3JTlngzojxK+hLQqwazun98fM8O6xzirkG1H2HMwfPp3hPUQQvf/gcPT7oxsk9pxnRdryqlZ6EV4AXT/R5lM2L/6ZC3bLYbXZO7AzBZrUjXTJF/juAb5APIxcPpV7bWu73Ym7E8vW7PxAdEUO1ZpX5/qNlblmCW/T79GWeHZ7S3rRwOpz0rfYWEZcjEULQeXB7XpvcI8NjTh84x88zfqFoucI8/95T97cSz6VI+wlkzDhAh/Abj9DnH8lhAFfkC2Dfj/poaEIU3ITQ/beahORWhBD7pJT1MzM2O1bqQ4ETgG82zJWKHqO7EVgkgNB/L/FEn9ZZcuig6oCLpJJ2l8OZZju7g38ewWZJkgUw6BgwrScLPviRqLBo9xiztxmX00VMZKxbLnfhmJ+o2iSYZVPXUKxCYXqO6+4OQdyO0WykatLqNbhBBZ4b0YUFH/zo/tyeaGP5tF8AiPw1KsWxBrMBRaekWHHHRMby8fPTWRm5wP2eb6APb89TNeAvnLjEog9/SjGP3qCjebf0V+mXTl1hwdif8PT2oOOAtkRcjnRfy7alO+/q1CvUKZtmPn9+Qkb1UVexCOSNVxGFNue0SdmLTKpSdb9O/buskfu5r5RGIUQJoANw/zlq6aAoCh37t2XQ9N6UrVH67gfcQY0WVajeNBi9UY+HtwcBhf3Ys/5AijGNOtTD7GXCaDZQvEIRmnZpgN2e7EQNJgNvz+vPJxtH4+WfHM/Xm/S802Ycf6/azerP1/PFmwsYOuc1zF4mFJ1CndbVqfNYDfpOfokaLasw8aWZdPR6iT3rDvDIUw3VjByjLlVnJFBDIHqDjuAG5SlQPHWYJ61GFrcoXaUEH699nwZP1MHkYUTRKTzzdifsVjs9yg3iSY8XWfjhUvd4l8vFWy1Gs23pTjYu2sLsN+bj4e2BolMwehip0bIKn/T6nGeLvMonPT/7T8rtSukC163+sRJcETlqz4NA+I5RG0+jA88XEPq0Wx5q5G7uK/wihFgOTAJ8gOFphV+EEP2AfgClSpWqd+HChXs+370ipeTItuO8/+QEbBY7Jk8jo5a8RZNO6tOM0+lk85K/iY9OoE2P5ozrNo3DW4/jdDgRimDs8mHUfrQ6bzQdxZXT13A4nCkWNLcoX7sM5WqW5sCmI1SoU4Yhs19j5sB57NtwELOXGbvNjs1iBwHlapTmf9vGY/Y00SWgJ9Y7xMUMJj0Nn6zL2/MGcGjLUT7uPkMNwyQVLb23aAgtn717KpmUEqfDid6g3oAObT6KlGDyMPLlwamUqFiUhFgLTwf1djtrnwAvvjwwhV/mbsS/sB9Gk4Evhy2IzfLPAAAeA0lEQVTCmmDF5Gli8Mzedy3oyo+4YqZCwkL1hfcgFO+BGR+QB5HSCdgRwnzXsRoPj6yEX+55pS6E6AiESSn3ZTROSvmVlLK+lLJ+wYLZU0mYVYQQXDx5xV1YYU2wsX/TEffnOp2ONj1a0GVwe7z8vAg9ccnt4PR6HVUaV+KP77dz7Xw4DnvaDt1g1ONXwIc/vt9GxOUb7PplPwPqvMPhbcdxOlwkxNy2aSnh/LFQFk9cgU6vo2mn1P9WdquDU3vO4OnjwcyBX+NyudAZdNRrW5Nf43/IlEO/de23NOOlS6YQB5MudbXv6eNB/Xa13J2TOg1sR6FSBekz4UWefqMD1gQbLqf6fbicTuKjE1Kd57+A4jscUeAXRIF1+dKhAwih0xx6Hud+YurNgM5CiCdRy8t8hRDfSykzDr7mELe0SAxGPYpe7SWaHs+81ZGFY39CURSqNqlEQGF/tZxeSbvaTtErlKtThmvnw1O0p4u7GY/RrG4YKjqB3qh3x6ldTsmlk1foX1tNPSxZpTgXT1xOMa/T6SLuZrzqRKW6WXv20IV7lr195q2OHNl+ApfTRdVmwZQMLu7+bNyqERzachyzl8kd/79F254tWfvlBsJCwylYskC66pb/BYQ+6yFADY2HyX1nvwAIIVqRTvjldu4l+yU7OX/sIvs2HCK4YQWqN8u4tPn0wXPE30ygevPK6HQ6nE4nMwfOY+eavVSqXw6fAG/CL0Uikfy7KwS71YHBZMBus4NUV8ilq5eg0RN1+fWrP0iIVTdsnXYnepMeg1FPg/Z1+HvlPzgdanGSEMLdQFrRKUz4dST12tZkeOsPCdl3Fiklz7zdkTqta7BzzR5qtqhG0y4N3DY7nU6WfrqaE7tC6NCvDY061EtxTX2qvsnFf5NvHHMPTsW/kC+xUfGUqlw8wxJxl8tFdEQsfgV8skVL/WZ4NNERsZQMLqZps2to3IWshF/+U079QbB9xT9M6f25O32wcad6lK1RitWfr8dhdxJUxJ+eHz3PrEHz3GmMtVpVY+zPw/n2gyWsm7cJp8OJ2dtMvykvc2TbCXyDvOk57jn++fUAx3edomW3xiTGW/Hy88ToYeTNR0a7VRU7DmjL0C/6ASRVlS51x74/2zkhxebyU0G9iIuKd79+rEdzti/fhVAU6rapwbiVIzJ07NL6FzL6PUCP8P8fwpj+005G7Pn9IOOengJCUK1pMJPWj9Icu4ZGBjyUmPrtSCm33M2h53XibsZz9ex1bt0Ej/79LzvX7qVqk0p4eJnx9PHA5Gni6aEdUIRCYrwVm8XGjWs3CQuNSCrbB7OXiVbdm+IT4M1LH3SjWIUiCEXQ6Mk6dHitDSN/GMrrs/qyY/VeZgz4irVf/M6ojpMoUrYQ1R+pwsndp9WngSR+/eoP4mPUGHfI/nPuQi1Fp+4j3E7H/m3dPyt6haN//Yst0Y41wcr+jYe5cuYa6SGlRN4cDK4wcF1B3nw93bGqdMBVEhPS7iy1cMxPWC02rAlWju88ybkjoXf59jWyinTF4Yr7Glfc10hX6jTe/IKUicjEzUj78Zw2JdeQ6ytKcwMH/jzC6M6fIKWkTuvqVG5YkZ8+XYUQgjLVS/LVkWkc33GKkpWLU6JiUUJPXMZg1GO12BCKoGRwMWbtnMjWZTsoW72Uu6ozqGgA84/PSFOm9ci248kOWlE4feA8pauWpM5jNRAIZNJurXRJ5o9azOuz+tL2lRZsXbYDgcBgNFC7dXVcLhfbl+8iPjqBF95/GktcIr9+tRGjyYhvkA8Rl24kZ70EemfwLbhAJt9MVOnV1FjiExnSeCTXzoVhNBuYtmUcpauWSLESL1y6AGcOnsNhdyJdEr8Cmdds18gcMqov2I+pP1v/QAT9eJcj8h5S2pCRz4DzCkgn0ncMime3ux+Yz9GceiaYP2qJ28Ee2nKMk3vOuFUazxw8jz3R7k6PBOjQrw0h+8+yY/UeipQtRNkapSheoShlqnVPc/60Qh4tuzdjy9IdqnCTIqjZUm2pV6JSMd77/g0+7fmZO1d9w4It1GtTi23Ld6JTFOw2B3abHZ1ex2eDv+aP77chpWTV579x/UI4DpsTh83CpX+vUL99bSIuRdJ34osZNsQQQof0GQWxEwAFfMenOW7nmr1cPx+GNUGVWuhfaxg+gT5M3fwhZauXAmDonH44HU6ung3j5bHPUqB4UJpzZRfSGYaM+xKEHuE9AKGkzvvPd9gPo7aoA+yHcMXPh8Q/wdwW4flK/uhq5TgNzstqrwCAhO9Ac+qaU79FfHQ8h7edoHjFopSqXDzFZ0HFAtDpFZwOFy6XpGzNUhzdfgKHzYnJ04RvgeRi2qjrNxn/3DRC9p7Fbndwev853mgykh8ufInZM23hr7Ro0K42/9s6njMHz1Ovbc0UcsGtX3iEs4fOs2zaWlxOF1JCzI04Tu07iz0pbVK6JJFXovhr5T/uG9DFk1fQ6ZNXzE6Xi4/XvJdpmxSvl5Ce3QCBEGmrLPrfoaIppVoB+9XwRUxa/wGgyhx8uGJEps97v8gbL4MzFBBI2x5EgZUP7dw5hrER2ParP+tLQ9xMtWLUfgR0xcHcJmftyw50RW97YQKttyqgOXVAdeiv1nibhBgLToeTscuHp2iOMXROP6wJNq6dC+PlD5+lcYd6fDd+GdHhMal0Tma/MZ8TO0NSVF3aLHYiLkUSH2PhyLbj1GxZlUr17q4bUqle+XTHdR7cno2LthIbFYenjwdR16No37c1i8YuRdEJipYtTPEKRajZoiq7ftmH0+EksKg/fSa+yLQ+c5BS8tZX/TP9HblcLhRFQYiMb0x1Wlfn+Xe78uu8P4i8EoXL6ULRKZi8Mn9Dy06kdKlCZLeKCxync8SOh40ImAuWNYDaXJr42Umf2MH58AsAHwRCCYDARci4r0FXEuGTv2UqMku2ZL9kluzIfvnp01UsnbqGouUK8+GKd9JUSswMYRcj+LTX59y8Hk3L55qyfNpaVagLqFC3HN3e6kiLZxtjMGZOmEpKiZSS99tPcHcKAlXDvVCpAgyfP4h3H/8Ip8OFTqcwbcs4ghtUID46nm9H/8jN8BheGvWMO0QRGxXHhBdmcP5oKF2HPMHz76ZuM+awO3jzkQ84e1jdaCxSpiDt+zxGsYqFadCuNiYPEzarnbVzfifuZjydBjxOYJEAnEmFRGlpxd/JhROXGNFmPFHXb9JlcHsGzeid5qP7pVNXGPfMVG6Gx/DqJy/Rruej/DDhZ5ZNXUORMoUYv+ZdCpXMGTVFV9QgsO1QX5jaofh/kiN25BTSEYqMfAr1xqYgglZpEgB5jIee0phZ7tepnz92kdcbvofVYkPRKTzyVENGLx12T3O91WI0x3ecxOWSaoGQENiS5hWKWqRUrVllJieFDDLi4OajjOn6CTaLnS5D2rP+mz+xJdqpVL88HV5tQ7OnG7L68/UsHPsTLqcLnV6h1/jnef69pxjVcRL7/ziM0+7Ay8+Ln658hdFsZOageayf/ycOmwOTp0m9CdRPvWpvb+yeQgfG7G2mUYe6fLDkrRTjNi3ezrp5f1CtWTA9P+yept5MWrzX7iP2/3FYlRbwNPLZzolpavAMbTaKE7tOqZ3VTAYWh87Bv2DWGpo8KKR0qj1EhQ6MLRDiv5c+KV03wH4KDMHqClcjT/GwVRofGonxiYikqk6X0+VeWd8LN65GuaV1JTD864FsW7aDHWv24rK7cNqd7P/jMC6Xi/Xf/sk/v+yjbc9HeaRrw1RzTXt1jjtPfc3n6/nx8ldIKVM4tZotqqgZMYk29AY91ZtXAeDckQtu+QBboo3oiFhMHkbOHw11v68ogrg01CUBqjQJ5sTOU+5wT2JcIn+v2J1izMk9p5neby7WBCsn95zBx987UxK8AIpep4q5SwkSd4eoO0mItaSQILBbHWmOywmE0IH5v6dVcztCCVQbS2vke/LUkiW4QQUad6yHTq/DJ8CLV+8iB5sRr07ugdFswGg20LB9HYxmA0Nmv0aVRhXRG/XoDXrK1SjN0qlrmP7aXHas3su4p6fw220NlaWUnD54LoU0AEJgNBvcDt0Sn8ihLccoVLogE38bRY/R3Zj42yiqN6tMQqyFJp3qY/Qw4uFjplSV4rzbbjzdCvfl1N4zAElKjRWo/Wj1VNcgpSTsQngKvXWdQUfZGin7pV45c90tcWBNsHLhxKVMf0+vz+pD0XKFMZoNPPN2R0pXTd2LVR3X192yr+OAtqn6wGpoaDwc8lT45dCWY3w7egn+BX0Z+mV/Agr5EXMjlun95nLlzDVeHvMsjzzVKNPzRUfEcHznKSa8MB2dXocQgunbP+LvlbtxOV10feMJ+tcaTsTl5C71BUsGsfjClwBMfGkmO9fswelwYkjaLB00vTftej8KQHxMAv1qDSMuKh6X08Wk30ZR/RF1hR5x5QYD67yDzarKCgya1YcfPl7O1TPXU9jYaeDjDPn81TTj2Jb4RLr693Q7db1Rz1NvqPF336Dk9MTYqDj61RxGQqwFl9PF1D8/JLhBhUx/T5nFYXdgS7Tj6eNx98EaGhqZJl+GX+JuxjOq4ySsCVb0Bh3T+n7Bx2vfZ9bgr9m1di8Ou5PJPWbx7clZmV4l+hXw5cj2E26RLaPZwNHtJ+gxOjnXtWiFIimcemARf5xOJwkxFrYt3+nuiOTt78XSqyll5Q9tPkbsjTh3aGbV5+vdTn3b0p3ExyRgtzpQdAphoeHE39HmzuRpokqjSiTGJzLhxZmE7D3D471a0WfCiwgh8PAyU61ZMCH7zwFqqmO/T19JdZ0+Ad58c3wGIfvOUrxS0XveXL4beoPerQipoaGRM+SZ8Et0RAy3grYOu5MrSSva6+fCVDlcQCiC6PCYLM1boU5Zd/64UBTKJGWf3GLssmEULBUEQtU4D9l/jueKvqa2rLvV7U4RFC6Tuu1X0XKF3I2nTR5GylRLzjgoXKage7PSYDJQpEwh+k97BYPJoKYkli/Ms8M6sXjiz3T2fYXd6/Zz49pNVn32Gwf+POqeZ/Lvoxk2bwAjfxjK0DmvpXudnj4e1GqVdl9VDQ2N/EOecerFyhehevMqaociDyM9Rj8DwCvjumPyMGLyNFG5YUXK1ix1l5lS8ujzzeg/7RXK1iyNdLmY/Moszh1N1iLxK+DL4vNfMnXTh+gNelxOF7GRcYzrNtWtA6M36Biz7O1Uc5euVpLHejQnqFgADZ+si5efl7vrUtMuDegxuhuV6pfn2eGdadGtMTfDouk6pD1zD05jUcjnRFy54b553YrbCyFS6JkbTQZadW9Gk071NVEsDQ2NvBVTdzqdnDl4Hr8CvhQundxwIyosmujwGEpVKX5Pju3GtSh6lB3sVj4MbliBz3dNSjHm5N4zDGs11i0XcCdFyhZi+DeDqNUquartdtVEAJ1eQW/U89onPegy+An3uIjLkQxt9gERlyJBQIESQXx3ZjbT+89l44ItOJNi5jqDjkr1yjF187j7auzsdDhZN+8PIq9G8UTfx1J8lxoaGrmPh67S+LDQ6XRUqlc+lRMKKORHmWol73mlenv2CJCiyfMtguuXp/uILugNd+R3J+1fXjsXxuguk7n9Jnlw87EUNwGnw4U1wcbG77almOKdx8YTFhqByyVxOSU3rt4kLiqeXuOfp0RwMXR6HW1eacHi0C+Z+feE+3LoALPf/Ja5wxexZNJKBjd8L101RQ0NjbxHnnLqD4oCxYPoPqILik7BO8CLN7/sl+a4l8c8S/k6Zd2vjWZDCgdrs9hxuZJvEI+/0hKTpwlFn/Jr9vBObhcmpUwleVu6agl8Ar0JKhrA10ens972I+8uGEJgYf/7FmIKvxTJhgVbsFpsuJwurBYbYaH5r4myhsZ/Fc2pJ9FzXHfWJS5mRcS3VG5Y0f1+6L+X+Xn6Lxz96wQAfSe+iMnThNFsoH672rz+WV81r92op8/EF1KU3rd8rimfbhzNYy+1SFHBeWzHScJCwwG4GR6Dl5+aAqjoFKo2DWb6tvFu5339Qjgn955xl/bfC9ERMXz/8XKWT1/LuGempnh68A30pkjZ1Ju8GhoaeRMt/+w27tRCuXz6KoMbvIvD7kSnVxi9dBiNnqzLT1e+Ii4qnkKlCiCEoMWzTZAuibe/V6o5qzYJpkrjSoTsP8P5IxcBtUl1yP5zFCpVkPkjF7s3PnUGHa9OehEPb9XJb126g097z0bRKQQ3KM+nG8dkOcQkpeTNRz7g6rkwdDolRdWnolP4ZOPo+w7naGho5B60lXoGHP3rX0CNsVsTbOxcswcAL19PCpcu6F5Ne/p44OmbfsGNEILuI7pi8jShN+pBQnADVcfFbrW7Ha1Or8NuS16Rf/fRcmwWG4lxifz7z2kunbqa5WuwxCVy5ex1nHYntkQ7QlHDRiZPI48+34wSFYtleU4NDY3ci7ZSz4DKjSq6Ha7J00i9trVSjTm4+Shju36KLdFGv6mv8NSQJ9Ocq81LLfAv6Mf5o6E06Vzf3Rii5/juHNp6jMgrUTRoV4vajyZnz5SoWITLp64k5eFL/Av6pjl3Rnh4mylfqwyhJy4hhOCRpxrR++PnscQlUqqKptSnoZHfyFMpjTnByb1n2PXLXio3qECjDvVSfd6j7CCuX1Dj43qjnhUR893hk8wipcRhd6SS+Y25EcvsN+Zz/UI4Pcd1p07rGlma9/Lpq0x4YQbR4TE06lCXak0r0+r5ppmS3NXQ0Mg95EuZgJwiuH55/Av6kl7Sye2dhARqVWpWEUKkqdvuG+jD+98PTfW+y+Ui6no0/gV9M5TQnfCCKg0A8Pu3m3l1cg/NoWto5HO0mPpd+G78MnpXGUrvykP5/uPlqT5/d9EQAgr74+FtZuicfllqWXcvWOIsDKw7gpfLD+alMgMJvxQJqKv90H8vExYazuFtx+lXexinkzRhQBXbslpsD9Q2DQ2NnEcLv2TAhROXeLVacrMJvVHPb4lLHrodUkoO/HkUa4KVm2HRfDH0WxITrCg6hU6D2qEogj3rDxKWFAZyuWSqAipPXw9W31z00G3X0NC4f7TwSzYxb8R3KV57+Xlm6jin08nmJX+TEGPhsR7N8fLN3HHpMeetBfz2zZ8IAcUqFHFXseoNOnau2UPk5Rspuh/dic6go1rTyvdlg4aGRt7gnsMvQoiSQojNQojjQohjQojUwd88jsFkcHdaEopgyOd9M3Xc9H5zmTnwK+a+s4ihzT5IUWV6O2ps/GaKJtVpsWHhFhLjE7HEJXLuSChte7YisGgAjTvWI/ZGXAqHLhSBf0FfDGYDeqOe0tVK0vrFR3jve60pr4bGf4H7Wak7gGFSyv1CCB9gnxBio5TyeDbZluMMnN6LyyFXuXYujKfe7EDLZ5tm6rhdv+wjMV6t2rwccpW4qPgUTStAbXDxVvPRhJ64hG+QD5/tmpSuDnzZmqX5958QXA4ngUX8GfJZX974/FUAZr3+NRsXbkG6JHqjnsqNKvLW3P4IAQazkYBCuaNPqIaGxsPhnp26lPIqcDXp51ghxAmgOJBvnHqhkgX46tC0LB9X+9Fq7Fy7D5fDScESQXgHpK40/evnf7gcchW71UHU9WhWzVrHa5++nOZ841eN4PuPlmOJS+SlUc9wMzyGb977nsQEK73GP0+zLg2wJdpp+ESdTDeU1tDQyJ9kS0xdCFEGqAP8k8Zn/YB+AKVKZU3rPLdwat8ZPug0mfib8fSd9BJPD+2Q5rgT/4Qw6aWZ2K122vV+lALFA+nwWps0S/u9/JPj7DqDDu/A1I7/Fj4B3gz8Xy/36yFNRhKy7wwup+TQluMsvTrvvoW+NDQ08gf3nf0ihPAGtgITpJQrMhqb17JfbtGv9jDOHVYbZxhMepZcnItfgdTVnS+U6k/EJbX1ncnTyJqY79LVapFSMuetBWz56W+qNavMe98NweSRuXTIrgE93Xoxik5hdfSiB55KqaGhkXM8tOwXIYQB+Bn44W4OPS9zq+uQ+gLSuxHeiqMDOGxO9bh0tqKFEAya0ZtBM3pn2Z7Og9qxctY6hBDUbVtTc+gaGhpu7if7RQDfACeklP/LPpNyH2/PG4hvkA96g45eHz+Pf8G0Nx+HzH4VRad+pRLJvo2Hs9WO3xdsZnCj94iPSWDCulGMX/0uY5YNy9ZzaGho5G3uOfwihHgE2A4cAW7l1I2UUq5L75i8Gn65hZQyw9i11WKli98r7hTDoGIB/Hjpq2w596l9Z3i75RisCTaMHka6v9OZVz7sni1za2ho5G4eSvhFSvkX7jKY/wZ324wUioKi07mduiEbdcrDQiPcTwE2i43Lp6/d5QgNDY3/Ipr2SzZiNBl4d9Hr+AR6U6BEICMXv5ltc9drW5PAwv54+nhg9janm4GjoaHx30bTfsnl2Kx29AYdiqJgS7Rx9vAFipQtlG5cX0NDI/+RlfCLtlLPxcwdvpBO3j14Oqg3/+4OwWg2UrlhRc2ha2hopIvm1HMpV89dZ80Xv+NyuoiPTuCz17/JaZM0NDTyAJpTz6UYjHp3Kz0h1L6iGhoaGndDc+q5lALFg+g3pQdefp4Ur1SMYV8PzGmTNDQ08gDaRmke5urZ6xhMencTaw0NjfyJtlH6H+CLN7/l1Rpv80qFIaya/VtOm6OhoZFL0Jx6HsSWaGP17PXYLDbsVjuLxi7NaZM0NDRyCZpTz4PoDDrMXqqIlxCCgML+OWyRhoZGbkFz6nkQnU7HpPUfUKl+eao3r8K4VSNy2iQNDY1cgtZ4Oo9StXElZu+enNNmaGho5DK0lbqGhoZGPkJz6hoaGhr5CM2pa2hoaOQjNKeuoaGhkY/QnLqGhoZGPkJz6hoaGhr5CM2pa2hoaOQjHqqglxAiHLjw0E54/xQAInLaiPtAsz9n0ezPWfK6/ZB8DaWllAUzc8BDdep5DSHE3swqo+VGNPtzFs3+nCWv2w/3dg1a+EVDQ0MjH6E5dQ0NDY18hObUM+arnDbgPtHsz1k0+3OWvG4/3MM1aDF1DQ0NjXyEtlLX0NDQyEdoTl1DQ0MjH6E59XQQQuiEEAeEEL/ktC33ghDCXwixXAjxrxDihBCiSU7blBWEEG8JIY4JIY4KIZYIIcw5bVNGCCHmCyHChBBHb3svUAixUQgRkvT/gJy0MSPSsX9K0u/PYSHESiFErm2xlZb9t302TAghhRAFcsK2zJCe/UKIIUn/BseEEJ9mZi7NqafPUOBEThtxH8wE1kspKwO1yEPXIoQoDrwB1JdSVgd0wPM5a9VdWQC0v+O994BNUsqKwKak17mVBaS2fyNQXUpZEzgFvP+wjcoCC0htP0KIksDjQOjDNiiLLOAO+4UQjwJdgFpSymrA1MxMpDn1NBBClAA6AF/ntC33ghDCD2gBfAMgpbRJKW/mrFVZRg94CCH0gCdwJYftyRAp5Tbgxh1vdwEWJv28EOj6UI3KAmnZL6XcIKV0JL3cBZR46IZlknS+f4DpwAggV2eEpGP/QGCylNKaNCYsM3NpTj1tZqD+Irhy2pB7pCwQDnybFEL6WgjhldNGZRYp5WXUVUkocBWIllJuyFmr7onCUsqrST9fAwrnpDH3SR/gt5w2IisIIboAl6WUh3LalnukEtBcCPGPEGKrEKJBZg7SnPodCCE6AmFSyn05bct9oAfqAnOklHWAeHL3o38KkmLPXVBvTsUALyFEj5y16v6Qau5wrl4tpocQYhTgAH7IaVsyixDCExgJjMlpW+4DPRAINAbeAZYKIcTdDtKcemqaAZ2FEOeBH4HWQojvc9akLHMJuCSl/Cfp9XJUJ59XaAOck1KGSyntwAqgaQ7bdC9cF0IUBUj6f6Yen3MTQoheQEfgJZm3ilrKoy4KDiX9LZcA9gshiuSoVVnjErBCquxGjRzcdbNXc+p3IKV8X0pZQkpZBnVz7k8pZZ5aJUoprwEXhRDBSW89BhzPQZOySijQWAjhmbQyeYw8tNF7G2uAnkk/9wRW56AtWUYI0R41DNlZSpmQ0/ZkBSnlESllISllmaS/5UtA3aS/jbzCKuBRACFEJcBIJlQnNaeefxkC/CCEOAzUBibmsD2ZJukJYzmwHziC+nuaq0u+hRBLgJ1AsBDikhCiLzAZaCuECEF9+pickzZmRDr2fw74ABuFEAeFEF/mqJEZkI79eYZ07J8PlEtKc/wR6JmZpyVNJkBDQ0MjH6Gt1DU0NDTyEZpT19DQ0MhHaE5dQ0NDIx+hOXUNDQ2NfITm1DU0NDTyEZpT19DQ0MhHaE5dQ0NDIx/xf6q74p+QBv6gAAAAAElFTkSuQmCC\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# data generation\n", + "np.random.seed(314)\n", + "data_size_1 = 300\n", + "x1_1 = np.random.normal(loc=5.0, scale=1.0, size=data_size_1)\n", + "x2_1 = np.random.normal(loc=4.0, scale=1.0, size=data_size_1)\n", + "y_1 = [0 for _ in range(data_size_1)]\n", + "\n", + "data_size_2 = 400\n", + "x1_2 = np.random.normal(loc=10.0, scale=2.0, size=data_size_2)\n", + "x2_2 = np.random.normal(loc=8.0, scale=2.0, size=data_size_2)\n", + "y_2 = [1 for _ in range(data_size_2)]\n", + "\n", + "x1 = np.concatenate((x1_1, x1_2), axis=0)\n", + "x2 = np.concatenate((x2_1, x2_2), axis=0)\n", + "x = np.hstack((x1.reshape(-1,1), x2.reshape(-1,1)))\n", + "y = np.concatenate((y_1, y_2), axis=0)\n", + "\n", + "data_size_all = data_size_1+data_size_2\n", + "shuffled_index = np.random.permutation(data_size_all)\n", + "x = x[shuffled_index]\n", + "y = y[shuffled_index]\n", + "\n", + "split_index = int(data_size_all*0.7)\n", + "x_train = x[:split_index]\n", + "y_train = y[:split_index]\n", + "x_test = x[split_index:]\n", + "y_test = y[split_index:]\n", + "\n", + "# visualize data\n", + "plt.scatter(x_train[:,0], x_train[:,1], c=y_train, marker='.')\n", + "plt.title(\"train data\")\n", + "plt.show()\n", + "plt.scatter(x_test[:,0], x_test[:,1], c=y_test, marker='.')\n", + "plt.title(\"test data\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Program" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import operator\n", + "\n", + "class KNN(object):\n", + "\n", + " def __init__(self, k=3):\n", + " self.k = k\n", + "\n", + " def fit(self, x, y):\n", + " self.x = x\n", + " self.y = y\n", + "\n", + " def _square_distance(self, v1, v2):\n", + " return np.sum(np.square(v1-v2))\n", + "\n", + " def _vote(self, ys):\n", + " ys_unique = np.unique(ys)\n", + " vote_dict = {}\n", + " for y in ys:\n", + " if y not in vote_dict.keys():\n", + " vote_dict[y] = 1\n", + " else:\n", + " vote_dict[y] += 1\n", + " sorted_vote_dict = sorted(vote_dict.items(), key=operator.itemgetter(1), reverse=True)\n", + " return sorted_vote_dict[0][0]\n", + "\n", + " def predict(self, x):\n", + " y_pred = []\n", + " for i in range(len(x)):\n", + " dist_arr = [self._square_distance(x[i], self.x[j]) for j in range(len(self.x))]\n", + " sorted_index = np.argsort(dist_arr)\n", + " top_k_index = sorted_index[:self.k]\n", + " y_pred.append(self._vote(ys=self.y[top_k_index]))\n", + " return np.array(y_pred)\n", + "\n", + " def score(self, y_true=None, y_pred=None):\n", + " if y_true is None and y_pred is None:\n", + " y_pred = self.predict(self.x)\n", + " y_true = self.y\n", + " score = 0.0\n", + " for i in range(len(y_true)):\n", + " if y_true[i] == y_pred[i]:\n", + " score += 1\n", + " score /= len(y_true)\n", + " return score" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train accuracy: 0.986\n", + "test accuracy: 0.957\n" + ] + } + ], + "source": [ + "# data preprocessing\n", + "x_train = (x_train - np.min(x_train, axis=0)) / (np.max(x_train, axis=0) - np.min(x_train, axis=0))\n", + "x_test = (x_test - np.min(x_test, axis=0)) / (np.max(x_test, axis=0) - np.min(x_test, axis=0))\n", + "\n", + "# knn classifier\n", + "clf = KNN(k=3)\n", + "clf.fit(x_train, y_train)\n", + "\n", + "print('train accuracy: {:.3}'.format(clf.score()))\n", + "\n", + "y_test_pred = clf.predict(x_test)\n", + "print('test accuracy: {:.3}'.format(clf.score(y_test, y_test_pred)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## sklearn program" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature dimensions: (1797, 64)\n", + "Label dimensions: (1797,)\n" + ] + } + ], + "source": [ + "% matplotlib inline\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from sklearn import datasets, neighbors, linear_model\n", + "\n", + "# load data\n", + "digits = datasets.load_digits()\n", + "X_digits = digits.data\n", + "y_digits = digits.target\n", + "\n", + "print(\"Feature dimensions: \", X_digits.shape)\n", + "print(\"Label dimensions: \", y_digits.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot sample images\n", + "nplot = 10\n", + "fig, axes = plt.subplots(nrows=1, ncols=nplot)\n", + "\n", + "for i in range(nplot):\n", + " img = X_digits[i].reshape(8, 8)\n", + " axes[i].imshow(img)\n", + " axes[i].set_title(y_digits[i])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# split train / test data\n", + "n_samples = len(X_digits)\n", + "n_train = int(0.4 * n_samples)\n", + "\n", + "X_train = X_digits[:n_train]\n", + "y_train = y_digits[:n_train]\n", + "X_test = X_digits[n_train:]\n", + "y_test = y_digits[n_train:]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "KNN score: 0.953661\n", + "LogisticRegression score: 0.908248\n" + ] + } + ], + "source": [ + "# do KNN classification\n", + "knn = neighbors.KNeighborsClassifier()\n", + "logistic = linear_model.LogisticRegression()\n", + "\n", + "print('KNN score: %f' % knn.fit(X_train, y_train).score(X_test, y_test))\n", + "print('LogisticRegression score: %f' % logistic.fit(X_train, y_train).score(X_test, y_test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "* [Digits Classification Exercise](http://scikit-learn.org/stable/auto_examples/exercises/plot_digits_classification_exercise.html)\n", + "* [knn算法的原理与实现](https://zhuanlan.zhihu.com/p/36549000)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/3_kmeans/1-k-means.ipynb b/3_kmeans/1-k-means.ipynb index de01400..9c6b800 100644 --- a/3_kmeans/1-k-means.ipynb +++ b/3_kmeans/1-k-means.ipynb @@ -989,7 +989,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/3_kmeans/1-k-means_EN.ipynb b/3_kmeans/1-k-means_EN.ipynb new file mode 100644 index 0000000..9c6b800 --- /dev/null +++ b/3_kmeans/1-k-means_EN.ipynb @@ -0,0 +1,997 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# k-Means" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 方法\n", + "\n", + "由于具有出色的速度和良好的可扩展性，K-Means聚类算法算得上是最著名的聚类方法。***K-Means算法是一个重复移动类中心点的过程，把类的中心点，也称重心（centroids），移动到其包含成员的平均位置，然后重新划分其内部成员。***\n", + "\n", + "K是算法计算出的超参数，表示类的数量；K-Means可以自动分配样本到不同的类，但是不能决定究竟要分几个类。\n", + "\n", + "K必须是一个比训练集样本数小的正整数。有时，类的数量是由问题内容指定的。例如，一个鞋厂有三种新款式，它想知道每种新款式都有哪些潜在客户，于是它调研客户，然后从数据里找出三类。也有一些问题没有指定聚类的数量，最优的聚类数量是不确定的。\n", + "\n", + "K-Means的参数是类的重心位置和其内部观测值的位置。与广义线性模型和决策树类似，K-Means参数的最优解也是以成本函数最小化为目标。K-Means成本函数公式如下：\n", + "$$\n", + "J = \\sum_{k=1}^{K} \\sum_{i \\in C_k} | x_i - u_k|^2\n", + "$$\n", + "\n", + "$u_k$是第$k$个类的重心位置，定义为：\n", + "$$\n", + "u_k = \\frac{1}{|C_k|} \\sum_{x \\in C_k} x\n", + "$$\n", + "\n", + "\n", + "成本函数是各个类畸变程度（distortions）之和。每个类的畸变程度等于该类重心与其内部成员位置距离的平方和。若类内部的成员彼此间越紧凑则类的畸变程度越小，反之，若类内部的成员彼此间越分散则类的畸变程度越大。\n", + "\n", + "求解成本函数最小化的参数就是一个重复配置每个类包含的观测值，并不断移动类重心的过程。\n", + "1. 首先，类的重心是随机确定的位置。实际上，重心位置等于随机选择的观测值的位置。\n", + "2. 每次迭代的时候，K-Means会把观测值分配到离它们最近的类，然后把重心移动到该类全部成员位置的平均值那里。\n", + "3. 若达到最大迭代步数或两次迭代差小于设定的阈值则算法结束，否则重复步骤2。\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "% matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "X0 = np.array([7, 5, 7, 3, 4, 1, 0, 2, 8, 6, 5, 3])\n", + "X1 = np.array([5, 7, 7, 3, 6, 4, 0, 2, 7, 8, 5, 7])\n", + "plt.figure()\n", + "plt.axis([-1, 9, -1, 9])\n", + "plt.grid(True)\n", + "plt.plot(X0, X1, 'k.');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "假设K-Means初始化时，将第一个类的重心设置在第5个样本，第二个类的重心设置在第11个样本.那么我们可以把每个实例与两个重心的距离都计算出来，将其分配到最近的类里面。计算结果如下表所示：\n", + "![data_0](images/data_0.png)\n", + "\n", + "新的重心位置和初始聚类结果如下图所示。第一类用X表示，第二类用点表示。重心位置用稍大的点突出显示。\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "C1 = [1, 4, 5, 9, 11]\n", + "C2 = list(set(range(12)) - set(C1))\n", + "X0C1, X1C1 = X0[C1], X1[C1]\n", + "X0C2, X1C2 = X0[C2], X1[C2]\n", + "plt.figure()\n", + "plt.title('1st iteration results')\n", + "plt.axis([-1, 9, -1, 9])\n", + "plt.grid(True)\n", + "plt.plot(X0C1, X1C1, 'rx')\n", + "plt.plot(X0C2, X1C2, 'g.')\n", + "plt.plot(4,6,'rx',ms=12.0)\n", + "plt.plot(5,5,'g.',ms=12.0);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "现在我们重新计算两个类的重心，把重心移动到新位置，并重新计算各个样本与新重心的距离，并根据距离远近为样本重新归类。结果如下表所示：\n", + "\n", + "![data_1](images/data_1.png)\n", + "\n", + "画图结果如下：" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "C1 = [1, 2, 4, 8, 9, 11]\n", + "C2 = list(set(range(12)) - set(C1))\n", + "X0C1, X1C1 = X0[C1], X1[C1]\n", + "X0C2, X1C2 = X0[C2], X1[C2]\n", + "plt.figure()\n", + "plt.title('2nd iteration results')\n", + "plt.axis([-1, 9, -1, 9])\n", + "plt.grid(True)\n", + "plt.plot(X0C1, X1C1, 'rx')\n", + "plt.plot(X0C2, X1C2, 'g.')\n", + "plt.plot(3.8,6.4,'rx',ms=12.0)\n", + "plt.plot(4.57,4.14,'g.',ms=12.0);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "我们再重复一次上面的做法，把重心移动到新位置，并重新计算各个样本与新重心的距离，并根据距离远近为样本重新归类。结果如下表所示：\n", + "![data_2](images/data_2.png)\n", + "\n", + "画图结果如下：\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "C1 = [0, 1, 2, 4, 8, 9, 10, 11]\n", + "C2 = list(set(range(12)) - set(C1))\n", + "X0C1, X1C1 = X0[C1], X1[C1]\n", + "X0C2, X1C2 = X0[C2], X1[C2]\n", + "plt.figure()\n", + "plt.title('3rd iteration results')\n", + "plt.axis([-1, 9, -1, 9])\n", + "plt.grid(True)\n", + "plt.plot(X0C1, X1C1, 'rx')\n", + "plt.plot(X0C2, X1C2, 'g.')\n", + "plt.plot(5.5,7.0,'rx',ms=12.0)\n", + "plt.plot(2.2,2.8,'g.',ms=12.0);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "再重复上面的方法就会发现类的重心不变了，K-Means会在条件满足的时候停止重复聚类过程。通常，条件是前后两次迭代的成本函数值的差达到了限定值，或者是前后两次迭代的重心位置变化达到了限定值。如果这些停止条件足够小，K-Means就能找到最优解。不过这个最优解不一定是全局最优解。\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Program" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
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" + ], + "text/plain": [ + " sepal-length sepal-width petal-length petal-width class\n", + "0 5.1 3.5 1.4 0.2 Iris-setosa\n", + "1 4.9 3.0 1.4 0.2 Iris-setosa\n", + "2 4.7 3.2 1.3 0.2 Iris-setosa\n", + "3 4.6 3.1 1.5 0.2 Iris-setosa\n", + "4 5.0 3.6 1.4 0.2 Iris-setosa" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This line configures matplotlib to show figures embedded in the notebook, \n", + "# instead of opening a new window for each figure. More about that later. \n", + "# If you are using an old version of IPython, try using '%pylab inline' instead.\n", + "%matplotlib inline\n", + "\n", + "# import librarys\n", + "from numpy import *\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import random\n", + "\n", + "# Load dataset\n", + "names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\n", + "dataset = pd.read_csv(\"iris.csv\", header=0, index_col=0)\n", + "dataset.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " \n", + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n", + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + " after removing the cwd from sys.path.\n" + ] + } + ], + "source": [ + "#对类别进行编码，3个类别分别赋值0，1，2\n", + "dataset['class'][dataset['class']=='Iris-setosa']=0\n", + "dataset['class'][dataset['class']=='Iris-versicolor']=1\n", + "dataset['class'][dataset['class']=='Iris-virginica']=2" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "def originalDatashow(dataSet):\n", + " #绘制原始的样本点\n", + " num,dim=shape(dataSet)\n", + " marksamples=['ob'] #样本图形标记\n", + " for i in range(num):\n", + " plt.plot(datamat.iat[i,0],datamat.iat[i,1],marksamples[0],markersize=5)\n", + " plt.title('original dataset')\n", + " plt.xlabel('sepal length')\n", + " plt.ylabel('sepal width') \n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "lines_to_end_of_cell_marker": 2, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#获取样本数据\n", + "datamat = dataset.loc[:, ['sepal-length', 'sepal-width']]\n", + "# 真实的标签\n", + "labels = dataset.loc[:, ['class']]\n", + "#原始数据显示\n", + "originalDatashow(datamat)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "\n", + "def randChosenCent(dataSet,k):\n", + " \"\"\"初始化聚类中心：通过在区间范围随机产生的值作为新的中心点\"\"\"\n", + "\n", + " # 样本数\n", + " m=shape(dataSet)[0]\n", + " # 初始化列表\n", + " centroidsIndex=[]\n", + " \n", + " #生成类似于样本索引的列表\n", + " dataIndex=list(range(m))\n", + " if False:\n", + " for i in range(k):\n", + " #生成随机数\n", + " randIndex=random.randint(0,len(dataIndex))\n", + " #将随机产生的样本的索引放入centroidsIndex\n", + " centroidsIndex.append(dataIndex[randIndex])\n", + " #删除已经被抽中的样本\n", + " del dataIndex[randIndex]\n", + " else:\n", + " random.shuffle(dataIndex)\n", + " centroidsIndex = dataIndex[:k]\n", + " \n", + " #根据索引获取样本\n", + " centroids = dataSet.iloc[centroidsIndex]\n", + " return mat(centroids)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def distEclud(vecA, vecB):\n", + " \"\"\"算距离, 两个向量间欧式距离\"\"\"\n", + " return sqrt(sum(power(vecA - vecB, 2))) #la.norm(vecA-vecB)\n", + "\n", + "\n", + "def kMeans(dataSet, k):\n", + " # 样本总数\n", + " m = shape(dataSet)[0]\n", + " # 分配样本到最近的簇：存[簇序号,距离的平方] (m行 x 2 列)\n", + " clusterAssment = mat(zeros((m, 2)))\n", + "\n", + " # step1: 通过随机产生的样本点初始化聚类中心\n", + " centroids = randChosenCent(dataSet, k)\n", + " print('最初的中心=', centroids)\n", + "\n", + " # 标志位，如果迭代前后样本分类发生变化值为Tree，否则为False\n", + " clusterChanged = True\n", + " # 查看迭代次数\n", + " iterTime = 0\n", + " \n", + " # 所有样本分配结果不再改变，迭代终止\n", + " while clusterChanged:\n", + " clusterChanged = False\n", + " \n", + " # step2:分配到最近的聚类中心对应的簇中\n", + " for i in range(m):\n", + " # 初始定义距离为无穷大\n", + " minDist = inf;\n", + " # 初始化索引值\n", + " minIndex = -1\n", + " # 计算每个样本与k个中心点距离\n", + " for j in range(k):\n", + " # 计算第i个样本到第j个中心点的距离\n", + " distJI = distEclud(centroids[j, :], dataSet.values[i, :])\n", + " # 判断距离是否为最小\n", + " if distJI < minDist:\n", + " # 更新获取到最小距离\n", + " minDist = distJI\n", + " # 获取对应的簇序号\n", + " minIndex = j\n", + " # 样本上次分配结果跟本次不一样，标志位clusterChanged置True\n", + " if clusterAssment[i, 0] != minIndex:\n", + " clusterChanged = True\n", + " clusterAssment[i, :] = minIndex, minDist ** 2 # 分配样本到最近的簇\n", + " \n", + " iterTime += 1\n", + " sse = sum(clusterAssment[:, 1])\n", + " print('the SSE of %d' % iterTime + 'th iteration is %f' % sse)\n", + " \n", + " # step3:更新聚类中心\n", + " for cent in range(k): # 样本分配结束后，重新计算聚类中心\n", + " # 获取该簇所有的样本点\n", + " ptsInClust = dataSet.iloc[nonzero(clusterAssment[:, 0].A == cent)[0]]\n", + " # 更新聚类中心：axis=0沿列方向求均值。\n", + " centroids[cent, :] = mean(ptsInClust, axis=0)\n", + " return centroids, clusterAssment\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "最初的中心= [[6.2 2.2]\n", + " [6.3 2.5]\n", + " [7.7 3.8]]\n", + "the SSE of 1th iteration is 189.420000\n", + "the SSE of 2th iteration is 70.447978\n", + "the SSE of 3th iteration is 56.041643\n", + "the SSE of 4th iteration is 49.785857\n", + "the SSE of 5th iteration is 45.985699\n", + "the SSE of 6th iteration is 43.078623\n", + "the SSE of 7th iteration is 40.594295\n", + "the SSE of 8th iteration is 37.791783\n", + "the SSE of 9th iteration is 37.235470\n", + "the SSE of 10th iteration is 37.201302\n", + "the SSE of 11th iteration is 37.155048\n", + "the SSE of 12th iteration is 37.141172\n" + ] + } + ], + "source": [ + "# 进行k-means聚类\n", + "k = 3 # 用户定义聚类数\n", + "mycentroids, clusterAssment = kMeans(datamat, k)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def datashow(dataSet, k, centroids, clusterAssment): # 二维空间显示聚类结果\n", + " from matplotlib import pyplot as plt\n", + " num, dim = shape(dataSet) # 样本数num ,维数dim\n", + "\n", + " if dim != 2:\n", + " print('sorry,the dimension of your dataset is not 2!')\n", + " return 1\n", + " marksamples = ['or', 'ob', 'og', 'ok', '^r', '^b', ' len(marksamples):\n", + " print('sorry,your k is too large,please add length of the marksample!')\n", + " return 1\n", + " # 绘所有样本\n", + " for i in range(num):\n", + " markindex = int(clusterAssment[i, 0]) # 矩阵形式转为int值, 簇序号\n", + " # 特征维对应坐标轴x,y；样本图形标记及大小\n", + " plt.plot(dataSet.iat[i, 0], dataSet.iat[i, 1], marksamples[markindex], markersize=6)\n", + "\n", + " # 绘中心点\n", + " markcentroids = ['o', '*', '^'] # 聚类中心图形标记\n", + " label = ['0', '1', '2']\n", + " c = ['yellow', 'pink', 'red']\n", + " for i in range(k):\n", + " plt.plot(centroids[i, 0], centroids[i, 1], markcentroids[i], markersize=15, label=label[i], c=c[i])\n", + " plt.legend(loc='upper left')\n", + " plt.xlabel('sepal length')\n", + " plt.ylabel('sepal width')\n", + "\n", + " plt.title('k-means cluster result') # 标题\n", + " plt.show()\n", + " \n", + " \n", + "# 画出实际图像\n", + "def trgartshow(dataSet, k, labels):\n", + " from matplotlib import pyplot as plt\n", + "\n", + " num, dim = shape(dataSet)\n", + " label = ['0', '1', '2']\n", + " marksamples = ['ob', 'or', 'og', 'ok', '^r', '^b', '" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# 绘图显示\n", + "datashow(datamat, k, mycentroids, clusterAssment)\n", + "trgartshow(datamat, 3, labels)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How to use sklearn to do the classifiction\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import load_digits\n", + "import matplotlib.pyplot as plt \n", + "from sklearn.cluster import KMeans\n", + "\n", + "# load digital data\n", + "digits, dig_label = load_digits(return_X_y=True)\n", + "\n", + "# draw one digital\n", + "plt.gray() \n", + "plt.matshow(digits[0].reshape([8, 8])) \n", + "plt.show() \n", + "\n", + "# calculate train/test data number\n", + "N = len(digits)\n", + "N_train = int(N*0.8)\n", + "N_test = N - N_train\n", + "\n", + "# split train/test data\n", + "x_train = digits[:N_train, :]\n", + "y_train = dig_label[:N_train]\n", + "x_test = digits[N_train:, :]\n", + "y_test = dig_label[N_train:]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# do kmeans\n", + "kmeans = KMeans(n_clusters=10, random_state=0).fit(x_train)\n", + "\n", + "# kmeans.labels_ - output label\n", + "# kmeans.cluster_centers_ - cluster centers\n", + "\n", + "# draw cluster centers\n", + "fig, axes = plt.subplots(nrows=1, ncols=10)\n", + "for i in range(10):\n", + " img = kmeans.cluster_centers_[i].reshape(8, 8)\n", + " axes[i].imshow(img)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exerciese - How to caluate the accuracy?\n", + "\n", + "1. How to match cluster label to groundtruth label\n", + "2. How to solve the uncertainty of some digital" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 评估聚类性能\n", + "\n", + "方法1： 如果被用来评估的数据本身带有正确的类别信息，则利用Adjusted Rand Index(ARI)，ARI与分类问题中计算准确性的方法类似，兼顾了类簇无法和分类标记一一对应的问题。\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ari_train = 0.687021\n" + ] + } + ], + "source": [ + "from sklearn.metrics import adjusted_rand_score\n", + "\n", + "ari_train = adjusted_rand_score(y_train, kmeans.labels_)\n", + "print(\"ari_train = %f\" % ari_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given the contingency table:\n", + "![ARI_ct](images/ARI_ct.png)\n", + "\n", + "the adjusted index is:\n", + "![ARI_define](images/ARI_define.png)\n", + "\n", + "* [ARI reference](https://davetang.org/muse/2017/09/21/adjusted-rand-index/)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "方法2： 如果被用来评估的数据没有所属类别，则使用轮廓系数(Silhouette Coefficient)来度量聚类结果的质量，评估聚类的效果。**轮廓系数同时兼顾了聚类的凝聚度和分离度，取值范围是[-1,1]，轮廓系数越大，表示聚类效果越好。** \n", + "\n", + "轮廓系数的具体计算步骤： \n", + "1. 对于已聚类数据中第i个样本$x_i$，计算$x_i$与其同一类簇内的所有其他样本距离的平均值，记作$a_i$，用于量化簇内的凝聚度 \n", + "2. 选取$x_i$外的一个簇$b$，计算$x_i$与簇$b$中所有样本的平均距离，遍历所有其他簇，找到最近的这个平均距离，记作$b_i$，用于量化簇之间分离度 \n", + "3. 对于样本$x_i$，轮廓系数为$sc_i = \\frac{b_i−a_i}{max(b_i,a_i)}$ \n", + "4. 最后，对所以样本集合$\\mathbf{X}$求出平均值，即为当前聚类结果的整体轮廓系数。" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import silhouette_score\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.rcParams['figure.figsize']=(10,10)\n", + "plt.subplot(3,2,1)\n", + "\n", + "x1=np.array([1,2,3,1,5,6,5,5,6,7,8,9,7,9]) #初始化原始数据\n", + "x2=np.array([1,3,2,2,8,6,7,6,7,1,2,1,1,3])\n", + "X=np.array(list(zip(x1,x2))).reshape(len(x1),2)\n", + "\n", + "plt.xlim([0,10])\n", + "plt.ylim([0,10])\n", + "plt.title('Instances')\n", + "plt.scatter(x1,x2)\n", + "\n", + "colors=['b','g','r','c','m','y','k','b']\n", + "markers=['o','s','D','v','^','p','*','+']\n", + "\n", + "clusters=[2,3,4,5,8]\n", + "subplot_counter=1\n", + "sc_scores=[]\n", + "for t in clusters:\n", + " subplot_counter +=1\n", + " plt.subplot(3,2,subplot_counter)\n", + " kmeans_model=KMeans(n_clusters=t).fit(X) #KMeans建模\n", + "\n", + " for i,l in enumerate(kmeans_model.labels_):\n", + " plt.plot(x1[i],x2[i],color=colors[l],marker=markers[l],ls='None')\n", + "\n", + " plt.xlim([0,10])\n", + " plt.ylim([0,10])\n", + "\n", + " sc_score=silhouette_score(X,kmeans_model.labels_,metric='euclidean') #计算轮廓系数\n", + " sc_scores.append(sc_score)\n", + "\n", + " plt.title('k=%s,silhouette coefficient=%0.03f'%(t,sc_score))\n", + "\n", + "plt.figure()\n", + "plt.plot(clusters,sc_scores,'*-') #绘制类簇数量与对应轮廓系数关系\n", + "plt.xlabel('Number of Clusters')\n", + "plt.ylabel('Silhouette Coefficient Score')\n", + "\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How to determin the 'k'?\n", + "\n", + "利用“肘部观察法”可以粗略地估计相对合理的聚类个数。K-means模型最终期望*所有数据点到其所属的类簇距离的平方和趋于稳定，所以可以通过观察这个值随着K的走势来找出最佳的类簇数量。理想条件下，这个折线在不断下降并且趋于平缓的过程中会有斜率的拐点，这表示从这个拐点对应的K值开始，类簇中心的增加不会过于破坏数据聚类的结构*。\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import numpy as np\n", + "from sklearn.cluster import KMeans\n", + "from scipy.spatial.distance import cdist\n", + "import matplotlib.pyplot as plt\n", + "\n", + "cluster1=np.random.uniform(0.5,1.5,(2,10))\n", + "cluster2=np.random.uniform(5.5,6.5,(2,10))\n", + "cluster3=np.random.uniform(3,4,(2,10))\n", + "\n", + "X=np.hstack((cluster1,cluster2,cluster3)).T\n", + "plt.scatter(X[:,0],X[:,1])\n", + "plt.xlabel('x1')\n", + "plt.ylabel('x2')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "K=range(1,10)\n", + "meandistortions=[]\n", + "\n", + "for k in K:\n", + " kmeans=KMeans(n_clusters=k)\n", + " kmeans.fit(X)\n", + " meandistortions.append(\\\n", + " sum(np.min(cdist(X,kmeans.cluster_centers_,'euclidean'),\\\n", + " axis=1))/X.shape[0])\n", + "\n", + "plt.plot(K,meandistortions,'bx-')\n", + "plt.xlabel('k')\n", + "plt.ylabel('Average Dispersion')\n", + "plt.title('Selecting k with the Elbow Method')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "从上图可见，类簇数量从1降到2再降到3的过程，更改K值让整体聚类结构有很大改变，这意味着新的聚类数量让算法有更大的收敛空间，这样的K值不能反映真实的类簇数量。而当K=3以后再增大K，平均距离的下降速度显著变缓慢，这意味着进一步增加K值不再会有利于算法的收敛，同时也暗示着K=3是相对最佳的类簇数量。" + ] + } + ], + "metadata": { + "jupytext_formats": "ipynb,py", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/3_kmeans/2-kmeans-color-vq.ipynb b/3_kmeans/2-kmeans-color-vq.ipynb index 39cd5ce..52351a3 100644 --- a/3_kmeans/2-kmeans-color-vq.ipynb +++ b/3_kmeans/2-kmeans-color-vq.ipynb @@ -233,7 +233,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/3_kmeans/2-kmeans-color-vq_EN.ipynb b/3_kmeans/2-kmeans-color-vq_EN.ipynb new file mode 100644 index 0000000..52351a3 --- /dev/null +++ b/3_kmeans/2-kmeans-color-vq_EN.ipynb @@ -0,0 +1,241 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Color Quantization by K-Means\n", + "\n", + "Performs a pixel-wise **Vector Quantization (VQ)** of an image of the summer palace (China), reducing the number of colors required to show the image from 96,615 unique colors to 64, while preserving the overall appearance quality.\n", + "\n", + "In this example, pixels are represented in a 3D-space and K-means is used to find 64 color clusters. In the image processing literature, the codebook obtained from K-means (the cluster centers) is called the color palette. Using a single byte, up to 256 colors can be addressed, whereas an RGB encoding requires 3 bytes per pixel. The GIF file format, for example, uses such a palette.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "% matplotlib inline\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import pairwise_distances_argmin\n", + "from sklearn.datasets import load_sample_image\n", + "from sklearn.utils import shuffle\n", + "from time import time" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/sklearn/datasets/base.py:762: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " images = [imread(filename) for filename in filenames]\n", + "/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/sklearn/datasets/base.py:762: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", + "Use ``imageio.imread`` instead.\n", + " images = [imread(filename) for filename in filenames]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting model on a small sub-sample of the data\n", + " done in 0.185s.\n", + "Predicting color indices on the full image (k-means)\n", + " done in 0.128s.\n", + "Predicting color indices on the full image (random)\n", + " done in 0.095s.\n" + ] + } + ], + "source": [ + "n_colors = 64\n", + "\n", + "# Load the Summer Palace photo\n", + "china = load_sample_image(\"china.jpg\")\n", + "\n", + "# Convert to floats instead of the default 8 bits integer coding. Dividing by\n", + "# 255 is important so that plt.imshow behaves works well on float data (need to\n", + "# be in the range [0-1])\n", + "china = np.array(china, dtype=np.float64) / 255\n", + "\n", + "# Load Image and transform to a 2D numpy array.\n", + "w, h, d = original_shape = tuple(china.shape)\n", + "assert d == 3\n", + "image_array = np.reshape(china, (w * h, d))\n", + "\n", + "print(\"Fitting model on a small sub-sample of the data\")\n", + "t0 = time()\n", + "image_array_sample = shuffle(image_array, random_state=0)[:1000]\n", + "kmeans = KMeans(n_clusters=n_colors, random_state=0).fit(image_array_sample)\n", + "print(\" done in %0.3fs.\" % (time() - t0))\n", + "\n", + "# Get labels for all points\n", + "print(\"Predicting color indices on the full image (k-means)\")\n", + "t0 = time()\n", + "labels = kmeans.predict(image_array)\n", + "print(\" done in %0.3fs.\" % (time() - t0))\n", + "\n", + "\n", + "codebook_random = shuffle(image_array, random_state=0)[:n_colors + 1]\n", + "print(\"Predicting color indices on the full image (random)\")\n", + "t0 = time()\n", + "labels_random = pairwise_distances_argmin(codebook_random,\n", + " image_array,\n", + " axis=0)\n", + "print(\" done in %0.3fs.\" % (time() - t0))\n", + "\n", + "\n", + "def recreate_image(codebook, labels, w, h):\n", + " \"\"\"Recreate the (compressed) image from the code book & labels\"\"\"\n", + " d = codebook.shape[1]\n", + " image = np.zeros((w, h, d))\n", + " label_idx = 0\n", + " for i in range(w):\n", + " for j in range(h):\n", + " image[i][j] = codebook[labels[label_idx]]\n", + " label_idx += 1\n", + " return image" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# draw original image\n", + "plt.figure(1)\n", + "plt.clf()\n", + "ax = plt.axes([0, 0, 1, 1])\n", + "plt.axis('off')\n", + "plt.title('Original image (96,615 colors)')\n", + "plt.imshow(china)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# 64 VQ image\n", + "plt.figure(2)\n", + "plt.clf()\n", + "ax = plt.axes([0, 0, 1, 1])\n", + "plt.axis('off')\n", + "plt.title('Quantized image (64 colors, K-Means)')\n", + "plt.imshow(recreate_image(kmeans.cluster_centers_, labels, w, h))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Random VQ image\n", + "plt.figure(3)\n", + "plt.clf()\n", + "ax = plt.axes([0, 0, 1, 1])\n", + "plt.axis('off')\n", + "plt.title('Quantized image (64 colors, Random)')\n", + "plt.imshow(recreate_image(codebook_random, labels_random, w, h))\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/3_kmeans/3-ClusteringAlgorithms.ipynb b/3_kmeans/3-ClusteringAlgorithms.ipynb index 0e84c9a..8d14f1f 100644 --- a/3_kmeans/3-ClusteringAlgorithms.ipynb +++ b/3_kmeans/3-ClusteringAlgorithms.ipynb @@ -215,7 +215,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.8" }, "main_language": "python" }, diff --git a/3_kmeans/3-ClusteringAlgorithms_EN.ipynb b/3_kmeans/3-ClusteringAlgorithms_EN.ipynb new file mode 100644 index 0000000..8d14f1f --- /dev/null +++ b/3_kmeans/3-ClusteringAlgorithms_EN.ipynb @@ -0,0 +1,224 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comparing different clustering algorithms on toy datasets\n", + "\n", + "This example shows characteristics of different clustering algorithms on datasets that are “interesting” but still in 2D. With the exception of the last dataset, the parameters of each of these dataset-algorithm pairs has been tuned to produce good clustering results. Some algorithms are more sensitive to parameter values than others.\n", + "The last dataset is an example of a ‘null’ situation for clustering: the data is homogeneous, and there is no good clustering. For this example, the null dataset uses the same parameters as the dataset in the row above it, which represents a mismatch in the parameter values and the data structure.\n", + "While these examples give some intuition about the algorithms, this intuition might not apply to very high dimensional data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "% matplotlib inline\n", + "\n", + "import time\n", + "import warnings\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn import cluster, datasets, mixture\n", + "from sklearn.neighbors import kneighbors_graph\n", + "from sklearn.preprocessing import StandardScaler\n", + "from itertools import cycle, islice\n", + "\n", + "np.random.seed(0)\n", + "\n", + "# ============\n", + "# Generate datasets. We choose the size big enough to see the scalability\n", + "# of the algorithms, but not too big to avoid too long running times\n", + "# ============\n", + "n_samples = 1500\n", + "noisy_circles = datasets.make_circles(n_samples=n_samples, factor=.5,\n", + " noise=.05)\n", + "noisy_moons = datasets.make_moons(n_samples=n_samples, noise=.05)\n", + "blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)\n", + "no_structure = np.random.rand(n_samples, 2), None\n", + "\n", + "# Anisotropicly distributed data\n", + "random_state = 170\n", + "X, y = datasets.make_blobs(n_samples=n_samples, random_state=random_state)\n", + "transformation = [[0.6, -0.6], [-0.4, 0.8]]\n", + "X_aniso = np.dot(X, transformation)\n", + "aniso = (X_aniso, y)\n", + "\n", + "# blobs with varied variances\n", + "varied = datasets.make_blobs(n_samples=n_samples,\n", + " cluster_std=[1.0, 2.5, 0.5],\n", + " random_state=random_state)\n", + "\n", + "\n", + "# ============\n", + "# Set up cluster parameters\n", + "# ============\n", + "plt.figure(figsize=(9 * 2 + 3, 12.5))\n", + "plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05,\n", + " hspace=.01)\n", + "\n", + "plot_num = 1\n", + "\n", + "default_base = {'quantile': .3,\n", + " 'eps': .3,\n", + " 'damping': .9,\n", + " 'preference': -200,\n", + " 'n_neighbors': 10,\n", + " 'n_clusters': 3}\n", + "\n", + "datasets = [\n", + " (noisy_circles, {'damping': .77, 'preference': -240,\n", + " 'quantile': .2, 'n_clusters': 2}),\n", + " (noisy_moons, {'damping': .75, 'preference': -220, 'n_clusters': 2}),\n", + " (varied, {'eps': .18, 'n_neighbors': 2}),\n", + " (aniso, {'eps': .15, 'n_neighbors': 2}),\n", + " (blobs, {}),\n", + " (no_structure, {})]\n", + "\n", + "for i_dataset, (dataset, algo_params) in enumerate(datasets):\n", + " # update parameters with dataset-specific values\n", + " params = default_base.copy()\n", + " params.update(algo_params)\n", + "\n", + " X, y = dataset\n", + "\n", + " # normalize dataset for easier parameter selection\n", + " X = StandardScaler().fit_transform(X)\n", + "\n", + " # estimate bandwidth for mean shift\n", + " bandwidth = cluster.estimate_bandwidth(X, quantile=params['quantile'])\n", + "\n", + " # connectivity matrix for structured Ward\n", + " connectivity = kneighbors_graph(\n", + " X, n_neighbors=params['n_neighbors'], include_self=False)\n", + " # make connectivity symmetric\n", + " connectivity = 0.5 * (connectivity + connectivity.T)\n", + "\n", + " # ============\n", + " # Create cluster objects\n", + " # ============\n", + " ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)\n", + " two_means = cluster.MiniBatchKMeans(n_clusters=params['n_clusters'])\n", + " ward = cluster.AgglomerativeClustering(\n", + " n_clusters=params['n_clusters'], linkage='ward',\n", + " connectivity=connectivity)\n", + " spectral = cluster.SpectralClustering(\n", + " n_clusters=params['n_clusters'], eigen_solver='arpack',\n", + " affinity=\"nearest_neighbors\")\n", + " dbscan = cluster.DBSCAN(eps=params['eps'])\n", + " affinity_propagation = cluster.AffinityPropagation(\n", + " damping=params['damping'], preference=params['preference'])\n", + " average_linkage = cluster.AgglomerativeClustering(\n", + " linkage=\"average\", affinity=\"cityblock\",\n", + " n_clusters=params['n_clusters'], connectivity=connectivity)\n", + " birch = cluster.Birch(n_clusters=params['n_clusters'])\n", + " gmm = mixture.GaussianMixture(\n", + " n_components=params['n_clusters'], covariance_type='full')\n", + "\n", + " clustering_algorithms = (\n", + " ('MiniBatchKMeans', two_means),\n", + " ('AffinityPropagation', affinity_propagation),\n", + " ('MeanShift', ms),\n", + " ('SpectralClustering', spectral),\n", + " ('Ward', ward),\n", + " ('AgglomerativeClustering', average_linkage),\n", + " ('DBSCAN', dbscan),\n", + " ('Birch', birch),\n", + " ('GaussianMixture', gmm)\n", + " )\n", + "\n", + " for name, algorithm in clustering_algorithms:\n", + " t0 = time.time()\n", + "\n", + " # catch warnings related to kneighbors_graph\n", + " with warnings.catch_warnings():\n", + " warnings.filterwarnings(\n", + " \"ignore\",\n", + " message=\"the number of connected components of the \" +\n", + " \"connectivity matrix is [0-9]{1,2}\" +\n", + " \" > 1. Completing it to avoid stopping the tree early.\",\n", + " category=UserWarning)\n", + " warnings.filterwarnings(\n", + " \"ignore\",\n", + " message=\"Graph is not fully connected, spectral embedding\" +\n", + " \" may not work as expected.\",\n", + " category=UserWarning)\n", + " algorithm.fit(X)\n", + "\n", + " t1 = time.time()\n", + " if hasattr(algorithm, 'labels_'):\n", + " y_pred = algorithm.labels_.astype(np.int)\n", + " else:\n", + " y_pred = algorithm.predict(X)\n", + "\n", + " plt.subplot(len(datasets), len(clustering_algorithms), plot_num)\n", + " if i_dataset == 0:\n", + " plt.title(name, size=18)\n", + "\n", + " colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',\n", + " '#f781bf', '#a65628', '#984ea3',\n", + " '#999999', '#e41a1c', '#dede00']),\n", + " int(max(y_pred) + 1))))\n", + " plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[y_pred])\n", + "\n", + " plt.xlim(-2.5, 2.5)\n", + " plt.ylim(-2.5, 2.5)\n", + " plt.xticks(())\n", + " plt.yticks(())\n", + " plt.text(.99, .01, ('%.2fs' % (t1 - t0)).lstrip('0'),\n", + " transform=plt.gca().transAxes, size=15,\n", + " horizontalalignment='right')\n", + " plot_num += 1\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reference\n", + "* [Comparing different clustering algorithms on toy datasets](http://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + }, + "main_language": "python" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/4_logistic_regression/1-Least_squares.ipynb b/4_logistic_regression/1-Least_squares.ipynb index e2af4ce..690605e 100644 --- a/4_logistic_regression/1-Least_squares.ipynb +++ b/4_logistic_regression/1-Least_squares.ipynb @@ -5146,7 +5146,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/4_logistic_regression/1-Least_squares_EN.ipynb b/4_logistic_regression/1-Least_squares_EN.ipynb new file mode 100644 index 0000000..690605e --- /dev/null +++ b/4_logistic_regression/1-Least_squares_EN.ipynb @@ -0,0 +1,5154 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 最小二乘（Generalized Least Squares）\n", + "\n", + "## 1. 最小二乘的基本\n", + "\n", + "最小二乘法（generalized least squares）是一种数学优化技术，它通过最小化误差的平方和找到一组数据的最佳函数匹配。 最小二乘法通常用于曲线拟合、求解模型。很多其他的优化问题也可通过最小化能量或最大化熵用最小二乘形式表达。最小二乘原理的一般形式为：\n", + "$$\n", + "L = \\sum (V_{obv} - V_{target}(\\theta))^2\n", + "$$\n", + "其中$V_{obv}$是我们观测的多组样本值，$V_{target}$是我们假设拟合函数的输出值，$\\theta$为构造模型的参数。$L$是目标函数，如果通过调整模型参数$\\theta$，使得$L$下降到最小则表明，拟合函数与观测最为接近，也就是找到了最优的模型。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 示例\n", + "\n", + "假设我们有下面的一些观测数据，我们希望找到他们内在的规律。" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import sklearn\n", + "from sklearn import datasets\n", + "\n", + "# load data\n", + "d = datasets.load_diabetes()\n", + "\n", + "X = d.data[:, 2]\n", + "Y = d.target\n", + "\n", + "# draw original data\n", + "plt.scatter(X, Y)\n", + "plt.xlabel(\"X\")\n", + "plt.ylabel(\"Y\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 数学原理\n", + "有$N$个观测数据为：\n", + "$$\n", + "\\mathbf{X} = \\{x_1, x_2, ..., x_N \\} \\\\\n", + "\\mathbf{Y} = \\{y_1, y_2, ..., y_N \\}\n", + "$$\n", + "其中$\\mathbf{X}$为自变量，$\\mathbf{Y}$为因变量。\n", + "\n", + "我们希望找到一个模型能够解释这些数据，假设我们使用最简单的线性模型来拟合数据：\n", + "$$\n", + "y = ax + b\n", + "$$\n", + "那么问题就变成求解参数$a$, $b$能够使得模型输出尽可能和观测数据有比较小的误差。\n", + "\n", + "如何构建函数来评估模型输出与观测数据之间的误差是一个关键问题，这里我们使用观测数据与模型输出的平方和来作为评估函数（也被称为损失函数Loss function):\n", + "$$\n", + "L = \\sum_{i=1}^{N} (y_i - a x_i - b)^2 \\\\\n", + "L = \\sum_{i=1}^{N} \\{y_i - (a x_i + b)\\}^2\n", + "$$\n", + "\n", + "使误差函数最小，那么我们就可以求出模型的参数:\n", + "$$\n", + "\\frac{\\partial L}{\\partial a} = -2 \\sum_{i=1}^{N} (y_i - a x_i - b) x_i \\\\\n", + "\\frac{\\partial L}{\\partial b} = -2 \\sum_{i=1}^{N} (y_i - a x_i - b)\n", + "$$\n", + "既当偏微分为0时，误差函数为最小，因此我们可以得到:\n", + "$$\n", + "-2 \\sum_{i=1}^{N} (y_i - a x_i - b) x_i = 0 \\\\\n", + "-2 \\sum_{i=1}^{N} (y_i - a x_i - b) = 0 \\\\\n", + "$$\n", + "\n", + "将上式调整一下顺序可以得到：\n", + "$$\n", + "a \\sum x_i^2 + b \\sum x_i = \\sum y_i x_i \\\\\n", + "a \\sum x_i + b N = \\sum y_i\n", + "$$\n", + "通过求解二元一次方程组，我们即可求出模型的最优参数。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 求解程序" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a = 949.435260, b = 152.133484\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "N = X.shape[0]\n", + "\n", + "S_X2 = np.sum(X*X)\n", + "S_X = np.sum(X)\n", + "S_XY = np.sum(X*Y)\n", + "S_Y = np.sum(Y)\n", + "\n", + "A1 = np.array([[S_X2, S_X], \n", + " [S_X, N]])\n", + "B1 = np.array([S_XY, S_Y])\n", + "\n", + "coeff = np.linalg.inv(A1).dot(B1)\n", + "\n", + "print('a = %f, b = %f' % (coeff[0], coeff[1]))\n", + "\n", + "x_min = np.min(X)\n", + "x_max = np.max(X)\n", + "y_min = coeff[0] * x_min + coeff[1]\n", + "y_max = coeff[0] * x_max + coeff[1]\n", + "\n", + "plt.scatter(X, Y, label='original data')\n", + "plt.plot([x_min, x_max], [y_min, y_max], 'r', label='model')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. 如何使用迭代的方法求出模型参数\n", + "\n", + "当数据比较多的时候，或者模型比较复杂，无法直接使用解析的方式求出模型参数。因此更为常用的方式是，通过迭代的方式逐步逼近模型的参数。\n", + "\n", + "### 2.1 梯度下降法\n", + "在机器学习算法中，对于很多监督学习模型，需要对原始的模型构建损失函数，接下来便是通过优化算法对损失函数进行优化，以便寻找到最优的参数。在求解机器学习参数的优化算法中，使用较多的是基于梯度下降的优化算法(Gradient Descent, GD)。\n", + "\n", + "梯度下降法有很多优点，其中最主要的优点是，在梯度下降法的求解过程中只需求解损失函数的一阶导数，计算的代价比较小，这使得梯度下降法能在很多大规模数据集上得到应用。梯度下降法的含义是通过当前点的梯度方向寻找到新的迭代点。\n", + "\n", + "梯度下降法的基本思想可以类比为一个下山的过程。假设这样一个场景：一个人被困在山上，需要从山上下来(i.e. 找到山的最低点，也就是山谷)。但此时山上的浓雾很大，导致可视度很低。因此，下山的路径就无法确定，他必须利用自己周围的信息去找到下山的路径。这个时候，他就可以利用梯度下降算法来帮助自己下山。具体来说就是，以他当前的所处的位置为基准，寻找这个位置最陡峭的地方，然后朝着山的高度下降的地方走，同理，如果我们的目标是上山，也就是爬到山顶，那么此时应该是朝着最陡峭的方向往上走。然后每走一段距离，都反复采用同一个方法，最后就能成功的抵达山谷。\n", + "\n", + "\n", + "我们同时可以假设这座山最陡峭的地方是无法通过肉眼立马观察出来的，而是需要一个复杂的工具来测量，同时，这个人此时正好拥有测量出最陡峭方向的能力。所以，此人每走一段距离，都需要一段时间来测量所在位置最陡峭的方向，这是比较耗时的。那么为了在太阳下山之前到达山底，就要尽可能的减少测量方向的次数。这是一个两难的选择，如果测量的频繁，可以保证下山的方向是绝对正确的，但又非常耗时，如果测量的过少，又有偏离轨道的风险。所以需要找到一个合适的测量方向的频率，来确保下山的方向不错误，同时又不至于耗时太多！\n", + "\n", + "\n", + "![gradient_descent](images/gradient_descent.png)\n", + "\n", + "如上图所示，得到了局部最优解。x,y表示的是$\\theta_0$和$\\theta_1$，z方向表示的是花费函数，很明显出发点不同，最后到达的收敛点可能不一样。当然如果是碗状的，那么收敛点就应该是一样的。\n", + "\n", + "对于某一个损失函数\n", + "$$\n", + "L = \\sum_{i=1}^{N} (y_i - a x_i - b)^2\n", + "$$\n", + "\n", + "我们更新的策略是：\n", + "$$\n", + "\\theta^1 = \\theta^0 - \\alpha \\triangledown L(\\theta)\n", + "$$\n", + "其中$\\theta$代表了模型中的参数，例如$a$, $b$\n", + "\n", + "此公式的意义是：$L$是关于$\\theta$的一个函数，我们当前所处的位置为$\\theta_0$点，要从这个点走到L的最小值点，也就是山底。首先我们先确定前进的方向，也就是梯度的反向，然后走一段距离的步长，也就是$\\alpha$，走完这个段步长，就到达了$\\theta_1$这个点！\n", + "\n", + "下面就这个公式的几个常见的疑问：\n", + "\n", + "* **$\\alpha$是什么含义？**\n", + "$\\alpha$在梯度下降算法中被称作为学习率或者步长，意味着我们可以通过$\\alpha$来控制每一步走的距离，以保证不要步子跨的太大，错过了最低点。同时也要保证不要走的太慢，导致太阳下山了，还没有走到山下。所以$\\alpha$的选择在梯度下降法中往往是很重要的。\n", + "![gd_stepsize](images/gd_stepsize.png)\n", + "\n", + "* **为什么要梯度要乘以一个负号？**\n", + "梯度前加一个负号，就意味着朝着梯度相反的方向前进！梯度的方向实际就是函数在此点上升最快的方向，而我们需要朝着下降最快的方向走，自然就是负的梯度的方向，所以此处需要加上负号。\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 示例代码" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 0: loss = 4303817.496892, a = 2.826518, b = 90.332322\n", + "epoch 1: loss = 2886806.756007, a = 4.689320, b = 127.204624\n", + "epoch 2: loss = 2650260.562182, a = 6.564882, b = 142.423411\n", + "epoch 3: loss = 2610244.478756, a = 8.443497, b = 148.704446\n", + "epoch 4: loss = 2601826.873313, a = 10.321163, b = 151.296317\n", + "epoch 5: loss = 2598022.583256, a = 12.196235, b = 152.365440\n", + "epoch 6: loss = 2594693.583475, a = 14.068035, b = 152.806033\n", + "epoch 7: loss = 2591324.277053, a = 15.936290, b = 152.987193\n", + "epoch 8: loss = 2587904.879022, a = 17.800890, b = 153.061271\n", + "epoch 9: loss = 2584465.875668, a = 19.661796, b = 153.091151\n", + "epoch 10: loss = 2581025.689621, a = 21.518994, b = 153.102788\n", + "epoch 11: loss = 2577592.886376, a = 23.372484, b = 153.106897\n", + "epoch 12: loss = 2574171.135769, a = 25.222270, b = 153.107901\n", + "epoch 13: loss = 2570761.947715, a = 27.068358, b = 153.107623\n", + "epoch 14: loss = 2567365.917653, a = 28.910755, b = 153.106819\n", + "epoch 15: loss = 2563983.260031, a = 30.749468, b = 153.105798\n", + "epoch 16: loss = 2560614.031657, a = 32.584504, b = 153.104690\n", + "epoch 17: loss = 2557258.224357, a = 34.415871, b = 153.103546\n", + "epoch 18: loss = 2553915.803272, a = 36.243576, b = 153.102390\n", + "epoch 19: loss = 2550586.722666, a = 38.067626, b = 153.101229\n", + "epoch 20: loss = 2547270.932442, a = 39.888028, b = 153.100068\n", + "epoch 21: loss = 2543968.380830, a = 41.704790, b = 153.098909\n", + "epoch 22: loss = 2540679.015496, a = 43.517919, b = 153.097751\n", + "epoch 23: loss = 2537402.783996, a = 45.327423, b = 153.096595\n", + "epoch 24: loss = 2534139.633964, a = 47.133308, b = 153.095442\n", + "epoch 25: loss = 2530889.513189, a = 48.935582, b = 153.094291\n", + "epoch 26: loss = 2527652.369647, a = 50.734252, b = 153.093142\n", + "epoch 27: loss = 2524428.151512, a = 52.529326, b = 153.091995\n", + "epoch 28: loss = 2521216.807162, a = 54.320809, b = 153.090851\n", + "epoch 29: loss = 2518018.285178, a = 56.108711, b = 153.089709\n", + "epoch 30: loss = 2514832.534347, a = 57.893037, b = 153.088570\n", + "epoch 31: loss = 2511659.503661, a = 59.673795, b = 153.087432\n", + "epoch 32: loss = 2508499.142314, a = 61.450992, b = 153.086297\n", + "epoch 33: loss = 2505351.399704, a = 63.224636, b = 153.085164\n", + "epoch 34: loss = 2502216.225431, a = 64.994732, b = 153.084034\n", + "epoch 35: loss = 2499093.569295, a = 66.761289, b = 153.082906\n", + "epoch 36: loss = 2495983.381300, a = 68.524314, b = 153.081780\n", + "epoch 37: loss = 2492885.611645, a = 70.283813, b = 153.080656\n", + "epoch 38: loss = 2489800.210732, a = 72.039794, b = 153.079534\n", + "epoch 39: loss = 2486727.129159, a = 73.792263, b = 153.078415\n", + "epoch 40: loss = 2483666.317722, a = 75.541228, b = 153.077298\n", + "epoch 41: loss = 2480617.727412, a = 77.286695, b = 153.076183\n", + "epoch 42: loss = 2477581.309419, a = 79.028673, b = 153.075070\n", + "epoch 43: loss = 2474557.015125, a = 80.767166, b = 153.073960\n", + "epoch 44: loss = 2471544.796108, a = 82.502184, b = 153.072852\n", + "epoch 45: loss = 2468544.604139, a = 84.233732, b = 153.071746\n", + "epoch 46: loss = 2465556.391180, a = 85.961817, b = 153.070642\n", + "epoch 47: loss = 2462580.109388, a = 87.686447, b = 153.069541\n", + "epoch 48: loss = 2459615.711109, a = 89.407628, b = 153.068441\n", + "epoch 49: loss = 2456663.148878, a = 91.125368, b = 153.067344\n", + "epoch 50: loss = 2453722.375424, a = 92.839672, b = 153.066249\n", + "epoch 51: loss = 2450793.343662, a = 94.550548, b = 153.065157\n", + "epoch 52: loss = 2447876.006693, a = 96.258004, b = 153.064066\n", + "epoch 53: loss = 2444970.317810, a = 97.962044, b = 153.062978\n", + "epoch 54: loss = 2442076.230490, a = 99.662678, b = 153.061892\n", + "epoch 55: loss = 2439193.698395, a = 101.359910, b = 153.060808\n", + "epoch 56: loss = 2436322.675374, a = 103.053749, b = 153.059726\n", + "epoch 57: loss = 2433463.115460, a = 104.744201, b = 153.058646\n", + "epoch 58: loss = 2430614.972868, a = 106.431272, b = 153.057569\n", + "epoch 59: loss = 2427778.201998, a = 108.114970, b = 153.056493\n", + "epoch 60: loss = 2424952.757431, a = 109.795301, b = 153.055420\n", + "epoch 61: loss = 2422138.593930, a = 111.472271, b = 153.054349\n", + "epoch 62: loss = 2419335.666438, a = 113.145889, b = 153.053280\n", + "epoch 63: loss = 2416543.930078, a = 114.816160, b = 153.052213\n", + "epoch 64: loss = 2413763.340154, a = 116.483090, b = 153.051148\n", + "epoch 65: loss = 2410993.852146, a = 118.146688, b = 153.050086\n", + "epoch 66: loss = 2408235.421713, a = 119.806958, b = 153.049026\n", + "epoch 67: loss = 2405488.004692, a = 121.463909, b = 153.047967\n", + "epoch 68: loss = 2402751.557095, a = 123.117547, b = 153.046911\n", + "epoch 69: loss = 2400026.035111, a = 124.767877, b = 153.045857\n", + "epoch 70: loss = 2397311.395102, a = 126.414908, b = 153.044805\n", + "epoch 71: loss = 2394607.593607, a = 128.058645, b = 153.043755\n", + "epoch 72: loss = 2391914.587336, a = 129.699095, b = 153.042707\n", + "epoch 73: loss = 2389232.333175, a = 131.336265, b = 153.041662\n", + "epoch 74: loss = 2386560.788178, a = 132.970161, b = 153.040618\n", + "epoch 75: loss = 2383899.909575, a = 134.600789, b = 153.039577\n", + "epoch 76: loss = 2381249.654764, a = 136.228157, b = 153.038537\n", + "epoch 77: loss = 2378609.981313, a = 137.852271, b = 153.037500\n", + "epoch 78: loss = 2375980.846962, a = 139.473137, b = 153.036465\n", + "epoch 79: loss = 2373362.209618, a = 141.090762, b = 153.035432\n", + "epoch 80: loss = 2370754.027356, a = 142.705152, b = 153.034401\n", + "epoch 81: loss = 2368156.258420, a = 144.316314, b = 153.033371\n", + "epoch 82: loss = 2365568.861218, a = 145.924254, b = 153.032344\n", + "epoch 83: loss = 2362991.794327, a = 147.528979, b = 153.031320\n", + "epoch 84: loss = 2360425.016488, a = 149.130495, b = 153.030297\n", + "epoch 85: loss = 2357868.486607, a = 150.728809, b = 153.029276\n", + "epoch 86: loss = 2355322.163754, a = 152.323926, b = 153.028257\n", + "epoch 87: loss = 2352786.007164, a = 153.915853, b = 153.027240\n", + "epoch 88: loss = 2350259.976233, a = 155.504598, b = 153.026226\n", + "epoch 89: loss = 2347744.030520, a = 157.090165, b = 153.025213\n", + "epoch 90: loss = 2345238.129745, a = 158.672562, b = 153.024202\n", + "epoch 91: loss = 2342742.233790, a = 160.251794, b = 153.023194\n", + "epoch 92: loss = 2340256.302696, a = 161.827869, b = 153.022187\n", + "epoch 93: loss = 2337780.296663, a = 163.400791, b = 153.021182\n", + "epoch 94: loss = 2335314.176054, a = 164.970569, b = 153.020180\n", + "epoch 95: loss = 2332857.901385, a = 166.537207, b = 153.019179\n", + "epoch 96: loss = 2330411.433334, a = 168.100713, b = 153.018180\n", + "epoch 97: loss = 2327974.732732, a = 169.661092, b = 153.017184\n", + "epoch 98: loss = 2325547.760572, a = 171.218351, b = 153.016189\n", + "epoch 99: loss = 2323130.477996, a = 172.772496, b = 153.015197\n", + "epoch 100: loss = 2320722.846308, a = 174.323534, b = 153.014206\n", + "epoch 101: loss = 2318324.826961, a = 175.871469, b = 153.013217\n", + "epoch 102: loss = 2315936.381566, a = 177.416310, b = 153.012231\n", + "epoch 103: loss = 2313557.471885, a = 178.958061, b = 153.011246\n", + "epoch 104: loss = 2311188.059833, a = 180.496729, b = 153.010263\n", + "epoch 105: loss = 2308828.107479, a = 182.032320, b = 153.009282\n", + "epoch 106: loss = 2306477.577040, a = 183.564841, b = 153.008304\n", + "epoch 107: loss = 2304136.430888, a = 185.094297, b = 153.007327\n", + "epoch 108: loss = 2301804.631543, a = 186.620695, b = 153.006352\n", + "epoch 109: loss = 2299482.141674, a = 188.144040, b = 153.005379\n", + "epoch 110: loss = 2297168.924101, a = 189.664339, b = 153.004408\n", + "epoch 111: loss = 2294864.941791, a = 191.181598, b = 153.003439\n", + "epoch 112: loss = 2292570.157861, a = 192.695823, b = 153.002472\n", + "epoch 113: loss = 2290284.535573, a = 194.207021, b = 153.001506\n", + "epoch 114: loss = 2288008.038337, a = 195.715196, b = 153.000543\n", + "epoch 115: loss = 2285740.629708, a = 197.220355, b = 152.999582\n", + "epoch 116: loss = 2283482.273389, a = 198.722505, b = 152.998622\n", + "epoch 117: loss = 2281232.933225, a = 200.221651, b = 152.997665\n", + "epoch 118: loss = 2278992.573208, a = 201.717799, b = 152.996709\n", + "epoch 119: loss = 2276761.157473, a = 203.210955, b = 152.995756\n", + "epoch 120: loss = 2274538.650297, a = 204.701125, b = 152.994804\n", + "epoch 121: loss = 2272325.016100, a = 206.188316, b = 152.993854\n", + "epoch 122: loss = 2270120.219447, a = 207.672532, b = 152.992906\n", + "epoch 123: loss = 2267924.225041, a = 209.153781, b = 152.991960\n", + "epoch 124: loss = 2265736.997727, a = 210.632068, b = 152.991016\n", + "epoch 125: loss = 2263558.502491, a = 212.107398, b = 152.990074\n", + "epoch 126: loss = 2261388.704460, a = 213.579779, b = 152.989133\n", + "epoch 127: loss = 2259227.568898, a = 215.049215, b = 152.988195\n", + "epoch 128: loss = 2257075.061208, a = 216.515713, b = 152.987258\n", + "epoch 129: loss = 2254931.146933, a = 217.979278, b = 152.986323\n", + "epoch 130: loss = 2252795.791751, a = 219.439916, b = 152.985390\n", + "epoch 131: loss = 2250668.961481, a = 220.897634, b = 152.984459\n", + "epoch 132: loss = 2248550.622075, a = 222.352437, b = 152.983530\n", + "epoch 133: loss = 2246440.739622, a = 223.804331, b = 152.982603\n", + "epoch 134: loss = 2244339.280347, a = 225.253321, b = 152.981677\n", + "epoch 135: loss = 2242246.210609, a = 226.699414, b = 152.980754\n", + "epoch 136: loss = 2240161.496903, a = 228.142616, b = 152.979832\n", + "epoch 137: loss = 2238085.105855, a = 229.582931, b = 152.978912\n", + "epoch 138: loss = 2236017.004229, a = 231.020366, b = 152.977994\n", + "epoch 139: loss = 2233957.158916, a = 232.454927, b = 152.977078\n", + "epoch 140: loss = 2231905.536944, a = 233.886619, b = 152.976163\n", + "epoch 141: loss = 2229862.105469, a = 235.315448, b = 152.975251\n", + "epoch 142: loss = 2227826.831783, a = 236.741420, b = 152.974340\n", + "epoch 143: loss = 2225799.683303, a = 238.164541, b = 152.973431\n", + "epoch 144: loss = 2223780.627579, a = 239.584815, b = 152.972524\n", + "epoch 145: loss = 2221769.632292, a = 241.002250, b = 152.971619\n", + "epoch 146: loss = 2219766.665250, a = 242.416850, b = 152.970715\n", + "epoch 147: loss = 2217771.694390, a = 243.828622, b = 152.969813\n", + "epoch 148: loss = 2215784.687776, a = 245.237570, b = 152.968913\n", + "epoch 149: loss = 2213805.613603, a = 246.643701, b = 152.968015\n", + "epoch 150: loss = 2211834.440190, a = 248.047021, b = 152.967119\n", + "epoch 151: loss = 2209871.135983, a = 249.447534, b = 152.966225\n", + "epoch 152: loss = 2207915.669555, a = 250.845246, b = 152.965332\n", + "epoch 153: loss = 2205968.009603, a = 252.240164, b = 152.964441\n", + "epoch 154: loss = 2204028.124951, a = 253.632292, b = 152.963552\n", + "epoch 155: loss = 2202095.984547, a = 255.021636, b = 152.962664\n", + "epoch 156: loss = 2200171.557461, a = 256.408203, b = 152.961779\n", + "epoch 157: loss = 2198254.812889, a = 257.791996, b = 152.960895\n", + "epoch 158: loss = 2196345.720150, a = 259.173022, b = 152.960013\n", + "epoch 159: loss = 2194444.248684, a = 260.551287, b = 152.959133\n", + "epoch 160: loss = 2192550.368053, a = 261.926796, b = 152.958254\n", + "epoch 161: loss = 2190664.047943, a = 263.299554, b = 152.957377\n", + "epoch 162: loss = 2188785.258158, a = 264.669567, b = 152.956502\n", + "epoch 163: loss = 2186913.968625, a = 266.036841, b = 152.955629\n", + "epoch 164: loss = 2185050.149391, a = 267.401380, b = 152.954757\n", + "epoch 165: loss = 2183193.770620, a = 268.763191, b = 152.953888\n", + "epoch 166: loss = 2181344.802598, a = 270.122278, b = 152.953020\n", + "epoch 167: loss = 2179503.215730, a = 271.478648, b = 152.952153\n", + "epoch 168: loss = 2177668.980536, a = 272.832306, b = 152.951289\n", + "epoch 169: loss = 2175842.067656, a = 274.183256, b = 152.950426\n", + "epoch 170: loss = 2174022.447849, a = 275.531506, b = 152.949565\n", + "epoch 171: loss = 2172210.091986, a = 276.877059, b = 152.948705\n", + "epoch 172: loss = 2170404.971060, a = 278.219921, b = 152.947848\n", + "epoch 173: loss = 2168607.056175, a = 279.560099, b = 152.946992\n", + "epoch 174: loss = 2166816.318553, a = 280.897596, b = 152.946138\n", + "epoch 175: loss = 2165032.729530, a = 282.232419, b = 152.945285\n", + "epoch 176: loss = 2163256.260558, a = 283.564572, b = 152.944434\n", + "epoch 177: loss = 2161486.883202, a = 284.894062, b = 152.943585\n", + "epoch 178: loss = 2159724.569140, a = 286.220893, b = 152.942738\n", + "epoch 179: loss = 2157969.290163, a = 287.545071, b = 152.941892\n", + "epoch 180: loss = 2156221.018176, a = 288.866601, b = 152.941048\n", + "epoch 181: loss = 2154479.725196, a = 290.185489, b = 152.940205\n", + "epoch 182: loss = 2152745.383351, a = 291.501739, b = 152.939365\n", + "epoch 183: loss = 2151017.964881, a = 292.815357, b = 152.938526\n", + "epoch 184: loss = 2149297.442136, a = 294.126348, b = 152.937688\n", + "epoch 185: loss = 2147583.787577, a = 295.434718, b = 152.936853\n", + "epoch 186: loss = 2145876.973776, a = 296.740471, b = 152.936019\n", + "epoch 187: loss = 2144176.973412, a = 298.043614, b = 152.935186\n", + "epoch 188: loss = 2142483.759277, a = 299.344150, b = 152.934356\n", + "epoch 189: loss = 2140797.304267, a = 300.642086, b = 152.933527\n", + "epoch 190: loss = 2139117.581390, a = 301.937426, b = 152.932699\n", + "epoch 191: loss = 2137444.563760, a = 303.230176, b = 152.931874\n", + "epoch 192: loss = 2135778.224600, a = 304.520341, b = 152.931050\n", + "epoch 193: loss = 2134118.537237, a = 305.807926, b = 152.930227\n", + "epoch 194: loss = 2132465.475108, a = 307.092937, b = 152.929407\n", + "epoch 195: 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"epoch 209: loss = 2108446.828347, a = 326.062600, b = 152.917291\n", + "epoch 210: loss = 2106896.242470, a = 327.307108, b = 152.916496\n", + "epoch 211: loss = 2105351.846122, a = 328.549127, b = 152.915703\n", + "epoch 212: loss = 2103813.614585, a = 329.788662, b = 152.914911\n", + "epoch 213: loss = 2102281.523241, a = 331.025719, b = 152.914121\n", + "epoch 214: loss = 2100755.547567, a = 332.260302, b = 152.913332\n", + "epoch 215: loss = 2099235.663141, a = 333.492417, b = 152.912545\n", + "epoch 216: loss = 2097721.845637, a = 334.722067, b = 152.911760\n", + "epoch 217: loss = 2096214.070828, a = 335.949259, b = 152.910976\n", + "epoch 218: loss = 2094712.314580, a = 337.173997, b = 152.910194\n", + "epoch 219: loss = 2093216.552860, a = 338.396285, b = 152.909413\n", + "epoch 220: loss = 2091726.761728, a = 339.616130, b = 152.908634\n", + "epoch 221: loss = 2090242.917340, a = 340.833535, b = 152.907857\n", + "epoch 222: loss = 2088764.995948, a = 342.048506, b = 152.907081\n", + "epoch 223: loss = 2087292.973899, a = 343.261047, b = 152.906306\n", + "epoch 224: loss = 2085826.827634, a = 344.471164, b = 152.905533\n", + "epoch 225: loss = 2084366.533687, a = 345.678860, b = 152.904762\n", + "epoch 226: loss = 2082912.068689, a = 346.884142, b = 152.903992\n", + "epoch 227: loss = 2081463.409360, a = 348.087014, b = 152.903224\n", + "epoch 228: loss = 2080020.532516, a = 349.287480, b = 152.902457\n", + "epoch 229: loss = 2078583.415065, a = 350.485545, b = 152.901692\n", + "epoch 230: loss = 2077152.034007, a = 351.681215, b = 152.900928\n", + "epoch 231: loss = 2075726.366435, a = 352.874494, b = 152.900166\n", + "epoch 232: loss = 2074306.389530, a = 354.065387, b = 152.899405\n", + "epoch 233: loss = 2072892.080568, a = 355.253899, b = 152.898646\n", + "epoch 234: loss = 2071483.416915, a = 356.440033, b = 152.897889\n", + "epoch 235: loss = 2070080.376026, a = 357.623796, b = 152.897133\n", + "epoch 236: loss = 2068682.935446, a = 358.805192, b = 152.896378\n", + "epoch 237: loss = 2067291.072812, a = 359.984226, b = 152.895625\n", + "epoch 238: loss = 2065904.765847, a = 361.160901, b = 152.894874\n", + "epoch 239: loss = 2064523.992367, a = 362.335224, b = 152.894124\n", + "epoch 240: loss = 2063148.730273, a = 363.507199, b = 152.893375\n", + "epoch 241: loss = 2061778.957556, a = 364.676830, b = 152.892628\n", + "epoch 242: loss = 2060414.652294, a = 365.844122, b = 152.891882\n", + "epoch 243: loss = 2059055.792654, a = 367.009079, b = 152.891138\n", + "epoch 244: loss = 2057702.356890, a = 368.171708, b = 152.890396\n", + "epoch 245: loss = 2056354.323340, a = 369.332011, b = 152.889655\n", + "epoch 246: loss = 2055011.670433, a = 370.489995, b = 152.888915\n", + "epoch 247: loss = 2053674.376681, a = 371.645662, b = 152.888177\n", + "epoch 248: loss = 2052342.420683, a = 372.799019, b = 152.887440\n", + "epoch 249: loss = 2051015.781122, a = 373.950069, b = 152.886705\n", + "epoch 250: loss = 2049694.436769, a = 375.098818, b = 152.885972\n", + "epoch 251: loss = 2048378.366478, a = 376.245269, b = 152.885239\n", + "epoch 252: loss = 2047067.549187, a = 377.389428, b = 152.884509\n", + "epoch 253: loss = 2045761.963919, a = 378.531300, b = 152.883779\n", + "epoch 254: loss = 2044461.589780, a = 379.670887, b = 152.883051\n", + "epoch 255: loss = 2043166.405962, a = 380.808196, b = 152.882325\n", + "epoch 256: loss = 2041876.391735, a = 381.943231, b = 152.881600\n", + "epoch 257: loss = 2040591.526458, a = 383.075996, b = 152.880877\n", + "epoch 258: loss = 2039311.789567, a = 384.206496, b = 152.880154\n", + "epoch 259: loss = 2038037.160585, a = 385.334735, b = 152.879434\n", + "epoch 260: loss = 2036767.619112, a = 386.460718, b = 152.878715\n", + "epoch 261: loss = 2035503.144833, a = 387.584449, b = 152.877997\n", + "epoch 262: loss = 2034243.717512, a = 388.705934, b = 152.877281\n", + "epoch 263: loss = 2032989.316996, a = 389.825176, b = 152.876566\n", + "epoch 264: loss = 2031739.923211, a = 390.942179, b = 152.875852\n", + "epoch 265: loss = 2030495.516162, a = 392.056949, b = 152.875140\n", + "epoch 266: loss = 2029256.075938, a = 393.169490, b = 152.874430\n", + "epoch 267: loss = 2028021.582702, a = 394.279807, b = 152.873721\n", + "epoch 268: loss = 2026792.016702, a = 395.387902, b = 152.873013\n", + "epoch 269: loss = 2025567.358259, a = 396.493783, b = 152.872307\n", + "epoch 270: loss = 2024347.587778, a = 397.597451, b = 152.871602\n", + "epoch 271: loss = 2023132.685738, a = 398.698913, b = 152.870898\n", + "epoch 272: loss = 2021922.632700, a = 399.798172, b = 152.870196\n", + "epoch 273: loss = 2020717.409298, a = 400.895233, b = 152.869495\n", + "epoch 274: loss = 2019516.996248, a = 401.990100, b = 152.868796\n", + "epoch 275: loss = 2018321.374339, a = 403.082778, b = 152.868098\n", + "epoch 276: loss = 2017130.524440, a = 404.173271, b = 152.867402\n", + "epoch 277: loss = 2015944.427495, a = 405.261583, b = 152.866707\n", + "epoch 278: loss = 2014763.064523, a = 406.347719, b = 152.866013\n", + "epoch 279: loss = 2013586.416620, a = 407.431683, b = 152.865321\n", + "epoch 280: loss = 2012414.464959, a = 408.513480, b = 152.864630\n", + "epoch 281: loss = 2011247.190786, a = 409.593113, b = 152.863940\n", + "epoch 282: loss = 2010084.575421, a = 410.670588, b = 152.863252\n", + "epoch 283: loss = 2008926.600262, a = 411.745907, b = 152.862565\n", + "epoch 284: loss = 2007773.246779, a = 412.819077, b = 152.861880\n", + "epoch 285: loss = 2006624.496517, a = 413.890100, b = 152.861196\n", + "epoch 286: loss = 2005480.331093, a = 414.958982, b = 152.860513\n", + "epoch 287: loss = 2004340.732199, a = 416.025727, b = 152.859832\n", + "epoch 288: loss = 2003205.681600, a = 417.090338, b = 152.859152\n", + "epoch 289: loss = 2002075.161134, a = 418.152820, b = 152.858473\n", + "epoch 290: loss = 2000949.152710, a = 419.213178, b = 152.857796\n", + "epoch 291: loss = 1999827.638312, a = 420.271415, b = 152.857120\n", + "epoch 292: loss = 1998710.599993, a = 421.327537, b = 152.856445\n", + "epoch 293: loss = 1997598.019879, a = 422.381546, b = 152.855772\n", + "epoch 294: loss = 1996489.880169, a = 423.433448, b = 152.855100\n", + "epoch 295: loss = 1995386.163130, a = 424.483246, b = 152.854430\n", + "epoch 296: loss = 1994286.851102, a = 425.530945, b = 152.853761\n", + "epoch 297: loss = 1993191.926495, a = 426.576549, b = 152.853093\n", + "epoch 298: loss = 1992101.371788, a = 427.620062, b = 152.852426\n", + "epoch 299: loss = 1991015.169533, a = 428.661489, b = 152.851761\n", + "epoch 300: loss = 1989933.302349, a = 429.700833, b = 152.851097\n", + "epoch 301: loss = 1988855.752925, a = 430.738098, b = 152.850435\n", + "epoch 302: loss = 1987782.504019, a = 431.773290, b = 152.849774\n", + "epoch 303: loss = 1986713.538460, a = 432.806411, b = 152.849114\n", + "epoch 304: loss = 1985648.839142, a = 433.837466, b = 152.848455\n", + "epoch 305: loss = 1984588.389031, a = 434.866460, b = 152.847798\n", + "epoch 306: loss = 1983532.171157, a = 435.893396, b = 152.847142\n", + "epoch 307: loss = 1982480.168622, a = 436.918279, b = 152.846488\n", + "epoch 308: loss = 1981432.364593, a = 437.941112, b = 152.845834\n", + "epoch 309: loss = 1980388.742304, a = 438.961900, b = 152.845182\n", + "epoch 310: loss = 1979349.285057, a = 439.980646, b = 152.844532\n", + "epoch 311: loss = 1978313.976221, a = 440.997355, b = 152.843882\n", + "epoch 312: loss = 1977282.799230, a = 442.012032, b = 152.843234\n", + "epoch 313: loss = 1976255.737586, a = 443.024679, b = 152.842588\n", + "epoch 314: loss = 1975232.774855, a = 444.035301, b = 152.841942\n", + "epoch 315: loss = 1974213.894670, a = 445.043903, b = 152.841298\n", + "epoch 316: loss = 1973199.080729, a = 446.050487, b = 152.840655\n", + "epoch 317: loss = 1972188.316794, a = 447.055059, b = 152.840013\n", + "epoch 318: loss = 1971181.586695, a = 448.057622, b = 152.839373\n", + "epoch 319: loss = 1970178.874323, a = 449.058180, b = 152.838734\n", + "epoch 320: loss = 1969180.163634, a = 450.056737, b = 152.838096\n", + "epoch 321: loss = 1968185.438651, a = 451.053298, b = 152.837460\n", + "epoch 322: loss = 1967194.683457, a = 452.047865, b = 152.836824\n", + "epoch 323: loss = 1966207.882201, a = 453.040444, b = 152.836190\n", + "epoch 324: loss = 1965225.019095, a = 454.031038, b = 152.835558\n", + "epoch 325: loss = 1964246.078413, a = 455.019652, b = 152.834926\n", + "epoch 326: loss = 1963271.044493, a = 456.006288, b = 152.834296\n", + "epoch 327: loss = 1962299.901734, a = 456.990951, b = 152.833667\n", + "epoch 328: loss = 1961332.634599, a = 457.973646, b = 152.833040\n", + "epoch 329: loss = 1960369.227613, a = 458.954375, b = 152.832413\n", + "epoch 330: loss = 1959409.665362, a = 459.933143, b = 152.831788\n", + "epoch 331: loss = 1958453.932493, a = 460.909954, b = 152.831164\n", + "epoch 332: loss = 1957502.013716, a = 461.884812, b = 152.830542\n", + "epoch 333: 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"epoch 347: loss = 1943670.710054, a = 476.275919, b = 152.821350\n", + "epoch 348: loss = 1942777.786279, a = 477.220049, b = 152.820747\n", + "epoch 349: loss = 1941888.425799, a = 478.162292, b = 152.820145\n", + "epoch 350: loss = 1941002.614385, a = 479.102651, b = 152.819545\n", + "epoch 351: loss = 1940120.337867, a = 480.041129, b = 152.818945\n", + "epoch 352: loss = 1939241.582129, a = 480.977731, b = 152.818347\n", + "epoch 353: loss = 1938366.333114, a = 481.912460, b = 152.817750\n", + "epoch 354: loss = 1937494.576819, a = 482.845319, b = 152.817154\n", + "epoch 355: loss = 1936626.299297, a = 483.776313, b = 152.816560\n", + "epoch 356: loss = 1935761.486660, a = 484.705446, b = 152.815966\n", + "epoch 357: loss = 1934900.125071, a = 485.632721, b = 152.815374\n", + "epoch 358: loss = 1934042.200750, a = 486.558141, b = 152.814783\n", + "epoch 359: loss = 1933187.699974, a = 487.481711, b = 152.814193\n", + "epoch 360: loss = 1932336.609071, a = 488.403434, b = 152.813604\n", + "epoch 361: loss = 1931488.914427, a = 489.323313, b = 152.813017\n", + "epoch 362: loss = 1930644.602481, a = 490.241354, b = 152.812430\n", + "epoch 363: loss = 1929803.659727, a = 491.157558, b = 152.811845\n", + "epoch 364: loss = 1928966.072710, a = 492.071931, b = 152.811261\n", + "epoch 365: loss = 1928131.828033, a = 492.984475, b = 152.810678\n", + "epoch 366: loss = 1927300.912349, a = 493.895194, b = 152.810097\n", + "epoch 367: loss = 1926473.312366, a = 494.804092, b = 152.809516\n", + "epoch 368: loss = 1925649.014846, a = 495.711173, b = 152.808937\n", + "epoch 369: loss = 1924828.006601, a = 496.616440, b = 152.808359\n", + "epoch 370: loss = 1924010.274499, a = 497.519896, b = 152.807782\n", + "epoch 371: loss = 1923195.805457, a = 498.421546, b = 152.807206\n", + "epoch 372: loss = 1922384.586448, a = 499.321393, b = 152.806631\n", + "epoch 373: loss = 1921576.604494, a = 500.219440, b = 152.806057\n", + "epoch 374: loss = 1920771.846670, a = 501.115692, b = 152.805485\n", + "epoch 375: loss = 1919970.300103, a = 502.010152, b = 152.804914\n", + "epoch 376: loss = 1919171.951971, a = 502.902823, b = 152.804344\n", + "epoch 377: loss = 1918376.789502, a = 503.793708, b = 152.803775\n", + "epoch 378: loss = 1917584.799978, a = 504.682813, b = 152.803207\n", + "epoch 379: loss = 1916795.970729, a = 505.570139, b = 152.802640\n", + "epoch 380: loss = 1916010.289136, a = 506.455691, b = 152.802074\n", + "epoch 381: loss = 1915227.742632, a = 507.339473, b = 152.801510\n", + "epoch 382: loss = 1914448.318700, a = 508.221487, b = 152.800947\n", + "epoch 383: loss = 1913672.004870, a = 509.101737, b = 152.800384\n", + "epoch 384: loss = 1912898.788726, a = 509.980227, b = 152.799823\n", + "epoch 385: loss = 1912128.657898, a = 510.856960, b = 152.799263\n", + "epoch 386: loss = 1911361.600068, a = 511.731940, b = 152.798704\n", + "epoch 387: loss = 1910597.602966, a = 512.605171, b = 152.798147\n", + "epoch 388: loss = 1909836.654372, a = 513.476655, b = 152.797590\n", + "epoch 389: loss = 1909078.742112, a = 514.346397, b = 152.797035\n", + "epoch 390: loss = 1908323.854064, a = 515.214399, b = 152.796480\n", + "epoch 391: loss = 1907571.978154, a = 516.080666, b = 152.795927\n", + "epoch 392: loss = 1906823.102353, a = 516.945201, b = 152.795375\n", + "epoch 393: loss = 1906077.214684, a = 517.808006, b = 152.794824\n", + "epoch 394: loss = 1905334.303215, a = 518.669087, b = 152.794274\n", + "epoch 395: loss = 1904594.356064, a = 519.528445, b = 152.793725\n", + "epoch 396: loss = 1903857.361395, a = 520.386085, b = 152.793177\n", + "epoch 397: loss = 1903123.307419, a = 521.242011, b = 152.792630\n", + "epoch 398: loss = 1902392.182395, a = 522.096224, b = 152.792085\n", + "epoch 399: loss = 1901663.974628, a = 522.948729, b = 152.791540\n", + "epoch 400: loss = 1900938.672471, a = 523.799530, b = 152.790997\n", + "epoch 401: loss = 1900216.264323, a = 524.648629, b = 152.790455\n", + "epoch 402: loss = 1899496.738628, a = 525.496031, b = 152.789913\n", + "epoch 403: loss = 1898780.083878, a = 526.341738, b = 152.789373\n", + "epoch 404: loss = 1898066.288609, a = 527.185753, b = 152.788834\n", + "epoch 405: loss = 1897355.341406, a = 528.028081, b = 152.788296\n", + "epoch 406: loss = 1896647.230897, a = 528.868725, b = 152.787759\n", + "epoch 407: loss = 1895941.945754, a = 529.707688, b = 152.787223\n", + "epoch 408: loss = 1895239.474699, a = 530.544973, b = 152.786689\n", + "epoch 409: loss = 1894539.806495, a = 531.380583, b = 152.786155\n", + "epoch 410: loss = 1893842.929951, a = 532.214523, b = 152.785622\n", + "epoch 411: loss = 1893148.833922, a = 533.046795, b = 152.785091\n", + "epoch 412: loss = 1892457.507305, a = 533.877403, b = 152.784560\n", + "epoch 413: loss = 1891768.939043, a = 534.706350, b = 152.784031\n", + "epoch 414: loss = 1891083.118123, a = 535.533639, b = 152.783502\n", + "epoch 415: loss = 1890400.033577, a = 536.359274, b = 152.782975\n", + "epoch 416: loss = 1889719.674478, a = 537.183258, b = 152.782449\n", + "epoch 417: loss = 1889042.029945, a = 538.005595, b = 152.781924\n", + "epoch 418: loss = 1888367.089140, a = 538.826287, b = 152.781399\n", + "epoch 419: loss = 1887694.841269, a = 539.645337, b = 152.780876\n", + "epoch 420: loss = 1887025.275578, a = 540.462750, b = 152.780354\n", + "epoch 421: loss = 1886358.381359, a = 541.278529, b = 152.779833\n", + "epoch 422: loss = 1885694.147947, a = 542.092676, b = 152.779313\n", + "epoch 423: loss = 1885032.564717, a = 542.905195, b = 152.778794\n", + "epoch 424: loss = 1884373.621089, a = 543.716089, b = 152.778276\n", + "epoch 425: loss = 1883717.306523, a = 544.525362, b = 152.777759\n", + "epoch 426: loss = 1883063.610524, a = 545.333017, b = 152.777244\n", + "epoch 427: loss = 1882412.522636, a = 546.139056, b = 152.776729\n", + "epoch 428: loss = 1881764.032446, a = 546.943484, b = 152.776215\n", + "epoch 429: loss = 1881118.129583, a = 547.746303, b = 152.775702\n", + "epoch 430: loss = 1880474.803717, a = 548.547517, b = 152.775190\n", + "epoch 431: loss = 1879834.044558, a = 549.347128, b = 152.774680\n", + "epoch 432: loss = 1879195.841860, a = 550.145141, b = 152.774170\n", + "epoch 433: loss = 1878560.185416, a = 550.941558, b = 152.773661\n", + "epoch 434: loss = 1877927.065059, a = 551.736382, b = 152.773154\n", + "epoch 435: loss = 1877296.470664, a = 552.529617, b = 152.772647\n", + "epoch 436: loss = 1876668.392146, a = 553.321265, b = 152.772141\n", + "epoch 437: loss = 1876042.819461, a = 554.111331, b = 152.771637\n", + "epoch 438: loss = 1875419.742604, a = 554.899817, b = 152.771133\n", + "epoch 439: loss = 1874799.151611, a = 555.686726, b = 152.770631\n", + "epoch 440: loss = 1874181.036556, a = 556.472061, b = 152.770129\n", + "epoch 441: loss = 1873565.387555, a = 557.255826, b = 152.769629\n", + "epoch 442: loss = 1872952.194761, a = 558.038024, b = 152.769129\n", + "epoch 443: loss = 1872341.448369, a = 558.818658, b = 152.768630\n", + "epoch 444: loss = 1871733.138612, a = 559.597731, b = 152.768133\n", + "epoch 445: loss = 1871127.255760, a = 560.375245, b = 152.767636\n", + "epoch 446: loss = 1870523.790126, a = 561.151205, b = 152.767141\n", + "epoch 447: loss = 1869922.732057, a = 561.925614, b = 152.766646\n", + "epoch 448: loss = 1869324.071943, a = 562.698474, b = 152.766152\n", + "epoch 449: loss = 1868727.800209, a = 563.469788, b = 152.765660\n", + "epoch 450: loss = 1868133.907320, a = 564.239560, b = 152.765168\n", + "epoch 451: loss = 1867542.383779, a = 565.007792, b = 152.764677\n", + "epoch 452: loss = 1866953.220126, a = 565.774489, b = 152.764188\n", + "epoch 453: loss = 1866366.406939, a = 566.539652, b = 152.763699\n", + "epoch 454: loss = 1865781.934835, a = 567.303285, b = 152.763211\n", + "epoch 455: loss = 1865199.794467, a = 568.065392, b = 152.762724\n", + "epoch 456: loss = 1864619.976525, a = 568.825974, b = 152.762239\n", + "epoch 457: loss = 1864042.471739, a = 569.585035, b = 152.761754\n", + "epoch 458: loss = 1863467.270872, a = 570.342579, b = 152.761270\n", + "epoch 459: loss = 1862894.364726, a = 571.098607, b = 152.760787\n", + "epoch 460: loss = 1862323.744141, a = 571.853124, b = 152.760305\n", + "epoch 461: loss = 1861755.399991, a = 572.606132, b = 152.759824\n", + "epoch 462: loss = 1861189.323188, a = 573.357635, b = 152.759344\n", + "epoch 463: loss = 1860625.504680, a = 574.107634, b = 152.758865\n", + "epoch 464: loss = 1860063.935451, a = 574.856134, b = 152.758387\n", + "epoch 465: loss = 1859504.606521, a = 575.603137, b = 152.757910\n", + "epoch 466: loss = 1858947.508946, a = 576.348646, b = 152.757434\n", + "epoch 467: loss = 1858392.633818, a = 577.092665, b = 152.756959\n", + "epoch 468: loss = 1857839.972264, a = 577.835195, b = 152.756485\n", + "epoch 469: loss = 1857289.515447, a = 578.576241, b = 152.756011\n", + "epoch 470: loss = 1856741.254564, a = 579.315805, b = 152.755539\n", + "epoch 471: loss = 1856195.180850, a = 580.053891, b = 152.755067\n", + "epoch 472: loss = 1855651.285571, a = 580.790500, b = 152.754597\n", + "epoch 473: loss = 1855109.560032, a = 581.525636, b = 152.754127\n", + "epoch 474: loss = 1854569.995569, a = 582.259302, b = 152.753659\n", + "epoch 475: loss = 1854032.583556, a = 582.991501, b = 152.753191\n", + "epoch 476: loss = 1853497.315398, a = 583.722236, b = 152.752725\n", + "epoch 477: loss = 1852964.182538, a = 584.451510, b = 152.752259\n", + "epoch 478: loss = 1852433.176450, a = 585.179326, b = 152.751794\n", + "epoch 479: loss = 1851904.288645, a = 585.905686, b = 152.751330\n", + "epoch 480: loss = 1851377.510664, a = 586.630593, b = 152.750867\n", + "epoch 481: loss = 1850852.834086, a = 587.354051, b = 152.750405\n", + "epoch 482: loss = 1850330.250520, a = 588.076063, b = 152.749944\n", + "epoch 483: loss = 1849809.751612, a = 588.796630, b = 152.749484\n", + "epoch 484: loss = 1849291.329038, a = 589.515757, b = 152.749024\n", + "epoch 485: loss = 1848774.974510, a = 590.233445, b = 152.748566\n", + "epoch 486: loss = 1848260.679771, a = 590.949699, b = 152.748108\n", + "epoch 487: loss = 1847748.436598, a = 591.664520, b = 152.747652\n", + "epoch 488: loss = 1847238.236802, a = 592.377912, b = 152.747196\n", + "epoch 489: loss = 1846730.072224, a = 593.089877, b = 152.746741\n", + "epoch 490: loss = 1846223.934739, a = 593.800419, b = 152.746288\n", + "epoch 491: loss = 1845719.816255, a = 594.509540, b = 152.745835\n", + "epoch 492: loss = 1845217.708712, a = 595.217242, b = 152.745383\n", + "epoch 493: loss = 1844717.604082, a = 595.923530, b = 152.744932\n", + "epoch 494: loss = 1844219.494368, a = 596.628405, b = 152.744481\n", + "epoch 495: loss = 1843723.371607, a = 597.331871, b = 152.744032\n", + "epoch 496: loss = 1843229.227867, a = 598.033930, b = 152.743584\n", + "epoch 497: loss = 1842737.055246, a = 598.734585, b = 152.743136\n", + "epoch 498: loss = 1842246.845876, a = 599.433839, b = 152.742690\n", + "epoch 499: loss = 1841758.591920, a = 600.131695, b = 152.742244\n", + "epoch 500: loss = 1841272.285571, a = 600.828155, b = 152.741799\n", + "epoch 501: loss = 1840787.919054, a = 601.523222, b = 152.741355\n", + "epoch 502: loss = 1840305.484625, a = 602.216900, b = 152.740912\n", + "epoch 503: loss = 1839824.974571, a = 602.909191, b = 152.740470\n", + "epoch 504: loss = 1839346.381209, a = 603.600097, b = 152.740029\n", + "epoch 505: loss = 1838869.696888, a = 604.289621, b = 152.739588\n", + "epoch 506: loss = 1838394.913987, a = 604.977767, b = 152.739149\n", + "epoch 507: loss = 1837922.024916, a = 605.664537, b = 152.738710\n", + "epoch 508: loss = 1837451.022113, a = 606.349933, b = 152.738272\n", + "epoch 509: loss = 1836981.898050, a = 607.033959, b = 152.737835\n", + "epoch 510: loss = 1836514.645225, a = 607.716617, b = 152.737399\n", + "epoch 511: loss = 1836049.256168, a = 608.397910, b = 152.736964\n", + "epoch 512: loss = 1835585.723441, a = 609.077841, b = 152.736530\n", + "epoch 513: loss = 1835124.039631, a = 609.756412, b = 152.736097\n", + "epoch 514: loss = 1834664.197358, a = 610.433626, b = 152.735664\n", + "epoch 515: loss = 1834206.189270, a = 611.109486, b = 152.735232\n", + "epoch 516: loss = 1833750.008046, a = 611.783994, b = 152.734802\n", + "epoch 517: loss = 1833295.646392, a = 612.457153, b = 152.734372\n", + "epoch 518: loss = 1832843.097045, a = 613.128967, b = 152.733943\n", + "epoch 519: loss = 1832392.352771, a = 613.799437, b = 152.733514\n", + "epoch 520: loss = 1831943.406362, a = 614.468566, b = 152.733087\n", + "epoch 521: loss = 1831496.250642, a = 615.136357, b = 152.732660\n", + "epoch 522: loss = 1831050.878463, a = 615.802813, b = 152.732235\n", + "epoch 523: loss = 1830607.282705, a = 616.467937, b = 152.731810\n", + "epoch 524: loss = 1830165.456276, a = 617.131730, b = 152.731386\n", + "epoch 525: loss = 1829725.392114, a = 617.794196, b = 152.730963\n", + "epoch 526: loss = 1829287.083183, a = 618.455337, b = 152.730541\n", + "epoch 527: loss = 1828850.522477, a = 619.115156, b = 152.730119\n", + "epoch 528: loss = 1828415.703016, a = 619.773655, b = 152.729699\n", + "epoch 529: loss = 1827982.617850, a = 620.430838, b = 152.729279\n", + "epoch 530: loss = 1827551.260056, a = 621.086707, b = 152.728860\n", + "epoch 531: loss = 1827121.622739, a = 621.741264, b = 152.728442\n", + "epoch 532: loss = 1826693.699029, a = 622.394512, b = 152.728025\n", + "epoch 533: loss = 1826267.482087, a = 623.046454, b = 152.727608\n", + "epoch 534: loss = 1825842.965099, a = 623.697093, b = 152.727193\n", + "epoch 535: loss = 1825420.141279, a = 624.346430, b = 152.726778\n", + "epoch 536: loss = 1824999.003869, a = 624.994469, b = 152.726364\n", + "epoch 537: loss = 1824579.546136, a = 625.641212, b = 152.725951\n", + "epoch 538: loss = 1824161.761376, a = 626.286662, b = 152.725539\n", + "epoch 539: loss = 1823745.642910, a = 626.930821, b = 152.725127\n", + "epoch 540: loss = 1823331.184086, a = 627.573691, b = 152.724717\n", + "epoch 541: loss = 1822918.378279, a = 628.215277, b = 152.724307\n", + "epoch 542: loss = 1822507.218891, a = 628.855579, b = 152.723898\n", + "epoch 543: loss = 1822097.699350, a = 629.494601, b = 152.723490\n", + "epoch 544: loss = 1821689.813109, a = 630.132345, b = 152.723083\n", + "epoch 545: loss = 1821283.553649, a = 630.768814, b = 152.722676\n", + "epoch 546: loss = 1820878.914476, a = 631.404010, b = 152.722270\n", + "epoch 547: loss = 1820475.889122, a = 632.037936, b = 152.721865\n", + "epoch 548: loss = 1820074.471145, a = 632.670595, b = 152.721461\n", + "epoch 549: loss = 1819674.654128, a = 633.301988, b = 152.721058\n", + "epoch 550: loss = 1819276.431682, a = 633.932119, b = 152.720656\n", + "epoch 551: loss = 1818879.797440, a = 634.560989, b = 152.720254\n", + "epoch 552: loss = 1818484.745063, a = 635.188602, b = 152.719853\n", + "epoch 553: loss = 1818091.268237, a = 635.814960, b = 152.719453\n", + "epoch 554: loss = 1817699.360671, a = 636.440066, b = 152.719054\n", + "epoch 555: loss = 1817309.016104, a = 637.063922, b = 152.718655\n", + "epoch 556: loss = 1816920.228294, a = 637.686530, b = 152.718258\n", + "epoch 557: loss = 1816532.991028, a = 638.307893, b = 152.717861\n", + "epoch 558: loss = 1816147.298117, a = 638.928013, b = 152.717465\n", + "epoch 559: loss = 1815763.143396, a = 639.546894, b = 152.717070\n", + "epoch 560: loss = 1815380.520724, a = 640.164537, b = 152.716675\n", + "epoch 561: loss = 1814999.423987, a = 640.780944, b = 152.716281\n", + "epoch 562: loss = 1814619.847094, a = 641.396120, b = 152.715888\n", + "epoch 563: loss = 1814241.783977, a = 642.010065, b = 152.715496\n", + "epoch 564: loss = 1813865.228594, a = 642.622782, b = 152.715105\n", + "epoch 565: loss = 1813490.174926, a = 643.234274, b = 152.714714\n", + "epoch 566: loss = 1813116.616980, a = 643.844544, b = 152.714325\n", + "epoch 567: loss = 1812744.548785, a = 644.453593, b = 152.713936\n", + "epoch 568: loss = 1812373.964394, a = 645.061424, b = 152.713547\n", + "epoch 569: loss = 1812004.857885, a = 645.668039, b = 152.713160\n", + "epoch 570: loss = 1811637.223358, a = 646.273442, b = 152.712773\n", + "epoch 571: loss = 1811271.054937, a = 646.877634, b = 152.712387\n", + "epoch 572: loss = 1810906.346771, a = 647.480618, b = 152.712002\n", + "epoch 573: loss = 1810543.093031, a = 648.082396, b = 152.711618\n", + "epoch 574: loss = 1810181.287911, a = 648.682971, b = 152.711234\n", + "epoch 575: loss = 1809820.925628, a = 649.282345, b = 152.710852\n", + "epoch 576: loss = 1809462.000425, a = 649.880520, b = 152.710469\n", + "epoch 577: loss = 1809104.506563, a = 650.477499, b = 152.710088\n", + "epoch 578: loss = 1808748.438331, a = 651.073284, b = 152.709708\n", + "epoch 579: loss = 1808393.790037, a = 651.667878, b = 152.709328\n", + "epoch 580: loss = 1808040.556015, a = 652.261283, b = 152.708949\n", + "epoch 581: loss = 1807688.730617, a = 652.853502, b = 152.708571\n", + "epoch 582: loss = 1807338.308223, a = 653.444536, b = 152.708193\n", + "epoch 583: loss = 1806989.283232, a = 654.034388, b = 152.707816\n", + "epoch 584: loss = 1806641.650065, a = 654.623061, b = 152.707440\n", + "epoch 585: loss = 1806295.403168, a = 655.210556, b = 152.707065\n", + "epoch 586: loss = 1805950.537008, a = 655.796877, b = 152.706691\n", + "epoch 587: loss = 1805607.046072, a = 656.382025, b = 152.706317\n", + "epoch 588: loss = 1805264.924872, a = 656.966004, b = 152.705944\n", + "epoch 589: loss = 1804924.167940, a = 657.548814, b = 152.705572\n", + "epoch 590: loss = 1804584.769832, a = 658.130459, b = 152.705200\n", + "epoch 591: loss = 1804246.725122, a = 658.710941, b = 152.704829\n", + "epoch 592: loss = 1803910.028410, a = 659.290262, b = 152.704459\n", + "epoch 593: loss = 1803574.674315, a = 659.868425, b = 152.704090\n", + "epoch 594: loss = 1803240.657477, a = 660.445432, b = 152.703722\n", + "epoch 595: loss = 1802907.972559, a = 661.021284, b = 152.703354\n", + "epoch 596: loss = 1802576.614245, a = 661.595986, b = 152.702987\n", + "epoch 597: loss = 1802246.577240, a = 662.169538, b = 152.702620\n", + "epoch 598: loss = 1801917.856269, a = 662.741943, b = 152.702255\n", + "epoch 599: loss = 1801590.446081, a = 663.313203, b = 152.701890\n", + "epoch 600: loss = 1801264.341442, a = 663.883321, b = 152.701526\n", + "epoch 601: loss = 1800939.537143, a = 664.452299, b = 152.701163\n", + "epoch 602: loss = 1800616.027992, a = 665.020140, b = 152.700800\n", + "epoch 603: loss = 1800293.808821, a = 665.586845, b = 152.700438\n", + "epoch 604: loss = 1799972.874480, a = 666.152416, b = 152.700077\n", + "epoch 605: loss = 1799653.219842, a = 666.716857, b = 152.699716\n", + "epoch 606: loss = 1799334.839799, a = 667.280169, b = 152.699356\n", + "epoch 607: loss = 1799017.729262, a = 667.842355, b = 152.698997\n", + "epoch 608: loss = 1798701.883166, a = 668.403416, b = 152.698639\n", + "epoch 609: loss = 1798387.296463, a = 668.963355, b = 152.698281\n", + "epoch 610: loss = 1798073.964127, a = 669.522175, b = 152.697924\n", + "epoch 611: loss = 1797761.881151, a = 670.079877, b = 152.697568\n", + "epoch 612: loss = 1797451.042549, a = 670.636464, b = 152.697213\n", + "epoch 613: loss = 1797141.443353, a = 671.191938, b = 152.696858\n", + "epoch 614: loss = 1796833.078618, a = 671.746302, b = 152.696504\n", + "epoch 615: loss = 1796525.943415, a = 672.299557, b = 152.696150\n", + "epoch 616: loss = 1796220.032837, a = 672.851705, b = 152.695798\n", + "epoch 617: loss = 1795915.341998, a = 673.402749, b = 152.695446\n", + "epoch 618: loss = 1795611.866027, a = 673.952692, b = 152.695095\n", + "epoch 619: loss = 1795309.600077, a = 674.501534, b = 152.694744\n", + "epoch 620: loss = 1795008.539318, a = 675.049280, b = 152.694394\n", + "epoch 621: loss = 1794708.678940, a = 675.595930, b = 152.694045\n", + "epoch 622: loss = 1794410.014151, a = 676.141486, b = 152.693697\n", + "epoch 623: loss = 1794112.540180, a = 676.685952, b = 152.693349\n", + "epoch 624: loss = 1793816.252274, a = 677.229329, b = 152.693002\n", + "epoch 625: loss = 1793521.145700, a = 677.771620, b = 152.692656\n", + "epoch 626: loss = 1793227.215741, a = 678.312826, b = 152.692310\n", + "epoch 627: loss = 1792934.457703, a = 678.852950, b = 152.691965\n", + "epoch 628: loss = 1792642.866907, a = 679.391994, b = 152.691621\n", + "epoch 629: loss = 1792352.438696, a = 679.929959, b = 152.691277\n", + "epoch 630: loss = 1792063.168428, a = 680.466850, b = 152.690934\n", + "epoch 631: loss = 1791775.051483, a = 681.002666, b = 152.690592\n", + "epoch 632: loss = 1791488.083257, a = 681.537411, b = 152.690250\n", + "epoch 633: loss = 1791202.259166, a = 682.071087, b = 152.689909\n", + "epoch 634: loss = 1790917.574643, a = 682.603696, b = 152.689569\n", + "epoch 635: loss = 1790634.025140, a = 683.135239, b = 152.689230\n", + "epoch 636: loss = 1790351.606127, a = 683.665720, b = 152.688891\n", + "epoch 637: loss = 1790070.313092, a = 684.195140, b = 152.688553\n", + "epoch 638: loss = 1789790.141542, a = 684.723501, b = 152.688215\n", + "epoch 639: loss = 1789511.087000, a = 685.250806, b = 152.687879\n", + "epoch 640: loss = 1789233.145008, a = 685.777056, b = 152.687542\n", + "epoch 641: loss = 1788956.311125, a = 686.302254, b = 152.687207\n", + "epoch 642: loss = 1788680.580931, a = 686.826401, b = 152.686872\n", + "epoch 643: loss = 1788405.950018, a = 687.349501, b = 152.686538\n", + "epoch 644: loss = 1788132.414001, a = 687.871555, b = 152.686205\n", + "epoch 645: loss = 1787859.968509, a = 688.392564, b = 152.685872\n", + "epoch 646: loss = 1787588.609190, a = 688.912532, b = 152.685540\n", + "epoch 647: loss = 1787318.331709, a = 689.431460, b = 152.685208\n", + "epoch 648: loss = 1787049.131749, a = 689.949350, b = 152.684878\n", + "epoch 649: loss = 1786781.005009, a = 690.466205, b = 152.684548\n", + "epoch 650: loss = 1786513.947206, a = 690.982026, b = 152.684218\n", + "epoch 651: loss = 1786247.954073, a = 691.496816, b = 152.683889\n", + "epoch 652: loss = 1785983.021362, a = 692.010576, b = 152.683561\n", + "epoch 653: loss = 1785719.144840, a = 692.523309, b = 152.683234\n", + "epoch 654: loss = 1785456.320293, a = 693.035016, b = 152.682907\n", + "epoch 655: loss = 1785194.543521, a = 693.545701, b = 152.682581\n", + "epoch 656: loss = 1784933.810344, a = 694.055364, b = 152.682255\n", + "epoch 657: loss = 1784674.116596, a = 694.564008, b = 152.681930\n", + "epoch 658: loss = 1784415.458129, a = 695.071635, b = 152.681606\n", + "epoch 659: loss = 1784157.830811, a = 695.578247, b = 152.681282\n", + "epoch 660: loss = 1783901.230528, a = 696.083846, b = 152.680960\n", + "epoch 661: loss = 1783645.653179, a = 696.588433, b = 152.680637\n", + "epoch 662: loss = 1783391.094684, a = 697.092012, b = 152.680316\n", + "epoch 663: loss = 1783137.550975, a = 697.594584, b = 152.679995\n", + "epoch 664: loss = 1782885.018003, a = 698.096150, b = 152.679674\n", + "epoch 665: loss = 1782633.491734, a = 698.596714, b = 152.679355\n", + "epoch 666: loss = 1782382.968150, a = 699.096277, b = 152.679036\n", + "epoch 667: loss = 1782133.443251, a = 699.594841, b = 152.678717\n", + "epoch 668: loss = 1781884.913049, a = 700.092408, b = 152.678399\n", + "epoch 669: loss = 1781637.373576, a = 700.588980, b = 152.678082\n", + "epoch 670: loss = 1781390.820878, a = 701.084559, b = 152.677766\n", + "epoch 671: loss = 1781145.251017, a = 701.579147, b = 152.677450\n", + "epoch 672: loss = 1780900.660070, a = 702.072746, b = 152.677134\n", + "epoch 673: loss = 1780657.044131, a = 702.565358, b = 152.676820\n", + "epoch 674: loss = 1780414.399308, a = 703.056984, b = 152.676506\n", + "epoch 675: loss = 1780172.721727, a = 703.547628, b = 152.676192\n", + "epoch 676: loss = 1779932.007527, a = 704.037291, b = 152.675880\n", + "epoch 677: loss = 1779692.252863, a = 704.525974, b = 152.675568\n", + "epoch 678: loss = 1779453.453906, a = 705.013681, b = 152.675256\n", + "epoch 679: loss = 1779215.606843, a = 705.500412, b = 152.674945\n", + "epoch 680: loss = 1778978.707874, a = 705.986169, b = 152.674635\n", + "epoch 681: loss = 1778742.753217, a = 706.470956, b = 152.674325\n", + "epoch 682: loss = 1778507.739101, a = 706.954773, b = 152.674016\n", + "epoch 683: loss = 1778273.661775, a = 707.437622, b = 152.673708\n", + "epoch 684: loss = 1778040.517499, a = 707.919506, b = 152.673400\n", + "epoch 685: loss = 1777808.302550, a = 708.400426, b = 152.673093\n", + "epoch 686: loss = 1777577.013219, a = 708.880385, b = 152.672786\n", + "epoch 687: loss = 1777346.645813, a = 709.359384, b = 152.672481\n", + "epoch 688: loss = 1777117.196652, a = 709.837425, b = 152.672175\n", + "epoch 689: loss = 1776888.662073, a = 710.314510, b = 152.671870\n", + "epoch 690: loss = 1776661.038424, a = 710.790641, b = 152.671566\n", + "epoch 691: loss = 1776434.322072, a = 711.265820, b = 152.671263\n", + "epoch 692: loss = 1776208.509395, a = 711.740049, b = 152.670960\n", + "epoch 693: loss = 1775983.596787, a = 712.213329, b = 152.670658\n", + "epoch 694: loss = 1775759.580657, a = 712.685664, b = 152.670356\n", + "epoch 695: loss = 1775536.457426, a = 713.157053, b = 152.670055\n", + "epoch 696: loss = 1775314.223532, a = 713.627500, b = 152.669754\n", + "epoch 697: loss = 1775092.875426, a = 714.097006, b = 152.669455\n", + "epoch 698: loss = 1774872.409572, a = 714.565574, b = 152.669155\n", + "epoch 699: loss = 1774652.822451, a = 715.033204, b = 152.668857\n", + "epoch 700: loss = 1774434.110556, a = 715.499899, b = 152.668559\n", + "epoch 701: loss = 1774216.270393, a = 715.965661, b = 152.668261\n", + "epoch 702: loss = 1773999.298484, a = 716.430492, b = 152.667964\n", + "epoch 703: loss = 1773783.191366, a = 716.894393, b = 152.667668\n", + "epoch 704: loss = 1773567.945585, a = 717.357367, b = 152.667372\n", + "epoch 705: loss = 1773353.557706, a = 717.819415, b = 152.667077\n", + "epoch 706: loss = 1773140.024305, a = 718.280538, b = 152.666783\n", + "epoch 707: loss = 1772927.341972, a = 718.740740, b = 152.666489\n", + "epoch 708: loss = 1772715.507311, a = 719.200021, b = 152.666195\n", + "epoch 709: loss = 1772504.516939, a = 719.658384, b = 152.665903\n", + "epoch 710: loss = 1772294.367487, a = 720.115831, b = 152.665610\n", + "epoch 711: loss = 1772085.055600, a = 720.572363, b = 152.665319\n", + "epoch 712: loss = 1771876.577936, a = 721.027981, b = 152.665028\n", + "epoch 713: loss = 1771668.931165, a = 721.482689, b = 152.664737\n", + "epoch 714: loss = 1771462.111971, a = 721.936488, b = 152.664448\n", + "epoch 715: loss = 1771256.117053, a = 722.389379, b = 152.664158\n", + "epoch 716: loss = 1771050.943122, a = 722.841364, b = 152.663870\n", + "epoch 717: loss = 1770846.586900, a = 723.292446, b = 152.663582\n", + "epoch 718: loss = 1770643.045126, a = 723.742625, b = 152.663294\n", + "epoch 719: loss = 1770440.314550, a = 724.191904, b = 152.663007\n", + "epoch 720: loss = 1770238.391933, a = 724.640285, b = 152.662721\n", + "epoch 721: loss = 1770037.274053, a = 725.087770, b = 152.662435\n", + "epoch 722: loss = 1769836.957698, a = 725.534359, b = 152.662150\n", + "epoch 723: loss = 1769637.439670, a = 725.980056, b = 152.661865\n", + "epoch 724: loss = 1769438.716783, a = 726.424861, b = 152.661581\n", + "epoch 725: loss = 1769240.785864, a = 726.868776, b = 152.661297\n", + "epoch 726: loss = 1769043.643754, a = 727.311805, b = 152.661014\n", + "epoch 727: loss = 1768847.287304, a = 727.753947, b = 152.660732\n", + "epoch 728: loss = 1768651.713380, a = 728.195205, b = 152.660450\n", + "epoch 729: loss = 1768456.918858, a = 728.635580, b = 152.660169\n", + "epoch 730: loss = 1768262.900630, a = 729.075075, b = 152.659888\n", + "epoch 731: loss = 1768069.655598, a = 729.513692, b = 152.659608\n", + "epoch 732: loss = 1767877.180675, a = 729.951431, b = 152.659328\n", + "epoch 733: loss = 1767685.472790, a = 730.388294, b = 152.659049\n", + "epoch 734: loss = 1767494.528882, a = 730.824285, b = 152.658771\n", + "epoch 735: loss = 1767304.345902, a = 731.259403, b = 152.658493\n", + "epoch 736: loss = 1767114.920814, a = 731.693651, b = 152.658216\n", + "epoch 737: loss = 1766926.250593, a = 732.127031, b = 152.657939\n", + "epoch 738: loss = 1766738.332229, a = 732.559544, b = 152.657663\n", + "epoch 739: loss = 1766551.162720, a = 732.991193, b = 152.657387\n", + "epoch 740: loss = 1766364.739080, a = 733.421978, b = 152.657112\n", + "epoch 741: loss = 1766179.058330, a = 733.851902, b = 152.656837\n", + "epoch 742: loss = 1765994.117509, a = 734.280966, b = 152.656563\n", + "epoch 743: loss = 1765809.913662, a = 734.709172, b = 152.656290\n", + "epoch 744: loss = 1765626.443849, a = 735.136522, b = 152.656017\n", + "epoch 745: loss = 1765443.705143, a = 735.563017, b = 152.655744\n", + "epoch 746: loss = 1765261.694624, a = 735.988660, b = 152.655472\n", + "epoch 747: loss = 1765080.409388, a = 736.413451, b = 152.655201\n", + "epoch 748: loss = 1764899.846541, a = 736.837393, b = 152.654930\n", + "epoch 749: loss = 1764720.003201, a = 737.260487, b = 152.654660\n", + "epoch 750: loss = 1764540.876496, a = 737.682735, b = 152.654390\n", + "epoch 751: loss = 1764362.463568, a = 738.104139, b = 152.654121\n", + "epoch 752: loss = 1764184.761567, a = 738.524700, b = 152.653853\n", + "epoch 753: loss = 1764007.767659, a = 738.944420, b = 152.653585\n", + "epoch 754: loss = 1763831.479016, a = 739.363301, b = 152.653317\n", + "epoch 755: loss = 1763655.892826, a = 739.781344, b = 152.653050\n", + "epoch 756: loss = 1763481.006286, a = 740.198551, b = 152.652784\n", + "epoch 757: loss = 1763306.816603, a = 740.614924, b = 152.652518\n", + "epoch 758: loss = 1763133.320998, a = 741.030464, b = 152.652252\n", + "epoch 759: loss = 1762960.516702, a = 741.445174, b = 152.651987\n", + "epoch 760: loss = 1762788.400955, a = 741.859054, b = 152.651723\n", + "epoch 761: loss = 1762616.971010, a = 742.272106, b = 152.651459\n", + "epoch 762: loss = 1762446.224132, a = 742.684333, b = 152.651196\n", + "epoch 763: loss = 1762276.157595, a = 743.095735, b = 152.650933\n", + "epoch 764: loss = 1762106.768685, a = 743.506314, b = 152.650671\n", + "epoch 765: loss = 1761938.054697, a = 743.916073, b = 152.650409\n", + "epoch 766: loss = 1761770.012940, a = 744.325012, b = 152.650148\n", + "epoch 767: loss = 1761602.640730, a = 744.733133, b = 152.649887\n", + "epoch 768: loss = 1761435.935396, a = 745.140439, b = 152.649627\n", + "epoch 769: loss = 1761269.894279, a = 745.546929, b = 152.649368\n", + "epoch 770: loss = 1761104.514727, a = 745.952607, b = 152.649109\n", + "epoch 771: loss = 1760939.794101, a = 746.357474, b = 152.648850\n", + "epoch 772: loss = 1760775.729772, a = 746.761531, b = 152.648592\n", + "epoch 773: loss = 1760612.319122, a = 747.164780, b = 152.648334\n", + "epoch 774: loss = 1760449.559542, a = 747.567223, b = 152.648077\n", + "epoch 775: loss = 1760287.448435, a = 747.968861, b = 152.647821\n", + "epoch 776: loss = 1760125.983214, a = 748.369696, b = 152.647565\n", + "epoch 777: loss = 1759965.161302, a = 748.769729, b = 152.647309\n", + "epoch 778: loss = 1759804.980131, a = 749.168963, b = 152.647054\n", + "epoch 779: loss = 1759645.437147, a = 749.567398, b = 152.646800\n", + "epoch 780: loss = 1759486.529802, a = 749.965036, b = 152.646546\n", + "epoch 781: loss = 1759328.255560, a = 750.361879, b = 152.646292\n", + "epoch 782: loss = 1759170.611896, a = 750.757929, b = 152.646039\n", + "epoch 783: loss = 1759013.596294, a = 751.153187, b = 152.645787\n", + "epoch 784: loss = 1758857.206248, a = 751.547654, b = 152.645535\n", + "epoch 785: loss = 1758701.439262, a = 751.941332, b = 152.645284\n", + "epoch 786: loss = 1758546.292851, a = 752.334224, b = 152.645033\n", + "epoch 787: loss = 1758391.764538, a = 752.726329, b = 152.644782\n", + "epoch 788: loss = 1758237.851858, a = 753.117651, b = 152.644532\n", + "epoch 789: loss = 1758084.552355, a = 753.508190, b = 152.644283\n", + "epoch 790: loss = 1757931.863581, a = 753.897948, b = 152.644034\n", + "epoch 791: loss = 1757779.783102, a = 754.286926, b = 152.643785\n", + "epoch 792: loss = 1757628.308489, a = 754.675127, b = 152.643538\n", + "epoch 793: loss = 1757477.437325, a = 755.062552, b = 152.643290\n", + "epoch 794: loss = 1757327.167204, a = 755.449202, b = 152.643043\n", + "epoch 795: loss = 1757177.495727, a = 755.835078, b = 152.642797\n", + "epoch 796: loss = 1757028.420505, a = 756.220183, b = 152.642551\n", + "epoch 797: loss = 1756879.939161, a = 756.604518, b = 152.642305\n", + "epoch 798: loss = 1756732.049325, a = 756.988085, b = 152.642060\n", + "epoch 799: loss = 1756584.748637, a = 757.370884, b = 152.641816\n", + "epoch 800: loss = 1756438.034746, a = 757.752918, b = 152.641572\n", + "epoch 801: loss = 1756291.905312, a = 758.134188, b = 152.641328\n", + "epoch 802: loss = 1756146.358003, a = 758.514696, b = 152.641085\n", + "epoch 803: loss = 1756001.390496, a = 758.894442, b = 152.640843\n", + "epoch 804: loss = 1755857.000479, a = 759.273430, b = 152.640601\n", + "epoch 805: loss = 1755713.185647, a = 759.651659, b = 152.640359\n", + "epoch 806: loss = 1755569.943706, a = 760.029132, b = 152.640118\n", + "epoch 807: loss = 1755427.272371, a = 760.405851, b = 152.639877\n", + "epoch 808: loss = 1755285.169365, a = 760.781816, b = 152.639637\n", + "epoch 809: loss = 1755143.632421, a = 761.157029, b = 152.639398\n", + "epoch 810: loss = 1755002.659280, a = 761.531492, b = 152.639158\n", + "epoch 811: loss = 1754862.247693, a = 761.905206, b = 152.638920\n", + "epoch 812: loss = 1754722.395421, a = 762.278173, b = 152.638681\n", + "epoch 813: loss = 1754583.100231, a = 762.650394, b = 152.638444\n", + "epoch 814: loss = 1754444.359902, a = 763.021870, b = 152.638206\n", + "epoch 815: loss = 1754306.172220, a = 763.392604, b = 152.637970\n", + "epoch 816: loss = 1754168.534980, a = 763.762597, b = 152.637733\n", + "epoch 817: loss = 1754031.445986, a = 764.131849, b = 152.637498\n", + "epoch 818: loss = 1753894.903051, a = 764.500363, b = 152.637262\n", + "epoch 819: loss = 1753758.903998, a = 764.868141, b = 152.637027\n", + "epoch 820: loss = 1753623.446655, a = 765.235183, b = 152.636793\n", + "epoch 821: loss = 1753488.528863, a = 765.601491, b = 152.636559\n", + "epoch 822: loss = 1753354.148468, a = 765.967066, b = 152.636325\n", + "epoch 823: loss = 1753220.303328, a = 766.331911, b = 152.636092\n", + "epoch 824: loss = 1753086.991306, a = 766.696025, b = 152.635860\n", + "epoch 825: loss = 1752954.210276, a = 767.059412, b = 152.635628\n", + "epoch 826: loss = 1752821.958120, a = 767.422072, b = 152.635396\n", + "epoch 827: loss = 1752690.232728, a = 767.784007, b = 152.635165\n", + "epoch 828: loss = 1752559.031999, a = 768.145218, b = 152.634934\n", + "epoch 829: loss = 1752428.353839, a = 768.505707, b = 152.634704\n", + "epoch 830: loss = 1752298.196165, a = 768.865475, b = 152.634474\n", + "epoch 831: loss = 1752168.556899, a = 769.224524, b = 152.634245\n", + "epoch 832: loss = 1752039.433974, a = 769.582854, b = 152.634016\n", + "epoch 833: loss = 1751910.825331, a = 769.940468, b = 152.633788\n", + "epoch 834: loss = 1751782.728917, a = 770.297367, b = 152.633560\n", + "epoch 835: loss = 1751655.142689, a = 770.653552, b = 152.633332\n", + "epoch 836: loss = 1751528.064612, a = 771.009025, b = 152.633105\n", + "epoch 837: loss = 1751401.492660, a = 771.363788, b = 152.632879\n", + "epoch 838: loss = 1751275.424813, a = 771.717840, b = 152.632652\n", + "epoch 839: loss = 1751149.859059, a = 772.071185, b = 152.632427\n", + "epoch 840: loss = 1751024.793398, a = 772.423823, b = 152.632201\n", + "epoch 841: loss = 1750900.225833, a = 772.775756, b = 152.631977\n", + "epoch 842: loss = 1750776.154377, a = 773.126986, b = 152.631752\n", + "epoch 843: loss = 1750652.577052, a = 773.477513, b = 152.631529\n", + "epoch 844: loss = 1750529.491887, a = 773.827339, b = 152.631305\n", + "epoch 845: loss = 1750406.896918, a = 774.176465, b = 152.631082\n", + "epoch 846: loss = 1750284.790190, a = 774.524893, b = 152.630860\n", + "epoch 847: loss = 1750163.169756, a = 774.872625, b = 152.630637\n", + "epoch 848: loss = 1750042.033675, a = 775.219661, b = 152.630416\n", + "epoch 849: loss = 1749921.380017, a = 775.566004, b = 152.630195\n", + "epoch 850: loss = 1749801.206855, a = 775.911653, b = 152.629974\n", + "epoch 851: loss = 1749681.512274, a = 776.256612, b = 152.629754\n", + "epoch 852: loss = 1749562.294365, a = 776.600881, b = 152.629534\n", + "epoch 853: loss = 1749443.551226, a = 776.944461, b = 152.629314\n", + "epoch 854: loss = 1749325.280964, a = 777.287354, b = 152.629095\n", + "epoch 855: loss = 1749207.481692, a = 777.629562, b = 152.628877\n", + "epoch 856: loss = 1749090.151532, a = 777.971085, b = 152.628658\n", + "epoch 857: loss = 1748973.288613, a = 778.311925, b = 152.628441\n", + "epoch 858: loss = 1748856.891070, a = 778.652084, b = 152.628224\n", + "epoch 859: loss = 1748740.957047, a = 778.991563, b = 152.628007\n", + "epoch 860: loss = 1748625.484697, a = 779.330363, b = 152.627790\n", + "epoch 861: loss = 1748510.472176, a = 779.668485, b = 152.627574\n", + "epoch 862: loss = 1748395.917651, a = 780.005931, b = 152.627359\n", + "epoch 863: loss = 1748281.819296, a = 780.342702, b = 152.627144\n", + "epoch 864: loss = 1748168.175290, a = 780.678800, b = 152.626929\n", + "epoch 865: loss = 1748054.983822, a = 781.014226, b = 152.626715\n", + "epoch 866: loss = 1747942.243086, a = 781.348981, b = 152.626501\n", + "epoch 867: loss = 1747829.951284, a = 781.683067, b = 152.626288\n", + "epoch 868: loss = 1747718.106627, a = 782.016484, b = 152.626075\n", + "epoch 869: loss = 1747606.707330, a = 782.349235, b = 152.625862\n", + "epoch 870: loss = 1747495.751617, a = 782.681321, b = 152.625650\n", + "epoch 871: loss = 1747385.237718, a = 783.012742, b = 152.625438\n", + "epoch 872: loss = 1747275.163873, a = 783.343501, b = 152.625227\n", + "epoch 873: loss = 1747165.528325, a = 783.673598, b = 152.625016\n", + "epoch 874: loss = 1747056.329325, a = 784.003035, b = 152.624806\n", + "epoch 875: loss = 1746947.565134, a = 784.331814, b = 152.624596\n", + "epoch 876: loss = 1746839.234017, a = 784.659935, b = 152.624386\n", + "epoch 877: loss = 1746731.334246, a = 784.987400, b = 152.624177\n", + "epoch 878: loss = 1746623.864101, a = 785.314210, b = 152.623968\n", + "epoch 879: loss = 1746516.821869, a = 785.640366, b = 152.623760\n", + "epoch 880: loss = 1746410.205842, a = 785.965871, b = 152.623552\n", + "epoch 881: loss = 1746304.014321, a = 786.290724, b = 152.623345\n", + "epoch 882: loss = 1746198.245613, a = 786.614928, b = 152.623138\n", + "epoch 883: loss = 1746092.898031, a = 786.938483, b = 152.622931\n", + "epoch 884: loss = 1745987.969896, a = 787.261392, b = 152.622725\n", + "epoch 885: loss = 1745883.459534, a = 787.583655, b = 152.622519\n", + "epoch 886: loss = 1745779.365280, a = 787.905273, b = 152.622314\n", + "epoch 887: loss = 1745675.685474, a = 788.226248, b = 152.622109\n", + "epoch 888: loss = 1745572.418463, a = 788.546582, b = 152.621904\n", + "epoch 889: loss = 1745469.562600, a = 788.866275, b = 152.621700\n", + "epoch 890: loss = 1745367.116247, a = 789.185328, b = 152.621496\n", + "epoch 891: loss = 1745265.077768, a = 789.503744, b = 152.621293\n", + "epoch 892: loss = 1745163.445539, a = 789.821523, b = 152.621090\n", + "epoch 893: loss = 1745062.217938, a = 790.138666, b = 152.620887\n", + "epoch 894: loss = 1744961.393352, a = 790.455175, b = 152.620685\n", + "epoch 895: loss = 1744860.970173, a = 790.771052, b = 152.620483\n", + "epoch 896: loss = 1744760.946801, a = 791.086296, b = 152.620282\n", + "epoch 897: loss = 1744661.321640, a = 791.400911, b = 152.620081\n", + "epoch 898: loss = 1744562.093104, a = 791.714896, b = 152.619880\n", + "epoch 899: loss = 1744463.259609, a = 792.028253, b = 152.619680\n", + "epoch 900: loss = 1744364.819581, a = 792.340984, b = 152.619480\n", + "epoch 901: loss = 1744266.771451, a = 792.653089, b = 152.619281\n", + "epoch 902: loss = 1744169.113654, a = 792.964570, b = 152.619082\n", + "epoch 903: loss = 1744071.844635, a = 793.275429, b = 152.618884\n", + "epoch 904: loss = 1743974.962844, a = 793.585665, b = 152.618685\n", + "epoch 905: loss = 1743878.466735, a = 793.895282, b = 152.618488\n", + "epoch 906: loss = 1743782.354770, a = 794.204279, b = 152.618290\n", + "epoch 907: loss = 1743686.625419, a = 794.512658, b = 152.618093\n", + "epoch 908: loss = 1743591.277154, a = 794.820421, b = 152.617897\n", + "epoch 909: loss = 1743496.308456, a = 795.127568, b = 152.617701\n", + "epoch 910: loss = 1743401.717811, a = 795.434101, b = 152.617505\n", + "epoch 911: loss = 1743307.503711, a = 795.740021, b = 152.617310\n", + "epoch 912: loss = 1743213.664656, a = 796.045330, b = 152.617115\n", + "epoch 913: loss = 1743120.199149, a = 796.350028, b = 152.616920\n", + "epoch 914: loss = 1743027.105700, a = 796.654116, b = 152.616726\n", + "epoch 915: loss = 1742934.382825, a = 796.957597, b = 152.616532\n", + "epoch 916: loss = 1742842.029048, a = 797.260470, b = 152.616338\n", + "epoch 917: loss = 1742750.042895, a = 797.562738, b = 152.616145\n", + "epoch 918: loss = 1742658.422902, a = 797.864402, b = 152.615953\n", + "epoch 919: loss = 1742567.167606, a = 798.165462, b = 152.615760\n", + "epoch 920: loss = 1742476.275556, a = 798.465920, b = 152.615568\n", + "epoch 921: loss = 1742385.745300, a = 798.765778, b = 152.615377\n", + "epoch 922: loss = 1742295.575398, a = 799.065036, b = 152.615186\n", + "epoch 923: loss = 1742205.764411, a = 799.363695, b = 152.614995\n", + "epoch 924: loss = 1742116.310909, a = 799.661757, b = 152.614805\n", + "epoch 925: loss = 1742027.213466, a = 799.959223, b = 152.614615\n", + "epoch 926: loss = 1741938.470661, a = 800.256095, b = 152.614425\n", + "epoch 927: loss = 1741850.081082, a = 800.552373, b = 152.614236\n", + "epoch 928: loss = 1741762.043319, a = 800.848058, b = 152.614047\n", + "epoch 929: loss = 1741674.355969, a = 801.143152, b = 152.613859\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 930: loss = 1741587.017634, a = 801.437656, b = 152.613670\n", + "epoch 931: loss = 1741500.026924, a = 801.731571, b = 152.613483\n", + "epoch 932: loss = 1741413.382452, a = 802.024898, b = 152.613295\n", + "epoch 933: loss = 1741327.082837, a = 802.317639, b = 152.613108\n", + "epoch 934: loss = 1741241.126703, a = 802.609794, b = 152.612922\n", + "epoch 935: loss = 1741155.512682, a = 802.901365, b = 152.612736\n", + "epoch 936: loss = 1741070.239409, a = 803.192353, b = 152.612550\n", + "epoch 937: loss = 1740985.305526, a = 803.482759, b = 152.612364\n", + "epoch 938: loss = 1740900.709678, a = 803.772585, b = 152.612179\n", + "epoch 939: loss = 1740816.450518, a = 804.061831, b = 152.611994\n", + "epoch 940: loss = 1740732.526703, a = 804.350498, b = 152.611810\n", + "epoch 941: loss = 1740648.936897, a = 804.638588, b = 152.611626\n", + "epoch 942: loss = 1740565.679766, a = 804.926103, b = 152.611442\n", + "epoch 943: loss = 1740482.753986, a = 805.213042, b = 152.611259\n", + "epoch 944: loss = 1740400.158233, a = 805.499407, b = 152.611076\n", + "epoch 945: loss = 1740317.891193, a = 805.785200, b = 152.610894\n", + "epoch 946: loss = 1740235.951555, a = 806.070421, b = 152.610712\n", + "epoch 947: loss = 1740154.338013, a = 806.355072, b = 152.610530\n", + "epoch 948: loss = 1740073.049267, a = 806.639154, b = 152.610348\n", + "epoch 949: loss = 1739992.084021, a = 806.922668, b = 152.610167\n", + "epoch 950: loss = 1739911.440986, a = 807.205615, b = 152.609986\n", + "epoch 951: loss = 1739831.118877, a = 807.487996, b = 152.609806\n", + "epoch 952: loss = 1739751.116414, a = 807.769812, b = 152.609626\n", + "epoch 953: loss = 1739671.432323, a = 808.051065, b = 152.609446\n", + "epoch 954: loss = 1739592.065335, a = 808.331755, b = 152.609267\n", + "epoch 955: loss = 1739513.014184, a = 808.611884, b = 152.609088\n", + "epoch 956: loss = 1739434.277612, a = 808.891453, b = 152.608910\n", + "epoch 957: loss = 1739355.854363, a = 809.170463, b = 152.608732\n", + "epoch 958: loss = 1739277.743190, a = 809.448915, b = 152.608554\n", + "epoch 959: loss = 1739199.942847, a = 809.726810, b = 152.608376\n", + "epoch 960: loss = 1739122.452096, a = 810.004149, b = 152.608199\n", + "epoch 961: loss = 1739045.269701, a = 810.280934, b = 152.608022\n", + "epoch 962: loss = 1738968.394433, a = 810.557165, b = 152.607846\n", + "epoch 963: loss = 1738891.825068, a = 810.832844, b = 152.607670\n", + "epoch 964: loss = 1738815.560385, a = 811.107972, b = 152.607494\n", + "epoch 965: loss = 1738739.599171, a = 811.382550, b = 152.607319\n", + "epoch 966: loss = 1738663.940215, a = 811.656578, b = 152.607144\n", + "epoch 967: loss = 1738588.582311, a = 811.930059, b = 152.606969\n", + "epoch 968: loss = 1738513.524261, a = 812.202992, b = 152.606795\n", + "epoch 969: loss = 1738438.764867, a = 812.475380, b = 152.606621\n", + "epoch 970: loss = 1738364.302940, a = 812.747223, b = 152.606447\n", + "epoch 971: loss = 1738290.137292, a = 813.018523, b = 152.606274\n", + "epoch 972: loss = 1738216.266744, a = 813.289280, b = 152.606101\n", + "epoch 973: loss = 1738142.690118, a = 813.559496, b = 152.605928\n", + "epoch 974: loss = 1738069.406242, a = 813.829171, b = 152.605756\n", + "epoch 975: loss = 1737996.413950, a = 814.098307, b = 152.605584\n", + "epoch 976: loss = 1737923.712078, a = 814.366905, b = 152.605413\n", + "epoch 977: loss = 1737851.299469, a = 814.634966, b = 152.605241\n", + "epoch 978: loss = 1737779.174969, a = 814.902490, b = 152.605071\n", + "epoch 979: loss = 1737707.337430, a = 815.169480, b = 152.604900\n", + "epoch 980: loss = 1737635.785708, a = 815.435936, b = 152.604730\n", + "epoch 981: loss = 1737564.518663, a = 815.701859, b = 152.604560\n", + "epoch 982: loss = 1737493.535160, a = 815.967250, b = 152.604390\n", + "epoch 983: loss = 1737422.834069, a = 816.232111, b = 152.604221\n", + "epoch 984: loss = 1737352.414263, a = 816.496442, b = 152.604052\n", + "epoch 985: loss = 1737282.274622, a = 816.760244, b = 152.603884\n", + "epoch 986: loss = 1737212.414028, a = 817.023519, b = 152.603716\n", + "epoch 987: loss = 1737142.831368, a = 817.286268, b = 152.603548\n", + "epoch 988: loss = 1737073.525536, a = 817.548491, b = 152.603381\n", + "epoch 989: loss = 1737004.495426, a = 817.810189, b = 152.603213\n", + "epoch 990: loss = 1736935.739941, a = 818.071365, b = 152.603047\n", + "epoch 991: loss = 1736867.257984, a = 818.332018, b = 152.602880\n", + "epoch 992: loss = 1736799.048465, a = 818.592150, b = 152.602714\n", + "epoch 993: loss = 1736731.110299, a = 818.851761, b = 152.602548\n", + "epoch 994: loss = 1736663.442403, a = 819.110854, b = 152.602383\n", + "epoch 995: loss = 1736596.043701, a = 819.369428, b = 152.602218\n", + "epoch 996: loss = 1736528.913118, a = 819.627486, b = 152.602053\n", + "epoch 997: loss = 1736462.049586, a = 819.885027, b = 152.601888\n", + "epoch 998: loss = 1736395.452040, a = 820.142053, b = 152.601724\n", + "epoch 999: loss = 1736329.119420, a = 820.398566, b = 152.601560\n", + "epoch 1000: loss = 1736263.050670, a = 820.654565, b = 152.601397\n", + "epoch 1001: loss = 1736197.244737, a = 820.910053, b = 152.601234\n", + "epoch 1002: loss = 1736131.700574, a = 821.165029, b = 152.601071\n", + "epoch 1003: loss = 1736066.417138, a = 821.419496, b = 152.600908\n", + "epoch 1004: loss = 1736001.393389, a = 821.673454, b = 152.600746\n", + "epoch 1005: loss = 1735936.628292, a = 821.926904, b = 152.600584\n", + "epoch 1006: loss = 1735872.120815, a = 822.179847, b = 152.600422\n", + "epoch 1007: loss = 1735807.869932, a = 822.432285, b = 152.600261\n", + "epoch 1008: loss = 1735743.874619, a = 822.684217, b = 152.600100\n", + "epoch 1009: loss = 1735680.133859, a = 822.935646, b = 152.599940\n", + "epoch 1010: loss = 1735616.646635, a = 823.186572, b = 152.599780\n", + "epoch 1011: loss = 1735553.411939, a = 823.436997, b = 152.599620\n", + "epoch 1012: loss = 1735490.428761, a = 823.686920, b = 152.599460\n", + "epoch 1013: loss = 1735427.696101, a = 823.936344, b = 152.599301\n", + "epoch 1014: loss = 1735365.212960, a = 824.185269, b = 152.599142\n", + "epoch 1015: loss = 1735302.978342, a = 824.433697, b = 152.598983\n", + "epoch 1016: loss = 1735240.991257, a = 824.681627, b = 152.598825\n", + "epoch 1017: loss = 1735179.250719, a = 824.929062, b = 152.598667\n", + "epoch 1018: loss = 1735117.755744, a = 825.176002, b = 152.598509\n", + "epoch 1019: loss = 1735056.505353, a = 825.422448, b = 152.598351\n", + "epoch 1020: loss = 1734995.498573, a = 825.668401, b = 152.598194\n", + "epoch 1021: loss = 1734934.734430, a = 825.913863, b = 152.598038\n", + "epoch 1022: loss = 1734874.211960, a = 826.158833, b = 152.597881\n", + "epoch 1023: loss = 1734813.930197, a = 826.403314, b = 152.597725\n", + "epoch 1024: loss = 1734753.888182, a = 826.647306, b = 152.597569\n", + "epoch 1025: loss = 1734694.084961, a = 826.890810, b = 152.597414\n", + "epoch 1026: loss = 1734634.519580, a = 827.133827, b = 152.597258\n", + "epoch 1027: loss = 1734575.191092, a = 827.376358, b = 152.597104\n", + "epoch 1028: loss = 1734516.098553, a = 827.618405, b = 152.596949\n", + "epoch 1029: loss = 1734457.241022, a = 827.859967, b = 152.596795\n", + "epoch 1030: loss = 1734398.617562, a = 828.101046, b = 152.596641\n", + "epoch 1031: loss = 1734340.227240, a = 828.341643, b = 152.596487\n", + "epoch 1032: loss = 1734282.069127, a = 828.581759, b = 152.596334\n", + "epoch 1033: loss = 1734224.142298, a = 828.821394, b = 152.596181\n", + "epoch 1034: loss = 1734166.445830, a = 829.060551, b = 152.596028\n", + "epoch 1035: loss = 1734108.978805, a = 829.299229, b = 152.595875\n", + "epoch 1036: loss = 1734051.740309, a = 829.537430, b = 152.595723\n", + "epoch 1037: loss = 1733994.729431, a = 829.775155, b = 152.595571\n", + "epoch 1038: loss = 1733937.945263, a = 830.012405, b = 152.595420\n", + "epoch 1039: loss = 1733881.386903, a = 830.249179, b = 152.595269\n", + "epoch 1040: loss = 1733825.053449, a = 830.485481, b = 152.595118\n", + "epoch 1041: loss = 1733768.944005, a = 830.721310, b = 152.594967\n", + "epoch 1042: loss = 1733713.057679, a = 830.956667, b = 152.594817\n", + "epoch 1043: loss = 1733657.393581, a = 831.191554, b = 152.594667\n", + "epoch 1044: loss = 1733601.950825, a = 831.425971, b = 152.594517\n", + "epoch 1045: loss = 1733546.728530, a = 831.659919, b = 152.594368\n", + "epoch 1046: loss = 1733491.725816, a = 831.893400, b = 152.594218\n", + "epoch 1047: loss = 1733436.941808, a = 832.126413, b = 152.594070\n", + "epoch 1048: loss = 1733382.375635, a = 832.358961, b = 152.593921\n", + "epoch 1049: loss = 1733328.026428, a = 832.591044, b = 152.593773\n", + "epoch 1050: loss = 1733273.893322, a = 832.822662, b = 152.593625\n", + "epoch 1051: loss = 1733219.975457, a = 833.053817, b = 152.593477\n", + "epoch 1052: loss = 1733166.271975, a = 833.284511, b = 152.593330\n", + "epoch 1053: loss = 1733112.782020, a = 833.514742, b = 152.593183\n", + "epoch 1054: loss = 1733059.504742, a = 833.744514, b = 152.593036\n", + "epoch 1055: loss = 1733006.439293, a = 833.973826, b = 152.592890\n", + "epoch 1056: loss = 1732953.584830, a = 834.202679, b = 152.592744\n", + "epoch 1057: loss = 1732900.940510, a = 834.431075, b = 152.592598\n", + "epoch 1058: loss = 1732848.505497, a = 834.659014, b = 152.592452\n", + "epoch 1059: loss = 1732796.278956, a = 834.886497, b = 152.592307\n", + "epoch 1060: loss = 1732744.260056, a = 835.113526, b = 152.592162\n", + "epoch 1061: loss = 1732692.447971, a = 835.340100, b = 152.592017\n", + "epoch 1062: loss = 1732640.841875, a = 835.566221, b = 152.591873\n", + "epoch 1063: loss = 1732589.440947, a = 835.791890, b = 152.591729\n", + "epoch 1064: loss = 1732538.244371, a = 836.017108, b = 152.591585\n", + "epoch 1065: loss = 1732487.251331, a = 836.241876, b = 152.591441\n", + "epoch 1066: loss = 1732436.461017, a = 836.466194, b = 152.591298\n", + "epoch 1067: loss = 1732385.872620, a = 836.690063, b = 152.591155\n", + "epoch 1068: loss = 1732335.485336, a = 836.913485, b = 152.591012\n", + "epoch 1069: loss = 1732285.298362, a = 837.136460, b = 152.590870\n", + "epoch 1070: loss = 1732235.310902, a = 837.358990, b = 152.590728\n", + "epoch 1071: loss = 1732185.522160, a = 837.581074, b = 152.590586\n", + "epoch 1072: loss = 1732135.931343, a = 837.802714, b = 152.590444\n", + "epoch 1073: loss = 1732086.537664, a = 838.023911, b = 152.590303\n", + "epoch 1074: loss = 1732037.340336, a = 838.244665, b = 152.590162\n", + "epoch 1075: loss = 1731988.338577, a = 838.464978, b = 152.590021\n", + "epoch 1076: loss = 1731939.531607, a = 838.684851, b = 152.589881\n", + "epoch 1077: loss = 1731890.918651, a = 838.904284, b = 152.589741\n", + "epoch 1078: loss = 1731842.498935, a = 839.123278, b = 152.589601\n", + "epoch 1079: loss = 1731794.271688, a = 839.341834, b = 152.589461\n", + "epoch 1080: loss = 1731746.236145, a = 839.559954, b = 152.589322\n", + "epoch 1081: loss = 1731698.391541, a = 839.777637, b = 152.589183\n", + "epoch 1082: loss = 1731650.737115, a = 839.994884, b = 152.589044\n", + "epoch 1083: loss = 1731603.272109, a = 840.211698, b = 152.588906\n", + "epoch 1084: loss = 1731555.995768, a = 840.428077, b = 152.588767\n", + "epoch 1085: loss = 1731508.907341, a = 840.644024, b = 152.588629\n", + "epoch 1086: loss = 1731462.006079, a = 840.859540, b = 152.588492\n", + "epoch 1087: loss = 1731415.291236, a = 841.074624, b = 152.588354\n", + "epoch 1088: loss = 1731368.762068, a = 841.289278, b = 152.588217\n", + "epoch 1089: loss = 1731322.417837, a = 841.503503, b = 152.588081\n", + "epoch 1090: loss = 1731276.257805, a = 841.717299, b = 152.587944\n", + "epoch 1091: loss = 1731230.281238, a = 841.930668, b = 152.587808\n", + "epoch 1092: loss = 1731184.487405, a = 842.143611, b = 152.587672\n", + "epoch 1093: loss = 1731138.875577, a = 842.356127, b = 152.587536\n", + "epoch 1094: loss = 1731093.445031, a = 842.568219, b = 152.587401\n", + "epoch 1095: loss = 1731048.195043, a = 842.779886, b = 152.587265\n", + "epoch 1096: loss = 1731003.124893, a = 842.991131, b = 152.587130\n", + "epoch 1097: loss = 1730958.233866, a = 843.201952, b = 152.586996\n", + "epoch 1098: loss = 1730913.521247, a = 843.412353, b = 152.586861\n", + "epoch 1099: loss = 1730868.986325, a = 843.622332, b = 152.586727\n", + "epoch 1100: loss = 1730824.628393, a = 843.831892, b = 152.586593\n", + "epoch 1101: loss = 1730780.446745, a = 844.041032, b = 152.586460\n", + "epoch 1102: loss = 1730736.440678, a = 844.249755, b = 152.586327\n", + "epoch 1103: loss = 1730692.609494, a = 844.458059, b = 152.586193\n", + "epoch 1104: loss = 1730648.952495, a = 844.665948, b = 152.586061\n", + "epoch 1105: loss = 1730605.468987, a = 844.873421, b = 152.585928\n", + "epoch 1106: loss = 1730562.158279, a = 845.080478, b = 152.585796\n", + "epoch 1107: loss = 1730519.019683, a = 845.287122, b = 152.585664\n", + "epoch 1108: loss = 1730476.052512, a = 845.493353, b = 152.585532\n", + "epoch 1109: loss = 1730433.256083, a = 845.699171, b = 152.585401\n", + "epoch 1110: loss = 1730390.629717, a = 845.904578, b = 152.585270\n", + "epoch 1111: loss = 1730348.172735, a = 846.109573, b = 152.585139\n", + "epoch 1112: loss = 1730305.884463, a = 846.314159, b = 152.585008\n", + "epoch 1113: loss = 1730263.764229, a = 846.518336, b = 152.584878\n", + "epoch 1114: loss = 1730221.811362, a = 846.722105, b = 152.584747\n", + "epoch 1115: loss = 1730180.025197, a = 846.925466, b = 152.584618\n", + "epoch 1116: loss = 1730138.405068, a = 847.128420, b = 152.584488\n", + "epoch 1117: loss = 1730096.950315, a = 847.330969, b = 152.584359\n", + "epoch 1118: loss = 1730055.660278, a = 847.533112, b = 152.584229\n", + "epoch 1119: loss = 1730014.534302, a = 847.734852, b = 152.584101\n", + "epoch 1120: loss = 1729973.571731, a = 847.936188, b = 152.583972\n", + "epoch 1121: loss = 1729932.771917, a = 848.137121, b = 152.583844\n", + "epoch 1122: loss = 1729892.134209, a = 848.337652, b = 152.583716\n", + "epoch 1123: loss = 1729851.657962, a = 848.537783, b = 152.583588\n", + "epoch 1124: loss = 1729811.342534, a = 848.737513, b = 152.583460\n", + "epoch 1125: loss = 1729771.187282, a = 848.936844, b = 152.583333\n", + "epoch 1126: loss = 1729731.191569, a = 849.135776, b = 152.583206\n", + "epoch 1127: loss = 1729691.354760, a = 849.334311, b = 152.583079\n", + "epoch 1128: loss = 1729651.676220, a = 849.532449, b = 152.582952\n", + "epoch 1129: loss = 1729612.155321, a = 849.730190, b = 152.582826\n", + "epoch 1130: loss = 1729572.791433, a = 849.927536, b = 152.582700\n", + "epoch 1131: loss = 1729533.583931, a = 850.124487, b = 152.582574\n", + "epoch 1132: loss = 1729494.532192, a = 850.321044, b = 152.582449\n", + "epoch 1133: loss = 1729455.635596, a = 850.517209, b = 152.582324\n", + "epoch 1134: loss = 1729416.893524, a = 850.712981, b = 152.582198\n", + "epoch 1135: loss = 1729378.305361, a = 850.908362, b = 152.582074\n", + "epoch 1136: loss = 1729339.870493, a = 851.103352, b = 152.581949\n", + "epoch 1137: loss = 1729301.588311, a = 851.297952, b = 152.581825\n", + "epoch 1138: loss = 1729263.458205, a = 851.492162, b = 152.581701\n", + "epoch 1139: loss = 1729225.479569, a = 851.685985, b = 152.581577\n", + "epoch 1140: loss = 1729187.651801, a = 851.879420, b = 152.581453\n", + "epoch 1141: loss = 1729149.974300, a = 852.072468, b = 152.581330\n", + "epoch 1142: loss = 1729112.446466, a = 852.265130, b = 152.581207\n", + "epoch 1143: loss = 1729075.067703, a = 852.457407, b = 152.581084\n", + "epoch 1144: loss = 1729037.837419, a = 852.649299, b = 152.580962\n", + "epoch 1145: loss = 1729000.755020, a = 852.840808, b = 152.580839\n", + "epoch 1146: loss = 1728963.819918, a = 853.031934, b = 152.580717\n", + "epoch 1147: loss = 1728927.031526, a = 853.222677, b = 152.580596\n", + "epoch 1148: loss = 1728890.389260, a = 853.413039, b = 152.580474\n", + "epoch 1149: loss = 1728853.892538, a = 853.603021, b = 152.580353\n", + "epoch 1150: loss = 1728817.540780, a = 853.792622, b = 152.580232\n", + "epoch 1151: loss = 1728781.333408, a = 853.981845, b = 152.580111\n", + "epoch 1152: loss = 1728745.269847, a = 854.170689, b = 152.579990\n", + "epoch 1153: loss = 1728709.349524, a = 854.359155, b = 152.579870\n", + "epoch 1154: loss = 1728673.571869, a = 854.547245, b = 152.579750\n", + "epoch 1155: loss = 1728637.936314, a = 854.734958, b = 152.579630\n", + "epoch 1156: loss = 1728602.442292, a = 854.922296, b = 152.579510\n", + "epoch 1157: loss = 1728567.089239, a = 855.109259, b = 152.579391\n", + "epoch 1158: loss = 1728531.876595, a = 855.295849, b = 152.579271\n", + "epoch 1159: loss = 1728496.803799, a = 855.482065, b = 152.579152\n", + "epoch 1160: loss = 1728461.870295, a = 855.667909, b = 152.579034\n", + "epoch 1161: loss = 1728427.075528, a = 855.853382, b = 152.578915\n", + "epoch 1162: loss = 1728392.418945, a = 856.038483, b = 152.578797\n", + "epoch 1163: loss = 1728357.899996, a = 856.223215, b = 152.578679\n", + "epoch 1164: loss = 1728323.518132, a = 856.407577, b = 152.578561\n", + "epoch 1165: loss = 1728289.272808, a = 856.591570, b = 152.578444\n", + "epoch 1166: loss = 1728255.163480, a = 856.775195, b = 152.578327\n", + "epoch 1167: loss = 1728221.189605, a = 856.958454, b = 152.578210\n", + "epoch 1168: loss = 1728187.350645, a = 857.141345, b = 152.578093\n", + "epoch 1169: loss = 1728153.646062, a = 857.323871, b = 152.577976\n", + "epoch 1170: loss = 1728120.075320, a = 857.506033, b = 152.577860\n", + "epoch 1171: loss = 1728086.637887, a = 857.687829, b = 152.577744\n", + "epoch 1172: loss = 1728053.333232, a = 857.869263, b = 152.577628\n", + "epoch 1173: loss = 1728020.160826, a = 858.050333, b = 152.577512\n", + "epoch 1174: loss = 1727987.120142, a = 858.231041, b = 152.577397\n", + "epoch 1175: loss = 1727954.210655, a = 858.411388, b = 152.577282\n", + "epoch 1176: loss = 1727921.431843, a = 858.591375, b = 152.577167\n", + "epoch 1177: loss = 1727888.783186, a = 858.771001, b = 152.577052\n", + "epoch 1178: loss = 1727856.264164, a = 858.950268, b = 152.576937\n", + "epoch 1179: loss = 1727823.874263, a = 859.129177, b = 152.576823\n", + "epoch 1180: loss = 1727791.612966, a = 859.307728, b = 152.576709\n", + "epoch 1181: loss = 1727759.479763, a = 859.485922, b = 152.576595\n", + "epoch 1182: loss = 1727727.474143, a = 859.663760, b = 152.576482\n", + "epoch 1183: loss = 1727695.595598, a = 859.841242, b = 152.576368\n", + "epoch 1184: loss = 1727663.843621, a = 860.018369, b = 152.576255\n", + "epoch 1185: loss = 1727632.217709, a = 860.195142, b = 152.576142\n", + "epoch 1186: loss = 1727600.717360, a = 860.371562, b = 152.576030\n", + "epoch 1187: loss = 1727569.342073, a = 860.547628, b = 152.575917\n", + "epoch 1188: loss = 1727538.091351, a = 860.723343, b = 152.575805\n", + "epoch 1189: loss = 1727506.964697, a = 860.898706, b = 152.575693\n", + "epoch 1190: loss = 1727475.961618, a = 861.073719, b = 152.575581\n", + "epoch 1191: loss = 1727445.081620, a = 861.248381, b = 152.575470\n", + "epoch 1192: loss = 1727414.324214, a = 861.422695, b = 152.575358\n", + "epoch 1193: loss = 1727383.688912, a = 861.596659, b = 152.575247\n", + "epoch 1194: loss = 1727353.175228, a = 861.770276, b = 152.575136\n", + "epoch 1195: loss = 1727322.782676, a = 861.943546, b = 152.575026\n", + "epoch 1196: loss = 1727292.510775, a = 862.116469, b = 152.574915\n", + "epoch 1197: loss = 1727262.359044, a = 862.289047, b = 152.574805\n", + "epoch 1198: loss = 1727232.327005, a = 862.461279, b = 152.574695\n", + "epoch 1199: loss = 1727202.414181, a = 862.633167, b = 152.574585\n", + "epoch 1200: loss = 1727172.620097, a = 862.804711, b = 152.574476\n", + "epoch 1201: loss = 1727142.944280, a = 862.975912, b = 152.574366\n", + "epoch 1202: loss = 1727113.386259, a = 863.146771, b = 152.574257\n", + "epoch 1203: loss = 1727083.945566, a = 863.317288, b = 152.574148\n", + "epoch 1204: loss = 1727054.621733, a = 863.487464, b = 152.574039\n", + "epoch 1205: loss = 1727025.414294, a = 863.657300, b = 152.573931\n", + "epoch 1206: loss = 1726996.322787, a = 863.826797, b = 152.573823\n", + "epoch 1207: loss = 1726967.346749, a = 863.995954, b = 152.573715\n", + "epoch 1208: loss = 1726938.485720, a = 864.164773, b = 152.573607\n", + "epoch 1209: loss = 1726909.739243, a = 864.333255, b = 152.573499\n", + "epoch 1210: loss = 1726881.106862, a = 864.501399, b = 152.573392\n", + "epoch 1211: loss = 1726852.588122, a = 864.669208, b = 152.573285\n", + "epoch 1212: loss = 1726824.182571, a = 864.836680, b = 152.573178\n", + "epoch 1213: loss = 1726795.889757, a = 865.003818, b = 152.573071\n", + "epoch 1214: loss = 1726767.709233, a = 865.170622, b = 152.572964\n", + "epoch 1215: loss = 1726739.640550, a = 865.337092, b = 152.572858\n", + "epoch 1216: loss = 1726711.683264, a = 865.503229, b = 152.572752\n", + "epoch 1217: loss = 1726683.836931, a = 865.669034, b = 152.572646\n", + "epoch 1218: loss = 1726656.101109, a = 865.834508, b = 152.572540\n", + "epoch 1219: loss = 1726628.475358, a = 865.999650, b = 152.572435\n", + "epoch 1220: loss = 1726600.959240, a = 866.164463, b = 152.572330\n", + "epoch 1221: loss = 1726573.552318, a = 866.328946, b = 152.572225\n", + "epoch 1222: loss = 1726546.254158, a = 866.493100, b = 152.572120\n", + "epoch 1223: loss = 1726519.064326, a = 866.656925, b = 152.572015\n", + "epoch 1224: loss = 1726491.982391, a = 866.820423, b = 152.571911\n", + "epoch 1225: loss = 1726465.007924, a = 866.983594, b = 152.571807\n", + "epoch 1226: loss = 1726438.140496, a = 867.146439, b = 152.571702\n", + "epoch 1227: loss = 1726411.379682, a = 867.308958, b = 152.571599\n", + "epoch 1228: loss = 1726384.725056, a = 867.471152, b = 152.571495\n", + "epoch 1229: loss = 1726358.176197, a = 867.633022, b = 152.571392\n", + "epoch 1230: loss = 1726331.732682, a = 867.794568, b = 152.571289\n", + "epoch 1231: loss = 1726305.394093, a = 867.955792, b = 152.571186\n", + "epoch 1232: loss = 1726279.160012, a = 868.116692, b = 152.571083\n", + "epoch 1233: loss = 1726253.030023, a = 868.277271, b = 152.570980\n", + "epoch 1234: loss = 1726227.003711, a = 868.437529, b = 152.570878\n", + "epoch 1235: loss = 1726201.080663, a = 868.597467, b = 152.570776\n", + "epoch 1236: loss = 1726175.260468, a = 868.757084, b = 152.570674\n", + "epoch 1237: loss = 1726149.542718, a = 868.916383, b = 152.570572\n", + "epoch 1238: loss = 1726123.927003, a = 869.075362, b = 152.570471\n", + "epoch 1239: loss = 1726098.412918, a = 869.234024, b = 152.570369\n", + "epoch 1240: loss = 1726073.000058, a = 869.392369, b = 152.570268\n", + "epoch 1241: loss = 1726047.688020, a = 869.550397, b = 152.570167\n", + "epoch 1242: loss = 1726022.476403, a = 869.708109, b = 152.570066\n", + "epoch 1243: loss = 1725997.364807, a = 869.865506, b = 152.569966\n", + "epoch 1244: loss = 1725972.352833, a = 870.022588, b = 152.569866\n", + "epoch 1245: loss = 1725947.440086, a = 870.179356, b = 152.569765\n", + "epoch 1246: loss = 1725922.626169, a = 870.335810, b = 152.569665\n", + "epoch 1247: loss = 1725897.910690, a = 870.491951, b = 152.569566\n", + "epoch 1248: loss = 1725873.293257, a = 870.647781, b = 152.569466\n", + "epoch 1249: loss = 1725848.773479, a = 870.803298, b = 152.569367\n", + "epoch 1250: loss = 1725824.350968, a = 870.958505, b = 152.569268\n", + "epoch 1251: loss = 1725800.025336, a = 871.113401, b = 152.569169\n", + "epoch 1252: loss = 1725775.796198, a = 871.267988, b = 152.569070\n", + "epoch 1253: loss = 1725751.663170, a = 871.422265, b = 152.568972\n", + "epoch 1254: loss = 1725727.625869, a = 871.576234, b = 152.568873\n", + "epoch 1255: loss = 1725703.683913, a = 871.729895, b = 152.568775\n", + "epoch 1256: loss = 1725679.836924, a = 871.883249, b = 152.568677\n", + "epoch 1257: loss = 1725656.084523, a = 872.036296, b = 152.568579\n", + "epoch 1258: loss = 1725632.426334, a = 872.189037, b = 152.568482\n", + "epoch 1259: loss = 1725608.861981, a = 872.341473, b = 152.568384\n", + "epoch 1260: loss = 1725585.391092, a = 872.493604, b = 152.568287\n", + "epoch 1261: loss = 1725562.013294, a = 872.645430, b = 152.568190\n", + "epoch 1262: loss = 1725538.728217, a = 872.796953, b = 152.568094\n", + "epoch 1263: loss = 1725515.535491, a = 872.948173, b = 152.567997\n", + "epoch 1264: loss = 1725492.434749, a = 873.099091, b = 152.567901\n", + "epoch 1265: loss = 1725469.425625, a = 873.249707, b = 152.567804\n", + "epoch 1266: loss = 1725446.507753, a = 873.400021, b = 152.567708\n", + "epoch 1267: loss = 1725423.680772, a = 873.550035, b = 152.567613\n", + "epoch 1268: loss = 1725400.944319, a = 873.699749, b = 152.567517\n", + "epoch 1269: loss = 1725378.298033, a = 873.849164, b = 152.567421\n", + "epoch 1270: loss = 1725355.741556, a = 873.998280, b = 152.567326\n", + "epoch 1271: loss = 1725333.274530, a = 874.147098, b = 152.567231\n", + "epoch 1272: loss = 1725310.896600, a = 874.295618, b = 152.567136\n", + "epoch 1273: loss = 1725288.607410, a = 874.443841, b = 152.567042\n", + "epoch 1274: loss = 1725266.406608, a = 874.591768, b = 152.566947\n", + "epoch 1275: loss = 1725244.293841, a = 874.739399, b = 152.566853\n", + "epoch 1276: loss = 1725222.268760, a = 874.886735, b = 152.566759\n", + "epoch 1277: loss = 1725200.331014, a = 875.033776, b = 152.566665\n", + "epoch 1278: loss = 1725178.480257, a = 875.180523, b = 152.566571\n", + "epoch 1279: loss = 1725156.716143, a = 875.326977, b = 152.566478\n", + "epoch 1280: loss = 1725135.038326, a = 875.473137, b = 152.566384\n", + "epoch 1281: loss = 1725113.446463, a = 875.619006, b = 152.566291\n", + "epoch 1282: loss = 1725091.940212, a = 875.764583, b = 152.566198\n", + "epoch 1283: loss = 1725070.519232, a = 875.909868, b = 152.566105\n", + "epoch 1284: loss = 1725049.183184, a = 876.054864, b = 152.566013\n", + "epoch 1285: loss = 1725027.931730, a = 876.199569, b = 152.565920\n", + "epoch 1286: loss = 1725006.764533, a = 876.343985, b = 152.565828\n", + "epoch 1287: loss = 1724985.681258, a = 876.488112, b = 152.565736\n", + "epoch 1288: loss = 1724964.681570, a = 876.631951, b = 152.565644\n", + "epoch 1289: loss = 1724943.765137, a = 876.775502, b = 152.565552\n", + "epoch 1290: loss = 1724922.931629, a = 876.918766, b = 152.565461\n", + "epoch 1291: loss = 1724902.180714, a = 877.061744, b = 152.565370\n", + "epoch 1292: loss = 1724881.512064, a = 877.204436, b = 152.565278\n", + "epoch 1293: loss = 1724860.925351, a = 877.346842, b = 152.565188\n", + "epoch 1294: loss = 1724840.420250, a = 877.488964, b = 152.565097\n", + "epoch 1295: loss = 1724819.996436, a = 877.630802, b = 152.565006\n", + "epoch 1296: loss = 1724799.653585, a = 877.772356, b = 152.564916\n", + "epoch 1297: loss = 1724779.391376, a = 877.913626, b = 152.564826\n", + "epoch 1298: loss = 1724759.209486, a = 878.054615, b = 152.564735\n", + "epoch 1299: loss = 1724739.107597, a = 878.195321, b = 152.564646\n", + "epoch 1300: loss = 1724719.085390, a = 878.335746, b = 152.564556\n", + "epoch 1301: loss = 1724699.142548, a = 878.475891, b = 152.564466\n", + "epoch 1302: loss = 1724679.278756, a = 878.615755, b = 152.564377\n", + "epoch 1303: loss = 1724659.493698, a = 878.755339, b = 152.564288\n", + "epoch 1304: loss = 1724639.787061, a = 878.894644, b = 152.564199\n", + "epoch 1305: loss = 1724620.158533, a = 879.033671, b = 152.564110\n", + "epoch 1306: loss = 1724600.607805, a = 879.172419, b = 152.564022\n", + "epoch 1307: loss = 1724581.134564, a = 879.310891, b = 152.563933\n", + "epoch 1308: loss = 1724561.738505, a = 879.449085, b = 152.563845\n", + "epoch 1309: loss = 1724542.419319, a = 879.587003, b = 152.563757\n", + "epoch 1310: loss = 1724523.176700, a = 879.724645, b = 152.563669\n", + "epoch 1311: loss = 1724504.010344, a = 879.862012, b = 152.563581\n", + "epoch 1312: loss = 1724484.919948, a = 879.999104, b = 152.563494\n", + "epoch 1313: loss = 1724465.905208, a = 880.135922, b = 152.563406\n", + "epoch 1314: loss = 1724446.965825, a = 880.272467, b = 152.563319\n", + "epoch 1315: loss = 1724428.101498, a = 880.408738, b = 152.563232\n", + "epoch 1316: loss = 1724409.311929, a = 880.544737, b = 152.563145\n", + "epoch 1317: loss = 1724390.596819, a = 880.680464, b = 152.563058\n", + "epoch 1318: loss = 1724371.955874, a = 880.815920, b = 152.562972\n", + "epoch 1319: loss = 1724353.388797, a = 880.951104, b = 152.562886\n", + "epoch 1320: loss = 1724334.895295, a = 881.086019, b = 152.562799\n", + "epoch 1321: loss = 1724316.475075, a = 881.220663, b = 152.562713\n", + "epoch 1322: loss = 1724298.127846, a = 881.355039, b = 152.562628\n", + "epoch 1323: loss = 1724279.853317, a = 881.489145, b = 152.562542\n", + "epoch 1324: loss = 1724261.651198, a = 881.622984, b = 152.562456\n", + "epoch 1325: loss = 1724243.521203, a = 881.756555, b = 152.562371\n", + "epoch 1326: loss = 1724225.463043, a = 881.889859, b = 152.562286\n", + "epoch 1327: loss = 1724207.476433, a = 882.022896, b = 152.562201\n", + "epoch 1328: loss = 1724189.561089, a = 882.155667, b = 152.562116\n", + "epoch 1329: loss = 1724171.716726, a = 882.288173, b = 152.562032\n", + "epoch 1330: loss = 1724153.943063, a = 882.420413, b = 152.561947\n", + "epoch 1331: loss = 1724136.239817, a = 882.552390, b = 152.561863\n", + "epoch 1332: loss = 1724118.606710, a = 882.684102, b = 152.561779\n", + "epoch 1333: loss = 1724101.043462, a = 882.815551, b = 152.561695\n", + "epoch 1334: loss = 1724083.549795, a = 882.946737, b = 152.561611\n", + "epoch 1335: loss = 1724066.125431, a = 883.077661, b = 152.561527\n", + "epoch 1336: loss = 1724048.770097, a = 883.208323, b = 152.561444\n", + "epoch 1337: loss = 1724031.483516, a = 883.338724, b = 152.561361\n", + "epoch 1338: loss = 1724014.265415, a = 883.468864, b = 152.561277\n", + "epoch 1339: loss = 1723997.115522, a = 883.598743, b = 152.561194\n", + "epoch 1340: loss = 1723980.033566, a = 883.728363, b = 152.561112\n", + "epoch 1341: loss = 1723963.019276, a = 883.857724, b = 152.561029\n", + "epoch 1342: loss = 1723946.072383, a = 883.986826, b = 152.560947\n", + "epoch 1343: loss = 1723929.192619, a = 884.115670, b = 152.560864\n", + "epoch 1344: loss = 1723912.379717, a = 884.244256, b = 152.560782\n", + "epoch 1345: loss = 1723895.633411, a = 884.372586, b = 152.560700\n", + "epoch 1346: loss = 1723878.953436, a = 884.500658, b = 152.560618\n", + "epoch 1347: loss = 1723862.339528, a = 884.628475, b = 152.560537\n", + "epoch 1348: loss = 1723845.791425, a = 884.756036, b = 152.560455\n", + "epoch 1349: loss = 1723829.308864, a = 884.883341, b = 152.560374\n", + "epoch 1350: loss = 1723812.891585, a = 885.010393, b = 152.560293\n", + "epoch 1351: loss = 1723796.539328, a = 885.137190, b = 152.560212\n", + "epoch 1352: loss = 1723780.251834, a = 885.263733, b = 152.560131\n", + "epoch 1353: loss = 1723764.028847, a = 885.390024, b = 152.560050\n", + "epoch 1354: loss = 1723747.870109, a = 885.516062, b = 152.559970\n", + "epoch 1355: loss = 1723731.775364, a = 885.641848, b = 152.559890\n", + "epoch 1356: loss = 1723715.744359, a = 885.767382, b = 152.559809\n", + "epoch 1357: loss = 1723699.776840, a = 885.892666, b = 152.559729\n", + "epoch 1358: loss = 1723683.872554, a = 886.017699, b = 152.559649\n", + "epoch 1359: loss = 1723668.031249, a = 886.142482, b = 152.559570\n", + "epoch 1360: loss = 1723652.252676, a = 886.267015, b = 152.559490\n", + "epoch 1361: loss = 1723636.536584, a = 886.391299, b = 152.559411\n", + "epoch 1362: loss = 1723620.882726, a = 886.515335, b = 152.559332\n", + "epoch 1363: loss = 1723605.290853, a = 886.639123, b = 152.559253\n", + "epoch 1364: loss = 1723589.760719, a = 886.762663, b = 152.559174\n", + "epoch 1365: loss = 1723574.292078, a = 886.885956, b = 152.559095\n", + "epoch 1366: loss = 1723558.884687, a = 887.009003, b = 152.559016\n", + "epoch 1367: loss = 1723543.538300, a = 887.131804, b = 152.558938\n", + "epoch 1368: loss = 1723528.252677, a = 887.254359, b = 152.558860\n", + "epoch 1369: loss = 1723513.027574, a = 887.376669, b = 152.558782\n", + "epoch 1370: loss = 1723497.862752, a = 887.498734, b = 152.558704\n", + "epoch 1371: loss = 1723482.757971, a = 887.620555, b = 152.558626\n", + "epoch 1372: loss = 1723467.712991, a = 887.742133, b = 152.558548\n", + "epoch 1373: loss = 1723452.727575, a = 887.863468, b = 152.558471\n", + "epoch 1374: loss = 1723437.801486, a = 887.984560, b = 152.558393\n", + "epoch 1375: loss = 1723422.934489, a = 888.105410, b = 152.558316\n", + "epoch 1376: loss = 1723408.126347, a = 888.226018, b = 152.558239\n", + "epoch 1377: loss = 1723393.376828, a = 888.346385, b = 152.558162\n", + "epoch 1378: loss = 1723378.685697, a = 888.466511, b = 152.558085\n", + "epoch 1379: loss = 1723364.052724, a = 888.586397, b = 152.558009\n", + "epoch 1380: loss = 1723349.477675, a = 888.706043, b = 152.557932\n", + "epoch 1381: loss = 1723334.960322, a = 888.825451, b = 152.557856\n", + "epoch 1382: loss = 1723320.500434, a = 888.944619, b = 152.557780\n", + "epoch 1383: loss = 1723306.097784, a = 889.063549, b = 152.557704\n", + "epoch 1384: loss = 1723291.752143, a = 889.182241, b = 152.557628\n", + "epoch 1385: loss = 1723277.463285, a = 889.300696, b = 152.557553\n", + "epoch 1386: loss = 1723263.230984, a = 889.418914, b = 152.557477\n", + "epoch 1387: loss = 1723249.055015, a = 889.536895, b = 152.557402\n", + "epoch 1388: loss = 1723234.935154, a = 889.654641, b = 152.557327\n", + "epoch 1389: loss = 1723220.871178, a = 889.772151, b = 152.557252\n", + "epoch 1390: loss = 1723206.862865, a = 889.889427, b = 152.557177\n", + "epoch 1391: loss = 1723192.909994, a = 890.006467, b = 152.557102\n", + "epoch 1392: loss = 1723179.012343, a = 890.123274, b = 152.557027\n", + "epoch 1393: loss = 1723165.169694, a = 890.239847, b = 152.556953\n", + "epoch 1394: loss = 1723151.381827, a = 890.356187, b = 152.556879\n", + "epoch 1395: loss = 1723137.648526, a = 890.472294, b = 152.556804\n", + "epoch 1396: loss = 1723123.969572, a = 890.588170, b = 152.556730\n", + "epoch 1397: loss = 1723110.344750, a = 890.703813, b = 152.556656\n", + "epoch 1398: loss = 1723096.773845, a = 890.819225, b = 152.556583\n", + "epoch 1399: loss = 1723083.256642, a = 890.934407, b = 152.556509\n", + "epoch 1400: loss = 1723069.792927, a = 891.049358, b = 152.556436\n", + "epoch 1401: loss = 1723056.382489, a = 891.164079, b = 152.556363\n", + "epoch 1402: loss = 1723043.025115, a = 891.278571, b = 152.556289\n", + "epoch 1403: loss = 1723029.720594, a = 891.392834, b = 152.556216\n", + "epoch 1404: loss = 1723016.468716, a = 891.506868, b = 152.556144\n", + "epoch 1405: loss = 1723003.269272, a = 891.620675, b = 152.556071\n", + "epoch 1406: loss = 1722990.122053, a = 891.734253, b = 152.555998\n", + "epoch 1407: loss = 1722977.026852, a = 891.847605, b = 152.555926\n", + "epoch 1408: loss = 1722963.983461, a = 891.960730, b = 152.555854\n", + "epoch 1409: loss = 1722950.991676, a = 892.073629, b = 152.555782\n", + "epoch 1410: loss = 1722938.051290, a = 892.186302, b = 152.555710\n", + "epoch 1411: loss = 1722925.162100, a = 892.298750, b = 152.555638\n", + "epoch 1412: loss = 1722912.323901, a = 892.410972, b = 152.555566\n", + "epoch 1413: loss = 1722899.536492, a = 892.522971, b = 152.555495\n", + "epoch 1414: loss = 1722886.799670, a = 892.634745, b = 152.555423\n", + "epoch 1415: loss = 1722874.113234, a = 892.746296, b = 152.555352\n", + "epoch 1416: loss = 1722861.476984, a = 892.857624, b = 152.555281\n", + "epoch 1417: loss = 1722848.890720, a = 892.968730, b = 152.555210\n", + "epoch 1418: loss = 1722836.354243, a = 893.079613, b = 152.555139\n", + "epoch 1419: loss = 1722823.867356, a = 893.190274, b = 152.555068\n", + "epoch 1420: loss = 1722811.429862, a = 893.300714, b = 152.554998\n", + "epoch 1421: loss = 1722799.041564, a = 893.410934, b = 152.554927\n", + "epoch 1422: loss = 1722786.702267, a = 893.520932, b = 152.554857\n", + "epoch 1423: loss = 1722774.411775, a = 893.630711, b = 152.554787\n", + "epoch 1424: loss = 1722762.169895, a = 893.740271, b = 152.554717\n", + "epoch 1425: loss = 1722749.976434, a = 893.849611, b = 152.554647\n", + "epoch 1426: loss = 1722737.831199, a = 893.958733, b = 152.554578\n", + "epoch 1427: loss = 1722725.733998, a = 894.067636, b = 152.554508\n", + "epoch 1428: loss = 1722713.684641, a = 894.176322, b = 152.554439\n", + "epoch 1429: loss = 1722701.682937, a = 894.284790, b = 152.554369\n", + "epoch 1430: loss = 1722689.728697, a = 894.393042, b = 152.554300\n", + "epoch 1431: loss = 1722677.821732, a = 894.501077, b = 152.554231\n", + "epoch 1432: loss = 1722665.961855, a = 894.608895, b = 152.554162\n", + "epoch 1433: loss = 1722654.148878, a = 894.716499, b = 152.554094\n", + "epoch 1434: loss = 1722642.382614, a = 894.823887, b = 152.554025\n", + "epoch 1435: loss = 1722630.662879, a = 894.931060, b = 152.553957\n", + "epoch 1436: loss = 1722618.989486, a = 895.038020, b = 152.553888\n", + "epoch 1437: loss = 1722607.362253, a = 895.144765, b = 152.553820\n", + "epoch 1438: loss = 1722595.780995, a = 895.251297, b = 152.553752\n", + "epoch 1439: loss = 1722584.245530, a = 895.357615, b = 152.553684\n", + "epoch 1440: loss = 1722572.755676, a = 895.463721, b = 152.553616\n", + "epoch 1441: loss = 1722561.311251, a = 895.569615, b = 152.553549\n", + "epoch 1442: loss = 1722549.912075, a = 895.675298, b = 152.553481\n", + "epoch 1443: loss = 1722538.557968, a = 895.780769, b = 152.553414\n", + "epoch 1444: loss = 1722527.248752, a = 895.886029, b = 152.553347\n", + "epoch 1445: loss = 1722515.984246, a = 895.991078, b = 152.553280\n", + "epoch 1446: loss = 1722504.764275, a = 896.095917, b = 152.553213\n", + "epoch 1447: loss = 1722493.588661, a = 896.200547, b = 152.553146\n", + "epoch 1448: loss = 1722482.457227, a = 896.304968, b = 152.553079\n", + "epoch 1449: loss = 1722471.369798, a = 896.409180, b = 152.553012\n", + "epoch 1450: loss = 1722460.326199, a = 896.513183, b = 152.552946\n", + "epoch 1451: loss = 1722449.326257, a = 896.616978, b = 152.552880\n", + "epoch 1452: loss = 1722438.369797, a = 896.720566, b = 152.552814\n", + "epoch 1453: loss = 1722427.456646, a = 896.823947, b = 152.552748\n", + "epoch 1454: loss = 1722416.586633, a = 896.927121, b = 152.552682\n", + "epoch 1455: loss = 1722405.759587, a = 897.030088, b = 152.552616\n", + "epoch 1456: loss = 1722394.975336, a = 897.132850, b = 152.552550\n", + "epoch 1457: loss = 1722384.233710, a = 897.235407, b = 152.552485\n", + "epoch 1458: loss = 1722373.534540, a = 897.337758, b = 152.552419\n", + "epoch 1459: loss = 1722362.877657, a = 897.439904, b = 152.552354\n", + "epoch 1460: loss = 1722352.262894, a = 897.541846, b = 152.552289\n", + "epoch 1461: loss = 1722341.690083, a = 897.643585, b = 152.552224\n", + "epoch 1462: loss = 1722331.159057, a = 897.745120, b = 152.552159\n", + "epoch 1463: loss = 1722320.669650, a = 897.846452, b = 152.552095\n", + "epoch 1464: loss = 1722310.221696, a = 897.947581, b = 152.552030\n", + "epoch 1465: loss = 1722299.815032, a = 898.048508, b = 152.551965\n", + "epoch 1466: loss = 1722289.449493, a = 898.149233, b = 152.551901\n", + "epoch 1467: loss = 1722279.124916, a = 898.249757, b = 152.551837\n", + "epoch 1468: loss = 1722268.841137, a = 898.350080, b = 152.551773\n", + "epoch 1469: loss = 1722258.597995, a = 898.450202, b = 152.551709\n", + "epoch 1470: loss = 1722248.395329, a = 898.550124, b = 152.551645\n", + "epoch 1471: loss = 1722238.232977, a = 898.649846, b = 152.551581\n", + "epoch 1472: loss = 1722228.110779, a = 898.749369, b = 152.551518\n", + "epoch 1473: loss = 1722218.028576, a = 898.848693, b = 152.551454\n", + "epoch 1474: loss = 1722207.986209, a = 898.947818, b = 152.551391\n", + "epoch 1475: loss = 1722197.983520, a = 899.046745, b = 152.551328\n", + "epoch 1476: loss = 1722188.020350, a = 899.145474, b = 152.551265\n", + "epoch 1477: loss = 1722178.096544, a = 899.244006, b = 152.551202\n", + "epoch 1478: loss = 1722168.211944, a = 899.342341, b = 152.551139\n", + "epoch 1479: loss = 1722158.366395, a = 899.440479, b = 152.551076\n", + "epoch 1480: loss = 1722148.559742, a = 899.538420, b = 152.551014\n", + "epoch 1481: loss = 1722138.791830, a = 899.636166, b = 152.550951\n", + "epoch 1482: loss = 1722129.062505, a = 899.733717, b = 152.550889\n", + "epoch 1483: loss = 1722119.371614, a = 899.831072, b = 152.550827\n", + "epoch 1484: loss = 1722109.719005, a = 899.928233, b = 152.550765\n", + "epoch 1485: loss = 1722100.104525, a = 900.025199, b = 152.550703\n", + "epoch 1486: loss = 1722090.528022, a = 900.121972, b = 152.550641\n", + "epoch 1487: loss = 1722080.989347, a = 900.218551, b = 152.550579\n", + "epoch 1488: loss = 1722071.488348, a = 900.314937, b = 152.550518\n", + "epoch 1489: loss = 1722062.024876, a = 900.411130, b = 152.550456\n", + "epoch 1490: loss = 1722052.598782, a = 900.507131, b = 152.550395\n", + "epoch 1491: loss = 1722043.209917, a = 900.602939, b = 152.550334\n", + "epoch 1492: loss = 1722033.858134, a = 900.698557, b = 152.550273\n", + "epoch 1493: loss = 1722024.543285, a = 900.793983, b = 152.550212\n", + "epoch 1494: loss = 1722015.265224, a = 900.889218, b = 152.550151\n", + "epoch 1495: loss = 1722006.023804, a = 900.984263, b = 152.550090\n", + "epoch 1496: loss = 1721996.818880, a = 901.079117, b = 152.550030\n", + "epoch 1497: loss = 1721987.650307, a = 901.173782, b = 152.549969\n", + "epoch 1498: loss = 1721978.517941, a = 901.268258, b = 152.549909\n", + "epoch 1499: loss = 1721969.421637, a = 901.362545, b = 152.549849\n", + "epoch 1500: loss = 1721960.361253, a = 901.456643, b = 152.549789\n", + "epoch 1501: loss = 1721951.336645, a = 901.550553, b = 152.549729\n", + "epoch 1502: loss = 1721942.347673, a = 901.644276, b = 152.549669\n", + "epoch 1503: loss = 1721933.394193, a = 901.737811, b = 152.549609\n", + "epoch 1504: loss = 1721924.476066, a = 901.831159, b = 152.549549\n", + "epoch 1505: loss = 1721915.593151, a = 901.924320, b = 152.549490\n", + "epoch 1506: loss = 1721906.745308, a = 902.017295, b = 152.549431\n", + "epoch 1507: loss = 1721897.932397, a = 902.110084, b = 152.549371\n", + "epoch 1508: loss = 1721889.154280, a = 902.202687, b = 152.549312\n", + "epoch 1509: loss = 1721880.410820, a = 902.295105, b = 152.549253\n", + "epoch 1510: loss = 1721871.701877, a = 902.387339, b = 152.549194\n", + "epoch 1511: loss = 1721863.027315, a = 902.479388, b = 152.549135\n", + "epoch 1512: loss = 1721854.386998, a = 902.571253, b = 152.549077\n", + "epoch 1513: loss = 1721845.780790, a = 902.662934, b = 152.549018\n", + "epoch 1514: loss = 1721837.208555, a = 902.754432, b = 152.548960\n", + "epoch 1515: loss = 1721828.670157, a = 902.845747, b = 152.548901\n", + "epoch 1516: loss = 1721820.165464, a = 902.936879, b = 152.548843\n", + "epoch 1517: loss = 1721811.694341, a = 903.027829, b = 152.548785\n", + "epoch 1518: loss = 1721803.256654, a = 903.118598, b = 152.548727\n", + "epoch 1519: loss = 1721794.852271, a = 903.209184, b = 152.548669\n", + "epoch 1520: loss = 1721786.481060, a = 903.299590, b = 152.548612\n", + "epoch 1521: loss = 1721778.142889, a = 903.389815, b = 152.548554\n", + "epoch 1522: loss = 1721769.837627, a = 903.479859, b = 152.548496\n", + "epoch 1523: loss = 1721761.565142, a = 903.569724, b = 152.548439\n", + "epoch 1524: loss = 1721753.325306, a = 903.659408, b = 152.548382\n", + "epoch 1525: loss = 1721745.117988, a = 903.748914, b = 152.548325\n", + "epoch 1526: loss = 1721736.943059, a = 903.838240, b = 152.548268\n", + "epoch 1527: loss = 1721728.800390, a = 903.927388, b = 152.548211\n", + "epoch 1528: loss = 1721720.689854, a = 904.016357, b = 152.548154\n", + "epoch 1529: loss = 1721712.611323, a = 904.105149, b = 152.548097\n", + "epoch 1530: loss = 1721704.564670, a = 904.193763, b = 152.548041\n", + "epoch 1531: loss = 1721696.549768, a = 904.282199, b = 152.547984\n", + "epoch 1532: loss = 1721688.566492, a = 904.370459, b = 152.547928\n", + "epoch 1533: loss = 1721680.614715, a = 904.458543, b = 152.547871\n", + "epoch 1534: loss = 1721672.694312, a = 904.546450, b = 152.547815\n", + "epoch 1535: loss = 1721664.805160, a = 904.634182, b = 152.547759\n", + "epoch 1536: loss = 1721656.947133, a = 904.721738, b = 152.547703\n", + "epoch 1537: loss = 1721649.120109, a = 904.809119, b = 152.547647\n", + "epoch 1538: loss = 1721641.323964, a = 904.896325, b = 152.547592\n", + "epoch 1539: loss = 1721633.558576, a = 904.983357, b = 152.547536\n", + "epoch 1540: loss = 1721625.823823, a = 905.070215, b = 152.547481\n", + "epoch 1541: loss = 1721618.119582, a = 905.156899, b = 152.547425\n", + "epoch 1542: loss = 1721610.445734, a = 905.243410, b = 152.547370\n", + "epoch 1543: loss = 1721602.802156, a = 905.329748, b = 152.547315\n", + "epoch 1544: loss = 1721595.188730, a = 905.415913, b = 152.547260\n", + "epoch 1545: loss = 1721587.605335, a = 905.501906, b = 152.547205\n", + "epoch 1546: loss = 1721580.051852, a = 905.587727, b = 152.547150\n", + "epoch 1547: loss = 1721572.528163, a = 905.673376, b = 152.547095\n", + "epoch 1548: loss = 1721565.034149, a = 905.758854, b = 152.547041\n", + "epoch 1549: loss = 1721557.569692, a = 905.844161, b = 152.546986\n", + "epoch 1550: loss = 1721550.134675, a = 905.929298, b = 152.546932\n", + "epoch 1551: loss = 1721542.728982, a = 906.014264, b = 152.546878\n", + "epoch 1552: loss = 1721535.352495, a = 906.099060, b = 152.546824\n", + "epoch 1553: loss = 1721528.005099, a = 906.183687, b = 152.546770\n", + "epoch 1554: loss = 1721520.686678, a = 906.268145, b = 152.546716\n", + "epoch 1555: loss = 1721513.397118, a = 906.352433, b = 152.546662\n", + "epoch 1556: loss = 1721506.136304, a = 906.436553, b = 152.546608\n", + "epoch 1557: loss = 1721498.904121, a = 906.520505, b = 152.546554\n", + "epoch 1558: loss = 1721491.700457, a = 906.604289, b = 152.546501\n", + "epoch 1559: loss = 1721484.525197, a = 906.687906, b = 152.546448\n", + "epoch 1560: loss = 1721477.378230, a = 906.771355, b = 152.546394\n", + "epoch 1561: loss = 1721470.259443, a = 906.854638, b = 152.546341\n", + "epoch 1562: loss = 1721463.168723, a = 906.937754, b = 152.546288\n", + "epoch 1563: loss = 1721456.105960, a = 907.020703, b = 152.546235\n", + "epoch 1564: loss = 1721449.071043, a = 907.103487, b = 152.546182\n", + "epoch 1565: loss = 1721442.063862, a = 907.186106, b = 152.546129\n", + "epoch 1566: loss = 1721435.084305, a = 907.268559, b = 152.546077\n", + "epoch 1567: loss = 1721428.132264, a = 907.350847, b = 152.546024\n", + "epoch 1568: loss = 1721421.207629, a = 907.432970, b = 152.545972\n", + "epoch 1569: loss = 1721414.310292, a = 907.514930, b = 152.545919\n", + "epoch 1570: loss = 1721407.440144, a = 907.596725, b = 152.545867\n", + "epoch 1571: loss = 1721400.597077, a = 907.678357, b = 152.545815\n", + "epoch 1572: loss = 1721393.780984, a = 907.759826, b = 152.545763\n", + "epoch 1573: loss = 1721386.991758, a = 907.841132, b = 152.545711\n", + "epoch 1574: loss = 1721380.229293, a = 907.922275, b = 152.545659\n", + "epoch 1575: loss = 1721373.493481, a = 908.003256, b = 152.545607\n", + "epoch 1576: loss = 1721366.784218, a = 908.084075, b = 152.545556\n", + "epoch 1577: loss = 1721360.101397, a = 908.164733, b = 152.545504\n", + "epoch 1578: loss = 1721353.444915, a = 908.245229, b = 152.545453\n", + "epoch 1579: loss = 1721346.814666, a = 908.325564, b = 152.545402\n", + "epoch 1580: loss = 1721340.210546, a = 908.405739, b = 152.545350\n", + "epoch 1581: loss = 1721333.632452, a = 908.485753, b = 152.545299\n", + "epoch 1582: loss = 1721327.080281, a = 908.565607, b = 152.545248\n", + "epoch 1583: loss = 1721320.553929, a = 908.645302, b = 152.545197\n", + "epoch 1584: loss = 1721314.053295, a = 908.724837, b = 152.545147\n", + "epoch 1585: loss = 1721307.578275, a = 908.804213, b = 152.545096\n", + "epoch 1586: loss = 1721301.128770, a = 908.883430, b = 152.545045\n", + "epoch 1587: loss = 1721294.704676, a = 908.962489, b = 152.544995\n", + "epoch 1588: loss = 1721288.305894, a = 909.041390, b = 152.544944\n", + "epoch 1589: loss = 1721281.932323, a = 909.120133, b = 152.544894\n", + "epoch 1590: loss = 1721275.583863, a = 909.198719, b = 152.544844\n", + "epoch 1591: loss = 1721269.260415, a = 909.277148, b = 152.544794\n", + "epoch 1592: loss = 1721262.961879, a = 909.355419, b = 152.544744\n", + "epoch 1593: loss = 1721256.688156, a = 909.433534, b = 152.544694\n", + "epoch 1594: loss = 1721250.439148, a = 909.511493, b = 152.544644\n", + "epoch 1595: loss = 1721244.214757, a = 909.589296, b = 152.544594\n", + "epoch 1596: loss = 1721238.014885, a = 909.666944, b = 152.544545\n", + "epoch 1597: loss = 1721231.839435, a = 909.744436, b = 152.544495\n", + "epoch 1598: loss = 1721225.688310, a = 909.821773, b = 152.544446\n", + "epoch 1599: loss = 1721219.561414, a = 909.898956, b = 152.544397\n", + "epoch 1600: loss = 1721213.458651, a = 909.975984, b = 152.544347\n", + "epoch 1601: loss = 1721207.379924, a = 910.052859, b = 152.544298\n", + "epoch 1602: loss = 1721201.325138, a = 910.129579, b = 152.544249\n", + "epoch 1603: loss = 1721195.294200, a = 910.206146, b = 152.544200\n", + "epoch 1604: loss = 1721189.287013, a = 910.282560, b = 152.544152\n", + "epoch 1605: loss = 1721183.303483, a = 910.358821, b = 152.544103\n", + "epoch 1606: loss = 1721177.343518, a = 910.434930, b = 152.544054\n", + "epoch 1607: loss = 1721171.407023, a = 910.510887, b = 152.544006\n", + "epoch 1608: loss = 1721165.493905, a = 910.586691, b = 152.543957\n", + "epoch 1609: loss = 1721159.604072, a = 910.662344, b = 152.543909\n", + "epoch 1610: loss = 1721153.737431, a = 910.737846, b = 152.543861\n", + "epoch 1611: loss = 1721147.893890, a = 910.813197, b = 152.543813\n", + "epoch 1612: loss = 1721142.073358, a = 910.888397, b = 152.543765\n", + "epoch 1613: loss = 1721136.275743, a = 910.963447, b = 152.543717\n", + "epoch 1614: loss = 1721130.500955, a = 911.038346, b = 152.543669\n", + "epoch 1615: loss = 1721124.748902, a = 911.113096, b = 152.543621\n", + "epoch 1616: loss = 1721119.019496, a = 911.187697, b = 152.543574\n", + "epoch 1617: loss = 1721113.312646, a = 911.262148, b = 152.543526\n", + "epoch 1618: loss = 1721107.628262, a = 911.336450, b = 152.543478\n", + "epoch 1619: loss = 1721101.966255, a = 911.410604, b = 152.543431\n", + "epoch 1620: loss = 1721096.326537, a = 911.484609, b = 152.543384\n", + "epoch 1621: loss = 1721090.709020, a = 911.558467, b = 152.543337\n", + "epoch 1622: loss = 1721085.113615, a = 911.632177, b = 152.543290\n", + "epoch 1623: loss = 1721079.540234, a = 911.705739, b = 152.543243\n", + "epoch 1624: loss = 1721073.988791, a = 911.779154, b = 152.543196\n", + "epoch 1625: loss = 1721068.459198, a = 911.852423, b = 152.543149\n", + "epoch 1626: loss = 1721062.951369, a = 911.925545, b = 152.543102\n", + "epoch 1627: loss = 1721057.465217, a = 911.998521, b = 152.543056\n", + "epoch 1628: loss = 1721052.000656, a = 912.071351, b = 152.543009\n", + "epoch 1629: loss = 1721046.557601, a = 912.144035, b = 152.542963\n", + "epoch 1630: loss = 1721041.135967, a = 912.216574, b = 152.542916\n", + "epoch 1631: loss = 1721035.735668, a = 912.288967, b = 152.542870\n", + "epoch 1632: loss = 1721030.356621, a = 912.361217, b = 152.542824\n", + "epoch 1633: loss = 1721024.998740, a = 912.433321, b = 152.542778\n", 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b = 152.542188\n", + "epoch 1647: loss = 1720952.164523, a = 913.427776, b = 152.542143\n", + "epoch 1648: loss = 1720947.114257, a = 913.497748, b = 152.542098\n", + "epoch 1649: loss = 1720942.083855, a = 913.567580, b = 152.542053\n", + "epoch 1650: loss = 1720937.073238, a = 913.637272, b = 152.542009\n", + "epoch 1651: loss = 1720932.082327, a = 913.706825, b = 152.541965\n", + "epoch 1652: loss = 1720927.111044, a = 913.776239, b = 152.541920\n", + "epoch 1653: loss = 1720922.159311, a = 913.845514, b = 152.541876\n", + "epoch 1654: loss = 1720917.227051, a = 913.914650, b = 152.541832\n", + "epoch 1655: loss = 1720912.314187, a = 913.983648, b = 152.541788\n", + "epoch 1656: loss = 1720907.420641, a = 914.052509, b = 152.541744\n", + "epoch 1657: loss = 1720902.546338, a = 914.121231, b = 152.541700\n", + "epoch 1658: loss = 1720897.691201, a = 914.189816, b = 152.541656\n", + "epoch 1659: loss = 1720892.855154, a = 914.258264, b = 152.541612\n", + "epoch 1660: loss = 1720888.038121, a = 914.326575, b = 152.541569\n", + "epoch 1661: loss = 1720883.240027, a = 914.394750, b = 152.541525\n", + "epoch 1662: loss = 1720878.460797, a = 914.462788, b = 152.541482\n", + "epoch 1663: loss = 1720873.700356, a = 914.530690, b = 152.541438\n", + "epoch 1664: loss = 1720868.958630, a = 914.598457, b = 152.541395\n", + "epoch 1665: loss = 1720864.235544, a = 914.666087, b = 152.541352\n", + "epoch 1666: loss = 1720859.531025, a = 914.733583, b = 152.541309\n", + "epoch 1667: loss = 1720854.845000, a = 914.800944, b = 152.541266\n", + "epoch 1668: loss = 1720850.177394, a = 914.868170, b = 152.541223\n", + "epoch 1669: loss = 1720845.528135, a = 914.935261, b = 152.541180\n", + "epoch 1670: loss = 1720840.897150, a = 915.002218, b = 152.541137\n", + "epoch 1671: loss = 1720836.284366, a = 915.069042, b = 152.541095\n", + "epoch 1672: loss = 1720831.689713, a = 915.135732, b = 152.541052\n", + "epoch 1673: loss = 1720827.113117, a = 915.202288, b = 152.541009\n", 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916.120224, b = 152.540423\n", + "epoch 1688: loss = 1720760.583133, a = 916.184812, b = 152.540382\n", + "epoch 1689: loss = 1720756.285922, a = 916.249271, b = 152.540341\n", + "epoch 1690: loss = 1720752.005590, a = 916.313601, b = 152.540300\n", + "epoch 1691: loss = 1720747.742072, a = 916.377802, b = 152.540259\n", + "epoch 1692: loss = 1720743.495299, a = 916.441875, b = 152.540218\n", + "epoch 1693: loss = 1720739.265205, a = 916.505819, b = 152.540177\n", + "epoch 1694: loss = 1720735.051726, a = 916.569636, b = 152.540136\n", + "epoch 1695: loss = 1720730.854794, a = 916.633325, b = 152.540095\n", + "epoch 1696: loss = 1720726.674344, a = 916.696887, b = 152.540055\n", + "epoch 1697: loss = 1720722.510311, a = 916.760321, b = 152.540014\n", + "epoch 1698: loss = 1720718.362630, a = 916.823629, b = 152.539974\n", + "epoch 1699: loss = 1720714.231237, a = 916.886810, b = 152.539934\n", + "epoch 1700: loss = 1720710.116066, a = 916.949865, b = 152.539893\n", + "epoch 1701: loss 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152.539338\n", + "epoch 1715: loss = 1720650.292133, a = 917.880698, b = 152.539299\n", + "epoch 1716: loss = 1720646.427958, a = 917.941766, b = 152.539260\n", + "epoch 1717: loss = 1720642.578948, a = 918.002711, b = 152.539221\n", + "epoch 1718: loss = 1720638.745042, a = 918.063534, b = 152.539182\n", + "epoch 1719: loss = 1720634.926181, a = 918.124236, b = 152.539143\n", + "epoch 1720: loss = 1720631.122305, a = 918.184817, b = 152.539104\n", + "epoch 1721: loss = 1720627.333355, a = 918.245276, b = 152.539066\n", + "epoch 1722: loss = 1720623.559271, a = 918.305614, b = 152.539027\n", + "epoch 1723: loss = 1720619.799996, a = 918.365832, b = 152.538989\n", + "epoch 1724: loss = 1720616.055469, a = 918.425930, b = 152.538950\n", + "epoch 1725: loss = 1720612.325634, a = 918.485907, b = 152.538912\n", + "epoch 1726: loss = 1720608.610431, a = 918.545764, b = 152.538874\n", + "epoch 1727: loss = 1720604.909803, a = 918.605501, b = 152.538836\n", + "epoch 1728: loss = 1720601.223691, a = 918.665120, b = 152.538798\n", + "epoch 1729: loss = 1720597.552040, a = 918.724618, b = 152.538760\n", + "epoch 1730: loss = 1720593.894791, a = 918.783998, b = 152.538722\n", + "epoch 1731: loss = 1720590.251887, a = 918.843259, b = 152.538684\n", + "epoch 1732: loss = 1720586.623272, a = 918.902402, b = 152.538646\n", + "epoch 1733: loss = 1720583.008889, a = 918.961426, b = 152.538608\n", + "epoch 1734: loss = 1720579.408681, a = 919.020333, b = 152.538571\n", + "epoch 1735: loss = 1720575.822593, a = 919.079121, b = 152.538533\n", + "epoch 1736: loss = 1720572.250569, a = 919.137792, b = 152.538496\n", + "epoch 1737: loss = 1720568.692553, a = 919.196346, b = 152.538458\n", + "epoch 1738: loss = 1720565.148489, a = 919.254783, b = 152.538421\n", + "epoch 1739: loss = 1720561.618323, a = 919.313102, b = 152.538384\n", + "epoch 1740: loss = 1720558.101999, a = 919.371305, b = 152.538347\n", + "epoch 1741: loss = 1720554.599463, a = 919.429392, b = 152.538310\n", 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920.230515, b = 152.537798\n", + "epoch 1756: loss = 1720503.679339, a = 920.286884, b = 152.537762\n", + "epoch 1757: loss = 1720500.390129, a = 920.343139, b = 152.537726\n", + "epoch 1758: loss = 1720497.113808, a = 920.399283, b = 152.537690\n", + "epoch 1759: loss = 1720493.850325, a = 920.455314, b = 152.537654\n", + "epoch 1760: loss = 1720490.599630, a = 920.511233, b = 152.537619\n", + "epoch 1761: loss = 1720487.361670, a = 920.567040, b = 152.537583\n", + "epoch 1762: loss = 1720484.136397, a = 920.622736, b = 152.537547\n", + "epoch 1763: loss = 1720480.923760, a = 920.678320, b = 152.537512\n", + "epoch 1764: loss = 1720477.723709, a = 920.733793, b = 152.537476\n", + "epoch 1765: loss = 1720474.536195, a = 920.789155, b = 152.537441\n", + "epoch 1766: loss = 1720471.361166, a = 920.844407, b = 152.537406\n", + "epoch 1767: loss = 1720468.198576, a = 920.899548, b = 152.537371\n", + "epoch 1768: loss = 1720465.048373, a = 920.954579, b = 152.537335\n", + "epoch 1769: loss = 1720461.910509, a = 921.009499, b = 152.537300\n", + "epoch 1770: loss = 1720458.784936, a = 921.064310, b = 152.537265\n", + "epoch 1771: loss = 1720455.671604, a = 921.119011, b = 152.537230\n", + "epoch 1772: loss = 1720452.570465, a = 921.173603, b = 152.537196\n", + "epoch 1773: loss = 1720449.481472, a = 921.228086, b = 152.537161\n", + "epoch 1774: loss = 1720446.404575, a = 921.282460, b = 152.537126\n", + "epoch 1775: loss = 1720443.339727, a = 921.336725, b = 152.537091\n", + "epoch 1776: loss = 1720440.286881, a = 921.390881, b = 152.537057\n", + "epoch 1777: loss = 1720437.245989, a = 921.444930, b = 152.537022\n", + "epoch 1778: loss = 1720434.217004, a = 921.498870, b = 152.536988\n", + "epoch 1779: loss = 1720431.199878, a = 921.552702, b = 152.536953\n", + "epoch 1780: loss = 1720428.194566, a = 921.606427, b = 152.536919\n", + "epoch 1781: loss = 1720425.201019, a = 921.660044, b = 152.536885\n", + "epoch 1782: loss = 1720422.219191, a = 921.713554, b = 152.536851\n", + "epoch 1783: loss = 1720419.249037, a = 921.766957, b = 152.536817\n", + "epoch 1784: loss = 1720416.290510, a = 921.820253, b = 152.536783\n", + "epoch 1785: loss = 1720413.343564, a = 921.873443, b = 152.536749\n", + "epoch 1786: loss = 1720410.408153, a = 921.926526, b = 152.536715\n", + "epoch 1787: loss = 1720407.484232, a = 921.979503, b = 152.536681\n", + "epoch 1788: loss = 1720404.571755, a = 922.032374, b = 152.536647\n", + "epoch 1789: loss = 1720401.670677, a = 922.085140, b = 152.536613\n", + "epoch 1790: loss = 1720398.780953, a = 922.137800, b = 152.536580\n", + "epoch 1791: loss = 1720395.902538, a = 922.190354, b = 152.536546\n", + "epoch 1792: loss = 1720393.035387, a = 922.242804, b = 152.536513\n", + "epoch 1793: loss = 1720390.179456, a = 922.295149, b = 152.536479\n", + "epoch 1794: loss = 1720387.334700, a = 922.347389, b = 152.536446\n", + "epoch 1795: loss = 1720384.501075, a = 922.399524, b = 152.536413\n", + "epoch 1796: loss = 1720381.678538, a = 922.451555, b = 152.536379\n", + "epoch 1797: loss = 1720378.867044, a = 922.503483, b = 152.536346\n", + "epoch 1798: loss = 1720376.066550, a = 922.555306, b = 152.536313\n", + "epoch 1799: loss = 1720373.277012, a = 922.607026, b = 152.536280\n", + "epoch 1800: loss = 1720370.498388, a = 922.658642, b = 152.536247\n", + "epoch 1801: loss = 1720367.730632, a = 922.710155, b = 152.536214\n", + "epoch 1802: loss = 1720364.973704, a = 922.761565, b = 152.536181\n", + "epoch 1803: loss = 1720362.227560, a = 922.812873, b = 152.536149\n", + "epoch 1804: loss = 1720359.492157, a = 922.864078, b = 152.536116\n", + "epoch 1805: loss = 1720356.767453, a = 922.915180, b = 152.536083\n", + "epoch 1806: loss = 1720354.053406, a = 922.966180, b = 152.536051\n", + "epoch 1807: loss = 1720351.349974, a = 923.017078, b = 152.536018\n", + "epoch 1808: loss = 1720348.657114, a = 923.067875, b = 152.535986\n", + "epoch 1809: loss = 1720345.974785, a = 923.118569, b = 152.535953\n", 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923.817744, b = 152.535507\n", + "epoch 1824: loss = 1720306.975637, a = 923.866939, b = 152.535475\n", + "epoch 1825: loss = 1720304.456252, a = 923.916036, b = 152.535444\n", + "epoch 1826: loss = 1720301.946713, a = 923.965035, b = 152.535413\n", + "epoch 1827: loss = 1720299.446979, a = 924.013936, b = 152.535381\n", + "epoch 1828: loss = 1720296.957014, a = 924.062739, b = 152.535350\n", + "epoch 1829: loss = 1720294.476778, a = 924.111444, b = 152.535319\n", + "epoch 1830: loss = 1720292.006232, a = 924.160052, b = 152.535288\n", + "epoch 1831: loss = 1720289.545339, a = 924.208563, b = 152.535257\n", + "epoch 1832: loss = 1720287.094060, a = 924.256977, b = 152.535226\n", + "epoch 1833: loss = 1720284.652358, a = 924.305294, b = 152.535195\n", + "epoch 1834: loss = 1720282.220194, a = 924.353514, b = 152.535165\n", + "epoch 1835: loss = 1720279.797531, a = 924.401638, b = 152.535134\n", + "epoch 1836: loss = 1720277.384331, a = 924.449666, b = 152.535103\n", + "epoch 1837: loss = 1720274.980557, a = 924.497598, b = 152.535073\n", + "epoch 1838: loss = 1720272.586172, a = 924.545433, b = 152.535042\n", + "epoch 1839: loss = 1720270.201138, a = 924.593174, b = 152.535011\n", + "epoch 1840: loss = 1720267.825419, a = 924.640818, b = 152.534981\n", + "epoch 1841: loss = 1720265.458978, a = 924.688368, b = 152.534951\n", + "epoch 1842: loss = 1720263.101779, a = 924.735822, b = 152.534920\n", + "epoch 1843: loss = 1720260.753784, a = 924.783182, b = 152.534890\n", + "epoch 1844: loss = 1720258.414957, a = 924.830446, b = 152.534860\n", + "epoch 1845: loss = 1720256.085263, a = 924.877617, b = 152.534830\n", + "epoch 1846: loss = 1720253.764664, a = 924.924692, b = 152.534800\n", + "epoch 1847: loss = 1720251.453126, a = 924.971674, b = 152.534770\n", + "epoch 1848: loss = 1720249.150613, a = 925.018562, b = 152.534740\n", + "epoch 1849: loss = 1720246.857088, a = 925.065356, b = 152.534710\n", + "epoch 1850: loss = 1720244.572517, a = 925.112057, b = 152.534680\n", + "epoch 1851: loss = 1720242.296863, a = 925.158664, b = 152.534650\n", + "epoch 1852: loss = 1720240.030093, a = 925.205178, b = 152.534621\n", + "epoch 1853: loss = 1720237.772169, a = 925.251599, b = 152.534591\n", + "epoch 1854: loss = 1720235.523059, a = 925.297927, b = 152.534561\n", + "epoch 1855: loss = 1720233.282727, a = 925.344162, b = 152.534532\n", + "epoch 1856: loss = 1720231.051138, a = 925.390305, b = 152.534502\n", + "epoch 1857: loss = 1720228.828258, a = 925.436356, b = 152.534473\n", + "epoch 1858: loss = 1720226.614052, a = 925.482314, b = 152.534444\n", + "epoch 1859: loss = 1720224.408486, a = 925.528181, b = 152.534414\n", + "epoch 1860: loss = 1720222.211527, a = 925.573956, b = 152.534385\n", + "epoch 1861: loss = 1720220.023139, a = 925.619640, b = 152.534356\n", + "epoch 1862: loss = 1720217.843290, a = 925.665232, b = 152.534327\n", + "epoch 1863: loss = 1720215.671945, a = 925.710733, b = 152.534298\n", + "epoch 1864: loss = 1720213.509072, a = 925.756143, b = 152.534269\n", + "epoch 1865: loss = 1720211.354635, a = 925.801462, b = 152.534240\n", + "epoch 1866: loss = 1720209.208604, a = 925.846690, b = 152.534211\n", + "epoch 1867: loss = 1720207.070943, a = 925.891828, b = 152.534182\n", + "epoch 1868: loss = 1720204.941620, a = 925.936876, b = 152.534153\n", + "epoch 1869: loss = 1720202.820602, a = 925.981834, b = 152.534125\n", + "epoch 1870: loss = 1720200.707857, a = 926.026702, b = 152.534096\n", + "epoch 1871: loss = 1720198.603351, a = 926.071480, b = 152.534067\n", + "epoch 1872: loss = 1720196.507053, a = 926.116169, b = 152.534039\n", + "epoch 1873: loss = 1720194.418929, a = 926.160768, b = 152.534010\n", + "epoch 1874: loss = 1720192.338948, a = 926.205278, b = 152.533982\n", + "epoch 1875: loss = 1720190.267077, a = 926.249699, b = 152.533953\n", + "epoch 1876: loss = 1720188.203284, a = 926.294031, b = 152.533925\n", + "epoch 1877: loss = 1720186.147538, a = 926.338275, b = 152.533897\n", + "epoch 1878: loss = 1720184.099807, a = 926.382430, b = 152.533869\n", + "epoch 1879: loss = 1720182.060059, a = 926.426497, b = 152.533841\n", + "epoch 1880: loss = 1720180.028263, a = 926.470475, b = 152.533812\n", + "epoch 1881: loss = 1720178.004387, a = 926.514366, b = 152.533784\n", + "epoch 1882: loss = 1720175.988400, a = 926.558169, b = 152.533756\n", + "epoch 1883: loss = 1720173.980271, a = 926.601884, b = 152.533729\n", + "epoch 1884: loss = 1720171.979968, a = 926.645512, b = 152.533701\n", + "epoch 1885: loss = 1720169.987462, a = 926.689053, b = 152.533673\n", + "epoch 1886: loss = 1720168.002721, a = 926.732507, b = 152.533645\n", + "epoch 1887: loss = 1720166.025715, a = 926.775873, b = 152.533617\n", + "epoch 1888: loss = 1720164.056412, a = 926.819153, b = 152.533590\n", + "epoch 1889: loss = 1720162.094784, a = 926.862347, b = 152.533562\n", + "epoch 1890: loss = 1720160.140799, a = 926.905454, b = 152.533535\n", + "epoch 1891: loss = 1720158.194427, a = 926.948475, b = 152.533507\n", + "epoch 1892: loss = 1720156.255638, a = 926.991410, b = 152.533480\n", + "epoch 1893: loss = 1720154.324403, a = 927.034259, b = 152.533452\n", + "epoch 1894: loss = 1720152.400692, a = 927.077022, b = 152.533425\n", + "epoch 1895: loss = 1720150.484474, a = 927.119700, b = 152.533398\n", + "epoch 1896: loss = 1720148.575721, a = 927.162292, b = 152.533371\n", + "epoch 1897: loss = 1720146.674402, a = 927.204800, b = 152.533343\n", + "epoch 1898: loss = 1720144.780489, a = 927.247222, b = 152.533316\n", + "epoch 1899: loss = 1720142.893953, a = 927.289560, b = 152.533289\n", + "epoch 1900: loss = 1720141.014763, a = 927.331812, b = 152.533262\n", + "epoch 1901: loss = 1720139.142892, a = 927.373981, b = 152.533235\n", + "epoch 1902: loss = 1720137.278311, a = 927.416065, b = 152.533209\n", + "epoch 1903: loss = 1720135.420990, a = 927.458065, b = 152.533182\n", + "epoch 1904: loss = 1720133.570901, a = 927.499981, b = 152.533155\n", + "epoch 1905: loss = 1720131.728016, a = 927.541813, b = 152.533128\n", + "epoch 1906: loss = 1720129.892306, a = 927.583561, b = 152.533102\n", + "epoch 1907: loss = 1720128.063742, a = 927.625226, b = 152.533075\n", + "epoch 1908: loss = 1720126.242298, a = 927.666808, b = 152.533048\n", + "epoch 1909: loss = 1720124.427944, a = 927.708306, b = 152.533022\n", + "epoch 1910: loss = 1720122.620652, a = 927.749721, b = 152.532995\n", + "epoch 1911: loss = 1720120.820396, a = 927.791054, b = 152.532969\n", + "epoch 1912: loss = 1720119.027146, a = 927.832304, b = 152.532943\n", + "epoch 1913: loss = 1720117.240876, a = 927.873472, b = 152.532916\n", + "epoch 1914: loss = 1720115.461558, a = 927.914557, b = 152.532890\n", + "epoch 1915: loss = 1720113.689165, a = 927.955560, b = 152.532864\n", + "epoch 1916: loss = 1720111.923668, a = 927.996481, b = 152.532838\n", + "epoch 1917: loss = 1720110.165042, a = 928.037320, b = 152.532812\n", + "epoch 1918: loss = 1720108.413258, a = 928.078078, b = 152.532786\n", + "epoch 1919: loss = 1720106.668291, a = 928.118754, b = 152.532760\n", + "epoch 1920: loss = 1720104.930113, a = 928.159348, b = 152.532734\n", + "epoch 1921: loss = 1720103.198697, a = 928.199862, b = 152.532708\n", + "epoch 1922: loss = 1720101.474017, a = 928.240295, b = 152.532682\n", + "epoch 1923: loss = 1720099.756046, a = 928.280646, b = 152.532656\n", + "epoch 1924: loss = 1720098.044757, a = 928.320917, b = 152.532631\n", + "epoch 1925: loss = 1720096.340125, a = 928.361108, b = 152.532605\n", + "epoch 1926: loss = 1720094.642123, a = 928.401218, b = 152.532579\n", + "epoch 1927: loss = 1720092.950726, a = 928.441248, b = 152.532554\n", + "epoch 1928: loss = 1720091.265906, a = 928.481197, b = 152.532528\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 1929: loss = 1720089.587638, a = 928.521067, b = 152.532503\n", + "epoch 1930: loss = 1720087.915897, a = 928.560858, b = 152.532477\n", + "epoch 1931: loss = 1720086.250656, a = 928.600568, b = 152.532452\n", + "epoch 1932: loss = 1720084.591889, a = 928.640200, b = 152.532427\n", + "epoch 1933: loss = 1720082.939573, a = 928.679752, b = 152.532401\n", + "epoch 1934: loss = 1720081.293680, a = 928.719224, b = 152.532376\n", + "epoch 1935: loss = 1720079.654186, a = 928.758618, b = 152.532351\n", + "epoch 1936: loss = 1720078.021066, a = 928.797934, b = 152.532326\n", + "epoch 1937: loss = 1720076.394293, a = 928.837170, b = 152.532301\n", + "epoch 1938: loss = 1720074.773845, a = 928.876328, b = 152.532276\n", + "epoch 1939: loss = 1720073.159694, a = 928.915408, b = 152.532251\n", + "epoch 1940: loss = 1720071.551818, a = 928.954410, b = 152.532226\n", + "epoch 1941: loss = 1720069.950190, a = 928.993333, b = 152.532201\n", + "epoch 1942: loss = 1720068.354786, a = 929.032179, b = 152.532176\n", + "epoch 1943: loss = 1720066.765582, a = 929.070948, b = 152.532152\n", + "epoch 1944: loss = 1720065.182554, a = 929.109638, b = 152.532127\n", + "epoch 1945: loss = 1720063.605677, a = 929.148251, b = 152.532102\n", + "epoch 1946: loss = 1720062.034926, a = 929.186787, b = 152.532078\n", + "epoch 1947: loss = 1720060.470278, a = 929.225246, b = 152.532053\n", + "epoch 1948: loss = 1720058.911709, a = 929.263629, b = 152.532028\n", + "epoch 1949: loss = 1720057.359194, a = 929.301934, b = 152.532004\n", + "epoch 1950: loss = 1720055.812710, a = 929.340163, b = 152.531980\n", + "epoch 1951: loss = 1720054.272234, a = 929.378315, b = 152.531955\n", + "epoch 1952: loss = 1720052.737740, a = 929.416391, b = 152.531931\n", + "epoch 1953: loss = 1720051.209207, a = 929.454391, b = 152.531907\n", + "epoch 1954: loss = 1720049.686610, a = 929.492315, b = 152.531882\n", + "epoch 1955: loss = 1720048.169926, a = 929.530163, b = 152.531858\n", + "epoch 1956: loss = 1720046.659132, a = 929.567935, b = 152.531834\n", + "epoch 1957: loss = 1720045.154204, a = 929.605632, b = 152.531810\n", + "epoch 1958: loss = 1720043.655120, a = 929.643253, b = 152.531786\n", + "epoch 1959: loss = 1720042.161857, a = 929.680800, b = 152.531762\n", + "epoch 1960: loss = 1720040.674390, a = 929.718271, b = 152.531738\n", + "epoch 1961: loss = 1720039.192699, a = 929.755667, b = 152.531714\n", + "epoch 1962: loss = 1720037.716759, a = 929.792988, b = 152.531690\n", + "epoch 1963: loss = 1720036.246549, a = 929.830235, b = 152.531667\n", + "epoch 1964: loss = 1720034.782046, a = 929.867408, b = 152.531643\n", + "epoch 1965: loss = 1720033.323226, a = 929.904506, b = 152.531619\n", + "epoch 1966: loss = 1720031.870069, a = 929.941529, b = 152.531596\n", + "epoch 1967: loss = 1720030.422552, a = 929.978479, b = 152.531572\n", + "epoch 1968: loss = 1720028.980651, a = 930.015355, b = 152.531548\n", + "epoch 1969: loss = 1720027.544347, a = 930.052157, b = 152.531525\n", + "epoch 1970: loss = 1720026.113615, a = 930.088886, b = 152.531501\n", + "epoch 1971: loss = 1720024.688435, a = 930.125541, b = 152.531478\n", + "epoch 1972: loss = 1720023.268784, a = 930.162123, b = 152.531455\n", + "epoch 1973: loss = 1720021.854641, a = 930.198631, b = 152.531431\n", + "epoch 1974: loss = 1720020.445984, a = 930.235067, b = 152.531408\n", + "epoch 1975: loss = 1720019.042792, a = 930.271430, b = 152.531385\n", + "epoch 1976: loss = 1720017.645042, a = 930.307720, b = 152.531362\n", + "epoch 1977: loss = 1720016.252715, a = 930.343937, b = 152.531338\n", + "epoch 1978: loss = 1720014.865787, a = 930.380082, b = 152.531315\n", + "epoch 1979: loss = 1720013.484238, a = 930.416155, b = 152.531292\n", + "epoch 1980: loss = 1720012.108047, a = 930.452156, b = 152.531269\n", + "epoch 1981: loss = 1720010.737193, a = 930.488085, b = 152.531246\n", + "epoch 1982: loss = 1720009.371655, a = 930.523941, b = 152.531224\n", + "epoch 1983: loss = 1720008.011411, a = 930.559727, b = 152.531201\n", + "epoch 1984: loss = 1720006.656442, a = 930.595440, b = 152.531178\n", + "epoch 1985: loss = 1720005.306725, a = 930.631082, b = 152.531155\n", + "epoch 1986: loss = 1720003.962241, a = 930.666653, b = 152.531132\n", + "epoch 1987: loss = 1720002.622969, a = 930.702153, b = 152.531110\n", + "epoch 1988: loss = 1720001.288889, a = 930.737582, b = 152.531087\n", + "epoch 1989: loss = 1719999.959979, a = 930.772940, b = 152.531064\n", + "epoch 1990: loss = 1719998.636221, a = 930.808227, b = 152.531042\n", + "epoch 1991: loss = 1719997.317593, a = 930.843444, b = 152.531019\n", + "epoch 1992: loss = 1719996.004075, a = 930.878590, b = 152.530997\n", + "epoch 1993: loss = 1719994.695647, a = 930.913666, b = 152.530975\n", + "epoch 1994: loss = 1719993.392289, a = 930.948672, b = 152.530952\n", + "epoch 1995: loss = 1719992.093981, a = 930.983607, b = 152.530930\n", + "epoch 1996: loss = 1719990.800704, a = 931.018473, b = 152.530908\n", + "epoch 1997: loss = 1719989.512438, a = 931.053270, b = 152.530885\n", + "epoch 1998: loss = 1719988.229162, a = 931.087996, b = 152.530863\n", + "epoch 1999: loss = 1719986.950857, a = 931.122653, b = 152.530841\n", + "epoch 2000: loss = 1719985.677504, a = 931.157241, b = 152.530819\n", + "epoch 2001: loss = 1719984.409083, a = 931.191760, b = 152.530797\n", + "epoch 2002: loss = 1719983.145576, a = 931.226210, b = 152.530775\n", + "epoch 2003: loss = 1719981.886961, a = 931.260591, b = 152.530753\n", + "epoch 2004: loss = 1719980.633221, a = 931.294903, b = 152.530731\n", + "epoch 2005: loss = 1719979.384336, a = 931.329146, b = 152.530709\n", + "epoch 2006: loss = 1719978.140286, a = 931.363321, b = 152.530687\n", + "epoch 2007: loss = 1719976.901054, a = 931.397428, b = 152.530666\n", + "epoch 2008: loss = 1719975.666620, a = 931.431466, b = 152.530644\n", + "epoch 2009: loss = 1719974.436965, a = 931.465437, b = 152.530622\n", + "epoch 2010: loss = 1719973.212070, a = 931.499339, b = 152.530601\n", + "epoch 2011: loss = 1719971.991917, a = 931.533174, b = 152.530579\n", + "epoch 2012: loss = 1719970.776487, a = 931.566941, b = 152.530557\n", + "epoch 2013: loss = 1719969.565762, a = 931.600640, b = 152.530536\n", + "epoch 2014: loss = 1719968.359722, a = 931.634272, b = 152.530514\n", + "epoch 2015: loss = 1719967.158350, a = 931.667837, b = 152.530493\n", + "epoch 2016: loss = 1719965.961627, a = 931.701335, b = 152.530472\n", + "epoch 2017: loss = 1719964.769535, a = 931.734766, b = 152.530450\n", + "epoch 2018: loss = 1719963.582056, a = 931.768130, b = 152.530429\n", + "epoch 2019: loss = 1719962.399172, a = 931.801427, b = 152.530408\n", + "epoch 2020: loss = 1719961.220864, a = 931.834657, b = 152.530386\n", + "epoch 2021: loss = 1719960.047114, a = 931.867821, b = 152.530365\n", + "epoch 2022: loss = 1719958.877905, a = 931.900919, b = 152.530344\n", + "epoch 2023: loss = 1719957.713219, a = 931.933951, b = 152.530323\n", + "epoch 2024: loss = 1719956.553038, a = 931.966917, b = 152.530302\n", + "epoch 2025: loss = 1719955.397344, a = 931.999816, b = 152.530281\n", + "epoch 2026: loss = 1719954.246120, a = 932.032650, b = 152.530260\n", + "epoch 2027: loss = 1719953.099348, a = 932.065418, b = 152.530239\n", + "epoch 2028: loss = 1719951.957011, a = 932.098121, b = 152.530218\n", + "epoch 2029: loss = 1719950.819091, a = 932.130758, b = 152.530197\n", + "epoch 2030: loss = 1719949.685570, a = 932.163330, b = 152.530176\n", + "epoch 2031: loss = 1719948.556433, a = 932.195837, b = 152.530156\n", + "epoch 2032: loss = 1719947.431660, a = 932.228279, b = 152.530135\n", + "epoch 2033: loss = 1719946.311236, a = 932.260656, b = 152.530114\n", + "epoch 2034: loss = 1719945.195143, a = 932.292968, b = 152.530094\n", + "epoch 2035: loss = 1719944.083364, a = 932.325216, b = 152.530073\n", + "epoch 2036: loss = 1719942.975882, a = 932.357399, b = 152.530052\n", + "epoch 2037: loss = 1719941.872681, a = 932.389518, b = 152.530032\n", + "epoch 2038: loss = 1719940.773742, a = 932.421573, b = 152.530012\n", + "epoch 2039: loss = 1719939.679051, a = 932.453563, b = 152.529991\n", + "epoch 2040: loss = 1719938.588590, a = 932.485490, b = 152.529971\n", + "epoch 2041: loss = 1719937.502342, a = 932.517352, b = 152.529950\n", + "epoch 2042: loss = 1719936.420291, a = 932.549151, b = 152.529930\n", + "epoch 2043: loss = 1719935.342420, a = 932.580887, b = 152.529910\n", + "epoch 2044: loss = 1719934.268714, a = 932.612559, b = 152.529890\n", + "epoch 2045: loss = 1719933.199154, a = 932.644167, b = 152.529869\n", + "epoch 2046: loss = 1719932.133727, a = 932.675713, b = 152.529849\n", + "epoch 2047: loss = 1719931.072414, a = 932.707195, b = 152.529829\n", + "epoch 2048: loss = 1719930.015201, a = 932.738614, b = 152.529809\n", + "epoch 2049: loss = 1719928.962070, a = 932.769971, b = 152.529789\n", + "epoch 2050: loss = 1719927.913006, a = 932.801265, b = 152.529769\n", + "epoch 2051: loss = 1719926.867993, a = 932.832496, b = 152.529749\n", + "epoch 2052: loss = 1719925.827015, a = 932.863665, b = 152.529729\n", + "epoch 2053: loss = 1719924.790056, a = 932.894771, b = 152.529709\n", + "epoch 2054: loss = 1719923.757100, a = 932.925815, b = 152.529689\n", + "epoch 2055: loss = 1719922.728132, a = 932.956798, b = 152.529670\n", + "epoch 2056: loss = 1719921.703136, a = 932.987718, b = 152.529650\n", + "epoch 2057: loss = 1719920.682097, a = 933.018576, b = 152.529630\n", + "epoch 2058: loss = 1719919.664998, a = 933.049373, b = 152.529611\n", + "epoch 2059: loss = 1719918.651824, a = 933.080108, b = 152.529591\n", + "epoch 2060: loss = 1719917.642561, a = 933.110782, b = 152.529571\n", + "epoch 2061: loss = 1719916.637192, a = 933.141394, b = 152.529552\n", + "epoch 2062: loss = 1719915.635703, a = 933.171945, b = 152.529532\n", + "epoch 2063: loss = 1719914.638078, a = 933.202435, b = 152.529513\n", + "epoch 2064: loss = 1719913.644301, a = 933.232864, b = 152.529493\n", + "epoch 2065: loss = 1719912.654359, a = 933.263232, b = 152.529474\n", + "epoch 2066: loss = 1719911.668235, a = 933.293540, b = 152.529455\n", + "epoch 2067: loss = 1719910.685915, a = 933.323787, b = 152.529435\n", + "epoch 2068: loss = 1719909.707384, a = 933.353973, b = 152.529416\n", + "epoch 2069: loss = 1719908.732628, a = 933.384099, b = 152.529397\n", + "epoch 2070: loss = 1719907.761630, a = 933.414165, b = 152.529378\n", + "epoch 2071: loss = 1719906.794377, a = 933.444170, b = 152.529358\n", + "epoch 2072: loss = 1719905.830853, a = 933.474116, b = 152.529339\n", + "epoch 2073: loss = 1719904.871045, a = 933.504002, b = 152.529320\n", + "epoch 2074: loss = 1719903.914938, a = 933.533828, b = 152.529301\n", + "epoch 2075: loss = 1719902.962516, a = 933.563594, b = 152.529282\n", + "epoch 2076: loss = 1719902.013766, a = 933.593301, b = 152.529263\n", + "epoch 2077: loss = 1719901.068673, a = 933.622949, b = 152.529244\n", + "epoch 2078: loss = 1719900.127223, a = 933.652537, b = 152.529225\n", + "epoch 2079: loss = 1719899.189402, a = 933.682066, b = 152.529206\n", + "epoch 2080: loss = 1719898.255195, a = 933.711536, b = 152.529188\n", + "epoch 2081: loss = 1719897.324588, a = 933.740947, b = 152.529169\n", + "epoch 2082: loss = 1719896.397567, a = 933.770299, b = 152.529150\n", + "epoch 2083: loss = 1719895.474119, a = 933.799592, b = 152.529131\n", + "epoch 2084: loss = 1719894.554228, a = 933.828827, b = 152.529113\n", + "epoch 2085: loss = 1719893.637881, a = 933.858004, b = 152.529094\n", + "epoch 2086: loss = 1719892.725064, a = 933.887122, b = 152.529075\n", + "epoch 2087: loss = 1719891.815764, a = 933.916182, b = 152.529057\n", + "epoch 2088: loss = 1719890.909966, a = 933.945183, b = 152.529038\n", + "epoch 2089: loss = 1719890.007657, a = 933.974127, b = 152.529020\n", + "epoch 2090: loss = 1719889.108823, a = 934.003013, b = 152.529001\n", + "epoch 2091: loss = 1719888.213450, a = 934.031841, b = 152.528983\n", + "epoch 2092: loss = 1719887.321526, a = 934.060612, b = 152.528965\n", + "epoch 2093: loss = 1719886.433035, a = 934.089325, b = 152.528946\n", + "epoch 2094: loss = 1719885.547966, a = 934.117980, b = 152.528928\n", + "epoch 2095: loss = 1719884.666304, a = 934.146579, b = 152.528910\n", + "epoch 2096: loss = 1719883.788036, a = 934.175120, b = 152.528892\n", + "epoch 2097: loss = 1719882.913149, a = 934.203604, b = 152.528873\n", + "epoch 2098: loss = 1719882.041630, a = 934.232031, b = 152.528855\n", + "epoch 2099: loss = 1719881.173465, a = 934.260401, b = 152.528837\n", + "epoch 2100: loss = 1719880.308641, a = 934.288714, b = 152.528819\n", + "epoch 2101: loss = 1719879.447146, a = 934.316971, b = 152.528801\n", + "epoch 2102: loss = 1719878.588965, a = 934.345171, b = 152.528783\n", + "epoch 2103: loss = 1719877.734087, a = 934.373315, b = 152.528765\n", + "epoch 2104: loss = 1719876.882498, a = 934.401403, b = 152.528747\n", + "epoch 2105: loss = 1719876.034185, a = 934.429435, b = 152.528729\n", + "epoch 2106: loss = 1719875.189135, a = 934.457410, b = 152.528711\n", + "epoch 2107: loss = 1719874.347336, a = 934.485329, b = 152.528693\n", + "epoch 2108: loss = 1719873.508776, a = 934.513193, b = 152.528676\n", + "epoch 2109: loss = 1719872.673440, a = 934.541001, b = 152.528658\n", + "epoch 2110: loss = 1719871.841317, a = 934.568753, b = 152.528640\n", + "epoch 2111: loss = 1719871.012394, a = 934.596450, b = 152.528622\n", + "epoch 2112: loss = 1719870.186658, a = 934.624092, b = 152.528605\n", + "epoch 2113: loss = 1719869.364098, a = 934.651678, b = 152.528587\n", + "epoch 2114: loss = 1719868.544700, a = 934.679209, b = 152.528570\n", + "epoch 2115: loss = 1719867.728452, a = 934.706685, b = 152.528552\n", + "epoch 2116: loss = 1719866.915342, a = 934.734106, b = 152.528535\n", + "epoch 2117: loss = 1719866.105358, a = 934.761472, b = 152.528517\n", + "epoch 2118: loss = 1719865.298486, a = 934.788784, b = 152.528500\n", + "epoch 2119: loss = 1719864.494716, a = 934.816041, b = 152.528482\n", + "epoch 2120: loss = 1719863.694035, a = 934.843243, b = 152.528465\n", + "epoch 2121: loss = 1719862.896431, a = 934.870391, b = 152.528447\n", + "epoch 2122: loss = 1719862.101892, a = 934.897485, b = 152.528430\n", + "epoch 2123: loss = 1719861.310405, a = 934.924524, b = 152.528413\n", + "epoch 2124: loss = 1719860.521959, a = 934.951509, b = 152.528396\n", + "epoch 2125: loss = 1719859.736543, a = 934.978441, b = 152.528378\n", + "epoch 2126: loss = 1719858.954143, a = 935.005319, b = 152.528361\n", + "epoch 2127: loss = 1719858.174749, a = 935.032142, b = 152.528344\n", + "epoch 2128: loss = 1719857.398348, a = 935.058913, b = 152.528327\n", + "epoch 2129: loss = 1719856.624929, a = 935.085629, b = 152.528310\n", + "epoch 2130: loss = 1719855.854480, a = 935.112293, b = 152.528293\n", + "epoch 2131: loss = 1719855.086989, a = 935.138903, b = 152.528276\n", + "epoch 2132: loss = 1719854.322446, a = 935.165459, b = 152.528259\n", + "epoch 2133: loss = 1719853.560837, a = 935.191963, b = 152.528242\n", + "epoch 2134: loss = 1719852.802153, a = 935.218414, b = 152.528225\n", + "epoch 2135: loss = 1719852.046381, a = 935.244811, b = 152.528208\n", + "epoch 2136: loss = 1719851.293510, a = 935.271156, b = 152.528191\n", + "epoch 2137: loss = 1719850.543529, a = 935.297449, b = 152.528175\n", + "epoch 2138: loss = 1719849.796427, a = 935.323688, b = 152.528158\n", + "epoch 2139: loss = 1719849.052191, a = 935.349876, b = 152.528141\n", + "epoch 2140: loss = 1719848.310811, a = 935.376010, b = 152.528125\n", + "epoch 2141: loss = 1719847.572276, a = 935.402093, b = 152.528108\n", + "epoch 2142: loss = 1719846.836575, a = 935.428123, b = 152.528091\n", + "epoch 2143: loss = 1719846.103696, a = 935.454102, b = 152.528075\n", + "epoch 2144: loss = 1719845.373629, a = 935.480028, b = 152.528058\n", + "epoch 2145: loss = 1719844.646362, a = 935.505903, b = 152.528042\n", + "epoch 2146: loss = 1719843.921885, a = 935.531726, b = 152.528025\n", + "epoch 2147: loss = 1719843.200186, a = 935.557497, b = 152.528009\n", + "epoch 2148: loss = 1719842.481255, a = 935.583217, b = 152.527992\n", + "epoch 2149: loss = 1719841.765080, a = 935.608885, b = 152.527976\n", + "epoch 2150: loss = 1719841.051652, a = 935.634502, b = 152.527959\n", + "epoch 2151: loss = 1719840.340960, a = 935.660068, b = 152.527943\n", + "epoch 2152: loss = 1719839.632992, a = 935.685582, b = 152.527927\n", + "epoch 2153: loss = 1719838.927738, a = 935.711046, b = 152.527911\n", + "epoch 2154: loss = 1719838.225187, a = 935.736458, b = 152.527894\n", + "epoch 2155: loss = 1719837.525329, a = 935.761820, b = 152.527878\n", + "epoch 2156: loss = 1719836.828154, a = 935.787131, b = 152.527862\n", + "epoch 2157: loss = 1719836.133650, a = 935.812392, b = 152.527846\n", + "epoch 2158: loss = 1719835.441808, a = 935.837602, b = 152.527830\n", + "epoch 2159: loss = 1719834.752617, a = 935.862761, b = 152.527814\n", + "epoch 2160: loss = 1719834.066066, a = 935.887871, b = 152.527798\n", + "epoch 2161: loss = 1719833.382146, a = 935.912930, b = 152.527782\n", + "epoch 2162: loss = 1719832.700845, a = 935.937939, b = 152.527766\n", + "epoch 2163: loss = 1719832.022154, a = 935.962898, b = 152.527750\n", + "epoch 2164: loss = 1719831.346063, a = 935.987807, b = 152.527734\n", + "epoch 2165: loss = 1719830.672562, a = 936.012666, b = 152.527718\n", + "epoch 2166: loss = 1719830.001639, a = 936.037475, b = 152.527702\n", + "epoch 2167: loss = 1719829.333286, a = 936.062235, b = 152.527686\n", + "epoch 2168: loss = 1719828.667491, a = 936.086946, b = 152.527670\n", + "epoch 2169: loss = 1719828.004246, a = 936.111607, b = 152.527655\n", + "epoch 2170: loss = 1719827.343541, a = 936.136218, b = 152.527639\n", + "epoch 2171: loss = 1719826.685364, a = 936.160781, b = 152.527623\n", + "epoch 2172: loss = 1719826.029707, a = 936.185294, b = 152.527608\n", + "epoch 2173: loss = 1719825.376559, a = 936.209758, b = 152.527592\n", + "epoch 2174: loss = 1719824.725911, a = 936.234174, b = 152.527576\n", + "epoch 2175: loss = 1719824.077753, a = 936.258540, b = 152.527561\n", + "epoch 2176: loss = 1719823.432075, a = 936.282858, b = 152.527545\n", + "epoch 2177: loss = 1719822.788867, a = 936.307127, b = 152.527530\n", + "epoch 2178: loss = 1719822.148121, a = 936.331348, b = 152.527514\n", + "epoch 2179: loss = 1719821.509826, a = 936.355520, b = 152.527499\n", + "epoch 2180: loss = 1719820.873972, a = 936.379644, b = 152.527484\n", + "epoch 2181: loss = 1719820.240551, a = 936.403720, b = 152.527468\n", + "epoch 2182: loss = 1719819.609552, a = 936.427747, b = 152.527453\n", + "epoch 2183: loss = 1719818.980966, a = 936.451727, b = 152.527437\n", + "epoch 2184: loss = 1719818.354784, a = 936.475658, b = 152.527422\n", + "epoch 2185: loss = 1719817.730997, a = 936.499542, b = 152.527407\n", + "epoch 2186: loss = 1719817.109594, a = 936.523378, b = 152.527392\n", + "epoch 2187: loss = 1719816.490568, a = 936.547166, b = 152.527377\n", + "epoch 2188: loss = 1719815.873907, a = 936.570907, b = 152.527361\n", + "epoch 2189: loss = 1719815.259604, a = 936.594600, b = 152.527346\n", + "epoch 2190: loss = 1719814.647649, a = 936.618246, b = 152.527331\n", + "epoch 2191: loss = 1719814.038033, a = 936.641844, b = 152.527316\n", + "epoch 2192: loss = 1719813.430746, a = 936.665396, b = 152.527301\n", + "epoch 2193: loss = 1719812.825780, a = 936.688900, b = 152.527286\n", + "epoch 2194: loss = 1719812.223125, a = 936.712357, b = 152.527271\n", + "epoch 2195: loss = 1719811.622773, a = 936.735768, b = 152.527256\n", + "epoch 2196: loss = 1719811.024714, a = 936.759131, b = 152.527241\n", + "epoch 2197: loss = 1719810.428940, a = 936.782448, b = 152.527226\n", + "epoch 2198: loss = 1719809.835442, a = 936.805718, b = 152.527211\n", + "epoch 2199: loss = 1719809.244210, a = 936.828942, b = 152.527197\n", + "epoch 2200: loss = 1719808.655236, a = 936.852119, b = 152.527182\n", + "epoch 2201: loss = 1719808.068512, a = 936.875250, b = 152.527167\n", + "epoch 2202: loss = 1719807.484027, a = 936.898335, b = 152.527152\n", + "epoch 2203: loss = 1719806.901775, a = 936.921373, b = 152.527138\n", + "epoch 2204: loss = 1719806.321745, a = 936.944365, b = 152.527123\n", + "epoch 2205: loss = 1719805.743929, a = 936.967312, b = 152.527108\n", + "epoch 2206: loss = 1719805.168319, a = 936.990212, b = 152.527094\n", + "epoch 2207: loss = 1719804.594905, a = 937.013067, b = 152.527079\n", + "epoch 2208: loss = 1719804.023681, a = 937.035876, b = 152.527064\n", + "epoch 2209: loss = 1719803.454636, a = 937.058640, b = 152.527050\n", + "epoch 2210: loss = 1719802.887762, a = 937.081357, b = 152.527035\n", + "epoch 2211: loss = 1719802.323051, a = 937.104030, b = 152.527021\n", + "epoch 2212: loss = 1719801.760495, a = 937.126657, b = 152.527006\n", + "epoch 2213: loss = 1719801.200084, a = 937.149239, b = 152.526992\n", + "epoch 2214: loss = 1719800.641811, a = 937.171776, b = 152.526978\n", + "epoch 2215: loss = 1719800.085668, a = 937.194267, b = 152.526963\n", + "epoch 2216: loss = 1719799.531645, a = 937.216714, b = 152.526949\n", + "epoch 2217: loss = 1719798.979736, a = 937.239116, b = 152.526935\n", + "epoch 2218: loss = 1719798.429930, a = 937.261473, b = 152.526920\n", + "epoch 2219: loss = 1719797.882221, a = 937.283785, b = 152.526906\n", + "epoch 2220: loss = 1719797.336601, a = 937.306053, b = 152.526892\n", + "epoch 2221: loss = 1719796.793060, a = 937.328276, b = 152.526878\n", + "epoch 2222: loss = 1719796.251591, a = 937.350455, b = 152.526863\n", + "epoch 2223: loss = 1719795.712185, a = 937.372589, b = 152.526849\n", + "epoch 2224: loss = 1719795.174836, a = 937.394679, b = 152.526835\n", + "epoch 2225: loss = 1719794.639534, a = 937.416725, b = 152.526821\n", + "epoch 2226: loss = 1719794.106272, a = 937.438727, b = 152.526807\n", + "epoch 2227: loss = 1719793.575042, a = 937.460685, b = 152.526793\n", + "epoch 2228: loss = 1719793.045835, a = 937.482599, b = 152.526779\n", + "epoch 2229: loss = 1719792.518645, a = 937.504469, b = 152.526765\n", + "epoch 2230: loss = 1719791.993463, a = 937.526295, b = 152.526751\n", + "epoch 2231: loss = 1719791.470281, a = 937.548078, b = 152.526737\n", + "epoch 2232: loss = 1719790.949091, a = 937.569817, b = 152.526723\n", + "epoch 2233: loss = 1719790.429886, a = 937.591513, b = 152.526709\n", + "epoch 2234: loss = 1719789.912658, a = 937.613165, b = 152.526696\n", + "epoch 2235: loss = 1719789.397400, a = 937.634774, b = 152.526682\n", + "epoch 2236: loss = 1719788.884103, a = 937.656340, b = 152.526668\n", + "epoch 2237: loss = 1719788.372760, a = 937.677863, b = 152.526654\n", + "epoch 2238: loss = 1719787.863363, a = 937.699343, b = 152.526641\n", + "epoch 2239: loss = 1719787.355906, a = 937.720779, b = 152.526627\n", + "epoch 2240: loss = 1719786.850379, a = 937.742173, b = 152.526613\n", + "epoch 2241: loss = 1719786.346776, a = 937.763524, b = 152.526600\n", + "epoch 2242: loss = 1719785.845089, a = 937.784833, b = 152.526586\n", + "epoch 2243: loss = 1719785.345311, a = 937.806098, b = 152.526572\n", + "epoch 2244: loss = 1719784.847435, a = 937.827322, b = 152.526559\n", + "epoch 2245: loss = 1719784.351452, a = 937.848502, b = 152.526545\n", + "epoch 2246: loss = 1719783.857356, a = 937.869641, b = 152.526532\n", + "epoch 2247: loss = 1719783.365139, a = 937.890737, b = 152.526518\n", + "epoch 2248: loss = 1719782.874794, a = 937.911791, b = 152.526505\n", + "epoch 2249: loss = 1719782.386313, a = 937.932803, b = 152.526492\n", + "epoch 2250: loss = 1719781.899690, a = 937.953773, b = 152.526478\n", + "epoch 2251: loss = 1719781.414916, a = 937.974701, b = 152.526465\n", + "epoch 2252: loss = 1719780.931986, a = 937.995587, b = 152.526451\n", + "epoch 2253: loss = 1719780.450892, a = 938.016431, b = 152.526438\n", + "epoch 2254: loss = 1719779.971625, a = 938.037234, b = 152.526425\n", + "epoch 2255: loss = 1719779.494181, a = 938.057995, b = 152.526412\n", + "epoch 2256: loss = 1719779.018550, a = 938.078714, b = 152.526398\n", + "epoch 2257: loss = 1719778.544727, a = 938.099392, b = 152.526385\n", 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938.384581, b = 152.526203\n", + "epoch 2272: loss = 1719771.649537, a = 938.404648, b = 152.526190\n", + "epoch 2273: loss = 1719771.203693, a = 938.424674, b = 152.526177\n", + "epoch 2274: loss = 1719770.759540, a = 938.444660, b = 152.526165\n", + "epoch 2275: loss = 1719770.317071, a = 938.464607, b = 152.526152\n", + "epoch 2276: loss = 1719769.876281, a = 938.484513, b = 152.526139\n", + "epoch 2277: loss = 1719769.437162, a = 938.504380, b = 152.526126\n", + "epoch 2278: loss = 1719768.999709, a = 938.524207, b = 152.526114\n", + "epoch 2279: loss = 1719768.563915, a = 938.543994, b = 152.526101\n", + "epoch 2280: loss = 1719768.129773, a = 938.563742, b = 152.526089\n", + "epoch 2281: loss = 1719767.697276, a = 938.583450, b = 152.526076\n", + "epoch 2282: loss = 1719767.266420, a = 938.603119, b = 152.526063\n", + "epoch 2283: loss = 1719766.837196, a = 938.622748, b = 152.526051\n", + "epoch 2284: loss = 1719766.409599, a = 938.642338, b = 152.526038\n", + "epoch 2285: loss 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152.525866\n", + "epoch 2299: loss = 1719760.186616, a = 938.931534, b = 152.525854\n", + "epoch 2300: loss = 1719759.784204, a = 938.950507, b = 152.525842\n", + "epoch 2301: loss = 1719759.383315, a = 938.969442, b = 152.525829\n", + "epoch 2302: loss = 1719758.983942, a = 938.988339, b = 152.525817\n", + "epoch 2303: loss = 1719758.586080, a = 939.007198, b = 152.525805\n", + "epoch 2304: loss = 1719758.189723, a = 939.026019, b = 152.525793\n", + "epoch 2305: loss = 1719757.794866, a = 939.044803, b = 152.525781\n", + "epoch 2306: loss = 1719757.401501, a = 939.063549, b = 152.525769\n", + "epoch 2307: loss = 1719757.009624, a = 939.082258, b = 152.525757\n", + "epoch 2308: loss = 1719756.619228, a = 939.100929, b = 152.525745\n", + "epoch 2309: loss = 1719756.230309, a = 939.119563, b = 152.525734\n", + "epoch 2310: loss = 1719755.842860, a = 939.138160, b = 152.525722\n", + "epoch 2311: loss = 1719755.456875, a = 939.156719, b = 152.525710\n", + "epoch 2312: loss = 1719755.072349, a = 939.175242, b = 152.525698\n", + "epoch 2313: loss = 1719754.689276, a = 939.193727, b = 152.525686\n", + "epoch 2314: loss = 1719754.307651, a = 939.212176, b = 152.525674\n", + "epoch 2315: loss = 1719753.927467, a = 939.230587, b = 152.525663\n", + "epoch 2316: loss = 1719753.548720, a = 939.248962, b = 152.525651\n", + "epoch 2317: loss = 1719753.171404, a = 939.267300, b = 152.525639\n", + "epoch 2318: loss = 1719752.795512, a = 939.285601, b = 152.525627\n", + "epoch 2319: loss = 1719752.421041, a = 939.303866, b = 152.525616\n", + "epoch 2320: loss = 1719752.047983, a = 939.322094, b = 152.525604\n", + "epoch 2321: loss = 1719751.676335, a = 939.340286, b = 152.525593\n", + "epoch 2322: loss = 1719751.306089, a = 939.358441, b = 152.525581\n", + "epoch 2323: loss = 1719750.937241, a = 939.376560, b = 152.525569\n", + "epoch 2324: loss = 1719750.569786, a = 939.394643, b = 152.525558\n", + "epoch 2325: loss = 1719750.203717, a = 939.412690, b = 152.525546\n", 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939.661587, b = 152.525387\n", + "epoch 2340: loss = 1719744.875511, a = 939.679100, b = 152.525376\n", + "epoch 2341: loss = 1719744.530916, a = 939.696577, b = 152.525365\n", + "epoch 2342: loss = 1719744.187619, a = 939.714020, b = 152.525354\n", + "epoch 2343: loss = 1719743.845615, a = 939.731428, b = 152.525343\n", + "epoch 2344: loss = 1719743.504900, a = 939.748802, b = 152.525332\n", + "epoch 2345: loss = 1719743.165468, a = 939.766140, b = 152.525321\n", + "epoch 2346: loss = 1719742.827314, a = 939.783444, b = 152.525310\n", + "epoch 2347: loss = 1719742.490434, a = 939.800713, b = 152.525298\n", + "epoch 2348: loss = 1719742.154822, a = 939.817948, b = 152.525287\n", + "epoch 2349: loss = 1719741.820473, a = 939.835148, b = 152.525276\n", + "epoch 2350: loss = 1719741.487384, a = 939.852314, b = 152.525266\n", + "epoch 2351: loss = 1719741.155548, a = 939.869445, b = 152.525255\n", + "epoch 2352: loss = 1719740.824960, a = 939.886542, b = 152.525244\n", + "epoch 2353: loss 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152.525093\n", + "epoch 2367: loss = 1719736.012774, a = 940.138936, b = 152.525082\n", + "epoch 2368: loss = 1719735.701524, a = 940.155495, b = 152.525072\n", + "epoch 2369: loss = 1719735.391444, a = 940.172020, b = 152.525061\n", + "epoch 2370: loss = 1719735.082527, a = 940.188512, b = 152.525051\n", + "epoch 2371: loss = 1719734.774771, a = 940.204971, b = 152.525040\n", + "epoch 2372: loss = 1719734.468171, a = 940.221397, b = 152.525030\n", + "epoch 2373: loss = 1719734.162722, a = 940.237791, b = 152.525019\n", + "epoch 2374: loss = 1719733.858420, a = 940.254151, b = 152.525009\n", + "epoch 2375: loss = 1719733.555260, a = 940.270479, b = 152.524998\n", + "epoch 2376: loss = 1719733.253238, a = 940.286775, b = 152.524988\n", + "epoch 2377: loss = 1719732.952350, a = 940.303037, b = 152.524978\n", + "epoch 2378: loss = 1719732.652590, a = 940.319268, b = 152.524967\n", + "epoch 2379: loss = 1719732.353956, a = 940.335465, b = 152.524957\n", + "epoch 2380: loss = 1719732.056441, a = 940.351631, b = 152.524947\n", + "epoch 2381: loss = 1719731.760043, a = 940.367764, b = 152.524936\n", + "epoch 2382: loss = 1719731.464757, a = 940.383864, b = 152.524926\n", + "epoch 2383: loss = 1719731.170578, a = 940.399933, b = 152.524916\n", + "epoch 2384: loss = 1719730.877503, a = 940.415969, b = 152.524906\n", + "epoch 2385: loss = 1719730.585526, a = 940.431974, b = 152.524895\n", + "epoch 2386: loss = 1719730.294644, a = 940.447946, b = 152.524885\n", + "epoch 2387: loss = 1719730.004853, a = 940.463887, b = 152.524875\n", + "epoch 2388: loss = 1719729.716147, a = 940.479795, b = 152.524865\n", + "epoch 2389: loss = 1719729.428524, a = 940.495672, b = 152.524855\n", + "epoch 2390: loss = 1719729.141979, a = 940.511517, b = 152.524844\n", + "epoch 2391: loss = 1719728.856507, a = 940.527330, b = 152.524834\n", + "epoch 2392: loss = 1719728.572105, a = 940.543112, b = 152.524824\n", + "epoch 2393: loss = 1719728.288769, a = 940.558862, b = 152.524814\n", 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940.776085, b = 152.524676\n", + "epoch 2408: loss = 1719724.163811, a = 940.791369, b = 152.524666\n", + "epoch 2409: loss = 1719723.896972, a = 940.806623, b = 152.524656\n", + "epoch 2410: loss = 1719723.631131, a = 940.821846, b = 152.524646\n", + "epoch 2411: loss = 1719723.366284, a = 940.837039, b = 152.524637\n", + "epoch 2412: loss = 1719723.102427, a = 940.852201, b = 152.524627\n", + "epoch 2413: loss = 1719722.839556, a = 940.867333, b = 152.524617\n", + "epoch 2414: loss = 1719722.577667, a = 940.882435, b = 152.524608\n", + "epoch 2415: loss = 1719722.316757, a = 940.897506, b = 152.524598\n", + "epoch 2416: loss = 1719722.056821, a = 940.912548, b = 152.524588\n", + "epoch 2417: loss = 1719721.797857, a = 940.927559, b = 152.524579\n", + "epoch 2418: loss = 1719721.539859, a = 940.942541, b = 152.524569\n", + "epoch 2419: loss = 1719721.282826, a = 940.957492, b = 152.524560\n", + "epoch 2420: loss = 1719721.026752, a = 940.972414, b = 152.524550\n", + "epoch 2421: loss = 1719720.771634, a = 940.987305, b = 152.524541\n", + "epoch 2422: loss = 1719720.517469, a = 941.002167, b = 152.524531\n", + "epoch 2423: loss = 1719720.264252, a = 941.016999, b = 152.524522\n", + "epoch 2424: loss = 1719720.011980, a = 941.031802, b = 152.524512\n", + "epoch 2425: loss = 1719719.760650, a = 941.046575, b = 152.524503\n", + "epoch 2426: loss = 1719719.510258, a = 941.061318, b = 152.524493\n", + "epoch 2427: loss = 1719719.260800, a = 941.076032, b = 152.524484\n", + "epoch 2428: loss = 1719719.012273, a = 941.090716, b = 152.524475\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 2429: loss = 1719718.764672, a = 941.105371, b = 152.524465\n", + "epoch 2430: loss = 1719718.517995, a = 941.119997, b = 152.524456\n", + "epoch 2431: loss = 1719718.272238, a = 941.134594, b = 152.524447\n", + "epoch 2432: loss = 1719718.027398, a = 941.149161, b = 152.524437\n", + "epoch 2433: loss = 1719717.783470, a = 941.163699, b = 152.524428\n", 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941.364208, b = 152.524300\n", + "epoch 2448: loss = 1719714.231731, a = 941.378316, b = 152.524291\n", + "epoch 2449: loss = 1719714.001940, a = 941.392396, b = 152.524282\n", + "epoch 2450: loss = 1719713.773003, a = 941.406448, b = 152.524273\n", + "epoch 2451: loss = 1719713.544917, a = 941.420472, b = 152.524264\n", + "epoch 2452: loss = 1719713.317680, a = 941.434467, b = 152.524255\n", + "epoch 2453: loss = 1719713.091289, a = 941.448435, b = 152.524246\n", + "epoch 2454: loss = 1719712.865738, a = 941.462375, b = 152.524237\n", + "epoch 2455: loss = 1719712.641027, a = 941.476287, b = 152.524228\n", + "epoch 2456: loss = 1719712.417151, a = 941.490171, b = 152.524219\n", + "epoch 2457: loss = 1719712.194107, a = 941.504027, b = 152.524211\n", + "epoch 2458: loss = 1719711.971892, a = 941.517856, b = 152.524202\n", + "epoch 2459: loss = 1719711.750502, a = 941.531657, b = 152.524193\n", + "epoch 2460: loss = 1719711.529935, a = 941.545430, b = 152.524184\n", + "epoch 2461: loss = 1719711.310188, a = 941.559176, b = 152.524175\n", + "epoch 2462: loss = 1719711.091257, a = 941.572894, b = 152.524167\n", + "epoch 2463: loss = 1719710.873139, a = 941.586585, b = 152.524158\n", + "epoch 2464: loss = 1719710.655831, a = 941.600249, b = 152.524149\n", + "epoch 2465: loss = 1719710.439329, a = 941.613885, b = 152.524140\n", + "epoch 2466: loss = 1719710.223632, a = 941.627494, b = 152.524132\n", + "epoch 2467: loss = 1719710.008735, a = 941.641075, b = 152.524123\n", + "epoch 2468: loss = 1719709.794636, a = 941.654630, b = 152.524114\n", + "epoch 2469: loss = 1719709.581332, a = 941.668157, b = 152.524106\n", + "epoch 2470: loss = 1719709.368819, a = 941.681658, b = 152.524097\n", + "epoch 2471: loss = 1719709.157094, a = 941.695131, b = 152.524089\n", + "epoch 2472: loss = 1719708.946155, a = 941.708578, b = 152.524080\n", + "epoch 2473: loss = 1719708.735998, a = 941.721997, b = 152.524071\n", + "epoch 2474: loss = 1719708.526620, a = 941.735390, b = 152.524063\n", + "epoch 2475: loss = 1719708.318019, a = 941.748756, b = 152.524054\n", + "epoch 2476: loss = 1719708.110192, a = 941.762095, b = 152.524046\n", + "epoch 2477: loss = 1719707.903135, a = 941.775408, b = 152.524037\n", + "epoch 2478: loss = 1719707.696845, a = 941.788694, b = 152.524029\n", + "epoch 2479: loss = 1719707.491320, a = 941.801953, b = 152.524020\n", + "epoch 2480: loss = 1719707.286557, a = 941.815186, b = 152.524012\n", + "epoch 2481: loss = 1719707.082552, a = 941.828392, b = 152.524003\n", + "epoch 2482: loss = 1719706.879303, a = 941.841572, b = 152.523995\n", + "epoch 2483: loss = 1719706.676807, a = 941.854726, b = 152.523987\n", + "epoch 2484: loss = 1719706.475060, a = 941.867853, b = 152.523978\n", + "epoch 2485: loss = 1719706.274061, a = 941.880954, b = 152.523970\n", + "epoch 2486: loss = 1719706.073807, a = 941.894029, b = 152.523961\n", + "epoch 2487: loss = 1719705.874293, a = 941.907078, b = 152.523953\n", + "epoch 2488: loss = 1719705.675518, a = 941.920100, b = 152.523945\n", + "epoch 2489: loss = 1719705.477479, a = 941.933097, b = 152.523937\n", + "epoch 2490: loss = 1719705.280173, a = 941.946068, b = 152.523928\n", + "epoch 2491: loss = 1719705.083597, a = 941.959012, b = 152.523920\n", + "epoch 2492: loss = 1719704.887748, a = 941.971931, b = 152.523912\n", + "epoch 2493: loss = 1719704.692623, a = 941.984824, b = 152.523903\n", + "epoch 2494: loss = 1719704.498220, a = 941.997691, b = 152.523895\n", + "epoch 2495: loss = 1719704.304537, a = 942.010533, b = 152.523887\n", + "epoch 2496: loss = 1719704.111569, a = 942.023348, b = 152.523879\n", + "epoch 2497: loss = 1719703.919315, a = 942.036138, b = 152.523871\n", + "epoch 2498: loss = 1719703.727771, a = 942.048903, b = 152.523863\n", + "epoch 2499: loss = 1719703.536936, a = 942.061642, b = 152.523854\n", + "epoch 2500: loss = 1719703.346805, a = 942.074356, b = 152.523846\n", + "epoch 2501: loss = 1719703.157378, a = 942.087044, b = 152.523838\n", + "epoch 2502: loss = 1719702.968650, a = 942.099706, b = 152.523830\n", + "epoch 2503: loss = 1719702.780619, a = 942.112344, b = 152.523822\n", + "epoch 2504: loss = 1719702.593283, a = 942.124956, b = 152.523814\n", + "epoch 2505: loss = 1719702.406639, a = 942.137543, b = 152.523806\n", + "epoch 2506: loss = 1719702.220684, a = 942.150105, b = 152.523798\n", + "epoch 2507: loss = 1719702.035415, a = 942.162641, b = 152.523790\n", + "epoch 2508: loss = 1719701.850830, a = 942.175153, b = 152.523782\n", + "epoch 2509: loss = 1719701.666927, a = 942.187640, b = 152.523774\n", + "epoch 2510: loss = 1719701.483702, a = 942.200101, b = 152.523766\n", + "epoch 2511: loss = 1719701.301154, a = 942.212538, b = 152.523758\n", + "epoch 2512: loss = 1719701.119279, a = 942.224950, b = 152.523750\n", + "epoch 2513: loss = 1719700.938075, a = 942.237337, b = 152.523742\n", + "epoch 2514: loss = 1719700.757539, a = 942.249699, b = 152.523734\n", + "epoch 2515: loss = 1719700.577669, a = 942.262036, b = 152.523726\n", + "epoch 2516: loss = 1719700.398462, a = 942.274349, b = 152.523719\n", + "epoch 2517: loss = 1719700.219916, a = 942.286637, b = 152.523711\n", + "epoch 2518: loss = 1719700.042028, a = 942.298901, b = 152.523703\n", + "epoch 2519: loss = 1719699.864796, a = 942.311140, b = 152.523695\n", + "epoch 2520: loss = 1719699.688217, a = 942.323355, b = 152.523687\n", + "epoch 2521: loss = 1719699.512289, a = 942.335545, b = 152.523679\n", + "epoch 2522: loss = 1719699.337009, a = 942.347711, b = 152.523672\n", + "epoch 2523: loss = 1719699.162374, a = 942.359852, b = 152.523664\n", + "epoch 2524: loss = 1719698.988383, a = 942.371970, b = 152.523656\n", + "epoch 2525: loss = 1719698.815032, a = 942.384063, b = 152.523649\n", + "epoch 2526: loss = 1719698.642320, a = 942.396132, b = 152.523641\n", + "epoch 2527: loss = 1719698.470244, a = 942.408176, b = 152.523633\n", + "epoch 2528: loss = 1719698.298801, a = 942.420197, b = 152.523625\n", + "epoch 2529: loss = 1719698.127989, a = 942.432193, b = 152.523618\n", + "epoch 2530: loss = 1719697.957806, a = 942.444166, b = 152.523610\n", + "epoch 2531: loss = 1719697.788248, a = 942.456115, b = 152.523602\n", + "epoch 2532: loss = 1719697.619315, a = 942.468039, b = 152.523595\n", + "epoch 2533: loss = 1719697.451003, a = 942.479940, b = 152.523587\n", + "epoch 2534: loss = 1719697.283310, a = 942.491817, b = 152.523580\n", + "epoch 2535: loss = 1719697.116234, a = 942.503671, b = 152.523572\n", + "epoch 2536: loss = 1719696.949772, a = 942.515500, b = 152.523565\n", + "epoch 2537: loss = 1719696.783922, a = 942.527306, b = 152.523557\n", + "epoch 2538: loss = 1719696.618682, a = 942.539089, b = 152.523549\n", + "epoch 2539: loss = 1719696.454049, a = 942.550847, b = 152.523542\n", + "epoch 2540: loss = 1719696.290020, a = 942.562583, b = 152.523534\n", + "epoch 2541: loss = 1719696.126595, a = 942.574295, b = 152.523527\n", + "epoch 2542: loss = 1719695.963770, a = 942.585983, b = 152.523520\n", + "epoch 2543: loss = 1719695.801543, a = 942.597648, b = 152.523512\n", + "epoch 2544: loss = 1719695.639912, a = 942.609290, b = 152.523505\n", + "epoch 2545: loss = 1719695.478874, a = 942.620908, b = 152.523497\n", + "epoch 2546: loss = 1719695.318427, a = 942.632503, b = 152.523490\n", + "epoch 2547: loss = 1719695.158570, a = 942.644075, b = 152.523482\n", + "epoch 2548: loss = 1719694.999299, a = 942.655624, b = 152.523475\n", + "epoch 2549: loss = 1719694.840613, a = 942.667150, b = 152.523468\n", + "epoch 2550: loss = 1719694.682509, a = 942.678653, b = 152.523460\n", + "epoch 2551: loss = 1719694.524985, a = 942.690133, b = 152.523453\n", + "epoch 2552: loss = 1719694.368039, a = 942.701589, b = 152.523446\n", + "epoch 2553: loss = 1719694.211668, a = 942.713023, b = 152.523438\n", + "epoch 2554: loss = 1719694.055871, a = 942.724434, b = 152.523431\n", + "epoch 2555: loss = 1719693.900645, a = 942.735822, b = 152.523424\n", + "epoch 2556: loss = 1719693.745988, a = 942.747188, b = 152.523417\n", + "epoch 2557: loss = 1719693.591898, a = 942.758531, b = 152.523409\n", + "epoch 2558: loss = 1719693.438373, a = 942.769851, b = 152.523402\n", + "epoch 2559: loss = 1719693.285410, a = 942.781148, b = 152.523395\n", + "epoch 2560: loss = 1719693.133008, a = 942.792423, b = 152.523388\n", + "epoch 2561: loss = 1719692.981164, a = 942.803675, b = 152.523380\n", + "epoch 2562: loss = 1719692.829876, a = 942.814905, b = 152.523373\n", + "epoch 2563: loss = 1719692.679142, a = 942.826112, b = 152.523366\n", + "epoch 2564: loss = 1719692.528960, a = 942.837297, b = 152.523359\n", + "epoch 2565: loss = 1719692.379328, a = 942.848459, b = 152.523352\n", + "epoch 2566: loss = 1719692.230244, a = 942.859600, b = 152.523345\n", + "epoch 2567: loss = 1719692.081705, a = 942.870717, b = 152.523338\n", + "epoch 2568: loss = 1719691.933710, a = 942.881813, b = 152.523331\n", + "epoch 2569: loss = 1719691.786256, a = 942.892887, b = 152.523324\n", + "epoch 2570: loss = 1719691.639342, a = 942.903938, b = 152.523316\n", + "epoch 2571: loss = 1719691.492965, a = 942.914967, b = 152.523309\n", + "epoch 2572: loss = 1719691.347123, a = 942.925974, b = 152.523302\n", + "epoch 2573: loss = 1719691.201815, a = 942.936960, b = 152.523295\n", + "epoch 2574: loss = 1719691.057038, a = 942.947923, b = 152.523288\n", + "epoch 2575: loss = 1719690.912790, a = 942.958864, b = 152.523281\n", + "epoch 2576: loss = 1719690.769069, a = 942.969783, b = 152.523274\n", + "epoch 2577: loss = 1719690.625873, a = 942.980681, b = 152.523267\n", + "epoch 2578: loss = 1719690.483201, a = 942.991557, b = 152.523260\n", + "epoch 2579: loss = 1719690.341050, a = 943.002411, b = 152.523254\n", + "epoch 2580: loss = 1719690.199418, a = 943.013243, b = 152.523247\n", + "epoch 2581: loss = 1719690.058303, a = 943.024054, b = 152.523240\n", + "epoch 2582: loss = 1719689.917704, a = 943.034843, b = 152.523233\n", + "epoch 2583: loss = 1719689.777618, a = 943.045610, b = 152.523226\n", + "epoch 2584: loss = 1719689.638043, a = 943.056356, b = 152.523219\n", + "epoch 2585: loss = 1719689.498977, a = 943.067081, b = 152.523212\n", + "epoch 2586: loss = 1719689.360419, a = 943.077784, b = 152.523205\n", + "epoch 2587: loss = 1719689.222367, a = 943.088465, b = 152.523199\n", + "epoch 2588: loss = 1719689.084818, a = 943.099126, b = 152.523192\n", + "epoch 2589: loss = 1719688.947771, a = 943.109765, b = 152.523185\n", + "epoch 2590: loss = 1719688.811224, a = 943.120382, b = 152.523178\n", + "epoch 2591: loss = 1719688.675174, a = 943.130979, b = 152.523171\n", + "epoch 2592: loss = 1719688.539621, a = 943.141554, b = 152.523165\n", + "epoch 2593: loss = 1719688.404562, a = 943.152108, b = 152.523158\n", + "epoch 2594: loss = 1719688.269995, a = 943.162641, b = 152.523151\n", + "epoch 2595: loss = 1719688.135918, a = 943.173153, b = 152.523145\n", + "epoch 2596: loss = 1719688.002330, a = 943.183644, b = 152.523138\n", + "epoch 2597: loss = 1719687.869228, a = 943.194114, b = 152.523131\n", + "epoch 2598: loss = 1719687.736611, a = 943.204563, b = 152.523124\n", + "epoch 2599: loss = 1719687.604477, a = 943.214991, b = 152.523118\n", + "epoch 2600: loss = 1719687.472824, a = 943.225398, b = 152.523111\n", + "epoch 2601: loss = 1719687.341651, a = 943.235784, b = 152.523105\n", + "epoch 2602: loss = 1719687.210955, a = 943.246150, b = 152.523098\n", + "epoch 2603: loss = 1719687.080734, a = 943.256495, b = 152.523091\n", + "epoch 2604: loss = 1719686.950988, a = 943.266819, b = 152.523085\n", + "epoch 2605: loss = 1719686.821713, a = 943.277123, b = 152.523078\n", + "epoch 2606: loss = 1719686.692909, a = 943.287406, b = 152.523072\n", + "epoch 2607: loss = 1719686.564573, a = 943.297668, b = 152.523065\n", + "epoch 2608: loss = 1719686.436703, a = 943.307910, b = 152.523058\n", + "epoch 2609: loss = 1719686.309299, a = 943.318132, b = 152.523052\n", + "epoch 2610: loss = 1719686.182358, a = 943.328333, b = 152.523045\n", + "epoch 2611: loss = 1719686.055878, a = 943.338513, b = 152.523039\n", + "epoch 2612: loss = 1719685.929858, a = 943.348673, b = 152.523032\n", + "epoch 2613: loss = 1719685.804295, a = 943.358813, b = 152.523026\n", + "epoch 2614: loss = 1719685.679189, a = 943.368933, b = 152.523019\n", + "epoch 2615: loss = 1719685.554537, a = 943.379032, b = 152.523013\n", + "epoch 2616: loss = 1719685.430338, a = 943.389112, b = 152.523007\n", + "epoch 2617: loss = 1719685.306590, a = 943.399171, b = 152.523000\n", + "epoch 2618: loss = 1719685.183291, a = 943.409210, b = 152.522994\n", + "epoch 2619: loss = 1719685.060440, a = 943.419228, b = 152.522987\n", + "epoch 2620: loss = 1719684.938034, a = 943.429227, b = 152.522981\n", + "epoch 2621: loss = 1719684.816073, a = 943.439206, b = 152.522975\n", + "epoch 2622: loss = 1719684.694554, a = 943.449165, b = 152.522968\n", + "epoch 2623: loss = 1719684.573476, a = 943.459104, b = 152.522962\n", + "epoch 2624: loss = 1719684.452837, a = 943.469023, b = 152.522956\n", + "epoch 2625: loss = 1719684.332636, a = 943.478922, b = 152.522949\n", + "epoch 2626: loss = 1719684.212870, a = 943.488802, b = 152.522943\n", + "epoch 2627: loss = 1719684.093539, a = 943.498662, b = 152.522937\n", + "epoch 2628: loss = 1719683.974640, a = 943.508502, b = 152.522930\n", + "epoch 2629: loss = 1719683.856171, a = 943.518322, b = 152.522924\n", + "epoch 2630: loss = 1719683.738133, a = 943.528123, b = 152.522918\n", + "epoch 2631: loss = 1719683.620521, a = 943.537904, b = 152.522912\n", + "epoch 2632: loss = 1719683.503336, a = 943.547665, b = 152.522905\n", + "epoch 2633: loss = 1719683.386575, a = 943.557407, b = 152.522899\n", + "epoch 2634: loss = 1719683.270237, a = 943.567130, b = 152.522893\n", + "epoch 2635: loss = 1719683.154320, a = 943.576833, b = 152.522887\n", + "epoch 2636: loss = 1719683.038822, a = 943.586516, b = 152.522880\n", + "epoch 2637: loss = 1719682.923743, a = 943.596181, b = 152.522874\n", + "epoch 2638: loss = 1719682.809080, a = 943.605826, b = 152.522868\n", + "epoch 2639: loss = 1719682.694831, a = 943.615451, b = 152.522862\n", + "epoch 2640: loss = 1719682.580996, a = 943.625058, b = 152.522856\n", + "epoch 2641: loss = 1719682.467573, a = 943.634645, b = 152.522850\n", + "epoch 2642: loss = 1719682.354560, a = 943.644213, b = 152.522844\n", + "epoch 2643: loss = 1719682.241955, a = 943.653762, b = 152.522838\n", + "epoch 2644: loss = 1719682.129757, a = 943.663292, b = 152.522831\n", + "epoch 2645: loss = 1719682.017965, a = 943.672803, b = 152.522825\n", + "epoch 2646: loss = 1719681.906577, a = 943.682294, b = 152.522819\n", + "epoch 2647: loss = 1719681.795591, a = 943.691767, b = 152.522813\n", + "epoch 2648: loss = 1719681.685006, a = 943.701221, b = 152.522807\n", + "epoch 2649: loss = 1719681.574821, a = 943.710656, b = 152.522801\n", + "epoch 2650: loss = 1719681.465033, a = 943.720072, b = 152.522795\n", + "epoch 2651: loss = 1719681.355642, a = 943.729469, b = 152.522789\n", + "epoch 2652: loss = 1719681.246645, a = 943.738848, b = 152.522783\n", + "epoch 2653: loss = 1719681.138042, a = 943.748208, b = 152.522777\n", + "epoch 2654: loss = 1719681.029831, a = 943.757548, b = 152.522771\n", + "epoch 2655: loss = 1719680.922010, a = 943.766871, b = 152.522765\n", + "epoch 2656: loss = 1719680.814578, a = 943.776174, b = 152.522759\n", + "epoch 2657: loss = 1719680.707534, a = 943.785460, b = 152.522753\n", + "epoch 2658: loss = 1719680.600876, a = 943.794726, b = 152.522748\n", + "epoch 2659: loss = 1719680.494602, a = 943.803974, b = 152.522742\n", + "epoch 2660: loss = 1719680.388711, a = 943.813203, b = 152.522736\n", + "epoch 2661: loss = 1719680.283202, a = 943.822414, b = 152.522730\n", + "epoch 2662: loss = 1719680.178073, a = 943.831607, b = 152.522724\n", + "epoch 2663: loss = 1719680.073323, a = 943.840781, b = 152.522718\n", + "epoch 2664: loss = 1719679.968950, a = 943.849937, b = 152.522712\n", + "epoch 2665: loss = 1719679.864953, a = 943.859075, b = 152.522706\n", + "epoch 2666: loss = 1719679.761330, a = 943.868194, b = 152.522701\n", + "epoch 2667: loss = 1719679.658081, a = 943.877295, b = 152.522695\n", + "epoch 2668: loss = 1719679.555203, a = 943.886378, b = 152.522689\n", + "epoch 2669: loss = 1719679.452696, a = 943.895443, b = 152.522683\n", + "epoch 2670: loss = 1719679.350557, a = 943.904489, b = 152.522677\n", + "epoch 2671: loss = 1719679.248786, a = 943.913518, b = 152.522672\n", + "epoch 2672: loss = 1719679.147381, a = 943.922528, b = 152.522666\n", + "epoch 2673: loss = 1719679.046340, a = 943.931520, b = 152.522660\n", + "epoch 2674: loss = 1719678.945663, a = 943.940495, b = 152.522654\n", + "epoch 2675: loss = 1719678.845348, a = 943.949451, b = 152.522649\n", + "epoch 2676: loss = 1719678.745393, a = 943.958390, b = 152.522643\n", + "epoch 2677: loss = 1719678.645798, a = 943.967311, b = 152.522637\n", + "epoch 2678: loss = 1719678.546560, a = 943.976213, b = 152.522632\n", + "epoch 2679: loss = 1719678.447679, a = 943.985098, b = 152.522626\n", + "epoch 2680: loss = 1719678.349153, a = 943.993966, b = 152.522620\n", + "epoch 2681: loss = 1719678.250981, a = 944.002815, b = 152.522615\n", + "epoch 2682: loss = 1719678.153162, a = 944.011647, b = 152.522609\n", + "epoch 2683: loss = 1719678.055694, a = 944.020461, b = 152.522603\n", + "epoch 2684: loss = 1719677.958575, a = 944.029258, b = 152.522598\n", + "epoch 2685: loss = 1719677.861805, a = 944.038037, b = 152.522592\n", + "epoch 2686: loss = 1719677.765383, a = 944.046798, b = 152.522587\n", + "epoch 2687: loss = 1719677.669306, a = 944.055542, b = 152.522581\n", + "epoch 2688: loss = 1719677.573574, a = 944.064269, b = 152.522575\n", + "epoch 2689: loss = 1719677.478185, a = 944.072978, b = 152.522570\n", + "epoch 2690: loss = 1719677.383138, a = 944.081669, b = 152.522564\n", + "epoch 2691: loss = 1719677.288432, a = 944.090343, b = 152.522559\n", + "epoch 2692: loss = 1719677.194065, a = 944.099000, b = 152.522553\n", + "epoch 2693: loss = 1719677.100036, a = 944.107640, b = 152.522548\n", + "epoch 2694: loss = 1719677.006344, a = 944.116262, b = 152.522542\n", + "epoch 2695: loss = 1719676.912988, a = 944.124867, b = 152.522537\n", + "epoch 2696: loss = 1719676.819967, a = 944.133455, b = 152.522531\n", + "epoch 2697: loss = 1719676.727278, a = 944.142025, b = 152.522526\n", + "epoch 2698: loss = 1719676.634921, a = 944.150579, b = 152.522520\n", + "epoch 2699: loss = 1719676.542895, a = 944.159115, b = 152.522515\n", + "epoch 2700: loss = 1719676.451198, a = 944.167634, b = 152.522509\n", + "epoch 2701: loss = 1719676.359830, a = 944.176137, b = 152.522504\n", + "epoch 2702: loss = 1719676.268788, a = 944.184622, b = 152.522498\n", + "epoch 2703: loss = 1719676.178072, a = 944.193090, b = 152.522493\n", + "epoch 2704: loss = 1719676.087680, a = 944.201542, b = 152.522488\n", + "epoch 2705: loss = 1719675.997611, a = 944.209976, b = 152.522482\n", + "epoch 2706: loss = 1719675.907865, a = 944.218394, b = 152.522477\n", + "epoch 2707: loss = 1719675.818439, a = 944.226794, b = 152.522472\n", + "epoch 2708: loss = 1719675.729333, a = 944.235178, b = 152.522466\n", + "epoch 2709: loss = 1719675.640545, a = 944.243546, b = 152.522461\n", + "epoch 2710: loss = 1719675.552074, a = 944.251896, b = 152.522456\n", + "epoch 2711: loss = 1719675.463919, a = 944.260230, b = 152.522450\n", + "epoch 2712: loss = 1719675.376079, a = 944.268547, b = 152.522445\n", + "epoch 2713: loss = 1719675.288553, a = 944.276847, b = 152.522440\n", + "epoch 2714: loss = 1719675.201339, a = 944.285131, b = 152.522434\n", + "epoch 2715: loss = 1719675.114437, a = 944.293399, b = 152.522429\n", + "epoch 2716: loss = 1719675.027844, a = 944.301649, b = 152.522424\n", + "epoch 2717: loss = 1719674.941560, a = 944.309884, b = 152.522418\n", + "epoch 2718: loss = 1719674.855585, a = 944.318101, b = 152.522413\n", + "epoch 2719: loss = 1719674.769915, a = 944.326303, b = 152.522408\n", + "epoch 2720: loss = 1719674.684551, a = 944.334488, b = 152.522403\n", + "epoch 2721: loss = 1719674.599492, a = 944.342656, b = 152.522398\n", + "epoch 2722: loss = 1719674.514736, a = 944.350809, b = 152.522392\n", + "epoch 2723: loss = 1719674.430281, a = 944.358945, b = 152.522387\n", + "epoch 2724: loss = 1719674.346128, a = 944.367064, b = 152.522382\n", + "epoch 2725: loss = 1719674.262274, a = 944.375168, b = 152.522377\n", + "epoch 2726: loss = 1719674.178719, a = 944.383255, b = 152.522372\n", + "epoch 2727: loss = 1719674.095462, a = 944.391326, b = 152.522366\n", + "epoch 2728: loss = 1719674.012501, a = 944.399381, b = 152.522361\n", + "epoch 2729: loss = 1719673.929835, a = 944.407420, b = 152.522356\n", + "epoch 2730: loss = 1719673.847464, a = 944.415443, b = 152.522351\n", + "epoch 2731: loss = 1719673.765385, a = 944.423450, b = 152.522346\n", + "epoch 2732: loss = 1719673.683599, a = 944.431440, b = 152.522341\n", + "epoch 2733: loss = 1719673.602103, a = 944.439415, b = 152.522336\n", + "epoch 2734: loss = 1719673.520897, a = 944.447374, b = 152.522331\n", + "epoch 2735: loss = 1719673.439980, a = 944.455317, b = 152.522326\n", + "epoch 2736: loss = 1719673.359351, a = 944.463244, b = 152.522321\n", + "epoch 2737: loss = 1719673.279008, a = 944.471155, b = 152.522315\n", + "epoch 2738: loss = 1719673.198950, a = 944.479050, b = 152.522310\n", + "epoch 2739: loss = 1719673.119177, a = 944.486930, b = 152.522305\n", + "epoch 2740: loss = 1719673.039688, a = 944.494793, b = 152.522300\n", + "epoch 2741: loss = 1719672.960480, a = 944.502642, b = 152.522295\n", + "epoch 2742: loss = 1719672.881554, a = 944.510474, b = 152.522290\n", + "epoch 2743: loss = 1719672.802908, a = 944.518291, b = 152.522285\n", + "epoch 2744: loss = 1719672.724541, a = 944.526092, b = 152.522280\n", + "epoch 2745: loss = 1719672.646452, a = 944.533877, b = 152.522275\n", + "epoch 2746: loss = 1719672.568640, a = 944.541647, b = 152.522270\n", + "epoch 2747: loss = 1719672.491105, a = 944.549401, b = 152.522266\n", + "epoch 2748: loss = 1719672.413844, a = 944.557140, b = 152.522261\n", + "epoch 2749: loss = 1719672.336857, a = 944.564864, b = 152.522256\n", + "epoch 2750: loss = 1719672.260143, a = 944.572572, b = 152.522251\n", + "epoch 2751: loss = 1719672.183701, a = 944.580264, b = 152.522246\n", + "epoch 2752: loss = 1719672.107530, a = 944.587941, b = 152.522241\n", + "epoch 2753: loss = 1719672.031629, a = 944.595603, b = 152.522236\n", + "epoch 2754: loss = 1719671.955996, a = 944.603250, b = 152.522231\n", + "epoch 2755: loss = 1719671.880631, a = 944.610881, b = 152.522226\n", + "epoch 2756: loss = 1719671.805534, a = 944.618497, b = 152.522221\n", + "epoch 2757: loss = 1719671.730702, a = 944.626097, b = 152.522217\n", + "epoch 2758: loss = 1719671.656135, a = 944.633683, b = 152.522212\n", + "epoch 2759: loss = 1719671.581832, a = 944.641253, b = 152.522207\n", + "epoch 2760: loss = 1719671.507791, a = 944.648808, b = 152.522202\n", + "epoch 2761: loss = 1719671.434013, a = 944.656348, b = 152.522197\n", + "epoch 2762: loss = 1719671.360495, a = 944.663873, b = 152.522192\n", + "epoch 2763: loss = 1719671.287238, a = 944.671383, b = 152.522188\n", + "epoch 2764: loss = 1719671.214239, a = 944.678878, b = 152.522183\n", + "epoch 2765: loss = 1719671.141498, a = 944.686358, b = 152.522178\n", + "epoch 2766: loss = 1719671.069015, a = 944.693823, b = 152.522173\n", + "epoch 2767: loss = 1719670.996787, a = 944.701273, b = 152.522169\n", + "epoch 2768: loss = 1719670.924815, a = 944.708708, b = 152.522164\n", + "epoch 2769: loss = 1719670.853097, a = 944.716129, b = 152.522159\n", + "epoch 2770: loss = 1719670.781632, a = 944.723534, b = 152.522154\n", + "epoch 2771: loss = 1719670.710419, a = 944.730925, b = 152.522150\n", + "epoch 2772: loss = 1719670.639458, a = 944.738301, b = 152.522145\n", + "epoch 2773: loss = 1719670.568747, a = 944.745662, b = 152.522140\n", + "epoch 2774: loss = 1719670.498286, a = 944.753008, b = 152.522135\n", + "epoch 2775: loss = 1719670.428073, a = 944.760340, b = 152.522131\n", + "epoch 2776: loss = 1719670.358107, a = 944.767657, b = 152.522126\n", + "epoch 2777: loss = 1719670.288389, a = 944.774959, b = 152.522121\n", + "epoch 2778: loss = 1719670.218916, a = 944.782247, b = 152.522117\n", + "epoch 2779: loss = 1719670.149688, a = 944.789521, b = 152.522112\n", + "epoch 2780: loss = 1719670.080704, a = 944.796779, b = 152.522108\n", + "epoch 2781: loss = 1719670.011963, a = 944.804023, b = 152.522103\n", + "epoch 2782: loss = 1719669.943464, a = 944.811253, b = 152.522098\n", + "epoch 2783: loss = 1719669.875206, a = 944.818468, b = 152.522094\n", + "epoch 2784: loss = 1719669.807189, a = 944.825669, b = 152.522089\n", + "epoch 2785: loss = 1719669.739411, a = 944.832856, b = 152.522084\n", + "epoch 2786: loss = 1719669.671871, a = 944.840028, b = 152.522080\n", + "epoch 2787: loss = 1719669.604569, a = 944.847185, b = 152.522075\n", + "epoch 2788: loss = 1719669.537504, a = 944.854329, b = 152.522071\n", + "epoch 2789: loss = 1719669.470675, a = 944.861458, b = 152.522066\n", + "epoch 2790: loss = 1719669.404081, a = 944.868573, b = 152.522062\n", + "epoch 2791: loss = 1719669.337721, a = 944.875673, b = 152.522057\n", + "epoch 2792: loss = 1719669.271594, a = 944.882760, b = 152.522053\n", + "epoch 2793: loss = 1719669.205700, a = 944.889832, b = 152.522048\n", + "epoch 2794: loss = 1719669.140037, a = 944.896890, b = 152.522044\n", + "epoch 2795: loss = 1719669.074604, a = 944.903934, b = 152.522039\n", + "epoch 2796: loss = 1719669.009402, a = 944.910964, b = 152.522035\n", + "epoch 2797: loss = 1719668.944428, a = 944.917980, b = 152.522030\n", + "epoch 2798: loss = 1719668.879683, a = 944.924982, b = 152.522026\n", + "epoch 2799: loss = 1719668.815165, a = 944.931970, b = 152.522021\n", + "epoch 2800: loss = 1719668.750873, a = 944.938943, b = 152.522017\n", + "epoch 2801: loss = 1719668.686807, a = 944.945903, b = 152.522012\n", + "epoch 2802: loss = 1719668.622965, a = 944.952849, b = 152.522008\n", + "epoch 2803: loss = 1719668.559348, a = 944.959781, b = 152.522003\n", + "epoch 2804: loss = 1719668.495953, a = 944.966700, b = 152.521999\n", + "epoch 2805: loss = 1719668.432781, a = 944.973604, b = 152.521995\n", + "epoch 2806: loss = 1719668.369830, a = 944.980495, b = 152.521990\n", + "epoch 2807: loss = 1719668.307100, a = 944.987371, b = 152.521986\n", + "epoch 2808: loss = 1719668.244589, a = 944.994234, b = 152.521981\n", + "epoch 2809: loss = 1719668.182297, a = 945.001084, b = 152.521977\n", + "epoch 2810: loss = 1719668.120224, a = 945.007919, b = 152.521973\n", + "epoch 2811: loss = 1719668.058368, a = 945.014741, b = 152.521968\n", + "epoch 2812: loss = 1719667.996728, a = 945.021550, b = 152.521964\n", + "epoch 2813: loss = 1719667.935304, a = 945.028344, b = 152.521960\n", + "epoch 2814: loss = 1719667.874095, a = 945.035126, b = 152.521955\n", + "epoch 2815: loss = 1719667.813100, a = 945.041893, b = 152.521951\n", + "epoch 2816: loss = 1719667.752318, a = 945.048647, b = 152.521947\n", + "epoch 2817: loss = 1719667.691749, a = 945.055388, b = 152.521942\n", + "epoch 2818: loss = 1719667.631391, a = 945.062115, b = 152.521938\n", + "epoch 2819: loss = 1719667.571245, a = 945.068828, b = 152.521934\n", + "epoch 2820: loss = 1719667.511309, a = 945.075529, b = 152.521929\n", + "epoch 2821: loss = 1719667.451582, a = 945.082215, b = 152.521925\n", + "epoch 2822: loss = 1719667.392063, a = 945.088889, b = 152.521921\n", + "epoch 2823: loss = 1719667.332753, a = 945.095549, b = 152.521917\n", + "epoch 2824: loss = 1719667.273649, a = 945.102196, b = 152.521912\n", + "epoch 2825: loss = 1719667.214752, a = 945.108829, b = 152.521908\n", + "epoch 2826: loss = 1719667.156061, a = 945.115449, b = 152.521904\n", + "epoch 2827: loss = 1719667.097574, a = 945.122056, b = 152.521900\n", + "epoch 2828: loss = 1719667.039291, a = 945.128650, b = 152.521896\n", + "epoch 2829: loss = 1719666.981211, a = 945.135230, b = 152.521891\n", + "epoch 2830: loss = 1719666.923334, a = 945.141798, b = 152.521887\n", + "epoch 2831: loss = 1719666.865659, a = 945.148352, b = 152.521883\n", + "epoch 2832: loss = 1719666.808185, a = 945.154893, b = 152.521879\n", + "epoch 2833: loss = 1719666.750911, a = 945.161421, b = 152.521875\n", + "epoch 2834: loss = 1719666.693836, a = 945.167936, b = 152.521870\n", + "epoch 2835: loss = 1719666.636960, a = 945.174438, b = 152.521866\n", + "epoch 2836: loss = 1719666.580283, a = 945.180927, b = 152.521862\n", + "epoch 2837: loss = 1719666.523802, a = 945.187403, b = 152.521858\n", + "epoch 2838: loss = 1719666.467519, a = 945.193866, b = 152.521854\n", + "epoch 2839: loss = 1719666.411431, a = 945.200317, b = 152.521850\n", + "epoch 2840: loss = 1719666.355538, a = 945.206754, b = 152.521846\n", + "epoch 2841: loss = 1719666.299839, a = 945.213178, b = 152.521842\n", + "epoch 2842: loss = 1719666.244335, a = 945.219590, b = 152.521837\n", + "epoch 2843: loss = 1719666.189023, a = 945.225988, b = 152.521833\n", + "epoch 2844: loss = 1719666.133903, a = 945.232374, b = 152.521829\n", + "epoch 2845: loss = 1719666.078976, a = 945.238747, b = 152.521825\n", + "epoch 2846: loss = 1719666.024238, a = 945.245108, b = 152.521821\n", + "epoch 2847: loss = 1719665.969691, a = 945.251456, b = 152.521817\n", + "epoch 2848: loss = 1719665.915334, a = 945.257791, b = 152.521813\n", + "epoch 2849: loss = 1719665.861165, a = 945.264113, b = 152.521809\n", + "epoch 2850: loss = 1719665.807184, a = 945.270423, b = 152.521805\n", + "epoch 2851: loss = 1719665.753391, a = 945.276720, b = 152.521801\n", + "epoch 2852: loss = 1719665.699784, a = 945.283004, b = 152.521797\n", + "epoch 2853: loss = 1719665.646364, a = 945.289276, b = 152.521793\n", + "epoch 2854: loss = 1719665.593128, a = 945.295535, b = 152.521789\n", + "epoch 2855: loss = 1719665.540077, a = 945.301782, b = 152.521785\n", + "epoch 2856: loss = 1719665.487210, a = 945.308016, b = 152.521781\n", + "epoch 2857: loss = 1719665.434526, a = 945.314238, b = 152.521777\n", + "epoch 2858: loss = 1719665.382025, a = 945.320448, b = 152.521773\n", + "epoch 2859: loss = 1719665.329706, a = 945.326645, b = 152.521769\n", + "epoch 2860: loss = 1719665.277568, a = 945.332829, b = 152.521765\n", + "epoch 2861: loss = 1719665.225610, a = 945.339002, b = 152.521761\n", + "epoch 2862: loss = 1719665.173832, a = 945.345162, b = 152.521757\n", + "epoch 2863: loss = 1719665.122234, a = 945.351309, b = 152.521753\n", + "epoch 2864: loss = 1719665.070814, a = 945.357445, b = 152.521749\n", + "epoch 2865: loss = 1719665.019572, a = 945.363568, b = 152.521746\n", + "epoch 2866: loss = 1719664.968507, a = 945.369678, b = 152.521742\n", + "epoch 2867: loss = 1719664.917619, a = 945.375777, b = 152.521738\n", + "epoch 2868: loss = 1719664.866907, a = 945.381863, b = 152.521734\n", + "epoch 2869: loss = 1719664.816370, a = 945.387938, b = 152.521730\n", + "epoch 2870: loss = 1719664.766007, a = 945.394000, b = 152.521726\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch 2871: loss = 1719664.715819, a = 945.400050, b = 152.521722\n", + "epoch 2872: loss = 1719664.665804, a = 945.406087, b = 152.521718\n", + "epoch 2873: loss = 1719664.615962, a = 945.412113, b = 152.521714\n", + "epoch 2874: loss = 1719664.566291, a = 945.418127, b = 152.521711\n", + "epoch 2875: loss = 1719664.516793, a = 945.424129, b = 152.521707\n", + "epoch 2876: loss = 1719664.467465, a = 945.430118, b = 152.521703\n", + "epoch 2877: loss = 1719664.418307, a = 945.436096, b = 152.521699\n", + "epoch 2878: loss = 1719664.369319, a = 945.442062, b = 152.521695\n", + "epoch 2879: loss = 1719664.320500, a = 945.448016, b = 152.521692\n", + "epoch 2880: loss = 1719664.271849, a = 945.453958, b = 152.521688\n", + "epoch 2881: loss = 1719664.223365, a = 945.459888, b = 152.521684\n", 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945.541674, b = 152.521632\n", + "epoch 2896: loss = 1719663.515827, a = 945.547429, b = 152.521628\n", + "epoch 2897: loss = 1719663.469945, a = 945.553172, b = 152.521624\n", + "epoch 2898: loss = 1719663.424220, a = 945.558903, b = 152.521621\n", + "epoch 2899: loss = 1719663.378653, a = 945.564623, b = 152.521617\n", + "epoch 2900: loss = 1719663.333241, a = 945.570332, b = 152.521613\n", + "epoch 2901: loss = 1719663.287985, a = 945.576030, b = 152.521610\n", + "epoch 2902: loss = 1719663.242885, a = 945.581716, b = 152.521606\n", + "epoch 2903: loss = 1719663.197939, a = 945.587390, b = 152.521603\n", + "epoch 2904: loss = 1719663.153148, a = 945.593053, b = 152.521599\n", + "epoch 2905: loss = 1719663.108510, a = 945.598705, b = 152.521595\n", + "epoch 2906: loss = 1719663.064024, a = 945.604346, b = 152.521592\n", + "epoch 2907: loss = 1719663.019692, a = 945.609975, b = 152.521588\n", + "epoch 2908: loss = 1719662.975511, a = 945.615593, b = 152.521585\n", + "epoch 2909: loss = 1719662.931481, a = 945.621200, b = 152.521581\n", + "epoch 2910: loss = 1719662.887602, a = 945.626796, b = 152.521577\n", + "epoch 2911: loss = 1719662.843874, a = 945.632380, b = 152.521574\n", + "epoch 2912: loss = 1719662.800295, a = 945.637953, b = 152.521570\n", + "epoch 2913: loss = 1719662.756865, a = 945.643515, b = 152.521567\n", + "epoch 2914: loss = 1719662.713583, a = 945.649066, b = 152.521563\n", + "epoch 2915: loss = 1719662.670450, a = 945.654606, b = 152.521560\n", + "epoch 2916: loss = 1719662.627464, a = 945.660135, b = 152.521556\n", + "epoch 2917: loss = 1719662.584625, a = 945.665653, b = 152.521553\n", + "epoch 2918: loss = 1719662.541932, a = 945.671160, b = 152.521549\n", + "epoch 2919: loss = 1719662.499386, a = 945.676655, b = 152.521546\n", + "epoch 2920: loss = 1719662.456984, a = 945.682140, b = 152.521542\n", + "epoch 2921: loss = 1719662.414727, a = 945.687614, b = 152.521539\n", + "epoch 2922: loss = 1719662.372615, a = 945.693077, b = 152.521535\n", + "epoch 2923: loss = 1719662.330646, a = 945.698529, b = 152.521532\n", + "epoch 2924: loss = 1719662.288821, a = 945.703970, b = 152.521528\n", + "epoch 2925: loss = 1719662.247138, a = 945.709400, b = 152.521525\n", + "epoch 2926: loss = 1719662.205597, a = 945.714819, b = 152.521521\n", + "epoch 2927: loss = 1719662.164198, a = 945.720227, b = 152.521518\n", + "epoch 2928: loss = 1719662.122941, a = 945.725625, b = 152.521514\n", + "epoch 2929: loss = 1719662.081823, a = 945.731012, b = 152.521511\n", + "epoch 2930: loss = 1719662.040846, a = 945.736388, b = 152.521507\n", + "epoch 2931: loss = 1719662.000008, a = 945.741753, b = 152.521504\n", + "epoch 2932: loss = 1719661.959310, a = 945.747108, b = 152.521501\n", + "epoch 2933: loss = 1719661.918750, a = 945.752451, b = 152.521497\n", + "epoch 2934: loss = 1719661.878328, a = 945.757785, b = 152.521494\n", + "epoch 2935: loss = 1719661.838044, a = 945.763107, b = 152.521490\n", + "epoch 2936: loss = 1719661.797896, a = 945.768419, b = 152.521487\n", + "epoch 2937: loss = 1719661.757886, a = 945.773720, b = 152.521484\n", + "epoch 2938: loss = 1719661.718011, a = 945.779011, b = 152.521480\n", + "epoch 2939: loss = 1719661.678272, a = 945.784291, b = 152.521477\n", + "epoch 2940: loss = 1719661.638668, a = 945.789560, b = 152.521473\n", + "epoch 2941: loss = 1719661.599198, a = 945.794819, b = 152.521470\n", + "epoch 2942: loss = 1719661.559863, a = 945.800068, b = 152.521467\n", + "epoch 2943: loss = 1719661.520661, a = 945.805306, b = 152.521463\n", + "epoch 2944: loss = 1719661.481592, a = 945.810533, b = 152.521460\n", + "epoch 2945: loss = 1719661.442656, a = 945.815750, b = 152.521457\n", + "epoch 2946: loss = 1719661.403852, a = 945.820957, b = 152.521453\n", + "epoch 2947: loss = 1719661.365180, a = 945.826153, b = 152.521450\n", + "epoch 2948: loss = 1719661.326639, a = 945.831339, b = 152.521447\n", + "epoch 2949: loss = 1719661.288228, a = 945.836514, b = 152.521443\n", 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945.907892, b = 152.521398\n", + "epoch 2964: loss = 1719660.727437, a = 945.912915, b = 152.521395\n", + "epoch 2965: loss = 1719660.691055, a = 945.917927, b = 152.521391\n", + "epoch 2966: loss = 1719660.654795, a = 945.922929, b = 152.521388\n", + "epoch 2967: loss = 1719660.618658, a = 945.927922, b = 152.521385\n", + "epoch 2968: loss = 1719660.582642, a = 945.932904, b = 152.521382\n", + "epoch 2969: loss = 1719660.546748, a = 945.937876, b = 152.521379\n", + "epoch 2970: loss = 1719660.510975, a = 945.942839, b = 152.521376\n", + "epoch 2971: loss = 1719660.475323, a = 945.947791, b = 152.521372\n", + "epoch 2972: loss = 1719660.439791, a = 945.952734, b = 152.521369\n", + "epoch 2973: loss = 1719660.404379, a = 945.957666, b = 152.521366\n", + "epoch 2974: loss = 1719660.369086, a = 945.962589, b = 152.521363\n", + "epoch 2975: loss = 1719660.333912, a = 945.967502, b = 152.521360\n", + "epoch 2976: loss = 1719660.298857, a = 945.972405, b = 152.521357\n", + "epoch 2977: loss = 1719660.263920, a = 945.977298, b = 152.521354\n", + "epoch 2978: loss = 1719660.229100, a = 945.982182, b = 152.521350\n", + "epoch 2979: loss = 1719660.194397, a = 945.987056, b = 152.521347\n", + "epoch 2980: loss = 1719660.159811, a = 945.991920, b = 152.521344\n", + "epoch 2981: loss = 1719660.125342, a = 945.996774, b = 152.521341\n", + "epoch 2982: loss = 1719660.090988, a = 946.001619, b = 152.521338\n", + "epoch 2983: loss = 1719660.056750, a = 946.006454, b = 152.521335\n", + "epoch 2984: loss = 1719660.022627, a = 946.011279, b = 152.521332\n", + "epoch 2985: loss = 1719659.988619, a = 946.016094, b = 152.521329\n", + "epoch 2986: loss = 1719659.954725, a = 946.020900, b = 152.521326\n", + "epoch 2987: loss = 1719659.920945, a = 946.025697, b = 152.521323\n", + "epoch 2988: loss = 1719659.887278, a = 946.030483, b = 152.521320\n", + "epoch 2989: loss = 1719659.853724, a = 946.035261, b = 152.521316\n", + "epoch 2990: loss = 1719659.820283, a = 946.040028, b = 152.521313\n", + "epoch 2991: loss = 1719659.786954, a = 946.044786, b = 152.521310\n", + "epoch 2992: loss = 1719659.753737, a = 946.049535, b = 152.521307\n", + "epoch 2993: loss = 1719659.720631, a = 946.054274, b = 152.521304\n", + "epoch 2994: loss = 1719659.687636, a = 946.059004, b = 152.521301\n", + "epoch 2995: loss = 1719659.654752, a = 946.063724, b = 152.521298\n", + "epoch 2996: loss = 1719659.621978, a = 946.068434, b = 152.521295\n", + "epoch 2997: loss = 1719659.589314, a = 946.073136, b = 152.521292\n", + "epoch 2998: loss = 1719659.556759, a = 946.077828, b = 152.521289\n", + "epoch 2999: loss = 1719659.524314, a = 946.082510, b = 152.521286\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "n_epoch = 3000 # epoch size\n", + "a, b = 1, 1 # initial parameters\n", + "epsilon = 0.001 # learning rate\n", + "\n", + "for i in range(n_epoch):\n", + " for j in range(N):\n", + " a = a + epsilon*2*(Y[j] - a*X[j] - b)*X[j]\n", + " b = b + epsilon*2*(Y[j] - a*X[j] - b)\n", + "\n", + " L = 0\n", + " for j in range(N):\n", + " L = L + (Y[j]-a*X[j]-b)**2\n", + " print(\"epoch %4d: loss = %f, a = %f, b = %f\" % (i, L, a, b))\n", + " \n", + "x_min = np.min(X)\n", + "x_max = np.max(X)\n", + "y_min = a * x_min + b\n", + "y_max = a * x_max + b\n", + "\n", + "plt.scatter(X, Y, label='original data')\n", + "plt.plot([x_min, x_max], [y_min, y_max], 'r', label='model')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. 如何可视化迭代过程" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
')\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " event.shiftKey = false;\n", + " // Send a \"J\" for go to next cell\n", + " event.which = 74;\n", + " event.keyCode = 74;\n", + " manager.command_mode();\n", + " manager.handle_keydown(event);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/matplotlib/cbook/__init__.py\", line 215, in process\n", + " func(*args, **kwargs)\n", + " File \"/home/bushuhui/.virtualenv/dl/lib/python3.5/site-packages/matplotlib/animation.py\", line 1462, in _stop\n", + " self.event_source.remove_callback(self._loop_delay)\n", + "AttributeError: 'NoneType' object has no attribute 'remove_callback'\n" + ] + } + ], + "source": [ + "%matplotlib nbagg\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.animation as animation\n", + "\n", + "n_epoch = 3000 # epoch size\n", + "a, b = 1, 1 # initial parameters\n", + "epsilon = 0.001 # learning rate\n", + "\n", + "fig = plt.figure()\n", + "imgs = []\n", + "\n", + "for i in range(n_epoch):\n", + " for j in range(N):\n", + " a = a + epsilon*2*(Y[j] - a*X[j] - b)*X[j]\n", + " b = b + epsilon*2*(Y[j] - a*X[j] - b)\n", + "\n", + " L = 0\n", + " for j in range(N):\n", + " L = L + (Y[j]-a*X[j]-b)**2\n", + " #print(\"epoch %4d: loss = %f, a = %f, b = %f\" % (i, L, a, b))\n", + " \n", + " if i % 50 == 0:\n", + " x_min = np.min(X)\n", + " x_max = np.max(X)\n", + " y_min = a * x_min + b\n", + " y_max = a * x_max + b\n", + "\n", + " img = plt.scatter(X, Y, label='original data')\n", + " img = plt.plot([x_min, x_max], [y_min, y_max], 'r', label='model')\n", + " imgs.append(img)\n", + " \n", + "ani = animation.ArtistAnimation(fig, imgs)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. 如何使用批次更新的方法？\n", + "\n", + "如果有一些数据包含比较大的错误（异常数据），因此每次更新仅仅使用一个数据会导致不精确，同时每次仅仅使用一个数据来计算更新也导致计算效率比较低。\n", + "\n", + "\n", + "* [梯度下降方法的几种形式](https://blog.csdn.net/u010402786/article/details/51188876)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. 如何拟合多项式函数？\n", + "\n", + "需要设计一个弹道导弹防御系统，通过观测导弹的飞行路径，预测未来导弹的飞行轨迹，从而完成摧毁的任务。按照物理学，可以得知模型为:\n", + "$$\n", + "y = at^2 + bt + c\n", + "$$\n", + "我们需要求解三个模型参数$a, b, c$。\n", + "\n", + "损失函数的定义为：\n", + "$$\n", + "L = \\sum_{i=1}^N (y_i - at^2 - bt - c)^2\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "t = np.array([2, 4, 6, 8])\n", + "#t = np.linspace(0, 10)\n", + "\n", + "pa = -20\n", + "pb = 90\n", + "pc = 800\n", + "\n", + "y = pa*t**2 + pb*t + pc\n", + "\n", + "\n", + "plt.scatter(t, y)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.1 如何得到更新项?\n", + "\n", + "$$\n", + "L = \\sum_{i=1}^N (y_i - at^2 - bt - c)^2\n", + "$$\n", + "\n", + "\\begin{eqnarray}\n", + "\\frac{\\partial L}{\\partial a} & = & - 2\\sum_{i=1}^N (y_i - at^2 - bt -c) t^2 \\\\\n", + "\\frac{\\partial L}{\\partial b} & = & - 2\\sum_{i=1}^N (y_i - at^2 - bt -c) t \\\\\n", + "\\frac{\\partial L}{\\partial c} & = & - 2\\sum_{i=1}^N (y_i - at^2 - bt -c)\n", + "\\end{eqnarray}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. 如何使用sklearn求解线性问题？\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a = 949.435260, b = 152.133484\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn import linear_model\n", + "\n", + "# load data\n", + "d = datasets.load_diabetes()\n", + "\n", + "X = d.data[:, np.newaxis, 2]\n", + "Y = d.target\n", + "\n", + "# create regression model\n", + "regr = linear_model.LinearRegression()\n", + "regr.fit(X, Y)\n", + "\n", + "a, b = regr.coef_, regr.intercept_\n", + "print(\"a = %f, b = %f\" % (a, b))\n", + "\n", + "x_min = np.min(X)\n", + "x_max = np.max(X)\n", + "y_min = a * x_min + b\n", + "y_max = a * x_max + b\n", + "\n", + "plt.scatter(X, Y)\n", + "plt.plot([x_min, x_max], [y_min, y_max], 'r')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. 如何使用sklearn拟合多项式函数？" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([800., 90., -20.])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fitting polynomial functions\n", + "\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.pipeline import Pipeline\n", + "\n", + "t = np.array([2, 4, 6, 8])\n", + "\n", + "pa = -20\n", + "pb = 90\n", + "pc = 800\n", + "\n", + "y = pa*t**2 + pb*t + pc\n", + "\n", + "model = Pipeline([('poly', PolynomialFeatures(degree=2)),\n", + " ('linear', LinearRegression(fit_intercept=False))])\n", + "model = model.fit(t[:, np.newaxis], y)\n", + "model.named_steps['linear'].coef_\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/4_logistic_regression/2-Logistic_regression.ipynb b/4_logistic_regression/2-Logistic_regression.ipynb index 954707d..c180550 100644 --- a/4_logistic_regression/2-Logistic_regression.ipynb +++ b/4_logistic_regression/2-Logistic_regression.ipynb @@ -697,7 +697,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/4_logistic_regression/2-Logistic_regression_EN.ipynb b/4_logistic_regression/2-Logistic_regression_EN.ipynb new file mode 100644 index 0000000..c180550 --- /dev/null +++ b/4_logistic_regression/2-Logistic_regression_EN.ipynb @@ -0,0 +1,705 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 逻辑回归 Logistic Regression\n", + "\n", + "逻辑回归(Logistic Regression, LR)模型其实仅在线性回归的基础上，套用了一个逻辑函数，但也就由于这个逻辑函数，使得逻辑回归模型成为了机器学习领域一颗耀眼的明星，更是计算广告学的核心。本节主要详述逻辑回归模型的基础。\n", + "\n", + "\n", + "## 1. 逻辑回归模型\n", + "回归是一种比较容易理解的模型，就相当于$y=f(x)$，表明自变量$x$与因变量$y$的关系。最常见问题有如医生治病时的望、闻、问、切，之后判定病人是否生病或生了什么病，其中的望闻问切就是获取自变量$x$，即特征数据，判断是否生病就相当于获取因变量$y$，即预测分类。\n", + "\n", + "最简单的回归是线性回归，在此借用Andrew NG的讲义，有如图所示，$X$为数据点——肿瘤的大小，$Y$为观测值——是否是恶性肿瘤。通过构建线性回归模型，如$h_\\theta(x)$所示，构建线性回归模型后，即可以根据肿瘤大小，预测是否为恶性肿瘤$h_\\theta(x)) \\ge 0.5$为恶性，$h_\\theta(x) \\lt 0.5$为良性。\n", + "\n", + "![LinearRegression](images/fig1.gif)\n", + "\n", + "然而线性回归的鲁棒性很差，例如在上图的数据集上建立回归，因最右边噪点的存在，使回归模型在训练集上表现都很差。这主要是由于线性回归在整个实数域内敏感度一致，而分类范围，需要在$[0,1]$。\n", + "\n", + "逻辑回归就是一种减小预测范围，将预测值限定为$[0,1]$间的一种回归模型，其回归方程与回归曲线如图2所示。逻辑曲线在$z=0$时，十分敏感，在$z>>0$或$z<<0$处，都不敏感，将预测值限定为$(0,1)$。\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.figure()\n", + "plt.axis([-10,10,0,1])\n", + "plt.grid(True)\n", + "X=np.arange(-10,10,0.1)\n", + "y=1/(1+np.e**(-X))\n", + "plt.plot(X,y,'b-')\n", + "plt.title(\"Logistic function\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 逻辑回归表达式\n", + "\n", + "这个函数称为Logistic函数(logistic function)，也称为Sigmoid函数(sigmoid function)。函数公式如下：\n", + "\n", + "$$\n", + "g(z) = \\frac{1}{1+e^{-z}}\n", + "$$\n", + "\n", + "Logistic函数当z趋近于无穷大时，g(z)趋近于1；当z趋近于无穷小时，g(z)趋近于0。Logistic函数的图形如上图所示。Logistic函数求导时有一个特性，这个特性将在下面的推导中用到，这个特性为：\n", + "$$\n", + "g'(z) = \\frac{d}{dz} \\frac{1}{1+e^{-z}} \\\\\n", + " = \\frac{1}{(1+e^{-z})^2}(e^{-z}) \\\\\n", + " = \\frac{1}{(1+e^{-z})} (1 - \\frac{1}{(1+e^{-z})}) \\\\\n", + " = g(z)(1-g(z))\n", + "$$\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.figure()\n", + "plt.axis([-10,10,0,1])\n", + "plt.grid(True)\n", + "X=np.arange(-10,10,0.1)\n", + "y=1/(1+np.e**(-X))\n", + "plt.plot(X,y,'b-')\n", + "plt.title(\"Logistic function\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "逻辑回归本质上是线性回归，只是在特征到结果的映射中加入了一层函数映射，即先把特征线性求和，然后使用函数$g(z)$将最为假设函数来预测。$g(z)$可以将连续值映射到0到1之间。线性回归模型的表达式带入$g(z)$，就得到逻辑回归的表达式:\n", + "\n", + "$$\n", + "h_\\theta(x) = g(\\theta^T x) = \\frac{1}{1+e^{-\\theta^T x}}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2 逻辑回归的软分类\n", + "\n", + "现在我们将y的取值$h_\\theta(x)$通过Logistic函数归一化到(0,1)间，$y$的取值有特殊的含义，它表示结果取1的概率，因此对于输入$x$分类结果为类别1和类别0的概率分别为：\n", + "\n", + "$$\n", + "P(y=1|x,\\theta) = h_\\theta(x) \\\\\n", + "P(y=0|x,\\theta) = 1 - h_\\theta(x)\n", + "$$\n", + "\n", + "对上面的表达式合并一下就是：\n", + "\n", + "$$\n", + "p(y|x,\\theta) = (h_\\theta(x))^y (1 - h_\\theta(x))^{1-y}\n", + "$$\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3 梯度上升\n", + "\n", + "得到了逻辑回归的表达式，下一步跟线性回归类似，构建似然函数，然后最大似然估计，最终推导出$\\theta$的迭代更新表达式。只不过这里用的不是梯度下降，而是梯度上升，因为这里是最大化似然函数。\n", + "\n", + "我们假设训练样本相互独立，那么似然函数表达式为：\n", + "![Loss](images/eq_loss.png)\n", + "\n", + "同样对似然函数取log，转换为：\n", + "![LogLoss](images/eq_logloss.png)\n", + "\n", + "转换后的似然函数对$\\theta$求偏导，在这里我们以只有一个训练样本的情况为例：\n", + "![LogLossDiff](images/eq_logloss_diff.png)\n", + "\n", + "这个求偏导过程中：\n", + "* 第一步是对$\\theta$偏导的转化，依据偏导公式：$y=lnx$, $y'=1/x$。\n", + "* 第二步是根据g(z)求导的特性g'(z) = g(z)(1 - g(z)) 。\n", + "* 第三步就是普通的变换。\n", + "\n", + "这样我们就得到了梯度上升每次迭代的更新方向，那么$\\theta$的迭代表达式为：\n", + "$$\n", + "\\theta_j := \\theta_j + \\alpha(y^i - h_\\theta(x^i)) x_j^i\n", + "$$\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4 示例程序" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "from __future__ import division\n", + "import numpy as np\n", + "import sklearn.datasets\n", + "import matplotlib.pyplot as plt\n", + "\n", + "np.random.seed(0)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "data = [[ 0.694565 0.42666408]\n", + " [ 1.68353008 -0.80016643]\n", + " [-0.25046823 0.24392224]\n", + " [-1.13337973 -0.6112787 ]\n", + " [ 1.76905577 -0.31025439]\n", + " [ 2.00225511 -0.18592 ]\n", + " [ 0.91169861 0.46995543]\n", + " [ 0.88211794 -0.46701178]\n", + " [ 0.75006972 0.33995342]\n", + " [ 1.30208867 -0.72334923]]\n", + "label = [0 1 1 0 1 1 0 1 0 1]\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Original Data')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# load sample data\n", + "data, label = sklearn.datasets.make_moons(200, noise=0.30)\n", + "\n", + "print(\"data = \", data[:10, :])\n", + "print(\"label = \", label[:10])\n", + "\n", + "plt.scatter(data[:,0], data[:,1], c=label)\n", + "plt.title(\"Original Data\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_decision_boundary(predict_func, data, label):\n", + " \"\"\"画出结果图\n", + " Args:\n", + " pred_func (callable): 预测函数\n", + " data (numpy.ndarray): 训练数据集合\n", + " label (numpy.ndarray): 训练数据标签\n", + " \"\"\"\n", + " x_min, x_max = data[:, 0].min() - .5, data[:, 0].max() + .5\n", + " y_min, y_max = data[:, 1].min() - .5, data[:, 1].max() + .5\n", + " h = 0.01\n", + "\n", + " xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n", + "\n", + " Z = predict_func(np.c_[xx.ravel(), yy.ravel()])\n", + " Z = Z.reshape(xx.shape)\n", + "\n", + " plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n", + " plt.scatter(data[:, 0], data[:, 1], c=label, cmap=plt.cm.Spectral)\n", + " plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def sigmoid(x):\n", + " return 1.0 / (1 + np.exp(-x))\n", + "\n", + "class Logistic(object):\n", + " \"\"\"logistic回归模型\"\"\"\n", + " def __init__(self, data, label):\n", + " self.data = data\n", + " self.label = label\n", + "\n", + " self.data_num, n = np.shape(data)\n", + " self.weights = np.ones(n)\n", + " self.b = 1\n", + "\n", + " def train(self, num_iteration=150):\n", + " \"\"\"随机梯度上升算法\n", + " Args:\n", + " data (numpy.ndarray): 训练数据集\n", + " labels (numpy.ndarray): 训练标签\n", + " num_iteration (int): 迭代次数\n", + " \"\"\"\n", + " for j in range(num_iteration):\n", + " data_index = list(range(self.data_num))\n", + " for i in range(self.data_num):\n", + " # 学习速率\n", + " alpha = 0.01\n", + " rand_index = int(np.random.uniform(0, len(data_index)))\n", + " error = self.label[rand_index] - sigmoid(sum(self.data[rand_index] * self.weights + self.b))\n", + " self.weights += alpha * error * self.data[rand_index]\n", + " self.b += alpha * error\n", + " del(data_index[rand_index])\n", + "\n", + " def predict(self, predict_data):\n", + " \"\"\"预测函数\"\"\"\n", + " result = list(map(lambda x: 1 if sum(self.weights * x + self.b) > 0 else 0,\n", + " predict_data))\n", + " return np.array(result)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "logistic = Logistic(data, label)\n", + "logistic.train(200)\n", + "plot_decision_boundary(lambda x: logistic.predict(x), data, label)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. 如何使用sklearn求解逻辑回归" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy train = 0.825000\n", + "accuracy test = 0.900000\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.linear_model.logistic import LogisticRegression\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import accuracy_score\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# calculate train/test data number\n", + "N = len(data)\n", + "N_train = int(N*0.8)\n", + "N_test = N - N_train\n", + "\n", + "# split train/test data\n", + "x_train = data[:N_train, :]\n", + "y_train = label[:N_train]\n", + "x_test = data[N_train:, :]\n", + "y_test = label[N_train:]\n", + "\n", + "# do logistic regression\n", + "lr=LogisticRegression()\n", + "lr.fit(x_train,y_train)\n", + "\n", + "pred_train = lr.predict(x_train)\n", + "pred_test = lr.predict(x_test)\n", + "\n", + "# calculate train/test accuracy\n", + "acc_train = accuracy_score(y_train, pred_train)\n", + "acc_test = accuracy_score(y_test, pred_test)\n", + "print(\"accuracy train = %f\" % acc_train)\n", + "print(\"accuracy test = %f\" % acc_test)\n", + "\n", + "# plot confusion matrix\n", + "cm = confusion_matrix(y_test,pred_test)\n", + "\n", + "plt.matshow(cm)\n", + "plt.title(u'Confusion Matrix')\n", + "plt.colorbar()\n", + "plt.ylabel(u'Groundtruth')\n", + "plt.xlabel(u'Predict')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. 多类识别问题" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 加载显示数据" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "from sklearn.datasets import load_digits\n", + "\n", + "# load data\n", + "digits = load_digits()\n", + "\n", + "# copied from notebook 02_sklearn_data.ipynb\n", + "fig = plt.figure(figsize=(6, 6)) # figure size in inches\n", + "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n", + "\n", + "# plot the digits: each image is 8x8 pixels\n", + "for i in range(64):\n", + " ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n", + " ax.imshow(digits.images[i], cmap=plt.cm.binary)\n", + " \n", + " # label the image with the target value\n", + " ax.text(0, 7, str(digits.target[i]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 可视化特征\n", + "\n", + "针对机器学习的问题，一个比较好的方法是通过降维的方法将原始的高维特征降到2-3维并可视化处理，通过这样的方法可以对所要处理的数据有一个初步的认识。这里介绍最简单的降维方法主成分分析(Principal Component Analysis, PCA).\n", + "\n", + "PCA寻求具有最大方差的特征的正交线性组合，因此可以更好地了解数据的结构。在这里，我们将使用Randomized PCA，因为当数据个数$N$比较大是，计算的效率更好。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.decomposition import PCA\n", + "pca = PCA(n_components=2, svd_solver=\"randomized\")\n", + "proj = pca.fit_transform(digits.data)\n", + "\n", + "plt.scatter(proj[:, 0], proj[:, 1], c=digits.target)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PCA的一个缺点是它可能会丢失数据中一些有趣的相互关系。如果想看到非线性的降维与映射\n", + "我们可以使用几种流形模块中的方法。在这里，我们将使用[Isomap](https://blog.csdn.net/VictoriaW/article/details/78497316)（串联\n", + "等距映射）是一种基于图论的流形降维方法。" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.manifold import Isomap\n", + "iso = Isomap(n_neighbors=5, n_components=2)\n", + "proj = iso.fit_transform(digits.data)\n", + "\n", + "plt.scatter(proj[:, 0], proj[:, 1], c=digits.target)\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3 示例程序" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1797, 64)\n", + "accuracy train = 0.998608, accuracy_test = 0.897222\n", + "score_train = 0.998608, score_test = 0.897222\n" + ] + } + ], + "source": [ + "from sklearn.datasets import load_digits\n", + "from sklearn.linear_model.logistic import LogisticRegression\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "import matplotlib.pyplot as plt \n", + "\n", + "# load digital data\n", + "digits, dig_label = load_digits(return_X_y=True)\n", + "print(digits.shape)\n", + "\n", + "# calculate train/test data number\n", + "N = len(digits)\n", + "N_train = int(N*0.8)\n", + "N_test = N - N_train\n", + "\n", + "# split train/test data\n", + "x_train = digits[:N_train, :]\n", + "y_train = dig_label[:N_train]\n", + "x_test = digits[N_train:, :]\n", + "y_test = dig_label[N_train:]\n", + "\n", + "# FIXME: need to use Isomap to transform data\n", + "\n", + "# do logistic regression\n", + "lr=LogisticRegression()\n", + "lr.fit(x_train,y_train)\n", + "\n", + "pred_train = lr.predict(x_train)\n", + "pred_test = lr.predict(x_test)\n", + "\n", + "# calculate train/test accuracy\n", + "acc_train = accuracy_score(y_train, pred_train)\n", + "acc_test = accuracy_score(y_test, pred_test)\n", + "print(\"accuracy train = %f, accuracy_test = %f\" % (acc_train, acc_test))\n", + "\n", + "score_train = lr.score(x_train, y_train)\n", + "score_test = lr.score(x_test, y_test)\n", + "print(\"score_train = %f, score_test = %f\" % (score_train, score_test))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "\n", + "# plot confusion matrix\n", + "cm = confusion_matrix(y_test,pred_test)\n", + "\n", + "plt.matshow(cm)\n", + "plt.title(u'Confusion Matrix')\n", + "plt.colorbar()\n", + "plt.ylabel(u'Groundtruth')\n", + "plt.xlabel(u'Predict')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. 练习 - 如何画出错误分类的数据？\n", + "\n", + "1. 如何得到错误分类数据的下标？\n", + "2. 如何根据下标，将这些错误的数据可视化出来？" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* [逻辑回归模型(Logistic Regression, LR)基础](https://www.cnblogs.com/sparkwen/p/3441197.html)\n", + "* [逻辑回归（Logistic Regression）](http://www.cnblogs.com/BYRans/p/4713624.html)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/4_logistic_regression/3-PCA_and_Logistic_Regression.ipynb b/4_logistic_regression/3-PCA_and_Logistic_Regression.ipynb index 0e9aeac..fa0725c 100644 --- a/4_logistic_regression/3-PCA_and_Logistic_Regression.ipynb +++ b/4_logistic_regression/3-PCA_and_Logistic_Regression.ipynb @@ -271,7 +271,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/4_logistic_regression/3-PCA_and_Logistic_Regression_EN.ipynb b/4_logistic_regression/3-PCA_and_Logistic_Regression_EN.ipynb new file mode 100644 index 0000000..fa0725c --- /dev/null +++ b/4_logistic_regression/3-PCA_and_Logistic_Regression_EN.ipynb @@ -0,0 +1,279 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Chaining a PCA and a logistic regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The PCA does an unsupervised dimensionality reduction, while the logistic regression does the prediction.\n", + "\n", + "We use a GridSearchCV to set the dimensionality of the PCA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "% matplotlib inline\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from sklearn import linear_model, decomposition, datasets\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "logistic = linear_model.LogisticRegression()\n", + "\n", + "pca = decomposition.PCA()\n", + "pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])\n", + "\n", + "digits = datasets.load_digits()\n", + "X_digits = digits.data\n", + "y_digits = digits.target\n", + "\n", + "# Plot the PCA spectrum\n", + "pca.fit(X_digits)\n", + "\n", + "plt.figure(1, figsize=(4, 3))\n", + "plt.clf()\n", + "plt.axes([.2, .2, .7, .7])\n", + "plt.plot(pca.explained_variance_, linewidth=2)\n", + "plt.axis('tight')\n", + "plt.xlabel('n_components')\n", + "plt.ylabel('explained_variance_')\n", + "\n", + "# Prediction\n", + "n_components = [20, 40, 64]\n", + "Cs = np.logspace(-4, 4, 3)\n", + "\n", + "# Parameters of pipelines can be set using ‘__’ separated parameter names:\n", + "estimator = GridSearchCV(pipe,\n", + " dict(pca__n_components=n_components,\n", + " logistic__C=Cs))\n", + "estimator.fit(X_digits, y_digits)\n", + "\n", + "plt.axvline(estimator.best_estimator_.named_steps['pca'].n_components,\n", + " linestyle=':', label='n_components chosen')\n", + "plt.legend(prop=dict(size=12))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1797, 64)\n" + ] + }, + { + "data": { + "text/plain": [ + "
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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Compare the performance\n", + "from sklearn.datasets import load_digits\n", + "from sklearn.linear_model.logistic import LogisticRegression\n", + "from sklearn import decomposition\n", + "from sklearn.metrics import confusion_matrix\n", + "from sklearn.metrics import accuracy_score\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "# load digital data\n", + "digits, dig_label = load_digits(return_X_y=True)\n", + "print(digits.shape)\n", + "\n", + "# draw one digital\n", + "plt.gray() \n", + "plt.matshow(digits[0].reshape([8, 8])) \n", + "plt.show() \n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy train = 0.998608, accuracy_test = 0.897222\n" + ] + } + ], + "source": [ + "\n", + "# calculate train/test data number\n", + "N = len(digits)\n", + "N_train = int(N*0.8)\n", + "N_test = N - N_train\n", + "\n", + "# split train/test data\n", + "x_train = digits[:N_train, :]\n", + "y_train = dig_label[:N_train]\n", + "x_test = digits[N_train:, :]\n", + "y_test = dig_label[N_train:]\n", + "\n", + "# do logistic regression\n", + "lr=LogisticRegression()\n", + "lr.fit(x_train,y_train)\n", + "\n", + "pred_train = lr.predict(x_train)\n", + "pred_test = lr.predict(x_test)\n", + "\n", + "# calculate train/test accuracy\n", + "acc_train = accuracy_score(y_train, pred_train)\n", + "acc_test = accuracy_score(y_test, pred_test)\n", + "print(\"accuracy train = %f, accuracy_test = %f\" % (acc_train, acc_test))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy train = 0.987474, accuracy_test = 0.894444\n" + ] + } + ], + "source": [ + "# do PCA with 'n_components=40'\n", + "pca = decomposition.PCA(n_components=40)\n", + "pca.fit(x_train)\n", + "\n", + "x_train_pca = pca.transform(x_train)\n", + "x_test_pca = pca.transform(x_test)\n", + "\n", + "# do logistic regression\n", + "lr=LogisticRegression()\n", + "lr.fit(x_train_pca,y_train)\n", + "\n", + "pred_train = lr.predict(x_train_pca)\n", + "pred_test = lr.predict(x_test_pca)\n", + "\n", + "# calculate train/test accuracy\n", + "acc_train = accuracy_score(y_train, pred_train)\n", + "acc_test = accuracy_score(y_test, pred_test)\n", + "print(\"accuracy train = %f, accuracy_test = %f\" % (acc_train, acc_test))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy train = 0.148921, accuracy_test = 0.102778\n" + ] + } + ], + "source": [ + "# do kernel PCA\n", + "# Ref: http://scikit-learn.org/stable/auto_examples/decomposition/plot_kernel_pca.html\n", + "\n", + "from sklearn.decomposition import PCA, KernelPCA\n", + "\n", + "kpca = KernelPCA(n_components=45, kernel=\"rbf\", fit_inverse_transform=True, gamma=10)\n", + "kpca.fit(x_train)\n", + "\n", + "x_train_pca = kpca.transform(x_train)\n", + "x_test_pca = kpca.transform(x_test)\n", + "\n", + "# do logistic regression\n", + "lr=LogisticRegression()\n", + "lr.fit(x_train_pca,y_train)\n", + "\n", + "pred_train = lr.predict(x_train_pca)\n", + "pred_test = lr.predict(x_test_pca)\n", + "\n", + "# calculate train/test accuracy\n", + "acc_train = accuracy_score(y_train, pred_train)\n", + "acc_test = accuracy_score(y_test, pred_test)\n", + "print(\"accuracy train = %f, accuracy_test = %f\" % (acc_train, acc_test))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "* [Pipelining: chaining a PCA and a logistic regression](http://scikit-learn.org/stable/auto_examples/plot_digits_pipe.html)\n", + "* [PCA进行无监督降维](https://ljalphabeta.gitbooks.io/python-/content/pca.html)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/5_nn/1-Perceptron_EN.ipynb b/5_nn/1-Perceptron_EN.ipynb new file mode 100644 index 0000000..f104f9b --- /dev/null +++ b/5_nn/1-Perceptron_EN.ipynb @@ -0,0 +1,256 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 感知机\n", + "\n", + "感知机（perceptron）是二分类的线性分类模型，输入为实例的特征向量，输出为实例的类别（取+1和-1）。感知机对应于输入空间中将实例划分为两类的分离超平面。感知机旨在求出该超平面，为求得超平面导入了基于误分类的损失函数，利用梯度下降法 对损失函数进行最优化（最优化）。感知机的学习算法具有简单而易于实现的优点，分为原始形式和对偶形式。感知机预测是用学习得到的感知机模型对新的实例进行预测的，因此属于判别模型。感知机由Rosenblatt于1957年提出的，是神经网络和支持向量机的基础。\n", + "\n", + "模仿的是生物神经系统内的神经元，它能够接受来自多个源的信号输入，然后将信号转化为便于传播的信号在进行输出(在生物体内表现为电信号)。\n", + "\n", + "![neuron](images/neuron.png)\n", + "\n", + "* dendrites - 树突\n", + "* nucleus - 细胞核\n", + "* axon - 轴突\n", + "\n", + "心理学家Rosenblatt构想了感知机，它作为简化的数学模型解释大脑神经元如何工作：它取一组二进制输入值（附近的神经元），将每个输入值乘以一个连续值权重（每个附近神经元的突触强度），并设立一个阈值，如果这些加权输入值的和超过这个阈值，就输出1，否则输出0（同理于神经元是否放电）。对于感知机，绝大多数输入值不是一些数据，就是别的感知机的输出值。\n", + "\n", + "唐纳德·赫布提出了一个出人意料并影响深远的想法，称知识和学习发生在大脑主要是通过神经元间突触的形成与变化，简要表述为赫布法则：\n", + "\n", + "> 当细胞A的轴突足以接近以激发细胞B，并反复持续地对细胞B放电，一些生长过程或代谢变化将发生在某一个或这两个细胞内，以致A作为对B放电的细胞中的一个，效率增加。\n", + "\n", + "\n", + "感知机并没有完全遵循这个想法，**但通过调输入值的权重，可以有一个非常简单直观的学习方案：给定一个有输入输出实例的训练集，感知机应该「学习」一个函数：对每个例子，若感知机的输出值比实例低太多，则增加它的权重，否则若设比实例高太多，则减少它的权重。**\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. 感知机模型\n", + "\n", + "假设输入空间(特征向量)为$X \\subseteq R^n$，输出空间为$Y=\\{-1, +1\\}$。输入$x \\in X$ 表示实例的特征向量，对应于输入空间的点；输出$y \\in Y$表示示例的类别。由输入空间到输出空间的函数为\n", + "\n", + "$$\n", + "f(x) = sign(w x + b)\n", + "$$\n", + "\n", + "称为感知机。其中，参数$w$叫做权值向量，$b$称为偏置。$w·x$表示$w$和$x$的内积。$sign$为符号函数，即\n", + "![sign_function](images/sign.png)\n", + "\n", + "### 1.1 几何解释 \n", + "感知机模型是线性分类模型，感知机模型的假设空间是定义在特征空间中的所有线性分类模型，即函数集合{f|f(x)=w·x+b}。线性方程 w·x+b=0对应于特征空间Rn中的一个超平面S，其中w是超平面的法向量，b是超平面的截踞。这个超平面把特征空间划分为两部分。位于两侧的点分别为正负两类。超平面S称为分离超平面，如下图：\n", + "![perceptron_geometry_def](images/perceptron_geometry_def.png)\n", + "\n", + "### 1.2 生物学类比\n", + "![perceptron_2](images/perceptron_2.PNG)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. 感知机学习策略\n", + "\n", + "假设训练数据集是线性可分的，感知机学习的目标是求得一个能够将训练数据的正负实例点完全分开的分离超平面，即最终求得参数w、b。这需要一个学习策略，即定义（经验）损失函数并将损失函数最小化。\n", + "\n", + "损失函数的一个自然的选择是误分类的点的总数。但是这样得到的损失函数不是参数w、b的连续可导函数，不宜优化。损失函数的另一个选择是误分类点到分类面的距离之和。\n", + "\n", + "首先，对于任意一点xo到超平面的距离为\n", + "$$\n", + "\\frac{1}{||w||} | w \\cdot xo + b |\n", + "$$\n", + "\n", + "其次，对于误分类点$(x_i,y_i)$来说 $-y_i(w \\cdot x_i + b) > 0$\n", + "\n", + "这样，假设超平面S的总的误分类点集合为M，那么所有误分类点到S的距离之和为\n", + "$$\n", + "-\\frac{1}{||w||} \\sum_{x_i \\in M} y_i (w \\cdot x_i + b)\n", + "$$\n", + "不考虑1/||w||，就得到了感知机学习的损失函数。\n", + "\n", + "### 经验风险函数\n", + "\n", + "给定数据集$T = \\{(x_1,y_1), (x_2, y_2), ... (x_N, y_N)\\}$（其中$x_i \\in R^n$, $y_i \\in \\{-1, +1\\}，i=1,2...N$），感知机sign(w·x+b)学习的损失函数定义为\n", + "$$\n", + "L(w, b) = - \\sum_{x_i \\in M} y_i (w \\cdot x_i + b)\n", + "$$\n", + "其中M为误分类点的集合，这个损失函数就是感知机学习的[经验风险函数](https://blog.csdn.net/zhzhx1204/article/details/70163099)。\n", + "\n", + "显然，损失函数L(w,b)是非负的。如果没有误分类点，那么L(w,b)为0，误分类点数越少，L(w,b)值越小。一个特定的损失函数：在误分类时是参数w,b的线性函数，在正确分类时，是0.因此，给定训练数据集T,损失函数L(w,b)是w,b的连续可导函数。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. 感知机学习算法\n", + "\n", + "\n", + "最优化问题：给定数据集$T = \\{(x_1,y_1), (x_2, y_2), ... (x_N, y_N)\\}$（其中$x_i \\in R^n$, $y_i \\in \\{-1, +1\\}，i=1,2...N$），求参数w,b,使其成为损失函数的解（M为误分类的集合）：\n", + "\n", + "$$\n", + "min_{w,b} L(w, b) = - \\sum_{x_i \\in M} y_i (w \\cdot x_i + b)\n", + "$$\n", + "\n", + "感知机学习是误分类驱动的，具体采用[随机梯度下降法](https://blog.csdn.net/zbc1090549839/article/details/38149561)。首先，任意选定$w_0$、$b_0$，然后用梯度下降法不断极小化目标函数，极小化的过程不是一次性的把M中的所有误分类点梯度下降，而是一次随机选取一个误分类点使其梯度下降。\n", + "\n", + "假设误分类集合M是固定的，那么损失函数L(w,b)的梯度为\n", + "$$\n", + "\\triangledown_w L(w, b) = - \\sum_{x_i \\in M} y_i x_i \\\\\n", + "\\triangledown_b L(w, b) = - \\sum_{x_i \\in M} y_i \\\\\n", + "$$\n", + "\n", + "随机选取一个误分类点$(x_i,y_i)$,对$w,b$进行更新：\n", + "$$\n", + "w = w + \\eta y_i x_i \\\\\n", + "b = b + \\eta y_i\n", + "$$\n", + "\n", + "式中$\\eta$（0 ≤ $ \\eta $ ≤ 1）是步长，在统计学是中成为学习速率。步长越大，梯度下降的速度越快，更能接近极小点。如果步长过大，有可能导致跨过极小点，导致函数发散；如果步长过小，有可能会耗很长时间才能达到极小点。\n", + "\n", + "直观解释：当一个实例点被误分类时，调整w,b，使分离超平面向该误分类点的一侧移动，以减少该误分类点与超平面的距离，直至超越该点被正确分类。\n", + "\n", + "\n", + "\n", + "算法\n", + "```\n", + "输入：T={(x1,y1),(x2,y2)...(xN,yN)}（其中xi∈X=Rn，yi∈Y={-1, +1}，i=1,2...N，学习速率为η）\n", + "输出：w, b;感知机模型f(x)=sign(w·x+b)\n", + "(1) 初始化w0,b0\n", + "(2) 在训练数据集中选取（xi, yi）\n", + "(3) 如果yi(w * xi+b)≤0\n", + " w = w + ηyixi\n", + " b = b + ηyi\n", + "(4) 如果所有的样本都正确分类，或者迭代次数超过设定值，则终止\n", + "(5) 否则，跳转至（2）\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. 示例程序\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "lines_to_end_of_cell_marker": 2 + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "update weight and bias: 1.0 2.5 0.5\n", + "update weight and bias: -2.5 1.0 0.0\n", + "update weight and bias: -1.5 3.5 0.5\n", + "update weight and bias: -5.0 2.0 0.0\n", + "update weight and bias: -4.0 4.5 0.5\n", + "w = [-4.0, 4.5]\n", + "b = 0.5\n", + "ground_truth: [1, 1, 1, 1, -1, -1, -1, -1]\n", + "predicted: [1, 1, 1, 1, -1, -1, -1, -1]\n" + ] + } + ], + "source": [ + "import random\n", + "import numpy as np\n", + "\n", + "# 符号函数\n", + "def sign(v):\n", + " if v > 0: return 1\n", + " else: return -1\n", + " \n", + "def perceptron_train(train_data, eta=0.5, n_iter=100):\n", + " weight = [0, 0] # 权重\n", + " bias = 0 # 偏置量\n", + " learning_rate = eta # 学习速率\n", + "\n", + " train_num = n_iter # 迭代次数\n", + "\n", + " for i in range(train_num):\n", + " #FIXME: the random chose sample is to slow\n", + " train = random.choice(train_data)\n", + " x1, x2, y = train\n", + " predict = sign(weight[0] * x1 + weight[1] * x2 + bias) # 输出\n", + " #print(\"train data: x: (%2d, %2d) y: %2d ==> predict: %2d\" % (x1, x2, y, predict))\n", + " \n", + " if y * predict <= 0: # 判断误分类点\n", + " weight[0] = weight[0] + learning_rate * y * x1 # 更新权重\n", + " weight[1] = weight[1] + learning_rate * y * x2\n", + " bias = bias + learning_rate * y # 更新偏置量\n", + " print(\"update weight and bias: \", weight[0], weight[1], bias)\n", + "\n", + " #print(\"stop training: \", weight[0], weight[1], bias)\n", + "\n", + " return weight, bias\n", + "\n", + "def perceptron_pred(data, w, b):\n", + " y_pred = []\n", + " for d in data:\n", + " x1, x2, y = d\n", + " yi = sign(w[0]*x1 + w[1]*x2 + b)\n", + " y_pred.append(yi)\n", + " \n", + " return y_pred\n", + "\n", + "# set training data\n", + "train_data = np.array([[1, 3, 1], [2, 5, 1], [3, 8, 1], [2, 6, 1], \n", + " [3, 1, -1], [4, 1, -1], [6, 2, -1], [7, 3, -1]])\n", + "\n", + "# do training\n", + "w, b = perceptron_train(train_data)\n", + "print(\"w = \", w)\n", + "print(\"b = \", b)\n", + "\n", + "# predict \n", + "y_pred = perceptron_pred(train_data, w, b)\n", + "\n", + "print(\"ground_truth: \", list(train_data[:, 2]))\n", + "print(\"predicted: \", y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reference\n", + "* [感知机（Python实现）](http://www.cnblogs.com/kaituorensheng/p/3561091.html)\n", + "* [Programming a Perceptron in Python](https://blog.dbrgn.ch/2013/3/26/perceptrons-in-python/)\n", + "* [损失函数、风险函数、经验风险最小化、结构风险最小化](https://blog.csdn.net/zhzhx1204/article/details/70163099)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/5_nn/2-mlp_bp_EN.ipynb b/5_nn/2-mlp_bp_EN.ipynb new file mode 100644 index 0000000..c33abc1 --- /dev/null +++ b/5_nn/2-mlp_bp_EN.ipynb @@ -0,0 +1,4929 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 多层神经网络和反向传播\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. 神经元\n", + "\n", + "神经元和感知器本质上是一样的，只不过我们说感知器的时候，它的激活函数是阶跃函数；而当我们说神经元时，激活函数往往选择为sigmoid函数或tanh函数。如下图所示：\n", + "\n", + "![neuron](images/neuron.gif)\n", + "\n", + "计算一个神经元的输出的方法和计算一个感知器的输出是一样的。假设神经元的输入是向量$\\vec{x}$，权重向量是$\\vec{w}$(偏置项是$w_0$)，激活函数是sigmoid函数，则其输出y：\n", + "$$\n", + "y = sigmod(\\vec{w}^T \\cdot \\vec{x})\n", + "$$\n", + "\n", + "sigmoid函数的定义如下：\n", + "$$\n", + "sigmod(x) = \\frac{1}{1+e^{-x}}\n", + "$$\n", + "将其带入前面的式子，得到\n", + "$$\n", + "y = \\frac{1}{1+e^{-\\vec{w}^T \\cdot \\vec{x}}}\n", + "$$\n", + "\n", + "sigmoid函数是一个非线性函数，值域是(0,1)。函数图像如下图所示\n", + "\n", + "![sigmod_function](images/sigmod.jpg)\n", + "\n", + "sigmoid函数的导数是：\n", + "\\begin{eqnarray}\n", + "y & = & sigmod(x) \\tag{1} \\\\\n", + "y' & = & y(1-y)\n", + "\\end{eqnarray}\n", + "\n", + "可以看到，sigmoid函数的导数非常有趣，它可以用sigmoid函数自身来表示。这样，一旦计算出sigmoid函数的值，计算它的导数的值就非常方便。\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. 神经网络是啥?\n", + "\n", + "![nn1](images/nn1.jpeg)\n", + "\n", + "神经网络其实就是按照一定规则连接起来的多个神经元。上图展示了一个全连接(full connected, FC)神经网络，通过观察上面的图，我们可以发现它的规则包括：\n", + "\n", + "* 神经元按照层来布局。最左边的层叫做输入层，负责接收输入数据；最右边的层叫输出层，我们可以从这层获取神经网络输出数据。输入层和输出层之间的层叫做隐藏层，因为它们对于外部来说是不可见的。\n", + "* 同一层的神经元之间没有连接。\n", + "* 第N层的每个神经元和第N-1层的所有神经元相连(这就是full connected的含义)，第N-1层神经元的输出就是第N层神经元的输入。\n", + "* 每个连接都有一个权值。\n", + "\n", + "上面这些规则定义了全连接神经网络的结构。事实上还存在很多其它结构的神经网络，比如卷积神经网络(CNN)、循环神经网络(RNN)，他们都具有不同的连接规则。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. 计算神经网络的输出\n", + "\n", + "神经网络实际上就是一个输入向量$\\vec{x}$到输出向量$\\vec{y}$的函数，即：\n", + "\n", + "$$\n", + "\\vec{y} = f_{network}(\\vec{x})\n", + "$$\n", + "根据输入计算神经网络的输出，需要首先将输入向量$\\vec{x}$的每个元素的值$x_i$赋给神经网络的输入层的对应神经元，然后根据式1依次向前计算每一层的每个神经元的值，直到最后一层输出层的所有神经元的值计算完毕。最后，将输出层每个神经元的值串在一起就得到了输出向量$\\vec{y}$。\n", + "\n", + "接下来举一个例子来说明这个过程，我们先给神经网络的每个单元写上编号。\n", + "\n", + "![nn2](images/nn2.png)\n", + "\n", + "如上图，输入层有三个节点，我们将其依次编号为1、2、3；隐藏层的4个节点，编号依次为4、5、6、7；最后输出层的两个节点编号为8、9。因为我们这个神经网络是全连接网络，所以可以看到每个节点都和上一层的所有节点有连接。比如，我们可以看到隐藏层的节点4，它和输入层的三个节点1、2、3之间都有连接，其连接上的权重分别为$w_{41}$,$w_{42}$,$w_{43}$。那么，我们怎样计算节点4的输出值$a_4$呢？\n", + "\n", + "\n", + "为了计算节点4的输出值，我们必须先得到其所有上游节点（也就是节点1、2、3）的输出值。节点1、2、3是输入层的节点，所以，他们的输出值就是输入向量$\\vec{x}$本身。按照上图画出的对应关系，可以看到节点1、2、3的输出值分别是$x_1$,$x_2$,$x_3$。我们要求输入向量的维度和输入层神经元个数相同，而输入向量的某个元素对应到哪个输入节点是可以自由决定的，你偏非要把$x_1$赋值给节点2也是完全没有问题的，但这样除了把自己弄晕之外，并没有什么价值。\n", + "\n", + "一旦我们有了节点1、2、3的输出值，我们就可以根据式1计算节点4的输出值$a_4$：\n", + "\n", + "![eqn_3_4](images/eqn_3_4.png)\n", + "\n", + "上式的$w_{4b}$是节点4的偏置项，图中没有画出来。而$w_{41}$,$w_{42}$,$w_{43}$分别为节点1、2、3到节点4连接的权重，在给权重$w_{ji}$编号时，我们把目标节点的编号$j$放在前面，把源节点的编号$i$放在后面。\n", + "\n", + "同样，我们可以继续计算出节点5、6、7的输出值$a_5$,$a_6$,$a_7$。这样，隐藏层的4个节点的输出值就计算完成了，我们就可以接着计算输出层的节点8的输出值$y_1$：\n", + "\n", + "![eqn_5_6](images/eqn_5_6.png)\n", + "\n", + "同理，我们还可以计算出$y_2$的值。这样输出层所有节点的输出值计算完毕，我们就得到了在输入向量$\\vec{x} = (x_1, x_2, x_3)^T$时，神经网络的输出向量$\\vec{y} = (y_1, y_2)^T$。这里我们也看到，输出向量的维度和输出层神经元个数相同。\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. 神经网络的矩阵表示\n", + "\n", + "神经网络的计算如果用矩阵来表示会很方便（当然逼格也更高），我们先来看看隐藏层的矩阵表示。\n", + "\n", + "首先我们把隐藏层4个节点的计算依次排列出来：\n", + "\n", + "![eqn_hidden_units](images/eqn_hidden_units.png)\n", + "\n", + "接着，定义网络的输入向量$\\vec{x}$和隐藏层每个节点的权重向量$\\vec{w}$。令\n", + "\n", + "![eqn_7_12](images/eqn_7_12.png)\n", + "\n", + "代入到前面的一组式子，得到：\n", + "\n", + "![eqn_13_16](images/eqn_13_16.png)\n", + "\n", + "现在，我们把上述计算$a_4$, $a_5$,$a_6$,$a_7$的四个式子写到一个矩阵里面，每个式子作为矩阵的一行，就可以利用矩阵来表示它们的计算了。令\n", + "\n", + "![eqn_matrix1](images/eqn_matrix1.png)\n", + "\n", + "带入前面的一组式子，得到\n", + "\n", + "![formular_2](images/formular_2.png)\n", + "\n", + "在式2中，$f$是激活函数，在本例中是$sigmod$函数；$W$是某一层的权重矩阵；$\\vec{x}$是某层的输入向量；$\\vec{a}$是某层的输出向量。式2说明神经网络的每一层的作用实际上就是先将输入向量左乘一个数组进行线性变换，得到一个新的向量，然后再对这个向量逐元素应用一个激活函数。\n", + "\n", + "每一层的算法都是一样的。比如，对于包含一个输入层，一个输出层和三个隐藏层的神经网络，我们假设其权重矩阵分别为$W_1$,$W_2$,$W_3$,$W_4$，每个隐藏层的输出分别是$\\vec{a}_1$,$\\vec{a}_2$,$\\vec{a}_3$，神经网络的输入为$\\vec{x}$，神经网络的输出为$\\vec{y}$，如下图所示：\n", + "\n", + "![nn_parameters_demo](images/nn_parameters_demo.png)\n", + "\n", + "则每一层的输出向量的计算可以表示为：\n", + "\n", + "![eqn_17_20](images/eqn_17_20.png)\n", + "\n", + "\n", + "这就是神经网络输出值的矩阵计算方法。\n", + "\n", + "如果写成一个公式：\n", + "$$\n", + "\\vec{y} = f(W4 \\cdot f(W3 \\cdot f(W2 \\cdot f(W1 \\cdot \\vec{x}))))\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "神经网络正向计算的过程比较简单，就是一层一层不断做运算就可以了，动态的演示如下图所示：\n", + "![](images/nn-forward.gif)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. 神经网络的训练 - 反向传播算法\n", + "\n", + "现在，我们需要知道一个神经网络的每个连接上的权值是如何得到的。我们可以说神经网络是一个模型，那么这些权值就是模型的参数，也就是模型要学习的东西。然而，一个神经网络的连接方式、网络的层数、每层的节点数这些参数，则不是学习出来的，而是人为事先设置的。对于这些人为设置的参数，我们称之为超参数(Hyper-Parameters)。\n", + "\n", + "反向传播算法其实就是链式求导法则的应用。然而，这个如此简单且显而易见的方法，却是在Roseblatt提出感知器算法将近30年之后才被发明和普及的。对此，Bengio这样回应道：\n", + "\n", + "> 很多看似显而易见的想法只有在事后才变得显而易见。\n", + "\n", + "按照机器学习的通用套路，我们先确定神经网络的目标函数，然后用随机梯度下降优化算法去求目标函数最小值时的参数值。\n", + "\n", + "我们取网络所有输出层节点的误差平方和作为目标函数：\n", + "\n", + "![bp_loss](images/bp_loss.png)\n", + "\n", + "其中，$E_d$表示是样本$d$的误差。\n", + "\n", + "然后，使用随机梯度下降算法对目标函数进行优化：\n", + "\n", + "![bp_weight_update](images/bp_weight_update.png)\n", + "\n", + "随机梯度下降算法也就是需要求出误差$E_d$对于每个权重$w_{ji}$的偏导数（也就是梯度），怎么求呢？\n", + "\n", + "![nn3](images/nn3.png)\n", + "\n", + "观察上图，我们发现权重$w_{ji}$仅能通过影响节点$j$的输入值影响网络的其它部分，设$net_j$是节点$j$的加权输入，即\n", + "\n", + "![eqn_21_22](images/eqn_21_22.png)\n", + "\n", + "$E_d$是$net_j$的函数，而$net_j$是$w_{ji}$的函数。根据链式求导法则，可以得到：\n", + "\n", + "![eqn_23_25](images/eqn_23_25.png)\n", + "\n", + "\n", + "上式中，$x_{ji}$是节点传递给节点$j$的输入值，也就是节点$i$的输出值。\n", + "\n", + "对于的$\\frac{\\partial E_d}{\\partial net_j}$推导，需要区分输出层和隐藏层两种情况。\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.1 输出层权值训练\n", + "\n", + "![nn3](images/nn3.png)\n", + "\n", + "对于输出层来说，$net_j$仅能通过节点$j$的输出值$y_j$来影响网络其它部分，也就是说$E_d$是$y_j$的函数，而$y_j$是$net_j$的函数，其中$y_j = sigmod(net_j)$。所以我们可以再次使用链式求导法则：\n", + "\n", + "![eqn_26](images/eqn_26.png)\n", + "\n", + "考虑上式第一项:\n", + "\n", + "![eqn_27_29](images/eqn_27_29.png)\n", + "\n", + "\n", + "考虑上式第二项：\n", + "\n", + "![eqn_30_31](images/eqn_30_31.png)\n", + "\n", + "将第一项和第二项带入，得到：\n", + "\n", + "![eqn_ed_net_j.png](images/eqn_ed_net_j.png)\n", + "\n", + "如果令$\\delta_j = - \\frac{\\partial E_d}{\\partial net_j}$，也就是一个节点的误差项$\\delta$是网络误差对这个节点输入的偏导数的相反数。带入上式，得到：\n", + "\n", + "![eqn_delta_j.png](images/eqn_delta_j.png)\n", + "\n", + "将上述推导带入随机梯度下降公式，得到：\n", + "\n", + "![eqn_32_34.png](images/eqn_32_34.png)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2 隐藏层权值训练\n", + "\n", + "现在我们要推导出隐藏层的$\\frac{\\partial E_d}{\\partial net_j}$。\n", + "\n", + "![nn3](images/nn3.png)\n", + "\n", + "首先，我们需要定义节点$j$的所有直接下游节点的集合$Downstream(j)$。例如，对于节点4来说，它的直接下游节点是节点8、节点9。可以看到$net_j$只能通过影响$Downstream(j)$再影响$E_d$。设$net_k$是节点$j$的下游节点的输入，则$E_d$是$net_k$的函数，而$net_k$是$net_j$的函数。因为$net_k$有多个，我们应用全导数公式，可以做出如下推导：\n", + "\n", + "![eqn_35_40](images/eqn_35_40.png)\n", + "\n", + "因为$\\delta_j = - \\frac{\\partial E_d}{\\partial net_j}$，带入上式得到：\n", + "\n", + "![eqn_delta_hidden.png](images/eqn_delta_hidden.png)\n", + "\n", + "\n", + "至此，我们已经推导出了反向传播算法。需要注意的是，我们刚刚推导出的训练规则是根据激活函数是sigmoid函数、平方和误差、全连接网络、随机梯度下降优化算法。如果激活函数不同、误差计算方式不同、网络连接结构不同、优化算法不同，则具体的训练规则也会不一样。但是无论怎样，训练规则的推导方式都是一样的，应用链式求导法则进行推导即可。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.3 具体解释\n", + "\n", + "我们假设每个训练样本为$(\\vec{x}, \\vec{t})$，其中向量$\\vec{x}$是训练样本的特征，而$\\vec{t}$是样本的目标值。\n", + "\n", + "![nn3](images/nn3.png)\n", + "\n", + "首先，我们根据上一节介绍的算法，用样本的特征$\\vec{x}$，计算出神经网络中每个隐藏层节点的输出$a_i$，以及输出层每个节点的输出$y_i$。\n", + "\n", + "然后，我们按照下面的方法计算出每个节点的误差项$\\delta_i$：\n", + "\n", + "* **对于输出层节点$i$**\n", + "\n", + "![formular_3.png](images/formular_3.png)\n", + "\n", + "其中，$\\delta_i$是节点$i$的误差项，$y_i$是节点$i$的输出值，$t_i$是样本对应于节点$i$的目标值。举个例子，根据上图，对于输出层节点8来说，它的输出值是$y_1$，而样本的目标值是$t_1$，带入上面的公式得到节点8的误差项应该是：\n", + "\n", + "![forumlar_delta8.png](images/forumlar_delta8.png)\n", + "\n", + "* **对于隐藏层节点**\n", + "\n", + "![formular_4.png](images/formular_4.png)\n", + "\n", + "其中，$a_i$是节点$i$的输出值，$w_{ki}$是节点$i$到它的下一层节点$k$的连接的权重，$\\delta_k$是节点$i$的下一层节点$k$的误差项。例如，对于隐藏层节点4来说，计算方法如下：\n", + "\n", + "![forumlar_delta4.png](images/forumlar_delta4.png)\n", + "\n", + "\n", + "\n", + "最后，更新每个连接上的权值：\n", + "\n", + "![formular_5.png](images/formular_5.png)\n", + "\n", + "其中，$w_{ji}$是节点$i$到节点$j$的权重，$\\eta$是一个成为学习速率的常数，$\\delta_j$是节点$j$的误差项，$x_{ji}$是节点$i$传递给节点$j$的输入。例如，权重$w_{84}$的更新方法如下：\n", + "\n", + "![eqn_w84_update.png](images/eqn_w84_update.png)\n", + "\n", + "类似的，权重$w_{41}$的更新方法如下：\n", + "\n", + "![eqn_w41_update.png](images/eqn_w41_update.png)\n", + "\n", + "\n", + "偏置项的输入值永远为1。例如，节点4的偏置项$w_{4b}$应该按照下面的方法计算：\n", + "\n", + "![eqn_w4b_update.png](images/eqn_w4b_update.png)\n", + "\n", + "我们已经介绍了神经网络每个节点误差项的计算和权重更新方法。显然，计算一个节点的误差项，需要先计算每个与其相连的下一层节点的误差项。这就要求误差项的计算顺序必须是从输出层开始，然后反向依次计算每个隐藏层的误差项，直到与输入层相连的那个隐藏层。这就是反向传播算法的名字的含义。当所有节点的误差项计算完毕后，我们就可以根据式5来更新所有的权重。\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. 为什么要使用激活函数\n", + "激活函数在神经网络中非常重要，使用激活函数也是非常必要的，前面我们从人脑神经元的角度理解了激活函数，因为神经元需要通过激活才能往后传播，所以神经网络中需要激活函数，下面我们从数学的角度理解一下激活函数的必要性。\n", + "\n", + "比如一个两层的神经网络，使用 A 表示激活函数，那么\n", + "\n", + "$$\n", + "y = w_2 A(w_1 x)\n", + "$$\n", + "\n", + "如果我们不使用激活函数，那么神经网络的结果就是\n", + "\n", + "$$\n", + "y = w_2 (w_1 x) = (w_2 w_1) x = \\bar{w} x\n", + "$$\n", + "\n", + "可以看到，我们将两层神经网络的参数合在一起，用 $\\bar{w}$ 来表示，两层的神经网络其实就变成了一层神经网络，只不过参数变成了新的 $\\bar{w}$，所以如果不使用激活函数，那么不管多少层的神经网络，$y = w_n \\cdots w_2 w_1 x = \\bar{w} x$，就都变成了单层神经网络，所以在每一层我们都必须使用激活函数。\n", + "\n", + "最后我们看看激活函数对神经网络的影响\n", + "\n", + "![](images/nn-activation-function.gif)\n", + "\n", + "可以看到使用了激活函数之后，神经网络可以通过改变权重实现任意形状，越是复杂的神经网络能拟合的形状越复杂，这就是著名的神经网络万有逼近定理。神经网络使用的激活函数都是非线性的，每个激活函数都输入一个值，然后做一种特定的数学运算得到一个结果。\n", + "\n", + "### 6.1 sigmoid 激活函数\n", + "\n", + "$$\\sigma(x) = \\frac{1}{1 + e^{-x}}$$\n", + "\n", + "![](images/act-sigmoid.jpg)\n", + "\n", + "### 6.2 tanh 激活函数\n", + "\n", + "$$tanh(x) = 2 \\sigma(2x) - 1$$\n", + "\n", + "![](images/act-tanh.jpg)\n", + "\n", + "### 6.3 ReLU 激活函数\n", + "\n", + "$$ReLU(x) = max(0, x)$$\n", + "\n", + "![](images/act-relu.jpg)\n", + "\n", + "当输入 $x<0$ 时，输出为 $0$，当 $x> 0$ 时，输出为 $x$。该激活函数使网络更快速地收敛。它不会饱和，即它可以对抗梯度消失问题，至少在正区域（$x> 0$ 时）可以这样，因此神经元至少在一半区域中不会把所有零进行反向传播。由于使用了简单的阈值化（thresholding），ReLU 计算效率很高。\n", + "\n", + "在网络中，不同的输入可能包含着大小不同关键特征，使用大小可变的数据结构去做容器，则更加灵活。假如神经元激活具有稀疏性，那么不同激活路径上：不同数量（选择性不激活）、不同功能（分布式激活）。两种可优化的结构生成的激活路径，可以更好地从有效的数据的维度上，学习到相对稀疏的特征，起到自动化解离效果。\n", + "\n", + "![](images/nn-sparse.png)\n", + "\n", + "在深度神经网络中，对非线性的依赖程度就少一些。另外，稀疏特征并不需要网络具有很强的处理线性不可分机制。因此在深度学习模型中，使用简单、速度快的线性激活函数可能更为合适。如图，一旦神经元与神经元之间改为线性激活，网络的非线性部分仅仅来自于神经元部分选择性激活。\n", + "\n", + "\n", + "更倾向于使用线性神经激活函数的另外一个原因是，减轻梯度法训练深度网络时的Vanishing Gradient Problem。\n", + "\n", + "看过BP推导的人都知道，误差从输出层反向传播算梯度时，在各层都要乘当前层的输入神经元值，激活函数的一阶导数。\n", + "$$\n", + "grad = error ⋅ sigmoid'(x) ⋅ x\n", + "$$\n", + "\n", + "使用双端饱和(即值域被限制)Sigmoid系函数会有两个问题：\n", + "\n", + "1. sigmoid'(x) ∈ (0,1) 导数缩放\n", + "2. x∈(0,1)或x∈(-1,1) 饱和值缩放\n", + "\n", + "这样，经过每一层时，Error都是成倍的衰减，一旦进行递推式的多层的反向传播，梯度就会不停的衰减，消失，使得网络学习变慢。而校正激活函数的梯度是1，且只有一端饱和，梯度很好的在反向传播中流动，训练速度得到了很大的提高。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. 示例程序" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "% matplotlib inline\n", + "\n", + "import numpy as np\n", + "from sklearn import datasets, linear_model\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# generate sample data\n", + "np.random.seed(0)\n", + "X, y = datasets.make_moons(200, noise=0.20)\n", + "\n", + "# generate nn output target\n", + "t = np.zeros((X.shape[0], 2))\n", + "t[np.where(y==0), 0] = 1\n", + "t[np.where(y==1), 1] = 1\n", + "\n", + "# plot data\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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rNfTXKVBTUSlOii3VUxCEmsAKRVGa+njtDmAsMACIAqYpitL+WvdTUz1Ljuy4JBY1fMRLrkDjZ+Ce07MRdRq0gX7luuOXbLOzqOHD5MSnuB2qoJHwqxbOsGO/IGqLf16Tdvgsm0ZOIv2wS8sotF1Dus16LV+tMRc2GEnmCa/5DgCCRkNElyb0/2NysdqrcmtSqto+giD8BvQAQgVBiAfeBrQAiqJ8A6zC5fhPArnAo8Uxrkrh8K8WTreZr7L5kY/+q85VIKJbc+bVuB9FljFVDaPT189TtW+7sjU2DySdlju3T2fHuOnELt0KgkD1wZ3pMG3sNR2/PcdM7NJt2NKzqXJba4Lq519tNLhJTQbv/QbLpQwESSrQTN1UNTRP5684HCTvPEbmqfNq83iVUkMt8rqFseeYufjXAUSNxPEfVhK/aqdH+0fJpGfApinlVr4gcdthzi74G0EjUef+XtctJEvcdpj1A15FURQUhxNQqP/EHURNGVPiq5yEtbvZOOxt917C1WiD/LhtybtU6t6iRO1Qufkp1SIvlfJJ+pGzHJm+hFNzNmL3sTmq9TNS7Y4OhLSs4ypuuqrvr2y2cWjSb4ArB/7c0q1c3HTQrWdflux4bjrr+rzMkamLODx5ASu7PMeB93/N83yn3cHGwW+6pB2yzJeb29s48dNq4lfvKnF7q/ZtR/tPR/tsHQkuAbngZqqOv0rpUa6yfW52FKeT7LMX0QaYik3e2Oc4isLWUZ9xes4foCgIWontY6bSd+3HhLX33ozNPpeEqNd6NKG/fCNiV2xnbZ+Xubj5IJJe62ozGOhH3/WfUKFh2WRkpew5TsyPq/6bRSsKstnKgfdnU/uB3gTU8i4hSdwa7TP//98U12oDokrabBqOHkTV/lEsbfkk9qxcd8Wxxs9AozGDrylKp6JS3Kgz/1IifvVO5kYOZ3HzJ5hb/T7W3P4SluR0wKWLf3LmOk7N3oA1regN1s8t3sKZ3/90zW4tNhxZZuwZOWwY9IZXa0KAwPqRvqUZAKfFzvkNrqpXe2Yu9iwzuecvsa7fK2VW2XpuyVbvB9Vl4lb6zp335fj/Jf3oOZa0fJJl7Z7m+HcrfH5Gvu6Xn/OuJqBGBEMO/kC9R/piqhJKcPPadPzyOdp8+GSB76WiUhTUmX8pkH7kLH/c8z+PeO/Fvw+wrv+rNHxmMDvGTkPQiICAIjvpOuMVat3d/br3lW12bGlZ6EODPDpAxfyw0qOAy32+2UbKrmNeaaL6Cv40HDuE418ty1/PXUXBmppFyq5jhEU1uv75xYxk0CJIIorT0/kKouhanfggonNTFNlHuEoUyD59AafN9XDYefQcCev20GvBOz7vkxETx9anJpO0ORpBEqg+pAsdv3rOXSWcH/yrhdPlh5eueY5ssyNbbGgDTOU660rlxkWd+ZcCh6ct9ppZKw6Z9KOxbB8zxTU7z7bgyHbFojc//JF7VeALpyyz+5XvmBMymPm1RvBbxDCOf7/iv9dtecxyBXDafc9W2300ijYfPZnvcJQgitgyc/J1bnFTa3hPnzIMitNJjSGdfV6jMRnoOuNVJKPeHXcXDToEQfD4vORcKwlrdpGyL8brHta0LFZ0epbETYdQnE6cdpnYJVtZ3fPFYlsFOXItbHniU2ZXGMSc0KEsbPAw5//4p1juraJyJarzLwWyTp33OetUnM7LWSdXIcDZRZvzvN++N37k6JdLcOS6wjq21Cx2vvAVZxduAqDOg7d5NTtx3VcgrIPvmbogCDQeM4Reiye6tPmvg9PuILxjE5J2HGFll3HM9B/A/DojOP7DyhIPBwXVi6T95GeQDDo0fgY0/kYko56uM1+95sOr5l1duevIz7R440EaP3cXkQPa+/y7OGUnSVsPex0/OXOdW/HTfa7dQfbZRC7+faBY3ttf97/H6TkbkS02dyXwxkFvkHatPskqKoVAdf6lQJVerTwExf5Fccg+M2cUp5JnTNtpd3B0+lKvlEE518r+iTMBqPPg7YR3borG3/UAEPVaJJOe7rPfQPKhXXMl4R0bY4wIzlOWQRBFJJOeqCnPkBETx5rb/o+kbYeRc61kn7nIrue/4uDlDKGSpOFTA7nnzGw6THuWjtPHMTx+br5CZf41Img54SGiPh9DSPM6CFrvFYSo02CsFOJ1PC36jM9UTcXpvH4v43yQHZvI+fV7vf72stXOoU/nFvn+KipXosb8S4EGowdyZPpiLI4MlMthF43JQLWBHYhdvt3LoQhAtTs7+ryXLSMnz43GnLhkwCXT0GfVh5xfv5eE9XvQhwZR98Hb8YsMu66tgiDQb8OnbBj8JpknE1x7CaJA7Qd6kxuXjCG8Ag1HDyS0bQPW3/m6V3qoI9fCwQ/n0OSFu9H4kHguTowRIdR79FpK4nlzduEmDk76zf33uBJJp6XaQO/PP7RNA878/qfXfoogCEVO00w/eo4TM9b6bImpyE7Sj/jWK1JRKSyq8y8F9MEBDN73Lfvf+5W45dvRVfCnyXPDqPtIX3Y+N50TP6/BkWsFASSDjmYvDc+z0lMfEoA2wITV6i31HNKyjvv/BVGkat92harQ9a8RwZD935NxIh57Zi4hzWv7rJpN3X/SIwTiRlEwn08hoHb5rFa1pmayaeQknD5WV37Vw7l95Yc+H1x1HryN/e/OdIVkLoeLRL2W4Oa1CevQuFC2OGWZTSM/JHbJNhDwubIQtBrC8wjXqagUFtX5lxLGiBA6fjGOjl+M8zgeNXUstYb35MzcP12Vqg/0JrRtgzzvI4gi7T4exfax0zwchWTS07aY0wWD6kVe8/XAepHknvfuKaw4nRgiiq9he3FhTcviwp/7SdoajSD6ajgsUGt4D4Kb1PR5vdbfJQu9a/w3xK3YjqTVUGdkH9p88HihM3JiflhF3NLtXjpLbgQBjVFHozGDOfbtchLW7cG/ejgNRw8iqEH+pSlUVK5Gdf75JOvsRXa/9A0Ja/egMelpMOpOWrz54HVj6NdDEAQiOjclorOXHp4XDouN2MVbyIlNovG4u4hfvYvcuCRCWtalzQdP+CzgKklavj3SFfq56iHU4Mk7yk1/4X85/v0Kdj73JaJOg9Pm8L2nouB7A/4K/KqG0fP3CUW2x56Vy8GPfif6k7l51iBo/I1U7tmSFhMeYsPgCeQmpODIsSBoJI5/t5KecyfkGR5UUbkeqvPPB5ZLGSxv/zS21GwUpxNHtpnoz+aTeuAUty19r1RsyI5LYmXHsdiycnFkm13NUipX5K7jM9w55tlxSex78ycS1uxGG2ii0bNDaTx2SIlp6lfu0ZLuv77Ozhe+Ijc+Gcmkp/GzQ2n1v0dKZLzCkhZ9hp3Pf4VsseW5kQ4gGXXUvLtbidvjtDtY2WUcmSfi83b8JgM9F7xDaJt6HJ2+hJzYRGSLK11YccjIDpnNj37MfRcXeNR4qKjkF9X554OY71biyLF6ZObIZivnN+wj/VhsqcgcbH3yM8yJae5YsyPLTLblIrtf+pbqgzuTfuQc0Z/MxZ6ViyI7sSSns/f1H0iPPkPn78aXmF01hnSh+uDOyGYrkkFXYg+aohDz02qv3gW+0AcHULH1tcXhioNzS7aSdeai25n7wmG28sddb6HITkSdxue5stVO+uGzhDSv4+MOKirXpvz9UsshyTuP+ozJilqJtEMln3/tdMhc2LjPKyfdaXdwcsZaNo+cxL63fsaWnu1xjpxr5dSs9eQkJJeofYIgoDEZyqXjB7BlZPuu7r0Ka2oWhz9fWOL2JG0/7LMlJPDfZ6goyLlWnFY7jmzfVdeKLKMNKFrzHZVbl/L5ay1nVGhSE9GHbIAiOwmsm3dGS058Mpsf/5jfq97DoqaPEfPjqsIXQOW1oai44sfk4dxEg47U/acKN+ZNQs2hXd01D9dCNluJ+WlVidvjX7MSklHvdVzQSL43yn18ZwRRJKhBNZ8idioq+UF1/vmg4dODkK6S4hV1WoKb1c5TQ96clMbS1k9xauZ6zBdSyThyjp3Pf8muF78q8PiiRqJK79Y+c8Cvh9PuyFeP2ZuZyAFRVOrW3HfV81Vc2WaxpKgzore3PIUoYAgNQhcc4PMaQatxdVgLMKEJMOJfM4Lei98tcVtVbl5U558P/CLD6PfnZCq2rocgiYg6DTWGdaXP6g/zvObItMXu+Pu/OHIsHP92BeaktALb0Pn78Rgrh6AJMIIouBzZdbILRZ2Gii3r5pm6eKsgiCK9l75H119eoea9Paj3aD/8qod7nSfptdR+4LYSt8dQMYh+f3xGYINIJIMOUa+lYqt6DNg8lSq9W/muOtZIDN7/PZ2/H8/tKz5gWMxM/GtElLitKjcvaievAuIwWxE10nV7xK7s+hxJW6O9jmuD/Og1/22q3NamwGPLNjuxS7aSERNPcLNa7H7lW7JivFsDCpKIIIlE9o+iy88vo6/gT8aJeFJ2H8e/ejjhnZveVEqR9mwzZxduwnwxlYguzQjv1OS67+/SPydY3Ws8TrsDOdeKxt9IQO3K3LFlGtp8aBsVB4qikBOfjKjVYLosJ5GTkMySFk9iz8hxTxw0fgaavzGCFq8+UCp2lRZZp8+TE59CcLNa6PNY8agUnFLt4XsrofERq/VFYN2qJO844r1Ja3P4nHXmB0mnpda9Pdz/9osMY03vyw7MbEPjb8Sveji9l0zEVKkiWn8jTlnmrxHvE7t4C6JWQlFc/WT7//EZpsoVC2VHeeLS/pOs6TUep0NGttiQ9FoiujbjtqXvXfMBXbFVPe45PZvTczaSfS6R8I5NqDawo0+10JJCEAT8q3l+F/yqhrmqwSfO5PyGfzBWCqbZS8OpOazkU1BLC1tGNhuHvkXyjqOIei1Oq53Gzw+jzfuFL5ZTKTjqzL+ESD14ihWdnvUogBJ1GsKiGjHg7ynFNo7lUganft1A1ukLRHRuSo2hXTyc3pEvFrHntR887BA0EhFdmtL/j8nFZkdZoCgKv1W6G+tV8teSSU+7j0fR6JkhZWSZyrXYMGQCCWt2e6TfavwMdPr6eeo8eHsZWnZzoPbwLWNCmteh57y3MVWp6NKQ12up2q89vYu5KMxQMYgmzw2jw9Sx1Lq3h9ds9+hXy7z0YhSHTNL2I1hSvPWBbiSiP53n5fjBleIa88PqMrBI5XpY07JIWLvbq+7CkWPh0Gfzy8iqoiFbbRz8+HcWNnyYBfVHsv/dWflrilTGqGGfEqTagCjujZtLTnwyukATuiD/UrfBV0cvcIUcHHnpydwgRE/O21nYs83seukbJJ2W2g/0LpNN7+xziWQciyWwfqSaknkZW0YOoiTiKzHZegNORhRFYV3/1zxqgQ5+OIe4Fdu5Y9sX5br6WnX+JYyvuG5+UBSFCxv3cWrORgRBoM5Dt1Ope4sCx0RrDO3C8W+Xe3X3MlYOyZfEc3lBttldmVaXf0xOh4wlKe9uZzmxiRyePB9BFDk8ZQGt33+cps/fXWq2bnroQ+KWb3fHtKv0aUuP3yeUuMz1v1hTM3HaHRgjvPsSlCX+1cPR+BtdKrZXIGgkqva9bqSiTFAUBdlqR9JrvX5/iZsPkbL7mEcRqGyxkX40loQ1u6l2R4fSNjffqGGfYkK22ji74G+OTFtE8q5jRe5mte3pz9k49C1O/rKWE7+sZcPANwpVI9BywkMYK4WgMV1u7KLToPEz0PWXV26IzbW0w2dZ0flZZpkGMMs0gL8eeA9rejaiRsIQlnffXKfN4RJqk53IZhv7Xv+xxCud/+Wft38hbsUOZIsNe0YOssXG+XV72PPKdyU+dk58Mqt6vMDvVe5hXs0HWNTkUVL2erekLCsEUaTj1y8gmfTuwkVRp0UX5EfLtx8uY+u8OfXbRuZVv49f/e9gTuhQDn061+O3nbzzKLLVW3rDkW0meceR0jS1wKgbvsVARkwcq7o9j8NsxWlzIGqkfGWc5EXK3hhWdX/eK1YvGfUM3PVVgUMY9mwzJ2eu4+Jf+wmoW5WGTw28IXLEzUlpLGzwMPbMXHeVq6jTENysFgN3fc2xb5ax56XvPOKrgiT6lHKQTHqiPh9DgyfvKHG7Z4cMxpae7XVcY9LzYNbKEnvoOmWZhfVGkhOX5PEZaANMDDsxE2N4+ZHZTtkbQ/TZN0AeAAAgAElEQVRn88g6dZ5KPVrS5IW73emu5YVzS7fy94j3PX6HGpOBlm89RLOX7wPg1JyNbBv9uZdch8ZkoN1no2n41MBStRnUDd9S5c97J2JJzsCRZXZpseRYuLjpIEe+WFyo+8Wv2ulTyEtxyMSv2lng+2n9jTR6ZjA9571N2w+euCEcP0DMj6tcje+v7Jlrc5BxLI7knUdpOHoQbT96En1oEIgCxkohVOnTFnxo9QuigOijeKokyGufxWG2+W5+U0xc2LAPy6UMnxpQJ2asLbFxC0Nom/r0mPMmA3d+RbuPRpU7xw/wz4SfvSZgjlwLByfNcXfTqzG0C5Je6yW/Imglat/Xs9RsLQyq8y8i2XFJrv6tV/2o5VwrMT8WLuNE42fw6agErVRqBUjlgbRDZ3xLMAsCmScSEASBRmOGcH/iQkbmrGLo0Z/JOnUenN4OVpEVqg/qVApWQ3jnJj6Ph7ZvWKLid9nnEn2uemSLrVh6DN9qZJ296PO4I8fqnulrjHoGbJpCcNOaSAYdkkFHYINq9P/jszJJ8CgIqvMvIopDzlN0zWl3oChKgeP/te7t4dtJKFBjWNfCmFnuURQF+ar0v9C2DVyx4atxOj165gqCgKTX8c9bv5B9LtH7fFGg26+vow8JLG6zfRI1dSzaABPiZT0oQatB42+k45fjrnNl0Qht57sDnMbfSESXZiU69s1IXp3StEEmDzXVCo1qMOTADwyLmcldx35h2NFf8tT8Kk+ozr+A5F5M5fyGvWSedMkq+NeshKmy95JV1GsRJJEZ+r7M0Pflj3vewZyYmq8x/CLD6Przy0hGPdpA1xdNYzLQ47c3y1XctjhQnE4OfDiHORWHMNPUn3k17+fsos0A1Husv6sj2BUPQsmgI6xDYyq2rOt1r9O//eEKE12NIBBZipkkIc1qMyT6RxqNHUJE12Y0HD2QIQe+J7R1/RIdt2KrelTq1txDMVTUaTBGBFNrePkOQZRH2k560kt9VTLpaf3e4z4nZ36RYfhXvzFCqqBu+OYbxelk+5ipnPhlLZJBh9PmILxTE3ov+h/px2JZe9tLLokBsxXJz4Bic6A4ne5luKCR8KsWzrBjv+R7E9iWmcP59XtBEKjap22xhHwyjseRdfo8wc1r41e17FM9973zC4c/ne+xaSuZ9PReNJGqfdqSHZvIrvFfk7B6N6JBS71H+9F64qM+ZTbmhA3FeinT67igkXgwfZk74+lGxp6VS8yPq4hbsQNjlYo0HjvUo32nbLNz+LP5HP9hJbLVTs1h3Wj19shSW/XcbCSs38Oel78j43gcpqqhtHrnYeqMKHnxv6KQ3w1f1fnnk8NTF7LvjZ88nJSo11Ljrq70mP2GS2Zh9kZyYhNRFIj5fqV3BkCAkW4zXqXGkC4FHj/z1Hl2Pj+d8xv2IRl01HukH20+eDzfWkO2zBw2Dp5A8q5jrj62Vju17+9Fp+9eLLNCFNlmZ07oEJ/NSkKjGjJw+5cFut/2MVOI+XG1Z02DKBDRpRkD/vq8qOaWObbMHJa1fZrchBRXXrkoIBl0dJw+jnqP9Ctr81TKCWq2TzFzZOoir5Jtp9XOuUWbcVhsLpmFcXfR/tOn0fobfXZqknOtZByNLfDYlpQMlkc9Q/zqXTitduwZORz/djkbBr+Z73tsG/05STuOIJut7tzz03P/5MjURQW2p7iwpmbl2TA96+T5At+v9ftPEFCnCprLKySNvxFDaBBdf3qpSHaWFuakNPa8/gPL2o5m47C3Sdx22OP1o18uITc++b+CIqer29eOcdNv+GptldLnlqjwVRSlyLnVtowc3y84FeRci0flZnCTmq4qxqseAJJJT4VCyAzEfL/SlXJ2RRaLbLGRtO0wqYdOE9Ks9jWvd1hsnFu0xUtPRc61cuSLRTR98Z4C21QcGEKDEPVanxk9hfmc9BX8GXLgB+JWbCf1wGkCalem5t3d8r06KktyL6aytOWT2NJzcNrsXPrnJAlrd9Ppmxeoe1ns7NziLT4/K0EUuPTPSSI6+c4yygtzUhoXNv6Dxs9AlT5tS636WKV8cNPO/GWrjV3jv2ZWwB38ormd5VHPkLz7WKHvV6V3K9+bPNXDvbovVR/SGUPFQIQr5IH/1WyPHBBV4LFT9sb4/NGLkkT64bPXvV42W/PML7dn5nodU5xOEtbtYefz09n/7qw8U96KiqiRaDnhIa9YvGTS0+a9xwp9zxpDutDq7ZHUfej2G8LxAxz8YDa2tOz/HtCXe/juHPcFTrsrjGWo6LuiWXE40QcXLK0w+rN5zK/5AFtHT2bTyA+ZW/lukrYfvv6FKnnidMgcnrKAhY0eYV6N+9n54ldYU733oMoLxeL8BUHoJwjCcUEQTgqC8KqP1x8RBCFZEIT9l/97ojjGvRabHvqQY98scxXcKAopu4+zptd4d5ZOQWk76Um0QSZEnauXryCJaEwGOn37oteqQtJpuXPHdGoO6+bK/TXqqHlvd+7Y+kWh9OKDm9fOs4dwXuloV6Kr4O+zlaMgilS53TM06HTIbBj0Bn/c/TZHpi3mwPu/srjJY+4MnGuhKAoX/trPwQ/ncGLGWux5NCm/kiYv3E3UtDH416yEqNcS0roety9/v9ymJp7fuI/l7Z9hVuCdLGnxBLHLtxXLfRPW7nY7+Stxyk4yLufoN37uLq8HpSCJBNSpTIVGNfI9VvKuY+x76xdkiw1Hlhl7Zi62jBzW3/E6stVHXYVKvvh7xPvsffMnMo/HkROXxLGvlrGs3TPlVuGzyBu+giBIQAxwOxAP7AbuVxTlyBXnPAK0VRRlbH7vW5QN3+y4JBY1eNhrtixoJOo/PoBOXz9fqPvmXrjE4SkLSdp2mKAG1Wg6/p4C/egKS/a5iyyo+5BXAU/F1vUYtOebfN3j4qaDrB/wGrLNjuKQEfVaNH4GBu3+2kNx8tScjWx7arJXlarG38j9iQvznEnLNjvr+r9Kyq5jrqYqRh2iRkP/PycT0qJOAd9x+SRh3R42Dn3LQ8RLMunp8uNL1C5iKmVend9EvZZ7Ts92N9458MFsDrz3K6JOgyI78YsMo8/ajwqUYrh19GRifljlVQynDTTRfc6bVCvE6vRW4uyizRz6+HfMiWlU6d2KlhNG4si1sKzt0x7fDXAVbEZ9/gz1nyh5WZF/Kc1OXu2Bk4qinL488O/AYKDMVI2yTib4jCUrDpnUAycLfV9T5Yq0+2hUUc0rMCdnrkfQSJ7OX3DN+pyyTOaJBHRBftfszFWpW3MG7fuGw1MWknEslvDOzWg8drCX6uOp2Rt8yhMIokDilkNUvd33d+ro9CUukavL5fD/ZvD8OXwidx395YYQkbseu1/+1uvHLeda2f3St0V2/k3H38Om/Sc9PntRpyGiSzOPv2uL10fQcPRAkncdwxBWwdVXuoCfrT3L7LMKGiVvaQoVFwc//p0DE//T6z85Yx3nFm+h5YSRCJL338GRY+HC3wdK1fnnl+Jw/lWBuCv+HQ/4mjoMEwShG65VwguKosT5OKdYCKwfidPXxphWomLr8l95dzUnflrtXbykuPrQ/hYxDKfVjtMhExbViJ5zJ+Qp4xtUvxqdvrr2qkfyEV76dzxJl8drwImf13jpoIBLZTLr9AUC61S55rg3AhnHfX9lcxNScNodhRLx+5caQ7rQ/PURHJg4E6dTQXHI6CsG0u6Tp7zO1YcEEtmvfaHHqjmsG3HLtnk5eqfdgV+1cFem0aaD6EODaPbScOo92u+meHgXFXuOmf0TZ3p8zxXZiT3bwoW/9/us9Bf12nL73S+tDd/lQE1FUZoD64EZvk4SBGGUIAh7BEHYk5xcePldv6ph1BjWzbs6T6+j6fh7C33fsuJfEamrURxObKlZOHIsOK12krYdZt2A14o0Vv3HB6Dx8y6GEnUawjs3zfvCa4UPy2ktSUHxqxrq87gu2N9jc7+wRHRqAqKIIsugKFhSMlnd4wXSj5wt8r2vpPrgTkR0a/7f31kUkUx6mr1yH+v6vUzskq1YL2WSeTyOnc9NZ99bPxfr+DcqGcfifO7ZKXYHmScSMIYHI0ieLlXUSOVy1g/F4/wTgCt3HSMvH3OjKMolRVH+fVz+ALTxdSNFUb5TFKWtoihtw8KKVn3a9eeXafLC3eiCAy73rG3GgE1TSr2jkj0rl5S9MfmWdrgah9lKQJ0qkI+Jl+KQyTweT+rBU9c9N/XAKc7M+4u06DMexyMHRFHvsf5IRh2SUY8mwIg20MRty9675mZ13Uf6ej1sAUxVKrrsvwlo+fbDXlpDGpOBFq+PKJaZ8banP3eFlS6HZBS7A3uWmd0vf1vke1+JKEnctuw9us9+gzoP3U6jZwZxx+ap5CakuJqsXPGwduRYOPzZfGyZeaQ630KYKod4NUX6l4Calej/12TCOzVB1GuRDDr8a1Wiz5qPym3TpOII++wG6gmCUAuX078PeODKEwRBqKwoyoXL/xwEHC2Gca+JqNXQ5r3HCp0yWFQUReGf/80g+uO5ropam4PIAVF0m/lqvmUGnHYHq7o975r5XTF5/nd24UvBUdBK5J6/REhz35us9hwz6+98nZTdx93a9+Edm3Db0nfRmAwIgkCHqWNpNHYIFzbsQ1fBn2qDOro0dq5B42eHErd8O5f+OYkj24zGz4Cgkejx+4SbJmRQ96HbcWSb2ffWz9izcpGMepq/ej9NXih6hzB7jpnMEz4y0RSFi5sOFfn+Hrd0OkGB6oM6eSidJm077BIqvApRpyHzRAKhbUpWm6i8Y6oSSqWeLbnwxz8eYVjJpKfZy/fhVzWMAX9PwZKcjsNiwy8yrFx/94vs/BVFcQiCMBZYC0jAT4qiHBYEYSKwR1GUZcA4QRAGAQ4gFXikqOOWF2SbneQdRxF1GkLbNXBLJZycsY7Dn85HttjcG8/xq3ay/ZmpdP3llXzd+9ySrWQcj0U2X5W1JIrUe2IAJ39Z4/Wa02q/5r7GrvHfkLzjqMeXN2lrNHte/4EOU/5LxgqqF0lQvch82QmukFr/PydzfuM+krcfwVi5IrWG90AX6Jfve9wINHx6EA2euhNbZi7aAGOxSWNIeh2iVoMse+9V6YKK5zO0Zeawc9x0Ts/9E6fdQXjHJnT65gV3c6DAelVJPxrrFaZz2hyY8gh53Wr0+O1NNj88ifg1uxE1EqJOS9TUMVTq1tx9jiGsQhlamH9UbR8fJO86xrGvlmJOSqf64E7UHdnHZ4pj3Mod/P3gB64fi6Kg8TPQe+l7hLVryKImj/qUchD1WkZcWpKv2f/WpyYT8/1Kr+OSSU+b9x8n+tN5WJLT3UtRjZ+Bhk8Pot3H3puE4FqNzPIf4PXAANAGGHkwYwVOu4P9787i6JdLsWflEt6hMVHTxvpU0VQpXraOnsypmes9stQkk55W7zxCs/8r2l6Voiis7Pwsl/ad+C90IQhoA00MOz4DY3gwyTuPsrrXeM9UVoOOyAFR9FrwTpHGvxFJPxbL3jd+JHHTQQxhQTR7+X7qPtwHQRCwpmZiuZRJQK3KhardKUlUbZ9Ccvz7Fazu9SInZ60nYc0udo3/mhUdx3oVamTHJfHn8InYM3KwZ+ZizzJjvpjGuj4v48i15NlcXBAEbD6qan3hFxnqLiq7ElGSCKhdmcH7vqXRmCEE1KlCaLsGdPr2RdpeJxXVV79RwN05bPNjHxP92XxsaVkoDpnELYdY1e15sk4XXGtHpWBEfT6Gqn3bIRl0aIP8kPRa6jx4G01fLHpY6dK+E6QdOuMZs1YUnFY7x79bAUBYVCN6/PYmpqqhiHotol5Lrft60m1W0ZIIbkSyTp9nRYcx7s3vjGNx7Bg7jX/+58pV0YcEElQvstw5/oJwS2j75Bd7tpmdL3zlkcol51rJPJnAiV/W0OiZIe7jp2at8xlzdzqdxC7bRqVuzTm3dKtXPrUuOABjRP40+es+0o9DH8311OQRBCSjjsh+7RG1Gtp/9jTtP3s6X/cTBIFKXZtzcdNBz6W9IFC5Vytyz6dwbsEmrweEbLER/dk8On5ZuOI4lfyhMerpvXgi2XFJZJ+5SFDDasXWvyHjeJzP9payxUbawdPuf1cf1IlqAztiSU539ZG4QeQxipsDH8x2qwP8iyPXQvQn82g2/l6PZi43KurM/wqSdx5F1Hg/D+VcK2cXbAJcrfLiVu4g43i8z8YhikPGmppFmw+fQOtv/C8FUBCQTHo6TB+X700g/2rh9Fr8PwxhQWj8jWhMBgLrVqX/n5MLnVPe8avn0Aaa3HIRkkGHLsiPqKljyIiJR/Qh7qU4ZC7tO1Go8VQKjn+1cCp1a15ox29Ny+LCX/vJiPmvLiG4aU3fje2NOkLbNfQ4JggCxvDgW9bxAyRtP+Lz8xK1UqElYsob6sz/CnRBfq5MCF+vVQjgrxHvE7t4i6t62GxFEEWf51fu2ZKg+tUY/M93HJz0G0lbowmoW5Xmr95PeIfGBbKp6u1tGX5+PmmHziAZdAQ1qFakDIIKjWow7NgvHP9uJZf+OUHF1vVoMOpO14/dZPD5QBM0EsEt1Jh/eUdRFPa9/TOHP52PqNfitDmo2KouvZe+S0jzOoR3bELS1mj3noIguvSp6j/ev4wtL38E1qnqc89Ottpvms1v1flfQcU29TGGVyDrquWeZNKjC/bnzNw/PbJ3EAQEjejWpNf4Gag9ordb7yegVmU6f/tike0SJalYN1yNESG0nPCQ13G/yDCqDepI3PLtHpvCkl5L0/FlI/usAtbUTHa/8h3nFmwCUaD2fT1p88ETXg3Cz8z7iyOfL/T4jqbsOc6GwRMwVAzk0t4YVwW3AIpToWqftkR9/oza5csHzV+7n/N/7PMIAUsGHdXu7HDTtFJVs32uIuNEPGtvfwlrWhaCIOK02Wn1zsMcnroQ8wXvQi1Bkojo2hTJoKP+E3dQY2iXcp3bez1km529r//I8e9W4MixENq2Ph2mjyPsqtCASungtDtY3PQxss8mulU/RZ2GoAauleWVMuPLo54hZfdx3zcSBPeERmMy0GjsYNpOKn2dqhuJs4u3sGPsNLcsc+37e9Hxy+fKfThMbeNYBBSnk6QdR7GlZhLeqQn6kEBmBd7pszuXIImMSF923SKoGxHF6fTZw+BmJCchmcOfLyBpazRBDarTZPw9122SUxqcXfA3mx//BEfWVS1B/Y30nDuByP7/yWjNrzOC7DP5670gGXTcd2G+1+pBxRPF6XRtfgf6lXun/y9qqmcREESRiE5NqHZnR/eSuErv1j6zJSo0rlGqjj87Lomc+MLrHhWEW8XxZ546z5JmT1xWJj3GqdkbWNFxLAnry76HdOrB016OH1xZOqlXZOkARPaPQtDmL/VQ1GvdfQJU8kYQRYwRITeM4y8It8avuxho9+lodEF+7iwZQSuh8XM1cykNUg+eYlGTR1nU4GEW1h/J4maPk3aNLl6K04k5MRWHD3XT8ow9x8z2Z6fxa9BAZhj7sWHIBLLPJZbomHte/Q5bZo47B16Rnci5VraNnkJZr4wD61Z19yS+Eo1RR2Ddqh7HWrwxAn1wAILu+lt5Tqsdv2rhxWanyo3HTen8Uw+c4uyizWSeKr7CpMA6VbjryM80/b/hVO7dmkZPD2bIge8LnL1TGOxZuazu8SIZR2Pdm3npR86xuvvz2HO8Z4Unf13P75XvYX6tEcypOJjtz07z2SWqvKEoCuv6vULMD6uwZ+XitNqJX7GD5e2fwZaRXWLjXvxzv099+9yE5DJvw1fz7m6uavAr95FEAV0Ff0LbN2TvhJ9Z3etFto+dij3bzNBDPxLaur7PVeq/SAYdkXdEYarkW/r7ZsdhsZFxIh57Vv6KLW9WbqpsH2t6Nuv7v0pa9BlXoxObg2oDO9J99hvFUolnjAihzbuPFoOlBePMvL+8nbeiINscnFu4mboj+7gPJ6zdzbbRn3tkKZz4aQ2K7Lyuln9pk7g1mn0Tfib98FkC60dS+4HepO4/5ZFuqjidOHLMnJyxjsbj7ioRO3QV/LGmZnm/IAj5FuErKTQmA20+fIJtT012i65Jei2t33+Cpa1GueW8E7dEc/KXdfRaMpHUA6d8N2sRXBpCtR/oTYcvni3ld1L2KIrCoU/mcuC9WYCA4pCpM/J2On4xrki9GG5Ubqp3vHXUZ1z654RHCXvcih0c+vh3Wrw+ogwtKxq5CSk+OyzJZiu5CSkex/a/O8urqYpstnLyl7W0+/gptD5CCGXB+T/+YcOgN9y2WpLTSdl9zGdDDEeulZS9MSVmS+Pnh7Hn1e89PjdRr6XmsG6FivXas81c2huDLtif4Ga1i5T9lXX2Ijueneahtilb7Gx94lMUh+yuM1EcMg6HzPanpyDkMes3hAdz79k5SHrvQr5bgVOz1nt04QI49esGNEY9UZ+Pue716UfOcmr2RmS7g5pDuxDesUlJmlvi3DTO32GxEbdsm5fetmy2cuzrZTe08w9t3xCNv9Er20gy6giNauRxLOus72wPQRJdWQul7PxzL6Zy8MM5JKzdjTEimKbj76X6oE7s/r9vvB5STpvDZ98CyagnuHnJZd40emYwmTHxxPywylUcZbVTqXsLOn3zQoHvdeybZez6v28QNRoUWcavejh9Vk3Cv0b+e+xeyYmf13jLLCuKp+THFWTHJiLmEfMPaVHnlnX8cFmy4SqNLjnXyvHvV9L2o1HuTnUpe45z7NvlWC9lUmNoV2oN78Gxb5ax97UfcNplFFnm+FfLqDPydqKmjSV52xHsmTmEd26KPjigLN5aobhpnL/TZkfxtdTlxu9LWrVPW4Kb1iT1wCl38ZVk1BHSsi6Ve7b0ODesXQNil233kuUVJLHUKxPNSWksbfkktrRsnHYHmTHxXNp3ghZvPkh6XpvVCghaDYr9P+VJyaCl/qP9SsxOQRTpMO1ZWk54iPQj5/CrHl6opj+JW6PZdfmhJuN6sGUej2ddv1cYeuTnQq0Acs9f8t1ARMCjx4P7sCDS9MV7ODx5gcf3XjLpaf2/Rwo8/s2EOTHN53HFIePINiOFaDn29TJ2vfSNq0jOqXB+/V6OTF1I2uGzHuFIR66FkzPWcnbBJhw5FmSr63xj5Yp0/u5Fqt3RobTeVqG5aTZ8dYF+BDWo5v2CKFK1X7vSN6gYEUSRfhs/o8UbDxJQtyqB9SJp+eZD9F33iZdDaTXxUTRXdZuSTHpavfvoNXvwlgSHP1+ALT3HY7/CkWNh/8RZGMKCfF6jq+BP7ft6umavokClHi24c/v0UqlCNYRVoFL3FoXu9nZk2iIvuWzF6SQnPpnU/ScLdc+qfdr6zPYRRNFn68iQ1nVp9c4jtP14FKbIUESdhopt69Nn1STCrlol3mqEtmvg87ghNAhdcAC2jGx2jf/atSK9PJF05Fhc3e58ZH3JZhvWlAyP7mvmC5f44+53OLtwU8m9kWLipnH+AF1++D80/kb3slcy6NCHBNB20pNlbFnR0Rj1tHh9BHfHzGTY8Rk0f+0BND5E2EKa1WbA5qlU7dcOfcVAgpvWoutPL9Pk2ZLZLL0W59fv9RmecNrsVB/axWszVWMy0OyV++g241Xuu7iAPqsm0e6TpwgsQFOZssSSlO7TSQiShPVS4bKGagzpTIVG1T3aR2r8DNS8t4fPsdL2nyInLolGTw9meOxcHrasZdCurz2ajdyqtPtolKtv8RUTJsmkp/2UMQiCQOKWaJ8S6k6bw6fIW144rfZib71ZEtw0YR9w6ZEPPfQjR79cQvqRc4R3bkKDUXdiqOh7lnmzUrFlXfqsmlTWZuBXLcynGqgiO0k9cIrmb47g0Ie/4ZRlBEGg8QvDaPbScKInz2ffmz8h6rUoDhlTZCh913xc6Lh5aVFtYEeSdx3zaIYCroddXrPO6yFqNfT/ewox363g1OwNaEwGGoweiDU1i9ilW732TRSnwpl5fxe5+cvNSMVW9bhj2xfsf2cGKXtjCKhdhZYTHqRyz1YAaANNPh+oCIKvCNs1yT5zsdxXyKvyDiolRuKWQ6zq8YLPtEPJoOPuU7+irxiIJSkdQ1gQkl7HhT//YcPAN1yNxC8jiCKBDaoxNPrHAsXN06LPcGLGWhzZZmoM6UKVPm1LVHfJnm1mWZunyIlP/m9vxqSn9buP0vSF4hXGi548n72v/+i9shJFWk54kFZvP1ys490KOGWZedXv89Lwkkx6mr8+goPvz0aQBHC6zlVkp8+exwCG8Arcf3FhaZjtRX7lHW6qmb9K6ZN9LpFdL35F/NrdSHot9R7pR+v3HkNj1BPRpRnG8GDMF70F8UStBktSOqbKFfGLDHMfPzJtsYfjh8tx89hE0g6dzrMx/dUc/Wopu1/61pUIIDs59esGqvZrT8+5E0psNqb1NzJozzcc+3Y5sUu2YgirQONxQ90zy+IkckAU+yb87HVcMmipdmfHYh/vVkCUJPqs+cjVje/y6s1pc9DqnYdp9n/DaTR6ILGXMwoj74ji3KIt7H7pG68Necmop8WbD5bFWygQ6sxfpdBY07NZ1OBhLJcy4XK+uWjQEd6xMf03fgbA9mencfy7FSh2zxmSxt/I/UmLvPYtVnYZR9K2w15jaYP86L14IpV7tPR67WosKRnMq36fRy9ccMXKe8x9i2oDovK48sZi9yvfcfTLJe5Vhsakp85Dt3sV81lTMzn2zXLOb9xHYJ0qNB53F8FNa5F+9BzHv1+J+WIq1QZEUfPeHqWeFFAeccoyF/8+iD0jh4huza4ZNk7acYSdz39J6j8nUZxONAEmWr71EE2eG1Zm6r7qzF+lxDnx8xqXvMQVDW2cFhvJO4+6GsW0qkeL1x7gzO9/YsvMcT8ANCY97T4e5XPDuvqQzlz656R33NzuyHfc/PyGvYhaCfmqDF9HjoWz8/++aZx/u49GUX1QJ079uh5FdlL7/l5UuurhmHsxlWWtn8KWno1ssZG46SCn5oneRh0AACAASURBVGyk0bNDOTptMU67A8UhE7d8O4enLmLApik3pYhZQRAliSq98rdaC+/QmIE7vkRxOrFn5aINMJXrOP+V3BhWqpRLUnYf89pwBFeMPu3QGQBMVUIZcuB7Go4ehH/tyugrBuJ0yOx782f2vPY98lUx64ZPDcSvWhiS8fKD4XL7y/afjs63eqpk0OGrWszVuermcmwRnZvS6esX6PzdeCr3bOU12zzw7kwslzLcq6B/ReuiP/4d2Wx1x6wdORbSj54j5qfVpf4ebgYEUUQX5H/DOH5Qnb9KEQhpVvuyo70KRSGw/n/pmaYqoTR/7QFsqVlYU7Nw2hxYUzM5Mm0xfw1/1+NSbYCJQXu+ofW7j1Gpewtq3dudvms/puHoQfm2q2of3yteUa+l7iN9vY47LDb2vzuL+XUfZH6tB9j7xo/YffRuuBGJW7nTK+QG+CwQk3OtnPn9z5I3qghY07M5+NFvrOr+PH8/+AHJu4+VtUk3LGrYR6XQ1H9iAIc+mYtstbtT5ESdhqCG1bwKio5+udS1iXbFHpNstpKwdjeZJxM85Im1/kaavngPTV8sXIaMxmSg95KJbBg8AUEQUBQFxe6g5dsjvTqSKYrCur4vk7LnuDt2fvjzBcSv3snA3V8jSkUXBCxLdEF+5BTgfG2gqcRsuRqnLHN69kZiflyF0yFTd2Qf6j/WP0+RNWtqJktbP4UlOd31txIEzi3ZQudvX6TOiNtKze6bBdX5XwNbZg7n1+8FRaFKn7boAv3K2qRyhSGsAndsmcrWUZNJ2nEEUZKoMawbHb98ziv8kLL7mM/m8KJOS/qRc17a9EWlcs9W3HdhPvGrduHIMVPl9jboAv1wyrKHQ0/cdJBL+054VObKFhuZJ88Tv3In1Qd1Kla7SpvGz9/Nzme/8NS00YhIGo3HQxtcG+INRw8sNdv+fuB94lftdMtQpB04zdmFm+g57y0u/nUAUSNRuXdr9x5E9OQFmBPT/vseKQpyrpXtY6ZS857u5Xaz2pyYSuzSbSiyk8g7O+BfTvoo3BLOX7ba2Pv6j8T8sBJ7joXwDo3pMH3cNZuin120mU0jP3Q7CqdDpuuMV6h1d/fSMvuGoELjmtyx5XK/AFHIc6Yc0rw2F//a75UW57Q7PEJExYnWz0ite7pzdtFmVnQci/liGpJeS6MxQ2j93mOIGslVlOWjCtmRbSZ519Eb3vnXe6QvaQdOcezb5Uh6HYpDpkKTGrT/fAx/3v0OjhwLCgqKXabhM4NLLU00ZW8McSt3eOwZOXItJG4+xO+V7kEyXHbkCvRa+A5VbmvjEm70MYEAV01HaOv6pWF6gTg1ewNbR312uYcy7Br/NW0mPUGTccPK2rRbw/n//cD7xK/Z5Z7dJW07zKpuzzPk4A8E1Kzkdb45MZVND32AbLZxZbR088hJRHRqgqlK6Qqk3QhcTw+90dghHPtmuYfzlwxawjs3oULD6u5jToeMIArX3DhTFIWLf+0nedcx/KqGUuOurnnq7l/48x82PfShO3vI4ZA5Mn0xDouNDlPG4Fc9HMmgw2G/SjHVZMC/evmuKM4PgiAQNWUMzV69n9R/TmCKDHP3Jr439ncu/PkP1pRMIro286i3KGku/n3AZ4HUv879yuK1jUPfYnj8XPShvvWdnHZHqWg/FZTci6lsffIzr5Tjva/9QGTf9r61yEqRm37DN+vsReJX7/IS3HJa7RyZ4rsC78z8TfjUFgbOzPu7uE28JfCvHsGAvz8ntH1DEAREvZY6D95O78UTAbi0/yQrOo5lhr4vM/0GsOWJT31uujosNlb3fJENgyew782f2PbMVObVuJ/0I2d9jvvP/2Z6pY3KuVZivl+JPcdM9cGdXWGFq8JUkk5Drft6Fs+bLweYKoUQ2T/Koym9qJGo+v/tnXd4VNXWh989PZNCSAgEQu+9d1A6goCIoGBFr4LlYr329lnutaNeFa/9ylVQURGRIh0VpPdeQk8oCQnp0/f3xwwxycykJzMD+32ePJk558zZa04ma/ZZe63fGtadpjcOrlbHD+6QoS8dHZ8IwfGf1tDuoQlubZ6Cu3RaanZs5nMSF2hOzFvrs6Oay+7k6JzV1W9QES5555954GR+392CuOwOzm/z1p0BcOTk+Wx76LI7Ql4eOpDEdmnBmPUzmGxdwm25i+n3yT/Qh4eRcyqFxQMeJmXDPrdWvdVO4qzlLB/7rNc59r7zA6mbDuDIzkM6XTiy87CmZbF60j99jpl5OMnndqERWM6mozMZuPqPfxPbpTkaox6NUU90u8aM/O0dtcZThTQa1x+hLZ37kQ4n9qw8Gl7Tlw6PT0JrMqCvEY4u3ER0u8YMmftiFVtbPlwOp++OalLicgS+reolH/aJatXA90KjXkesnxhhg1G92f7SVziL3JZqjHrqj7o0CoQCSdGWmvs+/Nmth14Al9VOyoZ9pO85Rs12jfO3H/pyiddMHinJPJxEzqkUrxlsbOfmnDqd5i3YJUR+f4MaLepzzeaPyDubhnRJzHVjK/YGFSWijwhjxLI3WTHueWwZOQjh7jnhsNhwWbz/Xy/Ksnd+7lbaTLuW1M0HCIuPKXQnE2w0HNOHzT7UPTVGPY3GXREAi4rYEWgDqprIxvHUv7qXVz661qSn3UO+F11qtm9Cqymj/pJ/FQJduIkWt48odpE4kDhtdjIPJ4VkU+q0HYk+G5ZodDoyD54qtK04ORJf+7q+fMdfBWMedGYTnZ69xaurVVidGOX4q5Fa3Vtxw/FvuPq3dxixYjqTzs2l0bX9C4V2dOEm2ky7lhoFZL2NNSNJGNY9qB0/QESjOvmfP6HTgkaDNsxI22nXBoUfuSy0fZxWG1uf+y8HPlmAI8dC7T7t6P3+/cR08i8SJqXkzG87SJy1AqSLZjcPJX5g52L1Opw2Ozv++TUHPl2IM89KwlU96PHm3VW+cLjn3z+y7fkvkS6Xu8z/5iH0mfFg0Ka+FWXbC1+y843vcBVZGNOGGRi77RNqtPxrYWz7y1+x89XZXotoNdo05Lo93kJnACkb9rHp8Y85v+0QYXVi6Pj0zbS4/aqAaa8o/COl5NTC9STOWoHGqKPF5KuqRBivolz0m6X5DF3Yd5wj361COlw0uq6/36wkKSXpO4+QdzadWt1blnsRu7TaPpeF868ull/zDMkrtuWHJYRWg6FmJOMPzKyy3p5H56xmzZ1vFm7ZF2ak+eThXgJfwUre2TTmtrkDW0ZOfnhGG2ag3pCuDJ3/r0LHOvKsLB70CBf2HseRnYcu3OTWvF/1drFf5pci0uUiZcM+bBk51O7TFkONiMofQ0ryzqajjwir9v7PwYg9O4+Nj35E4lfLcFptxA/oRJ8ZDxbKWCsPucmpLB35FFlHkhE6LS6rnY5P3UTn524t87mU869mLuw7zvzu93rFo7VhRrck7GMTq2TceZ2nkL7ziNd2rcnATefnhYxIV8ahU2x8eAbJK7ahCzfRasoourww2WfDcelykbRkEykb9hNevxaNbxhYpsXZI9+tYudr32A5k0adAZ3o+vIdhcIKoUDGgZMsGfEE1rRMhBC47E66vz6FttPGVdoYJxdt4M+738Z6PgPpgobX9qX/p4+ij6y+KuBgY9FAd2JC/jqiEBhqhHPd/i8Jq10TgLSdiZxZvQNT7WgaXtPXbxpyQX7peS/ntx0u1DFMF25iwOxnaDimbLUm1arqKYQYAfwb0AKfSSlfK7LfCPwP6AacByZKKY9VxtjBQtrOI2h0WopmLjvzrO4slioiN/m83322jJyQcf41WtRn2IJXS3Ws0GioP7IX9UeWffF946Mfsf/Dn/PDRsd/+J2kXzcyduvHRDatV+bzBQLpcrHkqsfJOZlSaCF785OfEtu1JXX6tqvwGOe3HWLVDS8WKsI68fOfrLzwIlf9+nqFzx+KnN9+mPObDxROIJESp8XGgU8W0Onpm/n9ttc4Pm8N0iXR6LWsu+/fjFjxFrFdWvg9b9aRZNL3HPNqFenIsbDn3R/L7PxLS4UXfIUQWmAGMBJoC9wohGhb5LA7gXQpZXPgHeCS+/RENauHdHn3+dQY9US3bVRl48b1buOVow6gjwwjrHZ0lY0batgysll45YPsefv7QusF0uXCkWtlx6uzA2hd2UjZsA9repZXBpMzz8b+D3+ulDF2vTXHa13FZbVz9vedZB07UyljhBoZ+08gfFSwOy020rYncmT2ivzWmi6LDUdWHrYL2ay49rliExWs6dn+9YxSy9f7uTRURrZPT+CwlPKIlNIGfAuMLXLMWGCm5/EPwBBxia22xXZrSXSbRvnN4y+iNehodffoKhu327/udMsUa4o0pX77vpCRl7Xn5HFq8QaSl2/xKbVQWi4u0h/8YjGpWw8W2vfHHW+Qst63AqR0OEleupkFfafxdfQY5nW6i+M/ry23HVWBy+Fkz7s/MLftHawc/4Jbl6coUmJJzaiU8bIOJfnMUdcY9eScOFcpYwQD1rRM0vccy+/cVRzRbRvhcnpXJWtNBmK7tuDApwt91gFZ07NI25Ho97w12zf2uV1j1NPw2qqTF6mMsE8CcLLA81NA0fvx/GOklA4hRAYQC6RWwvhBgRCC4Uvf4M973ubEvLVIlySmY1P6fvII4QlVVz0Z06EpYzbMYOsLM0lZv4/IJvF0evYWEoaVGPILCo5+/xtr7ngDoXN/UQmtliHzXiL+io5lOo8l5QKLB/+D7ONn3TNiKYnr3ZahC15B2h2cWrQRWUxhTU5SqjuMAqTvOspvN/+rXGqRjlwLmx7/hMMzl+C02okf2Ik+HzxQKGOpPKy6/gWSlm3x2T/hIjqzicbXVU7+eJ0rOpC20zsF12W1E92u6u5kqwuHxcbau97i2I+/uydsLkmn526l4+OT/L4mpmMz4nq1IWXd3r/uioRAG2ag1ZRRnFywzufr3Gsy/j97WqOBPjMeZO3db7vP65JowwyE1a5Ju4cmVOh9FkeFF3yFEBOAEVLKuzzPbwV6SSmnFThmt+eYU57niZ5jUoucayowFaBhw4bdjh8/XiHbAoXTZsdld5S6+cjlStbR0/zU/k6vRXJ9ZBgTk74vU3bJinHPcXJRYe16rclA2wevo+394/ih+a1eYYx8hPAuAgPC6sYy8dR3ZUoJXTzkH6T8ueevmblnQXD8gZmY4soXhju/7RALr3iwWMevNRup0bI+o/78wGeHtLKSk5TCvI53YcvIze/UpjUbaX3vNfR8854Knz/QrLnrLY58s7LQZ09nNtLvs0dpOmmw39c5ci1sfvJTd7GhxUbdwV3o/f791GhRn33/mc+mxz7y+jsZY6OYdOaHEuXBUzbtZ+97c8k9lUrCyJ60vnt0uTK4SrvgWxlxgSSg4LSmvmebz2OEEDqgBu6F30JIKT+RUnaXUnaPi6terZHKRGvQK8dfChK/Xo70cRstJZwoQ9jFabW5Z/ZFmpY4LTYO/fdXwurGYqzluw+rxqjPv+soils3vuRwwEXSdiaSsmFf4ZDMxQXBTxeW+jxFSdm432fzFYDI5gnUG9aNntPvZdTa9yvF8QOEJ8RxzeaPaDJxAKba0US1akCvd/5OjzfurpTzBxJHroXEWcu9/raOXCs7S1j70ZlN9H7vfm7NXMDttqVc9evr+ZliLe8cSVyP1ug8kxatyYDObGLgt8+Vqi9EXI/WDPjqaUauepuOj0+qktTdQu+lEs6xCWghhGiC28lPAm4qcsx8YDKwDpgArJTBmmOqqDas6Vk+K3ul04k9869KZSkl59btJX3nESKb1aXekK6F1jOk0+V3Qc1ptSOEoN8nj7Bywgu4rHak04Uw6NCbTVyz5SOWjniSzEOnvF6rCzf57lTmhwt7j6PR+sj4sthI3XLQ52tKQ3hCLZ+ZZFqTgZZ3jaTj4zeW+9zFEdmkLgNneesrhTpuOQnfd3N5Z9LKfV6tQc+IFW+RtGQTySu2ElYnhma3DsMcH1Puc1YlFXb+nhj+NGAJ7lTPL6SUe4QQLwGbpZTzgc+Br4QQh4E03F8QisucBlf34uBnC3FkF1kkk1BvWDfAvRi8dMQTpG1PRLokQqfBXDeWq39/Nz+vWmc2Edu5OambDxQ6jdBpaTjGrU9ff0RPRq/7gD3v/EDmwVPED+hE2wfGEVYnhs4vTObPKW/hyC0YAjDR4fFJZVo0j25TzIJgl/KX8ydc1QN9ZBj2nLxCi7BCp6XFZO+2lIriCatTE32U2TsMKAS1+3eo0LkrkoZc3agiL0XAkFKyYtzznF6xNT9LQhduouXUUfSafh8AGx/9D/s/nF/oH1XotNS/uhdD5/3V/zdt1xEWXfkQLpsdZ54NXbgJQ41wxmz6T6n0evZ/soCtz36OPTMXrdFA+8cm0umZm8ssAbF40COcW7+32CIgX9gyczj2w+9YzqZTu1976lzRodDYWUeSWTXxZS7sOQZCEFY3hgFfP03t3kWzqitG1rEzHPx0IdnHz1B3cFea3jg4ZGpFysKRb1ey9q6/vvCFVoPObGT0+hlEtwntBW1V4asICVxOJ8fnruHI7OVoDHpa3nk19YZ1y3d8s+PGYT3vness9FpuzVxQqALYkprBwS8Wk7H3OLV6taH5rcPKtGgsXS5sGTnoo8zl7t1rz8lj06Mfc/irpbisduIHdKL3Bw8UW/6fuuUgvw59FOlw4rTY0JoM1O7XnmG//Msr/zs3ORWnzUFEozqVrk2UvGIrK8Y+h8vhwGVzoAs3EV4/jtHrP6jy+HNVY8/KZfc7P3Ds+9/QmU20+ftYzA3i2PnKbLKPniaubzs6P3drqSq9bRnZZB8/S0Tj+KCU/VbOX3FJ8HX0NdgzvVuQC62GWzIXBPWsVEpZooPOS73Ajy1vw36h8HvUmo30eONu2txXtGSmapAuF981mEje6cIxb41RT4fHJtL1pTt8vi7zcBJ735tLxoGTxF/ZkVb3jMEU63txvTw2VUatisNiY363u8k+eib/DlIXbqLJpEH0//TRUp/H5XSy8eEPOfjZIjQGHS6bg1Z3j6bn9HuDqqamOrN9FIoqo+HYvm453IIIQa2erYPa8UPJio/ZJ84yt9XtXo4f3N3GDn2xuKpM8yLzUFKhRfaLuKx2v12nzvy2g587T2X/R7+QvGwLO/41i5/a/o2cUykVsuXMHzuZ13kKX+qG8XX0GLY887m7MUo5OfrtSnJOnCsUOnTkWDgyawWZicklvt5ld7Bvxjy+azCRfTPc0iD2zNz8LK6dr39bbtsCiXL+iqCm++tTMdeNydd415qNGKLD6f/5YwG2rOJsfupTbBnZfvdX51251mz00pa5SNHWieC2bc2db+LIteT34nVabFjTMtn6nG9p7dKQtjORpSOfzBcrtGfmsuffP7LuvnfLfc7k5Vt8Vt4KnZaUdXuKfa2UkmVjnmHTE59gOZPuLamRa2XP29+X27ZAcsl38lKENub4GK7b9yVHvl1F6qb9RLdpRLNbh1WZRHZ1kvTrJt9t/nBLWre4Y0S12RLRoDbR7RqRti2xkEaVzmyi9b3eoSdLygVykrwL9KXTxalFG0o1pj07j/NbD2GqFUV028YAPns1OHOtJH69nG6v3lWukFJ4gzpo9DqvKlshBGElpGGeXbOLc2t3F1tgZ7vg/ws8mFEzf0XQozObaPm3kfR48x6EVsMfk19j3bR/+23aHiroiikEjOvRmlZTq04TyheDvn+B8AZx6CPD0EWEoTUZaHz9lbT8m/eXkM5s8lkVDe4K7ZLY+/5PfFNnPMvHPsv8nvcxr/MUcpJSSNt5xLemkEFP9rGzZX9TQKspV6PRFwkdagSG6AjiB3Uu9rXn1uz2XxnuIaZzaPaRUDN/RUhgvZDNLz3uJe/0eRy5VoRWw6EvlzBg1jM0Gtsv0OaVizb3XsP2f31daFYptBpq92vPiFVvV0mnsfTdR0nbfpiIpvWo3adtoTEiG8czIfFrTq/eQW5SKrX7tCWqeYLP8+gjwqg/sienFm8sVKinNRtpM+1aXHYHp1duw5qeRfzAzoUKnU6v2sbmpz7FmWfNr7K9sOcYy0Y/Q2yXFmQeOOWlkOuy2YlsWrdc7zmyaT0G//giv9/2Go48K9LpJKpFfYbMfbHErK6w+Bi0JoPPsBHC3a+j17vTvPeFAMr5K0KCPe98T86plPz8eel04cy1subON2kwqrdXU/hQoP1jEzm/I5GT8/9EY9AhHS5qdmjCkHkvV7rjd9rsrBz/AqdXbkNo3Tf8kU3rMmL5W5gKSF8IjYZ6g0vXNrH/F4+z7OqnSN911N19ymanyfUDqHNlR76tf4P7byUlLpuDDk/dRJfnbwNgz7/neoVRpNNF5qFTdH1xMifmrcWRW6AzndlIiztGVCjUl3BVDyYmzyFj3wl0ZmOpezc0nnAlGx750HuHEDQY3ZsuL94eFP14y4Ny/oqQ4PjcNYWbaHhw2R1c2HuMmI6hd+ut0WkZ9O1zZCYmk77zCBFN4qvMkex8dTanV24rpGdzYd9x1tz1VqFiubJgrBnJ6HUfkLYzkezjZ4np1AxzQi3mNJyENaWwtPTuN74j/ooO1B3UBcvZdJ/n0+i0GGpGMmLldNY/+AHntxzEEB1Bu4fG0+GJiosCaLRaarZvUqbX6CPNjFj+FquufwGL5z0ZY6MYNOf/iOvZusI2BRLl/BUhgT7Kd+tA6XCiD1ChTermA6RtP0xks3rED+hU7lzvqGb1iGpWtV3EDnyy0EvITNqdJC3eiCPPWqG02ZiOzfK/fM+u2YU9O8/rGEeuhf0f/ULdQV2oP6o3qVsPegnxuexOYru0QB8Rxph1H5TbnsqmVreWTEicRcb+EyAlNdo0qpKQXHWjnL8iJGh7/zjW7jxSKPYqtBqi2zYmsnF8tdriyLOybPTTpG7YDwIQgvD6cYxc/XaxEg6BxN+ipZTSnQVTSTUTjhyLX8doy3DXM6TvOpKfHnoRodHQ7bW7grZJvBAi5GUfiqKyfRQhQZOJg2g5ZRRakwF9lBldRJh7IW/ui9Vuy/aXvyJl3V4cuRYcORYc2XlkHk5i7V1vVbstpaXB6N7exXK4u1NVpkRB7X7tcdm9C7J04SaaThpE6taDnFy43kuiWmPQERuCobtQRjl/RUgghKDX2/cx4fBXXPHfJ7hqyetct/9LIhrUrnZbDv33V6+ZtHQ4SVqyGUcJaYGBovtrUzDF1UBrds/wNSYD+kgz/b+o3GI5fUQYfWY8gDbMmL+wrAs3EdO5OU1vGsLpldu9wj3gvjNJXrG1Um1RFI8K+yhCCnO9WjQa17/QNmt6Fsd//B1bZi4JV/WgZrvGpTrXhf0nOLNqO8bYKBqM6VPquLevhWdwh1CKhjMqgsvu4NjcPzgx/09MsVG0nDKKmA5Ny3Uuc91Yrtv3JYdnLuHcur3UaN2QVlNGlUrxtKy0uH1Efk9ba2oGDa/tT+PrrkCj12GMiXTr4hQpuNKaDBhjoyrdFnD/XQ58upAd//way9k0arRuRM/p91BvaLcqGS9UUMJuipAmadlmVo57HoTAZXcitBpa3DGC3u/f7zf2LKVk3X3vcvh/SwGBRqdBaLVctexNanVrWeKYf9z+OomzVxR29EIQ06kZPd66h/D6tSrcs9dps/Pr0EdJ23bYHUfXatAY9PSZ8QAtbq++yt/KxpaRzZyGk7BnFV4U1plNTDjydZWsmeyePodtL8wstF6kDTMyfNGrxA/oVOnjBRol7Ka45HFYbKy6/kUcuVYcORaPlr+VwzOXkLzU/8Th+Nw/SPx6Oc48G848K/asPGwXsll+zTNexUW+6PbaFMLia+Zr3mhMBoRey4W9x1k5/v/4ucvdLOz/ANY0bynq0nJk9op8xw+euoY8K+umveczmyZUMNSIYNii1zDWqoE+0ow+yoyhZgRD5r1UJY7f5XCy/eWvvIq0nHlWtjz7RaWPF0qosI8iZDmzervP7Y4cC4dmLiHhqh4+9x/8bKHPik17dh6pmw4Q16tNsePm6w3NWkHKxv04cvI48cs6nLlWXDZ3SCh10wF+u+UVhi96rYzvys3ROat92qjR6Ti7Zhf1R/T0tj8nj+TlW0FK6g3tFrSZM3X6tWfS6e9J2bAfXC7ierXx6ltQHNLlInnZFs6t20tY3RiaTByEMdp3vwHr+QyfrUIBMvYdL5f9lwrK+StCFn8qlAC5p1Kwpmf5rAp1+onZCyFw2nzvK4o+PIxWU0fTaupofu461ati1WV3cHrVdiznM8olRuavrgEpsWfnseXZL8g6kkz8gE40u2UoZ1bvYPWkl/MXWaXDxRUzn6Dx+CvLPHZ1oNFqqdO3XZlf57DYWDLsMdJ2JOLIzkNnNrH5iU8ZseItnyE7Y0yUzywngEg/0hWXCyrsowg5nDY7h2YuYd+Mnwv13S1I6tZDfJdwAztemeW1r9nNQ33KFCNEibN+X1jTsnxu12g12DO8tfqTV25jfo97+SpqFD+1/xvHf1rjdUyrqWPc4mlFTdRrWXPHG+x+6zuOfruKTY9+xE/t/saqG17EkWPBnpmLPTMXR66F3297lZykimnrBxv73v+J81sP4fCEvhy5FuyZOaye+JJPCWyNXkeHx27wupYak4GOT1ZN4/tQQTl/RUjhtNlZPPBh1k97j6RfN+Yvuhad3TlzrTgtNna+MptTiwtLDDe/bThxvdqgi/DE7A16tGYjA75+Gq1BX2abEkb08Dm71EWEEd6oTqFtycu3sHzMM5zfchBHtoULe4/z262vcPirpYWOqze4C+0fn4jGqEcXGeaOjcdGoTXoPesb7lCGI8dCblKqz9x66ZIcm/Nbmd9PMHP4f0u9KpUBcs+kkeWnMUunZ2+l84uT/8om0giQktU3/pPfbnmlkI7Q5YQK+yhCiiPfrCR911GveLiUEqHTeqVaOnIt7Pn3XOqP7JW/TaPXMXzJ6yT9uomkJZsw1a5JHsuF+gAAGiJJREFU89uGEdGwsKMuLV2ev40TP63FlpmDy2pHaDRoTHr6fvyIl2rkpic+9XJezlwrm5/8lGa3DCuUodTl+dtoNWUUZ1bvwBAdTlTzBOZ1nuo1vr/wl8vuDOnFYZ/4U1WQgJ/sLiEE7R+5Ho1Bz8Z/fAgOV3667vG5f+ByOBj07fNVY28Qo5y/IqQ4/uMfPhdCtUb3jN3pI8/emprhtU2j1dJgVG8ajOpdYZvM9Woxbvfn7H3/J5JXbCWyaV3aP3I9sV1aeB2bsf+Ez3NYzl3AabF51RqY68bS9MbBAOSdTfOfjSSEl76+1qSn/kjvheFgxZaRjTU9m/AGcX6llpveOIRtL3zpVSgW3iDOr+SzlJLfJ7/G0dkrocj1c1psnJz/Z7nXZkIZ5fwVIYUxJtKnoxMaDdLp7fi1JoNXUVhVYIqLputLd/htdA5uJ2SoGUGej7CFPtKM1mQodoywOjHU6t6KlPV7vWf7UrrDGZ5GKLpwE00mDqJW91ZlfzPVjD0njzV3vsmJn/9Eo9WgNRvp/tpU7Fm5JC/bQkSTeNr8/VpMtWpw4KP5Xu9dFxHGoDnP+63rOLVoAyd+WuP3i1Oj15N3Jl05f4UimGl1zxiO/vCbV3aNPjKMLi/fwYYHPsCZZwMp0YYZMdeNoc20awNkbWE2Pf4xFh93ITqziY5P3lgqpciB3z7L4kH/IO9sGs5ca2FH6HKHvhqM6UPru8dQb1hoVLD+dvMrJC3ZhMtqxwU4cq2snfIWGoPeHUbTaTn0319JGN6dvLPpXp2+9OGmYqWaE2ev8N2MxYOULiKrWFU1GFHOXxFS1O7dlm7/upMtT32GxqAD6Z7lDv/1dWI6NCWmfVP2vjeX3ORU6o/qTaspoypVuKy8ZJ84y/4ZPyN95Jw3GNuX9o9NLNV5whPiGL//SxJnr2DNnW9BkVmw0Agim8STMLzEAk8cVhs7X5lF6sb9xHRqTsdnbsYQ6SfFtIrIPX2eZI/jL4T8S0ZDOpw4HU5OzP/TZ4tHW2YuWUdO++06VlyjH41RR5cXbkdXwl3XpYhy/qXEmp7Fvg/mcWrxBswJcbR7eEK58pQVFafdg+Npfttwzv6xC0ONcGr3b58fI47r2ZoBXz8dYAu9OfPbTr/55hqdtkz68EKjwVgzEp3ZiD2j8JeJy+YgbUdiiefIOHSKeR3uzM8aSlqymd3T5zB6/YxSSVxUFrnJ59EY9X5rLwrhp9m9y2pHV0xBW/PbhnN8ro+1Io2g/xdP0MyzpnK5oZx/KbCcz2B+17uxpGS41RzFfpIWb6DXe/fT8m8jA23eZYmxZiQNr+kbaDNKjTEmEqHx4eCFwFQ7uszni27T0KfAnMagp1a3kuP8S0c84VX5Kp0ufh36KLekzy+zPeWlRqsGuCoqhico1CO4KHUHd6Hl1FEc+OgXpEu67wSkZPDcl0p1h3SpovL8S8Get38gz5ONAYCUOHKtbHhoRtBK+CqCi4Th3X2HH6TML1gqC5FN65EwsifasALhCiHQmvS0ub/4NQ6HxUb20TM+99kzcsg4eKrM9pQXfUQYnZ65BZ25QJaTr7sgX1+cHi7Wa/hDCEGv6fdxzZaP6f7qFHq9+3duOPndZe34QTn/UnFy4XqfsyyhEVzYfTQAFikCTc6pFDY++h8W9LufNVPe4oKfFM6LaPQ6vwuwh2cuxZaRXWYbrvzqKRpe0xdduAmNXkfdIV0Y/ef7hCfEFf/CEsTrTvyyrsy2VIROT91E/y8eJ6ZLc8z13KmtTW8egtaoz2/cU6N1Q+IHds6Xr7iI0GtpdvOwUo0T3boh7R4aT8s7r65QM/hLBRX2KQWmON+35S670516qLisyDh4kl96/R1nnhWXzUHqxv0c/WYlwxYWLxF8Ya/vLwiNUU/G/pNlkpZI3XKQpSOfxGWzI7QahE5L00mDiW7buMTX6swm9JFm7Fm5vg8ohbJpZdPkhoE0uWFg/nN7Th7tHhxP9omzhNevTa0erchNTmVhvwewpWfhsNjRmvRENqlLt1furHZ7LwWU8y8F7R6eQMq6PYV0ZIROS82OTYlsevmliF3ubHrsY+yZufm1BtLpwpFr5c973uG6fV/6fV1UiwTSdx/1qlFwWe2ENyhhtl4Ap9XGkqsex1ZEU2j9/e9Tq0erUjV86f3hA/xxq7fiqDAEtjDMkWdl3b3vcuS7VQgh0NcIp/f79xPXszXhCXFMOPQVJxesJ/NwEjEdm1JvWDeEJvABjOyT58g+eoboNg39ThaDjcBftRCgwdW96Px/k9GGGdHXCEdrNhLTqRlDfnop0KYpAsCZ33Z4OXCAzMRk/7NpoMPjkwrH6HEXodUb3h1zvVqlHj952RafHcNcNjsHP19cqnM0v3kYTW8agtD95QI0YQZaTRlVbM58VfPH5Nc4Omc1Lqsdp8WG5Ww6f9z+OmfX7HLbqNfRaFx/Ojw2kYSregTc8TtyLSwf+yxzW01m+dhnmdPoRtbd/16p+kIEGjXzLyUdHptIq7tHc37rIcLq1CS6TaNAm6QIEPqocPfMvwhCqym2SjeuZ2sGfvMs6+59F2taFlJKGo2/gr4fPVym8W2ZuT4VLKXT5VfczBdXfvUUzW4ZSuLXy0EjaH7rsEppbWjLzOHINyvJOHiKWt1a0nj8FWiNJefRW1IucOKXdV7ra85cKztenc3wha9W2LbKZv3975O8bAtOiy0/IeTwf5cQ1TyBdg+OD7B1xaOcfxkwRIVTd2DnQJuhCDBt7x/HthdnFqoy1pr0NJk0uMSmJA3H9KXB6D7knU3HEGX2KdtcEnUHdvLboCR5xVYyDyf5LXgqiBCC+iN6+mwMU14yDpxkYf8HcFpsOHIs6CLC2Pb8fxm9YUaJ8gk5SalojXqfyRVl+VKrLpw2O4mzV3jZ68i1sOfdH4Pe+VfonkkIESOEWCaEOOT57bMPmxDCKYTY7vmpviRihaIKaPfIBJrdNAStyeAOA5oMxA/qQu/37y/V64UQmONjyuX4wS0k1+6hCT73uewOdr3xbbnOWxqklCTOWs7C/g/wc9ep7Hzj20KSyH/c8QbWtKz8gipHdh45J1PY8vTnJZ47qkWCT2lqodVQp1/7ynsTlYTTYvOrqGpLL3v2VnVT0YDZk8AKKWULYIXnuS/ypJSdPT/XVHBMhSKgaLRa+n3yDyYcmcXgH19k3J4vGL7wVfTh1dc2seHYvmh9NaRxukjZuL/Kxl07dTp/3vMO5/7cQ9r2RLY8/RlzmtxExqFT2HPySN18wHtB2+7g2I+/l3hufXgYHZ+6qfCXokagCzfR6ZmbK/utVBh9pJmIRj5kwIUgfkDH6jeojFTU+Y8FZnoezwSCQ0FLoagGzPEx1BvchcgmvqWEC+LItZD49TJ2vfkdZ//c4zNmXxYiGsd76foAoBFEt62a9aiMQ6c4MquISJpLYk3J4OdOU0jfddTv+/In0VyUTs/cTN+PHya6fWNMcTVoNK4/YzZ8GJRZdUII+n78CDqzMX/hWeh16CPD6PHG3QG2rmQqGvOvI6U87Xl8BvDXDcMkhNgMOIDXpJTzKjiuQhEypO06wuJBj+CyOXBabWgNeur078DQ+f8sU+PygpjjY2g4ti8n5v/pVjH1oK3C9oTn1u72KrK6iNNiY+nIJ31mQWmNeprdOrRUYwghaHbzUJrdXLrjA029wV0Yte4Ddr81hwv7TlC7T1va/+P6cjcGqk5K/OQJIZYD8T52PVPwiZRSCiH8TWcaSSmThBBNgZVCiF1SSi/1KSHEVGAqQMOGDUs0XqEIdqSUrJrwQqGcfIfdyZnfd7L/P/Np+8B15T73FV8+wabHPubg54tx2exENU+gz4cPEtOxWWWY7kVYnZrFplb66lcsdFpqdm5GlxdvrxKbgoGYDk25cqa/iHfwIipy+ymEOAAMlFKeFkLUBVZLKYtVlRJCfAkskFL+UNxx3bt3l5s3by63bQpFMJB5OIl5nad49R8AMNWOZtLpH8qk6OkLl9OJy+bw6gJ2ESkl0uEs911G/jgOJ3MaTiLvTFqpX6M1Gbg1Z1GF36Oi9AghtkgpSxQuqmjMfz4w2fN4MvCzD0NqCiGMnse1gH7A3gqOq1CEBMUV+1hSMtj95ncVHkOj1fp0/C6Hky3Pfs6s6DHMNI1gbrs7SF6xtfzj6LSMXPU2hpoRpX6Ny+5Qjj9Iqajzfw0YJoQ4BAz1PEcI0V0I8ZnnmDbAZiHEDmAV7pi/cv6Ky4KoFvUJ81fuLyW7KsH5+2P9/e+x590fsWflgZRk7DvB8rHPujNyykmNVg2YmDSHukO7ojHo0Ue5U12NsVHeapxCED/Qv9aRIrBUKOxTlaiwjyLr2BmOfLsSR46FBqN6E9erTUjOIlM27WdBr7/73X+7Y1mlyxRYL2TzXd0J3k1ShKDBmD4MnfdyhcfIOnqajP0nqNGqAU6bgwV9puXLMmhNBjRGPaPXfUB0a7V+V52UNuyjKnwVQUni7OWsvWs60uXCZXey950faTJxIP0+ezTkvgDierSmRttGZOw97rUvqmX9KtGnyTl5Do3BR4csKbngw47yENmkbqE01/H7v2T/xwtI23qImK4taH33aMLq+G+yoggsyvkrgg7rhWzWTpn+V/Mc3HnyR+espsmkQSQMC70mHL3fu5/lY57BmVdAEsJspOfb91XJeJGN43HZfUhAaASxXZpXyZhhdWLo8vxtVXJuReWjVD0VQUfy0s0+u145ciwc+XZlACyqOPUGd+GqpW8QP6gzptrR1LmyI8MXvkqDq3tVyXj6SDOtp12L1lx4IVhnMtLp2VuqZMxAkXX0NDtencWWZz7n3LqKF9BdLqiZvyLocDc6993vtqLpioGkTr/2jFwxvdrG6/HaFMzxMeyePgfr+Uxiu7Wk19v3lUrvvySyjp3BZXcQ1TwhoGG4xFnLWTt1OtLhwuVwsvffP9L4hoH0//yxkAsPVjeh+5+kuGRJGN7dp2CWzmyk+S2la9mnAKHR0P6R62n/yPWVds6MgydZOeFFsg4nITQajLFRDJj9TECE16wXslk7dXqhCmdHrpVj3/9G0xsHh2R4sDpRYR9F0KGPCGPgd8+hNRvRmU1ojHq0JgNtpo2jTv8OgTbvssVps7N4wMNc2HPMLdmcayHn5DmWjnySvLOlL/yqLJKXbUGj856/hnJ4sDpRM39FUNJgVG9uOP4NJ35agz3HQv2RPanRskGgzbqsObV4o7uVaZGYunQ4OfS/pXR8bFK12iO0Gp/RQYQotZDc5Yxy/oqgxRRbg5Z3jQq0GZVG3rl08k6nEdUiodxa/oEkL/k8Lh/tI50WGzknzlW7Pf7CgwBpu45y5o+dxF8R/NLKgUKFfRSKKsDlcOZnnThyLayc8AJzGt3IoisfYnbt69j5+jfVYoc9O4+za3eTeTipwueK69PW50xbFxFG/JXVX8nrDg8+jybMgCiYCCAlqRv2sXTkkxyds7ra7QoV1MxfoahEzq7Zxbpp75G+6yg6s5GWU0eRl5zGqUUbcFnt+S3/dvzzayKb1KXJDQOrzJbd73zP1mf/i0avw2V3ENOxKUN+fpmw2j4b7pVIbOfmJFzVg6Qlm/KF6rQmA1HN69Ho2n6VaXqpiWxaF0OEGcv5DK99zlwr6x94n8YTrgx4o/dgRMk7KBSVRPqeYyzodZ87Lu5BG2ZwV9m6vP/PYro0Z+yWj6vElqQlm1g5/oVCLRaFXktcj9aMWvNeuc/rcjg58MkCDnyyAJfNQdObBtPu4QnV2sXsIlJK5ra5ncxDST77CABow4yMPzCT8Ppx1Wxd4FDyDgpFNbPrjW9xWgrLKRRMQyxKWaSRy8ru6d8XcvwA0u7k/LZDZB07Q2RjXy06Skaj09LmvrG0uW9sZZhZITIOnCT3VIpfxw9uVVVDdOlVSC8n1L2QQlFJpO884lvC2Ve9mkZTpXFyf6mXGr0Oa6p3iCQUcVps4KezGLhDUk2uH4A+ovrvSkIB5fwVikoitntLn20OhU6LJsxQ6LkuwkTXl++oMlvqX90LjVHvtV26JNHtm1TZuNVJzQ5N0BoNPvcJnZb6o3vT96OHq9mq0EE5f4Wikujw+CS0RZqqaM1GWtwxgqsWvUbCiB5EtaxP88nDGbvtE6KaJ1SZLe3/cQOm2KhCXwBas5Ge0+9BZ/LtMEMNjVbLgK+fRms2ojG4I9jacBNRrRow/uD/GDzn/0Iypba6UAu+CkUlcn7bITY88iEp6/dhqBFO2wfH0+HxiQEpOrKcz2DPu3NJ+nUj5oRatH/keuKvvPTy3rOPn+XgF4vJPZVCvWHdaDz+ypDWgKoopV3wVc5foVAoLiGqq4evQqEIAew5eZz5bQfntx9WkscKQKV6KhSXPAc+X8TGBz9A6HRIpxNzvViGLXqNqGb1Am2aIoComb9CcQmTsmk/Gx78AEeuFXtmDo4cC5mHk1ky/DF1B3CZo5y/QnEJs//Dnwu1wwRASiwpGaRs2BcYoxRBgXL+CsUlTN6ZdJ/SEkIjsJ7PDIBFimBBOX+F4hKmwTV90RXp4wvgsjmo3adtACxSBAvK+SsUlzAtJg8nokndQsVnOrOJTs/fijEmKoCWKQKNyvZRKC5hdGYTo9d/wMHPFnH8xz8wxkbS5u/XUm9ot0CbpggwqshLoVAoLiFUkZdCoVAo/KKcv0KhUFyGqJi/QqGoFhy5FvZ9+DNHv1uNLtxEm/vG0vj6AQjho+GBospRzl+hUFQ5TquNBf0eIPPgyfzuZue3HOTMH7vo8/79Abbu8kSFfRQKRZVzdM5vZB1OKtTW0pFj4dDni8g6ejqAll2+KOevUCiqnKQlG3HkWLy2C52Wc2t3B8AihXL+CoWiyjEnxCH03g1thBCYatcMgEUK5fwVCkWV02rKKO/uWkKgjwyj7pAugTHqMqdCzl8Icb0QYo8QwiWE8FtUIIQYIYQ4IIQ4LIR4siJjKhSK0COqeQIDv3kWQ80I9JFmdOEmolokMGLl9IC0uFRUPNtnN3Ad8LG/A4QQWmAGMAw4BWwSQsyXUu6t4NgKhSKEaDimLzee+ZG0HYnowk3UaN1QpXkGkAo5fynlPqCkP2BP4LCU8ojn2G+BsYBy/grFZYZGr6NW91aBNkNB9cT8E4CTBZ6f8mxTKBQKRYAoceYvhFgOxPvY9YyU8ufKNEYIMRWYCtCwYcPKPLVCoVAoClCi85dSDq3gGElAgwLP63u2+RrrE+ATcKt6VnBchUKhUPihOsI+m4AWQogmQggDMAmYXw3jKhQKhcIPFU31HCeEOAX0ARYKIZZ4ttcTQiwCkFI6gGnAEmAfMEdKuadiZisUCoWiIlQ02+cn4Ccf25OBqws8XwQsqshYCoVCoag8graTlxAiBTheYFMtIDVA5pQFZWflouysXJSdlUsw2tlIShlX0kFB6/yLIoTYXJrWZIFG2Vm5KDsrF2Vn5RIqdvpCafsoFArFZYhy/gqFQnEZEkrO/5NAG1BKlJ2Vi7KzclF2Vi6hYqcXIRPzVygUCkXlEUozf4VCoVBUEkHr/MvQK+CYEGKXEGK7EGJzddroGT8kehoIIWKEEMuEEIc8v322TxJCOD3XcrsQotoqsUu6PkIIoxDiO8/+DUKIxtVlWxE7SrLzdiFESoFreFcAbPxCCHFOCOGzP6Jw857nPewUQnStbhs9dpRk50AhREaBa/l8ddvosaOBEGKVEGKv53/9QR/HBMU1LRNSyqD8AdoArYDVQPdijjsG1ApmOwEtkAg0BQzADqBtNdv5BvCk5/GTwOt+jssOwDUs8foA9wEfeR5PAr4LUjtvBz6obtuK2HAl0BXY7Wf/1cBiQAC9gQ1BaudAYEEgr6XHjrpAV8/jSOCgj797UFzTsvwE7cxfSrlPSnkg0HaURCntzO9pIKW0ARd7GlQnY4GZnsczgWurefziKM31KWj/D8AQUf2dQILh71giUsrfgbRiDhkL/E+6WQ9ECyHqVo91f1EKO4MCKeVpKeVWz+Ms3DI1RWXpg+KaloWgdf5lQAJLhRBbPJLQwUgw9DSoI6U87Xl8Bqjj5ziTEGKzEGK9EKK6viBKc33yj5FuvagMILZarPNhgwd/f8fxnlv/H4QQDXzsDzTB8HksLX2EEDuEEIuFEO0CbYwn3NgF2FBkVyhdU6DibRwrRCX1CugvpUwSQtQGlgkh9ntmFJVGdfY0qAjF2VnwiZRSCiH8pXk18lzPpsBKIcQuKWViZdt6CfML8I2U0iqEuBv33crgANsUqmzF/XnMFkJcDcwDWgTKGCFEBPAj8JCUMjNQdlQWAXX+suK9ApBSJnl+nxNC/IT71rxSnX8l2FnqngYVoTg7hRBnhRB1pZSnPbej5/yc4+L1PCKEWI17llPVzr801+fiMaeEEDqgBnC+iu0qSol2SikL2vQZ7rWWYKNaPo8VpaCDlVIuEkJ8KISoJaWsdi0dIYQet+OfJaWc6+OQkLimBQnpsI8QIlwIEXnxMTAcd1P5YCMYehrMByZ7Hk8GvO5YhBA1hRBGz+NaQD+qp9dyaa5PQfsnACulZ6WtGinRziJx3mtwx4eDjfnAbZ4Mld5ARoGQYNAghIi/uK4jhOiJ219V9xc+Hhs+B/ZJKd/2c1hIXNNCBHrF2d8PMA533MwKnAWWeLbXAxZ5HjfFnXGxA9iDOwwTdHbKv7IBDuKeRQfCzlhgBXAIWA7EeLZ3Bz7zPO4L7PJcz13AndVon9f1AV4CrvE8NgHfA4eBjUDTAH0uS7LzVc9ncQewCmgdABu/AU4Dds9n807gHuAez34BzPC8h10Uk00XYDunFbiW64G+AbKzP+61xZ3Ads/P1cF4Tcvyoyp8FQqF4jIkpMM+CoVCoSgfyvkrFArFZYhy/gqFQnEZopy/QqFQXIYo569QKBSXIcr5KxQKxWWIcv4KhUJxGaKcv0KhUFyG/D+Ue1SP4yiX7QAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# generate the NN model\n", + "class NN_Model:\n", + " epsilon = 0.01 # learning rate\n", + " n_epoch = 1000 # iterative number\n", + " \n", + "nn = NN_Model()\n", + "nn.n_input_dim = X.shape[1] # input size\n", + "nn.n_output_dim = 2 # output node size\n", + "nn.n_hide_dim = 4 # hidden node size\n", + "\n", + "nn.X = X\n", + "nn.y = y \n", + "\n", + "# initial weight array\n", + "nn.W1 = np.random.randn(nn.n_input_dim, nn.n_hide_dim) / np.sqrt(nn.n_input_dim)\n", + "nn.b1 = np.zeros((1, nn.n_hide_dim))\n", + "nn.W2 = np.random.randn(nn.n_hide_dim, nn.n_output_dim) / np.sqrt(nn.n_hide_dim)\n", + "nn.b2 = np.zeros((1, nn.n_output_dim))\n", + "\n", + "# defin sigmod & its derivate function\n", + "def sigmod(X):\n", + " return 1.0/(1+np.exp(-X))\n", + "\n", + "def sigmod_derivative(X):\n", + " f = sigmod(X)\n", + " return f*(1-f)\n", + "\n", + "# network forward calculation\n", + "def forward(n, X):\n", + " n.z1 = sigmod(X.dot(n.W1) + n.b1)\n", + " n.z2 = sigmod(n.z1.dot(n.W2) + n.b2)\n", + " return n\n", + "\n", + "\n", + "# use random weight to perdict\n", + "forward(nn, X)\n", + "y_pred = np.argmax(nn.z2, axis=1)\n", + "\n", + "# plot data\n", + "plt.scatter(X[:, 0], X[:, 1], c=y_pred, cmap=plt.cm.Spectral)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch [ 0] L = 103.723994, acc = 0.500000\n", + "epoch [ 1] L = 99.572368, acc = 0.500000\n", + "epoch [ 2] L = 96.298444, acc = 0.695000\n", + "epoch [ 3] L = 93.755262, acc = 0.745000\n", + "epoch [ 4] L = 91.723534, acc = 0.725000\n", + "epoch [ 5] L = 90.013431, acc = 0.710000\n", + "epoch [ 6] L = 88.493838, acc = 0.720000\n", + "epoch [ 7] L = 87.084077, acc = 0.740000\n", + "epoch [ 8] L = 85.737551, acc = 0.755000\n", + "epoch [ 9] L = 84.428420, acc = 0.760000\n", + "epoch [ 10] L = 83.142909, acc = 0.770000\n", + "epoch [ 11] L = 81.874127, acc = 0.775000\n", + "epoch [ 12] L = 80.619101, acc = 0.775000\n", + "epoch [ 13] L = 79.377104, acc = 0.775000\n", + "epoch [ 14] L = 78.148705, acc = 0.775000\n", + "epoch [ 15] L = 76.935216, acc = 0.785000\n", + "epoch [ 16] L = 75.738363, acc = 0.790000\n", + "epoch [ 17] L = 74.560076, acc = 0.795000\n", + "epoch [ 18] L = 73.402344, acc = 0.795000\n", + "epoch [ 19] L = 72.267120, acc = 0.800000\n", + "epoch [ 20] L = 71.156245, acc = 0.805000\n", + "epoch [ 21] L = 70.071400, acc = 0.805000\n", + "epoch [ 22] L = 69.014062, acc = 0.810000\n", + "epoch [ 23] L = 67.985488, acc = 0.810000\n", + "epoch [ 24] L = 66.986694, acc = 0.810000\n", + "epoch [ 25] L = 66.018452, acc = 0.810000\n", + "epoch [ 26] L = 65.081296, acc = 0.810000\n", + "epoch [ 27] L = 64.175529, acc = 0.810000\n", + "epoch [ 28] L = 63.301240, acc = 0.810000\n", + "epoch [ 29] L = 62.458318, acc = 0.810000\n", + "epoch [ 30] L = 61.646476, acc = 0.810000\n", + "epoch [ 31] L = 60.865270, acc = 0.810000\n", + "epoch [ 32] L = 60.114123, acc = 0.810000\n", + "epoch [ 33] L = 59.392345, acc = 0.815000\n", + "epoch [ 34] L = 58.699157, acc = 0.815000\n", + "epoch [ 35] L = 58.033705, acc = 0.815000\n", + "epoch [ 36] L = 57.395081, acc = 0.815000\n", + "epoch [ 37] L = 56.782341, acc = 0.815000\n", + "epoch [ 38] L = 56.194515, acc = 0.815000\n", + "epoch [ 39] L = 55.630622, acc = 0.815000\n", + "epoch [ 40] L = 55.089680, acc = 0.815000\n", + "epoch [ 41] L = 54.570712, acc = 0.825000\n", + "epoch [ 42] L = 54.072759, acc = 0.820000\n", + "epoch [ 43] L = 53.594882, acc = 0.820000\n", + "epoch [ 44] L = 53.136167, acc = 0.825000\n", + "epoch [ 45] L = 52.695728, acc = 0.825000\n", + "epoch [ 46] L = 52.272712, acc = 0.825000\n", + "epoch [ 47] L = 51.866299, acc = 0.825000\n", + "epoch [ 48] L = 51.475703, acc = 0.825000\n", + "epoch [ 49] L = 51.100173, acc = 0.825000\n", + "epoch [ 50] L = 50.738994, acc = 0.830000\n", + "epoch [ 51] L = 50.391484, acc = 0.830000\n", + "epoch [ 52] L = 50.056996, acc = 0.830000\n", + "epoch [ 53] L = 49.734917, acc = 0.830000\n", + "epoch [ 54] L = 49.424664, acc = 0.830000\n", + "epoch [ 55] L = 49.125686, acc = 0.830000\n", + "epoch [ 56] L = 48.837462, acc = 0.830000\n", + "epoch [ 57] L = 48.559498, acc = 0.830000\n", + "epoch [ 58] L = 48.291329, acc = 0.830000\n", + "epoch [ 59] L = 48.032513, acc = 0.830000\n", + "epoch [ 60] L = 47.782634, acc = 0.830000\n", + "epoch [ 61] L = 47.541299, acc = 0.830000\n", + "epoch [ 62] L = 47.308135, acc = 0.830000\n", + "epoch [ 63] L = 47.082790, acc = 0.830000\n", + "epoch [ 64] L = 46.864932, acc = 0.830000\n", + "epoch [ 65] L = 46.654245, acc = 0.830000\n", + "epoch [ 66] L = 46.450431, acc = 0.830000\n", + "epoch [ 67] L = 46.253208, acc = 0.830000\n", + "epoch [ 68] L = 46.062309, acc = 0.830000\n", + "epoch [ 69] L = 45.877480, acc = 0.830000\n", + "epoch [ 70] L = 45.698480, acc = 0.830000\n", + "epoch [ 71] L = 45.525081, acc = 0.835000\n", + "epoch [ 72] L = 45.357065, acc = 0.835000\n", + "epoch [ 73] L = 45.194228, acc = 0.835000\n", + "epoch [ 74] L = 45.036371, acc = 0.835000\n", + "epoch [ 75] L = 44.883309, acc = 0.835000\n", + "epoch [ 76] L = 44.734863, acc = 0.840000\n", + "epoch [ 77] L = 44.590864, acc = 0.840000\n", + "epoch [ 78] L = 44.451150, acc = 0.835000\n", + "epoch [ 79] L = 44.315567, acc = 0.835000\n", + "epoch [ 80] L = 44.183966, acc = 0.835000\n", + "epoch [ 81] L = 44.056207, acc = 0.835000\n", + "epoch [ 82] L = 43.932155, acc = 0.835000\n", + "epoch [ 83] L = 43.811681, acc = 0.835000\n", + "epoch [ 84] L = 43.694661, acc = 0.835000\n", + "epoch [ 85] L = 43.580977, acc = 0.835000\n", + "epoch [ 86] L = 43.470515, acc = 0.835000\n", + "epoch [ 87] L = 43.363166, acc = 0.835000\n", + "epoch [ 88] L = 43.258826, acc = 0.835000\n", + "epoch [ 89] L = 43.157394, acc = 0.835000\n", + "epoch [ 90] L = 43.058774, acc = 0.835000\n", + "epoch [ 91] L = 42.962874, acc = 0.835000\n", + "epoch [ 92] L = 42.869604, acc = 0.835000\n", + "epoch [ 93] L = 42.778879, acc = 0.835000\n", + "epoch [ 94] L = 42.690616, acc = 0.835000\n", + "epoch [ 95] L = 42.604736, acc = 0.835000\n", + "epoch [ 96] L = 42.521163, acc = 0.835000\n", + "epoch [ 97] L = 42.439823, acc = 0.830000\n", + "epoch [ 98] L = 42.360646, acc = 0.830000\n", + "epoch [ 99] L = 42.283563, acc = 0.830000\n", + "epoch [ 100] L = 42.208509, acc = 0.830000\n", + "epoch [ 101] L = 42.135420, acc = 0.830000\n", + "epoch [ 102] L = 42.064236, acc = 0.830000\n", + "epoch [ 103] L = 41.994896, acc = 0.830000\n", + "epoch [ 104] L = 41.927345, acc = 0.830000\n", + "epoch [ 105] L = 41.861528, acc = 0.830000\n", + "epoch [ 106] L = 41.797391, acc = 0.835000\n", + "epoch [ 107] L = 41.734884, acc = 0.835000\n", + "epoch [ 108] L = 41.673958, acc = 0.835000\n", + "epoch [ 109] L = 41.614563, acc = 0.835000\n", + "epoch [ 110] L = 41.556656, acc = 0.835000\n", + "epoch [ 111] L = 41.500191, acc = 0.835000\n", + "epoch [ 112] L = 41.445126, acc = 0.835000\n", + "epoch [ 113] L = 41.391418, acc = 0.835000\n", + "epoch [ 114] L = 41.339029, acc = 0.835000\n", + "epoch [ 115] L = 41.287919, acc = 0.835000\n", + "epoch [ 116] L = 41.238051, acc = 0.835000\n", + "epoch [ 117] L = 41.189388, acc = 0.835000\n", + "epoch [ 118] L = 41.141897, acc = 0.835000\n", + "epoch [ 119] L = 41.095542, acc = 0.835000\n", + "epoch [ 120] L = 41.050292, acc = 0.840000\n", + "epoch [ 121] L = 41.006114, acc = 0.840000\n", + "epoch [ 122] L = 40.962979, acc = 0.840000\n", + "epoch [ 123] L = 40.920857, acc = 0.840000\n", + "epoch [ 124] L = 40.879718, acc = 0.840000\n", + "epoch [ 125] L = 40.839536, acc = 0.840000\n", + "epoch [ 126] L = 40.800284, acc = 0.840000\n", + "epoch [ 127] L = 40.761935, acc = 0.845000\n", + "epoch [ 128] L = 40.724464, acc = 0.845000\n", + "epoch [ 129] L = 40.687848, acc = 0.845000\n", + "epoch [ 130] L = 40.652063, acc = 0.845000\n", + "epoch [ 131] L = 40.617086, acc = 0.845000\n", + "epoch [ 132] L = 40.582895, acc = 0.850000\n", + "epoch [ 133] L = 40.549468, acc = 0.850000\n", + "epoch [ 134] L = 40.516785, acc = 0.850000\n", + "epoch [ 135] L = 40.484827, acc = 0.850000\n", + "epoch [ 136] L = 40.453572, acc = 0.850000\n", + "epoch [ 137] L = 40.423004, acc = 0.850000\n", + "epoch [ 138] L = 40.393102, acc = 0.850000\n", + "epoch [ 139] L = 40.363851, acc = 0.850000\n", + "epoch [ 140] L = 40.335232, acc = 0.850000\n", + "epoch [ 141] L = 40.307229, acc = 0.850000\n", + "epoch [ 142] L = 40.279826, acc = 0.850000\n", + "epoch [ 143] L = 40.253008, acc = 0.850000\n", + "epoch [ 144] L = 40.226759, acc = 0.850000\n", + "epoch [ 145] L = 40.201064, acc = 0.850000\n", + "epoch [ 146] L = 40.175909, acc = 0.850000\n", + "epoch [ 147] L = 40.151281, acc = 0.850000\n", + "epoch [ 148] L = 40.127167, acc = 0.850000\n", + "epoch [ 149] L = 40.103552, acc = 0.850000\n", + "epoch [ 150] L = 40.080424, acc = 0.850000\n", + "epoch [ 151] L = 40.057772, acc = 0.850000\n", + "epoch [ 152] L = 40.035583, acc = 0.850000\n", + "epoch [ 153] L = 40.013846, acc = 0.850000\n", + "epoch [ 154] L = 39.992550, acc = 0.850000\n", + "epoch [ 155] L = 39.971683, acc = 0.850000\n", + "epoch [ 156] L = 39.951235, acc = 0.855000\n", + "epoch [ 157] L = 39.931196, acc = 0.855000\n", + "epoch [ 158] L = 39.911556, acc = 0.855000\n", + "epoch [ 159] L = 39.892306, acc = 0.855000\n", + "epoch [ 160] L = 39.873435, acc = 0.855000\n", + "epoch [ 161] L = 39.854934, acc = 0.855000\n", + "epoch [ 162] L = 39.836796, acc = 0.855000\n", + "epoch [ 163] L = 39.819011, acc = 0.855000\n", + "epoch [ 164] L = 39.801570, acc = 0.855000\n", + "epoch [ 165] L = 39.784466, acc = 0.855000\n", + "epoch [ 166] L = 39.767691, acc = 0.855000\n", + "epoch [ 167] L = 39.751236, acc = 0.855000\n", + "epoch [ 168] L = 39.735095, acc = 0.855000\n", + "epoch [ 169] L = 39.719261, acc = 0.855000\n", + "epoch [ 170] L = 39.703725, acc = 0.855000\n", + "epoch [ 171] L = 39.688481, acc = 0.855000\n", + "epoch [ 172] L = 39.673523, acc = 0.855000\n", + "epoch [ 173] L = 39.658844, acc = 0.855000\n", + "epoch [ 174] L = 39.644437, acc = 0.855000\n", + "epoch [ 175] L = 39.630297, acc = 0.855000\n", + "epoch [ 176] L = 39.616417, acc = 0.855000\n", + "epoch [ 177] L = 39.602791, acc = 0.855000\n", + "epoch [ 178] L = 39.589414, acc = 0.855000\n", + "epoch [ 179] L = 39.576281, acc = 0.855000\n", + "epoch [ 180] L = 39.563385, acc = 0.855000\n", + "epoch [ 181] L = 39.550721, acc = 0.855000\n", + "epoch [ 182] L = 39.538285, acc = 0.855000\n", + "epoch [ 183] L = 39.526072, acc = 0.855000\n", + "epoch [ 184] L = 39.514076, acc = 0.855000\n", + "epoch [ 185] L = 39.502292, acc = 0.855000\n", + "epoch [ 186] L = 39.490717, acc = 0.855000\n", + "epoch [ 187] L = 39.479346, acc = 0.855000\n", + "epoch [ 188] L = 39.468173, acc = 0.855000\n", + "epoch [ 189] L = 39.457196, acc = 0.855000\n", + "epoch [ 190] L = 39.446409, acc = 0.855000\n", + "epoch [ 191] L = 39.435809, acc = 0.855000\n", + "epoch [ 192] L = 39.425392, acc = 0.855000\n", + "epoch [ 193] L = 39.415154, acc = 0.855000\n", + "epoch [ 194] L = 39.405091, acc = 0.855000\n", + "epoch [ 195] L = 39.395199, acc = 0.855000\n", + "epoch [ 196] L = 39.385476, acc = 0.855000\n", + "epoch [ 197] L = 39.375916, acc = 0.855000\n", + "epoch [ 198] L = 39.366518, acc = 0.855000\n", + "epoch [ 199] L = 39.357277, acc = 0.855000\n", + "epoch [ 200] L = 39.348190, acc = 0.855000\n", + "epoch [ 201] L = 39.339255, acc = 0.855000\n", + "epoch [ 202] L = 39.330468, acc = 0.855000\n", + "epoch [ 203] L = 39.321826, acc = 0.855000\n", + "epoch [ 204] L = 39.313326, acc = 0.855000\n", + "epoch [ 205] L = 39.304965, acc = 0.855000\n", + "epoch [ 206] L = 39.296741, acc = 0.855000\n", + "epoch [ 207] L = 39.288650, acc = 0.855000\n", + "epoch [ 208] L = 39.280691, acc = 0.855000\n", + "epoch [ 209] L = 39.272860, acc = 0.855000\n", + "epoch [ 210] L = 39.265155, acc = 0.855000\n", + "epoch [ 211] L = 39.257573, acc = 0.855000\n", + "epoch [ 212] L = 39.250112, acc = 0.855000\n", + "epoch [ 213] L = 39.242770, acc = 0.855000\n", + "epoch [ 214] L = 39.235544, acc = 0.855000\n", + "epoch [ 215] L = 39.228431, acc = 0.855000\n", + "epoch [ 216] L = 39.221431, acc = 0.855000\n", + "epoch [ 217] L = 39.214540, acc = 0.855000\n", + "epoch [ 218] L = 39.207757, acc = 0.855000\n", + "epoch [ 219] L = 39.201079, acc = 0.855000\n", + "epoch [ 220] L = 39.194505, acc = 0.855000\n", + "epoch [ 221] L = 39.188032, acc = 0.855000\n", + "epoch [ 222] L = 39.181658, acc = 0.855000\n", + "epoch [ 223] L = 39.175382, acc = 0.855000\n", + "epoch [ 224] L = 39.169201, acc = 0.855000\n", + "epoch [ 225] L = 39.163115, acc = 0.855000\n", + "epoch [ 226] L = 39.157121, acc = 0.855000\n", + "epoch [ 227] L = 39.151217, acc = 0.855000\n", + "epoch [ 228] L = 39.145402, acc = 0.855000\n", + "epoch [ 229] L = 39.139673, acc = 0.855000\n", + "epoch [ 230] L = 39.134031, acc = 0.855000\n", + "epoch [ 231] L = 39.128472, acc = 0.855000\n", + "epoch [ 232] L = 39.122995, acc = 0.855000\n", + "epoch [ 233] L = 39.117600, acc = 0.855000\n", + "epoch [ 234] L = 39.112283, acc = 0.855000\n", + "epoch [ 235] L = 39.107045, acc = 0.855000\n", + "epoch [ 236] L = 39.101883, acc = 0.855000\n", + "epoch [ 237] L = 39.096796, acc = 0.855000\n", + "epoch [ 238] L = 39.091783, acc = 0.855000\n", + "epoch [ 239] L = 39.086842, acc = 0.855000\n", + "epoch [ 240] L = 39.081972, acc = 0.855000\n", + "epoch [ 241] L = 39.077172, acc = 0.855000\n", + "epoch [ 242] L = 39.072441, acc = 0.855000\n", + "epoch [ 243] L = 39.067776, acc = 0.855000\n", + "epoch [ 244] L = 39.063178, acc = 0.855000\n", + "epoch [ 245] L = 39.058645, acc = 0.855000\n", + "epoch [ 246] L = 39.054176, acc = 0.855000\n", + "epoch [ 247] L = 39.049770, acc = 0.855000\n", + "epoch [ 248] L = 39.045425, acc = 0.855000\n", + "epoch [ 249] L = 39.041140, acc = 0.855000\n", + "epoch [ 250] L = 39.036915, acc = 0.855000\n", + "epoch [ 251] L = 39.032748, acc = 0.855000\n", + "epoch [ 252] L = 39.028639, acc = 0.855000\n", + "epoch [ 253] L = 39.024586, acc = 0.855000\n", + "epoch [ 254] L = 39.020589, acc = 0.850000\n", + "epoch [ 255] L = 39.016646, acc = 0.850000\n", + "epoch [ 256] L = 39.012756, acc = 0.850000\n", + "epoch [ 257] L = 39.008920, acc = 0.850000\n", + "epoch [ 258] L = 39.005134, acc = 0.850000\n", + "epoch [ 259] L = 39.001400, acc = 0.850000\n", + "epoch [ 260] L = 38.997716, acc = 0.850000\n", + "epoch [ 261] L = 38.994081, acc = 0.850000\n", + "epoch [ 262] L = 38.990494, acc = 0.850000\n", + "epoch [ 263] L = 38.986955, acc = 0.850000\n", + "epoch [ 264] L = 38.983462, acc = 0.845000\n", + "epoch [ 265] L = 38.980015, acc = 0.845000\n", + "epoch [ 266] L = 38.976614, acc = 0.845000\n", + "epoch [ 267] L = 38.973256, acc = 0.845000\n", + "epoch [ 268] L = 38.969943, acc = 0.845000\n", + "epoch [ 269] L = 38.966672, acc = 0.845000\n", + "epoch [ 270] L = 38.963444, acc = 0.845000\n", + "epoch [ 271] L = 38.960257, acc = 0.845000\n", + "epoch [ 272] L = 38.957111, acc = 0.845000\n", + "epoch [ 273] L = 38.954005, acc = 0.845000\n", + "epoch [ 274] L = 38.950939, acc = 0.845000\n", + "epoch [ 275] L = 38.947911, acc = 0.845000\n", + "epoch [ 276] L = 38.944922, acc = 0.845000\n", + "epoch [ 277] L = 38.941970, acc = 0.845000\n", + "epoch [ 278] L = 38.939056, acc = 0.845000\n", + "epoch [ 279] L = 38.936178, acc = 0.845000\n", + "epoch [ 280] L = 38.933335, acc = 0.845000\n", + "epoch [ 281] L = 38.930528, acc = 0.845000\n", + "epoch [ 282] L = 38.927756, acc = 0.845000\n", + "epoch [ 283] L = 38.925018, acc = 0.845000\n", + "epoch [ 284] L = 38.922313, acc = 0.845000\n", + "epoch [ 285] L = 38.919641, acc = 0.845000\n", + "epoch [ 286] L = 38.917002, acc = 0.845000\n", + "epoch [ 287] L = 38.914395, acc = 0.845000\n", + "epoch [ 288] L = 38.911820, acc = 0.845000\n", + "epoch [ 289] L = 38.909275, acc = 0.845000\n", + "epoch [ 290] L = 38.906761, acc = 0.845000\n", + "epoch [ 291] L = 38.904278, acc = 0.845000\n", + "epoch [ 292] L = 38.901823, acc = 0.845000\n", + "epoch [ 293] L = 38.899398, acc = 0.845000\n", + "epoch [ 294] L = 38.897002, acc = 0.845000\n", + "epoch [ 295] L = 38.894633, acc = 0.845000\n", + "epoch [ 296] L = 38.892293, acc = 0.845000\n", + "epoch [ 297] L = 38.889980, acc = 0.845000\n", + "epoch [ 298] L = 38.887694, acc = 0.845000\n", + "epoch [ 299] L = 38.885434, acc = 0.845000\n", + "epoch [ 300] L = 38.883201, acc = 0.845000\n", + "epoch [ 301] L = 38.880993, acc = 0.845000\n", + "epoch [ 302] L = 38.878811, acc = 0.845000\n", + "epoch [ 303] L = 38.876653, acc = 0.845000\n", + "epoch [ 304] L = 38.874521, acc = 0.845000\n", + "epoch [ 305] L = 38.872412, acc = 0.845000\n", + "epoch [ 306] L = 38.870327, acc = 0.845000\n", + "epoch [ 307] L = 38.868266, acc = 0.845000\n", + "epoch [ 308] L = 38.866228, acc = 0.845000\n", + "epoch [ 309] L = 38.864212, acc = 0.845000\n", + "epoch [ 310] L = 38.862219, acc = 0.845000\n", + "epoch [ 311] L = 38.860249, acc = 0.845000\n", + "epoch [ 312] L = 38.858300, acc = 0.845000\n", + "epoch [ 313] L = 38.856372, acc = 0.845000\n", + "epoch [ 314] L = 38.854466, acc = 0.845000\n", + "epoch [ 315] L = 38.852580, acc = 0.845000\n", + "epoch [ 316] L = 38.850715, acc = 0.845000\n", + "epoch [ 317] L = 38.848870, acc = 0.845000\n", + "epoch [ 318] L = 38.847045, acc = 0.845000\n", + "epoch [ 319] L = 38.845240, acc = 0.845000\n", + "epoch [ 320] L = 38.843454, acc = 0.845000\n", + "epoch [ 321] L = 38.841687, acc = 0.845000\n", + "epoch [ 322] L = 38.839939, acc = 0.845000\n", + "epoch [ 323] L = 38.838209, acc = 0.845000\n", + "epoch [ 324] L = 38.836498, acc = 0.845000\n", + "epoch [ 325] L = 38.834804, acc = 0.845000\n", + "epoch [ 326] L = 38.833128, acc = 0.845000\n", + "epoch [ 327] L = 38.831470, acc = 0.845000\n", + "epoch [ 328] L = 38.829829, acc = 0.845000\n", + "epoch [ 329] L = 38.828205, acc = 0.845000\n", + "epoch [ 330] L = 38.826598, acc = 0.845000\n", + "epoch [ 331] L = 38.825007, acc = 0.845000\n", + "epoch [ 332] L = 38.823432, acc = 0.845000\n", + "epoch [ 333] L = 38.821874, acc = 0.845000\n", + "epoch [ 334] L = 38.820331, acc = 0.845000\n", + "epoch [ 335] L = 38.818804, acc = 0.845000\n", + "epoch [ 336] L = 38.817292, acc = 0.845000\n", + "epoch [ 337] L = 38.815795, acc = 0.845000\n", + "epoch [ 338] L = 38.814314, acc = 0.845000\n", + "epoch [ 339] L = 38.812847, acc = 0.845000\n", + "epoch [ 340] L = 38.811394, acc = 0.845000\n", + "epoch [ 341] L = 38.809956, acc = 0.845000\n", + "epoch [ 342] L = 38.808532, acc = 0.845000\n", + "epoch [ 343] L = 38.807122, acc = 0.845000\n", + "epoch [ 344] L = 38.805725, acc = 0.845000\n", + "epoch [ 345] L = 38.804342, acc = 0.845000\n", + "epoch [ 346] L = 38.802972, acc = 0.845000\n", + "epoch [ 347] L = 38.801616, acc = 0.845000\n", + "epoch [ 348] L = 38.800273, acc = 0.845000\n", + "epoch [ 349] L = 38.798942, acc = 0.845000\n", + "epoch [ 350] L = 38.797624, acc = 0.845000\n", + "epoch [ 351] L = 38.796318, acc = 0.845000\n", + "epoch [ 352] L = 38.795025, acc = 0.845000\n", + "epoch [ 353] L = 38.793744, acc = 0.845000\n", + "epoch [ 354] L = 38.792475, acc = 0.845000\n", + "epoch [ 355] L = 38.791217, acc = 0.845000\n", + "epoch [ 356] L = 38.789971, acc = 0.845000\n", + "epoch [ 357] L = 38.788737, acc = 0.845000\n", + "epoch [ 358] L = 38.787514, acc = 0.845000\n", + "epoch [ 359] L = 38.786302, acc = 0.845000\n", + "epoch [ 360] L = 38.785101, acc = 0.845000\n", + "epoch [ 361] L = 38.783911, acc = 0.845000\n", + "epoch [ 362] L = 38.782732, acc = 0.845000\n", + "epoch [ 363] L = 38.781564, acc = 0.845000\n", + "epoch [ 364] L = 38.780405, acc = 0.845000\n", + "epoch [ 365] L = 38.779258, acc = 0.845000\n", + "epoch [ 366] L = 38.778120, acc = 0.845000\n", + "epoch [ 367] L = 38.776992, acc = 0.845000\n", + "epoch [ 368] L = 38.775874, acc = 0.845000\n", + "epoch [ 369] L = 38.774766, acc = 0.845000\n", + "epoch [ 370] L = 38.773668, acc = 0.845000\n", + "epoch [ 371] L = 38.772579, acc = 0.845000\n", + "epoch [ 372] L = 38.771500, acc = 0.845000\n", + "epoch [ 373] L = 38.770430, acc = 0.845000\n", + "epoch [ 374] L = 38.769369, acc = 0.845000\n", + "epoch [ 375] L = 38.768317, acc = 0.845000\n", + "epoch [ 376] L = 38.767273, acc = 0.845000\n", + "epoch [ 377] L = 38.766239, acc = 0.845000\n", + "epoch [ 378] L = 38.765214, acc = 0.845000\n", + "epoch [ 379] L = 38.764197, acc = 0.845000\n", + "epoch [ 380] L = 38.763188, acc = 0.845000\n", + "epoch [ 381] L = 38.762188, acc = 0.845000\n", + "epoch [ 382] L = 38.761196, acc = 0.845000\n", + "epoch [ 383] L = 38.760212, acc = 0.845000\n", + "epoch [ 384] L = 38.759236, acc = 0.845000\n", + "epoch [ 385] L = 38.758269, acc = 0.845000\n", + "epoch [ 386] L = 38.757309, acc = 0.845000\n", + "epoch [ 387] L = 38.756356, acc = 0.845000\n", + "epoch [ 388] L = 38.755412, acc = 0.845000\n", + "epoch [ 389] L = 38.754475, acc = 0.845000\n", + "epoch [ 390] L = 38.753545, acc = 0.845000\n", + "epoch [ 391] L = 38.752623, acc = 0.845000\n", + "epoch [ 392] L = 38.751708, acc = 0.845000\n", + "epoch [ 393] L = 38.750800, acc = 0.845000\n", + "epoch [ 394] L = 38.749899, acc = 0.845000\n", + "epoch [ 395] L = 38.749006, acc = 0.845000\n", + "epoch [ 396] L = 38.748119, acc = 0.845000\n", + "epoch [ 397] L = 38.747239, acc = 0.845000\n", + "epoch [ 398] L = 38.746366, acc = 0.845000\n", + "epoch [ 399] L = 38.745499, acc = 0.845000\n", + "epoch [ 400] L = 38.744639, acc = 0.845000\n", + "epoch [ 401] L = 38.743785, acc = 0.850000\n", + "epoch [ 402] L = 38.742938, acc = 0.850000\n", + "epoch [ 403] L = 38.742097, acc = 0.850000\n", + "epoch [ 404] L = 38.741263, acc = 0.850000\n", + "epoch [ 405] L = 38.740435, acc = 0.850000\n", + "epoch [ 406] L = 38.739612, acc = 0.850000\n", + "epoch [ 407] L = 38.738796, acc = 0.850000\n", + "epoch [ 408] L = 38.737986, acc = 0.850000\n", + "epoch [ 409] L = 38.737181, acc = 0.850000\n", + "epoch [ 410] L = 38.736383, acc = 0.850000\n", + "epoch [ 411] L = 38.735590, acc = 0.850000\n", + "epoch [ 412] L = 38.734803, acc = 0.850000\n", + "epoch [ 413] L = 38.734021, acc = 0.850000\n", + "epoch [ 414] L = 38.733245, acc = 0.850000\n", + "epoch [ 415] L = 38.732475, acc = 0.850000\n", + "epoch [ 416] L = 38.731710, acc = 0.850000\n", + "epoch [ 417] L = 38.730950, acc = 0.850000\n", + "epoch [ 418] L = 38.730195, acc = 0.850000\n", + "epoch [ 419] L = 38.729446, acc = 0.850000\n", + "epoch [ 420] L = 38.728702, acc = 0.850000\n", + "epoch [ 421] L = 38.727963, acc = 0.850000\n", + "epoch [ 422] L = 38.727229, acc = 0.850000\n", + "epoch [ 423] L = 38.726500, acc = 0.850000\n", + "epoch [ 424] L = 38.725776, acc = 0.850000\n", + "epoch [ 425] L = 38.725057, acc = 0.850000\n", + "epoch [ 426] L = 38.724342, acc = 0.850000\n", + "epoch [ 427] L = 38.723633, acc = 0.850000\n", + "epoch [ 428] L = 38.722928, acc = 0.850000\n", + "epoch [ 429] L = 38.722227, acc = 0.850000\n", + "epoch [ 430] L = 38.721532, acc = 0.850000\n", + "epoch [ 431] L = 38.720840, acc = 0.850000\n", + "epoch [ 432] L = 38.720154, acc = 0.850000\n", + "epoch [ 433] L = 38.719471, acc = 0.850000\n", + "epoch [ 434] L = 38.718794, acc = 0.850000\n", + "epoch [ 435] L = 38.718120, acc = 0.850000\n", + "epoch [ 436] L = 38.717451, acc = 0.850000\n", + "epoch [ 437] L = 38.716786, acc = 0.850000\n", + "epoch [ 438] L = 38.716125, acc = 0.850000\n", + "epoch [ 439] L = 38.715468, acc = 0.850000\n", + "epoch [ 440] L = 38.714815, acc = 0.850000\n", + "epoch [ 441] L = 38.714167, acc = 0.850000\n", + "epoch [ 442] L = 38.713522, acc = 0.850000\n", + "epoch [ 443] L = 38.712881, acc = 0.850000\n", + "epoch [ 444] L = 38.712245, acc = 0.850000\n", + "epoch [ 445] L = 38.711612, acc = 0.850000\n", + "epoch [ 446] L = 38.710983, acc = 0.850000\n", + "epoch [ 447] L = 38.710357, acc = 0.850000\n", + "epoch [ 448] L = 38.709736, acc = 0.850000\n", + "epoch [ 449] L = 38.709118, acc = 0.850000\n", + "epoch [ 450] L = 38.708504, acc = 0.850000\n", + "epoch [ 451] L = 38.707893, acc = 0.850000\n", + "epoch [ 452] L = 38.707286, acc = 0.850000\n", + "epoch [ 453] L = 38.706683, acc = 0.850000\n", + "epoch [ 454] L = 38.706083, acc = 0.850000\n", + "epoch [ 455] L = 38.705486, acc = 0.850000\n", + "epoch [ 456] L = 38.704893, acc = 0.850000\n", + "epoch [ 457] L = 38.704304, acc = 0.850000\n", + "epoch [ 458] L = 38.703717, acc = 0.850000\n", + "epoch [ 459] L = 38.703134, acc = 0.850000\n", + "epoch [ 460] L = 38.702554, acc = 0.850000\n", + "epoch [ 461] L = 38.701978, acc = 0.850000\n", + "epoch [ 462] L = 38.701405, acc = 0.850000\n", + "epoch [ 463] L = 38.700834, acc = 0.850000\n", + "epoch [ 464] L = 38.700267, acc = 0.850000\n", + "epoch [ 465] L = 38.699704, acc = 0.850000\n", + "epoch [ 466] L = 38.699143, acc = 0.850000\n", + "epoch [ 467] L = 38.698585, acc = 0.850000\n", + "epoch [ 468] L = 38.698030, acc = 0.850000\n", + "epoch [ 469] L = 38.697478, acc = 0.850000\n", + "epoch [ 470] L = 38.696930, acc = 0.850000\n", + "epoch [ 471] L = 38.696384, acc = 0.850000\n", + "epoch [ 472] L = 38.695841, acc = 0.850000\n", + "epoch [ 473] L = 38.695300, acc = 0.850000\n", + "epoch [ 474] L = 38.694763, acc = 0.850000\n", + "epoch [ 475] L = 38.694228, acc = 0.850000\n", + "epoch [ 476] L = 38.693697, acc = 0.850000\n", + "epoch [ 477] L = 38.693168, acc = 0.850000\n", + "epoch [ 478] L = 38.692641, acc = 0.850000\n", + "epoch [ 479] L = 38.692118, acc = 0.850000\n", + "epoch [ 480] L = 38.691597, acc = 0.850000\n", + "epoch [ 481] L = 38.691078, acc = 0.850000\n", + "epoch [ 482] L = 38.690562, acc = 0.850000\n", + "epoch [ 483] L = 38.690049, acc = 0.850000\n", + "epoch [ 484] L = 38.689538, acc = 0.850000\n", + "epoch [ 485] L = 38.689030, acc = 0.850000\n", + "epoch [ 486] L = 38.688525, acc = 0.850000\n", + "epoch [ 487] L = 38.688021, acc = 0.850000\n", + "epoch [ 488] L = 38.687521, acc = 0.850000\n", + "epoch [ 489] L = 38.687022, acc = 0.850000\n", + "epoch [ 490] L = 38.686526, acc = 0.850000\n", + "epoch [ 491] L = 38.686033, acc = 0.850000\n", + "epoch [ 492] L = 38.685542, acc = 0.850000\n", + "epoch [ 493] L = 38.685053, acc = 0.850000\n", + "epoch [ 494] L = 38.684566, acc = 0.850000\n", + "epoch [ 495] L = 38.684082, acc = 0.850000\n", + "epoch [ 496] L = 38.683600, acc = 0.850000\n", + "epoch [ 497] L = 38.683120, acc = 0.850000\n", + "epoch [ 498] L = 38.682643, acc = 0.850000\n", + "epoch [ 499] L = 38.682167, acc = 0.850000\n", + "epoch [ 500] L = 38.681694, acc = 0.850000\n", + "epoch [ 501] L = 38.681223, acc = 0.850000\n", + "epoch [ 502] L = 38.680754, acc = 0.850000\n", + "epoch [ 503] L = 38.680287, acc = 0.850000\n", + "epoch [ 504] L = 38.679823, acc = 0.850000\n", + "epoch [ 505] L = 38.679360, acc = 0.850000\n", + "epoch [ 506] L = 38.678899, acc = 0.850000\n", + "epoch [ 507] L = 38.678441, acc = 0.850000\n", + "epoch [ 508] L = 38.677984, acc = 0.850000\n", + "epoch [ 509] L = 38.677530, acc = 0.850000\n", + "epoch [ 510] L = 38.677077, acc = 0.850000\n", + "epoch [ 511] L = 38.676627, acc = 0.850000\n", + "epoch [ 512] L = 38.676178, acc = 0.850000\n", + "epoch [ 513] L = 38.675731, acc = 0.850000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch [ 514] L = 38.675286, acc = 0.850000\n", + "epoch [ 515] L = 38.674843, acc = 0.850000\n", + "epoch [ 516] L = 38.674402, acc = 0.850000\n", + "epoch [ 517] L = 38.673963, acc = 0.850000\n", + "epoch [ 518] L = 38.673526, acc = 0.850000\n", + "epoch [ 519] L = 38.673090, acc = 0.850000\n", + "epoch [ 520] L = 38.672656, acc = 0.850000\n", + "epoch [ 521] L = 38.672224, acc = 0.850000\n", + "epoch [ 522] L = 38.671794, acc = 0.850000\n", + "epoch [ 523] L = 38.671365, acc = 0.850000\n", + "epoch [ 524] L = 38.670939, acc = 0.850000\n", + "epoch [ 525] L = 38.670514, acc = 0.850000\n", + "epoch [ 526] L = 38.670090, acc = 0.850000\n", + "epoch [ 527] L = 38.669669, acc = 0.850000\n", + "epoch [ 528] L = 38.669249, acc = 0.850000\n", + "epoch [ 529] L = 38.668830, acc = 0.850000\n", + "epoch [ 530] L = 38.668414, acc = 0.850000\n", + "epoch [ 531] L = 38.667999, acc = 0.850000\n", + "epoch [ 532] L = 38.667585, acc = 0.850000\n", + "epoch [ 533] L = 38.667173, acc = 0.850000\n", + "epoch [ 534] L = 38.666763, acc = 0.850000\n", + "epoch [ 535] L = 38.666354, acc = 0.850000\n", + "epoch [ 536] L = 38.665947, acc = 0.850000\n", + "epoch [ 537] L = 38.665542, acc = 0.850000\n", + "epoch [ 538] L = 38.665138, acc = 0.850000\n", + "epoch [ 539] L = 38.664735, acc = 0.850000\n", + "epoch [ 540] L = 38.664334, acc = 0.850000\n", + "epoch [ 541] L = 38.663935, acc = 0.850000\n", + "epoch [ 542] L = 38.663537, acc = 0.850000\n", + "epoch [ 543] L = 38.663140, acc = 0.850000\n", + "epoch [ 544] L = 38.662745, acc = 0.850000\n", + "epoch [ 545] L = 38.662351, acc = 0.850000\n", + "epoch [ 546] L = 38.661959, acc = 0.850000\n", + "epoch [ 547] L = 38.661568, acc = 0.850000\n", + "epoch [ 548] L = 38.661179, acc = 0.850000\n", + "epoch [ 549] L = 38.660791, acc = 0.850000\n", + "epoch [ 550] L = 38.660404, acc = 0.850000\n", + "epoch [ 551] L = 38.660019, acc = 0.850000\n", + "epoch [ 552] L = 38.659635, acc = 0.850000\n", + "epoch [ 553] L = 38.659253, acc = 0.850000\n", + "epoch [ 554] L = 38.658872, acc = 0.850000\n", + "epoch [ 555] L = 38.658492, acc = 0.850000\n", + "epoch [ 556] L = 38.658113, acc = 0.850000\n", + "epoch [ 557] L = 38.657736, acc = 0.850000\n", + "epoch [ 558] L = 38.657360, acc = 0.850000\n", + "epoch [ 559] L = 38.656986, acc = 0.850000\n", + "epoch [ 560] L = 38.656612, acc = 0.850000\n", + "epoch [ 561] L = 38.656240, acc = 0.850000\n", + "epoch [ 562] L = 38.655869, acc = 0.850000\n", + "epoch [ 563] L = 38.655500, acc = 0.850000\n", + "epoch [ 564] L = 38.655131, acc = 0.850000\n", + "epoch [ 565] L = 38.654764, acc = 0.850000\n", + "epoch [ 566] L = 38.654398, acc = 0.850000\n", + "epoch [ 567] L = 38.654034, acc = 0.850000\n", + "epoch [ 568] L = 38.653670, acc = 0.850000\n", + "epoch [ 569] L = 38.653308, acc = 0.850000\n", + "epoch [ 570] L = 38.652947, acc = 0.850000\n", + "epoch [ 571] L = 38.652587, acc = 0.850000\n", + "epoch [ 572] L = 38.652228, acc = 0.850000\n", + "epoch [ 573] L = 38.651870, acc = 0.850000\n", + "epoch [ 574] L = 38.651514, acc = 0.850000\n", + "epoch [ 575] L = 38.651159, acc = 0.850000\n", + "epoch [ 576] L = 38.650804, acc = 0.850000\n", + "epoch [ 577] L = 38.650451, acc = 0.850000\n", + "epoch [ 578] L = 38.650099, acc = 0.850000\n", + "epoch [ 579] L = 38.649748, acc = 0.850000\n", + "epoch [ 580] L = 38.649399, acc = 0.850000\n", + "epoch [ 581] L = 38.649050, acc = 0.850000\n", + "epoch [ 582] L = 38.648702, acc = 0.850000\n", + "epoch [ 583] L = 38.648356, acc = 0.850000\n", + "epoch [ 584] L = 38.648010, acc = 0.850000\n", + "epoch [ 585] L = 38.647666, acc = 0.850000\n", + "epoch [ 586] L = 38.647322, acc = 0.850000\n", + "epoch [ 587] L = 38.646980, acc = 0.850000\n", + "epoch [ 588] L = 38.646639, acc = 0.850000\n", + "epoch [ 589] L = 38.646299, acc = 0.850000\n", + "epoch [ 590] L = 38.645959, acc = 0.850000\n", + "epoch [ 591] L = 38.645621, acc = 0.850000\n", + "epoch [ 592] L = 38.645284, acc = 0.850000\n", + "epoch [ 593] L = 38.644948, acc = 0.850000\n", + "epoch [ 594] L = 38.644612, acc = 0.850000\n", + "epoch [ 595] L = 38.644278, acc = 0.850000\n", + "epoch [ 596] L = 38.643945, acc = 0.850000\n", + "epoch [ 597] L = 38.643612, acc = 0.850000\n", + "epoch [ 598] L = 38.643281, acc = 0.850000\n", + "epoch [ 599] L = 38.642951, acc = 0.850000\n", + "epoch [ 600] L = 38.642621, acc = 0.850000\n", + "epoch [ 601] L = 38.642293, acc = 0.850000\n", + "epoch [ 602] L = 38.641965, acc = 0.850000\n", + "epoch [ 603] L = 38.641638, acc = 0.850000\n", + "epoch [ 604] L = 38.641313, acc = 0.850000\n", + "epoch [ 605] L = 38.640988, acc = 0.850000\n", + "epoch [ 606] L = 38.640664, acc = 0.850000\n", + "epoch [ 607] L = 38.640341, acc = 0.850000\n", + "epoch [ 608] L = 38.640019, acc = 0.850000\n", + "epoch [ 609] L = 38.639698, acc = 0.850000\n", + "epoch [ 610] L = 38.639377, acc = 0.850000\n", + "epoch [ 611] L = 38.639058, acc = 0.850000\n", + "epoch [ 612] L = 38.638739, acc = 0.850000\n", + "epoch [ 613] L = 38.638422, acc = 0.850000\n", + "epoch [ 614] L = 38.638105, acc = 0.850000\n", + "epoch [ 615] L = 38.637789, acc = 0.850000\n", + "epoch [ 616] L = 38.637474, acc = 0.850000\n", + "epoch [ 617] L = 38.637160, acc = 0.850000\n", + "epoch [ 618] L = 38.636846, acc = 0.850000\n", + "epoch [ 619] L = 38.636534, acc = 0.850000\n", + "epoch [ 620] L = 38.636222, acc = 0.850000\n", + "epoch [ 621] L = 38.635911, acc = 0.850000\n", + "epoch [ 622] L = 38.635601, acc = 0.850000\n", + "epoch [ 623] L = 38.635292, acc = 0.850000\n", + "epoch [ 624] L = 38.634983, acc = 0.850000\n", + "epoch [ 625] L = 38.634676, acc = 0.850000\n", + "epoch [ 626] L = 38.634369, acc = 0.850000\n", + "epoch [ 627] L = 38.634063, acc = 0.850000\n", + "epoch [ 628] L = 38.633757, acc = 0.850000\n", + "epoch [ 629] L = 38.633453, acc = 0.850000\n", + "epoch [ 630] L = 38.633149, acc = 0.850000\n", + "epoch [ 631] L = 38.632846, acc = 0.850000\n", + "epoch [ 632] L = 38.632544, acc = 0.850000\n", + "epoch [ 633] L = 38.632243, acc = 0.850000\n", + "epoch [ 634] L = 38.631942, acc = 0.850000\n", + "epoch [ 635] L = 38.631642, acc = 0.850000\n", + "epoch [ 636] L = 38.631343, acc = 0.850000\n", + "epoch [ 637] L = 38.631045, acc = 0.850000\n", + "epoch [ 638] L = 38.630747, acc = 0.850000\n", + "epoch [ 639] L = 38.630451, acc = 0.850000\n", + "epoch [ 640] L = 38.630154, acc = 0.850000\n", + "epoch [ 641] L = 38.629859, acc = 0.850000\n", + "epoch [ 642] L = 38.629564, acc = 0.850000\n", + "epoch [ 643] L = 38.629271, acc = 0.850000\n", + "epoch [ 644] L = 38.628977, acc = 0.850000\n", + "epoch [ 645] L = 38.628685, acc = 0.850000\n", + "epoch [ 646] L = 38.628393, acc = 0.850000\n", + "epoch [ 647] L = 38.628102, acc = 0.850000\n", + "epoch [ 648] L = 38.627812, acc = 0.850000\n", + "epoch [ 649] L = 38.627522, acc = 0.850000\n", + "epoch [ 650] L = 38.627233, acc = 0.850000\n", + "epoch [ 651] L = 38.626945, acc = 0.850000\n", + "epoch [ 652] L = 38.626657, acc = 0.850000\n", + "epoch [ 653] L = 38.626371, acc = 0.850000\n", + "epoch [ 654] L = 38.626084, acc = 0.850000\n", + "epoch [ 655] L = 38.625799, acc = 0.850000\n", + "epoch [ 656] L = 38.625514, acc = 0.850000\n", + "epoch [ 657] L = 38.625230, acc = 0.850000\n", + "epoch [ 658] L = 38.624946, acc = 0.850000\n", + "epoch [ 659] L = 38.624664, acc = 0.850000\n", + "epoch [ 660] L = 38.624381, acc = 0.850000\n", + "epoch [ 661] L = 38.624100, acc = 0.850000\n", + "epoch [ 662] L = 38.623819, acc = 0.850000\n", + "epoch [ 663] L = 38.623539, acc = 0.850000\n", + "epoch [ 664] L = 38.623259, acc = 0.850000\n", + "epoch [ 665] L = 38.622980, acc = 0.850000\n", + "epoch [ 666] L = 38.622702, acc = 0.850000\n", + "epoch [ 667] L = 38.622424, acc = 0.850000\n", + "epoch [ 668] L = 38.622147, acc = 0.850000\n", + "epoch [ 669] L = 38.621871, acc = 0.850000\n", + "epoch [ 670] L = 38.621595, acc = 0.850000\n", + "epoch [ 671] L = 38.621320, acc = 0.850000\n", + "epoch [ 672] L = 38.621046, acc = 0.850000\n", + "epoch [ 673] L = 38.620772, acc = 0.850000\n", + "epoch [ 674] L = 38.620499, acc = 0.850000\n", + "epoch [ 675] L = 38.620226, acc = 0.850000\n", + "epoch [ 676] L = 38.619954, acc = 0.850000\n", + "epoch [ 677] L = 38.619682, acc = 0.850000\n", + "epoch [ 678] L = 38.619412, acc = 0.850000\n", + "epoch [ 679] L = 38.619141, acc = 0.850000\n", + "epoch [ 680] L = 38.618872, acc = 0.850000\n", + "epoch [ 681] L = 38.618603, acc = 0.850000\n", + "epoch [ 682] L = 38.618334, acc = 0.850000\n", + "epoch [ 683] L = 38.618066, acc = 0.850000\n", + "epoch [ 684] L = 38.617799, acc = 0.850000\n", + "epoch [ 685] L = 38.617532, acc = 0.850000\n", + "epoch [ 686] L = 38.617266, acc = 0.850000\n", + "epoch [ 687] L = 38.617001, acc = 0.850000\n", + "epoch [ 688] L = 38.616736, acc = 0.850000\n", + "epoch [ 689] L = 38.616471, acc = 0.850000\n", + "epoch [ 690] L = 38.616208, acc = 0.850000\n", + "epoch [ 691] L = 38.615944, acc = 0.850000\n", + "epoch [ 692] L = 38.615682, acc = 0.850000\n", + "epoch [ 693] L = 38.615420, acc = 0.850000\n", + "epoch [ 694] L = 38.615158, acc = 0.850000\n", + "epoch [ 695] L = 38.614897, acc = 0.850000\n", + "epoch [ 696] L = 38.614637, acc = 0.850000\n", + "epoch [ 697] L = 38.614377, acc = 0.850000\n", + "epoch [ 698] L = 38.614117, acc = 0.850000\n", + "epoch [ 699] L = 38.613859, acc = 0.850000\n", + "epoch [ 700] L = 38.613600, acc = 0.850000\n", + "epoch [ 701] L = 38.613343, acc = 0.850000\n", + "epoch [ 702] L = 38.613085, acc = 0.850000\n", + "epoch [ 703] L = 38.612829, acc = 0.850000\n", + "epoch [ 704] L = 38.612573, acc = 0.850000\n", + "epoch [ 705] L = 38.612317, acc = 0.850000\n", + "epoch [ 706] L = 38.612062, acc = 0.850000\n", + "epoch [ 707] L = 38.611808, acc = 0.850000\n", + "epoch [ 708] L = 38.611554, acc = 0.850000\n", + "epoch [ 709] L = 38.611300, acc = 0.850000\n", + "epoch [ 710] L = 38.611047, acc = 0.850000\n", + "epoch [ 711] L = 38.610795, acc = 0.850000\n", + "epoch [ 712] L = 38.610543, acc = 0.850000\n", + "epoch [ 713] L = 38.610291, acc = 0.850000\n", + "epoch [ 714] L = 38.610041, acc = 0.850000\n", + "epoch [ 715] L = 38.609790, acc = 0.850000\n", + "epoch [ 716] L = 38.609540, acc = 0.850000\n", + "epoch [ 717] L = 38.609291, acc = 0.850000\n", + "epoch [ 718] L = 38.609042, acc = 0.850000\n", + "epoch [ 719] L = 38.608794, acc = 0.850000\n", + "epoch [ 720] L = 38.608546, acc = 0.850000\n", + "epoch [ 721] L = 38.608299, acc = 0.850000\n", + "epoch [ 722] L = 38.608052, acc = 0.850000\n", + "epoch [ 723] L = 38.607805, acc = 0.850000\n", + "epoch [ 724] L = 38.607559, acc = 0.850000\n", + "epoch [ 725] L = 38.607314, acc = 0.850000\n", + "epoch [ 726] L = 38.607069, acc = 0.850000\n", + "epoch [ 727] L = 38.606825, acc = 0.850000\n", + "epoch [ 728] L = 38.606581, acc = 0.850000\n", + "epoch [ 729] L = 38.606337, acc = 0.850000\n", + "epoch [ 730] L = 38.606094, acc = 0.850000\n", + "epoch [ 731] L = 38.605852, acc = 0.850000\n", + "epoch [ 732] L = 38.605610, acc = 0.850000\n", + "epoch [ 733] L = 38.605368, acc = 0.850000\n", + "epoch [ 734] L = 38.605127, acc = 0.850000\n", + "epoch [ 735] L = 38.604887, acc = 0.850000\n", + "epoch [ 736] L = 38.604647, acc = 0.850000\n", + "epoch [ 737] L = 38.604407, acc = 0.850000\n", + "epoch [ 738] L = 38.604168, acc = 0.850000\n", + "epoch [ 739] L = 38.603929, acc = 0.850000\n", + "epoch [ 740] L = 38.603691, acc = 0.850000\n", + "epoch [ 741] L = 38.603453, acc = 0.850000\n", + "epoch [ 742] L = 38.603216, acc = 0.850000\n", + "epoch [ 743] L = 38.602979, acc = 0.850000\n", + "epoch [ 744] L = 38.602742, acc = 0.850000\n", + "epoch [ 745] L = 38.602506, acc = 0.850000\n", + "epoch [ 746] L = 38.602271, acc = 0.850000\n", + "epoch [ 747] L = 38.602036, acc = 0.850000\n", + "epoch [ 748] L = 38.601801, acc = 0.850000\n", + "epoch [ 749] L = 38.601567, acc = 0.850000\n", + "epoch [ 750] L = 38.601333, acc = 0.850000\n", + "epoch [ 751] L = 38.601100, acc = 0.850000\n", + "epoch [ 752] L = 38.600867, acc = 0.850000\n", + "epoch [ 753] L = 38.600635, acc = 0.850000\n", + "epoch [ 754] L = 38.600403, acc = 0.850000\n", + "epoch [ 755] L = 38.600171, acc = 0.850000\n", + "epoch [ 756] L = 38.599940, acc = 0.850000\n", + "epoch [ 757] L = 38.599709, acc = 0.850000\n", + "epoch [ 758] L = 38.599479, acc = 0.850000\n", + "epoch [ 759] L = 38.599249, acc = 0.850000\n", + "epoch [ 760] L = 38.599020, acc = 0.850000\n", + "epoch [ 761] L = 38.598791, acc = 0.850000\n", + "epoch [ 762] L = 38.598562, acc = 0.850000\n", + "epoch [ 763] L = 38.598334, acc = 0.850000\n", + "epoch [ 764] L = 38.598107, acc = 0.850000\n", + "epoch [ 765] L = 38.597879, acc = 0.850000\n", + "epoch [ 766] L = 38.597653, acc = 0.850000\n", + "epoch [ 767] L = 38.597426, acc = 0.850000\n", + "epoch [ 768] L = 38.597200, acc = 0.850000\n", + "epoch [ 769] L = 38.596975, acc = 0.850000\n", + "epoch [ 770] L = 38.596749, acc = 0.850000\n", + "epoch [ 771] L = 38.596525, acc = 0.850000\n", + "epoch [ 772] L = 38.596300, acc = 0.850000\n", + "epoch [ 773] L = 38.596076, acc = 0.850000\n", + "epoch [ 774] L = 38.595853, acc = 0.850000\n", + "epoch [ 775] L = 38.595630, acc = 0.850000\n", + "epoch [ 776] L = 38.595407, acc = 0.850000\n", + "epoch [ 777] L = 38.595185, acc = 0.850000\n", + "epoch [ 778] L = 38.594963, acc = 0.850000\n", + "epoch [ 779] L = 38.594741, acc = 0.850000\n", + "epoch [ 780] L = 38.594520, acc = 0.850000\n", + "epoch [ 781] L = 38.594299, acc = 0.850000\n", + "epoch [ 782] L = 38.594079, acc = 0.850000\n", + "epoch [ 783] L = 38.593859, acc = 0.850000\n", + "epoch [ 784] L = 38.593640, acc = 0.850000\n", + "epoch [ 785] L = 38.593421, acc = 0.850000\n", + "epoch [ 786] L = 38.593202, acc = 0.850000\n", + "epoch [ 787] L = 38.592984, acc = 0.850000\n", + "epoch [ 788] L = 38.592766, acc = 0.850000\n", + "epoch [ 789] L = 38.592548, acc = 0.850000\n", + "epoch [ 790] L = 38.592331, acc = 0.850000\n", + "epoch [ 791] L = 38.592114, acc = 0.850000\n", + "epoch [ 792] L = 38.591898, acc = 0.850000\n", + "epoch [ 793] L = 38.591682, acc = 0.850000\n", + "epoch [ 794] L = 38.591466, acc = 0.850000\n", + "epoch [ 795] L = 38.591251, acc = 0.850000\n", + "epoch [ 796] L = 38.591036, acc = 0.850000\n", + "epoch [ 797] L = 38.590821, acc = 0.850000\n", + "epoch [ 798] L = 38.590607, acc = 0.850000\n", + "epoch [ 799] L = 38.590394, acc = 0.850000\n", + "epoch [ 800] L = 38.590180, acc = 0.850000\n", + "epoch [ 801] L = 38.589967, acc = 0.850000\n", + "epoch [ 802] L = 38.589755, acc = 0.850000\n", + "epoch [ 803] L = 38.589542, acc = 0.850000\n", + "epoch [ 804] L = 38.589330, acc = 0.850000\n", + "epoch [ 805] L = 38.589119, acc = 0.850000\n", + "epoch [ 806] L = 38.588908, acc = 0.850000\n", + "epoch [ 807] L = 38.588697, acc = 0.850000\n", + "epoch [ 808] L = 38.588487, acc = 0.850000\n", + "epoch [ 809] L = 38.588277, acc = 0.850000\n", + "epoch [ 810] L = 38.588067, acc = 0.850000\n", + "epoch [ 811] L = 38.587858, acc = 0.850000\n", + "epoch [ 812] L = 38.587649, acc = 0.850000\n", + "epoch [ 813] L = 38.587440, acc = 0.850000\n", + "epoch [ 814] L = 38.587232, acc = 0.850000\n", + "epoch [ 815] L = 38.587024, acc = 0.850000\n", + "epoch [ 816] L = 38.586817, acc = 0.850000\n", + "epoch [ 817] L = 38.586609, acc = 0.850000\n", + "epoch [ 818] L = 38.586403, acc = 0.850000\n", + "epoch [ 819] L = 38.586196, acc = 0.850000\n", + "epoch [ 820] L = 38.585990, acc = 0.850000\n", + "epoch [ 821] L = 38.585784, acc = 0.850000\n", + "epoch [ 822] L = 38.585579, acc = 0.850000\n", + "epoch [ 823] L = 38.585374, acc = 0.850000\n", + "epoch [ 824] L = 38.585169, acc = 0.850000\n", + "epoch [ 825] L = 38.584965, acc = 0.850000\n", + "epoch [ 826] L = 38.584761, acc = 0.850000\n", + "epoch [ 827] L = 38.584557, acc = 0.850000\n", + "epoch [ 828] L = 38.584354, acc = 0.850000\n", + "epoch [ 829] L = 38.584151, acc = 0.850000\n", + "epoch [ 830] L = 38.583948, acc = 0.850000\n", + "epoch [ 831] L = 38.583746, acc = 0.850000\n", + "epoch [ 832] L = 38.583544, acc = 0.850000\n", + "epoch [ 833] L = 38.583342, acc = 0.850000\n", + "epoch [ 834] L = 38.583141, acc = 0.850000\n", + "epoch [ 835] L = 38.582940, acc = 0.850000\n", + "epoch [ 836] L = 38.582740, acc = 0.850000\n", + "epoch [ 837] L = 38.582539, acc = 0.850000\n", + "epoch [ 838] L = 38.582339, acc = 0.850000\n", + "epoch [ 839] L = 38.582140, acc = 0.850000\n", + "epoch [ 840] L = 38.581941, acc = 0.850000\n", + "epoch [ 841] L = 38.581742, acc = 0.850000\n", + "epoch [ 842] L = 38.581543, acc = 0.850000\n", + "epoch [ 843] L = 38.581345, acc = 0.850000\n", + "epoch [ 844] L = 38.581147, acc = 0.850000\n", + "epoch [ 845] L = 38.580949, acc = 0.850000\n", + "epoch [ 846] L = 38.580752, acc = 0.850000\n", + "epoch [ 847] L = 38.580555, acc = 0.850000\n", + "epoch [ 848] L = 38.580359, acc = 0.850000\n", + "epoch [ 849] L = 38.580162, acc = 0.850000\n", + "epoch [ 850] L = 38.579966, acc = 0.850000\n", + "epoch [ 851] L = 38.579771, acc = 0.850000\n", + "epoch [ 852] L = 38.579575, acc = 0.850000\n", + "epoch [ 853] L = 38.579380, acc = 0.850000\n", + "epoch [ 854] L = 38.579186, acc = 0.850000\n", + "epoch [ 855] L = 38.578991, acc = 0.850000\n", + "epoch [ 856] L = 38.578797, acc = 0.850000\n", + "epoch [ 857] L = 38.578603, acc = 0.850000\n", + "epoch [ 858] L = 38.578410, acc = 0.850000\n", + "epoch [ 859] L = 38.578217, acc = 0.850000\n", + "epoch [ 860] L = 38.578024, acc = 0.850000\n", + "epoch [ 861] L = 38.577832, acc = 0.850000\n", + "epoch [ 862] L = 38.577639, acc = 0.850000\n", + "epoch [ 863] L = 38.577448, acc = 0.850000\n", + "epoch [ 864] L = 38.577256, acc = 0.850000\n", + "epoch [ 865] L = 38.577065, acc = 0.850000\n", + "epoch [ 866] L = 38.576874, acc = 0.850000\n", + "epoch [ 867] L = 38.576683, acc = 0.850000\n", + "epoch [ 868] L = 38.576493, acc = 0.850000\n", + "epoch [ 869] L = 38.576303, acc = 0.850000\n", + "epoch [ 870] L = 38.576113, acc = 0.850000\n", + "epoch [ 871] L = 38.575924, acc = 0.850000\n", + "epoch [ 872] L = 38.575735, acc = 0.850000\n", + "epoch [ 873] L = 38.575546, acc = 0.850000\n", + "epoch [ 874] L = 38.575358, acc = 0.850000\n", + "epoch [ 875] L = 38.575169, acc = 0.850000\n", + "epoch [ 876] L = 38.574981, acc = 0.850000\n", + "epoch [ 877] L = 38.574794, acc = 0.850000\n", + "epoch [ 878] L = 38.574607, acc = 0.850000\n", + "epoch [ 879] L = 38.574420, acc = 0.850000\n", + "epoch [ 880] L = 38.574233, acc = 0.850000\n", + "epoch [ 881] L = 38.574047, acc = 0.850000\n", + "epoch [ 882] L = 38.573861, acc = 0.850000\n", + "epoch [ 883] L = 38.573675, acc = 0.850000\n", + "epoch [ 884] L = 38.573489, acc = 0.850000\n", + "epoch [ 885] L = 38.573304, acc = 0.850000\n", + "epoch [ 886] L = 38.573119, acc = 0.850000\n", + "epoch [ 887] L = 38.572934, acc = 0.850000\n", + "epoch [ 888] L = 38.572750, acc = 0.850000\n", + "epoch [ 889] L = 38.572566, acc = 0.850000\n", + "epoch [ 890] L = 38.572382, acc = 0.850000\n", + "epoch [ 891] L = 38.572199, acc = 0.850000\n", + "epoch [ 892] L = 38.572016, acc = 0.850000\n", + "epoch [ 893] L = 38.571833, acc = 0.850000\n", + "epoch [ 894] L = 38.571650, acc = 0.850000\n", + "epoch [ 895] L = 38.571468, acc = 0.850000\n", + "epoch [ 896] L = 38.571286, acc = 0.850000\n", + "epoch [ 897] L = 38.571104, acc = 0.850000\n", + "epoch [ 898] L = 38.570923, acc = 0.850000\n", + "epoch [ 899] L = 38.570742, acc = 0.850000\n", + "epoch [ 900] L = 38.570561, acc = 0.850000\n", + "epoch [ 901] L = 38.570380, acc = 0.850000\n", + "epoch [ 902] L = 38.570200, acc = 0.850000\n", + "epoch [ 903] L = 38.570020, acc = 0.850000\n", + "epoch [ 904] L = 38.569840, acc = 0.850000\n", + "epoch [ 905] L = 38.569660, acc = 0.850000\n", + "epoch [ 906] L = 38.569481, acc = 0.850000\n", + "epoch [ 907] L = 38.569302, acc = 0.850000\n", + "epoch [ 908] L = 38.569124, acc = 0.850000\n", + "epoch [ 909] L = 38.568945, acc = 0.850000\n", + "epoch [ 910] L = 38.568767, acc = 0.850000\n", + "epoch [ 911] L = 38.568589, acc = 0.850000\n", + "epoch [ 912] L = 38.568412, acc = 0.850000\n", + "epoch [ 913] L = 38.568234, acc = 0.850000\n", + "epoch [ 914] L = 38.568057, acc = 0.850000\n", + "epoch [ 915] L = 38.567881, acc = 0.850000\n", + "epoch [ 916] L = 38.567704, acc = 0.850000\n", + "epoch [ 917] L = 38.567528, acc = 0.850000\n", + "epoch [ 918] L = 38.567352, acc = 0.850000\n", + "epoch [ 919] L = 38.567176, acc = 0.850000\n", + "epoch [ 920] L = 38.567001, acc = 0.850000\n", + "epoch [ 921] L = 38.566826, acc = 0.850000\n", + "epoch [ 922] L = 38.566651, acc = 0.850000\n", + "epoch [ 923] L = 38.566476, acc = 0.850000\n", + "epoch [ 924] L = 38.566302, acc = 0.850000\n", + "epoch [ 925] L = 38.566128, acc = 0.850000\n", + "epoch [ 926] L = 38.565954, acc = 0.850000\n", + "epoch [ 927] L = 38.565780, acc = 0.850000\n", + "epoch [ 928] L = 38.565607, acc = 0.850000\n", + "epoch [ 929] L = 38.565434, acc = 0.850000\n", + "epoch [ 930] L = 38.565261, acc = 0.850000\n", + "epoch [ 931] L = 38.565089, acc = 0.850000\n", + "epoch [ 932] L = 38.564917, acc = 0.850000\n", + "epoch [ 933] L = 38.564745, acc = 0.850000\n", + "epoch [ 934] L = 38.564573, acc = 0.850000\n", + "epoch [ 935] L = 38.564401, acc = 0.850000\n", + "epoch [ 936] L = 38.564230, acc = 0.850000\n", + "epoch [ 937] L = 38.564059, acc = 0.850000\n", + "epoch [ 938] L = 38.563888, acc = 0.850000\n", + "epoch [ 939] L = 38.563718, acc = 0.850000\n", + "epoch [ 940] L = 38.563548, acc = 0.850000\n", + "epoch [ 941] L = 38.563378, acc = 0.850000\n", + "epoch [ 942] L = 38.563208, acc = 0.850000\n", + "epoch [ 943] L = 38.563039, acc = 0.850000\n", + "epoch [ 944] L = 38.562869, acc = 0.850000\n", + "epoch [ 945] L = 38.562701, acc = 0.850000\n", + "epoch [ 946] L = 38.562532, acc = 0.850000\n", + "epoch [ 947] L = 38.562363, acc = 0.850000\n", + "epoch [ 948] L = 38.562195, acc = 0.850000\n", + "epoch [ 949] L = 38.562027, acc = 0.850000\n", + "epoch [ 950] L = 38.561860, acc = 0.850000\n", + "epoch [ 951] L = 38.561692, acc = 0.850000\n", + "epoch [ 952] L = 38.561525, acc = 0.850000\n", + "epoch [ 953] L = 38.561358, acc = 0.850000\n", + "epoch [ 954] L = 38.561191, acc = 0.850000\n", + "epoch [ 955] L = 38.561025, acc = 0.850000\n", + "epoch [ 956] L = 38.560859, acc = 0.850000\n", + "epoch [ 957] L = 38.560693, acc = 0.850000\n", + "epoch [ 958] L = 38.560527, acc = 0.850000\n", + "epoch [ 959] L = 38.560361, acc = 0.850000\n", + "epoch [ 960] L = 38.560196, acc = 0.850000\n", + "epoch [ 961] L = 38.560031, acc = 0.850000\n", + "epoch [ 962] L = 38.559866, acc = 0.850000\n", + "epoch [ 963] L = 38.559702, acc = 0.850000\n", + "epoch [ 964] L = 38.559537, acc = 0.850000\n", + "epoch [ 965] L = 38.559373, acc = 0.850000\n", + "epoch [ 966] L = 38.559210, acc = 0.850000\n", + "epoch [ 967] L = 38.559046, acc = 0.850000\n", + "epoch [ 968] L = 38.558883, acc = 0.850000\n", + "epoch [ 969] L = 38.558719, acc = 0.850000\n", + "epoch [ 970] L = 38.558557, acc = 0.850000\n", + "epoch [ 971] L = 38.558394, acc = 0.850000\n", + "epoch [ 972] L = 38.558232, acc = 0.850000\n", + "epoch [ 973] L = 38.558069, acc = 0.850000\n", + "epoch [ 974] L = 38.557907, acc = 0.850000\n", + "epoch [ 975] L = 38.557746, acc = 0.850000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch [ 976] L = 38.557584, acc = 0.850000\n", + "epoch [ 977] L = 38.557423, acc = 0.850000\n", + "epoch [ 978] L = 38.557262, acc = 0.850000\n", + "epoch [ 979] L = 38.557101, acc = 0.850000\n", + "epoch [ 980] L = 38.556941, acc = 0.850000\n", + "epoch [ 981] L = 38.556780, acc = 0.850000\n", + "epoch [ 982] L = 38.556620, acc = 0.850000\n", + "epoch [ 983] L = 38.556460, acc = 0.850000\n", + "epoch [ 984] L = 38.556301, acc = 0.850000\n", + "epoch [ 985] L = 38.556141, acc = 0.850000\n", + "epoch [ 986] L = 38.555982, acc = 0.850000\n", + "epoch [ 987] L = 38.555823, acc = 0.850000\n", + "epoch [ 988] L = 38.555664, acc = 0.850000\n", + "epoch [ 989] L = 38.555506, acc = 0.850000\n", + "epoch [ 990] L = 38.555347, acc = 0.850000\n", + "epoch [ 991] L = 38.555189, acc = 0.850000\n", + "epoch [ 992] L = 38.555032, acc = 0.850000\n", + "epoch [ 993] L = 38.554874, acc = 0.850000\n", + "epoch [ 994] L = 38.554717, acc = 0.850000\n", + "epoch [ 995] L = 38.554559, acc = 0.850000\n", + "epoch [ 996] L = 38.554402, acc = 0.850000\n", + "epoch [ 997] L = 38.554246, acc = 0.850000\n", + "epoch [ 998] L = 38.554089, acc = 0.850000\n", + "epoch [ 999] L = 38.553933, acc = 0.850000\n", + "epoch [1000] L = 38.553777, acc = 0.850000\n", + "epoch [1001] L = 38.553621, acc = 0.850000\n", + "epoch [1002] L = 38.553465, acc = 0.850000\n", + "epoch [1003] L = 38.553310, acc = 0.850000\n", + "epoch [1004] L = 38.553154, acc = 0.850000\n", + "epoch [1005] L = 38.552999, acc = 0.850000\n", + "epoch [1006] L = 38.552845, acc = 0.850000\n", + "epoch [1007] L = 38.552690, acc = 0.850000\n", + "epoch [1008] L = 38.552536, acc = 0.850000\n", + "epoch [1009] L = 38.552382, acc = 0.850000\n", + "epoch [1010] L = 38.552228, acc = 0.850000\n", + "epoch [1011] L = 38.552074, acc = 0.850000\n", + "epoch [1012] L = 38.551920, acc = 0.850000\n", + "epoch [1013] L = 38.551767, acc = 0.850000\n", + "epoch [1014] L = 38.551614, acc = 0.850000\n", + "epoch [1015] L = 38.551461, acc = 0.850000\n", + "epoch [1016] L = 38.551308, acc = 0.850000\n", + "epoch [1017] L = 38.551156, acc = 0.850000\n", + "epoch [1018] L = 38.551004, acc = 0.850000\n", + "epoch [1019] L = 38.550852, acc = 0.850000\n", + "epoch [1020] L = 38.550700, acc = 0.850000\n", + "epoch [1021] L = 38.550548, acc = 0.850000\n", + "epoch [1022] L = 38.550397, acc = 0.850000\n", + "epoch [1023] L = 38.550245, acc = 0.850000\n", + "epoch [1024] L = 38.550094, acc = 0.850000\n", + "epoch [1025] L = 38.549944, acc = 0.850000\n", + "epoch [1026] L = 38.549793, acc = 0.850000\n", + "epoch [1027] L = 38.549642, acc = 0.850000\n", + "epoch [1028] L = 38.549492, acc = 0.850000\n", + "epoch [1029] L = 38.549342, acc = 0.850000\n", + "epoch [1030] L = 38.549192, acc = 0.850000\n", + "epoch [1031] L = 38.549043, acc = 0.850000\n", + "epoch [1032] L = 38.548893, acc = 0.850000\n", + "epoch [1033] L = 38.548744, acc = 0.850000\n", + "epoch [1034] L = 38.548595, acc = 0.850000\n", + "epoch [1035] L = 38.548446, acc = 0.850000\n", + "epoch [1036] L = 38.548298, acc = 0.850000\n", + "epoch [1037] L = 38.548149, acc = 0.850000\n", + "epoch [1038] L = 38.548001, acc = 0.850000\n", + "epoch [1039] L = 38.547853, acc = 0.850000\n", + "epoch [1040] L = 38.547705, acc = 0.850000\n", + "epoch [1041] L = 38.547558, acc = 0.850000\n", + "epoch [1042] L = 38.547410, acc = 0.850000\n", + "epoch [1043] L = 38.547263, acc = 0.850000\n", + "epoch [1044] L = 38.547116, acc = 0.850000\n", + "epoch [1045] L = 38.546969, acc = 0.850000\n", + "epoch [1046] L = 38.546823, acc = 0.850000\n", + "epoch [1047] L = 38.546676, acc = 0.850000\n", + "epoch [1048] L = 38.546530, acc = 0.850000\n", + "epoch [1049] L = 38.546384, acc = 0.845000\n", + "epoch [1050] L = 38.546238, acc = 0.845000\n", + "epoch [1051] L = 38.546092, acc = 0.845000\n", + "epoch [1052] L = 38.545947, acc = 0.845000\n", + "epoch [1053] L = 38.545802, acc = 0.845000\n", + "epoch [1054] L = 38.545656, acc = 0.845000\n", + "epoch [1055] L = 38.545512, acc = 0.845000\n", + "epoch [1056] L = 38.545367, acc = 0.845000\n", + "epoch [1057] L = 38.545222, acc = 0.845000\n", + "epoch [1058] L = 38.545078, acc = 0.845000\n", + "epoch [1059] L = 38.544934, acc = 0.845000\n", + "epoch [1060] L = 38.544790, acc = 0.845000\n", + "epoch [1061] L = 38.544646, acc = 0.845000\n", + "epoch [1062] L = 38.544502, acc = 0.845000\n", + "epoch [1063] L = 38.544359, acc = 0.845000\n", + "epoch [1064] L = 38.544216, acc = 0.845000\n", + "epoch [1065] L = 38.544073, acc = 0.845000\n", + "epoch [1066] L = 38.543930, acc = 0.845000\n", + "epoch [1067] L = 38.543787, acc = 0.845000\n", + "epoch [1068] L = 38.543645, acc = 0.845000\n", + "epoch [1069] L = 38.543502, acc = 0.845000\n", + "epoch [1070] L = 38.543360, acc = 0.845000\n", + "epoch [1071] L = 38.543218, acc = 0.845000\n", + "epoch [1072] L = 38.543077, acc = 0.845000\n", + "epoch [1073] L = 38.542935, acc = 0.845000\n", + "epoch [1074] L = 38.542794, acc = 0.845000\n", + "epoch [1075] L = 38.542652, acc = 0.845000\n", + "epoch [1076] L = 38.542511, acc = 0.845000\n", + "epoch [1077] L = 38.542370, acc = 0.845000\n", + "epoch [1078] L = 38.542230, acc = 0.845000\n", + "epoch [1079] L = 38.542089, acc = 0.845000\n", + "epoch [1080] L = 38.541949, acc = 0.845000\n", + "epoch [1081] L = 38.541809, acc = 0.845000\n", + "epoch [1082] L = 38.541669, acc = 0.845000\n", + "epoch [1083] L = 38.541529, acc = 0.845000\n", + "epoch [1084] L = 38.541389, acc = 0.845000\n", + "epoch [1085] L = 38.541250, acc = 0.845000\n", + "epoch [1086] L = 38.541111, acc = 0.845000\n", + "epoch [1087] L = 38.540972, acc = 0.845000\n", + "epoch [1088] L = 38.540833, acc = 0.845000\n", + "epoch [1089] L = 38.540694, acc = 0.845000\n", + "epoch [1090] L = 38.540555, acc = 0.845000\n", + "epoch [1091] L = 38.540417, acc = 0.845000\n", + "epoch [1092] L = 38.540279, acc = 0.845000\n", + "epoch [1093] L = 38.540141, acc = 0.845000\n", + "epoch [1094] L = 38.540003, acc = 0.845000\n", + "epoch [1095] L = 38.539865, acc = 0.845000\n", + "epoch [1096] L = 38.539728, acc = 0.845000\n", + "epoch [1097] L = 38.539590, acc = 0.845000\n", + "epoch [1098] L = 38.539453, acc = 0.845000\n", + "epoch [1099] L = 38.539316, acc = 0.845000\n", + "epoch [1100] L = 38.539179, acc = 0.845000\n", + "epoch [1101] L = 38.539043, acc = 0.845000\n", + "epoch [1102] L = 38.538906, acc = 0.845000\n", + "epoch [1103] L = 38.538770, acc = 0.845000\n", + "epoch [1104] L = 38.538634, acc = 0.845000\n", + "epoch [1105] L = 38.538498, acc = 0.845000\n", + "epoch [1106] L = 38.538362, acc = 0.845000\n", + "epoch [1107] L = 38.538226, acc = 0.845000\n", + "epoch [1108] L = 38.538090, acc = 0.845000\n", + "epoch [1109] L = 38.537955, acc = 0.845000\n", + "epoch [1110] L = 38.537820, acc = 0.845000\n", + "epoch [1111] L = 38.537685, acc = 0.845000\n", + "epoch [1112] L = 38.537550, acc = 0.845000\n", + "epoch [1113] L = 38.537415, acc = 0.845000\n", + "epoch [1114] L = 38.537281, acc = 0.845000\n", + "epoch [1115] L = 38.537147, acc = 0.845000\n", + "epoch [1116] L = 38.537012, acc = 0.845000\n", + "epoch [1117] L = 38.536878, acc = 0.845000\n", + "epoch [1118] L = 38.536744, acc = 0.845000\n", + "epoch [1119] L = 38.536611, acc = 0.845000\n", + "epoch [1120] L = 38.536477, acc = 0.845000\n", + "epoch [1121] L = 38.536344, acc = 0.845000\n", + "epoch [1122] L = 38.536211, acc = 0.845000\n", + "epoch [1123] L = 38.536078, acc = 0.845000\n", + "epoch [1124] L = 38.535945, acc = 0.845000\n", + "epoch [1125] L = 38.535812, acc = 0.845000\n", + "epoch [1126] L = 38.535679, acc = 0.845000\n", + "epoch [1127] L = 38.535547, acc = 0.845000\n", + "epoch [1128] L = 38.535415, acc = 0.845000\n", + "epoch [1129] L = 38.535283, acc = 0.845000\n", + "epoch [1130] L = 38.535151, acc = 0.845000\n", + "epoch [1131] L = 38.535019, acc = 0.845000\n", + "epoch [1132] L = 38.534887, acc = 0.845000\n", + "epoch [1133] L = 38.534756, acc = 0.845000\n", + "epoch [1134] L = 38.534624, acc = 0.845000\n", + "epoch [1135] L = 38.534493, acc = 0.845000\n", + "epoch [1136] L = 38.534362, acc = 0.845000\n", + "epoch [1137] L = 38.534231, acc = 0.845000\n", + "epoch [1138] L = 38.534101, acc = 0.845000\n", + "epoch [1139] L = 38.533970, acc = 0.845000\n", + "epoch [1140] L = 38.533840, acc = 0.845000\n", + "epoch [1141] L = 38.533709, acc = 0.845000\n", + "epoch [1142] L = 38.533579, acc = 0.845000\n", + "epoch [1143] L = 38.533449, acc = 0.845000\n", + "epoch [1144] L = 38.533320, acc = 0.845000\n", + "epoch [1145] L = 38.533190, acc = 0.845000\n", + "epoch [1146] L = 38.533060, acc = 0.845000\n", + "epoch [1147] L = 38.532931, acc = 0.845000\n", + "epoch [1148] L = 38.532802, acc = 0.845000\n", + "epoch [1149] L = 38.532673, acc = 0.845000\n", + "epoch [1150] L = 38.532544, acc = 0.845000\n", + "epoch [1151] L = 38.532415, acc = 0.845000\n", + "epoch [1152] L = 38.532287, acc = 0.845000\n", + "epoch [1153] L = 38.532158, acc = 0.845000\n", + "epoch [1154] L = 38.532030, acc = 0.845000\n", + "epoch [1155] L = 38.531902, acc = 0.845000\n", + "epoch [1156] L = 38.531774, acc = 0.845000\n", + "epoch [1157] L = 38.531646, acc = 0.845000\n", + "epoch [1158] L = 38.531518, acc = 0.845000\n", + "epoch [1159] L = 38.531391, acc = 0.845000\n", + "epoch [1160] L = 38.531263, acc = 0.845000\n", + "epoch [1161] L = 38.531136, acc = 0.845000\n", + "epoch [1162] L = 38.531009, acc = 0.845000\n", + "epoch [1163] L = 38.530882, acc = 0.845000\n", + "epoch [1164] L = 38.530755, acc = 0.845000\n", + "epoch [1165] L = 38.530628, acc = 0.845000\n", + "epoch [1166] L = 38.530502, acc = 0.845000\n", + "epoch [1167] L = 38.530376, acc = 0.845000\n", + "epoch [1168] L = 38.530249, acc = 0.845000\n", + "epoch [1169] L = 38.530123, acc = 0.845000\n", + "epoch [1170] L = 38.529997, acc = 0.845000\n", + "epoch [1171] L = 38.529871, acc = 0.845000\n", + "epoch [1172] L = 38.529746, acc = 0.845000\n", + "epoch [1173] L = 38.529620, acc = 0.845000\n", + "epoch [1174] L = 38.529495, acc = 0.845000\n", + "epoch [1175] L = 38.529369, acc = 0.845000\n", + "epoch [1176] L = 38.529244, acc = 0.845000\n", + "epoch [1177] L = 38.529119, acc = 0.845000\n", + "epoch [1178] L = 38.528995, acc = 0.845000\n", + "epoch [1179] L = 38.528870, acc = 0.845000\n", + "epoch [1180] L = 38.528745, acc = 0.845000\n", + "epoch [1181] L = 38.528621, acc = 0.845000\n", + "epoch [1182] L = 38.528497, acc = 0.845000\n", + "epoch [1183] L = 38.528372, acc = 0.845000\n", + "epoch [1184] L = 38.528248, acc = 0.845000\n", + "epoch [1185] L = 38.528125, acc = 0.845000\n", + "epoch [1186] L = 38.528001, acc = 0.845000\n", + "epoch [1187] L = 38.527877, acc = 0.845000\n", + "epoch [1188] L = 38.527754, acc = 0.845000\n", + "epoch [1189] L = 38.527630, acc = 0.845000\n", + "epoch [1190] L = 38.527507, acc = 0.845000\n", + "epoch [1191] L = 38.527384, acc = 0.845000\n", + "epoch [1192] L = 38.527261, acc = 0.845000\n", + "epoch [1193] L = 38.527139, acc = 0.845000\n", + "epoch [1194] L = 38.527016, acc = 0.845000\n", + "epoch [1195] L = 38.526893, acc = 0.845000\n", + "epoch [1196] L = 38.526771, acc = 0.845000\n", + "epoch [1197] L = 38.526649, acc = 0.845000\n", + "epoch [1198] L = 38.526527, acc = 0.845000\n", + "epoch [1199] L = 38.526405, acc = 0.845000\n", + "epoch [1200] L = 38.526283, acc = 0.845000\n", + "epoch [1201] L = 38.526161, acc = 0.845000\n", + "epoch [1202] L = 38.526040, acc = 0.845000\n", + "epoch [1203] L = 38.525918, acc = 0.845000\n", + "epoch [1204] L = 38.525797, acc = 0.845000\n", + "epoch [1205] L = 38.525676, acc = 0.845000\n", + "epoch [1206] L = 38.525555, acc = 0.845000\n", + "epoch [1207] L = 38.525434, acc = 0.845000\n", + "epoch [1208] L = 38.525313, acc = 0.845000\n", + "epoch [1209] L = 38.525192, acc = 0.845000\n", + "epoch [1210] L = 38.525072, acc = 0.845000\n", + "epoch [1211] L = 38.524951, acc = 0.845000\n", + "epoch [1212] L = 38.524831, acc = 0.845000\n", + "epoch [1213] L = 38.524711, acc = 0.845000\n", + "epoch [1214] L = 38.524591, acc = 0.845000\n", + "epoch [1215] L = 38.524471, acc = 0.845000\n", + "epoch [1216] L = 38.524351, acc = 0.845000\n", + "epoch [1217] L = 38.524231, acc = 0.845000\n", + "epoch [1218] L = 38.524112, acc = 0.845000\n", + "epoch [1219] L = 38.523993, acc = 0.845000\n", + "epoch [1220] L = 38.523873, acc = 0.845000\n", + "epoch [1221] L = 38.523754, acc = 0.845000\n", + "epoch [1222] L = 38.523635, acc = 0.845000\n", + "epoch [1223] L = 38.523516, acc = 0.845000\n", + "epoch [1224] L = 38.523397, acc = 0.845000\n", + "epoch [1225] L = 38.523279, acc = 0.845000\n", + "epoch [1226] L = 38.523160, acc = 0.845000\n", + "epoch [1227] L = 38.523042, acc = 0.845000\n", + "epoch [1228] L = 38.522924, acc = 0.845000\n", + "epoch [1229] L = 38.522806, acc = 0.845000\n", + "epoch [1230] L = 38.522688, acc = 0.845000\n", + "epoch [1231] L = 38.522570, acc = 0.845000\n", + "epoch [1232] L = 38.522452, acc = 0.845000\n", + "epoch [1233] L = 38.522334, acc = 0.845000\n", + "epoch [1234] L = 38.522217, acc = 0.845000\n", + "epoch [1235] L = 38.522099, acc = 0.845000\n", + "epoch [1236] L = 38.521982, acc = 0.845000\n", + "epoch [1237] L = 38.521865, acc = 0.845000\n", + "epoch [1238] L = 38.521748, acc = 0.845000\n", + "epoch [1239] L = 38.521631, acc = 0.845000\n", + "epoch [1240] L = 38.521514, acc = 0.845000\n", + "epoch [1241] L = 38.521397, acc = 0.845000\n", + "epoch [1242] L = 38.521281, acc = 0.845000\n", + "epoch [1243] L = 38.521164, acc = 0.845000\n", + "epoch [1244] L = 38.521048, acc = 0.845000\n", + "epoch [1245] L = 38.520932, acc = 0.845000\n", + "epoch [1246] L = 38.520816, acc = 0.845000\n", + "epoch [1247] L = 38.520700, acc = 0.845000\n", + "epoch [1248] L = 38.520584, acc = 0.845000\n", + "epoch [1249] L = 38.520468, acc = 0.845000\n", + "epoch [1250] L = 38.520353, acc = 0.845000\n", + "epoch [1251] L = 38.520237, acc = 0.845000\n", + "epoch [1252] L = 38.520122, acc = 0.845000\n", + "epoch [1253] L = 38.520006, acc = 0.845000\n", + "epoch [1254] L = 38.519891, acc = 0.845000\n", + "epoch [1255] L = 38.519776, acc = 0.845000\n", + "epoch [1256] L = 38.519661, acc = 0.845000\n", + "epoch [1257] L = 38.519547, acc = 0.845000\n", + "epoch [1258] L = 38.519432, acc = 0.845000\n", + "epoch [1259] L = 38.519317, acc = 0.845000\n", + "epoch [1260] L = 38.519203, acc = 0.845000\n", + "epoch [1261] L = 38.519089, acc = 0.845000\n", + "epoch [1262] L = 38.518974, acc = 0.845000\n", + "epoch [1263] L = 38.518860, acc = 0.845000\n", + "epoch [1264] L = 38.518746, acc = 0.845000\n", + "epoch [1265] L = 38.518632, acc = 0.845000\n", + "epoch [1266] L = 38.518519, acc = 0.845000\n", + "epoch [1267] L = 38.518405, acc = 0.845000\n", + "epoch [1268] L = 38.518291, acc = 0.845000\n", + "epoch [1269] L = 38.518178, acc = 0.845000\n", + "epoch [1270] L = 38.518065, acc = 0.845000\n", + "epoch [1271] L = 38.517951, acc = 0.845000\n", + "epoch [1272] L = 38.517838, acc = 0.845000\n", + "epoch [1273] L = 38.517725, acc = 0.845000\n", + "epoch [1274] L = 38.517612, acc = 0.845000\n", + "epoch [1275] L = 38.517500, acc = 0.845000\n", + "epoch [1276] L = 38.517387, acc = 0.845000\n", + "epoch [1277] L = 38.517274, acc = 0.845000\n", + "epoch [1278] L = 38.517162, acc = 0.845000\n", + "epoch [1279] L = 38.517050, acc = 0.845000\n", + "epoch [1280] L = 38.516937, acc = 0.845000\n", + "epoch [1281] L = 38.516825, acc = 0.845000\n", + "epoch [1282] L = 38.516713, acc = 0.845000\n", + "epoch [1283] L = 38.516601, acc = 0.845000\n", + "epoch [1284] L = 38.516490, acc = 0.845000\n", + "epoch [1285] L = 38.516378, acc = 0.845000\n", + "epoch [1286] L = 38.516266, acc = 0.845000\n", + "epoch [1287] L = 38.516155, acc = 0.845000\n", + "epoch [1288] L = 38.516044, acc = 0.845000\n", + "epoch [1289] L = 38.515932, acc = 0.845000\n", + "epoch [1290] L = 38.515821, acc = 0.845000\n", + "epoch [1291] L = 38.515710, acc = 0.845000\n", + "epoch [1292] L = 38.515599, acc = 0.845000\n", + "epoch [1293] L = 38.515488, acc = 0.845000\n", + "epoch [1294] L = 38.515378, acc = 0.845000\n", + "epoch [1295] L = 38.515267, acc = 0.845000\n", + "epoch [1296] L = 38.515157, acc = 0.845000\n", + "epoch [1297] L = 38.515046, acc = 0.845000\n", + "epoch [1298] L = 38.514936, acc = 0.845000\n", + "epoch [1299] L = 38.514826, acc = 0.845000\n", + "epoch [1300] L = 38.514716, acc = 0.845000\n", + "epoch [1301] L = 38.514606, acc = 0.845000\n", + "epoch [1302] L = 38.514496, acc = 0.845000\n", + "epoch [1303] L = 38.514386, acc = 0.845000\n", + "epoch [1304] L = 38.514276, acc = 0.845000\n", + "epoch [1305] L = 38.514167, acc = 0.845000\n", + "epoch [1306] L = 38.514057, acc = 0.845000\n", + "epoch [1307] L = 38.513948, acc = 0.845000\n", + "epoch [1308] L = 38.513839, acc = 0.845000\n", + "epoch [1309] L = 38.513729, acc = 0.845000\n", + "epoch [1310] L = 38.513620, acc = 0.845000\n", + "epoch [1311] L = 38.513511, acc = 0.845000\n", + "epoch [1312] L = 38.513403, acc = 0.845000\n", + "epoch [1313] L = 38.513294, acc = 0.845000\n", + "epoch [1314] L = 38.513185, acc = 0.845000\n", + "epoch [1315] L = 38.513077, acc = 0.845000\n", + "epoch [1316] L = 38.512968, acc = 0.845000\n", + "epoch [1317] L = 38.512860, acc = 0.845000\n", + "epoch [1318] L = 38.512752, acc = 0.845000\n", + "epoch [1319] L = 38.512643, acc = 0.845000\n", + "epoch [1320] L = 38.512535, acc = 0.845000\n", + "epoch [1321] L = 38.512427, acc = 0.845000\n", + "epoch [1322] L = 38.512320, acc = 0.845000\n", + "epoch [1323] L = 38.512212, acc = 0.845000\n", + "epoch [1324] L = 38.512104, acc = 0.845000\n", + "epoch [1325] L = 38.511997, acc = 0.845000\n", + "epoch [1326] L = 38.511889, acc = 0.845000\n", + "epoch [1327] L = 38.511782, acc = 0.845000\n", + "epoch [1328] L = 38.511674, acc = 0.845000\n", + "epoch [1329] L = 38.511567, acc = 0.845000\n", + "epoch [1330] L = 38.511460, acc = 0.845000\n", + "epoch [1331] L = 38.511353, acc = 0.845000\n", + "epoch [1332] L = 38.511246, acc = 0.845000\n", + "epoch [1333] L = 38.511140, acc = 0.845000\n", + "epoch [1334] L = 38.511033, acc = 0.845000\n", + "epoch [1335] L = 38.510926, acc = 0.845000\n", + "epoch [1336] L = 38.510820, acc = 0.845000\n", + "epoch [1337] L = 38.510713, acc = 0.845000\n", + "epoch [1338] L = 38.510607, acc = 0.845000\n", + "epoch [1339] L = 38.510501, acc = 0.845000\n", + "epoch [1340] L = 38.510395, acc = 0.845000\n", + "epoch [1341] L = 38.510289, acc = 0.845000\n", + "epoch [1342] L = 38.510183, acc = 0.845000\n", + "epoch [1343] L = 38.510077, acc = 0.845000\n", + "epoch [1344] L = 38.509971, acc = 0.845000\n", + "epoch [1345] L = 38.509866, acc = 0.845000\n", + "epoch [1346] L = 38.509760, acc = 0.845000\n", + "epoch [1347] L = 38.509655, acc = 0.845000\n", + "epoch [1348] L = 38.509549, acc = 0.845000\n", + "epoch [1349] L = 38.509444, acc = 0.845000\n", + "epoch [1350] L = 38.509339, acc = 0.845000\n", + "epoch [1351] L = 38.509234, acc = 0.845000\n", + "epoch [1352] L = 38.509129, acc = 0.845000\n", + "epoch [1353] L = 38.509024, acc = 0.845000\n", + "epoch [1354] L = 38.508919, acc = 0.845000\n", + "epoch [1355] L = 38.508814, acc = 0.845000\n", + "epoch [1356] L = 38.508710, acc = 0.845000\n", + "epoch [1357] L = 38.508605, acc = 0.845000\n", + "epoch [1358] L = 38.508501, acc = 0.845000\n", + "epoch [1359] L = 38.508396, acc = 0.845000\n", + "epoch [1360] L = 38.508292, acc = 0.845000\n", + "epoch [1361] L = 38.508188, acc = 0.845000\n", + "epoch [1362] L = 38.508084, acc = 0.845000\n", + "epoch [1363] L = 38.507980, acc = 0.845000\n", + "epoch [1364] L = 38.507876, acc = 0.845000\n", + "epoch [1365] L = 38.507772, acc = 0.845000\n", + "epoch [1366] L = 38.507669, acc = 0.845000\n", + "epoch [1367] L = 38.507565, acc = 0.845000\n", + "epoch [1368] L = 38.507461, acc = 0.845000\n", + "epoch [1369] L = 38.507358, acc = 0.845000\n", + "epoch [1370] L = 38.507255, acc = 0.845000\n", + "epoch [1371] L = 38.507151, acc = 0.845000\n", + "epoch [1372] L = 38.507048, acc = 0.845000\n", + "epoch [1373] L = 38.506945, acc = 0.845000\n", + "epoch [1374] L = 38.506842, acc = 0.845000\n", + "epoch [1375] L = 38.506739, acc = 0.845000\n", + "epoch [1376] L = 38.506636, acc = 0.845000\n", + "epoch [1377] L = 38.506534, acc = 0.845000\n", + "epoch [1378] L = 38.506431, acc = 0.845000\n", + "epoch [1379] L = 38.506328, acc = 0.845000\n", + "epoch [1380] L = 38.506226, acc = 0.845000\n", + "epoch [1381] L = 38.506123, acc = 0.845000\n", + "epoch [1382] L = 38.506021, acc = 0.845000\n", + "epoch [1383] L = 38.505919, acc = 0.845000\n", + "epoch [1384] L = 38.505817, acc = 0.845000\n", + "epoch [1385] L = 38.505715, acc = 0.845000\n", + "epoch [1386] L = 38.505613, acc = 0.845000\n", + "epoch [1387] L = 38.505511, acc = 0.845000\n", + "epoch [1388] L = 38.505409, acc = 0.845000\n", + "epoch [1389] L = 38.505307, acc = 0.845000\n", + "epoch [1390] L = 38.505206, acc = 0.845000\n", + "epoch [1391] L = 38.505104, acc = 0.845000\n", + "epoch [1392] L = 38.505003, acc = 0.845000\n", + "epoch [1393] L = 38.504901, acc = 0.845000\n", + "epoch [1394] L = 38.504800, acc = 0.845000\n", + "epoch [1395] L = 38.504699, acc = 0.845000\n", + "epoch [1396] L = 38.504598, acc = 0.845000\n", + "epoch [1397] L = 38.504497, acc = 0.845000\n", + "epoch [1398] L = 38.504396, acc = 0.845000\n", + "epoch [1399] L = 38.504295, acc = 0.845000\n", + "epoch [1400] L = 38.504194, acc = 0.845000\n", + "epoch [1401] L = 38.504093, acc = 0.845000\n", + "epoch [1402] L = 38.503993, acc = 0.845000\n", + "epoch [1403] L = 38.503892, acc = 0.845000\n", + "epoch [1404] L = 38.503792, acc = 0.845000\n", + "epoch [1405] L = 38.503691, acc = 0.845000\n", + "epoch [1406] L = 38.503591, acc = 0.845000\n", + "epoch [1407] L = 38.503491, acc = 0.845000\n", + "epoch [1408] L = 38.503391, acc = 0.845000\n", + "epoch [1409] L = 38.503291, acc = 0.845000\n", + "epoch [1410] L = 38.503191, acc = 0.845000\n", + "epoch [1411] L = 38.503091, acc = 0.845000\n", + "epoch [1412] L = 38.502991, acc = 0.845000\n", + "epoch [1413] L = 38.502891, acc = 0.845000\n", + "epoch [1414] L = 38.502792, acc = 0.845000\n", + "epoch [1415] L = 38.502692, acc = 0.845000\n", + "epoch [1416] L = 38.502593, acc = 0.845000\n", + "epoch [1417] L = 38.502493, acc = 0.845000\n", + "epoch [1418] L = 38.502394, acc = 0.845000\n", + "epoch [1419] L = 38.502295, acc = 0.845000\n", + "epoch [1420] L = 38.502195, acc = 0.845000\n", + "epoch [1421] L = 38.502096, acc = 0.845000\n", + "epoch [1422] L = 38.501997, acc = 0.845000\n", + "epoch [1423] L = 38.501898, acc = 0.845000\n", + "epoch [1424] L = 38.501800, acc = 0.845000\n", + "epoch [1425] L = 38.501701, acc = 0.845000\n", + "epoch [1426] L = 38.501602, acc = 0.845000\n", + "epoch [1427] L = 38.501503, acc = 0.845000\n", + "epoch [1428] L = 38.501405, acc = 0.845000\n", + "epoch [1429] L = 38.501307, acc = 0.845000\n", + "epoch [1430] L = 38.501208, acc = 0.845000\n", + "epoch [1431] L = 38.501110, acc = 0.845000\n", + "epoch [1432] L = 38.501012, acc = 0.845000\n", + "epoch [1433] L = 38.500913, acc = 0.845000\n", + "epoch [1434] L = 38.500815, acc = 0.845000\n", + "epoch [1435] L = 38.500717, acc = 0.845000\n", + "epoch [1436] L = 38.500619, acc = 0.845000\n", + "epoch [1437] L = 38.500522, acc = 0.845000\n", + "epoch [1438] L = 38.500424, acc = 0.845000\n", + "epoch [1439] L = 38.500326, acc = 0.845000\n", + "epoch [1440] L = 38.500229, acc = 0.845000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch [1441] L = 38.500131, acc = 0.845000\n", + "epoch [1442] L = 38.500034, acc = 0.845000\n", + "epoch [1443] L = 38.499936, acc = 0.845000\n", + "epoch [1444] L = 38.499839, acc = 0.845000\n", + "epoch [1445] L = 38.499742, acc = 0.845000\n", + "epoch [1446] L = 38.499644, acc = 0.845000\n", + "epoch [1447] L = 38.499547, acc = 0.845000\n", + "epoch [1448] L = 38.499450, acc = 0.845000\n", + "epoch [1449] L = 38.499353, acc = 0.845000\n", + "epoch [1450] L = 38.499257, acc = 0.845000\n", + "epoch [1451] L = 38.499160, acc = 0.845000\n", + "epoch [1452] L = 38.499063, acc = 0.845000\n", + "epoch [1453] L = 38.498966, acc = 0.845000\n", + "epoch [1454] L = 38.498870, acc = 0.845000\n", + "epoch [1455] L = 38.498773, acc = 0.845000\n", + "epoch [1456] L = 38.498677, acc = 0.845000\n", + "epoch [1457] L = 38.498581, acc = 0.845000\n", + "epoch [1458] L = 38.498484, acc = 0.845000\n", + "epoch [1459] L = 38.498388, acc = 0.845000\n", + "epoch [1460] L = 38.498292, acc = 0.845000\n", + "epoch [1461] L = 38.498196, acc = 0.845000\n", + "epoch [1462] L = 38.498100, acc = 0.845000\n", + "epoch [1463] L = 38.498004, acc = 0.845000\n", + "epoch [1464] L = 38.497908, acc = 0.845000\n", + "epoch [1465] L = 38.497812, acc = 0.845000\n", + "epoch [1466] L = 38.497717, acc = 0.845000\n", + "epoch [1467] L = 38.497621, acc = 0.845000\n", + "epoch [1468] L = 38.497526, acc = 0.845000\n", + "epoch [1469] L = 38.497430, acc = 0.845000\n", + "epoch [1470] L = 38.497335, acc = 0.845000\n", + "epoch [1471] L = 38.497239, acc = 0.845000\n", + "epoch [1472] L = 38.497144, acc = 0.845000\n", + "epoch [1473] L = 38.497049, acc = 0.845000\n", + "epoch [1474] L = 38.496954, acc = 0.845000\n", + "epoch [1475] L = 38.496859, acc = 0.845000\n", + "epoch [1476] L = 38.496764, acc = 0.845000\n", + "epoch [1477] L = 38.496669, acc = 0.845000\n", + "epoch [1478] L = 38.496574, acc = 0.845000\n", + "epoch [1479] L = 38.496479, acc = 0.845000\n", + "epoch [1480] L = 38.496385, acc = 0.845000\n", + "epoch [1481] L = 38.496290, acc = 0.845000\n", + "epoch [1482] L = 38.496195, acc = 0.845000\n", + "epoch [1483] L = 38.496101, acc = 0.845000\n", + "epoch [1484] L = 38.496006, acc = 0.845000\n", + "epoch [1485] L = 38.495912, acc = 0.845000\n", + "epoch [1486] L = 38.495818, acc = 0.845000\n", + "epoch [1487] L = 38.495724, acc = 0.845000\n", + "epoch [1488] L = 38.495629, acc = 0.845000\n", + "epoch [1489] L = 38.495535, acc = 0.845000\n", + "epoch [1490] L = 38.495441, acc = 0.845000\n", + "epoch [1491] L = 38.495347, acc = 0.845000\n", + "epoch [1492] L = 38.495254, acc = 0.845000\n", + "epoch [1493] L = 38.495160, acc = 0.845000\n", + "epoch [1494] L = 38.495066, acc = 0.845000\n", + "epoch [1495] L = 38.494972, acc = 0.845000\n", + "epoch [1496] L = 38.494879, acc = 0.845000\n", + "epoch [1497] L = 38.494785, acc = 0.845000\n", + "epoch [1498] L = 38.494692, acc = 0.845000\n", + "epoch [1499] L = 38.494598, acc = 0.845000\n", + "epoch [1500] L = 38.494505, acc = 0.845000\n", + "epoch [1501] L = 38.494412, acc = 0.845000\n", + "epoch [1502] L = 38.494319, acc = 0.845000\n", + "epoch [1503] L = 38.494225, acc = 0.845000\n", + "epoch [1504] L = 38.494132, acc = 0.845000\n", + "epoch [1505] L = 38.494039, acc = 0.845000\n", + "epoch [1506] L = 38.493946, acc = 0.845000\n", + "epoch [1507] L = 38.493854, acc = 0.845000\n", + "epoch [1508] L = 38.493761, acc = 0.845000\n", + "epoch [1509] L = 38.493668, acc = 0.845000\n", + "epoch [1510] L = 38.493575, acc = 0.845000\n", + "epoch [1511] L = 38.493483, acc = 0.845000\n", + "epoch [1512] L = 38.493390, acc = 0.845000\n", + "epoch [1513] L = 38.493298, acc = 0.845000\n", + "epoch [1514] L = 38.493205, acc = 0.845000\n", + "epoch [1515] L = 38.493113, acc = 0.845000\n", + "epoch [1516] L = 38.493021, acc = 0.845000\n", + "epoch [1517] L = 38.492929, acc = 0.845000\n", + "epoch [1518] L = 38.492836, acc = 0.845000\n", + "epoch [1519] L = 38.492744, acc = 0.845000\n", + "epoch [1520] L = 38.492652, acc = 0.845000\n", + "epoch [1521] L = 38.492560, acc = 0.845000\n", + "epoch [1522] L = 38.492468, acc = 0.845000\n", + "epoch [1523] L = 38.492377, acc = 0.845000\n", + "epoch [1524] L = 38.492285, acc = 0.845000\n", + "epoch [1525] L = 38.492193, acc = 0.845000\n", + "epoch [1526] L = 38.492101, acc = 0.845000\n", + "epoch [1527] L = 38.492010, acc = 0.845000\n", + "epoch [1528] L = 38.491918, acc = 0.845000\n", + "epoch [1529] L = 38.491827, acc = 0.845000\n", + "epoch [1530] L = 38.491736, acc = 0.845000\n", + "epoch [1531] L = 38.491644, acc = 0.845000\n", + "epoch [1532] L = 38.491553, acc = 0.845000\n", + "epoch [1533] L = 38.491462, acc = 0.845000\n", + "epoch [1534] L = 38.491371, acc = 0.845000\n", + "epoch [1535] L = 38.491280, acc = 0.845000\n", + "epoch [1536] L = 38.491189, acc = 0.845000\n", + "epoch [1537] L = 38.491098, acc = 0.845000\n", + "epoch [1538] L = 38.491007, acc = 0.845000\n", + "epoch [1539] L = 38.490916, acc = 0.845000\n", + "epoch [1540] L = 38.490825, acc = 0.845000\n", + "epoch [1541] L = 38.490735, acc = 0.845000\n", + "epoch [1542] L = 38.490644, acc = 0.845000\n", + "epoch [1543] L = 38.490553, acc = 0.845000\n", + "epoch [1544] L = 38.490463, acc = 0.845000\n", + "epoch [1545] L = 38.490372, acc = 0.845000\n", + "epoch [1546] L = 38.490282, acc = 0.845000\n", + "epoch [1547] L = 38.490192, acc = 0.845000\n", + "epoch [1548] L = 38.490101, acc = 0.845000\n", + "epoch [1549] L = 38.490011, acc = 0.845000\n", + "epoch [1550] L = 38.489921, acc = 0.845000\n", + "epoch [1551] L = 38.489831, acc = 0.845000\n", + "epoch [1552] L = 38.489741, acc = 0.845000\n", + "epoch [1553] L = 38.489651, acc = 0.845000\n", + "epoch [1554] L = 38.489561, acc = 0.845000\n", + "epoch [1555] L = 38.489471, acc = 0.845000\n", + "epoch [1556] L = 38.489381, acc = 0.845000\n", + "epoch [1557] L = 38.489292, acc = 0.845000\n", + "epoch [1558] L = 38.489202, acc = 0.845000\n", + "epoch [1559] L = 38.489112, acc = 0.845000\n", + "epoch [1560] L = 38.489023, acc = 0.845000\n", + "epoch [1561] L = 38.488933, acc = 0.845000\n", + "epoch [1562] L = 38.488844, acc = 0.845000\n", + "epoch [1563] L = 38.488755, acc = 0.845000\n", + "epoch [1564] L = 38.488665, acc = 0.845000\n", + "epoch [1565] L = 38.488576, acc = 0.845000\n", + "epoch [1566] L = 38.488487, acc = 0.845000\n", + "epoch [1567] L = 38.488398, acc = 0.845000\n", + "epoch [1568] L = 38.488309, acc = 0.845000\n", + "epoch [1569] L = 38.488220, acc = 0.845000\n", + "epoch [1570] L = 38.488131, acc = 0.845000\n", + "epoch [1571] L = 38.488042, acc = 0.845000\n", + "epoch [1572] L = 38.487953, acc = 0.845000\n", + "epoch [1573] L = 38.487864, acc = 0.845000\n", + "epoch [1574] L = 38.487775, acc = 0.845000\n", + "epoch [1575] L = 38.487687, acc = 0.845000\n", + "epoch [1576] L = 38.487598, acc = 0.845000\n", + "epoch [1577] L = 38.487510, acc = 0.845000\n", + "epoch [1578] L = 38.487421, acc = 0.845000\n", + "epoch [1579] L = 38.487333, acc = 0.845000\n", + "epoch [1580] L = 38.487244, acc = 0.845000\n", + "epoch [1581] L = 38.487156, acc = 0.845000\n", + "epoch [1582] L = 38.487068, acc = 0.845000\n", + "epoch [1583] L = 38.486979, acc = 0.845000\n", + "epoch [1584] L = 38.486891, acc = 0.845000\n", + "epoch [1585] L = 38.486803, acc = 0.845000\n", + "epoch [1586] L = 38.486715, acc = 0.845000\n", + "epoch [1587] L = 38.486627, acc = 0.845000\n", + "epoch [1588] L = 38.486539, acc = 0.845000\n", + "epoch [1589] L = 38.486451, acc = 0.845000\n", + "epoch [1590] L = 38.486364, acc = 0.845000\n", + "epoch [1591] L = 38.486276, acc = 0.845000\n", + "epoch [1592] L = 38.486188, acc = 0.845000\n", + "epoch [1593] L = 38.486101, acc = 0.845000\n", + "epoch [1594] L = 38.486013, acc = 0.845000\n", + "epoch [1595] L = 38.485925, acc = 0.845000\n", + "epoch [1596] L = 38.485838, acc = 0.845000\n", + "epoch [1597] L = 38.485750, acc = 0.845000\n", + "epoch [1598] L = 38.485663, acc = 0.845000\n", + "epoch [1599] L = 38.485576, acc = 0.845000\n", + "epoch [1600] L = 38.485489, acc = 0.845000\n", + "epoch [1601] L = 38.485401, acc = 0.845000\n", + "epoch [1602] L = 38.485314, acc = 0.845000\n", + "epoch [1603] L = 38.485227, acc = 0.845000\n", + "epoch [1604] L = 38.485140, acc = 0.845000\n", + "epoch [1605] L = 38.485053, acc = 0.845000\n", + "epoch [1606] L = 38.484966, acc = 0.845000\n", + "epoch [1607] L = 38.484879, acc = 0.845000\n", + "epoch [1608] L = 38.484792, acc = 0.845000\n", + "epoch [1609] L = 38.484706, acc = 0.845000\n", + "epoch [1610] L = 38.484619, acc = 0.845000\n", + "epoch [1611] L = 38.484532, acc = 0.845000\n", + "epoch [1612] L = 38.484446, acc = 0.845000\n", + "epoch [1613] L = 38.484359, acc = 0.845000\n", + "epoch [1614] L = 38.484273, acc = 0.845000\n", + "epoch [1615] L = 38.484186, acc = 0.845000\n", + "epoch [1616] L = 38.484100, acc = 0.845000\n", + "epoch [1617] L = 38.484014, acc = 0.845000\n", + "epoch [1618] L = 38.483927, acc = 0.845000\n", + "epoch [1619] L = 38.483841, acc = 0.845000\n", + "epoch [1620] L = 38.483755, acc = 0.845000\n", + "epoch [1621] L = 38.483669, acc = 0.845000\n", + "epoch [1622] L = 38.483583, acc = 0.845000\n", + "epoch [1623] L = 38.483497, acc = 0.845000\n", + "epoch [1624] L = 38.483411, acc = 0.845000\n", + "epoch [1625] L = 38.483325, acc = 0.845000\n", + "epoch [1626] L = 38.483239, acc = 0.845000\n", + "epoch [1627] L = 38.483153, acc = 0.845000\n", + "epoch [1628] L = 38.483067, acc = 0.845000\n", + "epoch [1629] L = 38.482982, acc = 0.845000\n", + "epoch [1630] L = 38.482896, acc = 0.845000\n", + "epoch [1631] L = 38.482810, acc = 0.845000\n", + "epoch [1632] L = 38.482725, acc = 0.845000\n", + "epoch [1633] L = 38.482639, acc = 0.845000\n", + "epoch [1634] L = 38.482554, acc = 0.845000\n", + "epoch [1635] L = 38.482469, acc = 0.845000\n", + "epoch [1636] L = 38.482383, acc = 0.845000\n", + "epoch [1637] L = 38.482298, acc = 0.845000\n", + "epoch [1638] L = 38.482213, acc = 0.845000\n", + "epoch [1639] L = 38.482127, acc = 0.845000\n", + "epoch [1640] L = 38.482042, acc = 0.845000\n", + "epoch [1641] L = 38.481957, acc = 0.845000\n", + "epoch [1642] L = 38.481872, acc = 0.845000\n", + "epoch [1643] L = 38.481787, acc = 0.845000\n", + "epoch [1644] L = 38.481702, acc = 0.845000\n", + "epoch [1645] L = 38.481617, acc = 0.845000\n", + "epoch [1646] L = 38.481533, acc = 0.845000\n", + "epoch [1647] L = 38.481448, acc = 0.845000\n", + "epoch [1648] L = 38.481363, acc = 0.845000\n", + "epoch [1649] L = 38.481278, acc = 0.845000\n", + "epoch [1650] L = 38.481194, acc = 0.845000\n", + "epoch [1651] L = 38.481109, acc = 0.845000\n", + "epoch [1652] L = 38.481025, acc = 0.845000\n", + "epoch [1653] L = 38.480940, acc = 0.845000\n", + "epoch [1654] L = 38.480856, acc = 0.845000\n", + "epoch [1655] L = 38.480771, acc = 0.845000\n", + "epoch [1656] L = 38.480687, acc = 0.845000\n", + "epoch [1657] L = 38.480603, acc = 0.845000\n", + "epoch [1658] L = 38.480519, acc = 0.845000\n", + "epoch [1659] L = 38.480434, acc = 0.845000\n", + "epoch [1660] L = 38.480350, acc = 0.845000\n", + "epoch [1661] L = 38.480266, acc = 0.845000\n", + "epoch [1662] L = 38.480182, acc = 0.845000\n", + "epoch [1663] L = 38.480098, acc = 0.845000\n", + "epoch [1664] L = 38.480014, acc = 0.845000\n", + "epoch [1665] L = 38.479930, acc = 0.845000\n", + "epoch [1666] L = 38.479847, acc = 0.845000\n", + "epoch [1667] L = 38.479763, acc = 0.845000\n", + "epoch [1668] L = 38.479679, acc = 0.845000\n", + "epoch [1669] L = 38.479595, acc = 0.845000\n", + "epoch [1670] L = 38.479512, acc = 0.845000\n", + "epoch [1671] L = 38.479428, acc = 0.845000\n", + "epoch [1672] L = 38.479345, acc = 0.845000\n", + "epoch [1673] L = 38.479261, acc = 0.845000\n", + "epoch [1674] L = 38.479178, acc = 0.845000\n", + "epoch [1675] L = 38.479094, acc = 0.845000\n", + "epoch [1676] L = 38.479011, acc = 0.845000\n", + "epoch [1677] L = 38.478928, acc = 0.845000\n", + "epoch [1678] L = 38.478844, acc = 0.845000\n", + "epoch [1679] L = 38.478761, acc = 0.845000\n", + "epoch [1680] L = 38.478678, acc = 0.845000\n", + "epoch [1681] L = 38.478595, acc = 0.845000\n", + "epoch [1682] L = 38.478512, acc = 0.845000\n", + "epoch [1683] L = 38.478429, acc = 0.845000\n", + "epoch [1684] L = 38.478346, acc = 0.845000\n", + "epoch [1685] L = 38.478263, acc = 0.845000\n", + "epoch [1686] L = 38.478180, acc = 0.845000\n", + "epoch [1687] L = 38.478097, acc = 0.845000\n", + "epoch [1688] L = 38.478014, acc = 0.845000\n", + "epoch [1689] L = 38.477932, acc = 0.845000\n", + "epoch [1690] L = 38.477849, acc = 0.845000\n", + "epoch [1691] L = 38.477766, acc = 0.845000\n", + "epoch [1692] L = 38.477684, acc = 0.845000\n", + "epoch [1693] L = 38.477601, acc = 0.845000\n", + "epoch [1694] L = 38.477519, acc = 0.845000\n", + "epoch [1695] L = 38.477436, acc = 0.845000\n", + "epoch [1696] L = 38.477354, acc = 0.845000\n", + "epoch [1697] L = 38.477271, acc = 0.845000\n", + "epoch [1698] L = 38.477189, acc = 0.845000\n", + "epoch [1699] L = 38.477107, acc = 0.845000\n", + "epoch [1700] L = 38.477025, acc = 0.845000\n", + "epoch [1701] L = 38.476942, acc = 0.845000\n", + "epoch [1702] L = 38.476860, acc = 0.845000\n", + "epoch [1703] L = 38.476778, acc = 0.845000\n", + "epoch [1704] L = 38.476696, acc = 0.845000\n", + "epoch [1705] L = 38.476614, acc = 0.845000\n", + "epoch [1706] L = 38.476532, acc = 0.845000\n", + "epoch [1707] L = 38.476450, acc = 0.845000\n", + "epoch [1708] L = 38.476368, acc = 0.845000\n", + "epoch [1709] L = 38.476287, acc = 0.845000\n", + "epoch [1710] L = 38.476205, acc = 0.845000\n", + "epoch [1711] L = 38.476123, acc = 0.845000\n", + "epoch [1712] L = 38.476041, acc = 0.845000\n", + "epoch [1713] L = 38.475960, acc = 0.845000\n", + "epoch [1714] L = 38.475878, acc = 0.845000\n", + "epoch [1715] L = 38.475797, acc = 0.845000\n", + "epoch [1716] L = 38.475715, acc = 0.845000\n", + "epoch [1717] L = 38.475634, acc = 0.845000\n", + "epoch [1718] L = 38.475552, acc = 0.845000\n", + "epoch [1719] L = 38.475471, acc = 0.845000\n", + "epoch [1720] L = 38.475390, acc = 0.845000\n", + "epoch [1721] L = 38.475308, acc = 0.845000\n", + "epoch [1722] L = 38.475227, acc = 0.845000\n", + "epoch [1723] L = 38.475146, acc = 0.845000\n", + "epoch [1724] L = 38.475065, acc = 0.845000\n", + "epoch [1725] L = 38.474984, acc = 0.845000\n", + "epoch [1726] L = 38.474903, acc = 0.845000\n", + "epoch [1727] L = 38.474822, acc = 0.845000\n", + "epoch [1728] L = 38.474741, acc = 0.845000\n", + "epoch [1729] L = 38.474660, acc = 0.845000\n", + "epoch [1730] L = 38.474579, acc = 0.845000\n", + "epoch [1731] L = 38.474498, acc = 0.845000\n", + "epoch [1732] L = 38.474417, acc = 0.845000\n", + "epoch [1733] L = 38.474337, acc = 0.845000\n", + "epoch [1734] L = 38.474256, acc = 0.845000\n", + "epoch [1735] L = 38.474175, acc = 0.845000\n", + "epoch [1736] L = 38.474095, acc = 0.845000\n", + "epoch [1737] L = 38.474014, acc = 0.845000\n", + "epoch [1738] L = 38.473934, acc = 0.845000\n", + "epoch [1739] L = 38.473853, acc = 0.845000\n", + "epoch [1740] L = 38.473773, acc = 0.845000\n", + "epoch [1741] L = 38.473692, acc = 0.845000\n", + "epoch [1742] L = 38.473612, acc = 0.845000\n", + "epoch [1743] L = 38.473532, acc = 0.845000\n", + "epoch [1744] L = 38.473451, acc = 0.845000\n", + "epoch [1745] L = 38.473371, acc = 0.845000\n", + "epoch [1746] L = 38.473291, acc = 0.845000\n", + "epoch [1747] L = 38.473211, acc = 0.845000\n", + "epoch [1748] L = 38.473131, acc = 0.845000\n", + "epoch [1749] L = 38.473051, acc = 0.845000\n", + "epoch [1750] L = 38.472970, acc = 0.845000\n", + "epoch [1751] L = 38.472891, acc = 0.845000\n", + "epoch [1752] L = 38.472811, acc = 0.845000\n", + "epoch [1753] L = 38.472731, acc = 0.845000\n", + "epoch [1754] L = 38.472651, acc = 0.845000\n", + "epoch [1755] L = 38.472571, acc = 0.845000\n", + "epoch [1756] L = 38.472491, acc = 0.845000\n", + "epoch [1757] L = 38.472412, acc = 0.845000\n", + "epoch [1758] L = 38.472332, acc = 0.845000\n", + "epoch [1759] L = 38.472252, acc = 0.845000\n", + "epoch [1760] L = 38.472173, acc = 0.845000\n", + "epoch [1761] L = 38.472093, acc = 0.845000\n", + "epoch [1762] L = 38.472014, acc = 0.845000\n", + "epoch [1763] L = 38.471934, acc = 0.845000\n", + "epoch [1764] L = 38.471855, acc = 0.845000\n", + "epoch [1765] L = 38.471775, acc = 0.845000\n", + "epoch [1766] L = 38.471696, acc = 0.845000\n", + "epoch [1767] L = 38.471617, acc = 0.845000\n", + "epoch [1768] L = 38.471537, acc = 0.845000\n", + "epoch [1769] L = 38.471458, acc = 0.845000\n", + "epoch [1770] L = 38.471379, acc = 0.845000\n", + "epoch [1771] L = 38.471300, acc = 0.845000\n", + "epoch [1772] L = 38.471221, acc = 0.845000\n", + "epoch [1773] L = 38.471142, acc = 0.845000\n", + "epoch [1774] L = 38.471063, acc = 0.845000\n", + "epoch [1775] L = 38.470984, acc = 0.845000\n", + "epoch [1776] L = 38.470905, acc = 0.845000\n", + "epoch [1777] L = 38.470826, acc = 0.845000\n", + "epoch [1778] L = 38.470747, acc = 0.845000\n", + "epoch [1779] L = 38.470668, acc = 0.845000\n", + "epoch [1780] L = 38.470589, acc = 0.845000\n", + "epoch [1781] L = 38.470511, acc = 0.845000\n", + "epoch [1782] L = 38.470432, acc = 0.845000\n", + "epoch [1783] L = 38.470353, acc = 0.845000\n", + "epoch [1784] L = 38.470275, acc = 0.845000\n", + "epoch [1785] L = 38.470196, acc = 0.845000\n", + "epoch [1786] L = 38.470118, acc = 0.845000\n", + "epoch [1787] L = 38.470039, acc = 0.845000\n", + "epoch [1788] L = 38.469961, acc = 0.845000\n", + "epoch [1789] L = 38.469882, acc = 0.845000\n", + "epoch [1790] L = 38.469804, acc = 0.845000\n", + "epoch [1791] L = 38.469725, acc = 0.845000\n", + "epoch [1792] L = 38.469647, acc = 0.845000\n", + "epoch [1793] L = 38.469569, acc = 0.845000\n", + "epoch [1794] L = 38.469491, acc = 0.845000\n", + "epoch [1795] L = 38.469413, acc = 0.845000\n", + "epoch [1796] L = 38.469334, acc = 0.845000\n", + "epoch [1797] L = 38.469256, acc = 0.845000\n", + "epoch [1798] L = 38.469178, acc = 0.845000\n", + "epoch [1799] L = 38.469100, acc = 0.845000\n", + "epoch [1800] L = 38.469022, acc = 0.845000\n", + "epoch [1801] L = 38.468944, acc = 0.845000\n", + "epoch [1802] L = 38.468866, acc = 0.845000\n", + "epoch [1803] L = 38.468789, acc = 0.845000\n", + "epoch [1804] L = 38.468711, acc = 0.845000\n", + "epoch [1805] L = 38.468633, acc = 0.845000\n", + "epoch [1806] L = 38.468555, acc = 0.845000\n", + "epoch [1807] L = 38.468477, acc = 0.845000\n", + "epoch [1808] L = 38.468400, acc = 0.845000\n", + "epoch [1809] L = 38.468322, acc = 0.845000\n", + "epoch [1810] L = 38.468245, acc = 0.845000\n", + "epoch [1811] L = 38.468167, acc = 0.845000\n", + "epoch [1812] L = 38.468089, acc = 0.845000\n", + "epoch [1813] L = 38.468012, acc = 0.845000\n", + "epoch [1814] L = 38.467935, acc = 0.845000\n", + "epoch [1815] L = 38.467857, acc = 0.845000\n", + "epoch [1816] L = 38.467780, acc = 0.845000\n", + "epoch [1817] L = 38.467702, acc = 0.845000\n", + "epoch [1818] L = 38.467625, acc = 0.845000\n", + "epoch [1819] L = 38.467548, acc = 0.845000\n", + "epoch [1820] L = 38.467471, acc = 0.845000\n", + "epoch [1821] L = 38.467394, acc = 0.845000\n", + "epoch [1822] L = 38.467316, acc = 0.845000\n", + "epoch [1823] L = 38.467239, acc = 0.845000\n", + "epoch [1824] L = 38.467162, acc = 0.845000\n", + "epoch [1825] L = 38.467085, acc = 0.845000\n", + "epoch [1826] L = 38.467008, acc = 0.845000\n", + "epoch [1827] L = 38.466931, acc = 0.845000\n", + "epoch [1828] L = 38.466854, acc = 0.845000\n", + "epoch [1829] L = 38.466777, acc = 0.845000\n", + "epoch [1830] L = 38.466701, acc = 0.845000\n", + "epoch [1831] L = 38.466624, acc = 0.845000\n", + "epoch [1832] L = 38.466547, acc = 0.845000\n", + "epoch [1833] L = 38.466470, acc = 0.845000\n", + "epoch [1834] L = 38.466394, acc = 0.845000\n", + "epoch [1835] L = 38.466317, acc = 0.845000\n", + "epoch [1836] L = 38.466240, acc = 0.845000\n", + "epoch [1837] L = 38.466164, acc = 0.845000\n", + "epoch [1838] L = 38.466087, acc = 0.845000\n", + "epoch [1839] L = 38.466011, acc = 0.845000\n", + "epoch [1840] L = 38.465934, acc = 0.845000\n", + "epoch [1841] L = 38.465858, acc = 0.845000\n", + "epoch [1842] L = 38.465781, acc = 0.845000\n", + "epoch [1843] L = 38.465705, acc = 0.845000\n", + "epoch [1844] L = 38.465629, acc = 0.845000\n", + "epoch [1845] L = 38.465553, acc = 0.845000\n", + "epoch [1846] L = 38.465476, acc = 0.845000\n", + "epoch [1847] L = 38.465400, acc = 0.845000\n", + "epoch [1848] L = 38.465324, acc = 0.845000\n", + "epoch [1849] L = 38.465248, acc = 0.845000\n", + "epoch [1850] L = 38.465172, acc = 0.845000\n", + "epoch [1851] L = 38.465096, acc = 0.845000\n", + "epoch [1852] L = 38.465020, acc = 0.845000\n", + "epoch [1853] L = 38.464944, acc = 0.845000\n", + "epoch [1854] L = 38.464868, acc = 0.845000\n", + "epoch [1855] L = 38.464792, acc = 0.845000\n", + "epoch [1856] L = 38.464716, acc = 0.845000\n", + "epoch [1857] L = 38.464640, acc = 0.845000\n", + "epoch [1858] L = 38.464564, acc = 0.845000\n", + "epoch [1859] L = 38.464488, acc = 0.845000\n", + "epoch [1860] L = 38.464413, acc = 0.845000\n", + "epoch [1861] L = 38.464337, acc = 0.845000\n", + "epoch [1862] L = 38.464261, acc = 0.845000\n", + "epoch [1863] L = 38.464186, acc = 0.845000\n", + "epoch [1864] L = 38.464110, acc = 0.845000\n", + "epoch [1865] L = 38.464034, acc = 0.845000\n", + "epoch [1866] L = 38.463959, acc = 0.845000\n", + "epoch [1867] L = 38.463883, acc = 0.845000\n", + "epoch [1868] L = 38.463808, acc = 0.845000\n", + "epoch [1869] L = 38.463733, acc = 0.845000\n", + "epoch [1870] L = 38.463657, acc = 0.845000\n", + "epoch [1871] L = 38.463582, acc = 0.845000\n", + "epoch [1872] L = 38.463506, acc = 0.845000\n", + "epoch [1873] L = 38.463431, acc = 0.845000\n", + "epoch [1874] L = 38.463356, acc = 0.845000\n", + "epoch [1875] L = 38.463281, acc = 0.845000\n", + "epoch [1876] L = 38.463206, acc = 0.845000\n", + "epoch [1877] L = 38.463130, acc = 0.845000\n", + "epoch [1878] L = 38.463055, acc = 0.845000\n", + "epoch [1879] L = 38.462980, acc = 0.845000\n", + "epoch [1880] L = 38.462905, acc = 0.845000\n", + "epoch [1881] L = 38.462830, acc = 0.845000\n", + "epoch [1882] L = 38.462755, acc = 0.845000\n", + "epoch [1883] L = 38.462680, acc = 0.845000\n", + "epoch [1884] L = 38.462605, acc = 0.845000\n", + "epoch [1885] L = 38.462530, acc = 0.845000\n", + "epoch [1886] L = 38.462456, acc = 0.845000\n", + "epoch [1887] L = 38.462381, acc = 0.845000\n", + "epoch [1888] L = 38.462306, acc = 0.845000\n", + "epoch [1889] L = 38.462231, acc = 0.845000\n", + "epoch [1890] L = 38.462157, acc = 0.845000\n", + "epoch [1891] L = 38.462082, acc = 0.845000\n", + "epoch [1892] L = 38.462007, acc = 0.845000\n", + "epoch [1893] L = 38.461933, acc = 0.845000\n", + "epoch [1894] L = 38.461858, acc = 0.845000\n", + "epoch [1895] L = 38.461784, acc = 0.845000\n", + "epoch [1896] L = 38.461709, acc = 0.845000\n", + "epoch [1897] L = 38.461635, acc = 0.845000\n", + "epoch [1898] L = 38.461560, acc = 0.845000\n", + "epoch [1899] L = 38.461486, acc = 0.845000\n", + "epoch [1900] L = 38.461412, acc = 0.845000\n", + "epoch [1901] L = 38.461337, acc = 0.845000\n", + "epoch [1902] L = 38.461263, acc = 0.845000\n", + "epoch [1903] L = 38.461189, acc = 0.845000\n", + "epoch [1904] L = 38.461114, acc = 0.845000\n", + "epoch [1905] L = 38.461040, acc = 0.845000\n", + "epoch [1906] L = 38.460966, acc = 0.845000\n", + "epoch [1907] L = 38.460892, acc = 0.845000\n", + "epoch [1908] L = 38.460818, acc = 0.845000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epoch [1909] L = 38.460744, acc = 0.845000\n", + "epoch [1910] L = 38.460670, acc = 0.845000\n", + "epoch [1911] L = 38.460596, acc = 0.845000\n", + "epoch [1912] L = 38.460522, acc = 0.845000\n", + "epoch [1913] L = 38.460448, acc = 0.845000\n", + "epoch [1914] L = 38.460374, acc = 0.845000\n", + "epoch [1915] L = 38.460300, acc = 0.845000\n", + "epoch [1916] L = 38.460226, acc = 0.845000\n", + "epoch [1917] L = 38.460153, acc = 0.845000\n", + "epoch [1918] L = 38.460079, acc = 0.845000\n", + "epoch [1919] L = 38.460005, acc = 0.845000\n", + "epoch [1920] L = 38.459931, acc = 0.845000\n", + "epoch [1921] L = 38.459858, acc = 0.845000\n", + "epoch [1922] L = 38.459784, acc = 0.845000\n", + "epoch [1923] L = 38.459711, acc = 0.845000\n", + "epoch [1924] L = 38.459637, acc = 0.845000\n", + "epoch [1925] L = 38.459563, acc = 0.845000\n", + "epoch [1926] L = 38.459490, acc = 0.845000\n", + "epoch [1927] L = 38.459416, acc = 0.845000\n", + "epoch [1928] L = 38.459343, acc = 0.845000\n", + "epoch [1929] L = 38.459270, acc = 0.845000\n", + "epoch [1930] L = 38.459196, acc = 0.845000\n", + "epoch [1931] L = 38.459123, acc = 0.845000\n", + "epoch [1932] L = 38.459050, acc = 0.845000\n", + "epoch [1933] L = 38.458976, acc = 0.845000\n", + "epoch [1934] L = 38.458903, acc = 0.845000\n", + "epoch [1935] L = 38.458830, acc = 0.845000\n", + "epoch [1936] L = 38.458757, acc = 0.845000\n", + "epoch [1937] L = 38.458684, acc = 0.845000\n", + "epoch [1938] L = 38.458610, acc = 0.845000\n", + "epoch [1939] L = 38.458537, acc = 0.845000\n", + "epoch [1940] L = 38.458464, acc = 0.845000\n", + "epoch [1941] L = 38.458391, acc = 0.845000\n", + "epoch [1942] L = 38.458318, acc = 0.845000\n", + "epoch [1943] L = 38.458245, acc = 0.845000\n", + "epoch [1944] L = 38.458172, acc = 0.845000\n", + "epoch [1945] L = 38.458100, acc = 0.845000\n", + "epoch [1946] L = 38.458027, acc = 0.845000\n", + "epoch [1947] L = 38.457954, acc = 0.845000\n", + "epoch [1948] L = 38.457881, acc = 0.845000\n", + "epoch [1949] L = 38.457808, acc = 0.845000\n", + "epoch [1950] L = 38.457736, acc = 0.845000\n", + "epoch [1951] L = 38.457663, acc = 0.845000\n", + "epoch [1952] L = 38.457590, acc = 0.845000\n", + "epoch [1953] L = 38.457518, acc = 0.845000\n", + "epoch [1954] L = 38.457445, acc = 0.845000\n", + "epoch [1955] L = 38.457372, acc = 0.845000\n", + "epoch [1956] L = 38.457300, acc = 0.845000\n", + "epoch [1957] L = 38.457227, acc = 0.845000\n", + "epoch [1958] L = 38.457155, acc = 0.845000\n", + "epoch [1959] L = 38.457082, acc = 0.845000\n", + "epoch [1960] L = 38.457010, acc = 0.845000\n", + "epoch [1961] L = 38.456938, acc = 0.845000\n", + "epoch [1962] L = 38.456865, acc = 0.845000\n", + "epoch [1963] L = 38.456793, acc = 0.845000\n", + "epoch [1964] L = 38.456721, acc = 0.845000\n", + "epoch [1965] L = 38.456648, acc = 0.845000\n", + "epoch [1966] L = 38.456576, acc = 0.845000\n", + "epoch [1967] L = 38.456504, acc = 0.845000\n", + "epoch [1968] L = 38.456432, acc = 0.845000\n", + "epoch [1969] L = 38.456360, acc = 0.845000\n", + "epoch [1970] L = 38.456287, acc = 0.845000\n", + "epoch [1971] L = 38.456215, acc = 0.845000\n", + "epoch [1972] L = 38.456143, acc = 0.845000\n", + "epoch [1973] L = 38.456071, acc = 0.845000\n", + "epoch [1974] L = 38.455999, acc = 0.845000\n", + "epoch [1975] L = 38.455927, acc = 0.845000\n", + "epoch [1976] L = 38.455855, acc = 0.845000\n", + "epoch [1977] L = 38.455784, acc = 0.845000\n", + "epoch [1978] L = 38.455712, acc = 0.845000\n", + "epoch [1979] L = 38.455640, acc = 0.845000\n", + "epoch [1980] L = 38.455568, acc = 0.845000\n", + "epoch [1981] L = 38.455496, acc = 0.845000\n", + "epoch [1982] L = 38.455425, acc = 0.845000\n", + "epoch [1983] L = 38.455353, acc = 0.845000\n", + "epoch [1984] L = 38.455281, acc = 0.845000\n", + "epoch [1985] L = 38.455209, acc = 0.845000\n", + "epoch [1986] L = 38.455138, acc = 0.845000\n", + "epoch [1987] L = 38.455066, acc = 0.845000\n", + "epoch [1988] L = 38.454995, acc = 0.845000\n", + "epoch [1989] L = 38.454923, acc = 0.845000\n", + "epoch [1990] L = 38.454852, acc = 0.845000\n", + "epoch [1991] L = 38.454780, acc = 0.845000\n", + "epoch [1992] L = 38.454709, acc = 0.845000\n", + "epoch [1993] L = 38.454637, acc = 0.845000\n", + "epoch [1994] L = 38.454566, acc = 0.845000\n", + "epoch [1995] L = 38.454494, acc = 0.845000\n", + "epoch [1996] L = 38.454423, acc = 0.845000\n", + "epoch [1997] L = 38.454352, acc = 0.845000\n", + "epoch [1998] L = 38.454281, acc = 0.845000\n", + "epoch [1999] L = 38.454209, acc = 0.845000\n" + ] + } + ], + "source": [ + "# FIXME: change variable name to math\n", + "\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "y_true = np.array(nn.y).astype(float)\n", + "\n", + "# back-propagation\n", + "def backpropagation(n, X, y):\n", + " for i in range(n.n_epoch):\n", + " # forward to calculate each node's output\n", + " forward(n, X)\n", + " \n", + " # print loss, accuracy\n", + " L = np.sum((n.z2 - y)**2)\n", + " \n", + " y_pred = np.argmax(nn.z2, axis=1)\n", + " acc = accuracy_score(y_true, y_pred)\n", + " \n", + " print(\"epoch [%4d] L = %f, acc = %f\" % (i, L, acc))\n", + " \n", + " # calc weights update\n", + " d2 = n.z2*(1-n.z2)*(y - n.z2)\n", + " d1 = n.z1*(1-n.z1)*(np.dot(d2, n.W2.T))\n", + " \n", + " # update weights\n", + " n.W2 += n.epsilon * np.dot(n.z1.T, d2)\n", + " n.b2 += n.epsilon * np.sum(d2, axis=0)\n", + " n.W1 += n.epsilon * np.dot(X.T, d1)\n", + " n.b1 += n.epsilon * np.sum(d1, axis=0)\n", + "\n", + "nn.n_epoch = 2000\n", + "backpropagation(nn, X, t)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot data\n", + "y_pred = np.argmax(nn.z2, axis=1)\n", + "\n", + "plt.scatter(X[:, 0], X[:, 1], c=nn.y, cmap=plt.cm.Spectral)\n", + "plt.title(\"ground truth\")\n", + "plt.show()\n", + "\n", + "plt.scatter(X[:, 0], X[:, 1], c=y_pred, cmap=plt.cm.Spectral)\n", + "plt.title(\"predicted\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. 如何使用类的方法封装多层神经网络?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn import datasets, linear_model\n", + "from sklearn.metrics import accuracy_score\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "# define sigmod\n", + "def sigmod(X):\n", + " return 1.0/(1+np.exp(-X))\n", + "\n", + "\n", + "# generate the NN model\n", + "class NN_Model:\n", + " def __init__(self, nodes=None):\n", + " self.epsilon = 0.01 # learning rate\n", + " self.n_epoch = 1000 # iterative number\n", + " \n", + " if not nodes:\n", + " self.nodes = [2, 6, 2] # default nodes size (from input -> output)\n", + " else:\n", + " self.nodes = nodes\n", + " \n", + " def init_weight(self):\n", + " W = []\n", + " B = []\n", + " \n", + " n_layer = len(self.nodes)\n", + " for i in range(n_layer-1):\n", + " w = np.random.randn(self.nodes[i], self.nodes[i+1]) / np.sqrt(self.nodes[i])\n", + " b = np.random.randn(1, self.nodes[i+1])\n", + " \n", + " W.append(w)\n", + " B.append(b)\n", + " \n", + " self.W = W\n", + " self.B = B\n", + " \n", + " def forward(self, X):\n", + " Z = []\n", + " x0 = X\n", + " for i in range(len(self.nodes)-1):\n", + " z = sigmod(np.dot(x0, self.W[i]) + self.B[i])\n", + " x0 = z\n", + " \n", + " Z.append(z)\n", + " \n", + " self.Z = Z\n", + " return Z[-1]\n", + " \n", + " # back-propagation\n", + " def backpropagation(self, X, y, n_epoch=None, epsilon=None):\n", + " if not n_epoch: n_epoch = self.n_epoch\n", + " if not epsilon: epsilon = self.epsilon\n", + " \n", + " self.X = X\n", + " self.Y = y\n", + " \n", + " for i in range(n_epoch):\n", + " # forward to calculate each node's output\n", + " self.forward(X)\n", + "\n", + " self.evaluate()\n", + " \n", + " # calc weights update\n", + " W = self.W\n", + " B = self.B\n", + " Z = self.Z\n", + " \n", + " D = []\n", + " d0 = y\n", + " n_layer = len(self.nodes)\n", + " for j in range(n_layer-1, 0, -1):\n", + " jj = j - 1\n", + " z = self.Z[jj]\n", + " \n", + " if j == n_layer - 1:\n", + " d = z*(1-z)*(d0 - z)\n", + " else:\n", + " d = z*(1-z)*np.dot(d0, W[j].T)\n", + " \n", + " d0 = d\n", + " D.insert(0, d)\n", + " \n", + " # update weights\n", + " for j in range(n_layer-1, 0, -1):\n", + " jj = j - 1\n", + " \n", + " if jj != 0:\n", + " W[jj] += epsilon * np.dot(Z[jj-1].T, D[jj])\n", + " else:\n", + " W[jj] += epsilon * np.dot(X.T, D[jj])\n", + " \n", + " B[jj] += epsilon * np.sum(D[jj], axis=0)\n", + " \n", + " def evaluate(self):\n", + " z = self.Z[-1]\n", + " \n", + " # print loss, accuracy\n", + " L = np.sum((z - self.Y)**2)\n", + " \n", + " y_pred = np.argmax(z, axis=1)\n", + " y_true = np.argmax(self.Y, axis=1)\n", + " acc = accuracy_score(y_true, y_pred)\n", + " \n", + " print(\"L = %f, acc = %f\" % (L, acc))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# generate sample data\n", + "np.random.seed(0)\n", + "X, y = datasets.make_moons(200, noise=0.20)\n", + "\n", + "# generate nn output target\n", + "t = np.zeros((X.shape[0], 2))\n", + "t[np.where(y==0), 0] = 1\n", + "t[np.where(y==1), 1] = 1\n", + "\n", + "# plot data\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L = 121.621107, acc = 0.500000\n", + "L = 115.928422, acc = 0.500000\n", + "L = 111.304997, acc = 0.500000\n", + "L = 107.789222, acc = 0.500000\n", + "L = 105.265297, acc = 0.500000\n", + "L = 103.533617, acc = 0.500000\n", + "L = 102.380546, acc = 0.500000\n", + "L = 101.622557, acc = 0.500000\n", + "L = 101.121698, acc = 0.500000\n", + "L = 100.782803, acc = 0.510000\n", + "L = 100.543751, acc = 0.530000\n", + "L = 100.365372, acc = 0.540000\n", + "L = 100.223492, acc = 0.520000\n", + "L = 100.103371, acc = 0.475000\n", + "L = 99.996073, acc = 0.460000\n", + "L = 99.896185, acc = 0.465000\n", + "L = 99.800411, acc = 0.465000\n", + "L = 99.706725, acc = 0.495000\n", + "L = 99.613854, acc = 0.515000\n", + "L = 99.520981, acc = 0.560000\n", + "L = 99.427551, acc = 0.585000\n", + "L = 99.333171, acc = 0.630000\n", + "L = 99.237541, acc = 0.660000\n", + "L = 99.140415, acc = 0.690000\n", + "L = 99.041582, acc = 0.705000\n", + "L = 98.940844, acc = 0.710000\n", + "L = 98.838015, acc = 0.720000\n", + "L = 98.732913, acc = 0.740000\n", + "L = 98.625357, acc = 0.745000\n", + "L = 98.515164, acc = 0.755000\n", + "L = 98.402148, acc = 0.785000\n", + "L = 98.286120, acc = 0.790000\n", + "L = 98.166887, acc = 0.800000\n", + "L = 98.044250, acc = 0.800000\n", + "L = 97.918005, acc = 0.805000\n", + "L = 97.787942, acc = 0.815000\n", + "L = 97.653845, acc = 0.830000\n", + "L = 97.515489, acc = 0.830000\n", + "L = 97.372644, acc = 0.830000\n", + "L = 97.225071, acc = 0.830000\n", + "L = 97.072523, acc = 0.830000\n", + "L = 96.914745, acc = 0.835000\n", + "L = 96.751472, acc = 0.835000\n", + "L = 96.582430, acc = 0.835000\n", + "L = 96.407335, acc = 0.835000\n", + "L = 96.225894, acc = 0.835000\n", + "L = 96.037800, acc = 0.835000\n", + "L = 95.842740, acc = 0.835000\n", + "L = 95.640384, acc = 0.835000\n", + "L = 95.430396, acc = 0.835000\n", + "L = 95.212423, acc = 0.835000\n", + "L = 94.986104, acc = 0.830000\n", + "L = 94.751064, acc = 0.830000\n", + "L = 94.506915, acc = 0.830000\n", + "L = 94.253259, acc = 0.830000\n", + "L = 93.989683, acc = 0.830000\n", + "L = 93.715765, acc = 0.830000\n", + "L = 93.431069, acc = 0.830000\n", + "L = 93.135151, acc = 0.830000\n", + "L = 92.827554, acc = 0.830000\n", + "L = 92.507814, acc = 0.830000\n", + "L = 92.175457, acc = 0.830000\n", + "L = 91.830004, acc = 0.835000\n", + "L = 91.470973, acc = 0.835000\n", + "L = 91.097875, acc = 0.835000\n", + "L = 90.710225, acc = 0.840000\n", + "L = 90.307539, acc = 0.845000\n", + "L = 89.889339, acc = 0.845000\n", + "L = 89.455160, acc = 0.845000\n", + "L = 89.004546, acc = 0.840000\n", + "L = 88.537066, acc = 0.840000\n", + "L = 88.052308, acc = 0.840000\n", + "L = 87.549895, acc = 0.840000\n", + "L = 87.029483, acc = 0.845000\n", + "L = 86.490773, acc = 0.845000\n", + "L = 85.933518, acc = 0.845000\n", + "L = 85.357526, acc = 0.845000\n", + "L = 84.762674, acc = 0.845000\n", + "L = 84.148911, acc = 0.845000\n", + "L = 83.516272, acc = 0.845000\n", + "L = 82.864878, acc = 0.845000\n", + "L = 82.194952, acc = 0.845000\n", + "L = 81.506820, acc = 0.840000\n", + "L = 80.800921, acc = 0.840000\n", + "L = 80.077810, acc = 0.840000\n", + "L = 79.338167, acc = 0.840000\n", + "L = 78.582791, acc = 0.840000\n", + "L = 77.812612, acc = 0.840000\n", + "L = 77.028680, acc = 0.840000\n", + "L = 76.232171, acc = 0.840000\n", + "L = 75.424374, acc = 0.840000\n", + "L = 74.606691, acc = 0.840000\n", + "L = 73.780620, acc = 0.840000\n", + "L = 72.947751, acc = 0.840000\n", + "L = 72.109745, acc = 0.840000\n", + "L = 71.268324, acc = 0.840000\n", + "L = 70.425252, acc = 0.840000\n", + "L = 69.582316, acc = 0.840000\n", + "L = 68.741307, acc = 0.840000\n", + "L = 67.904004, acc = 0.840000\n", + "L = 67.072151, acc = 0.840000\n", + "L = 66.247442, acc = 0.840000\n", + "L = 65.431502, acc = 0.840000\n", + "L = 64.625872, acc = 0.840000\n", + "L = 63.831996, acc = 0.840000\n", + "L = 63.051206, acc = 0.840000\n", + "L = 62.284717, acc = 0.840000\n", + "L = 61.533617, acc = 0.840000\n", + "L = 60.798864, acc = 0.840000\n", + "L = 60.081280, acc = 0.840000\n", + "L = 59.381556, acc = 0.840000\n", + "L = 58.700250, acc = 0.840000\n", + "L = 58.037794, acc = 0.840000\n", + "L = 57.394496, acc = 0.840000\n", + "L = 56.770551, acc = 0.840000\n", + "L = 56.166043, acc = 0.840000\n", + "L = 55.580959, acc = 0.840000\n", + "L = 55.015197, acc = 0.840000\n", + "L = 54.468573, acc = 0.840000\n", + "L = 53.940833, acc = 0.840000\n", + "L = 53.431659, acc = 0.840000\n", + "L = 52.940684, acc = 0.840000\n", + "L = 52.467494, acc = 0.840000\n", + "L = 52.011639, acc = 0.840000\n", + "L = 51.572642, acc = 0.840000\n", + "L = 51.150004, acc = 0.840000\n", + "L = 50.743209, acc = 0.840000\n", + "L = 50.351731, acc = 0.840000\n", + "L = 49.975042, acc = 0.840000\n", + "L = 49.612610, acc = 0.835000\n", + "L = 49.263906, acc = 0.835000\n", + "L = 48.928410, acc = 0.840000\n", + "L = 48.605606, acc = 0.840000\n", + "L = 48.294993, acc = 0.840000\n", + "L = 47.996079, acc = 0.840000\n", + "L = 47.708390, acc = 0.840000\n", + "L = 47.431462, acc = 0.840000\n", + "L = 47.164849, acc = 0.840000\n", + "L = 46.908123, acc = 0.840000\n", + "L = 46.660868, acc = 0.840000\n", + "L = 46.422687, acc = 0.840000\n", + "L = 46.193200, acc = 0.840000\n", + "L = 45.972040, acc = 0.840000\n", + "L = 45.758860, acc = 0.840000\n", + "L = 45.553325, acc = 0.840000\n", + "L = 45.355116, acc = 0.840000\n", + "L = 45.163929, acc = 0.835000\n", + "L = 44.979474, acc = 0.835000\n", + "L = 44.801473, acc = 0.835000\n", + "L = 44.629662, acc = 0.835000\n", + "L = 44.463789, acc = 0.835000\n", + "L = 44.303614, acc = 0.835000\n", + "L = 44.148907, acc = 0.835000\n", + "L = 43.999451, acc = 0.835000\n", + "L = 43.855036, acc = 0.835000\n", + "L = 43.715465, acc = 0.835000\n", + "L = 43.580546, acc = 0.835000\n", + "L = 43.450099, acc = 0.835000\n", + "L = 43.323950, acc = 0.835000\n", + "L = 43.201935, acc = 0.835000\n", + "L = 43.083894, acc = 0.835000\n", + "L = 42.969678, acc = 0.835000\n", + "L = 42.859141, acc = 0.835000\n", + "L = 42.752145, acc = 0.835000\n", + "L = 42.648557, acc = 0.835000\n", + "L = 42.548251, acc = 0.835000\n", + "L = 42.451106, acc = 0.835000\n", + "L = 42.357004, acc = 0.835000\n", + "L = 42.265834, acc = 0.835000\n", + "L = 42.177489, acc = 0.835000\n", + "L = 42.091866, acc = 0.845000\n", + "L = 42.008866, acc = 0.845000\n", + "L = 41.928395, acc = 0.845000\n", + "L = 41.850363, acc = 0.845000\n", + "L = 41.774680, acc = 0.845000\n", + "L = 41.701264, acc = 0.845000\n", + "L = 41.630034, acc = 0.845000\n", + "L = 41.560912, acc = 0.845000\n", + "L = 41.493823, acc = 0.845000\n", + "L = 41.428697, acc = 0.845000\n", + "L = 41.365463, acc = 0.845000\n", + "L = 41.304056, acc = 0.850000\n", + "L = 41.244412, acc = 0.850000\n", + "L = 41.186469, acc = 0.850000\n", + "L = 41.130168, acc = 0.850000\n", + "L = 41.075452, acc = 0.850000\n", + "L = 41.022266, acc = 0.850000\n", + "L = 40.970558, acc = 0.850000\n", + "L = 40.920276, acc = 0.850000\n", + "L = 40.871372, acc = 0.850000\n", + "L = 40.823798, acc = 0.850000\n", + "L = 40.777509, acc = 0.850000\n", + "L = 40.732461, acc = 0.855000\n", + "L = 40.688613, acc = 0.855000\n", + "L = 40.645922, acc = 0.855000\n", + "L = 40.604351, acc = 0.855000\n", + "L = 40.563861, acc = 0.855000\n", + "L = 40.524415, acc = 0.855000\n", + "L = 40.485980, acc = 0.855000\n", + "L = 40.448521, acc = 0.855000\n", + "L = 40.412004, acc = 0.855000\n", + "L = 40.376400, acc = 0.855000\n", + "L = 40.341678, acc = 0.855000\n", + "L = 40.307807, acc = 0.855000\n", + "L = 40.274761, acc = 0.855000\n", + "L = 40.242511, acc = 0.855000\n", + "L = 40.211032, acc = 0.855000\n", + "L = 40.180297, acc = 0.855000\n", + "L = 40.150284, acc = 0.855000\n", + "L = 40.120967, acc = 0.855000\n", + "L = 40.092325, acc = 0.855000\n", + "L = 40.064334, acc = 0.855000\n", + "L = 40.036975, acc = 0.855000\n", + "L = 40.010226, acc = 0.855000\n", + "L = 39.984068, acc = 0.855000\n", + "L = 39.958481, acc = 0.855000\n", + "L = 39.933446, acc = 0.855000\n", + "L = 39.908947, acc = 0.855000\n", + "L = 39.884966, acc = 0.855000\n", + "L = 39.861486, acc = 0.855000\n", + "L = 39.838490, acc = 0.855000\n", + "L = 39.815964, acc = 0.855000\n", + "L = 39.793892, acc = 0.855000\n", + "L = 39.772260, acc = 0.855000\n", + "L = 39.751053, acc = 0.855000\n", + "L = 39.730259, acc = 0.855000\n", + "L = 39.709863, acc = 0.855000\n", + "L = 39.689852, acc = 0.855000\n", + "L = 39.670216, acc = 0.855000\n", + "L = 39.650941, acc = 0.855000\n", + "L = 39.632017, acc = 0.855000\n", + "L = 39.613431, acc = 0.855000\n", + "L = 39.595173, acc = 0.855000\n", + "L = 39.577233, acc = 0.855000\n", + "L = 39.559600, acc = 0.855000\n", + "L = 39.542265, acc = 0.855000\n", + "L = 39.525218, acc = 0.855000\n", + "L = 39.508449, acc = 0.855000\n", + "L = 39.491950, acc = 0.855000\n", + "L = 39.475713, acc = 0.855000\n", + "L = 39.459727, acc = 0.855000\n", + "L = 39.443987, acc = 0.855000\n", + "L = 39.428483, acc = 0.855000\n", + "L = 39.413208, acc = 0.855000\n", + "L = 39.398154, acc = 0.855000\n", + "L = 39.383314, acc = 0.855000\n", + "L = 39.368682, acc = 0.855000\n", + "L = 39.354250, acc = 0.855000\n", + "L = 39.340012, acc = 0.855000\n", + "L = 39.325961, acc = 0.860000\n", + "L = 39.312091, acc = 0.860000\n", + "L = 39.298397, acc = 0.860000\n", + "L = 39.284872, acc = 0.860000\n", + "L = 39.271510, acc = 0.860000\n", + "L = 39.258306, acc = 0.860000\n", + "L = 39.245255, acc = 0.860000\n", + "L = 39.232351, acc = 0.860000\n", + "L = 39.219590, acc = 0.860000\n", + "L = 39.206966, acc = 0.860000\n", + "L = 39.194474, acc = 0.860000\n", + "L = 39.182111, acc = 0.860000\n", + "L = 39.169870, acc = 0.860000\n", + "L = 39.157749, acc = 0.860000\n", + "L = 39.145742, acc = 0.860000\n", + "L = 39.133846, acc = 0.850000\n", + "L = 39.122056, acc = 0.850000\n", + "L = 39.110369, acc = 0.850000\n", + "L = 39.098780, acc = 0.850000\n", + "L = 39.087286, acc = 0.850000\n", + "L = 39.075884, acc = 0.850000\n", + "L = 39.064569, acc = 0.850000\n", + "L = 39.053338, acc = 0.850000\n", + "L = 39.042188, acc = 0.850000\n", + "L = 39.031116, acc = 0.850000\n", + "L = 39.020118, acc = 0.850000\n", + "L = 39.009191, acc = 0.850000\n", + "L = 38.998332, acc = 0.850000\n", + "L = 38.987539, acc = 0.850000\n", + "L = 38.976808, acc = 0.850000\n", + "L = 38.966136, acc = 0.850000\n", + "L = 38.955522, acc = 0.850000\n", + "L = 38.944961, acc = 0.850000\n", + "L = 38.934453, acc = 0.850000\n", + "L = 38.923993, acc = 0.855000\n", + "L = 38.913579, acc = 0.855000\n", + "L = 38.903210, acc = 0.855000\n", + "L = 38.892883, acc = 0.855000\n", + "L = 38.882595, acc = 0.855000\n", + "L = 38.872344, acc = 0.855000\n", + "L = 38.862129, acc = 0.855000\n", + "L = 38.851946, acc = 0.855000\n", + "L = 38.841794, acc = 0.855000\n", + "L = 38.831671, acc = 0.855000\n", + "L = 38.821574, acc = 0.855000\n", + "L = 38.811503, acc = 0.855000\n", + "L = 38.801454, acc = 0.855000\n", + "L = 38.791426, acc = 0.855000\n", + "L = 38.781418, acc = 0.855000\n", + "L = 38.771427, acc = 0.855000\n", + "L = 38.761452, acc = 0.855000\n", + "L = 38.751491, acc = 0.855000\n", + "L = 38.741542, acc = 0.855000\n", + "L = 38.731604, acc = 0.855000\n", + "L = 38.721676, acc = 0.855000\n", + "L = 38.711755, acc = 0.855000\n", + "L = 38.701840, acc = 0.855000\n", + "L = 38.691929, acc = 0.855000\n", + "L = 38.682022, acc = 0.855000\n", + "L = 38.672117, acc = 0.855000\n", + "L = 38.662212, acc = 0.855000\n", + "L = 38.652306, acc = 0.855000\n", + "L = 38.642397, acc = 0.855000\n", + "L = 38.632485, acc = 0.855000\n", + "L = 38.622568, acc = 0.855000\n", + "L = 38.612645, acc = 0.855000\n", + "L = 38.602715, acc = 0.855000\n", + "L = 38.592775, acc = 0.855000\n", + "L = 38.582826, acc = 0.855000\n", + "L = 38.572866, acc = 0.855000\n", + "L = 38.562894, acc = 0.855000\n", + "L = 38.552908, acc = 0.855000\n", + "L = 38.542908, acc = 0.855000\n", + "L = 38.532892, acc = 0.855000\n", + "L = 38.522860, acc = 0.855000\n", + "L = 38.512811, acc = 0.855000\n", + "L = 38.502742, acc = 0.855000\n", + "L = 38.492655, acc = 0.855000\n", + "L = 38.482546, acc = 0.855000\n", + "L = 38.472416, acc = 0.855000\n", + "L = 38.462263, acc = 0.855000\n", + "L = 38.452087, acc = 0.855000\n", + "L = 38.441886, acc = 0.855000\n", + "L = 38.431660, acc = 0.855000\n", + "L = 38.421407, acc = 0.855000\n", + "L = 38.411128, acc = 0.855000\n", + "L = 38.400820, acc = 0.855000\n", + "L = 38.390483, acc = 0.855000\n", + "L = 38.380116, acc = 0.855000\n", + "L = 38.369719, acc = 0.855000\n", + "L = 38.359290, acc = 0.855000\n", + "L = 38.348829, acc = 0.855000\n", + "L = 38.338334, acc = 0.855000\n", + "L = 38.327806, acc = 0.855000\n", + "L = 38.317242, acc = 0.855000\n", + "L = 38.306643, acc = 0.855000\n", + "L = 38.296008, acc = 0.855000\n", + "L = 38.285335, acc = 0.855000\n", + "L = 38.274625, acc = 0.855000\n", + "L = 38.263875, acc = 0.855000\n", + "L = 38.253086, acc = 0.855000\n", + "L = 38.242257, acc = 0.855000\n", + "L = 38.231387, acc = 0.855000\n", + "L = 38.220475, acc = 0.855000\n", + "L = 38.209520, acc = 0.855000\n", + "L = 38.198523, acc = 0.855000\n", + "L = 38.187481, acc = 0.855000\n", + "L = 38.176394, acc = 0.855000\n", + "L = 38.165262, acc = 0.855000\n", + "L = 38.154084, acc = 0.855000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L = 38.142859, acc = 0.855000\n", + "L = 38.131586, acc = 0.855000\n", + "L = 38.120265, acc = 0.855000\n", + "L = 38.108895, acc = 0.855000\n", + "L = 38.097475, acc = 0.855000\n", + "L = 38.086004, acc = 0.855000\n", + "L = 38.074483, acc = 0.855000\n", + "L = 38.062909, acc = 0.855000\n", + "L = 38.051283, acc = 0.855000\n", + "L = 38.039603, acc = 0.855000\n", + "L = 38.027870, acc = 0.855000\n", + "L = 38.016082, acc = 0.855000\n", + "L = 38.004238, acc = 0.855000\n", + "L = 37.992338, acc = 0.860000\n", + "L = 37.980381, acc = 0.860000\n", + "L = 37.968367, acc = 0.860000\n", + "L = 37.956295, acc = 0.860000\n", + "L = 37.944163, acc = 0.860000\n", + "L = 37.931972, acc = 0.860000\n", + "L = 37.919720, acc = 0.860000\n", + "L = 37.907408, acc = 0.860000\n", + "L = 37.895033, acc = 0.860000\n", + "L = 37.882596, acc = 0.860000\n", + "L = 37.870096, acc = 0.860000\n", + "L = 37.857532, acc = 0.860000\n", + "L = 37.844903, acc = 0.860000\n", + "L = 37.832209, acc = 0.860000\n", + "L = 37.819448, acc = 0.860000\n", + "L = 37.806621, acc = 0.860000\n", + "L = 37.793727, acc = 0.860000\n", + "L = 37.780763, acc = 0.860000\n", + "L = 37.767731, acc = 0.860000\n", + "L = 37.754630, acc = 0.860000\n", + "L = 37.741457, acc = 0.860000\n", + "L = 37.728214, acc = 0.865000\n", + "L = 37.714898, acc = 0.865000\n", + "L = 37.701510, acc = 0.865000\n", + "L = 37.688048, acc = 0.865000\n", + "L = 37.674512, acc = 0.865000\n", + "L = 37.660902, acc = 0.865000\n", + "L = 37.647215, acc = 0.865000\n", + "L = 37.633452, acc = 0.865000\n", + "L = 37.619612, acc = 0.865000\n", + "L = 37.605693, acc = 0.865000\n", + "L = 37.591696, acc = 0.865000\n", + "L = 37.577620, acc = 0.865000\n", + "L = 37.563463, acc = 0.865000\n", + "L = 37.549225, acc = 0.870000\n", + "L = 37.534905, acc = 0.865000\n", + "L = 37.520503, acc = 0.865000\n", + "L = 37.506017, acc = 0.865000\n", + "L = 37.491447, acc = 0.865000\n", + "L = 37.476792, acc = 0.865000\n", + "L = 37.462052, acc = 0.865000\n", + "L = 37.447224, acc = 0.865000\n", + "L = 37.432310, acc = 0.865000\n", + "L = 37.417307, acc = 0.865000\n", + "L = 37.402215, acc = 0.865000\n", + "L = 37.387033, acc = 0.865000\n", + "L = 37.371761, acc = 0.865000\n", + "L = 37.356398, acc = 0.865000\n", + "L = 37.340942, acc = 0.865000\n", + "L = 37.325393, acc = 0.865000\n", + "L = 37.309751, acc = 0.865000\n", + "L = 37.294013, acc = 0.865000\n", + "L = 37.278181, acc = 0.865000\n", + "L = 37.262252, acc = 0.865000\n", + "L = 37.246226, acc = 0.865000\n", + "L = 37.230101, acc = 0.865000\n", + "L = 37.213879, acc = 0.865000\n", + "L = 37.197556, acc = 0.865000\n", + "L = 37.181133, acc = 0.865000\n", + "L = 37.164609, acc = 0.865000\n", + "L = 37.147983, acc = 0.865000\n", + "L = 37.131254, acc = 0.865000\n", + "L = 37.114421, acc = 0.865000\n", + "L = 37.097483, acc = 0.865000\n", + "L = 37.080440, acc = 0.865000\n", + "L = 37.063291, acc = 0.865000\n", + "L = 37.046035, acc = 0.865000\n", + "L = 37.028670, acc = 0.865000\n", + "L = 37.011197, acc = 0.865000\n", + "L = 36.993614, acc = 0.865000\n", + "L = 36.975921, acc = 0.865000\n", + "L = 36.958116, acc = 0.865000\n", + "L = 36.940199, acc = 0.865000\n", + "L = 36.922169, acc = 0.865000\n", + "L = 36.904025, acc = 0.865000\n", + "L = 36.885767, acc = 0.865000\n", + "L = 36.867393, acc = 0.870000\n", + "L = 36.848902, acc = 0.870000\n", + "L = 36.830294, acc = 0.870000\n", + "L = 36.811568, acc = 0.870000\n", + "L = 36.792724, acc = 0.870000\n", + "L = 36.773759, acc = 0.870000\n", + "L = 36.754674, acc = 0.870000\n", + "L = 36.735467, acc = 0.870000\n", + "L = 36.716138, acc = 0.870000\n", + "L = 36.696686, acc = 0.870000\n", + "L = 36.677111, acc = 0.870000\n", + "L = 36.657410, acc = 0.870000\n", + "L = 36.637584, acc = 0.870000\n", + "L = 36.617631, acc = 0.870000\n", + "L = 36.597551, acc = 0.870000\n", + "L = 36.577343, acc = 0.870000\n", + "L = 36.557006, acc = 0.870000\n", + "L = 36.536540, acc = 0.870000\n", + "L = 36.515943, acc = 0.875000\n", + "L = 36.495215, acc = 0.875000\n", + "L = 36.474354, acc = 0.875000\n", + "L = 36.453361, acc = 0.875000\n", + "L = 36.432234, acc = 0.875000\n", + "L = 36.410972, acc = 0.875000\n", + "L = 36.389576, acc = 0.875000\n", + "L = 36.368043, acc = 0.875000\n", + "L = 36.346373, acc = 0.875000\n", + "L = 36.324566, acc = 0.875000\n", + "L = 36.302620, acc = 0.875000\n", + "L = 36.280535, acc = 0.875000\n", + "L = 36.258310, acc = 0.875000\n", + "L = 36.235944, acc = 0.875000\n", + "L = 36.213437, acc = 0.875000\n", + "L = 36.190788, acc = 0.875000\n", + "L = 36.167996, acc = 0.875000\n", + "L = 36.145060, acc = 0.875000\n", + "L = 36.121980, acc = 0.875000\n", + "L = 36.098755, acc = 0.875000\n", + "L = 36.075384, acc = 0.875000\n", + "L = 36.051866, acc = 0.875000\n", + "L = 36.028201, acc = 0.875000\n", + "L = 36.004388, acc = 0.875000\n", + "L = 35.980427, acc = 0.875000\n", + "L = 35.956316, acc = 0.875000\n", + "L = 35.932055, acc = 0.875000\n", + "L = 35.907644, acc = 0.875000\n", + "L = 35.883081, acc = 0.875000\n", + "L = 35.858366, acc = 0.875000\n", + "L = 35.833499, acc = 0.875000\n", + "L = 35.808478, acc = 0.875000\n", + "L = 35.783303, acc = 0.875000\n", + "L = 35.757974, acc = 0.875000\n", + "L = 35.732489, acc = 0.875000\n", + "L = 35.706849, acc = 0.875000\n", + "L = 35.681052, acc = 0.875000\n", + "L = 35.655099, acc = 0.875000\n", + "L = 35.628988, acc = 0.875000\n", + "L = 35.602718, acc = 0.875000\n", + "L = 35.576290, acc = 0.875000\n", + "L = 35.549703, acc = 0.875000\n", + "L = 35.522956, acc = 0.875000\n", + "L = 35.496049, acc = 0.875000\n", + "L = 35.468980, acc = 0.875000\n", + "L = 35.441751, acc = 0.875000\n", + "L = 35.414359, acc = 0.875000\n", + "L = 35.386805, acc = 0.875000\n", + "L = 35.359088, acc = 0.875000\n", + "L = 35.331208, acc = 0.875000\n", + "L = 35.303164, acc = 0.875000\n", + "L = 35.274956, acc = 0.875000\n", + "L = 35.246582, acc = 0.875000\n", + "L = 35.218044, acc = 0.875000\n", + "L = 35.189340, acc = 0.875000\n", + "L = 35.160470, acc = 0.875000\n", + "L = 35.131434, acc = 0.875000\n", + "L = 35.102230, acc = 0.875000\n", + "L = 35.072860, acc = 0.875000\n", + "L = 35.043321, acc = 0.875000\n", + "L = 35.013615, acc = 0.875000\n", + "L = 34.983741, acc = 0.880000\n", + "L = 34.953697, acc = 0.880000\n", + "L = 34.923485, acc = 0.880000\n", + "L = 34.893103, acc = 0.880000\n", + "L = 34.862552, acc = 0.880000\n", + "L = 34.831831, acc = 0.880000\n", + "L = 34.800940, acc = 0.880000\n", + "L = 34.769878, acc = 0.880000\n", + "L = 34.738645, acc = 0.880000\n", + "L = 34.707242, acc = 0.880000\n", + "L = 34.675667, acc = 0.880000\n", + "L = 34.643920, acc = 0.880000\n", + "L = 34.612002, acc = 0.880000\n", + "L = 34.579912, acc = 0.880000\n", + "L = 34.547650, acc = 0.880000\n", + "L = 34.515216, acc = 0.880000\n", + "L = 34.482609, acc = 0.880000\n", + "L = 34.449830, acc = 0.880000\n", + "L = 34.416878, acc = 0.885000\n", + "L = 34.383754, acc = 0.885000\n", + "L = 34.350456, acc = 0.885000\n", + "L = 34.316985, acc = 0.885000\n", + "L = 34.283341, acc = 0.890000\n", + "L = 34.249524, acc = 0.890000\n", + "L = 34.215534, acc = 0.890000\n", + "L = 34.181370, acc = 0.890000\n", + "L = 34.147033, acc = 0.890000\n", + "L = 34.112523, acc = 0.890000\n", + "L = 34.077839, acc = 0.890000\n", + "L = 34.042982, acc = 0.890000\n", + "L = 34.007951, acc = 0.890000\n", + "L = 33.972747, acc = 0.890000\n", + "L = 33.937370, acc = 0.890000\n", + "L = 33.901819, acc = 0.890000\n", + "L = 33.866095, acc = 0.890000\n", + "L = 33.830199, acc = 0.890000\n", + "L = 33.794129, acc = 0.890000\n", + "L = 33.757886, acc = 0.890000\n", + "L = 33.721471, acc = 0.890000\n", + "L = 33.684882, acc = 0.890000\n", + "L = 33.648122, acc = 0.890000\n", + "L = 33.611189, acc = 0.890000\n", + "L = 33.574083, acc = 0.890000\n", + "L = 33.536806, acc = 0.890000\n", + "L = 33.499357, acc = 0.890000\n", + "L = 33.461737, acc = 0.895000\n", + "L = 33.423945, acc = 0.895000\n", + "L = 33.385982, acc = 0.895000\n", + "L = 33.347848, acc = 0.895000\n", + "L = 33.309543, acc = 0.895000\n", + "L = 33.271069, acc = 0.895000\n", + "L = 33.232424, acc = 0.895000\n", + "L = 33.193610, acc = 0.895000\n", + "L = 33.154626, acc = 0.895000\n", + "L = 33.115473, acc = 0.895000\n", + "L = 33.076151, acc = 0.895000\n", + "L = 33.036662, acc = 0.895000\n", + "L = 32.997004, acc = 0.895000\n", + "L = 32.957179, acc = 0.895000\n", + "L = 32.917186, acc = 0.895000\n", + "L = 32.877027, acc = 0.895000\n", + "L = 32.836701, acc = 0.895000\n", + "L = 32.796210, acc = 0.895000\n", + "L = 32.755553, acc = 0.895000\n", + "L = 32.714731, acc = 0.895000\n", + "L = 32.673745, acc = 0.895000\n", + "L = 32.632595, acc = 0.895000\n", + "L = 32.591282, acc = 0.895000\n", + "L = 32.549805, acc = 0.895000\n", + "L = 32.508166, acc = 0.895000\n", + "L = 32.466366, acc = 0.895000\n", + "L = 32.424404, acc = 0.895000\n", + "L = 32.382281, acc = 0.895000\n", + "L = 32.339998, acc = 0.895000\n", + "L = 32.297556, acc = 0.895000\n", + "L = 32.254955, acc = 0.895000\n", + "L = 32.212196, acc = 0.900000\n", + "L = 32.169279, acc = 0.900000\n", + "L = 32.126206, acc = 0.900000\n", + "L = 32.082976, acc = 0.900000\n", + "L = 32.039590, acc = 0.900000\n", + "L = 31.996050, acc = 0.900000\n", + "L = 31.952356, acc = 0.900000\n", + "L = 31.908508, acc = 0.900000\n", + "L = 31.864507, acc = 0.900000\n", + "L = 31.820355, acc = 0.900000\n", + "L = 31.776051, acc = 0.900000\n", + "L = 31.731597, acc = 0.900000\n", + "L = 31.686994, acc = 0.900000\n", + "L = 31.642241, acc = 0.900000\n", + "L = 31.597341, acc = 0.900000\n", + "L = 31.552294, acc = 0.900000\n", + "L = 31.507100, acc = 0.900000\n", + "L = 31.461761, acc = 0.900000\n", + "L = 31.416278, acc = 0.900000\n", + "L = 31.370651, acc = 0.900000\n", + "L = 31.324881, acc = 0.900000\n", + "L = 31.278969, acc = 0.900000\n", + "L = 31.232916, acc = 0.900000\n", + "L = 31.186724, acc = 0.900000\n", + "L = 31.140392, acc = 0.900000\n", + "L = 31.093922, acc = 0.900000\n", + "L = 31.047316, acc = 0.900000\n", + "L = 31.000573, acc = 0.900000\n", + "L = 30.953695, acc = 0.900000\n", + "L = 30.906683, acc = 0.900000\n", + "L = 30.859538, acc = 0.905000\n", + "L = 30.812261, acc = 0.905000\n", + "L = 30.764853, acc = 0.905000\n", + "L = 30.717315, acc = 0.905000\n", + "L = 30.669648, acc = 0.905000\n", + "L = 30.621854, acc = 0.905000\n", + "L = 30.573933, acc = 0.905000\n", + "L = 30.525886, acc = 0.910000\n", + "L = 30.477715, acc = 0.910000\n", + "L = 30.429421, acc = 0.910000\n", + "L = 30.381005, acc = 0.910000\n", + "L = 30.332468, acc = 0.910000\n", + "L = 30.283811, acc = 0.910000\n", + "L = 30.235036, acc = 0.910000\n", + "L = 30.186143, acc = 0.910000\n", + "L = 30.137135, acc = 0.910000\n", + "L = 30.088011, acc = 0.910000\n", + "L = 30.038774, acc = 0.910000\n", + "L = 29.989424, acc = 0.910000\n", + "L = 29.939963, acc = 0.910000\n", + "L = 29.890392, acc = 0.910000\n", + "L = 29.840713, acc = 0.910000\n", + "L = 29.790926, acc = 0.910000\n", + "L = 29.741034, acc = 0.910000\n", + "L = 29.691036, acc = 0.910000\n", + "L = 29.640935, acc = 0.910000\n", + "L = 29.590733, acc = 0.910000\n", + "L = 29.540429, acc = 0.910000\n", + "L = 29.490027, acc = 0.910000\n", + "L = 29.439526, acc = 0.915000\n", + "L = 29.388929, acc = 0.915000\n", + "L = 29.338237, acc = 0.915000\n", + "L = 29.287451, acc = 0.915000\n", + "L = 29.236573, acc = 0.915000\n", + "L = 29.185604, acc = 0.915000\n", + "L = 29.134546, acc = 0.915000\n", + "L = 29.083399, acc = 0.915000\n", + "L = 29.032166, acc = 0.915000\n", + "L = 28.980848, acc = 0.915000\n", + "L = 28.929446, acc = 0.915000\n", + "L = 28.877963, acc = 0.915000\n", + "L = 28.826398, acc = 0.915000\n", + "L = 28.774755, acc = 0.915000\n", + "L = 28.723034, acc = 0.915000\n", + "L = 28.671237, acc = 0.915000\n", + "L = 28.619366, acc = 0.915000\n", + "L = 28.567421, acc = 0.915000\n", + "L = 28.515405, acc = 0.915000\n", + "L = 28.463320, acc = 0.915000\n", + "L = 28.411166, acc = 0.915000\n", + "L = 28.358945, acc = 0.915000\n", + "L = 28.306660, acc = 0.915000\n", + "L = 28.254311, acc = 0.915000\n", + "L = 28.201900, acc = 0.915000\n", + "L = 28.149428, acc = 0.920000\n", + "L = 28.096899, acc = 0.920000\n", + "L = 28.044312, acc = 0.920000\n", + "L = 27.991670, acc = 0.920000\n", + "L = 27.938974, acc = 0.920000\n", + "L = 27.886226, acc = 0.920000\n", + "L = 27.833427, acc = 0.920000\n", + "L = 27.780580, acc = 0.920000\n", + "L = 27.727686, acc = 0.920000\n", + "L = 27.674747, acc = 0.920000\n", + "L = 27.621764, acc = 0.920000\n", + "L = 27.568739, acc = 0.920000\n", + "L = 27.515673, acc = 0.920000\n", + "L = 27.462569, acc = 0.925000\n", + "L = 27.409429, acc = 0.925000\n", + "L = 27.356253, acc = 0.925000\n", + "L = 27.303043, acc = 0.925000\n", + "L = 27.249802, acc = 0.925000\n", + "L = 27.196531, acc = 0.925000\n", + "L = 27.143232, acc = 0.925000\n", + "L = 27.089906, acc = 0.925000\n", + "L = 27.036556, acc = 0.925000\n", + "L = 26.983183, acc = 0.925000\n", + "L = 26.929788, acc = 0.925000\n", + "L = 26.876374, acc = 0.925000\n", + "L = 26.822943, acc = 0.925000\n", + "L = 26.769495, acc = 0.925000\n", + "L = 26.716034, acc = 0.925000\n", + "L = 26.662560, acc = 0.925000\n", + "L = 26.609075, acc = 0.925000\n", + "L = 26.555582, acc = 0.925000\n", + "L = 26.502081, acc = 0.925000\n", + "L = 26.448576, acc = 0.925000\n", + "L = 26.395067, acc = 0.925000\n", + "L = 26.341556, acc = 0.925000\n", + "L = 26.288045, acc = 0.925000\n", + "L = 26.234536, acc = 0.925000\n", + "L = 26.181031, acc = 0.930000\n", + "L = 26.127532, acc = 0.930000\n", + "L = 26.074040, acc = 0.930000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L = 26.020557, acc = 0.930000\n", + "L = 25.967084, acc = 0.930000\n", + "L = 25.913625, acc = 0.930000\n", + "L = 25.860180, acc = 0.930000\n", + "L = 25.806751, acc = 0.930000\n", + "L = 25.753340, acc = 0.930000\n", + "L = 25.699949, acc = 0.930000\n", + "L = 25.646580, acc = 0.930000\n", + "L = 25.593234, acc = 0.930000\n", + "L = 25.539913, acc = 0.930000\n", + "L = 25.486619, acc = 0.930000\n", + "L = 25.433354, acc = 0.930000\n", + "L = 25.380119, acc = 0.930000\n", + "L = 25.326917, acc = 0.930000\n", + "L = 25.273749, acc = 0.930000\n", + "L = 25.220617, acc = 0.930000\n", + "L = 25.167522, acc = 0.930000\n", + "L = 25.114466, acc = 0.930000\n", + "L = 25.061452, acc = 0.930000\n", + "L = 25.008480, acc = 0.930000\n", + "L = 24.955553, acc = 0.930000\n", + "L = 24.902673, acc = 0.930000\n", + "L = 24.849840, acc = 0.935000\n", + "L = 24.797058, acc = 0.935000\n", + "L = 24.744326, acc = 0.935000\n", + "L = 24.691648, acc = 0.935000\n", + "L = 24.639025, acc = 0.935000\n", + "L = 24.586459, acc = 0.935000\n", + "L = 24.533951, acc = 0.935000\n", + "L = 24.481502, acc = 0.935000\n", + "L = 24.429116, acc = 0.935000\n", + "L = 24.376793, acc = 0.935000\n", + "L = 24.324535, acc = 0.935000\n", + "L = 24.272343, acc = 0.940000\n", + "L = 24.220220, acc = 0.940000\n", + "L = 24.168167, acc = 0.940000\n", + "L = 24.116186, acc = 0.940000\n", + "L = 24.064277, acc = 0.940000\n", + "L = 24.012444, acc = 0.940000\n", + "L = 23.960687, acc = 0.940000\n", + "L = 23.909008, acc = 0.940000\n", + "L = 23.857408, acc = 0.940000\n", + "L = 23.805890, acc = 0.940000\n", + "L = 23.754455, acc = 0.940000\n", + "L = 23.703103, acc = 0.940000\n", + "L = 23.651838, acc = 0.940000\n", + "L = 23.600660, acc = 0.940000\n", + "L = 23.549570, acc = 0.940000\n", + "L = 23.498571, acc = 0.940000\n", + "L = 23.447664, acc = 0.940000\n", + "L = 23.396850, acc = 0.940000\n", + "L = 23.346131, acc = 0.940000\n", + "L = 23.295508, acc = 0.940000\n", + "L = 23.244983, acc = 0.940000\n", + "L = 23.194557, acc = 0.940000\n", + "L = 23.144231, acc = 0.940000\n", + "L = 23.094008, acc = 0.940000\n", + "L = 23.043887, acc = 0.940000\n", + "L = 22.993871, acc = 0.940000\n", + "L = 22.943961, acc = 0.940000\n", + "L = 22.894159, acc = 0.940000\n", + "L = 22.844465, acc = 0.940000\n", + "L = 22.794881, acc = 0.940000\n", + "L = 22.745409, acc = 0.940000\n", + "L = 22.696048, acc = 0.940000\n", + "L = 22.646802, acc = 0.940000\n", + "L = 22.597671, acc = 0.945000\n", + "L = 22.548656, acc = 0.945000\n", + "L = 22.499758, acc = 0.945000\n", + "L = 22.450980, acc = 0.945000\n", + "L = 22.402321, acc = 0.945000\n", + "L = 22.353783, acc = 0.945000\n", + "L = 22.305367, acc = 0.945000\n", + "L = 22.257075, acc = 0.945000\n", + "L = 22.208907, acc = 0.945000\n", + "L = 22.160865, acc = 0.945000\n", + "L = 22.112950, acc = 0.945000\n", + "L = 22.065162, acc = 0.945000\n", + "L = 22.017503, acc = 0.945000\n", + "L = 21.969973, acc = 0.945000\n", + "L = 21.922575, acc = 0.945000\n", + "L = 21.875308, acc = 0.945000\n", + "L = 21.828175, acc = 0.945000\n", + "L = 21.781174, acc = 0.945000\n", + "L = 21.734309, acc = 0.945000\n", + "L = 21.687579, acc = 0.945000\n", + "L = 21.640986, acc = 0.945000\n", + "L = 21.594530, acc = 0.945000\n", + "L = 21.548213, acc = 0.945000\n", + "L = 21.502034, acc = 0.945000\n", + "L = 21.455996, acc = 0.945000\n", + "L = 21.410098, acc = 0.945000\n", + "L = 21.364342, acc = 0.945000\n", + "L = 21.318729, acc = 0.945000\n", + "L = 21.273258, acc = 0.945000\n", + "L = 21.227932, acc = 0.945000\n", + "L = 21.182750, acc = 0.945000\n", + "L = 21.137714, acc = 0.945000\n", + "L = 21.092823, acc = 0.945000\n", + "L = 21.048079, acc = 0.945000\n", + "L = 21.003483, acc = 0.945000\n", + "L = 20.959034, acc = 0.945000\n", + "L = 20.914734, acc = 0.945000\n", + "L = 20.870584, acc = 0.945000\n", + "L = 20.826583, acc = 0.945000\n", + "L = 20.782732, acc = 0.945000\n", + "L = 20.739032, acc = 0.945000\n", + "L = 20.695484, acc = 0.945000\n", + "L = 20.652088, acc = 0.945000\n", + "L = 20.608844, acc = 0.945000\n", + "L = 20.565752, acc = 0.945000\n", + "L = 20.522815, acc = 0.945000\n", + "L = 20.480031, acc = 0.945000\n", + "L = 20.437401, acc = 0.945000\n", + "L = 20.394926, acc = 0.945000\n", + "L = 20.352605, acc = 0.945000\n", + "L = 20.310440, acc = 0.945000\n", + "L = 20.268431, acc = 0.945000\n", + "L = 20.226578, acc = 0.945000\n", + "L = 20.184881, acc = 0.945000\n", + "L = 20.143341, acc = 0.945000\n", + "L = 20.101957, acc = 0.945000\n", + "L = 20.060731, acc = 0.945000\n", + "L = 20.019662, acc = 0.945000\n", + "L = 19.978751, acc = 0.945000\n", + "L = 19.937997, acc = 0.945000\n", + "L = 19.897402, acc = 0.945000\n", + "L = 19.856965, acc = 0.945000\n", + "L = 19.816686, acc = 0.945000\n", + "L = 19.776566, acc = 0.945000\n", + "L = 19.736604, acc = 0.945000\n", + "L = 19.696802, acc = 0.945000\n", + "L = 19.657158, acc = 0.945000\n", + "L = 19.617673, acc = 0.945000\n", + "L = 19.578347, acc = 0.945000\n", + "L = 19.539180, acc = 0.945000\n", + "L = 19.500173, acc = 0.945000\n", + "L = 19.461324, acc = 0.945000\n", + "L = 19.422635, acc = 0.945000\n", + "L = 19.384105, acc = 0.945000\n", + "L = 19.345734, acc = 0.945000\n", + "L = 19.307522, acc = 0.945000\n", + "L = 19.269469, acc = 0.945000\n", + "L = 19.231575, acc = 0.945000\n", + "L = 19.193840, acc = 0.945000\n", + "L = 19.156264, acc = 0.945000\n", + "L = 19.118847, acc = 0.945000\n", + "L = 19.081588, acc = 0.945000\n", + "L = 19.044488, acc = 0.945000\n", + "L = 19.007546, acc = 0.945000\n", + "L = 18.970763, acc = 0.945000\n", + "L = 18.934137, acc = 0.945000\n", + "L = 18.897670, acc = 0.945000\n", + "L = 18.861360, acc = 0.945000\n", + "L = 18.825208, acc = 0.945000\n", + "L = 18.789212, acc = 0.945000\n", + "L = 18.753374, acc = 0.945000\n", + "L = 18.717693, acc = 0.945000\n", + "L = 18.682169, acc = 0.945000\n", + "L = 18.646800, acc = 0.945000\n", + "L = 18.611588, acc = 0.945000\n", + "L = 18.576532, acc = 0.945000\n", + "L = 18.541631, acc = 0.945000\n", + "L = 18.506885, acc = 0.945000\n", + "L = 18.472295, acc = 0.945000\n", + "L = 18.437858, acc = 0.945000\n", + "L = 18.403577, acc = 0.945000\n", + "L = 18.369449, acc = 0.945000\n", + "L = 18.335475, acc = 0.945000\n", + "L = 18.301654, acc = 0.945000\n", + "L = 18.267985, acc = 0.945000\n", + "L = 18.234470, acc = 0.945000\n", + "L = 18.201106, acc = 0.945000\n", + "L = 18.167895, acc = 0.945000\n", + "L = 18.134835, acc = 0.945000\n", + "L = 18.101925, acc = 0.945000\n", + "L = 18.069167, acc = 0.945000\n", + "L = 18.036558, acc = 0.945000\n", + "L = 18.004099, acc = 0.945000\n", + "L = 17.971790, acc = 0.945000\n", + "L = 17.939629, acc = 0.945000\n", + "L = 17.907617, acc = 0.945000\n", + "L = 17.875753, acc = 0.945000\n", + "L = 17.844036, acc = 0.945000\n", + "L = 17.812467, acc = 0.945000\n", + "L = 17.781044, acc = 0.945000\n", + "L = 17.749767, acc = 0.945000\n", + "L = 17.718636, acc = 0.945000\n", + "L = 17.687650, acc = 0.945000\n", + "L = 17.656809, acc = 0.945000\n", + "L = 17.626111, acc = 0.945000\n", + "L = 17.595558, acc = 0.945000\n", + "L = 17.565148, acc = 0.945000\n", + "L = 17.534880, acc = 0.945000\n", + "L = 17.504755, acc = 0.945000\n", + "L = 17.474771, acc = 0.945000\n", + "L = 17.444929, acc = 0.945000\n", + "L = 17.415227, acc = 0.945000\n", + "L = 17.385665, acc = 0.945000\n", + "L = 17.356243, acc = 0.945000\n", + "L = 17.326960, acc = 0.945000\n", + "L = 17.297815, acc = 0.945000\n", + "L = 17.268808, acc = 0.945000\n", + "L = 17.239939, acc = 0.945000\n", + "L = 17.211206, acc = 0.945000\n", + "L = 17.182610, acc = 0.950000\n", + "L = 17.154149, acc = 0.950000\n", + "L = 17.125824, acc = 0.950000\n", + "L = 17.097633, acc = 0.950000\n", + "L = 17.069577, acc = 0.950000\n", + "L = 17.041653, acc = 0.950000\n", + "L = 17.013863, acc = 0.950000\n", + "L = 16.986205, acc = 0.950000\n", + "L = 16.958679, acc = 0.950000\n", + "L = 16.931284, acc = 0.950000\n", + "L = 16.904020, acc = 0.950000\n", + "L = 16.876886, acc = 0.950000\n", + "L = 16.849881, acc = 0.950000\n", + "L = 16.823006, acc = 0.950000\n", + "L = 16.796258, acc = 0.950000\n", + "L = 16.769639, acc = 0.950000\n", + "L = 16.743146, acc = 0.950000\n", + "L = 16.716780, acc = 0.950000\n", + "L = 16.690540, acc = 0.950000\n", + "L = 16.664426, acc = 0.950000\n", + "L = 16.638436, acc = 0.950000\n", + "L = 16.612571, acc = 0.950000\n", + "L = 16.586829, acc = 0.950000\n", + "L = 16.561211, acc = 0.950000\n", + "L = 16.535715, acc = 0.950000\n", + "L = 16.510341, acc = 0.950000\n", + "L = 16.485088, acc = 0.950000\n", + "L = 16.459956, acc = 0.950000\n", + "L = 16.434944, acc = 0.950000\n", + "L = 16.410051, acc = 0.950000\n", + "L = 16.385278, acc = 0.950000\n", + "L = 16.360623, acc = 0.950000\n", + "L = 16.336085, acc = 0.950000\n", + "L = 16.311665, acc = 0.950000\n", + "L = 16.287362, acc = 0.950000\n", + "L = 16.263175, acc = 0.950000\n", + "L = 16.239103, acc = 0.955000\n", + "L = 16.215146, acc = 0.955000\n", + "L = 16.191303, acc = 0.955000\n", + "L = 16.167574, acc = 0.955000\n", + "L = 16.143958, acc = 0.955000\n", + "L = 16.120455, acc = 0.955000\n", + "L = 16.097064, acc = 0.955000\n", + "L = 16.073784, acc = 0.955000\n", + "L = 16.050615, acc = 0.955000\n", + "L = 16.027556, acc = 0.955000\n", + "L = 16.004606, acc = 0.955000\n", + "L = 15.981766, acc = 0.955000\n", + "L = 15.959035, acc = 0.955000\n", + "L = 15.936411, acc = 0.955000\n", + "L = 15.913895, acc = 0.955000\n", + "L = 15.891485, acc = 0.955000\n", + "L = 15.869182, acc = 0.955000\n", + "L = 15.846984, acc = 0.955000\n", + "L = 15.824892, acc = 0.955000\n", + "L = 15.802904, acc = 0.955000\n", + "L = 15.781020, acc = 0.955000\n", + "L = 15.759239, acc = 0.955000\n", + "L = 15.737561, acc = 0.955000\n", + "L = 15.715986, acc = 0.955000\n", + "L = 15.694512, acc = 0.955000\n", + "L = 15.673140, acc = 0.955000\n", + "L = 15.651868, acc = 0.955000\n", + "L = 15.630696, acc = 0.955000\n", + "L = 15.609624, acc = 0.955000\n", + "L = 15.588651, acc = 0.955000\n", + "L = 15.567776, acc = 0.955000\n", + "L = 15.546999, acc = 0.955000\n", + "L = 15.526320, acc = 0.955000\n", + "L = 15.505737, acc = 0.955000\n", + "L = 15.485251, acc = 0.955000\n", + "L = 15.464860, acc = 0.955000\n", + "L = 15.444565, acc = 0.955000\n", + "L = 15.424364, acc = 0.955000\n", + "L = 15.404258, acc = 0.955000\n", + "L = 15.384245, acc = 0.955000\n", + "L = 15.364325, acc = 0.955000\n", + "L = 15.344498, acc = 0.955000\n", + "L = 15.324763, acc = 0.955000\n", + "L = 15.305119, acc = 0.955000\n", + "L = 15.285567, acc = 0.955000\n", + "L = 15.266105, acc = 0.955000\n", + "L = 15.246733, acc = 0.955000\n", + "L = 15.227450, acc = 0.955000\n", + "L = 15.208257, acc = 0.955000\n", + "L = 15.189152, acc = 0.955000\n", + "L = 15.170135, acc = 0.955000\n", + "L = 15.151205, acc = 0.955000\n", + "L = 15.132362, acc = 0.955000\n", + "L = 15.113606, acc = 0.955000\n", + "L = 15.094936, acc = 0.955000\n", + "L = 15.076352, acc = 0.955000\n", + "L = 15.057852, acc = 0.955000\n", + "L = 15.039437, acc = 0.955000\n", + "L = 15.021106, acc = 0.955000\n", + "L = 15.002859, acc = 0.955000\n", + "L = 14.984695, acc = 0.955000\n", + "L = 14.966613, acc = 0.955000\n", + "L = 14.948613, acc = 0.960000\n", + "L = 14.930695, acc = 0.960000\n", + "L = 14.912859, acc = 0.960000\n", + "L = 14.895103, acc = 0.960000\n", + "L = 14.877427, acc = 0.960000\n", + "L = 14.859831, acc = 0.960000\n", + "L = 14.842314, acc = 0.960000\n", + "L = 14.824876, acc = 0.960000\n", + "L = 14.807517, acc = 0.960000\n", + "L = 14.790236, acc = 0.960000\n", + "L = 14.773032, acc = 0.960000\n", + "L = 14.755905, acc = 0.960000\n", + "L = 14.738855, acc = 0.960000\n", + "L = 14.721881, acc = 0.960000\n", + "L = 14.704982, acc = 0.960000\n", + "L = 14.688159, acc = 0.960000\n", + "L = 14.671411, acc = 0.960000\n", + "L = 14.654737, acc = 0.960000\n", + "L = 14.638137, acc = 0.960000\n", + "L = 14.621611, acc = 0.960000\n", + "L = 14.605157, acc = 0.960000\n", + "L = 14.588777, acc = 0.960000\n", + "L = 14.572468, acc = 0.960000\n", + "L = 14.556232, acc = 0.960000\n", + "L = 14.540067, acc = 0.960000\n", + "L = 14.523973, acc = 0.960000\n", + "L = 14.507949, acc = 0.960000\n", + "L = 14.491996, acc = 0.960000\n", + "L = 14.476113, acc = 0.960000\n", + "L = 14.460298, acc = 0.960000\n", + "L = 14.444553, acc = 0.960000\n", + "L = 14.428877, acc = 0.960000\n", + "L = 14.413268, acc = 0.960000\n", + "L = 14.397727, acc = 0.960000\n", + "L = 14.382254, acc = 0.960000\n", + "L = 14.366847, acc = 0.960000\n", + "L = 14.351507, acc = 0.960000\n", + "L = 14.336234, acc = 0.960000\n", + "L = 14.321026, acc = 0.960000\n", + "L = 14.305883, acc = 0.960000\n", + "L = 14.290805, acc = 0.960000\n", + "L = 14.275793, acc = 0.960000\n", + "L = 14.260844, acc = 0.960000\n", + "L = 14.245959, acc = 0.960000\n", + "L = 14.231138, acc = 0.960000\n", + "L = 14.216380, acc = 0.960000\n", + "L = 14.201684, acc = 0.960000\n", + "L = 14.187051, acc = 0.960000\n", + "L = 14.172480, acc = 0.960000\n", + "L = 14.157971, acc = 0.960000\n", + "L = 14.143523, acc = 0.960000\n", + "L = 14.129136, acc = 0.960000\n", + "L = 14.114810, acc = 0.960000\n", + "L = 14.100543, acc = 0.960000\n", + "L = 14.086337, acc = 0.960000\n", + "L = 14.072190, acc = 0.960000\n", + "L = 14.058102, acc = 0.960000\n", + "L = 14.044074, acc = 0.960000\n", + "L = 14.030103, acc = 0.960000\n", + "L = 14.016191, acc = 0.960000\n", + "L = 14.002337, acc = 0.960000\n", + "L = 13.988540, acc = 0.960000\n", + "L = 13.974800, acc = 0.960000\n", + "L = 13.961117, acc = 0.960000\n", + "L = 13.947491, acc = 0.960000\n", + "L = 13.933920, acc = 0.960000\n", + "L = 13.920406, acc = 0.960000\n", + "L = 13.906947, acc = 0.960000\n", + "L = 13.893543, acc = 0.960000\n", + "L = 13.880194, acc = 0.960000\n", + "L = 13.866899, acc = 0.960000\n", + "L = 13.853659, acc = 0.960000\n", + "L = 13.840472, acc = 0.960000\n", + "L = 13.827339, acc = 0.960000\n", + "L = 13.814260, acc = 0.960000\n", + "L = 13.801233, acc = 0.960000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L = 13.788259, acc = 0.960000\n", + "L = 13.775337, acc = 0.960000\n", + "L = 13.762467, acc = 0.965000\n", + "L = 13.749649, acc = 0.965000\n", + "L = 13.736883, acc = 0.965000\n", + "L = 13.724167, acc = 0.965000\n", + "L = 13.711503, acc = 0.965000\n", + "L = 13.698888, acc = 0.965000\n", + "L = 13.686324, acc = 0.965000\n", + "L = 13.673810, acc = 0.965000\n", + "L = 13.661346, acc = 0.965000\n", + "L = 13.648931, acc = 0.965000\n", + "L = 13.636565, acc = 0.965000\n", + "L = 13.624248, acc = 0.965000\n", + "L = 13.611980, acc = 0.965000\n", + "L = 13.599759, acc = 0.965000\n", + "L = 13.587587, acc = 0.965000\n", + "L = 13.575462, acc = 0.965000\n", + "L = 13.563385, acc = 0.965000\n", + "L = 13.551354, acc = 0.965000\n", + "L = 13.539371, acc = 0.965000\n", + "L = 13.527434, acc = 0.965000\n", + "L = 13.515544, acc = 0.965000\n", + "L = 13.503699, acc = 0.965000\n", + "L = 13.491901, acc = 0.965000\n", + "L = 13.480147, acc = 0.965000\n", + "L = 13.468439, acc = 0.965000\n", + "L = 13.456776, acc = 0.965000\n", + "L = 13.445158, acc = 0.965000\n", + "L = 13.433585, acc = 0.965000\n", + "L = 13.422055, acc = 0.965000\n", + "L = 13.410569, acc = 0.965000\n", + "L = 13.399128, acc = 0.965000\n", + "L = 13.387729, acc = 0.965000\n", + "L = 13.376374, acc = 0.965000\n", + "L = 13.365062, acc = 0.965000\n", + "L = 13.353792, acc = 0.965000\n", + "L = 13.342565, acc = 0.965000\n", + "L = 13.331380, acc = 0.965000\n", + "L = 13.320237, acc = 0.965000\n", + "L = 13.309136, acc = 0.965000\n", + "L = 13.298077, acc = 0.965000\n", + "L = 13.287059, acc = 0.965000\n", + "L = 13.276081, acc = 0.965000\n", + "L = 13.265145, acc = 0.965000\n", + "L = 13.254249, acc = 0.965000\n", + "L = 13.243393, acc = 0.965000\n", + "L = 13.232578, acc = 0.965000\n", + "L = 13.221802, acc = 0.965000\n", + "L = 13.211066, acc = 0.965000\n", + "L = 13.200370, acc = 0.965000\n", + "L = 13.189712, acc = 0.965000\n", + "L = 13.179094, acc = 0.965000\n", + "L = 13.168515, acc = 0.965000\n", + "L = 13.157973, acc = 0.965000\n", + "L = 13.147471, acc = 0.965000\n", + "L = 13.137006, acc = 0.965000\n", + "L = 13.126579, acc = 0.965000\n", + "L = 13.116190, acc = 0.965000\n", + "L = 13.105839, acc = 0.965000\n", + "L = 13.095525, acc = 0.965000\n", + "L = 13.085247, acc = 0.965000\n", + "L = 13.075007, acc = 0.965000\n", + "L = 13.064803, acc = 0.965000\n", + "L = 13.054636, acc = 0.965000\n", + "L = 13.044504, acc = 0.965000\n", + "L = 13.034409, acc = 0.965000\n", + "L = 13.024350, acc = 0.965000\n", + "L = 13.014326, acc = 0.965000\n", + "L = 13.004338, acc = 0.965000\n", + "L = 12.994385, acc = 0.965000\n", + "L = 12.984467, acc = 0.965000\n", + "L = 12.974583, acc = 0.965000\n", + "L = 12.964735, acc = 0.965000\n", + "L = 12.954920, acc = 0.965000\n", + "L = 12.945140, acc = 0.965000\n", + "L = 12.935394, acc = 0.965000\n", + "L = 12.925682, acc = 0.965000\n", + "L = 12.916004, acc = 0.965000\n", + "L = 12.906359, acc = 0.965000\n", + "L = 12.896747, acc = 0.965000\n", + "L = 12.887169, acc = 0.965000\n", + "L = 12.877623, acc = 0.965000\n", + "L = 12.868110, acc = 0.965000\n", + "L = 12.858630, acc = 0.965000\n", + "L = 12.849182, acc = 0.965000\n", + "L = 12.839766, acc = 0.965000\n", + "L = 12.830383, acc = 0.965000\n", + "L = 12.821031, acc = 0.965000\n", + "L = 12.811711, acc = 0.965000\n", + "L = 12.802422, acc = 0.965000\n", + "L = 12.793165, acc = 0.965000\n", + "L = 12.783939, acc = 0.965000\n", + "L = 12.774743, acc = 0.965000\n", + "L = 12.765579, acc = 0.965000\n", + "L = 12.756445, acc = 0.965000\n", + "L = 12.747342, acc = 0.965000\n", + "L = 12.738269, acc = 0.965000\n", + "L = 12.729226, acc = 0.965000\n", + "L = 12.720214, acc = 0.965000\n", + "L = 12.711231, acc = 0.965000\n", + "L = 12.702277, acc = 0.965000\n", + "L = 12.693354, acc = 0.965000\n", + "L = 12.684459, acc = 0.965000\n", + "L = 12.675594, acc = 0.965000\n", + "L = 12.666757, acc = 0.965000\n", + "L = 12.657950, acc = 0.965000\n", + "L = 12.649171, acc = 0.965000\n", + "L = 12.640421, acc = 0.965000\n", + "L = 12.631699, acc = 0.965000\n", + "L = 12.623006, acc = 0.965000\n", + "L = 12.614340, acc = 0.965000\n", + "L = 12.605703, acc = 0.965000\n", + "L = 12.597093, acc = 0.965000\n", + "L = 12.588511, acc = 0.965000\n", + "L = 12.579956, acc = 0.965000\n", + "L = 12.571429, acc = 0.965000\n", + "L = 12.562928, acc = 0.965000\n", + "L = 12.554455, acc = 0.965000\n", + "L = 12.546009, acc = 0.965000\n", + "L = 12.537590, acc = 0.965000\n", + "L = 12.529197, acc = 0.965000\n", + "L = 12.520831, acc = 0.965000\n", + "L = 12.512491, acc = 0.965000\n", + "L = 12.504177, acc = 0.965000\n", + "L = 12.495889, acc = 0.965000\n", + "L = 12.487627, acc = 0.965000\n", + "L = 12.479391, acc = 0.965000\n", + "L = 12.471180, acc = 0.965000\n", + "L = 12.462995, acc = 0.965000\n", + "L = 12.454836, acc = 0.965000\n", + "L = 12.446701, acc = 0.965000\n", + "L = 12.438592, acc = 0.965000\n", + "L = 12.430508, acc = 0.965000\n", + "L = 12.422448, acc = 0.965000\n", + "L = 12.414413, acc = 0.965000\n", + "L = 12.406403, acc = 0.965000\n", + "L = 12.398417, acc = 0.965000\n", + "L = 12.390456, acc = 0.965000\n", + "L = 12.382519, acc = 0.965000\n", + "L = 12.374605, acc = 0.965000\n", + "L = 12.366716, acc = 0.965000\n", + "L = 12.358851, acc = 0.965000\n", + "L = 12.351009, acc = 0.965000\n", + "L = 12.343190, acc = 0.965000\n", + "L = 12.335396, acc = 0.965000\n", + "L = 12.327624, acc = 0.965000\n", + "L = 12.319876, acc = 0.965000\n", + "L = 12.312151, acc = 0.965000\n", + "L = 12.304448, acc = 0.965000\n", + "L = 12.296769, acc = 0.965000\n", + "L = 12.289112, acc = 0.965000\n", + "L = 12.281478, acc = 0.965000\n", + "L = 12.273866, acc = 0.965000\n", + "L = 12.266277, acc = 0.965000\n", + "L = 12.258710, acc = 0.965000\n", + "L = 12.251165, acc = 0.965000\n", + "L = 12.243642, acc = 0.965000\n", + "L = 12.236141, acc = 0.965000\n", + "L = 12.228662, acc = 0.965000\n", + "L = 12.221204, acc = 0.965000\n", + "L = 12.213768, acc = 0.965000\n", + "L = 12.206354, acc = 0.965000\n", + "L = 12.198961, acc = 0.965000\n", + "L = 12.191589, acc = 0.965000\n", + "L = 12.184238, acc = 0.965000\n", + "L = 12.176908, acc = 0.965000\n", + "L = 12.169599, acc = 0.965000\n", + "L = 12.162311, acc = 0.965000\n", + "L = 12.155044, acc = 0.965000\n", + "L = 12.147797, acc = 0.965000\n", + "L = 12.140571, acc = 0.965000\n", + "L = 12.133365, acc = 0.965000\n", + "L = 12.126180, acc = 0.965000\n", + "L = 12.119015, acc = 0.965000\n", + "L = 12.111869, acc = 0.965000\n", + "L = 12.104744, acc = 0.965000\n", + "L = 12.097639, acc = 0.965000\n", + "L = 12.090553, acc = 0.965000\n", + "L = 12.083487, acc = 0.965000\n", + "L = 12.076441, acc = 0.965000\n", + "L = 12.069414, acc = 0.965000\n", + "L = 12.062407, acc = 0.965000\n", + "L = 12.055419, acc = 0.965000\n", + "L = 12.048450, acc = 0.965000\n", + "L = 12.041500, acc = 0.965000\n", + "L = 12.034569, acc = 0.965000\n", + "L = 12.027657, acc = 0.965000\n", + "L = 12.020764, acc = 0.965000\n", + "L = 12.013890, acc = 0.965000\n", + "L = 12.007034, acc = 0.965000\n", + "L = 12.000197, acc = 0.965000\n", + "L = 11.993379, acc = 0.965000\n", + "L = 11.986578, acc = 0.965000\n", + "L = 11.979796, acc = 0.965000\n", + "L = 11.973033, acc = 0.965000\n", + "L = 11.966287, acc = 0.965000\n", + "L = 11.959559, acc = 0.965000\n", + "L = 11.952849, acc = 0.965000\n", + "L = 11.946157, acc = 0.965000\n", + "L = 11.939483, acc = 0.965000\n", + "L = 11.932827, acc = 0.965000\n", + "L = 11.926188, acc = 0.965000\n", + "L = 11.919566, acc = 0.965000\n", + "L = 11.912962, acc = 0.965000\n", + "L = 11.906376, acc = 0.965000\n", + "L = 11.899806, acc = 0.965000\n", + "L = 11.893254, acc = 0.965000\n", + "L = 11.886718, acc = 0.965000\n", + "L = 11.880200, acc = 0.965000\n", + "L = 11.873699, acc = 0.965000\n", + "L = 11.867214, acc = 0.965000\n", + "L = 11.860747, acc = 0.965000\n", + "L = 11.854295, acc = 0.965000\n", + "L = 11.847861, acc = 0.965000\n", + "L = 11.841443, acc = 0.965000\n", + "L = 11.835041, acc = 0.965000\n", + "L = 11.828656, acc = 0.965000\n", + "L = 11.822287, acc = 0.965000\n", + "L = 11.815935, acc = 0.965000\n", + "L = 11.809598, acc = 0.965000\n", + "L = 11.803278, acc = 0.965000\n", + "L = 11.796973, acc = 0.965000\n", + "L = 11.790684, acc = 0.965000\n", + "L = 11.784412, acc = 0.965000\n", + "L = 11.778154, acc = 0.965000\n", + "L = 11.771913, acc = 0.965000\n", + "L = 11.765687, acc = 0.965000\n", + "L = 11.759477, acc = 0.965000\n", + "L = 11.753282, acc = 0.970000\n", + "L = 11.747103, acc = 0.970000\n", + "L = 11.740939, acc = 0.970000\n", + "L = 11.734790, acc = 0.970000\n", + "L = 11.728656, acc = 0.970000\n", + "L = 11.722538, acc = 0.970000\n", + "L = 11.716434, acc = 0.970000\n", + "L = 11.710346, acc = 0.970000\n", + "L = 11.704272, acc = 0.970000\n", + "L = 11.698213, acc = 0.970000\n", + "L = 11.692169, acc = 0.970000\n", + "L = 11.686140, acc = 0.970000\n", + "L = 11.680125, acc = 0.970000\n", + "L = 11.674125, acc = 0.970000\n", + "L = 11.668140, acc = 0.970000\n", + "L = 11.662169, acc = 0.970000\n", + "L = 11.656212, acc = 0.970000\n", + "L = 11.650269, acc = 0.970000\n", + "L = 11.644341, acc = 0.970000\n", + "L = 11.638427, acc = 0.970000\n", + "L = 11.632527, acc = 0.970000\n", + "L = 11.626641, acc = 0.970000\n", + "L = 11.620769, acc = 0.970000\n", + "L = 11.614911, acc = 0.970000\n", + "L = 11.609067, acc = 0.970000\n", + "L = 11.603237, acc = 0.970000\n", + "L = 11.597420, acc = 0.970000\n", + "L = 11.591618, acc = 0.970000\n", + "L = 11.585828, acc = 0.970000\n", + "L = 11.580053, acc = 0.970000\n", + "L = 11.574291, acc = 0.970000\n", + "L = 11.568542, acc = 0.970000\n", + "L = 11.562807, acc = 0.970000\n", + "L = 11.557085, acc = 0.970000\n", + "L = 11.551376, acc = 0.970000\n", + "L = 11.545680, acc = 0.970000\n", + "L = 11.539998, acc = 0.970000\n", + "L = 11.534329, acc = 0.970000\n", + "L = 11.528673, acc = 0.970000\n", + "L = 11.523029, acc = 0.970000\n", + "L = 11.517399, acc = 0.970000\n", + "L = 11.511782, acc = 0.970000\n", + "L = 11.506177, acc = 0.970000\n", + "L = 11.500585, acc = 0.970000\n", + "L = 11.495006, acc = 0.970000\n", + "L = 11.489440, acc = 0.970000\n", + "L = 11.483886, acc = 0.970000\n", + "L = 11.478345, acc = 0.970000\n", + "L = 11.472816, acc = 0.970000\n", + "L = 11.467300, acc = 0.970000\n", + "L = 11.461796, acc = 0.970000\n", + "L = 11.456304, acc = 0.970000\n", + "L = 11.450825, acc = 0.970000\n", + "L = 11.445358, acc = 0.970000\n", + "L = 11.439903, acc = 0.970000\n", + "L = 11.434461, acc = 0.970000\n", + "L = 11.429030, acc = 0.970000\n", + "L = 11.423612, acc = 0.970000\n", + "L = 11.418205, acc = 0.970000\n", + "L = 11.412810, acc = 0.970000\n", + "L = 11.407428, acc = 0.970000\n", + "L = 11.402057, acc = 0.970000\n", + "L = 11.396698, acc = 0.970000\n", + "L = 11.391351, acc = 0.970000\n", + "L = 11.386015, acc = 0.970000\n", + "L = 11.380691, acc = 0.970000\n", + "L = 11.375379, acc = 0.970000\n", + "L = 11.370078, acc = 0.970000\n", + "L = 11.364789, acc = 0.970000\n", + "L = 11.359511, acc = 0.970000\n", + "L = 11.354245, acc = 0.970000\n", + "L = 11.348990, acc = 0.970000\n", + "L = 11.343746, acc = 0.970000\n", + "L = 11.338514, acc = 0.970000\n", + "L = 11.333293, acc = 0.970000\n", + "L = 11.328083, acc = 0.970000\n", + "L = 11.322884, acc = 0.970000\n", + "L = 11.317696, acc = 0.970000\n", + "L = 11.312520, acc = 0.970000\n", + "L = 11.307354, acc = 0.970000\n", + "L = 11.302200, acc = 0.970000\n", + "L = 11.297056, acc = 0.970000\n", + "L = 11.291923, acc = 0.970000\n", + "L = 11.286802, acc = 0.970000\n", + "L = 11.281691, acc = 0.970000\n", + "L = 11.276590, acc = 0.970000\n", + "L = 11.271501, acc = 0.970000\n", + "L = 11.266422, acc = 0.970000\n", + "L = 11.261354, acc = 0.970000\n", + "L = 11.256296, acc = 0.970000\n", + "L = 11.251249, acc = 0.970000\n", + "L = 11.246213, acc = 0.970000\n", + "L = 11.241187, acc = 0.970000\n", + "L = 11.236171, acc = 0.970000\n", + "L = 11.231166, acc = 0.970000\n", + "L = 11.226172, acc = 0.970000\n", + "L = 11.221187, acc = 0.970000\n", + "L = 11.216213, acc = 0.970000\n", + "L = 11.211249, acc = 0.970000\n", + "L = 11.206296, acc = 0.970000\n", + "L = 11.201352, acc = 0.970000\n", + "L = 11.196419, acc = 0.970000\n", + "L = 11.191496, acc = 0.970000\n", + "L = 11.186583, acc = 0.970000\n", + "L = 11.181680, acc = 0.970000\n", + "L = 11.176787, acc = 0.970000\n", + "L = 11.171904, acc = 0.970000\n", + "L = 11.167031, acc = 0.970000\n", + "L = 11.162167, acc = 0.970000\n", + "L = 11.157314, acc = 0.970000\n", + "L = 11.152470, acc = 0.970000\n", + "L = 11.147637, acc = 0.970000\n", + "L = 11.142813, acc = 0.970000\n", + "L = 11.137998, acc = 0.970000\n", + "L = 11.133194, acc = 0.970000\n", + "L = 11.128399, acc = 0.970000\n", + "L = 11.123613, acc = 0.970000\n", + "L = 11.118838, acc = 0.970000\n", + "L = 11.114072, acc = 0.970000\n", + "L = 11.109315, acc = 0.970000\n", + "L = 11.104568, acc = 0.970000\n", + "L = 11.099830, acc = 0.970000\n", + "L = 11.095102, acc = 0.970000\n", + "L = 11.090383, acc = 0.970000\n", + "L = 11.085673, acc = 0.970000\n", + "L = 11.080973, acc = 0.970000\n", + "L = 11.076282, acc = 0.970000\n", + "L = 11.071600, acc = 0.970000\n", + "L = 11.066928, acc = 0.970000\n", + "L = 11.062264, acc = 0.970000\n", + "L = 11.057610, acc = 0.970000\n", + "L = 11.052965, acc = 0.970000\n", + "L = 11.048329, acc = 0.970000\n", + "L = 11.043702, acc = 0.970000\n", + "L = 11.039085, acc = 0.970000\n", + "L = 11.034476, acc = 0.970000\n", + "L = 11.029876, acc = 0.970000\n", + "L = 11.025285, acc = 0.970000\n", + "L = 11.020703, acc = 0.970000\n", + "L = 11.016130, acc = 0.970000\n", + "L = 11.011566, acc = 0.970000\n", + "L = 11.007011, acc = 0.970000\n", + "L = 11.002464, acc = 0.970000\n", + "L = 10.997927, acc = 0.970000\n", + "L = 10.993398, acc = 0.970000\n", + "L = 10.988877, acc = 0.970000\n", + "L = 10.984366, acc = 0.970000\n", + "L = 10.979863, acc = 0.970000\n", + "L = 10.975369, acc = 0.970000\n", + "L = 10.970883, acc = 0.970000\n", + "L = 10.966406, acc = 0.970000\n", + "L = 10.961938, acc = 0.970000\n", + "L = 10.957478, acc = 0.970000\n", + "L = 10.953026, acc = 0.970000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L = 10.948583, acc = 0.970000\n", + "L = 10.944149, acc = 0.970000\n", + "L = 10.939723, acc = 0.970000\n", + "L = 10.935305, acc = 0.970000\n", + "L = 10.930896, acc = 0.965000\n", + "L = 10.926495, acc = 0.965000\n", + "L = 10.922102, acc = 0.965000\n", + "L = 10.917718, acc = 0.965000\n", + "L = 10.913342, acc = 0.965000\n", + "L = 10.908974, acc = 0.965000\n", + "L = 10.904614, acc = 0.965000\n", + "L = 10.900263, acc = 0.965000\n", + "L = 10.895920, acc = 0.965000\n", + "L = 10.891585, acc = 0.965000\n", + "L = 10.887258, acc = 0.965000\n", + "L = 10.882939, acc = 0.965000\n", + "L = 10.878628, acc = 0.965000\n", + "L = 10.874325, acc = 0.965000\n", + "L = 10.870031, acc = 0.965000\n", + "L = 10.865744, acc = 0.965000\n", + "L = 10.861465, acc = 0.965000\n", + "L = 10.857195, acc = 0.965000\n", + "L = 10.852932, acc = 0.965000\n", + "L = 10.848677, acc = 0.965000\n", + "L = 10.844430, acc = 0.965000\n", + "L = 10.840191, acc = 0.965000\n", + "L = 10.835959, acc = 0.965000\n", + "L = 10.831736, acc = 0.965000\n", + "L = 10.827520, acc = 0.965000\n", + "L = 10.823312, acc = 0.965000\n", + "L = 10.819112, acc = 0.965000\n", + "L = 10.814919, acc = 0.965000\n", + "L = 10.810735, acc = 0.965000\n", + "L = 10.806558, acc = 0.965000\n", + "L = 10.802388, acc = 0.965000\n", + "L = 10.798226, acc = 0.965000\n", + "L = 10.794072, acc = 0.965000\n", + "L = 10.789926, acc = 0.965000\n", + "L = 10.785787, acc = 0.965000\n", + "L = 10.781655, acc = 0.965000\n", + "L = 10.777531, acc = 0.965000\n", + "L = 10.773415, acc = 0.965000\n", + "L = 10.769306, acc = 0.965000\n", + "L = 10.765205, acc = 0.965000\n", + "L = 10.761111, acc = 0.965000\n", + "L = 10.757024, acc = 0.965000\n", + "L = 10.752945, acc = 0.965000\n", + "L = 10.748873, acc = 0.965000\n", + "L = 10.744809, acc = 0.965000\n", + "L = 10.740752, acc = 0.965000\n", + "L = 10.736702, acc = 0.965000\n", + "L = 10.732660, acc = 0.965000\n", + "L = 10.728625, acc = 0.965000\n", + "L = 10.724597, acc = 0.965000\n", + "L = 10.720576, acc = 0.965000\n", + "L = 10.716563, acc = 0.965000\n", + "L = 10.712557, acc = 0.965000\n", + "L = 10.708558, acc = 0.965000\n", + "L = 10.704566, acc = 0.965000\n", + "L = 10.700582, acc = 0.965000\n", + "L = 10.696604, acc = 0.965000\n", + "L = 10.692634, acc = 0.965000\n", + "L = 10.688671, acc = 0.965000\n", + "L = 10.684715, acc = 0.965000\n", + "L = 10.680766, acc = 0.965000\n", + "L = 10.676824, acc = 0.965000\n", + "L = 10.672889, acc = 0.965000\n", + "L = 10.668961, acc = 0.965000\n", + "L = 10.665040, acc = 0.965000\n", + "L = 10.661126, acc = 0.965000\n", + "L = 10.657219, acc = 0.965000\n", + "L = 10.653318, acc = 0.965000\n", + "L = 10.649425, acc = 0.965000\n", + "L = 10.645539, acc = 0.965000\n", + "L = 10.641659, acc = 0.965000\n", + "L = 10.637787, acc = 0.965000\n", + "L = 10.633921, acc = 0.965000\n", + "L = 10.630062, acc = 0.965000\n", + "L = 10.626210, acc = 0.965000\n", + "L = 10.622365, acc = 0.965000\n", + "L = 10.618526, acc = 0.965000\n", + "L = 10.614695, acc = 0.965000\n", + "L = 10.610870, acc = 0.965000\n", + "L = 10.607051, acc = 0.965000\n", + "L = 10.603240, acc = 0.965000\n", + "L = 10.599435, acc = 0.965000\n", + "L = 10.595637, acc = 0.965000\n", + "L = 10.591845, acc = 0.965000\n", + "L = 10.588060, acc = 0.965000\n", + "L = 10.584282, acc = 0.965000\n", + "L = 10.580510, acc = 0.965000\n", + "L = 10.576745, acc = 0.965000\n", + "L = 10.572987, acc = 0.965000\n", + "L = 10.569235, acc = 0.965000\n", + "L = 10.565490, acc = 0.965000\n", + "L = 10.561751, acc = 0.965000\n", + "L = 10.558019, acc = 0.965000\n", + "L = 10.554293, acc = 0.965000\n", + "L = 10.550574, acc = 0.965000\n", + "L = 10.546861, acc = 0.965000\n", + "L = 10.543154, acc = 0.965000\n", + "L = 10.539454, acc = 0.965000\n", + "L = 10.535761, acc = 0.965000\n", + "L = 10.532074, acc = 0.965000\n", + "L = 10.528393, acc = 0.965000\n", + "L = 10.524719, acc = 0.965000\n", + "L = 10.521051, acc = 0.965000\n", + "L = 10.517389, acc = 0.965000\n", + "L = 10.513734, acc = 0.965000\n", + "L = 10.510085, acc = 0.965000\n", + "L = 10.506442, acc = 0.965000\n", + "L = 10.502806, acc = 0.965000\n", + "L = 10.499176, acc = 0.965000\n", + "L = 10.495552, acc = 0.965000\n", + "L = 10.491934, acc = 0.965000\n", + "L = 10.488323, acc = 0.965000\n", + "L = 10.484717, acc = 0.965000\n", + "L = 10.481118, acc = 0.965000\n", + "L = 10.477526, acc = 0.965000\n", + "L = 10.473939, acc = 0.965000\n", + "L = 10.470359, acc = 0.965000\n", + "L = 10.466784, acc = 0.965000\n", + "L = 10.463216, acc = 0.965000\n", + "L = 10.459654, acc = 0.965000\n", + "L = 10.456098, acc = 0.965000\n", + "L = 10.452548, acc = 0.965000\n", + "L = 10.449004, acc = 0.965000\n", + "L = 10.445466, acc = 0.965000\n", + "L = 10.441935, acc = 0.965000\n", + "L = 10.438409, acc = 0.965000\n", + "L = 10.434889, acc = 0.965000\n", + "L = 10.431376, acc = 0.965000\n", + "L = 10.427868, acc = 0.965000\n", + "L = 10.424366, acc = 0.965000\n", + "L = 10.420870, acc = 0.965000\n", + "L = 10.417381, acc = 0.965000\n", + "L = 10.413897, acc = 0.965000\n", + "L = 10.410419, acc = 0.965000\n", + "L = 10.406947, acc = 0.965000\n", + "L = 10.403481, acc = 0.965000\n", + "L = 10.400020, acc = 0.965000\n", + "L = 10.396566, acc = 0.965000\n", + "L = 10.393117, acc = 0.965000\n", + "L = 10.389675, acc = 0.965000\n", + "L = 10.386238, acc = 0.965000\n", + "L = 10.382807, acc = 0.965000\n", + "L = 10.379381, acc = 0.965000\n", + "L = 10.375962, acc = 0.965000\n", + "L = 10.372548, acc = 0.965000\n", + "L = 10.369140, acc = 0.965000\n", + "L = 10.365738, acc = 0.965000\n", + "L = 10.362341, acc = 0.965000\n", + "L = 10.358951, acc = 0.965000\n", + "L = 10.355566, acc = 0.965000\n", + "L = 10.352186, acc = 0.965000\n", + "L = 10.348813, acc = 0.965000\n", + "L = 10.345445, acc = 0.965000\n", + "L = 10.342082, acc = 0.965000\n", + "L = 10.338726, acc = 0.965000\n", + "L = 10.335375, acc = 0.965000\n", + "L = 10.332029, acc = 0.965000\n", + "L = 10.328690, acc = 0.965000\n", + "L = 10.325355, acc = 0.965000\n", + "L = 10.322027, acc = 0.965000\n", + "L = 10.318704, acc = 0.965000\n", + "L = 10.315386, acc = 0.965000\n", + "L = 10.312074, acc = 0.965000\n", + "L = 10.308768, acc = 0.965000\n", + "L = 10.305467, acc = 0.965000\n", + "L = 10.302172, acc = 0.965000\n", + "L = 10.298882, acc = 0.965000\n", + "L = 10.295597, acc = 0.965000\n", + "L = 10.292319, acc = 0.965000\n", + "L = 10.289045, acc = 0.965000\n", + "L = 10.285777, acc = 0.965000\n", + "L = 10.282515, acc = 0.965000\n", + "L = 10.279258, acc = 0.965000\n", + "L = 10.276006, acc = 0.965000\n", + "L = 10.272760, acc = 0.965000\n", + "L = 10.269519, acc = 0.965000\n", + "L = 10.266283, acc = 0.965000\n", + "L = 10.263053, acc = 0.965000\n", + "L = 10.259828, acc = 0.965000\n", + "L = 10.256609, acc = 0.965000\n", + "L = 10.253395, acc = 0.965000\n", + "L = 10.250186, acc = 0.965000\n", + "L = 10.246983, acc = 0.965000\n", + "L = 10.243785, acc = 0.965000\n", + "L = 10.240592, acc = 0.965000\n", + "L = 10.237404, acc = 0.965000\n", + "L = 10.234222, acc = 0.965000\n", + "L = 10.231045, acc = 0.965000\n", + "L = 10.227873, acc = 0.965000\n", + "L = 10.224707, acc = 0.965000\n", + "L = 10.221545, acc = 0.965000\n", + "L = 10.218389, acc = 0.965000\n", + "L = 10.215238, acc = 0.965000\n", + "L = 10.212092, acc = 0.965000\n", + "L = 10.208952, acc = 0.965000\n", + "L = 10.205816, acc = 0.965000\n", + "L = 10.202686, acc = 0.965000\n", + "L = 10.199561, acc = 0.965000\n", + "L = 10.196441, acc = 0.965000\n", + "L = 10.193326, acc = 0.965000\n", + "L = 10.190216, acc = 0.965000\n", + "L = 10.187112, acc = 0.965000\n", + "L = 10.184012, acc = 0.965000\n", + "L = 10.180918, acc = 0.965000\n", + "L = 10.177828, acc = 0.965000\n", + "L = 10.174744, acc = 0.965000\n", + "L = 10.171665, acc = 0.965000\n", + "L = 10.168591, acc = 0.965000\n", + "L = 10.165521, acc = 0.965000\n", + "L = 10.162457, acc = 0.965000\n", + "L = 10.159398, acc = 0.965000\n", + "L = 10.156344, acc = 0.965000\n", + "L = 10.153294, acc = 0.965000\n", + "L = 10.150250, acc = 0.965000\n", + "L = 10.147211, acc = 0.965000\n", + "L = 10.144176, acc = 0.965000\n", + "L = 10.141147, acc = 0.965000\n", + "L = 10.138122, acc = 0.965000\n", + "L = 10.135103, acc = 0.965000\n", + "L = 10.132088, acc = 0.965000\n", + "L = 10.129078, acc = 0.965000\n", + "L = 10.126073, acc = 0.965000\n", + "L = 10.123073, acc = 0.965000\n", + "L = 10.120078, acc = 0.965000\n", + "L = 10.117088, acc = 0.965000\n", + "L = 10.114103, acc = 0.965000\n", + "L = 10.111122, acc = 0.965000\n", + "L = 10.108146, acc = 0.965000\n", + "L = 10.105175, acc = 0.965000\n", + "L = 10.102209, acc = 0.965000\n", + "L = 10.099248, acc = 0.965000\n", + "L = 10.096291, acc = 0.965000\n", + "L = 10.093339, acc = 0.965000\n", + "L = 10.090392, acc = 0.965000\n", + "L = 10.087450, acc = 0.965000\n", + "L = 10.084512, acc = 0.965000\n", + "L = 10.081579, acc = 0.965000\n", + "L = 10.078651, acc = 0.965000\n", + "L = 10.075728, acc = 0.965000\n", + "L = 10.072809, acc = 0.965000\n", + "L = 10.069895, acc = 0.965000\n", + "L = 10.066986, acc = 0.965000\n", + "L = 10.064081, acc = 0.965000\n", + "L = 10.061181, acc = 0.965000\n", + "L = 10.058286, acc = 0.965000\n", + "L = 10.055395, acc = 0.965000\n", + "L = 10.052509, acc = 0.965000\n", + "L = 10.049628, acc = 0.965000\n", + "L = 10.046751, acc = 0.965000\n", + "L = 10.043879, acc = 0.965000\n", + "L = 10.041011, acc = 0.965000\n", + "L = 10.038148, acc = 0.965000\n", + "L = 10.035290, acc = 0.965000\n", + "L = 10.032436, acc = 0.965000\n", + "L = 10.029586, acc = 0.965000\n", + "L = 10.026742, acc = 0.965000\n", + "L = 10.023901, acc = 0.965000\n", + "L = 10.021066, acc = 0.965000\n", + "L = 10.018234, acc = 0.965000\n", + "L = 10.015408, acc = 0.965000\n", + "L = 10.012586, acc = 0.965000\n", + "L = 10.009768, acc = 0.965000\n", + "L = 10.006955, acc = 0.965000\n", + "L = 10.004146, acc = 0.965000\n", + "L = 10.001342, acc = 0.965000\n", + "L = 9.998542, acc = 0.965000\n", + "L = 9.995746, acc = 0.965000\n", + "L = 9.992955, acc = 0.965000\n", + "L = 9.990169, acc = 0.965000\n", + "L = 9.987387, acc = 0.965000\n", + "L = 9.984609, acc = 0.965000\n", + "L = 9.981835, acc = 0.965000\n", + "L = 9.979066, acc = 0.965000\n", + "L = 9.976302, acc = 0.965000\n", + "L = 9.973542, acc = 0.965000\n", + "L = 9.970786, acc = 0.965000\n", + "L = 9.968034, acc = 0.965000\n", + "L = 9.965287, acc = 0.965000\n", + "L = 9.962544, acc = 0.965000\n", + "L = 9.959806, acc = 0.965000\n", + "L = 9.957071, acc = 0.965000\n", + "L = 9.954342, acc = 0.965000\n", + "L = 9.951616, acc = 0.965000\n", + "L = 9.948895, acc = 0.965000\n", + "L = 9.946177, acc = 0.965000\n", + "L = 9.943465, acc = 0.970000\n", + "L = 9.940756, acc = 0.970000\n", + "L = 9.938052, acc = 0.970000\n", + "L = 9.935352, acc = 0.970000\n", + "L = 9.932656, acc = 0.970000\n", + "L = 9.929964, acc = 0.970000\n", + "L = 9.927277, acc = 0.970000\n", + "L = 9.924594, acc = 0.970000\n", + "L = 9.921915, acc = 0.970000\n", + "L = 9.919240, acc = 0.970000\n", + "L = 9.916569, acc = 0.970000\n", + "L = 9.913903, acc = 0.970000\n", + "L = 9.911240, acc = 0.970000\n", + "L = 9.908582, acc = 0.970000\n", + "L = 9.905928, acc = 0.970000\n", + "L = 9.903278, acc = 0.970000\n", + "L = 9.900632, acc = 0.970000\n", + "L = 9.897991, acc = 0.970000\n", + "L = 9.895353, acc = 0.970000\n", + "L = 9.892720, acc = 0.970000\n", + "L = 9.890090, acc = 0.970000\n", + "L = 9.887465, acc = 0.970000\n", + "L = 9.884844, acc = 0.970000\n", + "L = 9.882227, acc = 0.970000\n", + "L = 9.879614, acc = 0.970000\n", + "L = 9.877004, acc = 0.970000\n", + "L = 9.874399, acc = 0.970000\n", + "L = 9.871798, acc = 0.970000\n", + "L = 9.869201, acc = 0.970000\n", + "L = 9.866608, acc = 0.970000\n", + "L = 9.864019, acc = 0.970000\n", + "L = 9.861434, acc = 0.970000\n", + "L = 9.858853, acc = 0.970000\n", + "L = 9.856276, acc = 0.970000\n", + "L = 9.853703, acc = 0.970000\n", + "L = 9.851134, acc = 0.970000\n", + "L = 9.848569, acc = 0.970000\n", + "L = 9.846008, acc = 0.970000\n", + "L = 9.843450, acc = 0.970000\n", + "L = 9.840897, acc = 0.970000\n", + "L = 9.838348, acc = 0.970000\n", + "L = 9.835802, acc = 0.970000\n", + "L = 9.833261, acc = 0.970000\n", + "L = 9.830723, acc = 0.970000\n", + "L = 9.828189, acc = 0.970000\n", + "L = 9.825659, acc = 0.970000\n", + "L = 9.823133, acc = 0.970000\n", + "L = 9.820611, acc = 0.970000\n", + "L = 9.818092, acc = 0.970000\n", + "L = 9.815578, acc = 0.970000\n", + "L = 9.813067, acc = 0.970000\n", + "L = 9.810560, acc = 0.970000\n", + "L = 9.808057, acc = 0.970000\n", + "L = 9.805558, acc = 0.970000\n", + "L = 9.803062, acc = 0.970000\n", + "L = 9.800570, acc = 0.970000\n", + "L = 9.798082, acc = 0.970000\n", + "L = 9.795598, acc = 0.970000\n", + "L = 9.793118, acc = 0.970000\n", + "L = 9.790641, acc = 0.970000\n", + "L = 9.788168, acc = 0.970000\n", + "L = 9.785699, acc = 0.970000\n", + "L = 9.783234, acc = 0.970000\n", + "L = 9.780772, acc = 0.970000\n", + "L = 9.778314, acc = 0.970000\n", + "L = 9.775860, acc = 0.970000\n", + "L = 9.773410, acc = 0.970000\n", + "L = 9.770963, acc = 0.970000\n", + "L = 9.768520, acc = 0.970000\n", + "L = 9.766080, acc = 0.970000\n", + "L = 9.763645, acc = 0.970000\n", + "L = 9.761213, acc = 0.970000\n", + "L = 9.758784, acc = 0.970000\n", + "L = 9.756359, acc = 0.970000\n", + "L = 9.753938, acc = 0.970000\n", + "L = 9.751521, acc = 0.970000\n", + "L = 9.749107, acc = 0.970000\n", + "L = 9.746696, acc = 0.970000\n", + "L = 9.744290, acc = 0.970000\n", + "L = 9.741887, acc = 0.970000\n", + "L = 9.739487, acc = 0.970000\n", + "L = 9.737091, acc = 0.970000\n", + "L = 9.734699, acc = 0.970000\n", + "L = 9.732310, acc = 0.970000\n", + "L = 9.729925, acc = 0.970000\n", + "L = 9.727544, acc = 0.970000\n", + "L = 9.725166, acc = 0.970000\n", + "L = 9.722791, acc = 0.970000\n", + "L = 9.720420, acc = 0.970000\n", + "L = 9.718053, acc = 0.970000\n", + "L = 9.715689, acc = 0.970000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "L = 9.713328, acc = 0.970000\n", + "L = 9.710971, acc = 0.970000\n", + "L = 9.708618, acc = 0.970000\n", + "L = 9.706268, acc = 0.970000\n", + "L = 9.703921, acc = 0.970000\n", + "L = 9.701578, acc = 0.970000\n", + "L = 9.699239, acc = 0.970000\n", + "L = 9.696903, acc = 0.970000\n", + "L = 9.694570, acc = 0.970000\n", + "L = 9.692241, acc = 0.970000\n", + "L = 9.689915, acc = 0.970000\n", + "L = 9.687593, acc = 0.970000\n", + "L = 9.685274, acc = 0.970000\n", + "L = 9.682959, acc = 0.970000\n", + "L = 9.680647, acc = 0.970000\n", + "L = 9.678338, acc = 0.970000\n", + "L = 9.676033, acc = 0.970000\n", + "L = 9.673731, acc = 0.970000\n", + "L = 9.671432, acc = 0.970000\n", + "L = 9.669137, acc = 0.970000\n", + "L = 9.666845, acc = 0.970000\n", + "L = 9.664557, acc = 0.970000\n", + "L = 9.662272, acc = 0.970000\n", + "L = 9.659990, acc = 0.970000\n", + "L = 9.657712, acc = 0.970000\n", + "L = 9.655437, acc = 0.970000\n", + "L = 9.653165, acc = 0.970000\n", + "L = 9.650897, acc = 0.970000\n", + "L = 9.648631, acc = 0.970000\n", + "L = 9.646370, acc = 0.970000\n", + "L = 9.644111, acc = 0.970000\n", + "L = 9.641856, acc = 0.970000\n", + "L = 9.639604, acc = 0.970000\n", + "L = 9.637355, acc = 0.970000\n", + "L = 9.635110, acc = 0.970000\n", + "L = 9.632868, acc = 0.970000\n", + "L = 9.630629, acc = 0.970000\n", + "L = 9.628393, acc = 0.970000\n", + "L = 9.626160, acc = 0.970000\n", + "L = 9.623931, acc = 0.970000\n", + "L = 9.621705, acc = 0.970000\n", + "L = 9.619482, acc = 0.970000\n", + "L = 9.617263, acc = 0.970000\n", + "L = 9.615046, acc = 0.970000\n", + "L = 9.612833, acc = 0.970000\n", + "L = 9.610623, acc = 0.970000\n", + "L = 9.608416, acc = 0.970000\n", + "L = 9.606213, acc = 0.970000\n", + "L = 9.604012, acc = 0.970000\n", + "L = 9.601815, acc = 0.970000\n", + "L = 9.599621, acc = 0.970000\n", + "L = 9.597430, acc = 0.970000\n", + "L = 9.595242, acc = 0.970000\n", + "L = 9.593057, acc = 0.970000\n", + "L = 9.590876, acc = 0.970000\n", + "L = 9.588697, acc = 0.970000\n", + "L = 9.586522, acc = 0.970000\n", + "L = 9.584349, acc = 0.970000\n", + "L = 9.582180, acc = 0.970000\n", + "L = 9.580014, acc = 0.970000\n", + "L = 9.577851, acc = 0.970000\n", + "L = 9.575691, acc = 0.970000\n", + "L = 9.573534, acc = 0.970000\n", + "L = 9.571381, acc = 0.970000\n", + "L = 9.569230, acc = 0.970000\n", + "L = 9.567082, acc = 0.970000\n", + "L = 9.564937, acc = 0.970000\n", + "L = 9.562796, acc = 0.970000\n", + "L = 9.560657, acc = 0.970000\n", + "L = 9.558522, acc = 0.970000\n", + "L = 9.556389, acc = 0.970000\n", + "L = 9.554260, acc = 0.970000\n", + "L = 9.552133, acc = 0.970000\n", + "L = 9.550010, acc = 0.970000\n", + "L = 9.547889, acc = 0.970000\n", + "L = 9.545772, acc = 0.970000\n", + "L = 9.543657, acc = 0.970000\n", + "L = 9.541546, acc = 0.970000\n", + "L = 9.539437, acc = 0.970000\n", + "L = 9.537332, acc = 0.970000\n", + "L = 9.535229, acc = 0.970000\n", + "L = 9.533129, acc = 0.970000\n", + "L = 9.531032, acc = 0.970000\n", + "L = 9.528939, acc = 0.970000\n", + "L = 9.526848, acc = 0.970000\n", + "L = 9.524760, acc = 0.970000\n", + "L = 9.522675, acc = 0.970000\n", + "L = 9.520592, acc = 0.970000\n", + "L = 9.518513, acc = 0.970000\n", + "L = 9.516437, acc = 0.970000\n", + "L = 9.514363, acc = 0.970000\n", + "L = 9.512293, acc = 0.970000\n", + "L = 9.510225, acc = 0.970000\n", + "L = 9.508160, acc = 0.970000\n", + "L = 9.506098, acc = 0.970000\n", + "L = 9.504039, acc = 0.970000\n", + "L = 9.501983, acc = 0.970000\n", + "L = 9.499930, acc = 0.970000\n", + "L = 9.497879, acc = 0.970000\n", + "L = 9.495832, acc = 0.970000\n", + "L = 9.493787, acc = 0.975000\n", + "L = 9.491745, acc = 0.975000\n", + "L = 9.489706, acc = 0.975000\n", + "L = 9.487669, acc = 0.975000\n", + "L = 9.485636, acc = 0.975000\n", + "L = 9.483605, acc = 0.975000\n", + "L = 9.481577, acc = 0.975000\n", + "L = 9.479552, acc = 0.975000\n", + "L = 9.477529, acc = 0.975000\n", + "L = 9.475510, acc = 0.975000\n", + "L = 9.473493, acc = 0.975000\n", + "L = 9.471479, acc = 0.975000\n", + "L = 9.469467, acc = 0.975000\n", + "L = 9.467459, acc = 0.975000\n", + "L = 9.465453, acc = 0.975000\n", + "L = 9.463450, acc = 0.975000\n", + "L = 9.461450, acc = 0.975000\n", + "L = 9.459452, acc = 0.975000\n", + "L = 9.457457, acc = 0.975000\n", + "L = 9.455465, acc = 0.975000\n", + "L = 9.453475, acc = 0.975000\n", + "L = 9.451489, acc = 0.975000\n", + "L = 9.449505, acc = 0.975000\n", + "L = 9.447523, acc = 0.975000\n", + "L = 9.445545, acc = 0.975000\n", + "L = 9.443569, acc = 0.975000\n", + "L = 9.441595, acc = 0.975000\n", + "L = 9.439625, acc = 0.975000\n", + "L = 9.437657, acc = 0.975000\n", + "L = 9.435692, acc = 0.975000\n", + "L = 9.433729, acc = 0.975000\n" + ] + } + ], + "source": [ + "# use the NN model and training\n", + "nn = NN_Model([2, 6, 4, 2])\n", + "nn.init_weight()\n", + "nn.backpropagation(X, t, 2000)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# predict results & plot results\n", + "y_res = nn.forward(X)\n", + "y_pred = np.argmax(y_res, axis=1)\n", + "\n", + "# plot data\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)\n", + "plt.title(\"ground truth\")\n", + "plt.show()\n", + "\n", + "plt.scatter(X[:, 0], X[:, 1], c=y_pred, cmap=plt.cm.Spectral)\n", + "plt.title(\"predicted\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9. 深入分析与问题" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.03154963 0.97354996]\n", + " [0.30242346 0.68475421]\n", + " [0.84429554 0.17625119]\n", + " [0.04812804 0.95826417]\n", + " [0.04183504 0.96405488]\n", + " [0.80767817 0.17874873]\n", + " [0.05463129 0.94906635]\n", + " [0.83768873 0.14807047]\n", + " [0.05043638 0.95552076]]\n" + ] + } + ], + "source": [ + "# print some results\n", + "\n", + "print(y_res[1:10, :])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**问题**\n", + "1. 我们希望得到的每个类别的概率\n", + "2. 如何做多分类问题？\n", + "3. 如何能让神经网络更快的训练好？\n", + "4. 如何更好的构建网络的类定义，从而让神经网络的类支持更多的类型的处理层？" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "* 反向传播算法\n", + " * [零基础入门深度学习(3) - 神经网络和反向传播算法](https://www.zybuluo.com/hanbingtao/note/476663)\n", + " * [Neural Network Using Python and Numpy](https://www.python-course.eu/neural_networks_with_python_numpy.php)\n", + " * http://www.cedar.buffalo.edu/%7Esrihari/CSE574/Chap5/Chap5.3-BackProp.pdf\n", + " * https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/5_nn/3-softmax_ce_EN.ipynb b/5_nn/3-softmax_ce_EN.ipynb new file mode 100644 index 0000000..08a8a3b --- /dev/null +++ b/5_nn/3-softmax_ce_EN.ipynb @@ -0,0 +1,176 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Softmax & 交叉熵代价函数\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "softmax经常被添加在分类任务的神经网络中的输出层，神经网络的反向传播中关键的步骤就是求导，从这个过程也可以更深刻地理解反向传播的过程，还可以对梯度传播的问题有更多的思考。\n", + "\n", + "## 1. softmax 函数\n", + "\n", + "softmax(柔性最大值)函数，一般在神经网络中， softmax可以作为分类任务的输出层。其实可以认为softmax输出的是几个类别选择的概率，比如我有一个分类任务，要分为三个类，softmax函数可以根据它们相对的大小，输出三个类别选取的概率，并且概率和为1。\n", + "\n", + "softmax函数的公式是这种形式：\n", + "\n", + "$$\n", + "S_i = \\frac{e^{z_i}}{\\sum_k e^{z_k}}\n", + "$$\n", + "\n", + "* $S_i$是经过softmax的类别概率输出\n", + "* $z_k$是神经元的输出\n", + "\n", + "\n", + "更形象的如下图表示：\n", + "\n", + "![softmax_demo](images/softmax_demo.png)\n", + "\n", + "softmax直白来说就是将原来输出是$[3,1,-3]$通过softmax函数一作用，就映射成为(0,1)的值，而这些值的累和为1（满足概率的性质），那么我们就可以将它理解成概率，在最后选取输出结点的时候，我们就可以选取概率最大（也就是值对应最大的）结点，作为我们的预测目标！\n", + "\n", + "\n", + "\n", + "首先是神经元的输出，一个神经元如下图：\n", + "\n", + "![softmax_neuron](images/softmax_neuron.png)\n", + "\n", + "神经元的输出设为：\n", + "\n", + "$$\n", + "z_i = \\sum_{j} w_{ij} x_{j} + b\n", + "$$\n", + "\n", + "其中$W_{ij}$是第$i$个神经元的第$j$个权重，$b$是偏置。$z_i$表示该网络的第$i$个输出。\n", + "\n", + "给这个输出加上一个softmax函数，那就变成了这样：\n", + "\n", + "$$\n", + "a_i = \\frac{e^{z_i}}{\\sum_k e^{z_k}}\n", + "$$\n", + "\n", + "$a_i$代表softmax的第$i$个输出值，右侧套用了softmax函数。\n", + "\n", + "\n", + "### 1.1 损失函数 loss function\n", + "\n", + "在神经网络反向传播中，要求一个损失函数，这个损失函数其实表示的是真实值与网络的估计值的误差，知道误差了，才能知道怎样去修改网络中的权重。\n", + "\n", + "损失函数可以有很多形式，这里用的是交叉熵函数，主要是由于这个求导结果比较简单，易于计算，并且交叉熵解决某些损失函数学习缓慢的问题。**[交叉熵函数](https://blog.csdn.net/u014313009/article/details/51043064)**是这样的：\n", + "\n", + "$$\n", + "C = - \\sum_i y_i ln a_i\n", + "$$\n", + "\n", + "其中$y_i$表示真实的分类结果。\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. 推导过程\n", + "\n", + "首先，我们要明确一下我们要求什么，我们要求的是我们的$loss$对于神经元输出($z_i$)的梯度，即：\n", + "\n", + "$$\n", + "\\frac{\\partial C}{\\partial z_i}\n", + "$$\n", + "\n", + "根据复合函数求导法则：\n", + "\n", + "$$\n", + "\\frac{\\partial C}{\\partial z_i} = \\frac{\\partial C}{\\partial a_j} \\frac{\\partial a_j}{\\partial z_i}\n", + "$$\n", + "\n", + "有个人可能有疑问了，这里为什么是$a_j$而不是$a_i$，这里要看一下$softmax$的公式了，因为$softmax$公式的特性，它的分母包含了所有神经元的输出，所以，对于不等于i的其他输出里面，也包含着$z_i$，所有的$a$都要纳入到计算范围中，并且后面的计算可以看到需要分为$i = j$和$i \\ne j$两种情况求导。\n", + "\n", + "### 2.1 针对$a_j$的偏导\n", + "\n", + "$$\n", + "\\frac{\\partial C}{\\partial a_j} = \\frac{(\\partial -\\sum_j y_j ln a_j)}{\\partial a_j} = -\\sum_j y_j \\frac{1}{a_j}\n", + "$$\n", + "\n", + "### 2.2 针对$z_i$的偏导\n", + "\n", + "如果 $i=j$ :\n", + "\n", + "\\begin{eqnarray}\n", + "\\frac{\\partial a_i}{\\partial z_i} & = & \\frac{\\partial (\\frac{e^{z_i}}{\\sum_k e^{z_k}})}{\\partial z_i} \\\\\n", + " & = & \\frac{\\sum_k e^{z_k} e^{z_i} - (e^{z_i})^2}{\\sum_k (e^{z_k})^2} \\\\\n", + " & = & (\\frac{e^{z_i}}{\\sum_k e^{z_k}} ) (1 - \\frac{e^{z_i}}{\\sum_k e^{z_k}} ) \\\\\n", + " & = & a_i (1 - a_i)\n", + "\\end{eqnarray}\n", + "\n", + "如果 $i \\ne j$:\n", + "\\begin{eqnarray}\n", + "\\frac{\\partial a_j}{\\partial z_i} & = & \\frac{\\partial (\\frac{e^{z_j}}{\\sum_k e^{z_k}})}{\\partial z_i} \\\\\n", + " & = & \\frac{0 \\cdot \\sum_k e^{z_k} - e^{z_j} \\cdot e^{z_i} }{(\\sum_k e^{z_k})^2} \\\\\n", + " & = & - \\frac{e^{z_j}}{\\sum_k e^{z_k}} \\cdot \\frac{e^{z_i}}{\\sum_k e^{z_k}} \\\\\n", + " & = & -a_j a_i\n", + "\\end{eqnarray}\n", + "\n", + "当u，v都是变量的函数时的导数推导公式：\n", + "$$\n", + "(\\frac{u}{v})' = \\frac{u'v - uv'}{v^2} \n", + "$$\n", + "\n", + "### 2.3 整体的推导\n", + "\n", + "\\begin{eqnarray}\n", + "\\frac{\\partial C}{\\partial z_i} & = & (-\\sum_j y_j \\frac{1}{a_j} ) \\frac{\\partial a_j}{\\partial z_i} \\\\\n", + " & = & - \\frac{y_i}{a_i} a_i ( 1 - a_i) + \\sum_{j \\ne i} \\frac{y_j}{a_j} a_i a_j \\\\\n", + " & = & -y_i + y_i a_i + \\sum_{j \\ne i} y_j a_i \\\\\n", + " & = & -y_i + a_i \\sum_{j} y_j \\\\\n", + " & = & -y_i + a_i\n", + "\\end{eqnarray}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. 问题\n", + "如何将本节所讲的softmax，交叉熵代价函数应用到上节所讲的BP方法中？" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Softmax & 交叉熵\n", + " * [交叉熵代价函数（作用及公式推导）](https://blog.csdn.net/u014313009/article/details/51043064)\n", + " * [手打例子一步一步带你看懂softmax函数以及相关求导过程](https://www.jianshu.com/p/ffa51250ba2e)\n", + " * [简单易懂的softmax交叉熵损失函数求导](https://www.jianshu.com/p/c02a1fbffad6)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/README_EN.md b/README_EN.md new file mode 100644 index 0000000..5694f8e --- /dev/null +++ b/README_EN.md @@ -0,0 +1,110 @@ +# 机器学习 + +本教程主要讲解机器学习的基本原理与实现，通过本教程的引导来快速学习Python、Python常用库、机器学习的理论知识与实际编程，并学习如何解决实际问题。 + +由于**本课程需要大量的编程练习才能取得比较好的学习效果**，因此需要认真去完成[作业和报告](https://gitee.com/pi-lab/machinelearning_homework)，写作业的过程可以查阅网上的资料，但是不能直接照抄，需要自己独立思考并独立写出代码。 + +![Machine Learning Cover](images/machine_learning.png) + + +## 1. 内容 +1. [课程简介](CourseIntroduction.pdf) +2. [Python](0_python/) + - [Install Python](tips/InstallPython.md) + - [Python Basics](0_python/1_Basics.ipynb) + - [Print Statement](0_python/2_Print_Statement.ipynb) + - [Data Structure 1](0_python/3_Data_Structure_1.ipynb) + - [Data Structure 2](0_python/4_Data_Structure_2.ipynb) + - [Control Flow](0_python/5_Control_Flow.ipynb) + - [Function](0_python/6_Function.ipynb) + - [Class](0_python/7_Class.ipynb) +3. [numpy & matplotlib](1_numpy_matplotlib_scipy_sympy/) + - [numpy](1_numpy_matplotlib_scipy_sympy/numpy_tutorial.ipynb) + - [matplotlib](1_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.ipynb) + - [ipython & notebook](1_numpy_matplotlib_scipy_sympy/ipython_notebook.ipynb) +4. [knn](2_knn/knn_classification.ipynb) +5. [kMenas](3_kmeans/k-means.ipynb) +6. [Logistic Regression](4_logistic_regression/) + - [Least squares](4_logistic_regression/Least_squares.ipynb) + - [Logistic regression](4_logistic_regression/Logistic_regression.ipynb) +7. [Neural Network](5_nn/) + - [Perceptron](5_nn/Perceptron.ipynb) + - [Multi-layer Perceptron & BP](5_nn/mlp_bp.ipynb) + - [Softmax & cross-entroy](5_nn/softmax_ce.ipynb) +8. [PyTorch](6_pytorch/) + - Basic + - [short tutorial](6_pytorch/PyTorch_quick_intro.ipynb) + - [basic/Tensor-and-Variable](6_pytorch/0_basic/Tensor-and-Variable.ipynb) + - [basic/autograd](6_pytorch/0_basic/autograd.ipynb) + - [basic/dynamic-graph](6_pytorch/0_basic/dynamic-graph.ipynb) + - NN & Optimization + - [nn/linear-regression-gradient-descend](6_pytorch/1_NN/linear-regression-gradient-descend.ipynb) + - [nn/logistic-regression](6_pytorch/1_NN/logistic-regression.ipynb) + - [nn/nn-sequential-module](6_pytorch/1_NN/nn-sequential-module.ipynb) + - [nn/bp](6_pytorch/1_NN/bp.ipynb) + - [nn/deep-nn](6_pytorch/1_NN/deep-nn.ipynb) + - [nn/param_initialize](6_pytorch/1_NN/param_initialize.ipynb) + - [optim/sgd](6_pytorch/1_NN/optimizer/sgd.ipynb) + - [optim/adam](6_pytorch/1_NN/optimizer/adam.ipynb) + - CNN + - [CNN simple demo](demo_code/3_CNN_MNIST.py) + - [cnn/basic_conv](6_pytorch/2_CNN/basic_conv.ipynb) + - [cnn/minist (demo code)](./demo_code/3_CNN_MNIST.py) + - [cnn/batch-normalization](6_pytorch/2_CNN/batch-normalization.ipynb) + - [cnn/regularization](6_pytorch/2_CNN/regularization.ipynb) + - [cnn/lr-decay](6_pytorch/2_CNN/lr-decay.ipynb) + - [cnn/vgg](6_pytorch/2_CNN/vgg.ipynb) + - [cnn/googlenet](6_pytorch/2_CNN/googlenet.ipynb) + - [cnn/resnet](6_pytorch/2_CNN/resnet.ipynb) + - [cnn/densenet](6_pytorch/2_CNN/densenet.ipynb) + - RNN + - [rnn/pytorch-rnn](6_pytorch/3_RNN/pytorch-rnn.ipynb) + - [rnn/rnn-for-image](6_pytorch/3_RNN/rnn-for-image.ipynb) + - [rnn/lstm-time-series](6_pytorch/3_RNN/time-series/lstm-time-series.ipynb) + - GAN + - [gan/autoencoder](6_pytorch/4_GAN/autoencoder.ipynb) + - [gan/vae](6_pytorch/4_GAN/vae.ipynb) + - [gan/gan](6_pytorch/4_GAN/gan.ipynb) + + + +## 2. 学习的建议 +1. 为了更好的学习本课程，需要大家把Python编程的基础能力培养好，这样后续的机器学习方法学习才比较扎实。 +2. 每个课程前部分是理论基础，然后是代码实现。个人如果想学的更扎实，可以自己把各个方法的代码亲自实现一下。做的过程尽可能自己想解决办法，因为重要的学习目标不是代码本身，而是学会分析问题、解决问题的能力。 + + +## 3. 其他参考资料 +* 资料速查 + * [相关学习参考资料汇总](References.md) + * [一些速查手册](tips/cheatsheet) + +* 机器学习方面技巧等 + * [Confusion Matrix](tips/confusion_matrix.ipynb) + * [Datasets](tips/datasets.ipynb) + * [构建深度神经网络的一些实战建议](tips/构建深度神经网络的一些实战建议.md) + * [Intro to Deep Learning](tips/Intro_to_Deep_Learning.pdf) + +* Python技巧等 + * [安装Python环境](tips/InstallPython.md) + * [Python tips](tips/python) + +* Git + * [Git Tips - 常用方法速查，快速入门](https://gitee.com/pi-lab/learn_programming/blob/master/6_tools/git/git-tips.md) + * [Git快速入门 - Git初体验](https://my.oschina.net/dxqr/blog/134811) + * [在win7系统下使用TortoiseGit(乌龟git)简单操作Git](https://my.oschina.net/longxuu/blog/141699) + * [Git系统学习 - 廖雪峰的Git教程](https://www.liaoxuefeng.com/wiki/0013739516305929606dd18361248578c67b8067c8c017b000) + +* Markdown + * [Markdown——入门指南](https://www.jianshu.com/p/1e402922ee32) + + +## 4. 相关学习资料参考 + +在上述内容学习完成之后，可以进行更进一步机器学习、计算机视觉方面的学习与研究，具体的资料可以参考： +1. [《一步一步学编程》](https://gitee.com/pi-lab/learn_programming) +2. 智能系统实验室-培训教程与作业 + - [《智能系统实验室-暑期培训教程》](https://gitee.com/pi-lab/SummerCamp) + - [《智能系统实验室-暑期培训作业》](https://gitee.com/pi-lab/SummerCampHomework) +3. [《智能系统实验室研究课题》](https://gitee.com/pi-lab/pilab_research_fields) +4. [《编程代码参考、技巧集合》](https://gitee.com/pi-lab/code_cook) + - 可以在这个代码、技巧集合中找到某项功能的示例，从而加快自己代码的编写 diff --git a/tips/InstallPython_EN.md b/tips/InstallPython_EN.md new file mode 100644 index 0000000..af7f7a6 --- /dev/null +++ b/tips/InstallPython_EN.md @@ -0,0 +1,89 @@ +# Installing Python Environments + +由于Python的库比较多，并且依赖关系比较复杂，所以请仔细阅读下面的说明，使用下面的说明来安装能够减少问题的可能。*不过所列的安装方法，里面存在较多的细节，也许和你的系统并不适配，所以会遇到问题。如果遇到问题请通过搜索引擎去查找解决的办法*，通过这个方式锻炼自己解决问题的能力。 + +可以参考后面所列的`1.Winodws`或者`2.Linux`章节所列的将Python环境安装到计算机里。如果想一次性把所有的所需要的软件都安装到机器上，可以在本项目的根目录下执行下面的命令，需要Python 3.5版本，如果出现问题，则可以参考`requirements.txt`里面所列的软件包名字，手动一个一个安装。 +``` +pip install -r requirements.txt +``` + + +## 1. Windows + +### 安装Anaconda + +由于Anaconda集成了大部分的python包，因此能够很方便的开始使用。由于网络下载速度较慢，因此推荐使用镜像来提高下载的速度。镜像的使用方法可以参考[Anaconda镜像的说明文档](https://mirrors.tuna.tsinghua.edu.cn/help/anaconda) + +在这里找到适合自己的安装文件，然后下载 +https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/ + + +设置软件源 https://mirror.tuna.tsinghua.edu.cn/help/anaconda/ +``` +conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ +conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/ +conda config --set show_channel_urls yes +``` + +### 安装Pytorch +``` +conda install pytorch -c pytorch +pip3 install torchvision +``` + + + + +## 2. Linux + +### 安装pip +``` +sudo apt-get install python3-pip +``` + + + +### 设置PIP源 + +``` +pip config set global.index-url 'https://mirrors.ustc.edu.cn/pypi/web/simple' +``` + + + +### 安装常用的包 + +``` +pip install -r requirements.txt +``` + +或者手动安装 +``` +sudo pip install scipy +sudo pip install scikit-learn +sudo pip install numpy +sudo pip install matplotlib +sudo pip install pandas +sudo pip install ipython +sudo pip install jupyter +``` + + + +### 安装pytorch + +到[pytorch 官网](https://pytorch.org)，根据自己的操作系统、CUDA版本，选择合适的安装命令。 + +例如Linux, Python3.5, CUDA 9.0： +``` +pip3 install torch torchvision +``` + + + +## 3. [Python技巧](python/) + +- [pip的安装、使用等](python/pip.md) +- [virtualenv的安装、使用](python/virtualenv.md) +- [virtualenv便捷管理工具：virtualenv_wrapper](python/virtualenv_wrapper.md) +