diff --git a/.gitignore b/.gitignore index 32b46f9..790125a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ .ipynb_checkpoints .idea +references diff --git a/0_python/02 Print Statement.ipynb b/0_python/02 Print Statement.ipynb index 8bd718e..0919c42 100644 --- a/0_python/02 Print Statement.ipynb +++ b/0_python/02 Print Statement.ipynb @@ -20,10 +20,10 @@ "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\" % " + " - print(\"Hello World\")\n", + " - print(\"Hello\", )\n", + " - print(\"Hello\" + )\n", + " - print(\"Hello %s\" % )" ] }, { @@ -377,7 +377,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#PrecisionWidth and FieldWidth" + "# PrecisionWidth and FieldWidth" ] }, { diff --git a/0_python/02 Print Statement.py b/0_python/02 Print Statement.py new file mode 100644 index 0000000..487ed44 --- /dev/null +++ b/0_python/02 Print Statement.py @@ -0,0 +1,134 @@ +# --- +# jupyter: +# jupytext_format_version: '1.2' +# 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 +# --- + +# All the IPython Notebooks in this lecture series are available at https://github.com/rajathkumarmp/Python-Lectures + +# # Print Statement + +# The **print** statement can be used in the following different ways : +# +# - print("Hello World") +# - print("Hello", ) +# - print("Hello" + ) +# - print("Hello %s" % ) + +print("Hello World") + +# In Python, single, double and triple quotes are used to denote a string. +# Most use single quotes when declaring a single character. +# Double quotes when declaring a line and triple quotes when declaring a paragraph/multiple lines. + +print('Hey') + +print("""My name is Rajath Kumar M.P. + +I love Python.""") + +# Strings can be assigned to variable say _string1_ and _string2_ which can called when using the print statement. + +# + {"scrolled": true} +string1 = 'World' +print('Hello', string1) + +string2 = '!' +print('Hello', string1, string2) +# - + +# String concatenation is the "addition" of two strings. Observe that while concatenating there will be no space between the strings. + +print('Hello' + string1 + string2) + +# **%s** is used to refer to a variable which contains a string. + +print("Hello %s" % string1) + +# Similarly, when using other data types +# +# - %s -> string +# - %d -> Integer +# - %f -> Float +# - %o -> Octal +# - %x -> Hexadecimal +# - %e -> exponential +# +# This can be used for conversions inside the print statement itself. + +print("Actual Number = %d" % 18) +print("Float of the number = %f" % 18) +print("Octal equivalent of the number = %o" % 18) +print("Hexadecimal equivalent of the number = %x" % 18) +print("Exponential equivalent of the number = %e" % 18) + +# When referring to multiple variables parenthesis is used. + +print "Hello %s %s" %(string1,string2) + +# ## Other Examples + +# The following are other different ways the print statement can be put to use. + +print("I want %%d to be printed %s" %'here') + +print('_A'*10) + +print("Jan\nFeb\nMar\nApr\nMay\nJun\nJul\nAug") + +print("\n".join("Jan Feb Mar Apr May Jun Jul Aug".split(" "))) + +print("I want \\n to be printed.") + +print """ +Routine: +\t- Eat +\t- Sleep\n\t- Repeat +""" + +# # PrecisionWidth and FieldWidth + +# 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. +# +# The default Precision Width is set to 6. + +"%f" % 3.121312312312 + +# Notice upto 6 decimal points are returned. To specify the number of decimal points, '%(fieldwidth).(precisionwidth)f' is used. + +"%.5f" % 3.121312312312 + +# If the field width is set more than the necessary than the data right aligns itself to adjust to the specified values. + +"%9.5f" % 3.121312312312 + +# Zero padding is done by adding a 0 at the start of fieldwidth. + +"%020.5f" % 3.121312312312 + +# 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. + +print "% 9f" % 3.121312312312 +print "% 9f" % -3.121312312312 + +# '+' sign can be returned at the beginning of a positive number by adding a + sign at the beginning of the field width. + +print "%+9f" % 3.121312312312 +print "% 9f" % -3.121312312312 + +# 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. + +"%-9.3f" % 3.121312312312 diff --git a/0_python/03 Data Structure.py b/0_python/03 Data Structure.py new file mode 100644 index 0000000..38775c7 --- /dev/null +++ b/0_python/03 Data Structure.py @@ -0,0 +1,386 @@ +# --- +# jupyter: +# jupytext_format_version: '1.2' +# 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 +# --- + +# All the IPython Notebooks in this lecture series are available at https://github.com/rajathkumarmp/Python-Lectures + +# # Data Structures + +# In simple terms, It is the the collection or group of data in a particular structure. + +# ## Lists + +# 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. +# +# Lists are declared by just equating a variable to '[ ]' or list. + +a = [] + +print(type(a)) + +# One can directly assign the sequence of data to a list x as shown. + +x = ['apple', 'orange', 'peach'] + +# ### Indexing + +# 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. + +x[0] + +# 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. + +x[-1] + +# 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. + +y = ['carrot','potato'] + +# 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. + +z = [x,y] +print(z) + +# 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. +# +# Let us access the data 'apple' in the above nested list. +# 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'. + +z1 = z[0] +print(z1) + +# Now observe that z1 is not at all a nested list thus to access 'apple', z1 should be indexed at 0. + +z1[0] + +# Instead of doing the above, In python, you can access 'apple' by just writing the index values each time side by side. + +z[0][0] + +# If there was a list inside a list inside a list then you can access the innermost value by executing z[ ][ ][ ]. + +# ### Slicing + +# 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. +# +# 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. + +num = [0,1,2,3,4,5,6,7,8,9] + +print(num[0:4]) +print(num[4:]) + +# You can also slice a parent list with a fixed length or step length. + +num[:9:3] + +# ### Built in List Functions + +# To find the length of the list or the number of elements in a list, **len( )** is used. + +len(num) + +# If the list consists of all integer elements then **min( )** and **max( )** gives the minimum and maximum value in the list. + +min(num) + +max(num) + +# 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. + +[1,2,3] + [5,4,7] + +# 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. + +names = ['Earth','Air','Fire','Water'] + +# 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. + +'Fire' in names + +'Rajath' in names + +# 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. + +mlist = ['bzaa','ds','nc','az','z','klm'] + +print(max(mlist)) +print(min(mlist)) + +# 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? + +nlist = ['1','94','93','1000'] + +print(max(nlist)) +print(min(nlist)) + +# 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. + +# 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. + +print(max(names, key=len)) +print(min(names, key=len)) + +# 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. +# +# Any other built in function can be used or lambda function (will be discussed later) in place of len. +# +# A string can be converted into a list by using the **list()** function. + +list('hello') + +# **append( )** is used to add a element at the end of the list. + +lst = [1,1,4,8,7] + +lst.append(1) +print(lst) + +# **count( )** is used to count the number of a particular element that is present in the list. + +lst.count(1) + +# **append( )** function can also be used to add a entire list at the end. Observe that the resultant list becomes a nested list. + +lst1 = [5,4,2,8] + +lst.append(lst1) +print(lst) + +# But if nested list is not what is desired then **extend( )** function can be used. + +lst.extend(lst1) +print(lst) + +# **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. + +lst.index(1) + +# **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. + +lst.insert(5, 'name') +print(lst) + +# **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. + +lst[5] = 'Python' +print(lst) + +# **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. + +lst.pop() + +# Index value can be specified to pop a ceratin element corresponding to that index value. + +lst.pop(0) + +# **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. + +lst.remove('Python') +print(lst) + +# Alternative to **remove** function but with using index value is **del** + +del lst[1] +print(lst) + +# The entire elements present in the list can be reversed by using the **reverse()** function. + +lst.reverse() +print(lst) + +# 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. +# +# Python offers built in operation **sort( )** to arrange the elements in ascending order. + +lst.sort() +print(lst) + +# 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. + +lst.sort(reverse=True) +print(lst) + +# 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. + +names.sort() +print(names) +names.sort(reverse=True) +print(names) + +# To sort based on length key=len should be specified as shown. + +names.sort(key=len) +print(names) +names.sort(key=len,reverse=True) +print(names) + +# ### Copying a list + +# Most of the new python programmers commit this mistake. Consider the following, + +lista= [2,1,4,3] + +listb = lista +print(listb) + +# 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. + +lista.pop() +print(lista) +lista.append(9) +print(lista) + +print listb + +# 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? +# +# 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. + +lista = [2,1,4,3] + +listb = lista[:] +print(listb) + +lista.pop() +print(lista) +lista.append(9) +print(lista) + +print(listb) + +# ## Tuples + +# 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. + +xyz = divmod(10,3) +print(xyz) +print(type(xyz)) + +# 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. + +# To define a tuple, A variable is assigned to paranthesis ( ) or tuple( ). + +tup = () +tup2 = tuple() + +# If you want to directly declare a tuple it can be done by using a comma at the end of the data. + +27, + +# 27 when multiplied by 2 yields 54, But when multiplied with a tuple the data is repeated twice. + +2*(27,) + +# 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. + +# + {"scrolled": true} +tup3 = tuple([1,2,3]) +print(tup3) +tup4 = tuple('Hello') +print(tup4) +# - + +# It follows the same indexing and slicing as Lists. + +print(tup3[1]) +tup5 = tup4[:3] +print(tup5) + +# ### Mapping one tuple to another + +(a,b,c)= ('alpha','beta','gamma') + +print(a,b,c) + +d = tuple('RajathKumarMP') +print(d) + +# ### Built In Tuple functions + +# **count()** function counts the number of specified element that is present in the tuple. + +d.count('a') + +# **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 + +d.index('a') + +# ## Sets + +# Sets are mainly used to eliminate repeated numbers in a sequence/list. It is also used to perform some standard set operations. +# +# Sets are declared as set() which will initialize a empty set. Also set([sequence]) can be executed to declare a set with elements + +set1 = set() +print(type(set1)) + +set0 = set([1,2,2,3,3,4]) +print(set0) + +# elements 2,3 which are repeated twice are seen only once. Thus in a set each element is distinct. + +# ### Built-in Functions + +set1 = set([1,2,3]) + +set2 = set([2,3,4,5]) + +# **union( )** function returns a set which contains all the elements of both the sets without repition. + +set1.union(set2) + +# **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. + +set1.add(0) +set1 + +# **intersection( )** function outputs a set which contains all the elements that are in both sets. + +set1.intersection(set2) + +# **difference( )** function ouptuts a set which contains elements that are in set1 and not in set2. + +set1.difference(set2) + +# **symmetric_difference( )** function ouputs a function which contains elements that are in one of the sets. + +set2.symmetric_difference(set1) + +# **issubset( ), isdisjoint( ), issuperset( )** is used to check if the set1/set2 is a subset, disjoint or superset of set2/set1 respectively. + +set1.issubset(set2) + +set2.isdisjoint(set1) + +set2.issuperset(set1) + +# **pop( )** is used to remove an arbitrary element in the set + +set1.pop() +print(set1) + +# **remove( )** function deletes the specified element from the set. + +set1.remove(2) +set1 + +# **clear( )** is used to clear all the elements and make that set an empty set. + +set1.clear() +set1 diff --git a/0_python/05 Control Flow.ipynb b/0_python/05 Control Flow.ipynb index 13693e2..17b1e04 100644 --- a/0_python/05 Control Flow.ipynb +++ b/0_python/05 Control Flow.ipynb @@ -318,7 +318,14 @@ "while i < 3:\n", " print(i ** 2)\n", " i = i+1\n", - "print('Bye')" + "print('Bye')\n", + "\n", + "# do-untile\n", + "while True:\n", + " #do something\n", + " \n", + " # check \n", + " if xxxx: break" ] }, { diff --git a/0_python/05 Control Flow.py b/0_python/05 Control Flow.py new file mode 100644 index 0000000..17588c4 --- /dev/null +++ b/0_python/05 Control Flow.py @@ -0,0 +1,175 @@ +# --- +# jupyter: +# jupytext_format_version: '1.2' +# 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 +# --- + +# All the IPython Notebooks in this lecture series are available at https://github.com/rajathkumarmp/Python-Lectures + +# # Control Flow Statements + +# ## If + +# if some_condition: +# +# algorithm + +x = 12 +if x >10: + print("Hello") + +# ## If-else + +# if some_condition: +# +# algorithm +# +# else: +# +# algorithm + +x = 12 +if x > 10: + print("hello") +else: + print("world") + +# ## if-elif + +# if some_condition: +# +# algorithm +# +# elif some_condition: +# +# algorithm +# +# else: +# +# algorithm + +x = 10 +y = 12 +if x > y: + print("x>y") +elif x < y: + print("x y: + print("x>y") +elif x < y: + print("x=7: + break + +# ## Continue + +# 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. + +for i in range(10): + if i>4: + print("The end.") + continue + elif i<7: + print(i) + +# ## List Comprehensions + +# 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, + +res = [] +for i in range(1,11): + x = 27*i + res.append(x) +print res + +# Since you are generating another list altogether and that is what is required, List comprehensions is a more efficient way to solve this problem. + +[27*x for x in range(1,11)] + +# That's it!. Only remember to enclose it in square brackets + +# 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. + +[27*x for x in range(1,20) if x<=10] + +# Let me add one more loop to make you understand better, + +[27*z for i in range(50) if i==27 for z in range(1,11)] diff --git a/1_kmeans/README.md b/1_kmeans/README.md deleted file mode 100755 index e554c62..0000000 --- a/1_kmeans/README.md +++ /dev/null @@ -1,243 +0,0 @@ -[TOC] -# **$ \color {gray}Unsupervised \quad Learning $** -​ 利用无标签的数据学习 *数据的分布* 或 *数据与数据之间的关系* 被称作无监督学习 -有监督学习和无监督学习的最大区别在于数据是否有标签 -无监督学习最常应用的场景是**聚类**(clustering)和**降维**(DimensionReduction) -## 聚类 - 聚类(clustering)，就是根据数据的"相似性"将数据分为多类的过程 -![](./pic/01.jpeg) -### 聚类算法 -+ 分散型聚类（k-means） -+ 结构型聚类（层次聚类） -### 距离函数 - (相似性或相异性)：度量两个数据点的相似程度 -### 聚类评价 -+ 类内差异(聚类内部距离)：最小化 -+ 类间差异(聚类外部距离)：最大化 -聚类结果的质量与**算法、距离函数**和**应用领域**有很大关系 -## $\color{red} k-均值聚类 $ -+ k-均值算法是划分聚类算法 -+ 根据某个距离函数反复地把数据分入k个聚类中 -+ **k-均值算法把给定的数据划分为k个聚类** - + 每个聚类中有一个聚类的中心，用来表示某个聚类，这个中心时聚类中所有数据点的均值 - + k是由用户指定的 -### k-均值算法执行步骤 -``` -1. 随机选取k个数据点作为初始聚类中心 -2. 计算每个数据点与各个中心之间的距离，把每个数据点分配给距离他最近的聚类中心 -3. 数据点分配以后，每个聚类的聚类中心会根据聚类现有的数据点重新计算 -4. 不断重复直到满足某个终止条件为止 -``` -### 终止条件 -+ 没有(或最小数目)数据点被重新分配给不同的聚类 -+ 没有(或最小数目)聚类中心再发生变化 -+ `误差平方和`(sum of squared error,SSE)局部最小 -$$ -\sum_{j=1}^k \sum {x\in C_j} dist(x,m_j)^2 -$$ -其中，$ C_j $表示第$j$个聚类，$m_j$是聚类$C_j$的聚类中心（$C_j$中所有数据点的均值向量）,$dist(x,m_j)$表示数据点$x$和聚类中心$m_j$之间的距离。 -### 示例 -![](./pic/02.jpeg) -![]./pic/03.jpeg) -### 距离计算 -+ 在那些均值能被定义和计算的数据集上均能使用k-均值算法 -+ 在`欧式空间`，聚类均值可以使用如下公式 $m_j = {1\over \mid C_j \mid} \sum_ {x_i \in C_j} X_i$ -+ 数据点与聚类中心的距离使用如下公式计算$dist(x_i,m_j)=\mid\mid x_i - m_j \mid\mid = \sqrt{(x_{i1} - m_{j1}) ^2 + (x_{i2} - m_{j2}) ^2 +...+ (x_{ir} - m_{jr}) ^2} $ -### 算法举例 -![](./pic/04.jpeg) -![](./pic/05.jpeg) -![](./pic/06.jpeg) -### 优势与劣势 -#### 优势 -+ 简单：容易被理解，同时也容易被实现 -+ 效率：时间复杂度为$O(tkn)$（其中，$k$是聚类的个数；$t$是循环次数） -+ 由于$k$和$t$通常都远远小于$n$，k-均值算法被认为相对于数据点的数目来说是线性的 -+ k-均值算法时聚类算法中最流行的一种算法 -#### 劣势 -+ 算法只能用于那些均值能够被定义的数据集上。 - + 对于`范畴数据`，有一种k-均值算法的变体——`k-模算法`。 -+ 需要事先指定聚类数目k -+ 算法对于`异常值`十分敏感 - + 异常值是指数据中那些与其他数据点相隔很远的数据点 - + 异常值可能是数据采集时产生的错误或者一些具有不同值的特殊点 -#### 解决异常值问题 -![](./pic/07.jpeg) -+ 一种方法：在聚类过程中去除那些比其他数据点离聚类中心都要远的数据点（一般需要在多次循环中监控这些潜在的异常值，然后再决定是否删除他们） -+ 另一种方法：`随机采样`，因为采样过程中只选择很少一部分的数据点，因此选中异常值的概率将会很小（可以先用采样点进行预先聚类然后把其他数据点分配给这些聚类） -#### 算法对初始种子十分敏感 -![](./pic/08.jpeg) -![](./pic/09.jpeg) -#### 算法不适合发现那些形状不是`超维椭圆体`（或超维球体）的聚类 -![](./pic/10.jpeg) -## 层次聚类 -![](./pic/11.jpeg) -### 合并（自下而上）聚类： -从树状图的最底层开始构建树： -+ 合并最相似（距离最近）的聚类来形成山一层中的聚类 -+ 整个过程当全部数据点都合并到一个聚类中时停止 -### 分裂（自上而下）聚类： -从一个包含全部数据点的聚类开始： -+ 根节点聚类分裂成一些子聚类。每个子聚类在递归地继续向下分裂 -+ 直到出现只包含一个数据点的单节点聚类出现时停止 -#### 样例 -![](./pic/12.jpeg) -![](./pic/13.jpeg) -![](./pic/14.jpeg) -![](./pic/15.jpeg) -### 两个聚类之间距离的计算 -+ 单链接方法 -+ 全链接方法 -+ 平均链接方法 -+ 聚类中心方法 -+ ... -#### 单链接方法 -+ 两个聚类中距离最近的两个数据点之间的距离 -+ 适合于寻找那些形状怪异的聚类 -+ 对数据中的噪声值十分敏感 -#### 全链接方法 -+ 两个聚类中所有数据点之间聚类的最大值 -+ 对异常值十分敏感 -#### 平均链接方法 -+ 介于全链接防范对于异常值的而敏感型和单链接方法形成长链（这种倡廉不符合聚类时紧密的椭圆体对象这一常识）的趋势之间的折中办法 -+ 两个聚类之间距离是两个聚类之中多个数据点对之间距离值和的平均值 -#### 聚类中心方法 -两个聚类之间的距离是两个聚类中心之间的距离 -#### 复杂度 -![](./pic/16.jpeg) - -## $\color{red} PCA$ - -### 问题 - -     真实的训练数据总是存在各种各样的问题： - -- 比如拿到一个汽车的样本，里面既有以“千米/每小时”度量的最大速度特征，也有“英里/小时”的最大速度特征，显然这两个特征有一个多余。 -- 拿到一个数学系的本科生期末考试成绩单，里面有三列，一列是对数学的兴趣程度，一列是复习时间，还有一列是考试成绩。我们知道要学好数学，需要有浓厚的兴趣，所以第二项与第一项强相关，第三项和第二项也是强相关。那是不是可以合并第一项和第二项呢？ --  拿到一个样本，特征非常多，而样例特别少，这样用回归去直接拟合非常困难，容易过度拟合。比如北京的房价：假设房子的特征是（大小、位置、朝向、是否学区房、建造年代、是否二手、层数、所在层数），搞了这么多特征，结果只有不到十个房子的样例。要拟合房子特征->房价的这么多特征，就会造成过度拟合。 - -下面探讨一种称作主成分分析（PCA）的方法来解决部分上述问题。PCA的思想是将$n$维特征映射到k维$（k" + ] + }, + "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": 5, + "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": 8, + "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": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "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" ] }, { @@ -118,42 +328,24 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/bushuhui/.virtualenv/fintech/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/fintech/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/fintech/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" - ] - } - ], + "execution_count": null, + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], "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\n" + "dataset['class'][dataset['class']=='Iris-virginica']=2" ] }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": null, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [], "source": [ "def originalDatashow(dataSet):\n", @@ -165,36 +357,24 @@ " plt.title('original dataset')\n", " plt.xlabel('sepal length')\n", " plt.ylabel('sepal width') \n", - " plt.show()\n", - " " + " plt.show()" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { + "lines_to_end_of_cell_marker": 2, "scrolled": true }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "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)\n", - "\n" + "originalDatashow(datamat)" ] }, { @@ -411,6 +591,330 @@ "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": 21, + "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": 31, + "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都和分离度，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": 34, + "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": 37, + "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", + "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": 38, + "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(sum(np.min(cdist(X,kmeans.cluster_centers_,'euclidean'),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": { diff --git a/1_kmeans/k-means.py b/1_kmeans/k-means.py index cd69f96..8c45808 100755 --- a/1_kmeans/k-means.py +++ b/1_kmeans/k-means.py @@ -19,7 +19,113 @@ # version: 3.5.2 # --- -# # k-means demo +# # k-means + +# ## Theory +# +# 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。 +# +# 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。 +# +# 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。 +# +# 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： +# $$ +# J = \sum_{k=1}^{K} \sum_{i \in C_k} | x_i - u_k|^2 +# $$ +# +# $u_k$是第$k$个类的重心位置，定义为： +# $$ +# u_k = \frac{1}{|C_k|} \sum_{x \in C_k} x +# $$ +# +# +# 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。 +# +# 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。 +# 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。 +# 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。 +# 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。 +# +# + +# + +% matplotlib inline +import matplotlib.pyplot as plt +import numpy as np + +X0 = np.array([7, 5, 7, 3, 4, 1, 0, 2, 8, 6, 5, 3]) +X1 = np.array([5, 7, 7, 3, 6, 4, 0, 2, 7, 8, 5, 7]) +plt.figure() +plt.axis([-1, 9, -1, 9]) +plt.grid(True) +plt.plot(X0, X1, 'k.'); +# - + +# 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： +# ![data_0](images/data_0.png) +# +# 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。 +# +# +# + +C1 = [1, 4, 5, 9, 11] +C2 = list(set(range(12)) - set(C1)) +X0C1, X1C1 = X0[C1], X1[C1] +X0C2, X1C2 = X0[C2], X1[C2] +plt.figure() +plt.title('1st iteration results') +plt.axis([-1, 9, -1, 9]) +plt.grid(True) +plt.plot(X0C1, X1C1, 'rx') +plt.plot(X0C2, X1C2, 'g.') +plt.plot(4,6,'rx',ms=12.0) +plt.plot(5,5,'g.',ms=12.0); + +# 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： +# +# ![data_1](images/data_1.png) +# +# 画图结果如下： + +C1 = [1, 2, 4, 8, 9, 11] +C2 = list(set(range(12)) - set(C1)) +X0C1, X1C1 = X0[C1], X1[C1] +X0C2, X1C2 = X0[C2], X1[C2] +plt.figure() +plt.title('2nd iteration results') +plt.axis([-1, 9, -1, 9]) +plt.grid(True) +plt.plot(X0C1, X1C1, 'rx') +plt.plot(X0C2, X1C2, 'g.') +plt.plot(3.8,6.4,'rx',ms=12.0) +plt.plot(4.57,4.14,'g.',ms=12.0); + +# 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： +# ![data_2](images/data_2.png) +# +# 画图结果如下： +# + +C1 = [0, 1, 2, 4, 8, 9, 10, 11] +C2 = list(set(range(12)) - set(C1)) +X0C1, X1C1 = X0[C1], X1[C1] +X0C2, X1C2 = X0[C2], X1[C2] +plt.figure() +plt.title('3rd iteration results') +plt.axis([-1, 9, -1, 9]) +plt.grid(True) +plt.plot(X0C1, X1C1, 'rx') +plt.plot(X0C2, X1C2, 'g.') +plt.plot(5.5,7.0,'rx',ms=12.0) +plt.plot(2.2,2.8,'g.',ms=12.0); + +# 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。 +# +# + +# ## Program # + # This line configures matplotlib to show figures embedded in the notebook, @@ -55,7 +161,7 @@ def originalDatashow(dataSet): plt.xlabel('sepal length') plt.ylabel('sepal width') plt.show() - + # + {"scrolled": true} #获取样本数据 @@ -213,3 +319,170 @@ def trgartshow(dataSet, k, labels): # 绘图显示 datashow(datamat, k, mycentroids, clusterAssment) trgartshow(datamat, 3, labels) + +# ## How to use sklearn to do the classifiction +# + +# + +from sklearn.datasets import load_digits +import matplotlib.pyplot as plt +from sklearn.cluster import KMeans + +# load digital data +digits, dig_label = load_digits(return_X_y=True) + +# draw one digital +plt.gray() +plt.matshow(digits[0].reshape([8, 8])) +plt.show() + +# calculate train/test data number +N = len(digits) +N_train = int(N*0.8) +N_test = N - N_train + +# split train/test data +x_train = digits[:N_train, :] +y_train = dig_label[:N_train] +x_test = digits[N_train:, :] +y_test = dig_label[N_train:] + + + +# + +# do kmeans +kmeans = KMeans(n_clusters=10, random_state=0).fit(x_train) + +# kmeans.labels_ - output label +# kmeans.cluster_centers_ - cluster centers + +# draw cluster centers +fig, axes = plt.subplots(nrows=1, ncols=10) +for i in range(10): + img = kmeans.cluster_centers_[i].reshape(8, 8) + axes[i].imshow(img) +# - + +# ## Exerciese - How to caluate the accuracy? +# +# 1. How to match cluster label to groundtruth label +# 2. How to solve the uncertainty of some digital + +# ## 评估聚类性能 +# +# 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。 +# +# + +# + +from sklearn.metrics import adjusted_rand_score + +ari_train = adjusted_rand_score(y_train, kmeans.labels_) +print("ari_train = %f" % ari_train) +# - + +# Given the contingency table: +# ![ARI_ct](images/ARI_ct.png) +# +# the adjusted index is: +# ![ARI_define](images/ARI_define.png) +# +# * [ARI reference](https://davetang.org/muse/2017/09/21/adjusted-rand-index/) + +# +# +# 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都和分离度，取值范围是[-1,1]，轮廓系数越大，表示聚类效果越好。 +# +# 轮廓系数的具体计算步骤： +# 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 +# 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 +# 3. 对于样本$x_i$，轮廓系数为$sc_i = \frac{b_i−a_i}{max(b_i,a_i)}$ +# 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。 + +# + +import numpy as np +from sklearn.cluster import KMeans +from sklearn.metrics import silhouette_score +import matplotlib.pyplot as plt + +plt.rcParams['figure.figsize']=(10,10) +plt.subplot(3,2,1) + +x1=np.array([1,2,3,1,5,6,5,5,6,7,8,9,7,9]) #初始化原始数据 +x2=np.array([1,3,2,2,8,6,7,6,7,1,2,1,1,3]) +X=np.array(list(zip(x1,x2))).reshape(len(x1),2) + +plt.xlim([0,10]) +plt.ylim([0,10]) +plt.title('Instances') +plt.scatter(x1,x2) + +colors=['b','g','r','c','m','y','k','b'] +markers=['o','s','D','v','^','p','*','+'] + +clusters=[2,3,4,5,8] +subplot_counter=1 +sc_scores=[] +for t in clusters: + subplot_counter +=1 + plt.subplot(3,2,subplot_counter) + kmeans_model=KMeans(n_clusters=t).fit(X) #KMeans建模 + + for i,l in enumerate(kmeans_model.labels_): + plt.plot(x1[i],x2[i],color=colors[l],marker=markers[l],ls='None') + + plt.xlim([0,10]) + plt.ylim([0,10]) + + sc_score=silhouette_score(X,kmeans_model.labels_,metric='euclidean') #计算轮廓系数 + sc_scores.append(sc_score) + + plt.title('k=%s,silhouette coefficient=%0.03f'%(t,sc_score)) + +plt.figure() +plt.plot(clusters,sc_scores,'*-') #绘制类簇数量与对应轮廓系数关系 +plt.xlabel('Number of Clusters') +plt.ylabel('Silhouette Coefficient Score') + +plt.show() +# - + +# ## How to determin the 'k'? +# +# 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*。 +# +# + +# + +import numpy as np +from sklearn.cluster import KMeans +from scipy.spatial.distance import cdist +import matplotlib.pyplot as plt + +cluster1=np.random.uniform(0.5,1.5,(2,10)) +cluster2=np.random.uniform(5.5,6.5,(2,10)) +cluster3=np.random.uniform(3,4,(2,10)) + +X=np.hstack((cluster1,cluster2,cluster3)).T +plt.scatter(X[:,0],X[:,1]) +plt.xlabel('x1') +plt.ylabel('x2') +plt.show() + +# + +K=range(1,10) +meandistortions=[] + +for k in K: + kmeans=KMeans(n_clusters=k) + kmeans.fit(X) + meandistortions.append(sum(np.min(cdist(X,kmeans.cluster_centers_,'euclidean'),axis=1))/X.shape[0]) + +plt.plot(K,meandistortions,'bx-') +plt.xlabel('k') +plt.ylabel('Average Dispersion') +plt.title('Selecting k with the Elbow Method') +plt.show() +# - + +# 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。 diff --git a/1_kmeans/pic/01.jpeg b/1_kmeans/pic/01.jpeg deleted file mode 100755 index 1689825..0000000 Binary files a/1_kmeans/pic/01.jpeg and /dev/null differ diff --git a/1_kmeans/pic/02.jpeg b/1_kmeans/pic/02.jpeg deleted file mode 100755 index f37c8cc..0000000 Binary files a/1_kmeans/pic/02.jpeg and /dev/null differ diff --git a/1_kmeans/pic/03.jpeg b/1_kmeans/pic/03.jpeg deleted file mode 100755 index 4c56596..0000000 Binary files a/1_kmeans/pic/03.jpeg and /dev/null differ diff --git a/1_kmeans/pic/04.jpeg b/1_kmeans/pic/04.jpeg deleted file mode 100755 index 20f36f9..0000000 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deleted file mode 100755 index 474011b..0000000 Binary files a/1_kmeans/pic/29.gif and /dev/null differ diff --git a/1_kmeans/pic/30.gif b/1_kmeans/pic/30.gif deleted file mode 100755 index 0fc17f6..0000000 Binary files a/1_kmeans/pic/30.gif and /dev/null differ diff --git a/1_knn/images/knn.png b/1_knn/images/knn.png new file mode 100644 index 0000000..97476a2 Binary files /dev/null and b/1_knn/images/knn.png differ diff --git a/1_knn/knn_classification.ipynb b/1_knn/knn_classification.ipynb index be5c04e..1e0266b 100644 --- a/1_knn/knn_classification.ipynb +++ b/1_knn/knn_classification.ipynb @@ -6,7 +6,37 @@ "source": [ "# KNN Classification\n", "\n", - "\n" + "\n", + "KNN最邻近规则，主要应用领域是对未知事物的识别，即判断未知事物属于哪一类，判断思想是，基于欧几里得定理，判断未知事物的特征和哪一类已知事物的的特征最接近；\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算法中，所选择的邻居都是已经正确分类的对象。该方法在定类决策上只依据最邻近的一个或者几个样本的类别来决定待分样本所属的类别。 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", + "\n", + "\n", + "算法步骤：\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": [ + "## Program" ] }, { diff --git a/1_knn/knn_classification.py b/1_knn/knn_classification.py index b0a1732..df44fe4 100644 --- a/1_knn/knn_classification.py +++ b/1_knn/knn_classification.py @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- # --- # jupyter: # jupytext_format_version: '1.2' @@ -20,7 +21,31 @@ # # KNN Classification # # +# KNN最邻近规则，主要应用领域是对未知事物的识别，即判断未知事物属于哪一类，判断思想是，基于欧几里得定理，判断未知事物的特征和哪一类已知事物的的特征最接近； # +# 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算法中，所选择的邻居都是已经正确分类的对象。该方法在定类决策上只依据最邻近的一个或者几个样本的类别来决定待分样本所属的类别。 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。 +# +# 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）。 +# +# 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。 +# +# 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 +# ![knn](images/knn.png) +# +# 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。 +# +# +# 算法步骤： +# +# * step.1---初始化距离为最大值 +# * step.2---计算未知样本和每个训练样本的距离dist +# * step.3---得到目前K个最临近样本中的最大距离maxdist +# * step.4---如果dist小于maxdist，则将该训练样本作为K-最近邻样本 +# * 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 +# * step.6---统计K-最近邻样本中每个类标号出现的次数 +# * step.7---选择出现频率最大的类标号作为未知样本的类标号 + +# ## Program # + % matplotlib inline diff --git a/1_logistic_regression/Logistic_regression.ipynb b/1_logistic_regression/Logistic_regression.ipynb index 53b24cf..cd04d57 100644 --- a/1_logistic_regression/Logistic_regression.ipynb +++ b/1_logistic_regression/Logistic_regression.ipynb @@ -47,6 +47,48 @@ ] }, { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5,1,'Logistic function')" + ] + }, + "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": [ + "%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\")" + ] + }, + { "cell_type": "markdown", "metadata": {}, "source": [ @@ -108,8 +150,15 @@ ] }, { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Program" + ] + }, + { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -125,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -151,7 +200,7 @@ "Text(0.5,1,'Original Data')" ] }, - "execution_count": 21, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, @@ -162,7 +211,9 @@ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -275,6 +326,203 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "## How to use sklearn to resolve the problem\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "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": [ + "## Multi-class recognition" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1797, 64)\n" + ] + }, + { + "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" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy train = 0.998608\n", + "accuracy test = 0.897222\n", + "score_train = 0.998608, score_test = 0.897222\n" + ] + }, + { + "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", + "\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", + "\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", + "\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", + "# 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": [ + "## Exercise - How to draw mis-classfied data?\n", + "\n", + "1. How to obtain the mis-classified index?\n", + "2. How to draw them?" + ] + }, + { + "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", diff --git a/1_logistic_regression/Logistic_regression.py b/1_logistic_regression/Logistic_regression.py index 971b871..1482ef3 100644 --- a/1_logistic_regression/Logistic_regression.py +++ b/1_logistic_regression/Logistic_regression.py @@ -56,6 +56,20 @@ # # +# + +# %matplotlib inline +import matplotlib.pyplot as plt +import numpy as np + +plt.figure() +plt.axis([-10,10,0,1]) +plt.grid(True) +X=np.arange(-10,10,0.1) +y=1/(1+np.e**(-X)) +plt.plot(X,y,'b-') +plt.title("Logistic function") +# - + # 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: # # $$ @@ -104,6 +118,8 @@ # # +# ## Program + # + # %matplotlib inline @@ -192,6 +208,108 @@ logistic = Logistic(data, label) logistic.train(200) plot_decision_boundary(lambda x: logistic.predict(x), data, label) +# ## How to use sklearn to resolve the problem +# + +# + +from sklearn.linear_model.logistic import LogisticRegression +from sklearn.metrics import confusion_matrix +from sklearn.metrics import accuracy_score +import matplotlib.pyplot as plt + +# calculate train/test data number +N = len(data) +N_train = int(N*0.8) +N_test = N - N_train + +# split train/test data +x_train = data[:N_train, :] +y_train = label[:N_train] +x_test = data[N_train:, :] +y_test = label[N_train:] + +# do logistic regression +lr=LogisticRegression() +lr.fit(x_train,y_train) + +pred_train = lr.predict(x_train) +pred_test = lr.predict(x_test) + +# calculate train/test accuracy +acc_train = accuracy_score(y_train, pred_train) +acc_test = accuracy_score(y_test, pred_test) +print("accuracy train = %f" % acc_train) +print("accuracy test = %f" % acc_test) + +# plot confusion matrix +cm = confusion_matrix(y_test,pred_test) + +plt.matshow(cm) +plt.title(u'Confusion Matrix') +plt.colorbar() +plt.ylabel(u'Groundtruth') +plt.xlabel(u'Predict') +plt.show() +# - + +# ## Multi-class recognition + +# + +from sklearn.datasets import load_digits +import matplotlib.pyplot as plt + +# load digital data +digits, dig_label = load_digits(return_X_y=True) +print(digits.shape) + +# draw one digital +plt.gray() +plt.matshow(digits[0].reshape([8, 8])) +plt.show() + +# calculate train/test data number +N = len(digits) +N_train = int(N*0.8) +N_test = N - N_train + +# split train/test data +x_train = digits[:N_train, :] +y_train = dig_label[:N_train] +x_test = digits[N_train:, :] +y_test = dig_label[N_train:] + +# do logistic regression +lr=LogisticRegression() +lr.fit(x_train,y_train) + +pred_train = lr.predict(x_train) +pred_test = lr.predict(x_test) + +# calculate train/test accuracy +acc_train = accuracy_score(y_train, pred_train) +acc_test = accuracy_score(y_test, pred_test) +print("accuracy train = %f, accuracy_test = %f" % (acc_train, acc_test) + +score_train = lr.score(x_train, y_train) +score_test = lr.score(x_test, y_test) +print("score_train = %f, score_test = %f" % (score_train, score_test)) + +# plot confusion matrix +cm = confusion_matrix(y_test,pred_test) + +plt.matshow(cm) +plt.title(u'Confusion Matrix') +plt.colorbar() +plt.ylabel(u'Groundtruth') +plt.xlabel(u'Predict') +plt.show() +# - + +# ## Exercise - How to draw mis-classfied data? +# +# 1. How to obtain the mis-classified index? +# 2. How to draw them? + # ## References # # * [逻辑回归模型(Logistic Regression, LR)基础](https://www.cnblogs.com/sparkwen/p/3441197.html) diff --git a/1_logistic_regression/PCA_and_Logistic_Regression.ipynb b/1_logistic_regression/PCA_and_Logistic_Regression.ipynb new file mode 100644 index 0000000..1b7b0b7 --- /dev/null +++ b/1_logistic_regression/PCA_and_Logistic_Regression.ipynb @@ -0,0 +1,241 @@ +{ + "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": 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", + "\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": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1797, 64)\n" + ] + }, + { + "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": [ + "# 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": 9, + "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": 19, + "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": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "* [Pipelining: chaining a PCA and a logistic regression](http://scikit-learn.org/stable/auto_examples/plot_digits_pipe.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": "3.5.2" + }, + "main_language": "python" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/1_logistic_regression/PCA_and_Logistic_Regression.py b/1_logistic_regression/PCA_and_Logistic_Regression.py new file mode 100644 index 0000000..9b28bec --- /dev/null +++ b/1_logistic_regression/PCA_and_Logistic_Regression.py @@ -0,0 +1,141 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext_format_version: '1.2' +# 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 +# --- + +# # Chaining a PCA and a logistic regression + +# The PCA does an unsupervised dimensionality reduction, while the logistic regression does the prediction. +# +# We use a GridSearchCV to set the dimensionality of the PCA + +# + +% matplotlib inline + +import numpy as np +import matplotlib.pyplot as plt + +from sklearn import linear_model, decomposition, datasets +from sklearn.pipeline import Pipeline +from sklearn.model_selection import GridSearchCV + +logistic = linear_model.LogisticRegression() + +pca = decomposition.PCA() +pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)]) + +digits = datasets.load_digits() +X_digits = digits.data +y_digits = digits.target + +# Plot the PCA spectrum +pca.fit(X_digits) + +plt.figure(1, figsize=(4, 3)) +plt.clf() +plt.axes([.2, .2, .7, .7]) +plt.plot(pca.explained_variance_, linewidth=2) +plt.axis('tight') +plt.xlabel('n_components') +plt.ylabel('explained_variance_') + +# Prediction +n_components = [20, 40, 64] +Cs = np.logspace(-4, 4, 3) + +# Parameters of pipelines can be set using ‘__’ separated parameter names: +estimator = GridSearchCV(pipe, + dict(pca__n_components=n_components, + logistic__C=Cs)) +estimator.fit(X_digits, y_digits) + +plt.axvline(estimator.best_estimator_.named_steps['pca'].n_components, + linestyle=':', label='n_components chosen') +plt.legend(prop=dict(size=12)) +plt.show() + +# + +# Compare the performance +from sklearn.datasets import load_digits +from sklearn.linear_model.logistic import LogisticRegression +from sklearn import decomposition +from sklearn.metrics import confusion_matrix +from sklearn.metrics import accuracy_score +import matplotlib.pyplot as plt + + +# load digital data +digits, dig_label = load_digits(return_X_y=True) +print(digits.shape) + +# draw one digital +plt.gray() +plt.matshow(digits[0].reshape([8, 8])) +plt.show() + + +# + + +# calculate train/test data number +N = len(digits) +N_train = int(N*0.8) +N_test = N - N_train + +# split train/test data +x_train = digits[:N_train, :] +y_train = dig_label[:N_train] +x_test = digits[N_train:, :] +y_test = dig_label[N_train:] + +# do logistic regression +lr=LogisticRegression() +lr.fit(x_train,y_train) + +pred_train = lr.predict(x_train) +pred_test = lr.predict(x_test) + +# calculate train/test accuracy +acc_train = accuracy_score(y_train, pred_train) +acc_test = accuracy_score(y_test, pred_test) +print("accuracy train = %f, accuracy_test = %f" % (acc_train, acc_test)) + + +# + +# do PCA with 'n_components=40' +pca = decomposition.PCA(n_components=40) +pca.fit(x_train) + +x_train_pca = pca.transform(x_train) +x_test_pca = pca.transform(x_test) + +# do logistic regression +lr=LogisticRegression() +lr.fit(x_train_pca,y_train) + +pred_train = lr.predict(x_train_pca) +pred_test = lr.predict(x_test_pca) + +# calculate train/test accuracy +acc_train = accuracy_score(y_train, pred_train) +acc_test = accuracy_score(y_test, pred_test) +print("accuracy train = %f, accuracy_test = %f" % (acc_train, acc_test)) + +# - + +# ## References +# * [Pipelining: chaining a PCA and a logistic regression](http://scikit-learn.org/stable/auto_examples/plot_digits_pipe.html) diff --git a/1_logistic_regression/demo1/3a - Linear regression 1D.ipynb b/1_logistic_regression/demo1/3a - Linear regression 1D.ipynb deleted file mode 100644 index b32d7d4..0000000 --- a/1_logistic_regression/demo1/3a - Linear regression 1D.ipynb +++ /dev/null @@ -1,408 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Linear regression \n", - "\n", - "\n", - "We load the dataset 'diabetes' using the sklearn load function: " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# matplotlib inline\n", - "from sklearn import datasets\n", - "\n", - "# Load the diabetes dataset\n", - "diabetes = datasets.load_diabetes()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The dataset consists of data and targets. Target tells us what is the desired output for specific example from data: " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(442, 10)\n", - "(442,)\n" - ] - } - ], - "source": [ - "X = diabetes.data\n", - "y = diabetes.target\n", - "print(X.shape)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Splitting the data\n", - "==================\n", - "We want to split the data into train set and test set. We fit the linear model on the train set, and we show that it performs good on test set. \n", - "\n", - "Before splitting the data, we shuffle (mix) the examples, because for some datasets the examples are ordered. \n", - "\n", - "If we wouldn't shuffle, train set and test set could be totally different, thus linear model fitted on train set wouldn't be valid on test set.\n", - "Now we shuffle:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(442, 10)\n", - "(442,)\n" - ] - } - ], - "source": [ - "from sklearn.utils import shuffle\n", - "X, y = shuffle(X, y, random_state=1)\n", - "print(X.shape)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each example of data has 10 columns in total.\n", - "\n", - "We want to work with 1-dim data because it is simple to visualize. Therefore select only one column, e.g column 2 and fit linear model on it:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(442, 10)\n", - "(442, 1)\n" - ] - } - ], - "source": [ - "# Use only one column from data\n", - "print(X.shape)\n", - "X = X[:, 2:3]\n", - "print(X.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Split the data into training/testing sets" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(250, 1)\n", - "(192, 1)\n" - ] - } - ], - "source": [ - "train_set_size = 250\n", - "X_train = X[:train_set_size] # selects first 250 rows (examples) for train set\n", - "X_test = X[train_set_size:] # selects from row 250 until the last one for test set\n", - "print(X_train.shape)\n", - "print(X_test.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Split the targets into training/testing sets" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(250,)\n", - "(192,)\n" - ] - } - ], - "source": [ - "y_train = y[:train_set_size] # selects first 250 rows (targets) for train set\n", - "y_test = y[train_set_size:] # selects from row 250 until the last one for test set\n", - "print(y_train.shape)\n", - "print(y_test.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can look at our train data. We can see that the examples have linear relation. \n", - "\n", - "Therefore, we can use linear model to make good classification of our examples.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.scatter(X_train, y_train)\n", - "plt.scatter(X_test, y_test)\n", - "plt.xlabel('Data')\n", - "plt.ylabel('Target');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Linear regression\n", - "=================\n", - "Create linear regression object, which we use later to apply linear regression on data" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn import linear_model\n", - "import numpy as np\n", - "regr = linear_model.LinearRegression()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fit the model using the training set" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "regr.fit(X_train, y_train);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We found the coefficients and the bias (the intercept)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[988.07836941]\n", - "150.80798145969447\n" - ] - } - ], - "source": [ - "print(regr.coef_)\n", - "print(regr.intercept_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we calculate the mean square error on the test set" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training error: 3960.4058766864073\n", - "Test error: 3811.1989929980004\n" - ] - } - ], - "source": [ - "# The mean square error\n", - "print(\"Training error: \", np.mean((regr.predict(X_train) - y_train) ** 2))\n", - "print(\"Test error: \", np.mean((regr.predict(X_test) - y_test) ** 2))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting data and linear model\n", - "==============================\n", - "Now we want to plot the train data and teachers (marked as dots). \n", - "\n", - "With line we represents the data and predictions (linear model that we found):\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0,0.5,'Target')" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualises dots, where each dot represent a data exaple and corresponding teacher\n", - "plt.scatter(X_train, y_train, color='black')\n", - "# Plots the linear model\n", - "plt.plot(X_train, regr.predict(X_train), color='blue', linewidth=3);\n", - "plt.xlabel('Data')\n", - "plt.ylabel('Target')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We do similar with test data, and show that linear model is valid for a test set:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Visualises dots, where each dot represent a data example and corresponding teacher\n", - "plt.scatter(X_test, y_test, color='black')\n", - "# Plots the linear model\n", - "plt.plot(X_test, regr.predict(X_test), color='blue', linewidth=3);\n", - "plt.xlabel('Data')\n", - "plt.ylabel('Target');" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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_logistic_regression/demo1/3b - Linear regression 2D.ipynb b/1_logistic_regression/demo1/3b - Linear regression 2D.ipynb deleted file mode 100644 index 0cec971..0000000 --- a/1_logistic_regression/demo1/3b - Linear regression 2D.ipynb +++ /dev/null @@ -1,433 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Loading data\n", - "=====================================================\n", - "We will work with \"Parkinsons Telemonitoring\" dataset of the University of Oxford. The original study used a range of linear regression methods to predict the clinician's Parkinson's disease symptom score on the UPDRS scale\n", - "\n", - "We load the dataset \"Parkinsons Telemonitoring\" using the numpy loadtxt function.\n", - "\n", - "The columns are separated by ',' delimiter, which we pass to the loadtxt function." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# matplotlib inline\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "data = np.loadtxt(\"data/artifical_lin.txt\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We want to work now with 2-dim data, in order to plot it in 3d space. Therefore we select 2 columns (attributes) from total of 12 columns. \n", - "\n", - "In this example we will select attributes \"Clinician's motor UPDRS score\" and \"Clinician's total UPDRS score\", which are 4th and 5th columns. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.00747581 0.43208362]\n", - " [0.49910584 0.20943748]\n", - " [0.11935362 0.59634898]\n", - " [0.47691878 0.91091956]\n", - " [0.73039367 0.88576849]\n", - " [0.96646013 0.75029941]\n", - " [0.0254202 0.74285026]\n", - " [0.17781366 0.59303845]\n", - " [0.44925923 0.89314114]\n", - " [0.08370431 0.26143735]]\n", - "[1.45054918 1.1025327 1.33827336 2.6022192 2.2101526 2.6110778\n", - " 1.71069895 2.23335293 3.10281928 0.86406978]\n" - ] - } - ], - "source": [ - "X = data[:, :-1]\n", - "y = data[:, -1]\n", - "print(X[:10, :])\n", - "print(y[:10])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We shuffle examples:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(500, 2)\n", - "(500,)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/bushuhui/.virtualenv/fintech/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", - " return f(*args, **kwds)\n", - "/home/bushuhui/.virtualenv/fintech/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", - " return f(*args, **kwds)\n", - "/home/bushuhui/.virtualenv/fintech/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", - " return f(*args, **kwds)\n" - ] - } - ], - "source": [ - "from sklearn.utils import shuffle\n", - "X, y = shuffle(X, y, random_state=1)\n", - "print(X.shape)\n", - "print(y.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we split the data into train and test set:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "train_set_size = 250 \n", - "(250, 2)\n", - "(250, 2)\n" - ] - } - ], - "source": [ - "train_set_size = int(X.shape[0] / 2)\n", - "print(\"train_set_size = %d \" % train_set_size)\n", - "\n", - "X_train = X[:train_set_size, :] # selects first train_set_size rows (examples) for train set\n", - "X_test = X[train_set_size:, :] # selects from row train_set_size until the last one for test set\n", - "print(X_train.shape)\n", - "print(X_test.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And we split the targets into train and test set in similar way as we splitted data:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(250,)\n", - "(250,)\n" - ] - } - ], - "source": [ - "y_train = y[:train_set_size] # selects first 15 rows (targets) for train set\n", - "y_test = y[train_set_size:] # selects from row 250 until the last one for test set\n", - "print(y_train.shape)\n", - "print(y_test.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the data in the 3d plot. There is some linear relationship in the data:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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d1FYF8ekKAoWhRJ67NrSiD3ax+6oOzlvDvDaURBQFwrZ5aEeQrVUKY8kMqbzB40cmCHlUvG6dJ46lyKYS3NbmDPeudDLJ9N7VkYEkx4aS1AbcdNb5l33/lVrrnqK8piitidmNsmEYqKq64m//a9UDEUKQyWQ4cODAjNusxGqDYK6fTZ2jqqpomlbx40//b7leeOEFrr/++hW9VmtJ13W8Xi9e7/xFtS+2DTV+TNupL+rSVSYzRW7fNn8pOdsWJHMmTWGXc41LUdBUhVzRuQbp0lX+91u38JPuGJNZg60NAXY2v76P4rPnokQSIzSFXFiWhbtg8Npojn3VRQqFAvF4vLwbhMfjwev1krR0nuzKkjJgX0eEd+5pQZ+n9/etV0f44rO9WMKpyPP9E+P82UM7Fi1GLoRgNGNx5NAQmgJ3bK1dcE3lfMeohIWGT2VPUVqybDbLwYMHl3TbqcY2k8lw4sQJdF1fdWM+V29iqY3+fMc/ePAg11xzzQW3WQ8mJiZobW3F41n+ejZp5cZTBQ70xCiYNjuaQ1zRtPDGvYvZ0hDk5lad3pyBJWDfhgh3bZ+/8oyqKnTW+emJZqgP6OQMpx5qbcDF8LhzG7euctvWmeskJ9JF/vHAACeG00xmijSG3LjcbrAUmsNBGhp8mKZJe3s74PSKCoUCY4kMX3i8h0zBQldsXhtMcLqrj7s7dHRdLwdnKpVCd7n40o9H8Ooquq4jhOD8eJaX++LctKlmwdehK5rjD56PI5Q0AviXQ8N87n07aQov7wvMWn4+ZU9RWpZAIMBNN920rPscOXKErVu3EghcvNlpy6GqKi6Xa/EbXgJvhqHnSlvt6zWZKfK1gwMoirNrxrmxNKYt2NUSXtVxd9Tq/Nfrr0CgLKn6ywO7m/jWkUF6JjJ4XRoP7WmiJuBmaJ7nly6Y/Pq3ThJNF9FUGE8VeaE7zpY6Py5d5aFrmqCYmHEfVVXx+XwMDucwhEZrrTMMalo2Z3IW//vaq7Asi3w+T6FQIJFIkE5nyBdN0ARGafvHogXnuvtpUZPlAPV6vXg8nhm9rn9+ZQzTFtQGnc9bPGfwr4dH+JXbNy75dazU8CnMX+ZNhqK0ptZLr0uSlqInmsWwbFoizs7zuqpwqDdGMmfw/ddGAbh3ZyM3bqpZ1nt7qjFXFYWCYRFNFwl4dcJefc7jBL06P3NtG9l8gZxh87WXhzn9TA9qIcMv1hTYPutL5vGhFLGsUa5f6nNrTKQN3r+/hS31AeqCboaHExc8DjjLLqZH7fRdN3RdJxgMEgwGmZiYoKmpiVu2aLzUG8fr0iiaNn6Xws1XtuHXnQBNp9PlIJ1a4uDxeJhIZFCFwDCdyymqImas9VwPZChKF4Xs7UiXC22OgBhK5HnmbJRqnxM4//f5Xjy6xr4NCxfvnsvBnhh/9sNzpPImQa/O+/e18sBVzfP2HnVV4W9f7KdrIkdd0M1gyuaLzw3yyXeGCU/bzUJVFGfjxhIFBa+usq+jqjwzdL5e1lWtYRpCHsZSBXTVKSH3yLWt84b+z9/cQbpg0RXN0lnr56N3dbK5MTjvc7Ysi0KhwK3j3fzjkUkKhoUtTIqmTbM9zoEDk7hcrgt6mVN/Tl8iUcme4lxs28btnr+u7RuNDMVLQA4BSpeThpCHvGFxcihFJKBj2c7EF79bI1BaCF+wbA70xJYdimOpAp97ugvTErREvCRzJv9yaJCtDQF2tFy4ZZQQgpxhc348R1PYaagDLoWCZTMYzxNuej2IdrWEaAx7GEqUgs0WvHNXw5KWSvjdGr97/1Z+cHKceM5kd0uI6zfO/dwO9qf4wgsjgFMw/PqNEbYvEIgAmqbh9/t519XNZAoGL44IdFXhkWtbeduO+vLynqneZT6fJxaLlf/fsqzypKCpiTDTA9TtdldscoycaCOtOTl8unLyC8XFI4SgeyLLk6fGqPG5GDIK5A2bO7bV860jgwzEcuiqQtjrwrBs/O7lN5zj6SIFwyZQWlQf8mqMpoqMpYrsmOc+bl1F1xTGUwV6JvNkcgZut1KuqTrF79b49INX8o1XhhlNFri6Lcz9uxoueI6nxvN861wPLk3hvp0NbKhxhomrfC7es7dlwfM3LJsvPD+E26Xj0VUsW/DtoyPcvrWWTUtYlqEA920N8qv3bZn5c0XB5XLhcrkIheae1DS1RGloaIhcLkehUCCZTJaDVAhnH8S5epter7c80W8xcvhUuihkwy6tZ0XT5tuvDvOjU2PkDJvOOj83bKrh/FiGJ0+O0V7t48xohuNDSeqDHupDHt66o3HZj1Pl1XHpCnnTxqWp5A0bXVVoDM8/u1hXFe7dUcenn+hCLy1r8rtUnj4bZXerM/nHsGxGkgU8usp/u6l93sb/+GiOrxyKEvT5MC2bR4+PckVTiC31ft6zt2VG2bq55AwbwxKEfM4XAqfKjUI0U1xSKK6Goih4vV58Ph8ul6s8g3a66ZOC8vk8qVSKiYkJ8vk8hmEA4Ha78Xg8FAoFhoeHZwzTapomK9pIa0/2dqT17kD3JGdG04Q8Oj6XYDCWpzaQIZYtoioKHTV+Ht7bwomRJFVeFz9/88Zlr68DaKv28Z69rXz1QD8jiQKqBu/f18a2xoWXfLRHfGxvDOBzaWTTNh0NIbqiOUzbKQX32985zXAijyXg7u21fOTOzjm3jnqqK41XU6j2uzg3nmE4WcSwU/RM5jg6lOJPfupK/O75e0l+l0JtwEUiZxL26eQNGwVor/Yt6fmv9fVATdMIBALzznSfWjM9NTxrmibpdLocoidOnOArX/kKkUiEo0eP8tnPfvYNP5QqQ/ESkMOnKye/UFwcw8kCQY+OS1PonsigqTCazKOr6rShTp2t9UHaq/0rCkRwfp/v3NPMtRsijKacdYRNVd5FPyMBj47XpdFU5SFqqORLZeQ0BT7/TA/9sRxVPh0BPHlqgmvaqrhjW+0Fx3HeSwq2LRhLFXFpCh5dpS7gZiJd5MxYmqvbLry2WT5/4ON3dfCnzwwxkSng1lR+457NNC3Q06201QSroii43W7cbvecvc39+/czOjpKJBLhuuuue8MHIshQvGRkwy7NlsqbPHt2grFUgaawh1u31q16R4eVagp7OD2aprXKg2XbnB7JUO13c9/ORn7SPclwslDed25P+/yhsVTNER/NkaX1rgA6a33cvKmGF7omSeVswi6LX7zVGSY9P5HF7y5VO8LZCLgrmuUOLgzF2zsD/P3hGPGcgWXb6KVeI5TuyOJh017t5cuP7CaVNwl4dPQlrLmcsp7KvM1HURRn2cktt6z6WPl8nttuu41CoYBpmjz88MN84hOfqMBZVo4MxUtA9nak2UzL5nvHRkgXTKr8LgbieR49NsJDe1uXtLC90q7vrGEokefceAZQeOeeJt65pxmXptJa7ePcWAaBoLM2QG3w4k/XVxSFh69pYl9HFa+eOMV1OzfQWuvM+NxY4+NQfwK37lxvVKA8eWa2q5r8/Lf9Gq/FVFQVxlJFiqZNpmjRWuXlisalFdhQFYUqX+ULXpwYTvGT7hh+t87bdtQveo1zLVRyoo3H4+FHP/oRwWAQwzC45ZZbuO+++7jhhhsqcvxKkKF4CcjhU2m2ZN4kljNoLu3/Vx/yMJLIOyG5ysZ2JV/A3LrKe/a2Es85kzGq/a7y+7bK51rResTpiqZNsiDIFq0Fr9ktRFEUNtX5SVVp1E0L5l+5fSO/+Z3TjKeL2EJw65Ya7pxj6BSc12ZXk597rmnGsgU/ODnO8eEUTSEP79rThNe18Lmttpe20P1f6onxie+dxbQFAviPoyN84f27LwjGqVrAa6WS+ykqikIw6Hx5MQwDwzDWXXsoQ/ESkT3Flbmce9mWLZjMOHXAagLuGT1At6aCEFi2QFMVLFuAoGLbD62k4VFVhZpAZXuB2aLFYCzHY6+N0tVrcMzo4v5djexcZcm46epDHr7wvl30x3K4dZW2yOLXKF8bTvGVF/tJFyzu3FrDe/e1zDkxp9IWCsUvP9+PpirlIfRoxuDJU+OLLhOptErXPrUsi3379nHu3Dl++Zd/ed0V0ZeheAlczg27tDJF0+ax10YZiOUAaIv4uG9XY3kXhaBXZ19HhIO9cVQVbBtu3FSz4l7UeiOE4MlT4zx5cpzToykiPhftLqgNuPne8RFaIl6q/csP4P5Yjh90FziSHeHWrfXlZRBuXWX2zvd5w+LR18YZSebZ1Rzi1i01CCEYSBT51DOnsYXApSr8w4FBDFvwgevalvS8VvKF48fnovzFUz0kckV21rv5vaY2Qt6ZzXHesNCmHVvgLAGp1DkslWmaFQ1FTdM4cuQI8XicBx98kOPHj7Nr166KHX+1ZCheAuttuEBae8cGkwzEcrRGnOHRgXiOo4MJ9m+oLt/mus4aWqt9pAsWIY9OS+TSbPWUzBkc7k/g0hR2tYQJV+Ba2enRNI+/NkZ9yI1bU8kUTXpyNtt1FVBI5c1yKMazBs+enSBTMLm6PcKVzXMvz+iP5fjsD7uJxQ0GjCSHBtL86u0b2dpw4XVAw7L5+LdPcXosg4Lgu8fG6IpmuasFjgznyJs2taVhSVVRePzE+JJCcSXOj2f4w8fPo6kKPl3h6EieTz95nk8+sH3G7d5yRR1fe3kIgTPK4NaUOavqXIwyb2uxeD8SiXDnnXfy/e9/X4aiJIdPV+NyfO2imQKBab2+gFsrD6VO17qMGZhr4cxoml//12NMZp1riZvrA3zqXTtoW+K6u/mMJPJoqrPcwetSMUybZMHpQYMo95KSOYNPPXaayYyBrik8cWqcX7x1I9d3XrgF0/NdMWwhqPaq1ATcxHMWT5+NzhmKx4dSnB/PUuV1ZqVatuAbrwxza2Mjbk2ZMcfUEmLRfRCnrCSQjg+nsITAr2uYpo3frXCo78LC5B+4rg0FhSdPT+B3q/z0vlY6a9e2IMBcKjnRZnx8HJfLRSQSIZfL8cQTT/Dxj3+8IseuFBmKq7SSb2hy+HTlLtdednPYy5mxNGGf85FLF81l75m31oQQ/NHjZ4jnDGoDbmwh6I5m+crzvfyfB65Y1bFrAm5M20YA2xtDvNwbQ0EQzRR5+64mvLpGbzTL0cEE0Uyx/OUgUzD5j1dH5gxF2xZMfztMhd1cipaNor7+/lFLtcJN2+bGDUF+cD7LZKkwgaLAB6+fu5doWDZfeLaXp85E8egKmwIGNcP9dNQGecfuxiUNd4e9To9UCGcCjWFBTfDCplhTFT50Qxv37aznY986ye99/ywK8Mu3beDBq5vLt7uceorDw8N86EMfwrIsbNvmve99Lw888EBFjl0pMhQvgcu1YZdWbkdLmGimyMmRFAC7msMVnVxSCQXTJpEzcOsaKM4woq4qjKYKqz72zpYw122s5lBfHFVRuG5jNVtVk/vv2EQ8a/DJ750iU7AYSeXJFS1aSgv4NVXBsC68jgZw46ZqftITJ12wUbIGRRtu2zL3pr5XNoUIuDVSeRO3rpIzbPZ3VOHVVbxenb98704efW2MdN7ixk3VXN029+/mb57r4z+Pj+F1qQzGC5weteiIxXH3pjjUl+BT79y+6OSoWzZXc2VTkFMjaUzLQgX+192b5r39//e9M/RO5gi4NSwBn3+2l22NQXaWhpUvRihWavbpVVddxeHDhytyrLUiQ/ESkT3FNxdNVbhjez03lHZiL5o2sWyRKp9rRiM6mSkylszj0jXaq31LHsarBI+u0lLlZTyVxOtSQTjnuat14ZJrS6GpCu/d18rtW+swLJuGkIdDB6P43Rp//sPzFC2bxioPfo/Gj89G6ZvMURNwk8gZ88627Kz185E7NvLVZ45RVx/gzu11XNk097mGvTp/+u4r+cKzvQzG8zSGVHKGxcceH6ba7+aDN7mWdA3x2XOTeHQFVSlNehHOcGttwEXvZI7Toxl2tSz8erk0lU8/eCUvdsfpHxlnU7WLaxdY4nJqNFMuRqArUDTh7FimHIprTRYEl9acHD5ducv9tfO6NF44H+WlnhiKAhGviwevaaHK52Ionud7x4YRKFjCpins5e27mi5aMCqKwsffto3/892T5cX5d26r40M3bJhxu6Jp84OTYxwdSOB3a9y/q5GWKh+fe7qLQ31xqnyc3h2JAAAgAElEQVQufun2TvZ2OA39SCLPQDxP0KOxrSGIqjpl1Q6PmZx8voejgwl2lMIs5NXZ3hikPuShyqfzwO5G7tpeX37snGHx3VdHGEwU2Fzn52076nnHZg+7drXhci08Iagt4uPj92zm5/7pKKdG0uRNp05pS1jwp0928btv19nTunDvPeTVSeSM8nIaRQFdVcs9NXuJ702XpnLblhqGA05ll4XUB91MZop4XVqpV+j8bMpa9xRlKEprTg6frr28YTGUyKMqCq0Rb8XW+61WfyzHi92TtIR9aKrCeLrAD0+N8e5rWnmxK0rQ6yqvSxuM5RiI59hUt7SqKpXQVu3jS49cXW6E5yoc8PiJUQ70xGiv9pM3LL7+8iBF0+LEcJraoJuCafFHj5/hsw/vJpYt8rfP92ILJzD2b4jwgeva+fQT53j0tIG3d5BYtkgiZ3DL5loM28brVvkft3eycdakEssW/OkT5zk5ksLr0jjYG6c7muWmkL3kz9Sjx8cYTRWwhChfV4xmLdo8bp47N7loKP7ybRv4re+cJl2w0FUFE2eHjmimSEPIM+ckn4UsJdD+33u38LFvn6Jo2dg23Lq5hhs3VS94n+Va6IumDEXporicezvrXTJn8C+HBknkDATQEvby8N4WPItUJ7kYUnkDFaXc04j4XIwmnVmoecOecY6qCqZ18d8nLk2lcY5JQEIIvnZwgM893eWUTqv1c/vWOoSAQ30J2mt8qIqC362TLRY5M5riiVPjVPlc+N1OL+dQb4LN9QGePjNBwAVVATcBj0b/ZJ7eySwel8pDV7dwbizNc+ei7GgOlXucfZNZzoylaQy5URQV4dU52Jdg99alP7d00SpN0HGqoiqKE7a2EEuaJHNNexWff98uDvXF0VWFE+f7SGs+Wqv9/Mz+Fnxr8B7b3RrmHz90NWfGMoS9OjubgzOCdK3rp67Vkoz1SobiJXC5DwGudy90TZIumLRUOTMYB+M5jg4muXZjZb9dr0SVz4UATNvZaT2WLdJe7fSItjcGOdgboz7koWDaaIpCwwp3n5htNY1mMmfwTwf6OdyX4NRoCr9LwxaCvskcB3vjtEa8hLzOtklT4VcwLE6NpOmN5tjZHCyfg6pCKmeiqgpTU0c9ukZ90Bly3VIf4He+c4rnu6KYtkBTFH7h5g14XRq9sSyZgkVdYFrvdZkfoxs2RvjqwUEUy8YwwRbg96gEPTr372xY/ADApjp/uUhAJ6Ps2LEFt3tllX+WGmh1QfeMUnYrOcZKVXKizeVAhuIlcDkMn671B22llvKFIpkz8Ltef2t7dJVUfuHrNosxLJtouoiiKNQH3U6jvgKtER+3bqnl+fNRFEWhNuDmriuca2bXdERQVYUzo2kCbp23XFFPpEIFoG0hiGWLBNz6sq5RFgyLj37zGL2TOdIFk3TBpMbvKvV0BecnMtyxrY77djby2SfPkS1YZIom8azB90+MEc0UGU7kuPuK+vIGwte0R6gPuukZzeI1bTJFk7ZqHztbwhwfSvJ8VxS7FIiWbfNnPzpPyKtj24Ki5QRuc8RHtmhyTXsVQdfkjPdq3rAYTxcJefQLXr/drWF+9/6t/NXTPSRdJg0+eMu2Gt6xd8OKtr96o3y5XejzLodPpWVbSc9vPX+Y1mMYLkdnXYCu0+P43U6PJmtYtFevfE1gpmDynaMjTKQLCJxZj/fubFzxdcprN1azsyWMYdkEPXp5KFVTFfZ2RMrDhZUymbP45GPnieWdsmE/e2NHeRbsYl4bTjEYz1NVWl+ZKZgkciY7W0JEswY7mkK8f38ruqbymYd3cXY8wxef6Sbk0Ql6daq8Ol0TWU6OpGkMebhlSw2KAp9+aBe/9bUXyGoaO1tCfOSuzbg0lUTORFMUROktaNoCWzi1YVWXgm5YpIsWm+t8bK4P8M7djRx7NVY+355olt///lnSBQsh4JFrW3nnVY0zntOd2+q4c1sdAF1dXYTDYepW0SNfbUHw1fbC1nr4VIaitOYuh+HT9dxTXMzejgjpgskrfQk0Fe7eXs/WRXZyX8iBnhiT2SItpQXlXRNZTo2k2b3IpIyFONevLk5D82+nspi6n6YqZ2LM377Qy4Zaf3lHjoVMvUsVRSHs00nmNLJFi0zBojHo4Tfu2Ype+nLQXuOnvcbPXz/bjbfUG3XpKrUBN61VHl4dTHJqJMX/fa6X37x3Gz+3y8NNN+2f8XhTywxMyy4XRldLayaBclWcj791y5zn++knu8gWbSI+F6Zl808HB9nVEioPd6436/VzNt2bLRTfPAPF68jlEIrr2WKvnaYq3Lm9no/evZmP3LV51dcS41ljxma/XpdKPHthibb1qGjajGet8nZDXpcGAoYT+TlvnzcsxlIF8oYFwI6mEA1hD/GsQbZgORvOVnnY1hjg42/bekF9ViEEjWEP3dEs8ZxBrmhRtGyePzeJbTu/G01V+KPHnS2RZqsPefjCz+yhMezFFrCtIUiVz0XRsjFtgSXg7u11FzymojiL/EeSBUIepwHXNRWF+Z/r9PuulBAC0xb8pDvGM2ejROco3bfW5ESbypI9RekCb5TQXul1v9laI176umOlSSSQK1rrrkTbXIQQHB9KkjcFg4k8G2p1Z2kEYs7Nas+Mpvj7n/RTNG1cmsoHb2jniqYQf/He3XzlhV6eOzdJ2KejKfBSd5yDvXH+z/3bueuKhvLj/c53TvJyT4xM0SKaKdJS5eXqtjAvdscRQpAumHhKFWXOTMLN9tTSCIFt2wgh2NHg49sf3ott29i2zcHeOH/1rLOt0y2dIT58Q+Ockz9cmkpDyE0yZxLy6s4+hELQFK7MZKW5FC3Br/3bKbqiWRQUXJrCX7xnJ1vql7Y0o1KBtlqLhaKcaCOtqfUeOut9OOdiu6YjQiJncmo0jaIIbuisZssS16Ol8iZnx9LYQrCpLlDx/QmnE0KUw8WyLL7+8hBPnY2CEJwdyzhr6YJu7tleQ1jJE43my8GTNyy++Owgbl0loCtkCzafe+IEv3R9HR4N3t4u6Bu26U8UGUxbeF0KuaLgjx89QXGin8aASlesyOPH06gI/JqCpUI0lUfJmFiGSdZ0tsQaM8CjwePdNt3//Cz3depoqopaWgSvzvr/WkXhk7eGyu/Lvp5u8nmn9+fxeMjn8wwPDxMIBPi12zv4oyd7iOcMhID372+5YAup2a/ZbKYt6JrIIoSgs9a/4MSkF4cMzowVyxVnskWLP32yi7/+6d2r/G0uTyU+swtdU9T1N09UvHme6Tqy3kMR1u9EoIsR2NPDZar3cktniP1tfhDODMpkMln+t6k/DcNgZGSkfL9U3uTRU3FypaFIBXjLJj8RrzrjfnP9OftnS/19TAVJsgjfO5qjxqfS4BXUBzQmchYPb3WxKVJkbGxsRvBM5ixSeRO/W6VoKoS8OpmiTRGdmoAHVVWpDuY4PFIk5HOC3cbG5VLJeGrZsrWO5EAaj/tMuUi3C6ee6ubODXRuEHzjlUEm00VcGly3qYaxiUlOZby8tX4Tt2yrn/9JTWNZFqZplj9D+XyeI0eOYFkWY2Nj5LJZfm5LgXhBoSbko6Uqz+joKH6/H0t180dPdnOgJ07ArfHRuzrp0Ga+p3KGxae+f45zYxlQoKXKy+/ev3XOIgYAsbyzxnHqGG5NYTy99CHUtR76XM4x5vNmu6YoQ/ESuBxCsRKW2/Av5TbJZJL+/n7GxsZWdP+lvu7TA2O+P2f/zLZtMplM+e/nJnIUbWguFbdO5C26Mxp3t1TPef+Fjj3131INxHLU9J2mIewhOjFBVSSCnjG59qor51yY3z2RYaIQQzfBpSrEioKGkJ8tG1rL11N/5iYfP+55lZzprLEMenWGEwX+9KlePvfjAe7d0YCqQN509v4rWjZNYQ9t1T6SOYOfvb6dbx4eoinsJZY1sQUIS/DDU+Psaqmidp51eLPfUwDZ0vBsXdCNruu0t7fPeH0syyKXy5HNZslms0SjUf7ypRjHJiy8GqRyKp989DT/z7V+9gcC5SHC7x0f4/RomrqAG0VRGIjn+NrLQ/z3WzfMeT6bq1SeGxHlCUEF0+bmzRe/0Lu8plg5MhQvkeWE4nLCpBL/lslkOH78OMC8t1mqhRr6pYTB1J+apqGqKrqu4/f7CQaDKz72WpmYmGDz5s3lvw9aUWqKSapL0/3VnIE/4Ka+fmm9otVoCHmoD3kYSxawLMF4qkhbTYDaeYZvD/bEuH5jhKODzl5/k5ki793fOmOC0bbGEJ94x5V85smz2LZgOFHAtAXNpW2mHj0+wk9d3cLTZyYYTRbY1hDkj9+9E5eq8InvnaY/liWVN8kUMnTU+MgDbl2lyutiIJZdcig+czbK7z12DnAm7nxwm2D/rNv1xwu81OMUNr9jaysbfS66f/QSYZ9e+lJqkylanBrL0+ofZmBgACEEr3QLbMOmWBSomoZHUxmI5eY9n511Gj8baeTvXxrEEoJ9HVV89K7ORZ/H9Oez3q8pWpYlrylKy9PX10c+n180eKb+nLrt4ODgkh+jkuHicrkWvF8ymWTDhg243e4Fj3kpxONxampqiEQqu5ZvLWypD3C4L04iZ6AqCsm8yS1baue8rRCCVN7Epan4llBubDFuXeWjd2/mnw/0c/hckt3NQT50c2d5+cRsmaJFR22AjtoAuaJFPGfQMcfGwrdsqUXXFB47NsJ4ehyf21nIrysKuqYyFM/x+Z/eA0LQEPLgcWn81VPniWcNOmv9jLqKnBlL0R9TCKnOmkFFVeY9r9mimSK/99g5bCFwaSpFy+bLxw3efaddLtN2dDDJx751koLp1ET96sEh/uandxP26sSyBh5dARR0TaM+4qezs53q6mqEEAy7Bjn50hCWbWMYJrGcyRXBHC+//DI+nw+/3z/jPyEEH7i+jUeua8OwbGd270V2Oe2neDmQoVgBgUAAr9e75KAaHR3FMAw6OzvX5aQWl8tFIBBYcemqtVaJb8ajyQI/6Z7EtGyu3VhNR03l17E1hr381NUtvNwbx7JtbtxUzZaG4AW3SxdM/u6FPs5PZFCAt17ZwNt2Nqz6vVETcPMrd27mcCTJ9u3t+P3z/z63NwY50BOjIeRB1xQ8ukbTHOsYX+mL84ePnWE4kSOVtxBAwbSoD3oomhZ9k1m++HQXYZ/Ohho/79jTzNmxDGGvxtHBJPGciWkJ4tki25tUFFWhyqvTucSi54PxPIoCrlLPxa2p5EwYTRXKu9J//tleLFsQ8jrN23i6yHePj/Ibb9nMb3/3NDnD2R1jW0OA61pen5mqKApv39NKf9Lk2XOToMAt2+v4tTs7calcMByby+XIZDIcPnyYQCCA3+8vB6fX611S72q9rFOUi/dfJ0OxAmpra5fVUGuaVp4sIF18o8k8f/z4WfKmjarAU2cm+OjdW9hcH8CyBc+eneDsWJqGkJe37mhYUqHo+bRV+2ibo8c13XdeHebcRJqWsBfLFjz62igdtT52NF+8a1PXbazGtgUHe+NoqsLbdjZQF7xwKcOXn+shW7TQVJWIXyGWNUnlLQwrT9irE/K60DWVyayBJbIc6Xdqo/74XJR41kTXQAiVoFejK17kV7fU0lkXWPJr3BT2YNkCoThDp2ZpA+LpWyml8iaaNu2zJQTJnMl1GyP8zc/s5thgioBH49bNNfR0nZvxOdRVhV+5fSMfvL4N2xZUlYZbAYLBIMHgzC81Bw8eZNeuXeXATCQSDA8Pk8/nEULg9XrLvUqfz0cgECiP1FSKrGhTWTIUL4H1HoZv9IlAz52LUjBtWko9oWimyOMnxvil2zv5+ssD/Oj0OAG3Rq4Y57WhJL/x1q1ruqdh10SGWr8zsUPXnMXtQ/H8RQ1F07I52Bfn3FiaupCHH54cJ+Jz0zhrjV+mYDnbLqkKLk0j6BVU+1xsbQhS43cjcKr1eHSVyXSBVN7kZ2/cwIGeOKZtg6IS8mrUBtwk0wY7W5b3HBtCHj5y50b+4ukeFOEUFX/kCteMa593bK3hX14ZRlWEU0NVVcpbLXXW+ss9yoWEvUtvGt1uN263m6qqqhk/n5odOxWY4+Pj9Pb2UiwWUVUVn89HoVDAsqxyeK4kfNb6s2rbtlySIa2tN3rorKVKvHaGJcr1RgE0xelx5A2LZ85M0BL2oqoKEZ+gdzJL72SWrXMMe1ZKU9jL6dE03tLuE5blXAsbiOVoCHmWHMhF0+bH56JMpItsaQiwt72q/AVsoS9iBcPiN/79NQ73J3BpCtpYhps2VfPEyVH+y/UdM257y5YavnZgAMN0rpHrikJj2Mud2+sZjufoi+Xw2RrCFuRMm7ZqH41hD79z/3Z+7RtH8bo0/G6nxmlHSKFo2sv+wvGuq5q4obOGkWSe1iovPaeOzvj3/3pjOwVT8MSpcTxujV+4uYO97VVzHmsthy8VRcHn8+Hz+aipmVlrdmp2bHd3N8Vikf7+fnK5XHlNYCAQmHENc7Hh2LXuKcqJNtKaW8+hWKnQPjeWJp4ziPhcc15Lu1Su3VjNs+eixLJFVEUhXTC5efO0CTCltsFZBuFsRAuUKrJYKAozeiar9e5rWvj8010MJ/PYtkAA3zw0gKaqNIQ8/M+7NlG9wPVAcHp6n/jPUxzojaHg7AzyoRs7eHhv66KP/3Kfs1lv0KPjc6kULJsjA4k5t6362Rs3IAT82ytDpAomTVVerttQzf27G3n69ARFy2Yw4ZSJu3t7HTtLvd2r26v4rfu285dPnSeaMcgULOIZmzs++2N+677tPLC7iaJp8/SZcYYTBbY1Brmhs3rehrop7ClXqumZ9W8uTeVX79jIr96xcdHnfqlomkYwGMTv9xOJRKitff39ZxhG+drlUoZjbXvpmyzPRw6fvk6G4iWw3odPK+EHJ8Z45sw4qqJgC8Ed2+q5Z8fS9qtba5vrA3zkrs08fmIU0xK8f38dezucXtWNm2p4/nyUoEcnW7RojfjYWOunYFh899gI58czCGBfR4S7t9dXpJRcTcDNx966leFkgdOjKf710CBt1c6GvSOpAt84NMgv3rrwNP/HXnNe74jfRcirky3a/NNL/bzzquZF12fmihYu1akdatlO7y+VN2mu8vL8+Si2gN0tYSJ+F25d5Rdv6+QtV9bz9YODCCBVNPnRyXHuvrKe9mofmYJJU9jDxrrAjPf6W3c08JYr6njXF18ia1hoKAgU/uCxM2xrCPK1gwO80hdHVZ19Dh+6poUP3tAx73lXyqX8PM4VRi6Xi6qqqjmHYwuFQjkwp4Zjk8kkx48fL0/2mR6alRj2lLNPpWVbbs9qvQ+frraRSOQMnjsXpSXiK+908ONzUa7rrJ63MshyVOK1294YZHvjhb3X/3J9O81hD6fHMjSHPdy3qwm3rvLU6XHOjWVojXgROOv6Wqq8S74mJoTgQE+Mn3RPoikqd2yrY9e0XTayhsVALMuxwWRpIbjzO4h4dQZi8xe0BmdG6NdfHiBTtLCAnGlTF3ARz5kcH0rytdeyBAe72buhlrftaLhguHJ7UwifW3Mq9RRMMgWLXc0hHj0+ykS6iKJAyKvzJ+/eVd5Z49Hjo9QG3eVNhY8OJdnTXsVVbXMPU77+PG0G43ls4VzvC+hOKDx3foJXBxLUh5xrq5Yt+PaRYR7e27qqiU6LWc+fw9kURcHr9eL1emcMxx47doxNmzYhhCgH5uTkJNlstjwcO3spyezhWLl4/3UyFC+R9f5hXM35FU0bSrMDwflTUUo/X6W1+lY/9Xxdmsq9u5q4d9a/D8XzRPzOrEEF8Lk0RpMFdrYs7fiH++L8x6vD6KqCaQu+eqCfX7h1I5vqAsSyRb760gBFy2YyU6QvlqMm4Cbs1YnnzAX3VxRC8B+vDtMW8dE1nkHYgnTexLYFG2r9fPfYCG4N6gNuDvXGcGkK9+6cub9ge7WP37x3O3/7fC/BXJHrNtbg1hS+fXSY+tIM1GimyD/+pJ+PvW0rti3IFCyaq5zmQ1EUNEUhbyz++z3anyBfuh6JcDaEDnp0qnwuVOX136+qONtWObNL37gNcqVmjk5N3Jk9OxaWNhwLUCwWKRQKuN3uGedUieHZy4kMxUvgcugprub8qv0u6oNuRpN5agJuJkvluObameFSs23BgZ5JDvUlUIDrOqvZ1xG5oBGoD3kYTOTxuTTOjKY4N57BsARXtYXnXLow25GBBKdHM6TyhnPJUoGXe2JsqgtweiRNwXJmwzaHPcRzBmfHM7RGfHRU+3jP3vmT1xZOabHGsIc97VWcGUmRLli0Vft4+JoWXulP4NEVVFWhIeTh9Gj6glAEZ0f6P3/v60WsP/PEufJaQACvrjKeLgDO7iPbGoOcHk3RHPaSKVpopeMv5t+ODNNR7WMgnkOUNhDe2hDkbVc28OixUSYzTnHtVN5kV0u4vNZwrayXdYKrtdBzWMpwbDQaJZ/Pc/LkyRmzY1955RVUVeXIkSNs3759ztB9o5GhKFWcrql84PoOvntshMFYjs31QR7Y3bTkqiUX02vDSV7sitES8SKE4Mdno4S9OttmbUp8y5ZaRpN5njnrlDBrq/aRNyy+8nwfv3RH56ITb/piOcbTBeoCLmzhFA949Pgod1/RUK6bCYCicFVrFTd01nDfzkZqAu4ZM2Vn01SFPW1hDvcl6KwNEPLoWLbg19+yhVjOcNYdlm6bNax5y7zNtrejiqfPjGNYNqri7P6wf8PrPdb7djaiKJQW5uu8f38rkSV96XEW1e9oCnF2NIkhnGObNnzqXTv40nM9DMfzXLuhmp+7acMbIrAWcikLgk8fjlVVp0j9tm3bgNdnx05V6/rc5z7Hzp07+fVf//VVnevlQIbiJXA59BRXK+J38YHr2ytwNjNV+rXrm8w5ewSqTumvgEejfzJ3QSiOJvMc6IlxbDBFXdDNproAYa/OYCLPUDzPtjmuT07XHPaiKZDKOyXUbNtmKJ7n88908dDVTk8wmimiqQrpvMnNm2vwuTSWMo/n3Ve34NU1Toyk6Kzz8649zbREfNSHPWypD/DSyQly0RxDqQIdNX6+fWSI+0vXSudzx7Y6RpIFvnloEFsI3r6riYeueb3H6nNrPHj1EseOp3nXVc38yRNn6ZvMUTRBUQTd0Swf/uph/vnn9vO7b79i2ceUVm92sE7Njr3vvvv4gz/4A77yla9UpF3o7+/ngx/8IKOjoyiKwoc//GE+8pGPrPq4lSRD8RJY76EI6/+a50qMJvP0RLN4XRo7mkO4NJWQR6fHsKkqFZ0pmDahWZOBMgWT33/0NAXTJuhRSRdMnjo9zgO7mxACdG3xxuKa9iqePjtBpmBSNFU0TWNTQwAhBMeGkrxvXysHemLYQmCYFn/5VBcIQUvEx/6OCC0RL3vaqubsbXtcGg9e08KDs37u0lR++to2AtkRfjBUxDAF46kC//rKEIPxPL90+/xlBhVF4aevbeP9+1vLf6+E27bVMRjP8YePn0FTwO91Ni3um3TWOG5aYrm3SrnUw6fraeuoi/E66LrOZz7zGfbu3UsqlWLfvn3cc8897NixY80fe6lkKEoXuBxCe7nOjqX562e7S7uxw9aGAP/9tk72bojQM5llKO7M8KwPedg9a0bpUCJPtmhRG3R2gxhOFEjkDM6OZdjdGp6zcPZs126s5uG9rfzdC72AM7llR1OQTNHiYG+Mr788gGEJNtX6SRZM2qp9DMay/OjUOOfG0rRGfJwcSfPIdW3Larx0TUVTIVO0aatxzjPo1XmpO8aHbugguMg1u7VoKK/dWI3frWMYBrqqlJaMOCEuLd+lDvblaG5uprm5GYBQKMSVV17J4OCgDMU3uzdi6FxMS3ntEjmDoXgeVVXYUOPjm4cG8bk1wl4XQjg70b82lOLq9iret6+VkWQBRXGqy7h1lVzRQlWcXljQo2MLsGxBTcCNYQsGJnO4dYV9G+buvc2mKAoPXdPCzuYQf/tCL3UBN7GsyYmRJP2TOSwhsC3BUDxHfchNa8TLcLJI2Kdj2IL2ah/Hh5KMJAvlZREred0UpTStE8pFCi62TXUB9rSFOdAVpWDY6JpThq0tsrLntRqVDBRbCF7qiTOWKrCx1s+e1sWX66yXQLsU59HT08Phw4e5/vrrL+rjLkaG4iWw3kNxLT8c0XSRfz8yxHAiT2ednwevbllWdZilnNt4qsA3XxlkOJEnmXMWkk9mi+WqMIqilDbDtQAn+DaU6mEals33Xxvl1EgaRRFc0x7hls21vOOqJr7z6jBFS9A3maU26KI3muMPHjvLb967javnKSM2W0eNn811fp44Oc5oMo8CxLIGfreGz6VRLJiMJAtYQgACwxKEPHr5nE17+e+b5qDGxhoP3bE8Xl0lb9jcsa2uolV5lkNVFf7qfXv41Dd+jOGv48qmEO+/dnk94PVGCMFnftjF02ecYgeaovDB61t5777lX3ddyWNfLsOnU9LpNA899BB//ud/Tjh88TdlXogMxUvgcvjwr0Vo5w2LLzzTRTJvEvbqvNIXJ541+ZU7KruF1kvdMXomsgzGc+iaSu9kltqAi0LRpiniJW84Myo3zLFd1OH+BCdHUrRUeRECDvbGaQh6eOS6NvZ1RPjawX4s2y5vNZXIGfz74aElhaJh2Xz5xz10TWSYzBTRNRXbFiil18ajq7g1FcOyiaYKTk1WYEONn5Fknvqgh8YlLHuYTVcVPnpXJ0+dSzCczLG1Pshbrrx41YUSOYPHjo+SLpjcsKmGXS1h3LrK2za6uOmmnRftPNbS+Yksz5ydJOzVURVnLeo/HBjk7bsaCCzw5WO9bDJ8MY9vGAYPPfQQjzzyCO9+97sreuxKkKF4iaznnuJaGUkWiOUMmsPOMFlz2Ev3RIZk3qxIpZspyZxBXyxLTcCDVqpfqikKu1rDpRqfGo9cu2HOYciBWI6qUsP2/7N33uFxnWXevk+d3qSRZBU3yS3uaU5CGsyUFUAAACAASURBVEkISUwCBBZIgAWW0Ou3LB/L7rIfbQtsY1nKLsvuwlISQkkgQCCEhPTEcRw7tuMuyypW12h6OfX740hjyerKyBrHc1+XLlujU94zM+f9ned5n4IAPkWiJ5lnXX2A8+oDrIz6ONSbLm7vTICzK0pwMp6nY9ixMiVRwKtKDGd0JEnEtCwKhoUkCmxbEeHTN64hp5kc7k3RFc+zJOjixg118+rWYds2XlXijdPkOy4UyZzOe3+wm75kAduGO3d28bnXrOOqNdEzPpbJKJWFlNFMZFEoViKSnK8POd3CN/fnmDmzUJZiqecp27a54447OO+88/jEJz5R0mOXioooLgJng/t0IcanjlhGlu2UMTMtG4S5BVjMZmwra7zkdQvLsjBtAcuCoF9h+8Y6ls7QTDjqV+kazhFwOyKd1c1xRQeuWh3lgQP9xLM6ouhMejdNkgw/HS5ZwiVLReuwMeRiMK3RGPYQ9ip87JpmmqM+0gWD9qEsdUEXdUEXxwezhDxOQ97BdIFf7e3lhZMJav1OObrZunDPJL8/NEBfskBw5KEnr5t887G2shHFUrGy2otLFkkVDLyKRKpgsDTioco3/cNeubg+pzuGUxi/NJ6cJ598ku9///ts2rSJrVu3AvB3f/d3bN++vSTHLwUVUVwEyt19ulDjqw+5uGh5hGdPDCMJAqZts31j3ZS1LXtGoj6rfMqELhEHupO82JPCo4hc1lxN9Zgms5esqOLSlRF2dybwu2Rq/ArLIl7qgjMHcly8PEJ3PE93PIcNNEe94+qbttT4+Oxr1nHPnm500+JV62q5vKVq6gOOoTHsZlnES3ssy5paHy90JfGoIqtqfHzsmjqq/Sorqp1xZgoGn773RU7G86QLBsmczsaGIDUBF9uWh3nw0ADPd8bJaU7JtF/t7+XTN6zhls31sxrLmSKnmeMeYiRRIKeZizii8ZTq4S/olvm7167lnx46Tk+iwIb6AP/3Vc1Fy3G685f7fFBKrrjiirI2CKAiiotGuX8xFmJ8o7lvGxuCxDIF6kMe1i2ZPOn9kSODPNc+jDji/nz9lnpWjuSwvdib4Q/tQwRUCc20ONib5n1Xrii6YEVR4PVbGygYFsNZnY0NId5wQcOsXI8eVeKPLmhgKKMhCAJRnzqhE8Z59QH+qn7tlMeIZTQePNhPf8qpfHP9ulr8bhlFEnnvlSv4w+EBBlIFXrelnvOXhUjmDDqHc4Q8SrFU2o4Tw3TH806UakbDo0p0DufY3BTivn299CXyaIaNWxHRDYtkzuDvf3uE85eGaIrM3ET3TLFtZYT/ebqD7Ih7saCbvOaCuVnWC02pRKmlxse/37Zp5g1LTLlYmy8XKqK4CJS7+/SlohlOw16/S54gKJIozOjm60vm2dXudKEQBcey+M3+Pj54tdM+aWdnmmqfB58qo5kWHbEsh3pTXLLSsdgO96a4a2cXXkWixi/QlypgmLN/v2VJnJVVORmaYfHT50+S0y3CHpm2wSz37unmbduWIo6sI75m05Li9k8cG+KfHjwKOHVMb9m0hHddthTNsLBxXM0AiihijHSRx3aiUAWcNJG8YWHZThTrB+98gW++dStLZ5E7eToHepI8dnQInypxy+b6cWXbuoZzdMVzRH3qnHpjrq718+Vb1/O1Pxwno5m8bvMS3nvlCsC5hruf62J/d5LlVV6n0MAiRcQuFuUiRuUyjnLg3PoGLhBz/TKV+5fvpYj27s44P3u+G8OyqQ24eOely8a5NmdDbiQ6dNT15FElBjMah/vSdMZ1Zy3OA52xHPu6k2QKBtmCyfIqL0tCbp46HiPklovrWCfjOQ70JLly9cKvYw1ndZIjvQgB6oIuehKOCzR4WjCRYVp85aFjeBQJlyKRyOr82x9a+dbjJ2iKOPtnCgaiKJDM65xXHyCW0VhW5cWwbPrTGgXNKqYdSqJARyzH1//QypffsHFO43782BB/9tN9TrCPIPD9HZ38+L3bCHsVHjs6yDcebUPAycV7/ZZ63rpt9iX8Lloe4X/fdeGE1390WOdw8gSSKPBka4xdHXH+6qa1/MPvjnJsIENL1Mtntq+d9wPKbHi5iEHFUiwdlRISi0Q5W4rzvTn6kgXufu4kIY9MQ8jNcFbjzp1dcz5O9UgR7HTBwLZtuuN5BtMFfr2vl8fa08TzJkf70uxsd8qihb0KblXif5/pcKqjnH49ZzBLXZWFYq9AGG19NHkwUVYz0U0LlyKhmxbHBjKYto0qO9ZtVjNpjHhYWe3lgqVhGkNuQh6Z122p5/aLmoBTefiSAAGXU8P1ha7knMf9Dw8cRTcsVElEEgUG0xr37ummoJv8x2NtBN0yUb9KtU/lFy/00Dmcm98bNEI8q7N3wCTgkfG7ZUIemWMDGd77g93sPDFMOu8UM3//D/eUpOVYuVIRo/KjYikuAmeD+3Q+4xtIFxBwoisBoj4nktMwrTl1yAi4Zd5wfgP37++lJ1nApYiEPQpLI15cRhpdUBjMCwQ9ClG/SkPIjUeR6Irn0AyLVzRXcdfOLgzLxrBsFFlkfX3pEoSTOZ2vPNTKro44IY/Mx65p5uIVjus24lU5f2mY/3riBJ2xHIos8p7Ll+OZJJgo4JZZEnTTn3LeN3skKlcZEaasZvLJ61cXrc7DfWk+9bP9/HpfP92JHCLgkgQKpo1pQ96wEGBeLboymjFucjYtm2TOIKOZWBa4RtZjJVFAEgWSOR3m4aIdZdQtfKo5iIBhOr0gR98rWYJYRufEUHbGgusvhYooVcR5LBVRXARerl++kEfBtG3MkbWvVN4g4lXm1TKqKeLhfVeuxLRsDvWm+N2BfsCZRFUJqvwqBdOi1u9CkUQSOZ2IV0GVRdYtCfD2bU3sPZlClQW2raiaswv3dHoSeZ4+HiOnmew8MczRgTRVXpW8bvG3vznC196yubjti91JuuN5PCNd6b+3o5NLVlZNmNgFQeCzN6/ji78+TOtgBhsIuWUKhkUipwPw30+e4M9vWIMsCnzqnv3Eshp+l4xh2ggC+FQJ3TSxAAGbGr+L2y9umjD+44NZHjnWjSgKbN9YVyw+MMqr1tVwz+5uJ2UGUGWRq9ZUE/IoVPudYJ+wVxnJxxNpDI8XRNOy+c5T7Tx4cICAW+LDr2zm/KVTN0eOeBXWRkROZA0UWUQ3bOqCLjpi2eIEbds2pm3jURbOobXYD6flIkblMo5yoCKKi8Ri34zTMV9Ltins5lXrannocD8iAi5F5J3bJk7Qc0ESBeqCLgTBWV8zLJtExuCVG5xglV/u7UUQwK1IvPPSZcUbe3VdgNWntX+aLx2xLJ/95UG6E3nSeYN4zqAu6IisV5UYzpoc7kszKjOPHBkk6JGLLtPhnM6u9uFJrZ3GsId/f+sW8rrFvz3cys/2dJPMGQC4ZIH7XujFp8pctCLCkb40AjCY1hBFcGoGCPhHhPSSlVW88fwGrltXM+4cJxIG33j0RQqm0+3+Z7u7+Y+3bqWl5lRHik9evxrDsvn9wQE8qsSfvaqlKGp/edMa/vF3RzkZzxPyKPzZq1fROZzj8WNDVPkULl1Zxbcea+Ou57qQRZGehM0nfrKf//rj88ed43T+eL3KYbuB/d1JllV5ec/ly/mXh47x0KFBTMsR31eujdIYdmOaTmqHpmlIklSZwM8Q5TxPLRQVUVwEys19Opgu8PtDA6TyBpsbggTmOTZBEHj1+lq2Lg2R1UyifrUk9TWjfhev31LPI0cGSWsWmxsCXNZchSKJbGoMkik4zXMnc1GWgl/u7WU4q2FZNlG/SjJvMJzR8akyEa/TNNjvlhld+fKqEkMZE2VkOCICnmneB0EQ8KgSn7phNb2pPA8eHMCtSMiiU+DgwUP9/OKFHmxH0xAERxBFwflBgA9dvZIPXLVy0uPf35pHNwWCIx0xUgWDu3Z28Zntp9JKVFnkr7ev5TM3rcGynMIH+Xwey7IIyRZfvGklOc1AFmx+e7CHO5/vx1k2tbmgwcuOjjSibSPYNrIA6YLJPU8f5JY1/uLxTv/R81m2BPrYvNzGtjMcfGGAG6ptqlrgZBoa/LAtOswzzzyDKIrFRri6riPLMn6/H7/fj2mamKaJJM39819sC2mxzz92HKI40SK3LGvS11/OVERxESiHm2CUZE7na384Tk4zcckS+08muShqsr1u/qJdO4/6nDOxIurjXVEfx4+beL3eohUW8aosdFpeXnfW1WTRqewR8Sok8jrDWQ2ALY1BVtf6OdjtvGcfvrqZz/7qEPGsjiBAfcjDdWvHW2+6YTKYLhB0ScgiRaFYEVJRJQFJANuyMU0LlwidGQ1VAs2E0WeWy5f7eMfWMF4ZfEqBAwcOYNv2BPFJ5XQMA3IjLlnDsDnR1c1TTw1NuFZBEIoCNPoz9jXTFvjBzkF8qlj8DHZ3ZwCwAGW0zJkoUhX0U1NTM+F4o8fctWsXF198cfH3Ua6Y5rPQNA3btjEMg3Q6TTqdRtM0du/e7ZSz83qLYun3+3G5zkCNtZcBU4nzfB82zmYqorhIlIuleKQ/QyJvsHRkjcijijx7cojtWxd5YNMwl/fO6dU3UShimQL7ulOI2Gxc4sUlCRO2Gd2vxafxu0KBjG5R0ARsE9ZWySwPKTT4BR5rH+C2/+hHsg3emXmalUGB96yzOTJs4ZYFLqgtsO/5Z4tjOpG0+NYLBfKmEzX67s0eLqh3I4oiF0TgHkUgqZkjPQYF3rE5wL/v1OlJGbhlMG0bWRS4/fxa1tT7pxUxURS5qmsXdx8xEWXH0nJJ8M5rzuMV80hRSeUN1CfSBH1KcRLV0Hjt1lru3NlF1nDyJ2sCbm69uJkq3/i1XMuyGcxoeGRnbPOdcBVFIRKJEIlE6O3t5aKLLsKyLLLZLOl0muHhYTo7O9E0DUVRCAQCRaH0er1F62exLbXFPv9MVESxwhlhtu5TzbD43cF+9nYlCLhlbtlcz4rq0ppFouAEaIxij7S9matoTyU+Y8VlNj8zbZtKpRBFka6urnH7TMfpIhHL23xtV5oRw4moT+KTl0edDgenCYskSVy8PIwtKvxg9wCpgklLjZsLl4a4eUOUj997hCFNRBYFNN3kp8cFvv32rVzic0062WmGxee++TSGqOB3OakY3z9k8tqrNhQt7J9v0Lh/fx95zeTK1VHW1PnZvNZJV0jlDUTb5mOvbObarctn9dlc0qBS37SEn+3pRRQF/njb0nnnbPpdEmvqfBzpSxPyKGQ1E7cicdvFTVy4PMLjRwcJuGVu3dowQRD7UwU+dNcLdA3nsGyb6xoFLrtsXsOYFFEUi8I3Fk3TSKVSpNNphoaGyGazAPh8PvL5PPF4nHA4jKKUrij92cZU4mxZVkUUKyw8s30y/O2LfTxxbIjagEoip/NfT5zg49e2EPWrJREYy7IQCwailuVQRwpZtMkZNpfWmLS2ttLe3j7heDNd10xWy1Q/kiShKMqM+5w8eRK32019ff247cARnMeODtI5nGNpxMNVq6OTlnb72/sPY0k6SwLOpD2UKbA/7eOdm5ZNeW23NsLrL1nNcFbHsm0iXpXW/gxd8QKKJKJbNikN0kNZ/uvJTt575XI8ikRWcyr7jI5jMK2R1yzcIwuOykiHjPahbFEUI16Vt52WHN9S4+OBj72C3mSBsEch4J5bD8qbN9byxgtnn3A/3bH+4sY1fOORNg70JGmKePjoNc2EPAoXLQ9z0fKpI07/+r4DtA9lUWUB0RZ4sEPn5tYhLm+pfsnjmg5VVamurqa6+tR5LMsik8mQTCYZGhqis7MTwzBwuVzj3K9er3fK+7UU3p5ysRSnc59W1hQrzJnRm2u2QmQYBqlUir179065DcD9Bwp4ZejPOF/WwZzN/Y8NsToizSgy0wnLqPiIokgoJPLhq8M825kmrVmsX+IjoMWIRCJEo9EJx1zsG1hRFGRZnvBUb9s2//t0Oy90JfGqErva4xwfzPCey1dMKDXXny4U8+7SBYNUzkmzeNu2pmnTRwRBGGf9DGYKTkNZUSCvmxRMwLJ4vHWQF7rirK8PIIoCHkXiTRc20hj2ODmEgtNb0RFEJ5eyLjjz2pciifMq3zb6/uzvTpLI6ayrC7ykFJWIVx0XpDNbDvakUSTnOyQAhgWHelMLLoqTIYoigUAAVVVZtWoVsixj2zaFQqG4VjkwMEA2mx1ngY7+yHJpps5yEsXJqIhihXmRy+VIJBITxGis9TP2Z9QNuHLlymlF7Yn0UUzLOhXBOZzjgq3L2NRY2k7VNUDzmMyJI0cyuFwu3G43uunU1ZTL/MYYTGvs607RGHaPBMPY7O9OMZjRJgT+XLw8wtH+NKmCwclYDgt4ui3G//nJPr765s1OfdFZ4HfJrKn1cXwwS7rgpFEEXDJ1fhcHelMsrfKybkmAVF7np8938+GrV+JRJT5z01r+5v7DGKaFadm85/IVE/IG58NAqsCxgQyqLLJppJEvOInyn7v/KI8dG0YSnR6QX3vLZjY3ndlWUw1hN60DGVyi456XR4KQygVBEHC73bjdbqLRU+5l0zSLQtnX10drayumaeJ2uykUCgwMDOD3+3G73WUhcPOlEmjjUBHFEhCNRolEIrPe3jAMZFkmEJg+j+61m+r47jOdxHMGtmWzMupjbYkre/Qk8nQn8oQ9Cs1Rb9EatG2bhw8N8PixIcDm/KVhbtpYN6feh2eKvmSB3+7vpSOWQRUhGpi+VuZbLmpkMF3gPx8/AQL4VQmPIrKnM8Fz7cPFwuIzsbrWz6bGEAG3zHPtcWzLZHm1F32kxJsiOZNMwK3QHc+T0y0Cksir19eysSFIeyxLfcjNimovw1mNL/32KIf6Uqys9vKXN62dUxRv60CGf3zQKdVmAauiPj55/SpcisS+AYPHjsaQRqy0vGHxV784yC8/fOmsj18KvnDLebz/h3uchwEb1ldL3LC+9oyO4XRm4wKVJIlQKEQodOohwrZtstkse/fuJZVK0dPTQz6fR5KkCVbldKJSTpZiRRQdKqK4SMzmZlxXH+Rj17TQHsvgUWTW1wfm1Xl9Kna0xfj2E+2AjWXDjRvqePOFjQAc6s/yh+O5EcsLnmsfJuJV5hWgkdNMnmwdojdZYGnEw2XNVfO+jtNv3MF0gf94rA3TslEliec6E6ypNVFlkfVLAkR9E92EiiTyvitXcuezXbgV4VQkouVEV84WjyrxkWuaebo1hkeV2dXahzJSAk2VRKpHzp0uGHhUcVxlloawm4awI96GafGBH+7h+GAWSYDueJ47vvc8P33fNlzK7CakO3d2Fbtn6KbFvu4EO9vjXLGqmqGc5USsCs75VUmgP1WY9XWWijV1fu79wCUc7E3hU0TSXQdnbZUvJPMRpVGrUlVVmpubi6+PTRXp6ekhnU5jWdakqSLlIIajTDUfVQJtKpwR5pK8P3byLCWaYfE/T3UQ8si4FQnTsnngxT5e0VyFIAh0JTS8qlictEJeheODmTmLomFa/Oi5LjpjOQJumWP9afpTBd40Ir7zYex7d6AnRV43aYp4iPpV9ncn0QyL126u5/rzaiasJ47iVSXWNwQ42JvGJdjopl0sBjAX/C6Z69fXcu26Gr7ysxhx1UeVT+XCZWGebovRk8ijyiJvvrBxyvXK7kSejuEc6oglJwOxrM6R/sy04znQk6RrOE9j2M1QusDuzjgZzYSRJP8XuhJcsaqaZUEJUTCK5fcKhsW6ElX7mSthr8JlzVWYpsnOk+UjCqVClmXC4TDh8KmAo1GrMp1Ok0gkOHnyJIVCAVmWyeVy9Pb2EgwG8fl8i7p+Vwm0caiIYgmYT+uoxc5TzOkmhmnhVhxrRhKdVk2pvIEChFwi+Zhe3D5bMKmum3si9FBGo2s4R+NIgEjALXOwN0Uqb8wpgnIqBJwKL6PX0Bz1EfQo3Lx5ybT7AfzLH23iM/cdZG9XgtqAi8/dvG7ebYokUeAVjQqveMV6AB45PMCx/gxg885Ll00bIKNKolOhZmTusW2n/qhrGmv6+8908v0dHQiCk0bjc0mk8qaTXiM4nTp+/kIPH35lM6siMu+/oo5vPdGBZds0hT186dYN87rOUlHubsNS7isIAj6fD5/PR13dqQbLuq6za9cuDMOgs7OTTMYpgjDWqhwNBlpoKu7TU1RE8Rwl4JKpC7oZTBWo9qukCyayJFAfcjOUFdhU76Vf02iPZRGAar+Lq+eV2+ZkQRaLPI++WqL5cENDkEeODNGbyJM3TNpjOS5dGaE3kWdJaHqBq/KpfPP2LS/p/LppkSmYhDynbqU/HB7gr35xsJi7+eEf7cWtiATdCp+4roXtm8YLdl3QxTVrozx8aABjpObnhcvDrJqibuhgusAPnu3AP9IqyrRsuoZzIIA90ihLlUSymlnc560XN/KWi5eR0QyqvOqUFnSFM4eiKEiSxLJlp2r2nl6AoKOjA13XUVV1QqpIKS24ivv0FBVRXATKwVIURYGPX9vCvz92nPahHGGvwkdeuYqwV2EIZ93pjy9ZSncij2VDQ8g9r3XAGr/K6ho/h/tTeBWZjGZw8fLIvGuinv7eVflUPnDVCn6+p5sfPtuFIMCDBwd4+vgw//rmTRO6OZSSx44O8tWHW9FNG1GAVDpL9f5nsUbE0K1IxHM6BcPCxtnmi/cfZknIzQXLTrnXBEHgLRc28HxH3Fl/VCRuu6hxSuFK5AwkQSi6tiVRwOeSMEaKo45aj9uWjw/+8qjSgtWHPRdZCGt3qgIEY1NFRgsQCIKA1+tF0zSGh4fx+/0vqQBBJXnfoSKKi0A5uI3AsVA+d/N5aIZVzB+DU+OTJfElpwqIosAfXdjArvY4A+kC9UEXHlXmqdYhagIuVtX4ZvV+PPBiH994tI10rsArlvv56KujRP0uJFGgJuCiO1Eg4JYJj/QSHMpo3LO7h49e0zzDkSdyqDfF3q4kAbfMlauq8U/i5u0czvEvv2/Fq4pohsGhPqf1U3cmiQB4FAm3IlEY6XEoICBLIjnNZEdbbJwoFnSTf/59K3VBF82qj4Ju8s1H29jUGJpQFQagMezGq8qk8gZ+l0S6YFLlc/GeK1bwbw8fp2CYXLIywhdeex5QPiUFy5FycOPO5vwulwuXyzWuAIFpmmSzWeLxOAMDA7S1tY0rQDBa2s7j8cx4jkry/ikqolhhUgvQtm16EnkGUgX8bpnm6NQtgGZCkUQubXbSHO7Z3c2DB/uL1szrttRz44a6afd/unWIz9x3EGwbzTC5Z/8wO7t3ceWqKB+6eiVVPpWMZiBLp25qSRDIFAx00+LBA/0cHcjQGHazfeMSvNNYS88cj/G1R44jIGBaFj9/oYcPXLWCNbV+fGOs244Rt7JLljiazJxyCwOWDQXTctyXI4LkUU816Q17VSzLZseJYRI5nfqQm4JhFaNVXYpEVrfoTxUmFUW3IvHlN2zg878+xMl4joaQh8/dvI6WGh9vvbipWFCgnCmVGJ3Lgi9JEoFAAEVRWLNmDcCEAgR9fX3kcrkZCxBMl7xfsRQrnHOM3hBjLcUXe7M83D5c/Pvlq6q5vKWKvmQBtyKxNOxmX3eS44NZqnwql66MFEuXTcVgusBDhwZYEnQjiQKGafGrfb1c3lI9bdDNfXt7MS0btyyS001EYCit0Z/M84MdnXzs2hZeta6G/d0pJNEppG3Z8Mo11fz3k+081RrDp0o83xHnYE+Kv7hxzZSRoHc91wW2TU8yTyKnY5g2wxmN5hoff3pdSzEQp9qnFhsqnz63CwJsaQqxNOJhOKuxuyOBbYNhOkEu2zfW8aG7XmBXR3ykDZRNQ9hNVjPxqlLR3VrjnzqwqaXGx/fedWExovTUuZ0OGxVmz0IH2pxJ5lqAwOPx4Pf7yeVyxQ4kY6+pYilWOKewbZuHDg3wq329WDZcu7aG125egmHZ/PbwMA3RMC5ZxLJsfnegn2fbYrgUCctywmeSeR2vSyGvmxzoSfLeK1ZMmdzfnyqwuyNOqqBTN5KUPipMOd2cVhRlSUAQwLQdS8wWHLdslU+lbciJ2Lv+vFryhsV9L/QiCnDbRU2srw/yrcdPUB9yIQoCIVvm+GCWjuHclJbvUFrjSH8G27adllGANmL13f3cST52bQsAa+v83LJ5Cb/c20vYq5DMGdg4YqxIAhcvD2NYNmvq/Lz/ypUc7kvjUSWuX1fDk60xnuuIY5gWguAEygylNfwumVjWREDgw1evnFUptnK3CBeaxbYUF1sUZ3v9UxUgyOVypNNpBgcH6erqor29fVwBgn379lVEscK5w84Tw/zw2S7qgi4USeDX+3sJuGWWiE49ztGUAFEU6BrOEfUFaQy5MS2bn+3pZtuKCNGRibszlqMjlpu003rrQIYf7OhEMyz6kxqJ7DBbGkMk8ga1ARdV3umDA95yYSN/ODxIpmBgjAjjxoYgibxBw0iZMEEQeO3mel67ub64XzKnT3q86eaRap+KYVlIggCCgIiT0B9wSfSNSXgXBIH3XrGCa9fWEM/pxDIaX7xvL2nDaQ78nac6WF3rR5FFnm+P8/e3bigKf18yj2meeiIXBchoFv/5tvPpTxeo8qrFtdGXK6W0shZLmBZbkEfHMN/rHw3U8Xq9xGIxGhoaCAaDxQIE8Xice++9lz179nDBBRfw3e9+l82bN5f4CsqPc+sRoEKRRE7nP59opyOWYd/JJLGMTsgts+9kAq8isiSg0JcsYNk28ayObUN0tOyYANhgmqcmhekian+9vxe/S2Z5tZfrz6sFBE4mcqys8fLhq5unLcINsLExxDdu28yr1kVpqVZZVe3Cp8p4FJE/vmTqzg8Bt8y2FVX0JArEszo9yQIrol6WVU0dkbptZZiwR0WVRURBwK9KqJLIUEafNOG9pcbHhcvC+F0yti2wotqDYdmIInTFc9QFXMSyGvu7k8V9NjeFkEShGKVqAxvqA8W125e7IL6cWGxLcSEYLUCwlJVKBgAAIABJREFUYsUK/vzP/5zt27ezc+dO1q9f/5KP/e53v5va2lo2btxYgpEuDBVL8Rzl3t3d6IaJIkm4FZG2oQz1QTcbG4MIgs3rN1TxVI9F62CWGr/KLVvq6EkUcAdFCrpJfchNIm/gUXWymkm1T6VpigT1bOGUezTgltnaFOLW8xvYunTqgtSpvMHvD/aT0UwuWh5my1Lnp6Ojg8GMQXXtEuqCrmnXMQVB4I7Ll7M04ubYQIaGsJvXbFwybf3W68+r46e7euhNmsiiQMG0iHhlNjQEeOMFDVPuN5TRiucEJ9CnYFiAzemPCluaQnzy1av4hweOYtk2LVEf//KmTVMeu0J5crZbirM5zmigTamCbd71rnfxkY98hHe84x0lOd5CUBHFc5QTsSybm8I8fXyIdMEkb5iIosDNm5aQGerFr4q8+/JT/QVzmsn9L/ZypC+NV5X41KtX0ZvUONqfJupTuXZdzZQCtakxyNPHY9SH3OR0E0F00gqmIp03+PiP99IdzwPwo51d/OVNa3hFSzWCIBD1ySybZbNlVRa5eYxLdSaeax+mN5EnNsb1qlnwvitWTFuHtDHsxsapOxryyAymNQJumf5UgYhXZWPD+HJtt13UxB+d30DesOads1mhPINdzkbOVEWbq666ihMnTpTseAtB5W48R6kLuIhldK5aHaUvWWAgXeCj1zQT9bvIDE3c3qNKvPH8xnE3z9olcPWaKLZtM5DWSMSyLAm5J1hi166toT2W5XBvmqaIm3dcuoyaaTpAPHZskO54vrhemdVMvvHIcdyKhJ7SqA8snHvx20+0k9HNca8d7Enxnac7+MBVKyfd57f7e/nZ7h7cksDJeJ6gS6al1sfWphBNYQ+3bm2YNJBIlkT8Zdh15ExxtotZOQjyQo/BMIxKSkaFc4M3nN/Ad5/uYDBdQBDg1q31bGp03JnTrQ+efgNals3XHjnOAwf6EQWoD7r5+1vXEx1JJ9BNiy/8+hAvdCUQBYGueI43nD99MfCcNl6UsgWD9pjGVx9uJZvLs31tkD9ZNnG/9qEsrYMZgm6Z85eG5xSZOZAqcLA3RTyrY5jWuL8Zls2h3tSk+x3pS3P3893U+FXskMhyT4jmah9/edOaRZ8wy5lycD2+HCjV+ziVuNq2XYk+rXBuUBd087FrWhjMaLhlcVrLbTqeaB3ity/2EfYqiILAyXiOrz58nC+OVFN59Mggz3cmiHhlBMFpq/SvD7fytbdMHcV2/rIwitxJuuCUM+tJFUa6YLhIYXD/4SSvubgwrt/gjrYY//zgMSzbWcW7aFmYT92wZlbCeGIoy9/cf5i8bjLaRmssogDnLZm8j2VPMo8ARes46lfpjOcqgniGWExrrRwsRSiNxV0pCH6Kc+sRoMI4PKrE0ohngiDOpTZr+1AW23a6uQP4XDKtA5ni3wfS4xOCPapEf3L6Xn7NUR9fvOU8GsIeFEmg1q+yttYRJVkUEAUnenYU27b55qNteFSJaEAl6lfZ1RFn78nEhGObls33nungQ3e9wBfvP8RQWuPOZzsxLZu6oJvzl4VxyU5Rbee9cNpMraqdvNVStU91igWMKGkyZ9AwQyHyxaAcJu/TKccxnW0stDCfi6JYsRRLwLl8cy+t8jpVWWwbURDIaCYXLPXz6329PH08hm5a2Dh9FSVRIJ03uWLV1FGno2xdGuKbt29BNy3+z4/3caQ/jSwKYBr4VZH6MS2eLBtSBYOakTVIQXDquE7WMPjvfnOYX+ztLSbOP35siA31wWKQkICAKks0RVRqA66idZvTJm8+vKE+wKvOq+HhQwMkCjZLIxJ3XL58Hu/kucXLwX1asRTnzu23384jjzzC4OAgTU1NfP7zn+eOO+4o2fFLQUUUX2YYpjVj3t8oOc3kZ7u7Odiboi7g4i0XNVEXdITAsqyZDwBctaqanSdqeOTIIKLgBPDUBl18+YGj2CPpCJIAybxQTLr/k8uWc6AniSSKrKz2Ttt9w7YhqxmcjOcxLae49ps2RsYV6ZZEga1NIfZ0Joj6VacUnCCwuna8y1M3Le7Z04MknHJ3JrI6IY9MbzKNKrnQLRu/S0JAwK1IaKaFKAqsXTK5pSgIAm+9uIlr19bw1I7nuOmV64u1VS3L5oEDfRzqS7O8ysvNm5bMq9NIhakpF2FaLBZ6TbHUXTLuuuuukh1roaiI4llOXjeJZ3UG0hq/O9BPRjNYGfVx65b6Sbs7jOXbT55gd0eCsFdhf3eS9t8d4Qu3nDen84uiwCevX8VbtzWR1y2awm5u+vrTqIqILDpu2HTB5I3n1/O6LfWAwPd3dJLXTWzbpiHs4e3bmqZMd9jVEacnUWBV1AsI5AoFHm1L8ZeWPa610p9et4p/e7iVPV0JIl6F/3t9M/WnuTFtG6cl/Wn3/rYVEVZW+3j06CAuWeQvblzLwd4UzxyP4VUlPn5NywSBHYsgOH0o6/3iuGLj//TgMe5/sRd7pArPk61D/OMbNk5oCWVaNl99qJWfPH8SYaRE3Ude2VyynocvB6tslHK6lnIQ5IWuDFSpfVrhrKIjluV/nuogkdM43JdhS2OQlhofHUNZfv5CD2+fptpLTjPZ05mgPuRYhl5Voi9Z4MRQlvAc+z0KglDsW2jbNtZIkWrbtknmDFIFg/v29rK7M8HGhiACNk0jeYodwzl+9FwXT7bGyOkml66s4j2XLy+KZMGwEASKN6YsCmTsEXftGHULuGX+avvaacepyiI3bKjl9wf7RyJMBcJehctbqgl5FN552TKeaYvx9785Qjync+GyMJ+9eR0hz9xTQGIZjd+82IdPlRBH3s/dnQmODmRYWzdeYH/4bCd373IE0bac3+uCjuVe4RSnF66vUDoqTYZPcW49AryMMC2b/326A0mEgFvBJQkc6U+T0y3qgi6OD2YwTw+jHIMkjhTZHtlmVMxkUeDZjjRfeaKPv77vAM8cj81pXIIg8Not9eR1i3TBIJU3UESB5qgPw7R5+NAAHuXU1y5T0Ll710ls27mOR48O8v0dncW/b2wI4JJFkiPNepMFkwvqPbN2EZ/OF245j3detpz1DUGuO6+GO++4CAEnevWBF/v49L0vksjpqLLIsyeG+ev7Ds7rPNqImJ8K2BEQBYGCYU7Y9tEjg5gja7KiKGBazmsvZxa6Esu5wpmoaDO2xdS5wLl1tS8jsppJWnMKYidzOqIoYNtOxwnbtgm45GnTEVRZ5PVb6vnZ893IkoBh2mxsDDKU0fjxviH8igB5g289fgKPIrJlaXjKY53Ox69tocqncN8LvUCBLU0hXLKIIgoMpAsMpnXqQyKGadOX0lAlsdgRvsqrsrM9znuucI4V9bv45z/axNf+0MpAWuOC+iBv2hCc+uQzoMoiH72mudh8uG0ww3u+v5u8bjqVfXSTJSNuV68qsasjjnWaq3Y21I40UD7Sn8YliRQMi5qAi9U1E92wUb86rki5jV3M86zg4Dy0WWU1SZeDIC/0mmLFfVrhrMGrSvhVmWReJ+iWqfGrnBjKkczqGC6J2y+e2fV286YlNEU8HB/IEvUrXNZczTcePY5XEfEoAn6XTMGweObE8JxEURIF3nXZcq5cFeVzvzqIMiIosazGhcvCXN5SzfMdcRRZ5Jo1Nfx6f2/xpszrZrFn4SgtNT7+9c1OXmN3dzeFwvQpHXPhyw8cJVUw8KkSLtMimbdI53X8bgXdsh335zzW9kRR4B/fuJGvPNTKod4Uy6u8fOJVLUXxH8tHXtnMjhPxkaIFNiGPwgevnrx6zmJiWda4H9M0J/3/bH7XNI1EIsHu3bsn/H0qRlOFLMvC7/cTDAbxer1ltc64GCykMJ+L7tOKKJ6lSKLAOy9bxnee6qAn6bQbev3WBpqjPmoCKhHvzL34BEHg/KVhzh8jeF5FQh8zLxmmhXeG5sFT0VLj4x2XLOOHO7uwbZvGsJsPXd1Mtd+plQpQ0E0O96VoHcgiCE4vwj95xSTlakpMbyLPj57rYndnHFV2rs+rOm2f8oaFWDAQRYFP3bhm3ucIeRQ+d/O6GbdbWuXlp++7mMeODiEIcPXqKFW+iZ+fbdtzFh/TNMnlcrS1tQHMat+pEAQBURSRJAlRFIs/0/0uSRKqqk74u6Zp6LrO2rVri9tLklRMp5kM27aLjXAzmQyJRIKTJ0+STCZ5/vnnCQaDBINBQqEQLtfCW9rlYikutPu0YilWOGtYVuXl0zesJp4z8LskfCUoLP2aTUvY0dpPf1onZebxu2Revb52zseJZ3WO9KepD7v55m2byRsWYY8ywepyKRKfu/k8nu+MU9At1tb5i+7LyZhLYYGp6EvmedO3d5LKO+uUtm2AreJRJSJehTde0EhDyM2mxiDrxqRijHXhTSYmhmEwMDAwL+GyLIvGkf8feuHElNc+FzEa+6/P5xsnTtPteyYm+mw2iyzLeL2zK+w+FlEUCQQCBAIBampqOHToEOvXryeZTJJIJIreBK/XSygUIhgMEggEFsTiWWxRLBVnKiXjbKAiiotIKZ7yXIpE3Twtucloinj40ysbeL5jmCV1dVy4PDzn9a3WgQyf+Mk+8oaFZdtcvDzMF245b0o3pCqLXLqyasbjjhWlQqEwJ/EZ+/+798VJZAtIooBLgrzhtH4K6gLXLpO5yNWLUBCIHYdHjzq5lqo00hJqGjExDIN4PD7h77Isz8nCKvVE293dTV1dXdmsxS0EiqJQXV1NdXU14HxXstksyWSSvr4+jh07BkAgEChakx7P1H01Z0M5uG0rFW1Kz8v3Lqkwb2r8Clcs99HSUjfudXukKe5Ma0lf/OVxUrkCXlXCti2ePDrAjx63uLTJMyvhmm6yMU0nkCiZTM7K6hFFcaL7zq1jky66hdxYVPlUfv6BbfjdCqIoYlo2n/3lQX65txeA69bV8KVbN0ybfB+Px1m9enUJPoEKs2UqURAEAZ/Ph8/no77eaR1mmiapVIpEIkFrayu5XK7oWh51vSrK2dXg+WxL3j8bqIjiIjHqBpzLU96oKM3VMppqu5xmcnAwTzJv0eSHep+IZVkUCgUsy6Kvr2/CGKYSn4IJT3QW6EmbHO7P4Fel4vVZQNoQCQaDs3bfTfW+9Pb2kslkaGlpme9bz2vPV/n5vkH0kVJvsizxxgsaCXpPWcT/+3QHv3mxj9FhPHJkkH9/9Dgfv27VvM9b4RSLsR4nSRLhcJhw+NQa+o4dO/D5fAwPD9Pe3o5hGPj9/qLb1efzTbmmVg5rirDwZd7OtgeFl0pFFEuAaZokEok5rR/lcjlefPHFKbedCkEQ5hToIIoiiqJM+Jtuwbee6mZHu45mWggIfPDqFbxuSwP9/f2k02lWrZpaAGzb5rFjQ+ztSlAXdNE9nKctaxP2y3hcOrGsTlPEhQ24LIELVzXgCQb41mNtHOpLs6LaywevWkn1JAElC82mxiD/+uZN/OtDrWQ1k9dsquP9V46P9nymLYZmOnmb4LSPeqZtmI+f8dG+dMrBzVcqSi1CoihSW1tLba2zbm5ZVjGIp7Ozk3Q6jSzLCxLEU4rP5Uy4T93u8itwv5BURLEEaJpGV1fXnCLwhoaGWLp06YT1prERePGsTnssiyQKrK7xTdv5fa7sPZlgZ1cKlyIT8UlkNYP/fqqTa9bWzSra7H+e6uB7z3Rg2rZTkVsQuGlDLbIkcvXqah48OEAipyNLIn9y2XIuXBbm4z/Zx5HeNB5VpHM4S+tAhv9465aSXtdsubylmstbqqf8e2PYgygMF38XgMbIS1uDqlB6Si0KY4N4RtF1fdIgHkVRMAzjZbPuVmkd5VARxRLg8XjYsGHDnPaRZRm/3z9l8ENvIs9/PnGCvGZiA41hN3dcvmLSPLf5kCmY5DWLUNBxjbhkiaymM5guEGT6p1jNsPjeMx24FQlJFDBNi95kgf6URkPYjUsWOX9piI+8spnl1V7cikRPIs+x/jThkb6KbkWiL1WgbSg7LsJzJs6Uu+qj1zTzxLEh4iMtqnxumU++quI6LRVnk/U6VRBPT09PMdcSJgbxzPRdLYWgnwlL8eUcoDUZ59bVLhDz+VLOtM8DB/rBPmWddA7neKErwaXNM0dpzoY1tX5UWSSZN/AoIpmCSdSv4HfJCNrkY+uO53nwYD9pzUAzrGLxa0kScSsifakChmUxlNFZV+dneZWn2JJJkZyKO6P1uEfXR5V5lGs7ExNqlU/l5x+8hGfahrGxuWRFFV3DOXZ1xKn2q2xbHilZwe5zlXJYj5sPo0E8VVXOvbhq1apJg3hcLlfR7bqQQTyV5P3SUhHFRWKmfLtUwcA9pkaoIgqkC5P39JsP1X6Vv7hxNV99+Dh53aLar3L7RU0sCbkZGEhNGFtPIs9n7jtATjMRRSd4Jp7V8btlNMOJ3nzTBfV845ETmLZNLKPx0bv38ZU3b8Lvkqn2qVy7NsrvDw0gCgKWbXPR8ggrq+eep3am8LlkrhspMnDfCz186/ETzvsiwFWrovz5DavP2on95UK5BLtMFsSTz+dJJpNTBvHMxpqciYV+QLQsq5K8X+HMMd0XemN9gN+82Icqi+imjW7ZNNf4Snr+K1ZF2dAQZDCt4XfJE1otjeWRI4NkNZO6oBNkIAownNVxySJLgm7+7PpVfPvxE0iiQMSjYts2xwcz/HyP061DEAT+7PrVbGwIcrg/zYoqL6/ZtGTO1tZiTICaYfHtJ07gd0kokohl2zx+bIjXb62fk+u3wsuLmQTZ7XbjdrunDeLJ5XIcO3Zs3kE8lTzF0lMRxUVipi/ylaujFAyLZ08Mo0git1/URHO0tKIIEPFOLAk3mRXrpC+c+l2RRLY0hfjb160vvtaXKhTz+EaDhfpTp+qUSqLA9k1L2P4Sx3ym16NyuollU4xEFQUBaQrLvVwsl7OBc+19Oj2IxzAM9uzZQyQSOeOVeGZLRRQrnDFmcp9KosANG+q4YUPdlNssFJNNVpe3VPO7A/0MZzUkUSBbMLnxtPJvFy4Lc8/ublRZHOn6YLO1KTTpOTTD4pm2GHnd4oJlobLuChF0y6yo9tI2mCHkUchqJookLMhDyrnCQiedny2IojivSjyj17zQ12/bdsV9WuHMUc4ReKePraXGx2e2r+XePT1ohsW162q4omV80M97Ll/OUEbj0aNDSAK8bdtSrlkbnXDsvG7yju/uom3QKQIuCQLfeecFZeuKFASBz9+yjn/83TEO9CapC7j55PWrJi3aXeHc4aUK0mT7z7YSz2gQDzBtXvNLpVIQvMIZoxSFrSfDsmz29yRJ5Awaw+6SWjPrlgT4ixunFi6XIvH/XrMOzbCQRGHKfo4/3nWSY/0ZrJFJIWea/L9fHuTH79024xgWyyqI+l18+Q1zS7upUKEUTBfE09fXRzKZJB6Pz7oSz1yopGRUOGMsxORu2zbfebqdHW3DI13fBd62rYkrV0+01mYa20sR7OnqgwKcjOfRTKuYjiGKAv3J2fdILGcLu8LCcrp1tZjfhYWwFGfLaBDPaAeUFStWLEglnsqaYoUzSqlv6BNDWXaeiNMQdiMKApphcfdzJ7m0uWpe+YCz4VBvir++7yA9iTwtNT7+5nXrWTpD5ZeLlof56fMnMS3n+gWEcT0dK5Seclt3K+Va2HyPUw4PV6V6D2ZTiUfTNDwez5yCeCqiWOGMsRDu07xuIQlOdCQ4CfOmbaMZ1pxEcbZjS+Z0Pnb3XnKaiVsRaR3I8PG79/Lj916MPM35XrWuhnddtoz/frId24YtS4N8/paZm/GWO+UmPBUWllJYigs5hrm009J1nWw2OyF3spK8X+GspiniwaVIDGc1Ai6ZgbTGqhpfsfJMqTk2kEE37WLpOa8qMZTR6EsVaAxPbS0KgsBHr2nh/VeuJG+YBN2zr/SxUGuxFc5Ozvbvwpks8zZdEM/g4OCkQTyaplUCbSqcGRZicg+4Zf70uha+v6OT/lSBTY0h3r6tac433my3D3kUDMumoJvkDAuwccsSAdfMX6tDvSk+fe8BepN5GsNuvnzrBlbV+uc0znKkHFMEylE4ysV9ulhrgqP7LzajQTyqqrJp0ybgVBDPI488wkMPPcTDDz/M9u3b+epXv7rIoz0zVERxkVgoi2dplZe/vGntSz7ObMbWHPWyqsbLjrZhRjP7farAcFYn6Jna+ssUDD5+9z7SmoFPlehNFPjo3Xu55/2XlKzgeYUK5U65FgQfDeJ585vfzDPPPMPtt9/OsmXLSnqOcubcsosrzIrZCrYgCOimTWPYTWPITUvUi0eVeLJ1aNr92mNZCqaJR3HaZHlUiZxmcjKem9U5K1QoBYttKZaCM1HmzeVyTdtbdS789re/Ze3ataxatYovfelLJTlmqamI4iLxclkbUyQRrypT7VfxuWRsG+QZ1iAiXhXDtLFGok9Ny8awbELTWJdjeTm8bxVKQzkI03w5G8ZeykAb0zT58Ic/zG9+8xsOHDjAXXfdxYEDB0py7FJSEcUSMN/WUeU6uc/let6+rYm8YZHI6cQyGiGPzNVrps+LrA+5eeu2JnTLomCYGJbFn7xiOTWB8i31VqG0nA2CMBPlcA0LPYZSdsl49tlnWbVqFc3Nzaiqym233cYvfvGLkhy7lFTWFBeJxb6ZZmK2gn3debX4XDKPHh0k4Ja5dWtDsZPGdHzo6mZe0VxNx3CW5VVetkxRI/WljK3CucFiBdq8VBb7/GPHMRWlzFM8efIkS5cuLf7e1NTEjh07SnLsUlIRxUWkXCf3uVqxlzZXzav58dalIbYunb0YQvk/TFQ4s5zNFW3OhjGci8n7FffpIlHO7tMKLz8We/I+nXIQlMWmXKJPpztGKdcUGxsb6ezsLP7e1dVFY2NjSY5dSiqW4iKxULVPS3Hsc32yqjB7Zvtgd/p2o9VVMpkMbrd7yu1Of90wTvWwFEURy7LOWvdpuTCT+7RUa4oXX3wxR48epa2tjcbGRn70ox9x5513luTYpaQiiotIKXvK/e5AP795sQ/bhuvW1XDzPLraL8TYSk05W9jlMMFOJypj/zbZdrN9bbrXp2Ls52bbNm63G7/fz759+9B1vVhpxe/3I0kSlmVhmuak/xqGgWmamKaJpmn4/X50XUcURQRBOGMVWMohpaNU/Q7PhKUoyzJf//rXueGGGzBNk3e/+91s2FB+nWcqorhIlHJyf/bEMPfs6aEu4EIQ4Nf7+wh6FF45QxTodGOrUBrma0nN5bWZzhMKhXj66acJBoNUVVURiURwuVzjRMq27UkFaPRnKoGazd8nQxRFJElClmVUVcU0TYaGhujp6cGyLNxud1EoXS5XcfvT/wVnshUEoXgu0zSxbRtJkqYVyYql6DDd+1Dq1lHbt29n+/btJTveQlARxUWilDfjgZ4UPlUqtmwKumVe7E7OWxShNJZiKm/wwIE+CrrFZS1VZdGpfiFEavQBRxRFjh49SjQaJRAIzOkzPv0haSrBsW17nOBMJ0ZjX1MUhUQiweDg4DjRkGUZSZImFZyx/47+f/RHUZRx+021ryAIc/6uW5ZFIpFgeHiYWCyGruuEQiGqqqoIhUKo6tTNnce+b3MVybnyUq20clpTnIpzMdCmIoolYj6WX6ksxbBHoWCceiLP6yYR7+yLbJ9OKQQ7kdN5638/x0C6gGXD1x89ztdv28KFy8a3iJqrSNm2jaZpGIYx6TjHitR8zjPd+WcSqSVLlpBMJjly5Ai5XA5VVXG73bhcrqIlM5l4TTa20cl7MuGZTIims6ZOFzdBEDAMoyg68XgcURSpqqqiurqaQCCwqEWgRVEkEokQiUQAZ2IeFcnOzk4MwyAcDhe3GSuSo9c4yuj7O/p+jx5v9LXFphxEcbpxVLpkVDhjlNJ9et26GvZ0JehO5MG2ifhUbtxQN+/jjbrTphvfTJbUT3Z10ZfMF61XzbD48m+PcOe7L5hy/9ng8XiQZZlnnnkGt9tdnBhH16FmsqBOF7Kp/j4ZMwmOJEl4vd6ilajrOplMhmQyiWVZBINBqquriUQiRQttVKTONLIsU1NTQ01NDQCFQoFYLEZXVxfJZBKPx0NVVRVVVVX4fL4zPsbT3bkul4va2lqqq6vRdZ1UKkVvby/Hjh0rulvdbndRIKf7Dowe37ZtQqEQuq7Py5J8ORQEh5ndp5UuGRXOCHO5mWa6eQJumf97/SqO9KexbVhd68Pvkqd9Ep7OkhIEgVQqxf79+6mpqSESiczqxhgrSP2JLIZlIxcbCUMsk6e3t3dSF9981qXy+TxdXV2cOHECAJfLhdfrxeVyIcvyOMFSFGVaa2vs7wsxCZimSSwWY2hoiM7OTlwuV7HXndfrXfS1LZfLRX19PfX19di2TS6XY2hoiNbWVrLZLIFAgEgkQjgcRlGUYqDLTA8gU/0734eQsf8PBoNFa7JQKJDNZhkaGsK2bQKBAKFQqGhJju57ujt3Okuy1O7WySgHS/FMpWScLVREsQQUCgVyudyMoeWnR//FYjECgcCkC9lzfYp0SbCp/lTrpaksotmIkWVZVFdXk8vlOHLkSLGn2ug60lRjGysyyxQTWbDRDRNBABu4cImXQqFQ3Gb0eDNZX7NZl9J1nVgsxsDAAMPDwwQCAWpqaqiqqkJR5u9KLhWSJI2zzEZF59ixY2SzWUKhENFolEgkMqfxzleUJhOnsf+e/hknEglisVjxe6WqKi6Xq2i5T/a5TfX6ZC7dUmKaJvF4nOHhYQ4fPoxt20V366iojzJ67tGJf6xIjrVWJxPJcrAUK8n7paciiiXgwIEDvOMd72DDhg3ceuutXHfddeMEcpSx61LV1dX09vayc+dOZFkmHA4X3W5TidbpE9nY3ydj7LrUbP6dal1KFEVyuVxxDUpRFGpra6mpqZn0OgHOB3w1vXztkTYKusWr1kX59A2ri+7UUqMoCnV1ddTV1WHbNslkksHBQdrb2xFFkWg0SjQaXXBX4FRrj1OFiy7eAAAdA0lEQVS58SKRCIFAgGw2S1tbGwcPHixej6IoRTf7VJ/xdEEuk4mRqqpTitPpDyJTMbrGF4vFiMViCIJQdGOHw+FFd7dJkjSu47xhGEWRbGtrAxi3Jjn2oXS2IjmTlTsbyiX6tWIpjkeY44daHk7wMiMWi3HHHXfQ29tLZ2cnw8PDRKNRZFnmi1/8IkuWLCluO9kEZJpm0dqUJIlAIEAwGCy6fWYz6Z3JmyuXyzEwMMDAwACmaRKNRqmtrV2UtafZkM/nGRgYYHBwkFwuRyAQIBwO4/M50bCzsZymE7bTmcuDyGTiZFkWyWSSRCJBJpPB7/dTXV1NNBrF5Sq/oum6ro8L2lFVtbgeOdco3JfCWNGazirWNI1MJkMmkyGbzQKgqmrxfgPGbT+dZ2TlypX4/f7iA+hc3K3Dw8MMDg6yevXqeV9zW1sbfr+/6IGYD7lcjmPHjhWbDI/lpptu4le/+hXhcHiSPc86ZvVFrIhiCTAMg66uLjweDx6PB5fLxXPPPcePf/xjHn74YbZs2cIb3vAGrrnmmhkntWw2S39/P/39/ciyTG1tLbW1tdOGoS8muq4zODhI//9v78yDojzvOP5dWITd5V5hVfBCQiIaLI02qDFKMokpTpgJQRrraNN4xFR7TGfUdnqMScfYGRPTTtXaJqYZawXRJKPTQWriBCdq1CgY8YSCGEEEdmHZi333evsH+z7dXfZ4d5XlVX+fmR12eV/geXmP7/M7n+5uWCwWpKenIzMzE6mpqUEfhqGSXSJ1CQYSKk+r1+FwwG63w2azQS6Xs+QYX0s5lEUVjckIz/MwmUzQ6XTQ6XRwOBwsS1QKVpk/rFYrsyKNRiOUSiVSU1PZRC+ccx7OefbnGREzGQEAs9kMk8kEk8mEmJgYVgKSnp4etE5PEE1/mcShRFL4H93NWoX3QhQtFgtaWlr8iuLzzz+Po0ePIikpKeLfLyFIFKWA0+nEqVOnUF1djS+++ALf/e53UVZWhgULFoQUuoGBASaQMTExTCCHy1rw5/YLNz41MDCAgYEB2O12r7o2X/EI5toNVPt2r+KPnpjNZmi1Wmi1WjgcDmaRpaSkSNLqFUopdDod9Hp9RAk7QuZluJMRMd/z97dkMhmcTiccDgdcLhcrVVEqlV7uXDHne7jikJ7Y7Xbo9XqvchVP93Awd2I4InkvRLG1tRXJyckYPTrymmSLxYLW1lZMnz59yLZnn30WdXV1UCqVEf9+CUGiKDWcTidOnDiB6upqHD9+HDNnzkRZWRnmz5/vN7nC8+FlsViYCxAYjImkpKQwd1u4DzN/yGSyeyZOMTExzMLp7e1FQkICSzSRqtXrcDig0+mg1WphMBiQmJjIYpHRSNbxTYQScy6tVivMZjObiAixSCEhSkwsUqwghdpfzIREcA0LguB0OpGWlsY67UgxfiW4h/v6+rxEUmgmEIlI8jyPvr4+GI3GuxLFlpYWlqQVKWazGW1tbX5brhUXF+PkyZOSdNtHAImiVGlra8PGjRvR3t6Omzdvor+/H2q1GrGxsdi8eTMyMzPZvv7ceTzPsxgkABaDTEhICGl1jUQMEhi88YQ4pEwmY3FIqc5AeZ6H0WhET08PdDodgMGkmJSUFCQkJETk9g2VoBFoUiJWmGQyGauL1Ov1LOEkkg470cLpdLJ4ZF9fH2JjY5nbMjk5OeruYV8L2t955DiOuVoHBgYgk8kQHx+PUaNGQS6Xi4o9C91wxo0bx1yfghUZzjG3tLQgNTWVJRVFgslkws2bN/2K4vz583H27Nl72uptBCFRlCpWqxW3bt2CQqGAUqmEXC7H2bNncfDgQZw4cQJFRUUoKyvDU089FfJi5DiOuVhdLhdzsSoUiigdTfhwHMcEkuM4jB49GhkZGUhOTg7b/XcvXIDBHl6eCRRCLNLhcCA+Ph4qlQpKpZLVQIbj+ovGw95ms7FYpNFoZAk7arVasjN/m83GrMj+/n4kJCSw+KlCoQhoSQc615Geb0/Xf7CJSWzsYNMIi8UCk8kEs9kMuVzOYpKC9RssuzOQu1WMSP73v/9lk4hIMZlM+Pbbb5Gfnz9k27x583D+/HlJWvARQKJ4P+JwOFBXV4fq6mqcOnUKc+bMQVlZGebMmRNSIG02GxNIh8OBzMxMaDSaqAmk2Jik53uHw8EyAW02G+Li4licKZT7T6wIhWNpiRFll8sFvV4PrVaL3t5exMfHMzerVCcjguUriKTT6bzrhJ1wJifhbPftASvEIl0uF2sgrlAovPqvhjrHga6F4YDjOOZu7e/vR1xcHHO3hrJ+wxXJeyGKRqMR7e3tmDp16pBt8+bNQ319vSQTuiKARPF+x26344svvkB1dTVOnz6NuXPnMoEMNXOz2Wzo6elBV1cX7HY71Go10tPTER8fH3HGn+c2f9eNv/hjOKIlxCGFh4lKpUJmZmbUYnqRYrFYWLKOzWbzStYZiYeJb62kv/Nns9lgMplgMBhYKZDQJi02Njascx7OeRZ7HQQ6LqPRyCxJm82G1NRUZpFJ9RoRRLK3txcGgwFxcXFszHcrkq2trazrVKQYDAZ0dHQEFMWGhgZJut4jgETxQeK9997DmTNn0NjYiM7OTkyePBkJCQmYP38+XnjhhaBp6sBg7Ebo8Si4bT1jkOEKWbRKEbq7u6HT6bx6dQZqGCAFnE4nS9YRhD0jIwNqtRpxcXERWU6htvsjXHESLHaj0QibzcZaqAkTqZGIQ4dCsNiFeCTP80hLS4NarQ6ZABNNfOOUQiMMg8EAs9mM2NhYqFQqZv36s76F955WMzB4vU2dOhVKpTJi17zBYMDt27fx2GOPDdn21FNP4cKFC5I79xFCovggcfToUbhcLhbDunDhAk6cOIHr169jzpw5ePHFF1FUVBTyQWC329HT04Pu7m4Wz9NoNEhMTAz6cyONv4YBGRkZrHD6bgiWXBGpUHnO8IVFcYHBTjXx8fGIi4sT1QYt1PvhECtBbHQ6Hfr6+hATE8NikVJN2AGGNhEQLLJA4/bXoSZYTFJs3NKXYHFKISYpJM5xHIe4uDiWPCe0gQxUkuJ5nXl+BbxXDAkmlP39/bhz5w4effTRIdtIFENDoigxOI7DZ599hv3796OhoQELFixAWVkZvve974WcMTocDiaQAwMDyMjIQGZm5j0RmntBIBcgx3HQ6/XQ6/XgOA5KpZI1Ag/lCg700IrUBRhoX3/xSSHxpaenB2azmaXSC5nHUoXjOBaLNJlMSEpKYiI5HOU1gWKVYsVL+Gqz2WCz2cBxHJxOJzs/wqLEwc57JO/DiUsHQ7AkBWtSWA1GTIcgTwtSEEnhfxpIJPV6Pbq7u5GXlzfk95H7NDQPrCjW1tbi5z//OZxOJ1auXIlf/epXXts5jsPy5ctx/vx5qNVq7N+/H5MmTRqZwQaA4zgcPXoU1dXVuHDhAoqLi1FWVoaZM2eKEkitVouuri4MDAx4WZCeN4SYB1YkVpbYGKXve+G4hfR4pVLJiqz99fmUSsKAy+Vii/4K/WSFZB2plqkAgw9boa9sX18fnE4nkpKSkJSUBKVSKfoaEHPuw52IBNtfJpOxJuy9vb0YGBhAcnIyS1KRajYuAK++w0ajEQqFgolkqAlsIJEE/m/BCkljJIrunUgUBy+YvLw8fPbZZ8jOzsasWbNQWVnplaK8c+dOXLx4Ebt27UJVVRU+/fRT7N+/fwRHHRyDwYDdu3fjyJEjaG5uxuOPP46CggKoVCoUFhZiwoQJQd1BVqsVNpuNZfwJLpxQrbQitbLuxU0nNAIX4pD3Q8MAgYGBAZasw3Ec0tPTMXr0aNGZoYH6fkbiDgzlCvQ9b0LLPCF7WKVSISkpCQkJCQEtKjGJNcOJy+Vi2bhCEwHPpB2p1uUJy3oJlqSnSKampvqtofWNSTqdTq+vVqsVCoUCU6ZMGWJJkiiG5oEUxa+++gqbNm3Cf/7zHwDAli1bAAC//vWv2T4LFy7Epk2bMHv2bDgcDowZM4YVokuRnp4ebNy4kbXS6ujoYA2x586dixdeeAE5OTkh41oulws6nQ5dXV0wmUxQq9XQaDSiawpHEs+GAQCYezialpivZS3GcrLb7bBYLDCbzeA4DnK5nJUfAAiaXHMv3YHhugJ5nmdrGup0OnAcxwrLpSw0wP+XmxLa58lkMmZF3m0WcSDPSqQTGM/zL1xfgcpW4uPjvSYj/s5xbGysV+MBAZfLhWeeeQbffPON5O91kYg6COlepVGko6MD48ePZ5+zs7Nx5syZgPsIxbk6ne6u2isNJxkZGfjwww+HfN9isaCmpgZ/+9vfcP36dTz33HMoKytDQUGB3xs/NjaWNQRwOgcXyr116xaMRiPS09Oh0Wgk2ydUpVJBpVJh0qRJ4DgOWq0W169fZwlGarUaiYmJIZMnwkm2EOsKDCRGQoPy9PR09tDiOA79/f3o6+sDAGb9Si3pRSaTsf/5hAkTvBJ2WltbIZfLmQUspbi1cO4UCgXGjh0LjUbD/uetra2wWCyIjY2FQqFg1m+wa8OXUIk2nt/zXRnH1/XvOVm1WCzsNTAwwFb9MJvN6O/vZ9fiX/7yF+ZuVSqVGBgY8Po5i8UCjuOGXLuCa/Vhg0QxQiwWC4qKigDAbwxy27Zt+OCDD1gpwYcffoiJEyeOxFC9UCqVKC8vR3l5OUwmE2pqavDee++hubkZzz33HF5++WVMnz49oEAKD2TBguzo6MDVq1eRlpYGjUYTcnUMMfi6Au8m2cJfgg3P8+js7ER7eztcLhfLCBUWzPUnVoHWIRwON7A/hNZ/wqokN2/ehMlkYs2g1Wq15CyxmJgYr8JyIWGnra0tZMJOKAs7UhexL4KlFGiikp6ejoyMDDidTta1xmq1spU/hI47nuUQvoIlvIxGI3sv9KsVXsJnX6HzJ1iCyAqlVUJnJaVSCYVC4fVZqVSitLQULS0tuHbtGn784x/j8ccfZ9uEfRUKBbt2pTBZGUmkdReNEFlZWbh16xb73N7ejqysLL/7ZGdns9ZqTU1NGD9+PGbNmoXS0lKvGGRhYSHOnTsHpVKJv/71r9iwYYPkYpCJiYmoqKhARUUFTCYT/v3vf2Pr1q1oaWnBwoUL8fLLLyM/P58JpG9RuFKpxIQJE2C3271m1QqFAomJiQGzQUM9qAAEfVD5WlaeSz4F298fgjXT09OD3t5eVlco1YYBcXFxGDt2LMaOHQue51myTltbG+RyuddiysOF2FKGQOddJpNBoVDAYrGgr68PV69eBc/zzJUvTC7EWNjCS4yFJSzg7U+wBCvLV7B8BUp4qVQqaDQaXLp0CRcvXoRMJmMrlQiC5StOvt9TqVRQq9VewpSYmOi1XaVSISEhgQQrilBMEYOZl3l5eTh27BiysrIwa9Ys7Nu3z6tB7o4dO9DY2Ihdu3bhrbfews6dO3Hnzh0A/mOQnjQ0NGDdunU4efLk8B/MXbJ48WLcvn0bnZ2dbHULhUKBJ554Aj/5yU9CWksxMTFeKzeoVCqWBBCo3koqN7pvwwDBdSz1hgECnospW61WJCcnIyUlBYmJiaJKHCJxCYsVrVC1li6Xi63LaTAYEBMTg+PHj2PatGlITU0VJVi+26xWKxuvTCYDz/PMDeorWL6vxMREr/2CCZbdbsfp06cxZswYv7V+hGSgRJtwqKmpwS9+8Qs4nU689tpr+M1vfoPf//73mDlzJkpLS2G1WrFs2TI0NDQAAJ544glm+f3zn//EmTNnsH37dr+/e926dRgzZgx++9vfRu14IkXI2lQoFIiJiYHBYMDhw4dx4MAB3Lp1C9///vdRVlaGxx57LKSYCcvjdHV1Qa/XIyUlBRqNBmlpaZIpjQjGvW4Y4K9JwN26Bv0hiI2QtCNkhQoPe1/LOpCQCa5FYT1EX2HyjGX5ugB9vwYSLOH/6CtYGo0G3d3duHbtGiwWC5YuXcpiYr6C5etCFMSLLCzCBxLF4eLgwYOora3FBx98ACC4KO7duxfbt2/H8ePHJV0LJQa9Xs8E8vbt21i0aBFeeukl5OXliRJIvV6Prq4u9PX1ITk5GRqNBunp6ZIQSN9GAb7iIyw8azAYwHEcFAoFi0EGciX6IibhQmzdnaeVLUawOI5DTEwM4uPj8a9//QsWiwXp6elQqVResa1gguVrTQmtyYJZWPdCsDiOu+/vHUISkCgOF2JKOADg888/x09/+lMcP34c9fX1QZsDCHz88ccoLy/H119/jZkzZw7vgdwFfX19OHToEA4cOIDu7m6UlJTgpZdewiOPPCJKIPv7+9HV1YXe3l4kJSVBo9GwhAXffcWWNISThOOLZ8JFKGESCsGNRiPMZjOLDSmVSpaJG0qwfLMG/VlYQpzLMxmD4zg2XuHeFSNYgrtPWMXj2rVrqK+vx8qVK5Gfn08WFvEwQKI4XIiJQTY0NKC8vBy1tbXIyckJ2RwAGFzCZdGiRbDZbNi+fbukRdGTO3fuYO/evTh8+DB0Oh2KioowY8YMJCQkYMqUKaxRgD+xcjgcsNlssFqtsNvtiImJYc0ChAdyqGYA4VpYQv2dWMHyFCZ/309OTkZGRgY+/fRTGI1GpKamIjU1FcBg+Y6vNRVMsHzfe24nwSKIu4LqFIcLuVyO7du3Y+HChSwGOW3aNK8Y5Pr162EymbB48WK2+kBOTg4A4JVXXsGhQ4eGiOLvfvc7bNy4EVu3bh2Jw4oYrVaLL7/8EllZWZgwYQJu3LiBK1euQCaTYd68eSgtLUVmZibLEAwmVmazGV1dXdDpdBg1ahS++eYbzJw5EzKZDBaLBf39/UEFy19Ku5DW7ukOBIYKlmdShednlUrFMjqDCdaf/vQnNDc3o6mpCS+++OJInhKCICKELMUoICYGWV9fj82bN+Pjjz/GggUL8M4779w3lmIgtFotPvnkExw4cABdXV0YN24cMjMzAwqW57Uol8sxfvx49PT04PLlyxgzZgzmzZvnV7A8MwM9rS3P/cjCIoiHHrIU7xdcLhd++ctf4qOPPmLfO3XqFJYuXRo0BlldXY1NmzZBJpNhxowZ2LdvXxRHHZrRo0dj9erVWL16NZqamnD+/Hk88sgjYQsWz/Po6elhBewEQRDDBYliFAjVHMBoNOLSpUtYsGABAKCzsxMnTpzAwYMHUVJS4rc5QHNzM7Zs2YKTJ08iLS0N3d3dUTueSMjLy/PbhV8MMpmMBJEgiKhA7tMoICYxx5PCwkIkJCTgq6++AuA/u3XDhg3Iy8vDypUrh/8ACIIg7n9EuU9HvkDsIcAzMWfq1KmoqKhgiTmHDx8esr/NZoNGo2Gfs7Oz0dHR4bVPU1MTmpqaMHfuXBQVFaG2tnbYj4MgiPuP1157DZmZmZg+fbrf7TzP42c/+xlyc3NRUFCA+vr6KI9QWpD7NEqUlJSgpKTE63tvvfWW333ffPPNkCLncDjQ3NyMuro6tLe34+mnn0ZjYyMrBSAIggCAV199FevWrcPy5cv9bhfWXG1ubsaZM2fwxhtvDFkl6GGCLEUJIqZBeXZ2NkpLSxEXF4fJkycjLy8Pe/bswaOPPorc3Fz88Y9/HPJ7v/32WxQXF6OwsBAFBQWoqakZ9mMhiAed2tpaSd93Tz/9NFupxB+HDh3C8uXLIZPJUFRUBL1ej87OziiOUGIIHUNEvogoYLfb+cmTJ/Otra08x3F8QUEBf+nSJa99jhw5wi9fvpzneZ7v6enhs7Ky+EmTJvEtLS3sZy5fvuz1M6tWreJ37tzJ8zzPX758mZ84cWJUjocgHlQcDgefk5Mj+fvuxo0b/LRp0/xuW7RoEf/ll1+yz8888wz/9ddfR2to0USUzpGlKEHExCAXLlwItVqN/Px8FBcXY/Xq1cjLy0NOTg5GjRrFGgR4IpPJYDAYAAD9/f0YN25c1I+NIMIhlBUGDJYm5efnY9q0afjhD38Y1fGdPXsWubm5dN89SIhVT54sRUlz4MABfsWKFezznj17+LVr13rtc/v2bX769Ol8VlYWn5qayp87dy7awyQkwpEjR/i8vDx+ypQp/JYtWwLud/DgQR7AiFgOYqywpqYm/jvf+Q7f29vL8zzPd3V1RXWM98t9F8xSXL16Nb9v3z72OS8vj799+3a0hhZNyFIkvKmsrMSrr76K9vZ21NTUYNmyZQEX+SUeXJxOJ9auXYsjR47gypUrqKysxJUrV4bsZzQa8ec//xlPPvnkCIxSnBX2/vvvY+3atUhLSwMASdazSv2+Ky0txZ49e8DzPE6fPo2UlBSMHTt2pIc1YpAoPiCISc7ZvXs3KioqAACzZ8+G1WrF0qVLKV37HhHK1bdt2zbk5+ejoKAAzz77LG7evDkCoxQnNsD/e/GO1ALLHR0dGD9+PPssxdKkSO87rVYbtTEuWbIEs2fPxvXr15GdnY3du3dj165d2LVrF4DBzPicnBzk5uZi1apV2LlzZ9TGJkVIFB8QZs2ahebmZty4cQM2mw1VVVUoLS312mfChAk4duwYAODq1auwWq1Ys2ZN0AeJZ7r23//+d7zxxhvDehz3K2Ksr8LCQpw7dw4XL15EeXk5NmzYMCJjFSM29fX1uHXrFhYtWhTt4YWFZ2lSZWUlVq1aBb1eH7W/H+l9l5GREbUxVlZWorOzE3a7He3t7VixYgXWrFmDNWvWABiMee7YsQMtLS1obGy873su3y0kig8IYpJz3n33Xbz//vuYMWMGlixZgo8++gjz58+XdLp2KOuL4zj84Ac/QG5uLp588km0tbVFbWyeiLG+iouLoVQqAQBFRUVob28fiaGGROjF++67747oOCItTWpubo7aGCO976gpvYQRG3zkKdHmgUWq6dpiEi127NjBv/766zzP83xlZSVfUVERlbH5IibhwpO1a9fyf/jDH6IxtCGcOnWKf/7559nnt99+m3/77bfZZ71ez6vVan7ixIn8xIkT+fj4eH7s2LFRT7aJpDQpOzub12q1UR0ncd9AiTbE/Y0Y6+vQoUP40Y9+BAAoLy/HsWPHvJagkiJ79+7FuXPnsH79+hH5+6FcfikpKdBqtWhra0NbWxuKiopw+PDhqLvVIilN2rp1K9RqdVTHSTxYUJs3IihiXFjDhb/Yl2/7Kc995HI5UlJSoNPpMHr06KiMUUDs/+nzzz/H5s2bcfz4ccTHx0dziAwxi2RLhVDtEWUyGbZt24Zt27ZFe2jEAwqJIhGU0tJSbN++Ha+88grOnDnz0KdrB8LT+srKykJVVdWQ9S0bGhrw+uuvo7a2dsRLB8LpxVtXVxeFERGENCBRfMhZsmQJ6urqoNVqkZ2djTfffBN2ux0AsGbNGpSUlKCmpga5ublQKpX4xz/+EbWxibG+hH2ys7PhcDjQ398/Iu4zMdbX+vXrYTKZsHjxYgCDWYn+VkkhCGLkoPUUCckiZh3KHTt2oLGxEbt27UJVVRU++eQTVFdXj+CoCYKQKKJSfslSJCSLGOtrxYoVWLZsGXJzc5Geno6qqqqRHjZBEPcxZCkSBEEQDwOiLEUqySAIgiAINySKBEEQBOGGRJEgCIIg3JAoEgRBEIQbEkWCIAiCcEOiSBAEQRBuSBQJgiAIwg2JIkEQBEG4IVEkCIIgCDckigRBEAThhkSRIAiCINyQKBIEQRCEGxJFgiAIgnBDokgQBEEQbkgUCYIgCMINiSJBEARBuCFRJAiCIAg3JIoEQRAE4YZEkSAIgiDckCgSBEEQhBsSRYIgCIJwQ6JIEARBEG5IFAmCIAjCDYkiQRAEQbghUSQIgiAINySKBEEQBOGGRJEgCIIg3JAoEgRBEIQbEkWCIAiCcCMPc3/ZsIyCIAiCICQAWYoEQRAE4YZEkSAIgiDckCgSBEEQhBsSRYIgCIJwQ6JIEARBEG5IFAmCIAjCDYkiQRAEQbghUSQIgiAINySKBEEQBOGGRJEgCIIg3PwPXASDPkkydyQAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "fig = plt.figure()\n", - "ax = Axes3D(fig)\n", - "ax.scatter3D(X_train[:500, 0], X_train[:500, 1], y_train[:500])\n", - "ax.view_init(6,-20)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Linear regression\n", - "=================\n", - "Create linear regression object, which we use later to apply linear regression on data" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/bushuhui/.virtualenv/fintech/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n", - " return f(*args, **kwds)\n" - ] - } - ], - "source": [ - "from sklearn import linear_model\n", - "regr = linear_model.LinearRegression()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fit the model using the training set" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "regr.fit(X_train, y_train);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We found the coefficients and the bias (the intercept)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.08241879 2.96344602]\n", - "0.09703581706766884\n" - ] - } - ], - "source": [ - "print(regr.coef_)\n", - "print(regr.intercept_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we calculate the mean square error on the test set" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training error: 0.1527714636459691\n", - "Test error: 0.16965042383819598\n" - ] - } - ], - "source": [ - "# The mean square error\n", - "print(\"Training error: \", np.mean((regr.predict(X_train) - y_train) ** 2))\n", - "print(\"Test error: \", np.mean((regr.predict(X_test) - y_test) ** 2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting data and linear model\n", - "==============================\n", - "Now we want to plot the train data and teachers in 3d plot (marked as dots). \n", - "\n", - "With plane we represents the data and predictions (linear model that we found).\n", - "\n", - "We first scatter the 3d points using mplot3d:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "fig = plt.figure()\n", - "ax = Axes3D(fig)\n", - "\n", - "\n", - "ax.scatter3D(X_train[:500, 0], X_train[:500, 1], y_train[:500]) # plots 3d points, 500 is number of points which are visualized\n", - "\n", - "# here we create plane which we want to plot, using the train data and predictions (you don't need to understand it)\n", - "range_x = np.linspace(X_train[:, 0].min(), X_train[:, 0].max(), num=10)\n", - "range_y = np.linspace(X_train[:, 1].min(), X_train[:, 1].max(), num=10)\n", - "xx, yy = np.meshgrid(range_x, range_y)\n", - "zz = np.vstack([xx.ravel(), yy.ravel()]).T\n", - "pred = regr.predict(zz)\n", - "pred = pred.reshape(10, 10)\n", - "\n", - "ax.plot_surface(xx, yy, pred, alpha=.1) # plots the plane\n", - "ax.view_init(6,-20)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we plot the data and the plane in similar way for test data:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Dq7a3FrGUeNMRL16ISZmCeghDeZ0TRwtoWlIOaeHPwteWvq9pGnEcE0URrute9p6u65e9Vog9HMclm00s1ZyAsOQzuGUbjqmveqyFf6/F2bNniYROa0cP9SDim0fH+E+HL1DxJG0pi0okKdYDtnTniMIAqxagWzrSsOnMSySSQgSOHtJixmxtN5mpVfnaC29wR4tExjG6ruHYDqmUQzqVIpVySKVSpNMZbNu65nXajfQ+3d2b5ZP7+vjbg6MYusDQBL/5wTvWbP96W4pKFBU3hOspOhshBp7nMTk5iWEYN0Ro1isYALVajVdeeWXRgL6WMKz293KvrdT2an/X63UuXLjA7t271zzeWrwtjDl0vshU1acnb3P/YMu6PRHnGJ11OTI8SxSFmJrD9u3b17VfKh/SeqqKjyBnG0yUPO7qb6W9Jc/pqSpuGNGXd+hIX33SajeIGJ71qUWC7Y7PD87M8Mr5Wap+iK0Lyl5Ia8pARgGjU0V6cjZuEJO2dPpaLI6P10hbGm4QA4Kso9HWkic2fDLtLdy3txuAWMZ4no/neXi+R6VYwZss4nkeQRAmfXFd/vw7hzg0HuFYBh/e08Hevjy2bWIaRnINhUDXNDRtfdfvavm5A4P82O5OirWAwbYUeWftIfpGTJ9a1iZmXb/BKFG8RsIwZHh4+IqFwvd9SqUSR44cWdbquJrpqIVcqziEYUi1WsWyrMve0zRtXcKy2t/LvTfX77U4ePAge/bsIZVa3SvvRiOEwDCMDemXZWgc2LF2kduVGC7U+bPvn0UTgiCKKEx73H+/O+/5uRo5x+Dzj27l7w5fZLoasLs3x0fv6+VLB0d4/WIZXYCmCX72wBA7OjNX1C83iKh6Ea+NzPLnL03iRdByrAJIevMpNCGQQBwG+K5PiyXoztmU3JDunMOurhRZW6c9HSCRBJEkY2q0O4IoivHCeJG3piY0nJRDOpsF7fLhLo5jvvTsQf5x2EfImCiscvjcNJ+726HDCpEyKZtk23bjx8FxbFJOYnlWq3UqdY9yzW0Ip0BoGtqckOoaUkq+/voETx6dxDaT8JAHBvMrnqP+Fof+luYJGVGONoorJoqi+cHdMIx1WS2u6xJFETt37ly3kNxIKpUKW7dubTrhaXaaZcr5xTMzmLpGZ9YmjEJmpiUvnyvy4/f2rmv/wbYUv/rEzvm/T01Wef1imcFWByEEFS/k74+M8ms/tmtd7XlBRMULuVCoM1Px+LMfXCAIJFlbxw1jhgt10rbO/X0pDp0rEEYRgW7y6I52/qf37kKIZK11uFCj6vp85O4eUpZGFEu+fmSYg+eLxGbI+3Z3sbsnKeorBWiagdBXH+ZeHo+xNEnGkAjbolATDMet/Nj9SW3FMAoTS9P1cD2XYnGWcW983tqMo4jZciURzHnxtLHTGUzL5tvHp/mLF0ewDEEUS/71197kt3/iLu7syaJriQNRGEWJkF6F+Kjp041FieI1YhgGW7deeYb7arWKYRhksytX5d5Mmn1NsRlpJuepIJJoC/ojSALdr5Z6EKGLS58xbepMVLw19/OCiKofEUSSI8NF3hyrUHUDLhbqdKcFpq7hpAyKZUm1XCVvGdzXn8O2dN6/p5u372if94AVSAZaHMKMQQScn6njhjEPD2bZ3ynZsWM7uhBIAWIdYoiUyChChB7EIBY45iy8lIZuYGR00pkMYskpLBSLFAoF+np7cT0Pz3Op1F1mShU8f5QwjPjKaz4yBISGKTQqvuQfX7tIR6ofS9cpV12mi5XGgRPrVtNEQyQbMzMCdE2DuWncxntz39PrLYoqo43iutNMA+jNhhLstXl4WxtHR8sI4RNGEWEM+4auPqSiL++gaYKyG6BrgsmKxwODK4c+LBRDgJoXcmK8kqwNpgxSlsZU1SdnCupxFUeXfP7tW/ECiWUItrSn5kMTkDJRqcbUaigl3zo2xXDBRdPAdT32d8MuTYBmMFEJ+ePvn2ai7HNvf47PPzKIvTDXqUy8eWUUQhzxtl6drw9LSm5ALMHUBe/cmUxdz91lQgoWfmNLbsCFootbrdOqgZNySGUyCN1crKhA+4Xj1Ap1DEMg45goDnErs7x1rEAQBNTqNV5//XVsx140TWtbFrZtoWl6oyfLjBkCAt9ntuoyW6mRdmxMY2OtOhWSobhhNPPArkT75mZnV4bPPbqFF8/MEEYR96ZshtrTV91eR9biE/sG+L+fPUWxFtCddxhodS6zLvwwpuKF82I4RxjLxnKAIGXq7BvI891jw0yUQjIpmw/s7WFbR3rxfbdADBcyWnQZLrr0tSa5Twsy4NBEyOOGTdmP+Y2/e5NZN0ATguGiy3jZ43/98J1Jk3GEjEKElAgST9872g1+/Y5tfPetaXRN8Mj2NtK2TiglxjLfg9GSxx8+d5ZaEBP4PoN5g1/faa0oRj/1QC+//8wZqn5y7LwJnziwg+5s0v9Dhw9x1+675qdoPc9jpjqN73m4vt/wotWxLOvSuqZtz4toGEYYukY25aBfoTPWQlZztFFriorrzs1g7TR7/xSrs7Mrw86uDL7vc+TItWfEma76PLazg66cTRxLToxX2NqRZqA1taIYzpF1DFodg+lSlYwp6M0K3rMjw7aODHcO9bKtc4EgzhtFyz+YJQKbbCd0A8tJEXo1EII3LpaoBxHWnGUjJS+fn6XmBThaPC+GS7mnP8+evhxfOTTKlw6OAjDY5vDZA4NkrMXD5FcOjeKHMZ05By8wOFsOODxS5uGty1vO+4by/Kv3buOFtyawDY337umaF8Q5ITINE9MwyWQyyXmQiy3DKI7mBdP1PErlEt6Uh4xCDA2iMOSNN17HcZzLfkzTXNdDrlpTTFCiuEkoS0xxI9mI+2264tOSMoHE+9TQBTNVn5SpryiGCRJNRrx9W5bXhiNOT1UxNcE9PSnu6MnS252Z22xJp5dvrTNrYZsm5cggpetMVSO2tSYhA7qWTLGKhpUZxSGx76PHAUJcbu2EseSNqZBjBy9SdkNOjFfpbbURJHUYv3lsio/fv9g5qeDGpNMpNMNARBIhAmbr4TIfWyLjCOKQPV0Oe7q2rHKG5kRQNM7D4g+vazoYFicmfKS02b+tj57WDKahU61WOXPmDDt37sR1XVzXpVqtMj09jeu6BEEAgGVZy4qmZVmrWoJKFBU3jGa2xJrZkm3mvm0kUkrOz9SpeCFtaYv+Ta7s0JW1ODtTpTvnUKn7HB8pYMmIrNlG2jSQApANT2khEo/RuLF2hyRlamRsAz+MiXTBwck61UjnAz2S+RW7ZYSwUA04X6hhCMGO7gz5bIaP3O/w/KkZZusBd3Wl2JUPKdQC+vI23VmLi8UqURCia/Dhu7uxlplWlFLytSNjPD8S0FUrc6HgEkZxIopCkLENhov1SztoGkI3uau/lR+cKWKbkrDxMDC0MGl3QwxlHCXivAqXrDOx0jNAcg5qPv/6749TdGM006T18DR/+Kl76MrpSCnRNI1UKrWit7iUkiAI5kXTdV1KpRKu6+J5HlJKXNflxIkTpNPpecEECIJATZ8qrj+3y8CuuHqeOTHFj84WEo9KCe/d08W+LTc2r+dC9m1ppVD1OHZhiqeOTuBHktdHinzz6Bi/8b47aE0nVqQEhKYjNQM0AyF0wiji9FSFb705zVC7Q0vKgnqZMwWf2VpIa8Zc9pgXiy5//sI5AjSkZtCbr/AzD/URxDFVL0RKOF+sc27Kp3jyHEQxezotHuy1KNRC7u3P8YG9XZe1K4GqH/HKhRJttkZr2iIIY46NV6l5IRnboOqF7O7Ozovh3NrmR+/roexFHLxQpFj12dFmkLESS2rpmuVqyAWm8dJtD4/M8g+vjiOBH7+nhyMjZQq+IJdN1oVnqgF/+sIF/tUH1hcSI4TAsiwsyyKfXz5G8uDBgwwNDRGGIa7rUigUeOmll/jyl7/Ml770Jb74xS/y9NNPr+t4NzNKFDeJZp8+VaK9ucxUfV45V2SgJfH6DKKYZ45PcU9//oprEG7EdQzCiLrrsq8/zemxAkKDLS2JVTJV8fiH10b5uQNbGgJisLAATyQlTx6b5OREldMFj7FqyAP9WYSWBLsnE4+Lvw/JkqHkqaPj6KZJRyaJjxwp1DkyUuLYaBUJ5Bydkark0Pkqb99hYzmCqUpAdzbFzz92eaiUBISQCJk42QguxQB3Zi1aSz4ztRA3iBloTfOB+/oRxuJsLilT5727O3n5fBFT1zhfDPmdfzzOv3zPVra2OWuKIaJhISKIiS8bC14dKfHvv3Uq2VTTOf7cMNs7sugLwiJ0XTBeTkJiNjLVXDabXRR+sWvXLk6cOME73vEOPvzhD19z+67r8s53vhPP8wjDkE984hP81m/91jW3u5EoUdxElOhcPbf6ufPDGNHIHANg6hoSuWbFBIC3Jip89fAoNT9k35ZWnth19dZlEEZU6i6en0iXpglKXoizoA+OmazrCdNioRjW/ZCZWkCx5nNupsZQm4PreUzM1njtgs9QJiKfdmhZJpVZcnUFFT/G1oAwQAJa5HNuvMChM0VsyyAWGhYRcRQTJ4pH3jHmBeNSew0BRCZR/UDONtjdk+ZHpyrYXkjdi9i3Jc+n9g8gDIv2rI2xQm7Ubx6dJJLQakMUBLh+xFNHx/nnywjx4l7MhXesLGLfOjaJFIJ8OoWm61S9JLwlyXAlQSRJwx/acn3qQy5lLiTDNJe35q8E27Z5+umnyWazBEHA448/zoc+9CEeeeSRDejpxqBEcZNodkusmfvXrFb2RvarPWORtQ2mqz4tjsF0NaA375C2Vnd4GJ11+ePnz5KxdWxD4ztvThGFIYMrbD9T9Zmp+uQck568Pf/6UjFcyF3dWX50tkA2Ttay6pHGnoE2FgrihZka/+VHF/DDmLIXYuuS3pRgR7uNLiPGyh7dGZOHty3J6ZqU4WDOctzbl+OZ41N05WyiOCaWktGih6ZrpGwLXdcZmamCbqCZFsLQKLs+O7rSCNNGyhiiEK3R7NzqZeLLIvjkg33IygxhxqY37/Bje3vJOGvn+fSCAC30QMQgJboQeGG8wtaXJHnpHTJnMc5hGDpOysGwfLSGc4tEsr0jxYFtrfz9a+Mg4UN3d/HphwYutXGTZLQRQswnLAmCgCAImu77rETxGrnaC9psN4Ji86n5EcdGS7hhzI7ODJ/cP8A3j44zWfbpb7ExNI0/ev4sHRmT9+zuojNrX9bGmakqUSzJO8lTfU/e4tBwicH+y493bLTElw9dREqIG2uWB7a1Uql7uF6wYj8f29nBeCXk6bdmQMBjd3TxvkYCbkgG1795eQRDE+QzBik95NXzJfozBp1ZC8cy+Mi9bWyzqqSshiDOiaFseGA2eMcdHQRRzOHhWSxD4+P7Bnj2ZIF7t+R4a6KKDJP0aPv6bGbrASDozlm8b3cnNDw/aVRnbPybPxyAH8UUPIkvfdozNqYAohApxNxc67zjEABxTBwFPL4ty5Fz01RlYsFJTfLYrstz1SbyvroTDYCua2RTNo5t8emHhnj5wjHKbuKgVPNjjo5WmK4G/Id/sof7BnKLHiRuRELwjfQ+jaKI/fv3c/LkSX75l3+ZAwcObFjbG4ESxU2kWS0xaG5LsZm52nNW9yP+/AfnmCglxXOfFlP8zMODfObhIaRMiv5OlF06szYVL+RrR0b5mYeHSC2wHAs1n28eneDVkVmmKj57+rJ4jcoSS/HDmK8eGaMtbeGYOn4Y8o0jw3RYct5hZlk0Dd0w+Om3beNjD21BSi6r3uGGMWXXpTtjQAQZ02CgLYUQgloQcd9Ankd3tDNyodow2S4XwzkMTeP9e3v4wN29oJtEccyxyTqFWsS+oTwVL2K6JHj/ToetWwaZrQV0pjQqrst/PjjBWMljsNXhx+/pnn9QmCOMYv7Dd85yYizEscu8dH6WF8/O8G8/fCfpBbGJEhKLcwEPDOT5xceG+NqhEWIZ87H9Q9w/0HBgERLZyICz8BNFUvL9UzNcnPXY3pHmbVtb0DWNbNqmszU3v93d/Tl+9+N7+Oqr4xwZLhFLH4CRosu/+foJ/uCTd7Ot4+oTMVwpGx28r+s6hw8fplgs8rGPfYzXX3+de+65Z8Pav1aUKG4SSnSunlvx3J0q3rAnAAAgAElEQVSYqDBR8uazzpTcgKePT7KzK4MXxozOuvQ1Kie0pExGSy7FejAvin4Y8wfPnGaq4pOxDc5OV5koe9zTn+enH+ynPlZZdDwvjAmjGFMX1F2XIAiJwpDhYp1aENGWNkktTI0mNKbrIaenamia4K6eLDlnGfGUEbb0abU0Zms+LSkLP4xImTqfemiA9szc1GRSSXkuHGElhBCgGyDm+hLz3js7efLNSWZqAboQvGdHDi/0+OIzpyhWXUwtyZyja0npq5OTVf7ypRF+4bGtC+IY4XzB5Xyhjq3DTC0kiiWHLpT4jb97k9/5ibsaMZnLlzSTwP6trWxJ+QRBQP9AaxIHqRvJb9n4fMjEAI5jfv/p07x8rkgUx0RSkks5bO3M8rZ2ydIVtb19Ofb25fipP3mFtpSBoWtYQLEecOhCaZEo3kzTpwtpbW3liSee4Mknn2wqUbx9gk+ajGafPm32/t1q+GG8KIG3qWt4QQSA0Rjg/caaVdxwuFgYezdV8ZmoePS3Ouzf0sqDW1ppSxt8/u1b2NV1eXknRxdkDMnwxCxBEFJxQ8bLHi+cnObbx8b52uGLFKoBCA1hmIxWI/7m4Cgvnyvww9Mz/O0rI5TdBVOscYQMPWSYrBF95uFBLENjsuxR8kI+/mA/7RlrgcAkYSYrIgTCMMGwFwhiQj5l8Ml9/Xz2wCBfeHSQobzG196Ypub5dOUsYuD10TJZW8c0kkohk2U/mV6Vl5LlyEbYRMWXRLGcL/c0XfX52qtjSX7UdTx8SSHAsBCmjdD0pO+alvy/kZj8Qink8GidbDaNnUpRCQ3GqiHnZmr85VGPH5yeWbbtlKktSuQuhMAxFw/bG+l9er1FcXJykmKxCEC9Xuepp55i9+7dG9L2RqEsxU2k2a2dZu/frcS2jjS6LijUfGxDY6ri8/7GOp2ha7z7zk6eOjaJEIko7htqpSN7ySHE1AVxDHEs0TVBR8YiiiWDrSlY4PYfRTGVukfd8/nQni7+2+ujjJVc4ljSl7cZak+mOWfdiBcvlPnQvcm03svnCti6lsQXAmMll+PjFR4ayiPjkKUK15O3+e/fs5NyPSJlaTgNq3PpoOtHMd8+Ns65mTptaYsn7uqmPecsW/twDiGSqcm0DsQBVTeg4sUM5ZN90raOlFD1YyxDJ4pipExqVC40Sre0pxhodZgs+0gkMeCYGoaAySXeq9MVn9/9zmlOTtbozJr8D09s587ePBgWumY2knavjBdKDF1gaIKqF6E1Ko7YhkbgwzePTfHoMvUzf/6xLfz7p05RrwUIkdRafMcya5fXk40UxdHRUT73uc8RRRFxHPPJT36Sj3zkIxvS9kahRHGTaPYpwGbv361GV87ms49s4ek3J3GDiA/e3cOBbW3z7+/uzdGZtSjWkinTpUVoO7MWj+9s5xtvjFMPIpDwkft6aU2beJ5HFMWUKnXqvj+vX20Zk587sIUgijk3U+N7p6aTtSPdIJsxKXmX1tHCKBHbOTRiAt9FRivX2zQ0jbbM6pNRz5ycZdIVdGQtJmoRX3ltip89MISz0m6Nck/IaP6llKlhaOAGMY6poYtkirnkBvhhRCwl776zk6y9eLgzdY1/+Z5t/C9//wZnSjFpS6PNNqiGknv6L5V0m674/PxfvUalUSHEjyX/9ltn+eJnHkiswXiZFG9z50kI0pbOg0N5co5JsZaElsSA05gBiGFRiMtC3nVHB50Zi1cuzJKzdd63p+uyz7GRluJybKSjzX333cehQ4c2pK3rhRJFxU3HrSrYQ20pPvfoyvkxO7P2sh6nkJyTfVtbOTlZRSLJ2gYCmCy5yKDObNWl5jWsn0szmIDA1AUtjoEUBgEGJoLpasAdC6Zd9/blefLoOFLGhEFAFEbsXMbZwwsjjo9VqXgB7RmLXd0ZjBWcNPxIMjzrs7W3A003sIHxssdUxWewbRmxjUPi0Kdc9yjUQlpSBq0pE13AR+7M8dT5kKofEcWSzx3opydnU6yHdGQsdnQu75iStgS/+ECKHxYyPHuyQC2UfHBPJ+/b3ciCIyW/+53TVNwAoWlIw8RHx5E6p6ZqDNrLC5ImBClTI23p8+//3k/t4Xe/c4aTE1UmpUfa0im5EaYm+Ol9fcv2DxLHm7v7cyu+f71RVTIUN4RmX7Nr9v4pLufkRJV7BvKkLZ04lpyfKnH0wiTb2qxFKcUW+bVIyYvnZzk1VcfUBTOVOhLY2p7hoaFcEtYgYHd3Ghm288bFAoZl8vC2brpyiwU6jGNeOD1NuR5imzqHh2d56tgE2zsy3DeQZ8siERXoho5m2sRCa8QRysb0b+JJ6xgaKctIpmejxBp742KJP37+LHN24j99eICdGWhL6RzYnqXqhuzb0sKurtWLd0uSrDZSSkxd4+cf38oXHh1CCLHIIp51IybKPpppo+mJwEWxJIjjyyy2uVObtnRSlr5ojRhgsDXF7//UXgBOT9X45tEJoijiDrvEzmXWfdfL9bYUVUJwxRVzq1ouzfyZmrlvm4Wpa1S8ABGHeL6P5/vo2kqDrQBN46uHx3jy6DimrhFGkm2dKX7tPTtxzMbQIOMkvEDAnv48e/obYQeN8x9LidaIMSzXQyZnk8D6i7NVpqs+hibQBLxwZgYvjvnhmQJT1Yi7+lq4I2Xw6NYUL4/5aAKiGLa2p/jT751lrOQRy4if2NvJB+/uARIr9E+/fw5T18hZOkEY85cvjfAz97Tw10cKxKIMQnBouMSvv2fHZaINc2H0ksa/RRiXJQ0XpBwLw07RoyfiGMeJN+k9fTnu7E4zPl6eX+NMWTrpZcRwOXZ0pvkX79xGvV7n5MnamtuvhhLFjUWJomJFmlV4NmIAmKp41PxoPnPMzU4cx+xst/nG8BRCSGIpyVhm4mgjFwbiC6TQ0DQNP4x56s0JOrI2RiOK4PxMnfMzde7sziZZWMQKgeeNayAAGgHsZS/gpfMFDC1xFNI1wbb2NI6pUfXhD757jljopEydk1Nj7GqBzz+yhR0DXUxXPNK2wddfG2O0WKMtpRGF8NUjo2zvTHNXT46SGxLEkkwquV6mrkEQ8d3TJcJI0tuWrLNOVz2ePjHFp/YPzHd3zjJcnIZ7ZTTDBM3AAT65v5//+spFurI2XhRxd2+O3/zgrvn70DGSNdH1iOHNiBJFhYJbe/r02eOTfPv4JFrD+++zB4auqSr9RuEFEWEsyVyBSMdxTLXuUXN9MqbkfXu6GJt10TWNobYUjqWRLCUK0HSE0OZFbs7q0eeqNjXWGMMoqUW42h0gZVKwFxnPi8zxsQopw0AKiWNqTFU8hJZGN00m6j7VULClzabsRQgheOVijU8HMQMdDoOtDkEYcOjMBJqUVIRJzjYAyVjJ466eHC2Oga1r1LyItKXjhdF84vCFRp6haVS9qNHPuWoVy4vhpfcTNN1M4iIX8N7dnWxrTzE869KWNrmvP5fcO6ZOi61hGuuzDpc/j9du5d1MjjY3A0oUFctyq4ri6KzLt9+cpLfFwdAEZTfkv748wm+8/45rbvtqz5mUkuffmub5k9Mg4I7uLD95Xy+2ufJAFMcxNden5nrEDYs+iiUpU+eu3uyCvohECOeCyhfgWDr7trTwyrkiGcvACyPaUhbbVnBKAbhYrDFZdunP23RmF+cIrfoRWztShLGkxTGRwkDqNuOViHzKxDE0LhRqjBQ9JJIwjPnOW0V+rqcd3/f4j985xUTJxQ9jjJLHUJuDhqC1UdjY0jV+6bFt/OHzZyjWAzQBX3hkkImpAmdmPNww8br1opgHBvPzAfRrWYZSgNDNRnWP5dnVnWFXoxiyY+pkLB1dE5RWSBh+K6EcbRSKBs06fQpX37eSGyZV4xuDWc4xGCnW11V94nr16+RklWfemmKwxUETcGK8zHffMnj/3p5l208sw0tiCEkC7hfPFIikpCNj8fad7aRtG6FpoEWXtTPHZw9soSNjcXy8QlfO5mP39y9KcQZQ80MOny/y3IkJDp4vkDJ1JPDL79rB27ZfChvZ1pHm3HSV3tYMuZyBZnk8trOdofY07WmTc9M1nn9rGt0QCClosSXfOjbGh+/tZnTW4+homR2dGc5O1wiimLNTdX72wCD3DeSYWwG8sy/Lb//EnRTrEa0pg5Spczyq8o6taY4XQTcEn767lwcGL+2zGkLT0c30qoI4h20kRZJXqpxxNdwMlmIURYvKSd3q3D6fVHHLcC0DQGcjzZgbREnJo4pHX4uzIYJ4tVwsuji6Nu/12J62OD9TX7SNlJKa61GtLxZDgFI95IXTM7SkLSxdo1AL+dH5Ck/ctXIM4dxAaps6H39wYMXtvCDkSy+d5/xMjR+cnkHXBI6pY+kaX3zuDA8MtmCZGki4f0s7HgaHh0voWsT793ahCcHXjlwEYP+WVo6MlNCRpLQYkxgv1qh4IW6QBLQ7psYd3RncIKJUD/jUQ4OX0mk3fjmGTl/eQErJD84U+OFbsyBj9vS18uG9XbSlL1mwc3fKgjw6yW9dR2omhJJV54lZXQyvtyCth+v98KrWFBUKbl2P2o6sxSf3DfCVwxeZqQV0Ziw+tX+lwko3hra0iRvG8wNsyQ3Z05vEpa0mhnOUvQCJwDYM0DTasjqTZW/ZAXvutbUGcolExDHD0xWmyh5pM/HEiaVguuKztSON54WUvIAuOwO6jkDjkR2dPLKjE0gs3j974RzZRn7W0xMVckaMJpOQhrOFANPQmCh57OjKYOgaZTfEMTXcMOb+oVbsZVKazTFa8nh1pERnRkfGGlEU871TM3z03t7LPs/c2iO6gdCMJWumy58LS9fI2PplCc83ko0SVbWmuHEoUVQsy2Y//a7FtQj2PQN57uzJ4oUxGUufL+S7Wezty3FiosKbYxU0kViK776zg2rdXVEMZ6o+w4U6hqaRT1sgdGIEGkkJqpxjLL6GSamHdYvhnAPN3Krc6KyHF0qESKZix8s+PfkUbfnsZY4pc7xyfhZb18haGjKOCPSIrYN5Dl8ocm6mjobEEvB/PHmC3/zgTn71HYP8fy9dpFgLuLc3zacf6KBQKBDH8XwMYxSFGIZJOp1iuuIxMltHBiHtKY1teZOZ+uXZZSQSoRtoDTFc9J6US1+6IjFsFkvxek+fqjVFhYLmXVPciAHAMrRNnTJdiKFrfPyBfiYrHkEUkzU1PNelLudkaTETJY+vvz6WCKGEjK2zvSPNyckqQkpMQ/D49laq1SpxHON5HkEYUCgWG+ISzwvN3E8URcgoJI4jZJy8FssYzw+plMqMTHl0pwVjtQhdM3AjwUe26xx9443L2ptjbNhnYsrHs5LXSr4kF2poYUTOkJgaCBHj+jF/99IZPrUnzWd3GwhhIjSNyuw0VSHQNA0hBJrQiOII3ytyulLjr4/WOVOWWBqc0jRm6jHbOzP8zcGL5B2Dx3e2k0tZiUfpOu4ZU9eSJOJLxDCIYv7o+XP8w+sTVLyIbe0Ov/KubcvmKr1SboY1RWUpKhS3KXOD+sJBfuHvlf4/99vzPOr1OufPn191u+Xei6II1w+peUFSYT5evkKDlJKXx0JKgSDbKNA75oFVNdie1YnRSJsahbEys5oGjWlw3/eZnp5OxKUhMnNCI4RESImu6wjDQGgampa836ZpvFemePN7F4mEzt2DaXb3ZKmFksce3ELaMha1mZTUlRCHDG6r8YfPncYLkhyqnVmNn3nndv7j06eY8itoxOhCwxCSrs4O7r5nx5rXKAhCZBzzxlgZ49xF9uRheKpCEElOTtU5MVVFxoDQ+fsjo/zqI510tmZJpVKkUikMY7EFLWVShaQ1Za74kPTF58/xpYOjVLzECj06VuXffOMt/q+P7aGlCSzF641aU1QouPY1xYUCs16hWa/4FItFgiBgbGzsisRnPcxbJZq26P9Lfy/3/yiK5p/a9UZKsLXaEULgBRF1P2wUem+8jmhUfV94TZJBfPzVMWpBRKaxVjdZ8dg52MYDW1qQMlk9SwrHJ+tmnu9z4sQJdu3atfACEcsYsaR4LoAfxNTDiJxtEEaS587PIE0HgcZ4JSKQNX7ivl5aMktCN2QMcYCMkynWnhabX3n3Dg5fmAXg/sEWuvM2n9w/wL/7x+O4fozQkxCHj9x7uaft4rZZtPQXxkl8YUfGxpZJUP+RUZfOXJq044AmKNYCztYs2vMxU1NT1Go1giBE1zUcxyGfzZC2NDKGZLVJg2dPzOA1SntpjTRvdT/iuZMzfHTb5lt5KqPNxqJE8SZgOYG5FqFZj/jMzs4yNTXFyMjIVQvMagKyXqGZ+79pmvOvlUol8vk8LS0ta7az1DK6nriuS7lcZmhoaF3b1z2fSt0nkgLDXLnafSJzl6oz3dWb5dnjUwgScYglDLQ5c1vB/LZJjF4cJdYVMiaec7aU8bwn5kJePlvgq0dGiSV05VK8e08Pk/WYu/tbGC/5eGGIH8Q8vrPj0k4yhihEystDP7pyNu9rlMCaY/+WVn7ro3v48g/eIpNJ8dMPb2V75zLp6KSgkYbmMl+Y7e1p0pZOoRoQR5JKIEmn05i2BXPrX0Kg2w59fV2Lz6eMEZFP6NUpFouUy2VeeeUVpJQ4jkM6nV78Y2mXueIIkVTnkDK+pvuqGdYk1yKOYxWSobgyRkZGqNfr67JyFr5XrVZ54YUX1nWMaxGVhb91XV9XO8PDw6TTaXp6ejZFYFZjZmaGlpYW2ttvbF25jaLu+VRdnzC63EpbyLwYLnl9V1cWKeH4eIU4jnlwqIXWlMHqzyqJqaUJGutr2qXXG4mxx0t1vnxknDbHxHEcpmoBTx6bBEDXBANtDnEsmar4yVTjKmK4ShdAwH0DeVL35+nq6qK1dYX8rGLlD9SSMvn5x7bwzTenGS9q7Ot0KEubbx+fJmVCGMVYmuDeBdUldE2QsfRGbccU0EI6nVi7u3fvRkqJ53nUajWq1Srj4+PUajWe6HY5NyVxQwlSouvQnjL40N4u/OL4+j77dUQ52mwsShQ3ANu2V5wuW018XnzxRR599NGmfFK0LAvbtnEcZ+2NFfOsZkXX/YBq3VtTDJFJmrWVWhIC7uzJUvZCnn5zgrcma+Qdg0/s6ydlGZTdgLSpk3WMxvaC44WIP/nbV6n7EQ9va+Pzj2xpZMwRyT8hGK8ElOo+pycDYjnLUGsKV4fBlhTnZqo4pk49lDy+swNHl8SBu757d4EYQpJ554VT0zz7epn2fMxHHrDYukwZqlXRNHracnzusRZGR0fRNI2Ozi5sU+fFM0WytsU/fXiAgVYHTQgydpJzdTWEEDiOg+M4ix64HnwQ3nZfkW+8Nsa5mRpb8xqP9GqMvPU6nudh2zalUol0Ok0mk0ks1lUs/0WnRk2fNh1KFDeAzs7Oq1p/22yLazWatV9z3EjPWD+M0RsFYf0w5uKsSywlvXmHtLX2YDEnhofPF3nh9AyGJnhidxfblxECGcPRsTKnp6qkTIP9W1ppyyweYAUwNuvx7WOTZB2DmhtxoVDjL354npxtEETJdPu77urinv4Wzsx4fOlNl3w6haELvntiCiHgFx7fPndUZBxTqHicmKg21s7g9dESu3tz/It37eDZ45NcLLoYGgzkdC4WqvS3phpm7LJZRS8Twzm+f2qap45NokUxM7WAv/jhBX7pndvoXlDVYqLkUfND+ltSSXKAuc8uBNK0kuK+SzB0jU/v7+fT+/uBSwV+U6a26v28nnv93sFW7h1svez106dPzz881mo1xsbGGmuXAbquL5qGzWQyOI5zXayujRDWlVDep4or5lYNdG/Wz3SjBNsLIr52ZJTXLpbRNcF77uxgtOQzXnIba0o6n9g3QGt6eatgoWX42kiJ//SD86RNHSklb46X+ZV37yRt6hwbKwOwtz/HhUKdH56eoTVlUai6XJwd5eMP9JN1jEXTqUU3IIhiTk14gCSKYt64WOZDd3fT0+IQRJLn3iow2JbmxHiFSCa5TiFJbffy2SK/8JhMVh0b1/mtiQqWrhFJOe/0E0SSlKnxnt1d/NWL5xkt+UxUfL53eoaPP9DHHT05WBIGL4Vo5Fm9NDUrGuWlJHD4/CwdGZNqJMjYBkVPcnqqSnfORkrJHz9/lm8enUATgrxj8ts/uYe+1lSSn1TESRzlKqxXDGFj7nHbtuno6KCjo2PR62EYUqvVqNVqlMtlxsfHqdfri9YuAXzfJwiCdVuXy30GJYobhxJFxbLcqkJ/JXznzUleGykx0OoQxpK/fnmE7pzNvQNJTcHJsseLZ2f4wJIcpa4fUHV9gvDSWtv3T02TtnRanGTgm6x4fPf4JIV6QNw4zYeHi1i6RlfWxjKSAPLRUp2xsssuJ7vIHss7BtMVHyEgbeuU6knofj2QgIapgxBJJfq0pS8y1oIopsUxkEs8Ty1DT1KaWUn8ox/F5J1E3E5PVRgv+/S3JNPpNT/imRNTDVG8JIYIbbFEigUPMcmfWLZF2fUTa08IYpkU+kUIfnSmwDden6DqJ+dlph7yO98+wx98Zl/SxjKCOCcKVyKGNwLDMMjn8+Tz+UWvSylxXZdarcbU1BTVapXXXnuNMAznrcu5adh0On3drMulrHTO4vjanIluNpQoKhQrcHKySnvGZKLsMVbyGC7UkbGcF8WUpVNxL2VQ8fyQ2ZpHsVK/rC1dSx4ykiK3ycA4POuStXW6s8m04VTVY6Ls0Za252ckpUxiCZfS1+JwV2+Ww8OzBDE4pslAh06pEUvnhRFCJFbhw1tb+OpBjULVJ45jLF3jC49uuazN9+/t4qmj45TcEFNPROZTDw0xV05qYeIfQxe4QZz0sxEPudawKWVilb53dyd/+aMLlGoR5Tikvy3Nnr4WQHCh6FHyIkBDExAjODoyS831kkTljTYWxqrMeYJ2ZMxlB+8wljx5dILhgsvunizvuqN9frtrtbKuZn8hxHzc5Jzz21y4zELrslQqMTo6iuu689blQrGcW7u8EeuSShQVtz3N/CW4UVZsR8bi0PkC07WAtKlh6BpjZY+xWZeunE2hFnDfQAteEFKte5SrdaJo+X49cWcn/89zZ5iJ5bxltL0jw3TVm99GE4ItbSkKdZ+qJwhlUvFioHV5Z6cP39OLRJCydBxTo1AN0YRgvOyia4L37+ki75i4MuSf3Zuimu6nHkbs6ckx1H55svCJsk8sJX4cI9H45XdtY9+WZB1tqC2FqWsUawG2qTFdCXjnnZ2IdUyrJTULL03T7urK8POPb+P5w2/R09XO23b1zq/NzsVe6ro2//BgaIKLxRo7uzLIufCSGISmMVEJ+ZOXpqmF8KF7BZ94sG/RvRtLyf/81WO8fH6WKE7a+tgDvfx3795+WT+bgfVYl3Nrl9VqlTAMCcMQx3Hwff+qrcu1vk/NPB5sNEoUFStyu0+ffvDuHr53aho3iIliuLM7S0vK4EKhTqEWcFd3mv6cTrFSWyMcAnZ1Z/mVJ3byyrkiuiZ4ZEcbQSj521dGmK0HQLKG+ZP392FoguFCHcfQuKMnh31ZZHnioLW1M8uH7hG8OVZG0wTvvrOb7pxN1Q9xDB1TF0kOUxlj6YJ9d3Rc3rEGpXrI//nNE2iaYKDFoR7E/M3LwzxxZyeWqdOatvjMw4M8e2KKWhDzxJ42DmxfPSRmoRguPT39LQ4P9tn09ubmBVEIePvODrLOWSpegKZpGELQmTXnxXJuu7RjUazH/PpX3qTi+uia4NR3z1HxIr7w6KU40ePjVQ5eKGEbyZRqLCVfOTzG5w4M0pLaGCvrWljv8Rdal0vXLk+fPj0fyzs7OztvXQLLxl2utHZ5OwnfaihR3GQ2+0u5GreqKI7Ouvzx82c5P1NjS3uaX3jHNvpaLrfGOrIW/+T+Pl4dnqUzZ9OWtjg/U+XUeInzfsDr56d57cIMv/CO7YkALUvigilI6g1uW+Jx+ol9A7xyrgDA/q29DLUlFtxy/ZkTw4VTh9s7M5cFvudsA4FENpJ6r4mUjM7WiYFUQ4BTpkbNj5mq+omXKZK+1jSfObAFEFwo1PnbV4bxQ8n9gy3cP5hfPCW5ghhefnaSvDtSJGeqJW3yz9++lb89ODJvUb9vTze9LQ4CQdo2SVkGuq7xD6+PUA8iHFNrtCH48uGxRaLoBhHJcqVonMHEIvfCmCCK8cI1wmPWPHWb//3VNI10Ok139+IkCQuty2q1yujoKLVajTAMMQxjkVBalrVC67cfShQ3kblpwP+fvfcOk+wqz31/a8fK1TlPzkozo5FmhIQECgTBJQmBMRiwwThd2/j4OlwffO6x/Tgc23CcOI8j+HLhGGyMABsHSURllEbSZE3o6emcu3LVDmvdP3ZVdaru6dH0TLeg3kel6aratfbau2qvd3/p/db6oqqF9TinuXilhO14kk8+fJps0aclZjE4U+CTD5/mD955bU3ty0NbmhhJlyi4PgPTWY70T5PKOXQkg0zJkyNZHj09wV27W2vsDSqZmWrBSwKBUpJNzWE2Nc+6Mk8MZzgylCZs6ty+vTnIbBWiKvu2HJYio4ueKyFojlpIqfDKLka3nP2TDJlBJqmYPZaRVIkvPtOPXbZG//3oCAB7NyQQcmXd7ivz0oSGWnBY79jfya7OOEMzRdoSFtd1JYjYJtGQhfR9ZDnZRl/Y3USpRXHNHW1RwqZOuuBhGBquJ9nSEuYrh4f558Mj+FJyQ5vJH2+RNSzyK48rmTm6nHXpui6FQoFcLkcqlSKbzZLJZHjmmWfmWZczMzM/VJmnMCtrUUcd87CeSfFy5jaaKZEquDTFTDRN0ByzmCm4jGdLNbdviJjctbOZnc02+7tiJCydeKji7hOYhsZIqvLZStHERaAqC9n843iub4a/e7yPw/0zPHJ6gj//9hnOTeb50jOD/N0TfTx5bgopF48fJLCU2z2VF8jxTIlnz0/zQv8M2VKQBetJVRXnXoiWuM37b+6h5EoKjsRxJT99+2ZiEatMxg42KXQAACAASURBVLNzPTuRDc5N2CBq6STDBi8MTIOUK6JDUVXVqVHEWMbujhh37W7h5s3NtCajJCIh9AUxsjt3NhO1DYquouQFEnY/dnC2YfJ03mUs4/DH79rDNZ1xLF3QFDERwBeeGcQ2BBFT8MJIib9+rO+i866FtUjUqYVLHcM0TRKJBJ2dnWzbto09e/aQTCa56aab2LZtG8lkEtd1+cd//EcGBwe58cYb+dSnPnXZ83w1oG4priHWe9nDep7bpSBb9HhpMEXJk3QkQkhF1SLyfIlS1CzCd1yPLz/Tz4MnhtEQdDeE6GkKc2G6gAAKriRVdNnQFLg6fSl5Yczl2DMDdDeEuXVb02JrZhk8fGKcZNjA8yUlX9E77fBn3zpLZ0MYy9D5j2MjOJ7PHTtaEAKUD0IstsyGZgp87/QkYVPDk3BqyOX8UJ7/9tj3UQpu3JDkV96wg4g9//J/295O9m9sYCwbuEw7k4uTcZ67MMP/+2QfE1mHazsTbG8N43k+VujiS4koq/Ss5FcVskxiIQtjQRsnT5al1oSgLW7zdx+4nk8/fIK8J3jL3m7etCew2B94YZj/9UgfuhCYuuATb9rOn33vPOmCx6nRHDnHx9Q1oqbA1ASH+9MrmNX6xGpmnwohqlYiwG/91m/xrW99i+effx7fX6Gc3zLo7+/nQx/6EKOjowgh+Kmf+ik+/vGPX/a4q4k6Ka4h1jsp/iAgW/T48++cZSLroAnQNMEtWxp58twUlCWx37Wvk8bIbEzF9XyyxRIvXpjhP44N0xqz0QUMTBeI2gZtCYtnz6cARUvMZiLr4EvF/35miGcuOLQ0pnn6/Ax9U3k+cHADiEq/igD5kk+q6GIbGk3R2f1KFOmSz3Tew9AgXXDIlwS7OhIYusDSbZ7pm+GOnS0Eyt7zyVCU43InR7LEbJ1YyEQpePL0NI9fcEhEbTQBhwfSfOaJPn7hzm2zH1ZBNueG5igbmmPzzmHJ9RnLOoGr+aEzSCXJlzyePDtBOhenqzHEa7cvncRT+Z2viAxNg1jYXkSGjif5y0d6+e6pcTQB9+/v5L59HfQ0hPnZg82EQiHa24OYWu9knv/1SB9GWYWo4Ep+8xunsA2NxoiFJxV5xydT9IiYJp5UtMdfWUxtvViKl4vl5lFZo1ZDFNwwDD71qU9x4403kslkOHDgAG94wxu45pprLnvs1UKdFOuoifVM2Jcyt8MDKSayTjWBZSbv4niS//vNuxjPlGiL2+xsD0igQoYlJ6j1G82UECLotwdBEsjgdAHb0LhzdzMhQ8fSBcdHsmxpTXNiNEdjSNAYtZBKcbg/xVuv65gn0zaSKvK905PlcjvFdV1xbuhJojSN27e38leP9BKydFw/UMxRCjIlj8aIie8rDEHZTRlUOGSKHiEzaJhcPSXl+F/lHI1mHfyydQVBm6Sjg2XLSClEuc6wVszy/GSeTz58mqLrM5Iu4Xs+zTELWzeYzDmcGM2QKrk8fGKMD96ysWbCkQq+tMWvz1mIQ6ZBNGRhGrXjV196doDvnBqnKWoifckXnxuiK2nzmq1Nixb0/ukimhBVKz1kakzn3Wrz4KaISarg4voKx5NELY1fWKclGivBlSTW1V4DOjs76ezsBCAej7Nnzx4GBwfrpFhHgB8U4lnPKLl+mQwCmIag6El2tcfYVSZDz/fJFhyKjjvvs00REymDWjdNCLIFj03NUSbzDi22UV3oNRFYMrN7CRI+hAisv+qrCp7snSJu64QsHakUR0eybGiO0RjVuX1HM0/1TjGcKhKxDXa0RXny7CTjmSKeJ3E8j/v3BzGzmbzLf//GSXrHcwC8/9AG3ndTNwjY3Rbje6cn8ZXCl4pEyMTQRZDpKQS+DGKpQghUJYmmku0yJ9SplOIvvnOWkuuTsA2mtSITrk/Cl/hS4XqKmG3QFrd5/OwUiZDJ/WXdUWBF3e4tQ6cpHsFaggwreL5/hphtBLqselCqcXQoy2u2Li4L6WkIunn4IkjGKXmSRMggZhukCi6GrtEctbh9WyP72k26bHfJWtCLYT1YiqtxnS43jyul0Xz+/HkOHz7MoUOHVn3sy0GdFNcQ65141uvcPKl4tj9Dqs+hOWpx67YmYnbtn/LujjgPHR8jVQgshYmsw7v2BQv3UmRYwXWdCQ5tbuLZC1NoQpAMm7znQBcPnxjn7ESOlqhJzvGxDI2dbTHa4iYnBiR6zqXkS3a0RmkKz7rlfKUoOpJQVEehoWmgCb9aFiCE4O17O/ne6Qmilo7rS27d2kR30sYHtrVE2dYalF/8z2+e4dx4DtvQkAq++PQAO9qiHNjYQFdjmNfvaqZvsoChC27u6eH3/i3LpAMoScTW+dnXbZ8vySbm/KsCjnQ8yXi6SHPUBCVpT4SYyrtkSl4QiwW6G8NBFwpL56WhNPffNJvoshwsQycRsWiIhZYkxDNjWU6PZYmHTJojFv1TRUJlcXBPKVpitevttrZE+OitG/jME/0oBaau8T/esYumiMXnnw7qQu/Y3sRbrmtjanKSdPrVG0+sYL132liIbDbLu9/9bv70T/90kVDBWqNOinW86vDkhTznM9DVrDE4U6B/usAHD22oWVLR0xjmY7dv5t+PjlLyJO/c18VrtjQyky0sSYYVaBq850Anr9vZjOtL2uKBJunbbujgoeOjHB/JMJN32dQY4d+PDJMMm5ga9E3l2N4W4203dDBXoe3cRI6HTo5TcCWNEZPbtjdjGxrx8OxluLsjjq1r9E7mCJuC6zoT1RZQc3FyNItZVn3RBRSl5OWRLAfKCjSdydlEmd7RGbpigiYjyq72OPfd2EUyvFwMLRDyNrWgVjNf8ojaBqYWJBvt60kyki4xNFMkGTaCejhf0hy9eFzOMnSiIQvbNBjUl9YoffzMJH/x3XPVbNtNzRGils5UzkEp2NwU5k3l5sW1FvT339zN3btamMoHVmCifA4/8ebtF53jpWC9WIrrIS65Uriuy7vf/W4+8IEPcN999631dBahTopriPVsKa7XuTme5PSUw4amKPGQQTxkMJQqMpYp0dO4OFsSYEdbjI/fFcOXkmyhxFQmt+w+KoUVlU4R7Ql73vsRS+eNe9oZzzo0RWwMDR49O0V7zMSREAkZXJgq8Nkn+/jp27fQFrdJlyR/9cgFmqM2OccnVXD49qkJfu/te4ha5ctQAUg2N4fY3Ly8O681ZtM3lS/HHRWmJmhbME8ISjN+58FzZHKScMThwvQ4G5oi3LOnbfGgan7XDCHgF+/cyqcePkOqLFz+gZs38Obr2im4kj968BSD0wXQNCKmzntu7KrWCioBQomqV9bUAzIMWcac3S39+/rME30kQgah8vFdmMrzc6/fiqYkmoDru+LlZsFLoz1hL/ru6qiNq0WsSik++tGPsmfPHn75l3/5iu/vlaBOiquAV/pjWq/Es54hREBac8v1KjG/peBLSa7oUii5wWJdLg1YSpvtYt+IAEYzRYquT3siTLbkEg8ZnB7PYyhFY8QkV5JkSz4PHB7izt3tSKXwlSIZNjEMDcsQpAoew6kivlR0JSwmcw4ALVGbGhrg1X0r4L/cs43/+rXjeH6gpXpdd4LX72zB9RUPHhvl3ESO7oYICEHeUUTtoOtGyRP8y0sj80lxARnOxebmCH/87usYy5SwDcF41uHIYJqtLVF+4827ODGSwZOK7a1RkmFz9vypoFDE0DSiIZuwvfK2SEoF2aEtscDyrHTA0IXg0ObGVe3asNaW3nqxFK8WKT7++ON8/vOf5/rrr2ffvn0A/P7v/z5vectbrvi+V4o6KdZRE+uVsE1dY29HiCMTDr5eouBKtrREaloEFTIsOl41A1LM+xdAoaQi6MdQtmwuctiKgJRPjeZ4ri9VLpcIYnuaEJRciRLQP10iXfCZKfo0Rix8qZjMOQzMFHA8ScGVfP6JPra3RzkzlquS4u7OOP/XPdtrKqxUpra9NcrffGAfL49lCZs613YmEAL+8pFzPNeXwjY1XhzKYumLv8d5z5Ra1EJqIUKmRkvU4s+/czbIyEWQDBv8wp1b2duTrPkZQ9eIhm3C1qX3CBRCcHBzI0+dm6I5ZlFwfAxdY0dbrOb2rzb34Wrj1XT8r33ta9flujIXdVJcQ6xX4lnvONAdoatZxzGiNIRNru9OziuSX0SGS6Aqi1ZOuaxYOPMgAAmI+Qo0T5+frna4KHpQ8iS72yKcHXOIEtTGCQG7OuI0RS2GZgrs35jgqy+MoAOeJ+mMG0wXXM5P5nl5PEtXMoSlaxwbSvPVw8O87+bFSSsFVzKUKtIYNmiMWGxvjVJ0fXylyBV8Dg9kaInb1d/WeNbB0AU5V4Hj43qK9x5oK5PhCkvpBXzv9ASj5RIWgImsw4PHxvjRgz3zNtU1jVjYImyvrO5vqcX8Z+7YgqlrHO6foTlm8bHXbqY9YeO6y8eBLxVrbemtF0JbL/NYD6iT4hpiPZPier5AhBDsaAnT0TE/LialIlt0FpFhyfWZKXjEbJ2obVySYPVsiYJAzVHCeXEgxc62KCVX4fg+eTdo5TQ+7HLBj/HiQIprO+PV4nxNE9zQleCl/hmips7QTJF4yCDr+EzmHCptgIUQWIbGmfHMItW402NZfuWfj+EpietJbtrUiK8kQmg0RCzec2P3ItE0Uxf8xMENfPtoP3Ysxm3bGrlje/NFrUOYU6mhAsk005gdPWQKpvJO9fmlkiEsH1OMWDq/cOfWFY/1w4xXk/v01YA6KdaxJNYrYS+ElIpcyaV/Ksc3T4wxkQ06xN+9p5VM0ePvHjtPzpGgFPft6+TQloaLk+ECQprKOXz35QmyjkfcNtA1gSchETFBGYxmHIRQPHSuQG+mhEIxnC7wpj3thEwdDdjUFGJne4zBqQINUZPJnIOpazSELcYyDoYWiIQ7nmRzc3SREfeJr58gW3LRNYEvJU+cneK2Hc00x0JMZh2+eXKcG3qSvNCfwjYFjqfY3BRh/4Y4LTLMtdesLPOyQoZzDcld7VG+f34azw7iedmSZE9HHF3TiIYswnbtBr+rjVq/yVfL77QW1kudItS+EX41n9tXijopriHWu6W4XudWQYUMCyWXouvz9ReGkErRGrcYzZT4t5dGODuew/UlLVED15d85YUBtrSEq27ARZibegoUXcmx4TSPnJ6kI27RHg+RKbq0RCxOjWWYzgX1j5uaIzx/IcXpaYltBVal4/o8dnaCe6/t4P4bu2hPhPmxQxv5x2cHOD2apTFisaU5QkciRMgQDMwUESKoR7xvf9e8abm+YjJbQhMVaQAdoUHWkTQDibDBcKrIf33zTv796BjnJrN0J8O87YZ28EpLJhUteegLNr9xYyMTuUC5Rim4a1cLb93bTSJiL7uoS6k4OZoh7/hsbIosfd4vE6+UWNba/amUuqRmwEvhShGrlHJV5vdqQp0U1xCvBuJZj1BAtuCgpfPVtXum4FFwJa1xm6GZIhem8uQdD5SqFrxbuoYGTOaceYvzkaEUDx0bQ9M0btvayM2bmxAi0E39o4dOM5wqMp0PMkz/jxs6cKXizESOzmSIvOOzv6eBd+3v5Fe+chSlQCqB5ysUQfLN2/d28eiZSR49O8Wejjgfe+3WqqqaUgolJe+9uZv+qQIQdLlfKCRu6oKGiMVMwUMXGmgSpCBcLnxPFVy2t0axTZ137e+cl1Gad4MM3W+dHOfIUJpEyMD1JKOZEhubIty/vzPQSV3mnAsBb7qmjTdf207EtoiFrYsuxL5U/M2jvTx3IYUmwNAFv3jnNnZ3xIPvse6yWxVcSfep7/s/dK2j6qRYR02sx8VKSkXecZnOOdi2IE6Q8DGWKSKVQkpF/1Se5y7MYGpQdLyyWowibBmEdIFU0DRH/PvBY6N8+ru9iLIE2qnRDFLBLVubeOjEGKOZEs0xi6InyTsez/dN40iFpWtsb40F5RnZEjnHp6nc+zBIsgn2VXAkn33iPBHTwDY1vnliDE9KbtnaRFkAFSEEOixqQFxxY0qp0DWd333ntfzaA8dQClwPbtzagFJBLWJrzOLd+7uWLK/4Zm+R58b7MDXBULqELxVtcZveiTz9UwX+6707l+3oIYQgGrKI2OaKLYdjw2meu5CiMxlYk5mix+eevMAfvOvalX3hK8Tl3FiuB0txPVxrdVKcRZ0U1xDr3VJcam6ZosdQqkjI0NjYFK4ueEcGUxQ9yY62WFWAuxbSBZfnL8xQ9CS7O+KLyKDWPAI3qYesZk3CIy+P82ffOUfR9QmbOgc2NXBqOI30fQoeREydnOPTN5UnZOhIBW+7oYP2hI0A0kWPL3y/H02DiGUgJQzPFHn8zBS3bG1iMhvE/HQtaMI7nC4GsT9dcH13MtCEJNA+zZZ83ntjF0+dn8H3CBRhdEEsZDAwVeBQWaNTFxaHL8xwy+aLxzUrbkxNBLHGPe0xvvSRA/RNFWiIWHQ3BK7cogeNER29vN1CSKV4atChJRkl7/jknaAF0OBMAVMXCAEj6SLdDYu/MyFEtcHvSslQlb+jdN5BKYmUEl9KhPQZS3nMzMwgpaRUKjE5OUkmk0FKueRDKVX92/M8wuEw8XiceDxe7Ri/HohlrXAliVVKWSfFOq4e1vOFvBRhD84U+NvH+nA8iVSKfT1J3nJ9O198eoCc42HoGs/2TXPfvi6216gryxY9/uaxoK+dqQseOzPJjx3sYXfnYv1DpRT5kke+5CLnzEUIQd7x+ZNv96IUxGyDouvz1NkJru2KM5wqUSqXKRRdSWvcYl9PEkPXGEkX8X2JrmukCm7ZjVkW9tYCInJVIO59TWeCZ/pm8KUibGrELIO7d7eSKricGc8xU3DLFleYhohFZ8Jic1IjJ21CpkYiZASdJSpzVwrP91EKvvHSCAVXctPmBrZc5KZgzhkhautc0xkrDyfLGbUCKSWu7yNl0Jle+rJKSNlcHqUUrucykp7fTNnxFGPpIsNDQzjTWkBCUiFV0IneMjSYQ0pziWo5CCFIlSCbKXF0OsVARuEpRXNY58iZC7RETRzHIZvNYts2mqZVH6Zpzns+9+G6Lvl8nunpaS5cuIDrunieR7FYxHVd4vE44XD4ql1b68FSvJKC4L7v12OKdVxdrGdLsRYeeH4IQwhakiGUUjzfP0PU1kkXvarMWrbk8cS5qZqkeLKsF1qxJDNFj2+fmphHikuR4VwMZxxKriRuawglCZuCTFFxXUeC02MjhA1ByVeYusA2dCiLVmdLHpLAXdkUtUiGLbKlAkU3sGaUEtx7bTsKuHVbE+PZEg8eHwPgnt2t3H9jFw8eG+GJs5OYhoGuaTi+x8tDU2xrsjjUrvPdkYBUp3NFTE3RHYHTg+MYQlB0fU5NFMm7gRLN554U/PSNSXY0GbOEUyalixKQEAihVbsYLCSQymu+53FtExyddnC8WUuyMmzM1tnYEkfXdTRNIxKyiIVtTMNYkpxW2jlBax7hv33jJJquaAmZhEydB3oFf/KePRw+fJjNmzdXG9quBI7jEI/H6ejoKB+D4tSpU4RCIQqFAuPj4+TzeXRdJxaLEYvFiMfjRKPRmhbPenFfXi6uZEyxTop1XDWsR/fpTN7lbx7t5djgNJ0R+PVuZ14D3qm8S7IsrlyR38qX/HldgjQhqkLOC+EvuPiC0oZg24AMXXLFoP8fUpVjhfPdaMVSEUsqTCEpOBJbD8ZQUrK7UbKzySRT8tCEwBYahUKRyWmfMQlbGg3Onj5VHfPNPT5fybmkigpDCO7dZuKOnObZkWB+PcBP7FDl4xrhxRdGOdrvsyEKlu5h6IKsC0d6h4k6NruTPvF4jMMjRWxd555tMdpjFmenHIo+nJsqcnikWBYvF7hS8i9nSvzJuzYHZKMtIDehlclvzknUtHlNi5eCQiGUIpfLcZ/nsV8189XDw/RN5gOrWIEuBB+8ZTNtbW2EbZNY2EZfxUXQMnW6kiEaI2b1O355NLvk7+NSIYRA13WSySSNjY3V1z3PI5fLkclkGBoaIpvNIqUkEokQj8erZHm5WC+W4pUi9npMsY6rivV0hypl0K/vN756jN6JPKYmGZjy+NUvv8Qfvm0bmgi26Ywojo/M0BYzcDxJLu/RqhucymR5OZ0KOsaXPG7tDnH8eG6R2y1b8JieLDA9ETTMzbqKWzo0vv29IYquXOQmDchBBOSgBSRcyOcxDIPXb7L5Vm+etBcQ8b074rRGdO7YGuPZgTwhQ6MxIpkp+DTHLba1RHjd9kZCloEmAgLaqWm8/gDkXEkybGEZes02gAJQZTIatkc4OpimNWEFRJ0qsXtbG9t7Yhw/foz37L2W9yxQi9m+Jfj3s4/3oUijaTqg0NEoeIpYvLaE2bw5CMGSoqhzURUfrXRzFGia4O3XdvKW6zr49HfO8dCJMTQheM+NXdx3YzfxiI1xBRa/5mj5HKlAnzbv+DRFTbRlknpWA4ZhkEwmSSZnZeiklBQKBTKZTNX9WrEqHcepWpaRSGRdXZtXA/VEm1nUSXENULF4lFKUSiUKhcKyiQYrecxNRljqsRyEEIwX4OXhAlFDoHxFSJOcn8jw3Kk+epJBosWhdkEqKzg/U8Q2NN66K8nOtihtiTAvDudxJNzZGmFXe3RJ19uOXQ6Pnp2m6Prsbo+yozWybBmdAkQlqxIYGBjAMAw+vreDt4xlGEuX6GwIs60lCkKwoQe29WQ5P5nnyGCaeExxXWeCe3a3zXLKnOJ8C4gukRcUZIBqs3qpwB07mrkwlWc0VUSpoITiwIYkmvIDy38ZtZibNzfy1ReGcf2g2wMqcNNe7Lu5FDJcTq3G0AS/dPc2Pn7XNkKWMY8Mp/MOX3l+iImsw8HNjdy5q+WyyWFvT4J79rTyrZMTaCLwDPzaG3de1pivFJqmEY1GiUaj1dcGBwdxXZdYLEY2m63pfq08roT7db1YikuNUU+0qeMVYXh4mAsXLqyYgCC4QCt3rQsTDZZ76Lo+LxGhVixpuTjQsseRKhI6fpho2MD3PBzHBWGyZ/euedmke68NWjgFmYvBmD3AdStsVbfBsrgvHiJbdJBSLUmIgftv9u+FEAJ2tsfZ2R6fc2zBv5tbovz1Y32cHs0CikfPTHF6LMfPvX5LZcBlEXTSEPPIsIJk2OQnb9vEUKqIALobbQwhKJR8UkVJ3vGJWLUXkhu6E/zy3dv4zBN9FD3FHTua+NhrtywxB60qMbcsylbpkq74Ba/blkEsZGPOae6bLXr84j8eYSxdRNME33l5nPFMiR+5uWfhaJcEIYLaxDdf20G64LKlJUJLzC5Pa3XcfpczjhACwzBoaWmhpaWl+vpc9+vw8PAVc7+uFq6k+7QeU6zjktHW1kYymbwkAgI4ceIEHR0d82Iha4mOhM1t25p45PQkSvq4vuTOPU30NCzu7Veroe9KUCi5VTJcClUCVLXJMFX0UQLapAriXzWI4+XRHL0TOSK2jkAgleLhk+N8+DWbiIV0lKoYi7PjKwVHh9I8fyGNaWi8dkczm5pqJ4HYhsbm5nCVcEbSBf78W2cZnszz5d5j/MiBbm7f0QyA6ymG0gXitkFT1OLO3a3cubt1yeO/FDJUlYlfhOWFENimUU6gWUzY3z8/xZmxLDkniA+3xGz+9zMDvPem7stecIUQ7Gq/uHt4LbAUoa7E/drf308qleL48eMkEolX5H59NWSf1i3FOi4ZhvHKT+N6SrQRQvBrb9zBvp4kR/snaNBLfOSeHatyF1ooueRKLr6/tBU9lwxrLfJKwZeeGeChYyNoQmPLqTy/eNe2ah+/uQjckyJoB6UgX/IouIrPPNnHO27orNZGCkS52B1eGpjhy4eHidkGvgoEuH/qji10JUOLJjLXRelLxR89eJrxTAlLCGK2zpeeGWBrS7CPX//acXIlHykV993YyYdv2Vjz+EVF5mYBGZ4Zy/KVw0PkHI+bNzXxluvagv6RKyBDANPUSURsGuNLZ3l+++Q4M4XZDhRD0wU6Fh73OsbViAHWcr8ePnyYHTt2UCqVyGQyV939eqXHqLtP67iqWI/Zp4au8ZbrOzjUZTI+Po6hX57rpOh4ZIvORcmwYrkttcgrBf/8/CBfe2mYuKmRtA0GUkW++MwgP3PH5kXbb2+LEguZTOVKeL6i5Ek64iFCusYDh4f48Gs2BkkgzFqMz/RNE7d1YiEdFIxnPI4OzNCZaEMTWrkoXeL6ium8g6VrJMMmDx4b4+xEnqilMV2U6DkHS9cZzZT4u8f7SOVdQqaOFPDA4WH29iTZN6cPoRAaj52d5HB/ipaYxdtv6CRqB5fmUKrIn33nLLoQmJrgX18awvd83rGv46Ln3jJ0omEbt6QxdZHv8fhIZsF3AsmwcUXJZjXdp5fz2ct1D9q2TSwWo7m5ufra1XS/XklLsU6KdVxVrOcMt8sl7JWSYfCHumiBwVdfGOafDw8zU/BIFyHvKboaLPqmcjXGhahl8D/eeQ1/+9h5numbZkNjlIObGzENjUzJYzRdpDlqzrogCZJAql5dARKFoQUDSnxAMJlz+ML3+0kVfECyf0OS3ok8jWGDkudj6YJ00SMZFjRHbQamClVXc1CqIumfKrCvJxm4SYEvPTvIF57ux/MDebhvnRzn0z+yl5Clc3I4jeNK2uIWoNCEwVO9k8uSomnoxMI2thlc3m5pyU2rSIZMLENDSoVUgcG6f2PtBsJ1XBwrdb9mMhkmJydJpVKXlf1ajymuHuqkuMZYb5bi5eJSyfBiUECq4PHtlydojVtkHR+DoMwiYrvcuLFh3rhiTgVfW9zmE/fu4jOP9+H6CtPQqgLcdvnvuXjdzlY+92Qfrh8U8odNnb0bGubF977+4gh5R9KesPGl4sneaaKmzmu2NvG90+MUfQXK5579XWxqDtPZEGKonKkblCVAZzJcjhsGNx5feLofAYRMHaUUo5kSz/RNc/uOFixdK2ezBnN1pSJu175sTUMnGrIIvYJuYeXyIAAAIABJREFU979w1zZ+7h9epOj56EDUNviJ12y65HHqWBq13K9nzpwhFothmuYrcr9CXRB8tVEnxTXEenSfVnCpcxtLFzg1nML3FRubwlX330LUEquuPYHgfxoCxw9q3JJhk8HpAqlCQLiJkMn7buqeQ4a1F4Y7d7fwhe9fYDilCJlaoLdaI4Fma0uEn7xtM0eG0pi64MDGxmqT4ApG0iUayjFMXYClaSTCJq5U3L69mdP9Y9y0o4f7b+wG4DfetJOPf/lFJnMlJII9HQmu705QKYb0pcKXCksPngshQCpKro+Skv09CR6M24xlSggEGvDhQxvmzcnQNaJhm/ArIMMKDmxs4HM/fiP/cXQU09B4597OqkLResflkMJ6KKmwLIumpqZX5H6taL9eLuqKNrOok+IaYj2T4kpRdD1e7JviS88OoGuQCFlEbZ133NBBPDS7SK+UDBWUhbZnL9CmqElb3OL5CzO4EiwjeLc9bpMMm8uqu6QKLl94sp/+6TyuH7SRevM17Sx1nW9oirBhGS3SrmSIwekCLXELT0oQinuvbWM84zCSKpDoMHjPTd1VAYCS59MSs+lMRLAtjXzJ55+eG+RDt2xEEdQNvmZLI0+dn0ZD4UlF2NDY25MAFBFb59fftJOneqcoOD7XdCWqCTyGXmnwuzoL4+6OeLWt09XCeg4hrCVW6n51HIdisUhvb281A3Y1xQd837+sRMJXI364jraOFeNiF1XJ9cgVHZ46O8nv/ccpHF+ha4K93Qk6G8I8dGKcazrjbGkOB9qjF82SFCBAq0Fwpqbxozf38MJAGktJ4qZOd8JiJF1kJBVok/7rkRGEgHft6+SDBzeU6wsVXzs8yFAqT3si0Grtny7w2NlJ7plTEhEU6Ff+Wh7v3NvB57/fz1imhFJw9642trXG2N4WLCBHjoxjlq0+BJyZyCOETmPZ4jQ0jZeG0tX9AvzqG3fw14/28lzfNM1Rm5+/cystsVmii9o6d8+Zr65pxMKrR4ZrhdW6IVzLG8uraWnWcr8qpXj22WdJJpPk8/lX5H5dbh71RJs6rirWu6VYa24VMnQ8H9+XfPq759AE2EYgwXa4P8W5iUBi7alzE7TFbH7mdVuILeFOrZDhxbQ8G8Mm3ckQrXGLQqEQ6KRKePjkGF97cQhdC0ov/um5QVoiBvde14ECBlNFItasVqupawynipU9owRcPM2nckKgIWLys6/bQqrgYusasVCN4yoTrECQKLtaKy7eousvatEUMnV+6a7aygeqPDtFoFMaWIZm3cJagLVyn641KsLsLS0t89ycl+p+XSoLV0r5qj4/rwR1UlxDrGdSXDi3uWRYQc6RlDxJPGQwXfAQECRqOIKd7Q1EbYPRdInvnp7ibde3zyt/EEqgVkCGlLdvilrs35DkuQszKN/H9RU3bW3i+HBQSqCVLUNX+jzZO8291wXZmVtbovRN5gmbWtAWypdsagoHSS4rPRkL6iaNcn/FpTYNIn8BDm1u5NEzk/RN5hEELs/3H+yZs23QX/KLzwwyli1y08ZG7r22rboQCQRCE8Rsk8hlkOF6XdjW67xWivUQk6w1xqW4Xy0r0KcNh8Mkk8l57td6ok0ddSyA4/nkCg4lz1v0XtzWaYpazBRckrZBuuQBgp6GUDXRxjY1pvMO8+ivomOqBEqUpdyEqFpTUI5BCoFQ5U8K+OAtG9jWGuVY3yjtMYO3HdzIJx86jS8lpkY5eUWjaU5Xj7fv7WQoVeT0aBaF4tZtTbx2+6yc11IQXIJbruJ6rVF4bxs6v/qGHRwbzuB4km0tEZrLrlEBFFyfn/mHF5jIOvhS8fjZKS5M5fmZO7YgNEH0MslwPWM93BC+2i3FS8FS7lfHcejt7cV1XXp7e6vu13A4zNe//nWmp6fJ5/OX1OLr1Yw6Ka4h1rOl6PmSdMFhKpOv+f54usR/HBulI2EzlXNQInAVvv36To6OpPFlUHiRdzx2tMaoFmLMO15V+a/6ukQGOadCVN+rLFmGpnHHjha2Rx2kkphC8MGDPRzun6Hg+IAiHtL5wMFZvc6wqfFLd29lJu+ja4JEePFP/shgmk8+fJqZvMuejhi/8eadNVVylIJHzkzw0kAKS9c4MpRhYLpAWzLEr79xB5ubaivAWLrGvp4EisUx06d6pwIrWwgMPWi59ZXDQ/zSPTtJRKwfmgX7crCWxLYeLEV45Ra3EALbtolEIliWVe1T6XkeU1NTeJ7HqVOnuPvuu/nsZz/Lnj17Lnuu6x11UlxDrEdSdD2fbMFhOlvEcf2a22SKHn/16HlcXxIyNboaQuzf0MB7DnRjCMG/HR3lsbOTCOCuna0c3NywsuOck3U6T5O08vac1hZKShSK7sYwf/mje3n6/DQCOLSliYaIuWBYjcZo7XTTkVSRT3ztOCUvIM3DAyn++7+e5E/fe/2ibf/h6X6+/PwQvlJkih5CaLTETcbTJX7zX07wl+9b/JlZi7e2o9j152flCk1DohEJrZ51uN5+Y+sJP0yW4qXAMAza2tp461vfimEY/MVf/MWqjPuRj3yEb3zjG7S1tXH06NFVGXO1USfFOoCADHNFh6IbuEmXImyhBOcn8+Qdn/ZE0O0gZOgcH8pg3hwE/d92Qwdvua4dhUJfyXqzkkVpnjj47LxOjKQZninR3RDmuq7E7FArXOiOD6eRSmGWZdCEgBMjmaDYf87kp3MuX3p+AFszKLkSqQLt0VxJkgwbeL7i/FRh7nQJnKli2cTbmzY1YOkaeU+C0DF1jbt2tFTn84OOHwRCWg+W4uXiahXv//iP/zg///M/z4c+9KFVG3O1USfFNcR6sBQXkuFSCGy0ILZnaNq8dd6TCn3OIq5UuVfgRSBWkvkZNFOsMR/BA4eHeODwUJWA3nxtGx84uDFoYFtxy5aHz5d8ToxkMDTBno44thko2lSyYivxTKWCRBqjvEBUMlq/eXKMTFGRxZm3eBQcH9sI6C9uG6TLZ2ZFy5yA7sYoX/jYIf7owTOMZkrcurWJ/3L3tpV8+lWP1SzJWA/E8kqwXuZ+tUjxjjvu4Pz586s23pVAnRRXAZfjz18rUkwXXF64MEnR8dnSEiG+oLSgckizDsvA/dc3mad3Io+vFH2TeaKWgSsl79zXAcv19FswtuIi2Z9zybDGhqmixwOHx4mFDDQh8BX85/Fx3rCng/akPY+VJjIOv/bAUdJFF6kU3ckwf/iua4nYOgc2NrCzLcapsQyOH/SI/KnbNwc9fYXgcP8MD5+coDFq05z3mMg4CBHItUkVLCZFV/Gma1vZ1BjiyODKfgsh2yAWstA1jUTE5m8/uH9Fn6tjdbFeSGmtsdR1W88+reMHHp4vGZ3J8+nvnGE8GyhFR22Dj922mZb4gjIDFdhglSXj/GSOfz86RsTW2NUa48J0nuu7EuztSbBzBf3yBKJcFxhgJu/ydO8UBVdyXXeCba3lrLhKCcSC6zRb9Hi2b4bhiQwJM6iLFAIcXyIQ6AKyjkc79rzP/f2TfUzl3XK9oqJ/psBXXhjiQ4c2omvwh++6hu+cnmA653JNZ5zryx0sBIKBmSKIoEZwa2uUnONTdHyaYxZtcYvpnMcb9rTw0ds248vaMdi5CFkG0ZB12d1H6vjBwHoi5XrxfoA6Ka4hrrSlKKUiU/IwdQ1LF2SLDkXH5bHTE4xnHToTQbbkZM7hW6fG+JGbyvVzlRKJBRfJ0cEMsZBOImSiQqCEZFNz6KKEuJAMIZBf+7NvnSVddDF0jUfPTPKRWzdyTWcgb7YQuZLPHz98mrGMg+s6QamG0Dg3XsCVEqkgbhm0LKwfVDA4U8LQxGxRvRAMpUrVxB7L1HjjNe3z4oCV+GV73EapoGOGLgRdyRDZokt7IoRUQSeJH7tl40UXtjoZLsYrIYRa18vlap/WUdc+nYs6Ka4hriQpZkseX35ukAuTORzP4+CGJIe2NQXF4iUPa07QzzZ00sWgxlAQuDeD+Nr8uelaUDJQaW6r5PLF98u5SV/sT5MuunQkQyggW3T5z2NjXNNZW3vzuQszjGVKdCTC5IuKfMkjXwJfSZRSmJpA0+DB46O896YeykmfIAQ3dCfom8zNaxN1XVeiOrYiaESRKXnEbL2qi6pQ3LypkZcGUjzdN4MmoC1m8Ztv3kmq6GLqOrs6YkEnC2rfadumQTRk1ux2X8elofJ7XG3Lai0ttfViKS61DtUtxTp+YPAfR4c5N5aiOWLimQaP907S2RhiY1OEHW1Rnjw3SdH10YVgpuDw2u1NzK0KXHihKmBTc4gnzk1g6YKGiIkpBCFDYyLrzNPqXEnM0JNBpwulggQXXRN4y7SbcjyF40mODKUoOj4hQ+AqQUPYxDaDi7bg+pwezVWL/Sv4wKEehtMFnjk/gwDu3tPGvde2VyXUTgxn+J1/O0nekURMjU+8dVeVNDUNPvbaTbz1unYcKelKBH0KKzIDaomjtEydWMiqk2EdrxosZSmupiD4j/7oj/Ld736XiYkJenp6+O3f/m0++tGPrtr4q4E6Ka4hRLnp7GrCl5JsweHUUIqYFSzIhha4CaeyDhubIuxuj/HOvZ1869QEni+5a1cbr9naBMzV2lRVy0qgmMgU+cZLI3ieYjrnMpIqEbF1BlMlfKW4Z08bd+1qDayuix03cE1njIeOC1L5wH2aLrjcuX+x0owoy7F1NYYYTjsEmjWQKvnEwya+mr3LVUqxqWWx6kbI0PnNe3eTczw0IQiZWtVFmi/5/NY3TlDyFLYhKHiS3/nGSf7+wweI2nrZ4hR0NYSrRKuqZ4rqfkVQoYGuQWM8jFUnw6uGtWwddblY6/1fbB6r7T794he/uGpjXSnUSXENsZruU19KckWHQslDoWiNmfRPFbHjFlKBVCoQpy4v8ge3NHFwS1PNseaWzWeKDmfH8zx5bhLHk2xuDYOCb5+aoFVY9DSE8ZXimyfG2dMRpyNZW9UFgmTSssAbHYkQP/e6rXzjyDDPnJ9hpuDymcf6yBY93nJ9R/XcVObi+YqOhE226OH4Ph1hA9syCVs6IykHiWJHW7zax7AWIpZRJfzKyEPpIt6cfoaWLvCVYmimwI622Oz5WG7dEkGD33jYIhEy64S4QqwXQqij7j6dizopvsohpSJbLFXJsILX72rlgcPDjKRLKKXY15MM+vAtX0sOzCabjOd8/ubrJyg4ksl8CdvQuD3ajCLo+6eJQO/TEAJNQLrg0ZGsMV4gc7rI1bixKYxtaBQ9n46EjetLvvD0IF0NEfZtTAbJPlIxMFNgMutg6ho726MUHSdowqvp/PH91zMwVUAI2NIcRa9RIFmxdit/z0VTxMKXgBYk0nhSIZWiMWKtqNjQ1HViYQvbNPD9i2efrhXq5LM+sZ5uDOqJNgHqpLiGuBxLUUpFruiQL7lVsnFcSabkEbUMkmGTHzvYw1Q+IJOFHeRrzqdKXoCAb54vUnBtGiIGugbnpwqcncjR3RjC1DVMQyuLWksQguaYhetJ/v6JPp7qncY2NN5/sJvbt7csycQvDmQImzogMA0d5bicGMmwb2MSz5f89SPnOVruP5gpuXhSoqSPkoqfvKOHsKFXLbqFWI4MK2iMmnzk1o189ok+0IJF6sOHNi4uT5lz3k+MZHF8yb6NTTQnfjhEktcDlFI1SeTV7D5dL6j3U5xFnRTXEK/kYqxFhgAXJvP80/ODlFyJpgnu29vJro4Y7Yml3ZlA4E6lTIZzpiOEYLqoCIeCu8SYbdAYMXB9Rdg0+D9ft5Wn+6YZy5bQhcb7buqmOWbxuScv8MjpSRrCBr5U/O1jfTTHbK5ZoqN7c9RkKu8QMmdvEBrL2qVPnJ3mxYE0ybCO4yuils6Gxgj7OyyaLcVt5W4XBdfnibOTZIoeuzvj7GqPXSQNZj7evreTvT1JhlJFupIhNjXXJjrfl/zuf57myHAWQ9cxtD4+/b4brnq3+rWEUgopJb7vI6WsPuY+X+rvhc9zuRxHjhypOeZSkFKiaRrxeJxEIkEymXxVl1WsF1K+Woo2rwbUSXGNsdILeikyBHA9yZefH0QXgta4Tcn1eeCFIX7hzm21m+BCtWRhYf3gXGxJahxPuVhRC18qQobOR27dxN4NgY/0tm1NZB2fkBFYjQCH+1PEwya6IdCBrCs5OZwt1x/O2TkAgh+/dRO/82+nyBRdFLCtJcqdu4Iu86OZItOFEqfH3fLWgohlcmhjM8VioDNacH3+4D9PMzCTRyj415dG+InbNnLbtuYVnNVZvZ5NzZElyRACabvv96U5MpwnZBoIIcg7Pr/776f4wkduWsG+Vg8rIaO5z3O5HNlslnPnzq34M8v9LnVdR9O06mPu81rv6bqOaZqLtpuammLbtm3VbSqvVxrnLkSlzZGUkkwmQzqd5uzZs8zMzHDixAkaGxtJJBIkEokVZ0yuB1Ja6/0vh7qlWMdVxUrcp1Iq8iWHXHExGVaQc3wKrqQ9bgOKkKWTLvqki+5iUlwBGUJAF3dtChGajnF4IIUmBPff2MXenllyE5qYlYcrV3Mkwwb90wWsyqKk1CIJuf7pIi/2z2AbOrdta+KT776Wk2NZLE3jhu5EQLAKQobGWKbci1EENZInR7PMbSxxZDDN4HSelpiFAEqez5efG7wIKVbI8OI3JLqmESt3u58pTOJJVRX5tnTBcKpIPp9HSonneXiex+Tk5CVbTbXeW3L2QixJQLX+hqCXXjgcviRCu9LQdZ1oNHrJ+9J1nYaGBhoaGgB44YUX2Lp1K4VCgcnJSXp7e5FSzrMm5zbOXU9YL1bucpaiaS5uo/aDjDopriGWu0iVmrUM5XIXjoKopWPpgpzjEbUNSq6PEBAPmfO2WwkZzk4ODA3ed3M3piGYzDrkHZ+Cqwhb82oTKpk5AHzo0Eb+4D9fZiYfWHcbmiLlpr4BER0ZTPP//OsJfD8oY/jyc0P86Xuv5zXlTFiBQpUzZJuiNrahU/ICy0XXAlm3TNHDUgrf98mVnMD95vtlTQFJvuQzPT2N9OeQjPRRUiJl8Dmp5KLnUsrqZyDISLUNbbYL+ZSP8l3yBRchoOQLrmkxOHXqVJVIHMdhfHy8JsnYtr2sRbXw79VaxNPpNKVSic7OzlUZby1R65xUegImEgna29sBqtZkKpWqNs61LItkMlm1JtfLYr8eyHo5UgyFLhKC+QFDnRTXGAvvFJVS5EsuuaJzUTKskJxpaLz3QDeff6qfgamga/b7b+4JLLTKdqyQDMsQaBQ8yV9+r5eC6xO1DZ7qnSJT9PmJWzdSEf+WUs0jnY4I/MY9G3l5NIehwTVtITLT46TKxPUnDw3guB5muZPFwFSOL3z3CLdvsJBKzSOyVMYD5WOWr1WJQkjF2MB5NBTTU1MIB6RbZDwVNPPNuYoDnSGmpqYCkhEaul4mH13HMCqkIxCahq5VyCh4bhoGsbBNPBJaREy3AkbLBf7m0fMAXNsR5VP3X09zWbhASsnTTz/N7t27L+FM/3DjShGCpmkkk0mSydl06FKpRCqVYnp6mvPnz1ctciEEpmkSjUavOkGtJ0uxFuoxxTquKpZynxq6RsQ2cTwf15Pz3aYVsezyZyukJKRPxBSgdHQheO7cGB0hD0MEP/i51pFfIR4lkb6PL+dYTWVy8zyf8+NZBsdyJG2NdB6EUjx5Ks0uYxRLD1yaAeksJpntseA9tyjxKxaRrpFzgvic0IJUGIkPdoyNGzsCEpozxj5NkIsN89nHL2DoQVun33n7LjaEHPL5Ips2bwJg4/YcX3xmkHTB5e6NSe7b1xVkxqr5yUPLQROCaMgiYi/f3PdDt2zkRw50U/QkiZBRMxOyjpVhNc/VSsjMtm3a2tpoa2sDgpuY48ePI6Wkr6+PXC6HaZpVazKZTF5xa3I9xDQruBqKNq8G/HAd7RVEJQHgUuJImUyGfD6P7/tLbud5Hp4vcX2J50s8OT+yqImAcL474FByIWEF1tGF8TwvnffY1mzPs4Y0XcfSjdnnmo6mz1pUokxgUkpG88eJJ2yaExYaAlcqtLzDwZuuqS1srWZdn0vV+N26vcA3T46jawJfSixDJ2Rb/NvJGba1xjiwqWHe9u850M2dO1uYyDp0JcMkIgYT4xPzFtQtLVE+ce/OyhRmLeMVrDWaEERsk2jIWvHiZJt6VVqujrXHKyVXTdMwTZP29vaqRVkqlUin08zMzHDhwgU8zyMWi1WJ8pXEQF8NqJdkzKJOiquAYrHIiy++uOJYka7rWJaF53lIKWltbV0y4WFhNp5SCteXuJ6P4/o4no9C8fjMWVTewTV02hMhVMGlraOFrRsWV9MLIJV3mco7RA2D1oS9aBvP9WiLaOzuiHN8OI1WFgN/2w2dNQnxqbNTfO3FYRxfctOmRu7f34VlLt7uZ+7YQsn3eeLsNAINQ4f/76kLIMDSdd5/sIcfO7Rh3mxb4jYtcRspA4k5T9bolDDn4CoKrssnEs2SobaSjsgXgVKq2q+xjpVjvVhJFdi2TWtrK62tQQZ0JXs3lUrR399PNpvFMIwqSV6uTON6sRSXU7T5QbwJWA51UlwFhMNhbr755kv+3MTEBFJKmptXUj4QQAiBZehYhk60HP8+M5bhwozD0EwBW4cz43m2t0fpbpgfIBcEltS5iRwPvDCEL4Pnt21r4rU75s9BaEHSy4dfs4EXB9LM5F16GsPsao8uGvP0WJYvPTdAMmwStw2+3ztFSBfcd2Ch5JrANjV+7Y07efTMJI+cmuA7L4+XiUTg+T6ff6qf+/Z1EbX1cgur4GI9OpjmN752nJLrown4+Vua2bxlztCzWubVp4vOXfkRtq1VI0MIBMV/8Z9eIlVw0ZTPn3dNc3Bz46qMvVpYDwvvlcKVJJZKTWQ8PluL6jgO6XSaVCpFsVjk6aefJhqNVokyFotdNSK50i7oekyxjquK1bqQv3d6iq1tcVoTEUYzRTIFl70bmuhpjuG4HkqqatapUop/eWmEiK0TMQ2kVDx+dopd7TFa43bV9Rj4HgWGri1yacL8ooZzE3kEQQsqgIaIybHhLPfN3XYOwQGcHs0StoP4J4IqMWoC0kUHsIjY5e4XJZ9f/+ox8o6PpglcX/IXT05w+3UuDVHz4tqkBJZh2A56GuqruGA5nuTnv/Qi6aKHZWgUSh6//OUj/MvP3bIiFaE61havhFAty6KlpYWWlhampqa46aabqtbkwMAAuVwOXdercclEIoFtL/bGvNL9XwnUi/dnUSfFNcal3Ok5nsTQxCILp+T5WLrO5mabzc0RhlNFOhoiNJRNSc+XOK6H40syeYeiJ2mIBBeppgk0ocg7Hgh7tsriIhfq3FknQnq1W4UQgoLj091glQXAq/w6D+FKTE5ApSTPExAxNH78c4dRChrCBrs64zSFTaSietyiXLd4YaZAMlo7EWJopsCRwTSxkMHrd7bREAutKhlWMJIuUnAltqGhCDqSQHCjUCfFHw4IIYjFYsRiMbq7A++I67pVa3JwcBDHcYhEIlWSjMfjq2JNrhapLrUOKaXq7tM6rh5Wqn2aLXo88MIQfVMFQobgHXs72dk+6865aWMD//z8EEIE3SQUij1zpMcMXcPQLSJAQzTEltY45yfz5EouuZJH1BQ0RhffyS6a2wIXZQU3bmjkyXPT9E2VLUZT5/79XdVEl5FUke/3TqOU4uCWRrr+//bOPbypMt/335Ws3NOmSdoU2hQslMptQIQKqIMiItviZrSDDJx55HHwMnhwz+1sHefMOBtnj+M+Z0bnmWfQzdnqbLfbRxDEEZ2peMdRUC4WBsqlLfeWljbX5p6VtfKeP5K1SNqkSds0DeX9PE+fdCUryZtkrfV9f7/3dynR4OYaE3Ye7kKpToWO3iBAgIoiJdyhWI5lmBfQ5RHQ4wtDrZDDH471foydnwQCQVIPx0SaLrjx5DsnAEYGmUyOt4868PJ9czASTe9N2lgqCYmLdpTEiqVb0tROHQ3GekRsIdY+VSgUMJvN0tIIIQSBQAC9vb3o7OyEz+eDTCZDMBiEzWZDSUnJkPIBczl+WhA8BhXFUSTbg/mdI13ocAVhLVEjFBGwvakT679ZLeXH1V1jBAGw/5wLSlaG26daYTVq0r7eshnj8OS7JxDiBChYGcqK1QhHAY1SAS7CIyJEIQ0tRYJ+X5QKGR69dRJae7wI8wSTSnUoidcv7XQH8evGVoQiMXPw/eM2/OzOKZho0uJSbwghQcAEoyZWrCASi7Bl5TKI/YaFKBAIx4QSDIGKZcELUdxVq8f4NG2qnv3oNCKQQyGTQSAEJy95set4N1bMyn3yul7N4p+X1uDZD0/FfMQEWLugChNMtFA45TIMw0Cn00Gn06GiogIAwPM8Dh48iEAggO7uboTDYWg0Gim/Uq/X5811OZD7lKZkUPJKplm8LxTBByd6QKIEF90hTB9fBBDA5gtLosgwDOZXmzA/TX/EvnT2hjDHaoBZrwQrY+AJ8TjS6YM7KOD/fX4Ofo7HdRVFmKuN9QmMZGiJxCBWQGBGRf9I1/eP9YDjo9JY3X4Ofz3aje8tnIjTjgDKdCrpZPRzwVjuYjyiL77MGEuzADDOoMb9CyegTMmjRB7u915qRWzN0MNddmMyTGwN0unjsvpuhsLK6ysxp6oEp20+ONvbsPqWSSP2Xlc7hbIGlwtYloVcLkd1dbXkNQoEAvB4POjq6oLX6wXDMElrk2q1Ounz59J9StcUY1BRHEUyuU8JIdhxqAv+sACNQobeYAQHzjkx0ayDTjn0AzVeTAaKuD+REMDu4/DnQ53Qq1iU6VU4fNEHt4bgrtu1iEYJOF4Ax/OI8NFkkcyQIB+MRCGXMdJJJ5fLEI4IUMhjOYJClICVx3IWA2EBURBwEZJkoIoWam+Qx/vHunF9pQYLyi+7dEQxVMQDfeZUGbD/vAuyuFuTlcukIuYjxeQyHarNGuzrPTOi70PpTyG6T4dCojUpluRiLfr0AAAgAElEQVTjeV4qV9fd3Y1QKAS1Wi2JpFY7sh6Jq1EUry5ncYGR6WT0cwLOOwNYUG1ClABRAjgCEUyx6AZ0j2biG5UGKOQyXOoNweYNw8/xMGoVEAigVsghYxiYdAqccsUsNpmMgVrJolirhrlYC4tBjxKdBjqVEsoMSew3TjIiIsQKlgc4ARwfxY2TTZDLY6XpfBwPd4BDZ28IYICaUi2sJeqYtzb+9YhLGsGIgEueMN5pduLtE16oWBamIi1K9BpJEAHg/zTMwOxKA4RoTBCfWDYFc6r6R9DmmkK5uFKuLAY6bliWhdFoxDXXXINZs2ahrq4ONTU1UKlU6OnpwZEjR+D1etHS0oJLly4hEAgMaQ2ZJu9fhlqKo8xAB7BSLoOMAXQqORZOMsEX5uH0c1g6rXxYF2CTTonv3TgBh9p7wQtRzKw04IIzII2HYRgEI1GkiWORRFKtZKXncPFiAhHhcmk6BsB1E0qwflE1/trcA0IIltVZcJ21BH/acx57TjnAMAwml2ngCfJwBnjIZHIYNDLYfLEKQYxMBiHuTjXrFChSx8rfHbwUgUGnTplraNQq8V/3z0VEiEXr5lOsxnpQy1ijkCzFbGAYBlqtFlqtFuPGjQPHcWhubobFYoHH48GpU6cQDAaTrMlsWmkNJIo00IaSNzK5T5WsDEumWrDrWDdkTCyy8eYac04iG0v1KiydZpG2xxWrMNtajKMdHsgYBnI5g29Nzu59GIaBSsFCpUgWyUi8duv8ajNuSFjv3Hm4C1+ccsCoU8BAgA53GDVlOvT4PQmNhlkQAkQBcPHGyaVFasjlLAgjQ1iIZByXYiTCTQEEOAG/fPcE/tZmh17F4uf/UIslCd8lJb+M1kSkUCZAMpkMRqMRRmOsYAQhBKFQCB6PB3a7HWfOnAEhBEVFRUlu12wmA1ej+5SK4iiSTUrGwkkmVBjUsPvCKFIrUFM2MpX8FXIZfnHntTjc4UGA43FtuR6nm5uG9FqpRDKxNN2xLg/UypibFkzsvY1aBaaVF6G1xwcGwMwKA/556RS0u4I43NGLvxyzwcMBSnkUHC/gjhpdzirSDJZfvnsCH5+0gRACe4TDT/98HK8Y1JgxvijzkykjwpVk7eWSVBYewzDQaDTQaDRSKy1BEODz+dDb24szZ84gGAxK7bYMBkPMK0MDbQBQUbwiyNQVPlewchnmJVSvOZ2j1+1bmu4aSwnOu21gWTbediqKccVqPLKoOra2CKDCoMbxbj9+8uYxEEYGhom5k++YZsE3LArMNvI5Gt3g+bzNcTlwiAE4IYp9Z11UFK9Ahus+LQQxzmYMcrm8Xyst0Zp0OBzw+/34+uuvk6xJnU5HRZGSX7JN3s8GQgjCfCzSc6TchrnivvlVONblhcsfS5OYPM6INfOvgYploFEpIEQJdGoFfr/1GEICEHOiAkKUoMqowdJri2Gz2UZt/HqVHMGIAHn8WqSQMyhS0VPpaqMQ3KfDGYNarYZarYbFYkFvby+uu+46qcDAuXPnsGvXLuzduxcMwyAUCmHJkiU5HHnhQs/kHDDU2WKuRDHACXh9fztOXPJCLmOwfOY4LJpiLohZbCrKilT4w73fwMnuWB7WjPFFUMejWBPtYVcged0wIhDY/COXb5gt//vOa/H4W82ICAQKOYPxBjXumjVutIc1IIV6LOSCKy1YJpfkMk9RrNdaXFyMqqoqzJw5Ey0tLbj22mvR0tJCRZFy5fDukUs40eVFRYkafJTg7cNdqChRY4pFP9pDS4tezWLexIE7Sdw82YTGY93g+NjEQa2Q4Zs1l8tmjRZLppbhv+6fi31nXShSsbhr1jholfKCsBwog2Os5DjmglSfRS6XY8mSJZg7d25O3mPXrl344Q9/CEEQ8OCDD+KJJ57IyevmEiqKo0iuLMW2Hh/M+liTXIU8Vh/0oitY0KKYDRvvmgZ/WMCnrXYo5TL8aMlkTDRp8HZzB/hgANdMFqAZRhGD4TCzohgzK4qT7htLF8grjav1ux/pguC5XFMUBAEbNmzAhx9+CKvVirq6OqxYsQLTp0/PyevnCiqKo8hwDuazdj+Od3mhVshRpJaj2xMrnE0IgRAFSrTDT9sghODjkzZ8ecaJEo0C986tRHmKhsQjhUYpx6Y1s6UT/8A5F/7HywcREaIAieL9cwfx2rp50I6SMF4pUAs2PVe6pTjSv20uk/f379+PmpoaTJoUK4O4evVq7Ny5k4oiJZmhHNTHOz145asLUMhk4KOxdS2GYdDVG0KUEFxnLcbMiuFHQn5+kcfnh05BIY+VY/vyrBN//M6svLdEEi88T7/Xgkg0lpAvCEC7K4h3/t6F1XXWvI6Hcplsj99U+0WjUTidzqQ2SgO1MAJiZc9EZDLZqItSIZCL7yDda+SyS8bFixdRVVUlbVutVuzbty8nr51LqCiOIkN1n3540oZiNYtidawTRbsriPoZFlhNWijlMlQZNTnJ4fv8ooDiIhZKNnZSOHwcDpx3Ydn08mG/9lBwB/lYbiMAMAz4KIErMPqBN4VEquOJECL9Zdovm/sGuj8dqY71yZMno729HV6vF0qlUsqZU6vVIITE0nUEAYIgIBqNIhqNgud56T7xLxQKIRKJSCI52Iv4lW4pjqT7lJZ5o+SVoR7MESGWeiEiYwCWled8DbHfecKkuG8AolECm4+DipVJraSGw42TjPjwhA0EBEKUQMnKUXdNdp1BRovhWFK5EimFQgGPx4N9+/ZJlU9KSkrSHn+EEEmIxP8ThUn8f6D70u2TCrlcDp7nYbPZcOnSJfA8D5ZloVarodFooFarwbIsZDIZWJaFUqmEXC6XRNBqtUImk0ndVQRBkKIpM4kkdS0PTC5bR1VWVqK9vV3a7ujokJoyFxJUFEeZoZyUCyeZ8OdDXRC0BBGeQCGX4dry3AfV3Fghx5d2HipWhohAoFexqMsQMSriCnD4yfZmnLH7QQjwrdnj8JPba4Y1q/1F/VRwPMHuVhvULIPH/6E2qdhANoyESCVaQRzH4dSpUzCZTNDr9Vl/3oG8BqLVlPg3kAglipp4q9frwfM8HA4HOjs7wfN8vGuJPElgxLHIZDLp/ky3SqUSMplMuk+8P9V2Nt8HIQTBYBAulwsulws2mw0ajQZGoxHFxcUDfq99vydgcCI5GMaSpTiQ+zRXlmJdXR3a2tpw9uxZVFZWYuvWrXj99ddz8tq5hIpijhiKK3So7tMbJ5nAyhgcau+FWiHD7VMtsBTlPgBmcRWL62dWY8/pWKDNd2+okvoiZuJ3H5xCW48vlqoA4O2/d2GW1ZBUbxUYnEipWQb/t2E63G43zp07h5nTzf2sj74iNdj3yXYsovD0FaApU6bA7XajpaVFavOj1Wqh0cS6mmQjaKkYSGj6bve1ptLdchwHl8sFp9MJn88HrVYLs9kMk8kkjXc0SCx6XVlZKYmk0+nEuXPnpLGKVm+iSIrfgUiitZtKJAtB2IbDlRRow7IsNm3ahGXLlkEQBKxbtw4zZszIyWvnEiqKo8hwkv4H01R4sIgnGsMwuHNGOe6cUd7vsUyW1LFOD5Qsg3h5UwhRguaLvVg8xZT2+dkiisyBAwegVCqlC7lKpcrKmhqMu0+8kPYlUZD63srlcpSWlsYaHEciCAQCuHTpEgAkNYwVm8wmPj+fHQlYlk0SHp/PB6fTiZMnTyIUCsFgMMBsNsNoNEKpzG9wVV9UKhUsFgvM5thEyO/3o7e3Fy0tLQgGg1AqldJxIZYP7Lsm2XctkhACjuMgCAI4jhuSJVkIgjqSY8hloA0A1NfXo76+PmevNxJQURxlshGHoe6TqYFxpn0VCgW+/vprlJaWorS0FCpV9tao1ajG4fYwWBkDEo21kSrTAG63O60IZSNWfS3DUCiE8+fPS50AFAoF1Go1VCqVtA6VSnhUKlVW1hfD5K71VCQSgdPphMPhwMWLFyXLzGw2D+q7HQkYhkFRURGKioowceJERKNR9Pb2wul04sKFCyCEoKSkBGazWVqPTCc86W7TTTwyWcoDuXOLi4tRUlICQRAQDodht9vBcRzUajWKioqkGp6pjgXRozCQJTmQSBbCeuRIW7o00IYyJMLhsNTDTCQbkRLdQna7HQaDIengzvUJJ65LpVpvSidQRqMR4XAY3d3dOHv2LAghUCqVUCgUkMlkScEYgQjBm20RnPdEUaZlcPsEBU7KBPiDPAgBas0KTNcF0N3NpbSyFApF1mtYA61/OJ1O2O12uN1u6HQ6SdBH29IBYpOM8vJylJeXx76zQAAOhwMnTpwAx3GS6JhMpkFfiPpe3IcrTom34mv39PSgs7MTgiDECkUoFFAqldIEY6DfTbSKM/22ubBKCCHw+/1wuVyw2+24cOECdDodTCYTjEZjUtsk8Vb8vhPPkcQI2HQiWQiW4khCC4JThsTx48exdu1azJgxA/fccw+WLFmSJJCpEC80NTU1aG9vx/Hjx1FUVISSkhLodLqUF7iB3Hx9BS0VgwmeENel9Ho9ysrKIJfLIQgCent74XK5EA6HYTKZYLFYYDAY8D+3HsUJtw9KlsUZH8GO8yxee2guLrpCULKxQCD5CLd6ksvlKCsrQ1lZmXRhtNlsOHLkCKLRKMxmM8rKylBUVJSXi1ligEw6cVKpVBg/fjwikQj8fj/OnTuH48ePS9asUqmULJp01rLIQC7dVC5eMUAmk1il+q7C4TCcTiecTic8Hg80Gg0MBgNMJhN0upFpb5YtDMNAr9dDr9ejqqpKOhacTidOnTqFQCAAvV4vrUkmiqQoeJlEEojlTI62tUgtxdxDRTEHzJkzB+vWrcPf/vY3PPXUU3j44Ycxfvx4KJVK3HXXXbj99ttTPi/xIqbT6RCJRNDe3o5IJAK1Wg29Xi+5fhKtqUxuv1y6/PpiNsdqjwqCIEUy7vv7CTRfjECrkIOVyaFQAd4Qj3ZnENdPyC46NCJE0e4KQimXobJEPezxJ14Yq6urEYlE4HA4cP78eXi9XhQVFUnRjAzDZGVRZbpNxWAmIqKoiFa41+uF1+tFMBiEXq9HeXk5zGYz1Orhfz/DRRTz8ePHSx4Ph8OB06dPIxAIoKioCCaTCSaTKeMEMdckWs2J1q7BYIBer5fWJF0uF9rb28FxHJRKJdRqteQFSTeZSXwPADAajeA4TrIgcxndmu1nHek1RSqKlCGxePFi3HTTTdBoNFCpVDh27Bg++ugjNDY24sKFC2hoaMDixYuzWjuKRqNwu93o7u7GpUuXYDAYYLFYpAtmISCXy2GxWGCxWGANcpB9vQ98NIpIKBQLrGHkiMWdZsbpD+PHbx5DpztWkeeGCcV47LYJkCF1jly2IpVOqFiWhd/vh8fjAcfF3LkajQY6na6fK1Bco8xkUY1EdZVx42KdNwghUt+75uZmEEJgMplgNpthMBhG/ZhIjBYVLTOv1wuHw4Fjx44hEomgpKQERqNRWibI5rfM9r5UZGMpFxcXw2g0SgFRfr8ffr8fHMdBp9NJY9bpdGknm6Lg9l0XF8eQb5HMNVejKDKDNP9Hf2X5CkMQBOzduxfbtm3Dp59+iuuvvx4NDQ249dZbs1rnIoRIAulyuVBcXCxF4Y3kyZZN4Evi/y8dsOGTM35ECQEDgupiBuuujUIWX3tKDGzoy6vHORyxR6FhY5GqQYHB6hk6LJmkH/CilunCl63FHAqFYLfbYbfbEQqFYDQaUVpaCqPRWJAXtMSAnd7e3qSAncGmUqSqHJOttTzQfX2r5yRGfTIMI1lmiYn5mX7fwa4xDxVR1MV0lVAoJFm+RqNxwO+4r0gmfg+pRPLAgQOoq6sb8lhFF3ZNTc2QX2OgcSxZsgS7d++GVjvyTc7zQFYHChXFPCIIAr744gts27YNn332GebNm4eGhgbccsstUCgGrvginmgulws9PT1wuVxSrpZer5defzDuPvEilQoxsTubGbdMJgMjk+GLcz60OcKoKlGhfnoZ1EoWPM9LFxee51FaWtpvXe++V5pg93FSOTlviMfymRb8r9uHd6IPBfE7ttlscLvdUKvVUrBOvtyA6dYiU/1+PM8jHA7D6/UiEAiA53moVCooFAqwLJu0FpnOch6sCGXaP1PATCQSkY4Jt9sNpVIpuVrztd47GERLXSwmEAqFJCszk3t4IJFkGAZNTU244YYbhjw2h8MBl8s1YqK4ePFi7NmzZ9Sjo3MEFcVC5l/+5V9w+PBhtLS0oKurC1arFQqFAnfffTcWLVqU8jl91w2j0SjC4TDC4bAUFKPX67OO5BypmXY6xIoqPT098Pl8MBqNKCsrwx+/tOFvp5zQq2JuGl9YwA8WV+Pu2ePzMq50iBGiohXJ83xScEbf4JehWlWpGIoYiW6uQCAAj8cDr9cLlmVhMplQWlqaVHi7kAiFQpLF4/V6+xURGA2RHMiC5nkePp9PWvMVJyLiX9816lSuXvG6q9PpUFtbC+By6slgfiOHwwG3243JkycP6/OmE8VbbrkF+/fvz1mpt1EmqwNpTHzSK5Fly5bhzjvvhEajgVKpxOHDh/Hxxx/jrbfeQkdHBxoaGnDzzTdndTCK7p7u7m709PRAq9WivLwcpaWlBbUewLKslJKQaJHdWOTEMWUUzlAUjEyGmyabcNc3sis6ni4NYbiuwHSTRbvdLtXnFF2AicFQfaN3s3H1juRFPxQKSQFGfr8fRUVFKC0thclkKog0FQBQq9WoqKhARUVFUqRoa2srgsEgiouLJZFUKBRpf7fB/P7ZBEllmoSI0eIMw4DjOKmggCAI0pqkaEmm+637WpKJ3ptsRDIf0aeFZrmPNNRSLDB4nsfu3buxbds27N27FzfeeCMaGhpw4403Zi2QPp8P3d3dsNvt0Gg0sFgsKCsry9tsL3GWnY0ICYIArz+A090eCFwIpRpGigQUZ92CMPjE7uG4BDNdCESXmt1uh8PhgEwmk9yso52SkI7EMTudzpwG7KQLhBqsOPUNrBKPJbE7hligQcyPHCj/MZtjYSSitaPRqORudTqdiEQiMBgMkpdhIFdkJndrX5G02+3weDxSj8Khks5S/OY3v4mmpqaC9DAMAeo+vdKJRCL49NNPsW3bNnz11Ve46aabJIHM1gJMFEilUilZCenSEIZiaaViqAIll8sRiUSkfEhCCMxmMywWS0GuN4mEw2E4HA7YbDYEAgGUlJRI3/VoWeupUhMSb8W1SI/Hg0AgAIVCIUVPi2khqX77dJOT4YpTqslKXwRBkMTG5XJBJpNJwl5cXFyQF+9oNCodzy6XSxJJMXBnIIs9k0g6nU74/X5UV1cPeXyEEBw8eDCtKB46dKhgz7tBQkVxLPHJJ5/gzTffxP79+3HhwgVMnjwZpaWlqKysxPe+972sgmcIIeB5HjzPS2kIWq027RpkNlbVSLv/IpEI7HY7enp6EAwGYTQaYbFYBmx9NNpEo7GUGtEiU6lUkhWp0WhSustyYVmlOpezsYpFa4nnefj9fvh8PgiCIOVyGo3GpGOkkISH47ikIgIqlUoK2hlMl5J8IP7miZM+0d2q1Wqh0+mk9J90v3tiP0lxu6KiAuPHj88qyCnduJqamjBv3rx+j9188804fPhwQX2Pw4CK4lhi9+7dOHv2LLRaLViWxcmTJ3HkyBG0t7fjpptuwtKlSzF37lypSkmmgzgQCKC7uxs2mw0sy0o5h4WyzpQKQYiVcbPZbOjt7UVxcTHKyspgNptzYo0NJj1hsI+LkxFCCFiWlVyAiekIg7GyRjpgShAEuN1uOBwOOJ1OKBQKKe2j0MQmEbGIgGhB6fV6SSTTpVKk+937/j+YtcxUHpRU1rQ4IYlEIgiHwwiFQiCEQKfTSbVoE+v09rWsRW9AokUpXtMTBXIgoaSi2GcnKopXNuFwGB9++CHeeOMNHDp0CLfeeisaGhpwww03ZD1jDAaDkkDKZDJJIAslDDtVlKcgCNK6jcfjAcuy0Ov1UsmukQquGIoLMPGCIgixSkB2ux29vb0FV581HWLAjsPhgN/vTwp+Galx93X/DjbARkxXCYfDUjcM8XdiWTZl/8ihTEiydf1miyAIUjF2l8uFaDQqFRIQrfZ0iMe16OYWtwkhaUVSEAQcPnwYc+fO7fd61H2amTErirt27cIPf/hDCIKABx98EE888UTS4+FwGGvXrsXXX38Ns9mMN954A9dcc83oDDYN4XAYH3zwAbZt24bDhw9j8eLFaGhowLx58wYlkD09Pejp6QHDMJJApsrFSnQBpptRDyW4oi99L1qpLkI8zyMQCMDv94NhGCmwQavVpp2dj/aJLgZFiSkfQKyMnpg+MdrjS4dYUCIxYKe4uBjFxcXQaDRZuYZTHTOpyEaIspmkiJ6ExKR8QoiUa1hSUjJqa7+ZEK12cU1SLFknjnsoIglcPq94nkdzczOuv/76fs+nopiZMSmKgiCgtrYWH374IaxWK+rq6rBlyxZMnz5d2ueFF17AkSNHsHnzZmzduhV//vOf8cYbb4ziqAcmFAph69ateOutt3DixAnMnTsXdXV10prc1KlTM4oXx3HSLBuIpVSI7j4gc0/BoVhcuVivElsI9fT0IBwOS4E6Yp3TQoXjOMmK9Pl8MBgMUrBOtpHDfV2BgxGldI+lW6tMjNSNRCKIRCLgOA4KhUJy/6XqoJHuOMn3b5NYWMLlckGhUEiu1kI+VkSRFIsfiG29EsvopfuNeZ6X1jUT7/P7/Zg9e3Y/S5KKYmbGpCh++eWX2LhxI95//30AwDPPPAMA+NnPfibts2zZMmzcuBELFy4Ez/MYN24cbDZbQR8s999/v9SA1WazweVyAQBmzZqFVatWYcKECSkb3aYSrEgkIlmQ0WgUZWVlKC8vH9UO7dkguit7enrg9XpHrY5spuCaVNuBQAA+nw/BYBAymUxag0yMHO5LLl2BQwmsEfMMRVer2BJLLJtXqNYY0L/zh1arlUQysZPGUEiVppTtcZBuW7x2pyqjp1AooFKppNSmgc5t8dhSq9VJx1Q0GsVtt92Gv//97wV9nRsENHk/Wy5evIiqqipp22q1Yt++fWn3YVkWBoMBDocDpaWleR3rYHjllVf63RcIBNDY2IgXX3wRLS0tWLp0KRoaGjBr1qwBL34qlQpVVVWoqqoCx3Ho6enBiRMnwPO8JJCFWB9RLr9cuFyMCrXZbGhra4NOp0NZWZkkkLkKtsgUCZpJiORyuVShaNy4cZJ7uLe3F263GxzHSdWACq0+a2J3kokTJ0pWjd1ux6lTpwoyYEcULIZhUFJSgqKiImlS4na70dnZCY7joFKp+qWspDo+snEFD3QMsCwrvUfi44mBOoQQhEIhBAIBBAIBBINB+P1+BINBqaJRNBoFy7JobGxER0eH5HKNRCJJzwsEAgiHw/2OWZlMBrfbnY+foKCgojhEAoEAFixYAAAp1yCfe+45vPTSS2BZFmVlZfjTn/6EiRMnjsZQk9BqtVi5ciVWrlwJn8+HxsZG/P73v0dbWxuWLl2Kb3/725g5c+aAF1qlUgmr1Qqr1QqO42Cz2XDy5ElJIC0WC3Q63bDHmi7AZjjilXjii+tL4uxavOj1bdGV+H827sCRcgWKEzBBEKRqQK2trdBqtVKwTqEER4nI5XJJBIHLATtnz57NKmAnVZ5lNsfCQI+lG2eq31I8f0VvidgYWhBiKSvFxcVgWVaKgBbPG1FsEv8S7xMFTPxLFLRMgiV6A8SUKp1OJ3UpEbu9iNs6nQ4LFizAhQsX0NLSAqVSifXr1yc9Lj6vb+PlqxUqigAqKyvR3t4ubXd0dKCysjLlPlarFeFwGD09PWhtbUVVVRXq6uqwYsWKpDXIOXPm4ODBg9Bqtfj3f/93PP744wW3BqnX67Fq1SqsWrUKPp8Pf/nLX/Db3/4Wp0+fxrJly/Dtb38b06dP7yeQfV1B4uxazBlrbm5GJBJJ6gc5lGjQgQJs+v4/kJBl4woMhUKw2Wyw2Wzw+/0oLS2FxWIpGGsmEblcLomg6K602+04evQootGoFKwz0utimYKpBtqWy+XQarUIBAJwuVzgOE5KV+kbDJXJwhLvE8vqpdpfvE/s/dhXsBL/EgUrlaAFAgFwHCfVlG1sbITD4ZDKK7Is20+c0gmW2WxOEiYxglp8XMxdpIKVP+iaImIL7rW1tfj4449RWVmJuro6vP7665gxY4a0z/PPP4+jR49i8+bN+NWvfoUXXngBly5dApB6DTKRQ4cO4dFHH8WePXtG/sMMk6effhpffvklzp8/j87OTjAMg+LiYpSXl+Ppp5+W9sskPkBstuzz+ZLKXOn1+n65eYkXwEJAbEjc09MDv9+fVDCgkFyVqRDHbrPZ4PV6pRqcqZopD8bCGkze3WC2E285jkN3d7eU9hGJRPDJJ5/guuuuQ1FRUUbB6vtYKBRKGishBHK5PKVg9f3T6/VJ+2USLKfTiT179uAf//EfC+Y4pvSDBtoMhsbGRvzoRz+CIAhYt24dfv7zn+OXv/wl5s2bhxUrViAUCuG+++7DoUOHAABz586VLL///u//xr59+7Bp06aUr/3oo49i3Lhx+MUvfpG3zzNUOjo6QAiRTn6O4/Duu+9i+/btaG9vx5133omGhgZMnTo165Of53kpGjQQCEhWWCGnHYhEo5fbdbndbuj1eqmf5VBqyaYKuBmua3Cg6FCxcEBiM2W9Xi8Vqc5khYu30Wi0n/j0Xcvqe382giX+/qkEy2Qy4dKlSzh27Bii0SgefPBB6HS6foLV14Uoihe1sCh9oKI4Urz55pvYtWsXXnrpJQADi+Jrr72GTZs24bPPPiu49Z7B4na78c4772D79u3o7OzE8uXLcc8996C2tjbri44gCLDb7eju7k5yUxZCCHymdAZBEOD3++F2u+Hz+aSLuJjDmeq5ww24ycbCEltcZRIsjuPAsiy0Wi3efvttdHZ2SutiHMdlFKy+1rQmzIsAAAtcSURBVJROp5PWtdJZWLkSLJ7nx0r7IsroQUVxpMgmhQMAPvroI/zTP/0TPvvsMzQ1NQ1YHEBkx44dWLlyJQ4cOJCy7FKh4HK5sHPnTmzfvh09PT2or6/HPffcgylTpgxKIBPTJUwmE8rLy6Vcq0TSBd0MdU0rU8BFNsIl9tXzeDxSxwmWZTF+/HhpjTMajaYUrHRRg30FTfxf3A6HwwAuuwPFMWcSLNHdJwqWQqFAW1sbmpqa8K1vfQs33HADtbAoYx0qiiNFNmuQhw4dwsqVK7Fr1y5MmjQpY3EAIBYNuXz5cnAch02bNhW0KCbicDiwdetW7NixA3a7HTfffDPmz58PtVoNk8mEmpqajELG8zxCoRDC4bAUiCEGXYgX5oHcfdkKWeJttoKVKEzp7o9Go5g4cSLOnDmD5uZmKBQKlJeXSxGtgxGsxP8TH6eCRaEMC5qnOFKwLItNmzZh2bJl0hrkjBkzktYgH3vsMfh8Ptx7773w+/3wer1Sz7PVq1dj586d/UTxySefxE9/+lP89re/HY2PNWQYhsH27duh1WpRXV2Nw4cPY+/evVAqlZg3bx7WrFkjNTxOl9KQmIcFXG7m6/F40N3dDY/Hg+nTpyeJUSrBShcCHw6Hk9yBAKQowVTh6YnbYn3SbAUrGAzik08+wR133FHQ9UwpFEp/qKWYB7JZg2xqasLTTz+NHTt24NZbb8Xvfve7K8ZSTIfdbsdbb72F7du3o7u7GxUVFbBYLGkFK/FYFAVLr9fDYDCgpaUFFy5cwKJFizB16tQkUUqMDEy0thKFjVpYFMpVD7UUrxSi0Sh+8pOfJFWg2bt3L7773e8OuAa5bds2bNy4EQzDYPbs2Xj99dfzOOrMlJaW4uGHH8bDDz+M1tZWfP3115gyZcqQBUsQYvVYC720HIVCuXKhopgHMhUH8Hq9aG5uxq233goA6OrqwhdffIE333wT9fX1KYsDtLW14ZlnnsGePXtgNBrR09OTt88zFGpra1FbWzus1xCjPSkUCmWkoO7TPJBNYE4ic+bMgVqtxpdffgkgdXTr448/jtraWjz44IMj/wEoFArlyicr92lhl+cYIyQG5kybNg2rVq2SAnPeeeedfvtzHIfy8nJp22q14uLFi0n7tLa2orW1FTfddBMWLFiAXbt2jfjnoFAoVybr1q2DxWLBzJkzUz5OCMEPfvAD1NTUYNasWWhqasrzCAsH6j7NE/X19aivr0+671e/+lXKfZ966qmMIsfzPNra2rB79250dHRg0aJFOHr0KEpKSnI2ZgqFMja4//778eijj2Lt2rUpH3/vvffQ1taGtrY27Nu3D4888ki/TkFXC9RSLECyKVButVqxYsUKKBQKVFdXo7a2Fm1tbdi1axeuvfZa1NTU4N/+7d/6vfaFCxewePFizJkzB7NmzUJjY+OIfx4KZaxT6OfdokWLYDKZ0j6+c+dOrF27FgzDYMGCBXC73ejq6srjCAsIQshg/ih5IBKJkOrqanLmzBkSDofJrFmzSHNzc9I+7733Hlm7di0hhBCbzUasVivp7u4mkyZNIqdPn5aed+zYsaTnPfTQQ+SFF14ghBBy7NgxMnHixLx8JgplKLz33nuktraWTJ48mTzzzDMp93njjTfItGnTyPTp08maNWvyPEJCeJ6/Is67s2fPkhkzZqR8bPny5eTzzz+Xtm+77TZy4MCBfA0tX2Slc9R9WoBkUxxg2bJl+OCDDzB9+nTI5XKp5VNNTc2ARQIYhoHH4wEA9Pb2oqKiYlQ+I4WSCUEQsGHDhqRKUIUYhb1//3563o0hqPu0QKmvr0draytOnz6Nn//85wBia5ArVqwAEDvJnnvuORw/fhxHjx7F6tWrcfHiRVRVVUmvkSpAZ+PGjXjttddgtVpRX1+PP/7xj/n7UJSCIpPLT2THjh1gGAYHDx7M4+iSxUapVEpik8iLL76IDRs2wGg0AgAsFktexwhgTJx32SzZXC1QUbzK2LJlC+6//350dHSgsbER9913X8peeZSxjWiFvffeezh+/Di2bNmC48eP99vP6/XiD3/4A+bPn5/3MWYjNldKFHahn3crVqzAq6++CkIIvvrqKxgMBowfP360hzUqUFEcQ2Qz23v55ZexatUqAMDChQvR3t5OQ7VzSCbr67nnnsP06dMxa9YsLFmyBOfPnx+FUWZnhQGX6/GK7bEKjcQo7C1btuChhx6C2+3O6xiGct6FQiHY7fa8jXHNmjVYuHAhWlpaYLVa8fLLL2Pz5s3YvHkzgJhnatKkSaipqcFDDz2EF154IW9jKzSoKI4h6urq0NbWhrNnz4LjOGzdulVyt4pMmDABH3/8MQDgxIkTUCqVUgusVCSGav/Hf/wHHnnkkRH9DFcy2Vhfc+bMwcGDB3HkyBGsXLkSjz/++KiMNRsrrKmpCe3t7Vi+fHm+hwdgeFHY+WQo510oFEJZWVnexrhlyxZ0dXUhEomgo6MDDzzwANavX4/169cDiC3HPP/88zh9+jSOHj16xdddHg5UFMcQ2RQJePbZZ/Hiiy9i9uzZWLNmDbZs2QKz2Zz2NQslVDuTBRYOh/Gd73wHNTU1mD9/Ps6dO5f3MWZjfS1evBharRYAsGDBAnR0dOR9nNkg1uN99tlnR20M2YjN3Xffjd27dwOIFaBvbW2VAl7yxVDOu1deeYUWpi9Usg1TJTQlY8xS6KHa2YS8P//88+T73/8+IYSQLVu2kFWrVuV1jIQQsn37dvLAAw9I26+++irZsGFD2v03bNhA/vVf/zUfQ+vH3r17yR133CFt/+Y3vyG/+c1vpG23203MZjOZOHEimThxIlGpVGT8+PF5/+3/+te/kilTppBJkyaRX//614QQQp588kmyc+dOQggh0WiU/PjHPybTpk0jM2fOJFu2bMnr+ChXFDQlgzI2yCbkfefOndi4cSMAYOXKlXj00UdBCCnY2fhrr72GgwcP4rPPPhuV90+0wiorK7F169akLisGgyFpzWu02pllqgQlRmE/99xzeR0XZexC3aeUASmEUO1s1r8S92FZFgaDAQ6HI6/jzPa7+uijj/D000/jnXfegUqlyucQJQZbj5dCuVqgliJlQFasWIFNmzZh9erV2Ldv31Udqp2JTNYXABw6dAjf//73sWvXrlHJqUtkMPV4xXU7CmWsQ0XxKmfNmjXYvXs37HY7rFYrnnrqKUQiEQDA+vXrUV9fj8bGRtTU1ECr1eI///M/8z7GbCwwcR+r1Qqe59Hb2ztgANFIkE0losceeww+nw/33nsvgFhUIrXMKJTCgfZTpBQ82fSjfP7553H06FFs3rwZW7duxVtvvYVt27aN4qgpFEqBkVWAAbUUKQVPNhbYAw88gPvuuw81NTUwmUzYunXraA+bQqFcgVBLkUKhUChXA1lZijT6lEKhUCiUOFQUKRQKhUKJQ0WRQqFQKJQ4VBQpFAqFQolDRZFCoVAolDhUFCkUCoVCiUNFkUKhUCiUOFQUKRQKhUKJQ0WRQqFQKJQ4VBQpFAqFQolDRZFCoVAolDhUFCkUCoVCiUNFkUKhUCiUOFQUKRQKhUKJQ0WRQqFQKJQ4VBQpFAqFQolDRZFCoVAolDhUFCkUCoVCiUNFkUKhUCiUOFQUKRQKhUKJQ0WRQqFQKJQ4VBQpFAqFQolDRZFCoVAolDhUFCkUCoVCiUNFkUKhUCiUOFQUKRQKhUKJQ0WRQqFQKJQ4VBQpFAqFQolDRZFCoVAolDjsIPdnRmQUFAqFQqEUANRSpFAoFAolDhVFCoVCoVDiUFGkUCgUCiUOFUUKhUKhUOJQUaRQKBQKJQ4VRQqFQqFQ4lBRpFAoFAolDhVFCoVCoVDiUFGkUCgUCiUOFUUKhUKhUOL8f6Ke9+hRmHLjAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from mpl_toolkits.mplot3d import Axes3D\n", - "fig = plt.figure()\n", - "ax = Axes3D(fig)\n", - "ax.scatter3D(X_test[:500, 0], X_test[:500, 1], y_test[:500]) # plots 3d points 500 is number of points which are visualized\n", - "\n", - "# here we create plane which we want to plot, using the train data and predictions (you don't need to understand it)\n", - "range_x = np.linspace(X_test[:, 0].min(), X_test[:, 0].max(), num=10)\n", - "range_y = np.linspace(X_test[:, 1].min(), X_test[:, 1].max(), num=10)\n", - "xx, yy = np.meshgrid(range_x, range_y)\n", - "zz = np.vstack([xx.ravel(), yy.ravel()]).T\n", - "pred = regr.predict(zz)\n", - "pred = pred.reshape(10, 10)\n", - "\n", - "ax.plot_surface(xx, yy, pred, alpha=.1) # plots the plane\n", - "ax.view_init(6,-20)\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Playing with this Notebook\n", - "==========================\n", - "\n", - "Do linear regression on dataset named 'artifical_lin\".\n", - "\n", - "Try to see what happens with the error, if you change the sizes of train set and test set. \n", - "\n", - "Add noise to the data, and fit the model again. How does the error changes when you add more noise?\n", - "\n", - "You add noise using normal distribution, with mean 0 and width 0.4 (you can vary this parameters).\n", - "noise = np.random.normal(0,0.4, (train_set_size,2))\n", - "\n", - "Add noise to data:\n", - "X = X + noise\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": 1 -} diff --git a/1_logistic_regression/demo1/4 - Logistic Regression.ipynb b/1_logistic_regression/demo1/4 - Logistic Regression.ipynb deleted file mode 100644 index 3f8d950..0000000 --- a/1_logistic_regression/demo1/4 - Logistic Regression.ipynb +++ /dev/null @@ -1,658 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Logistic Regression\n", - "===================" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we generate some data to train to train with." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.datasets import make_blobs\n", - "X, y = make_blobs(centers=2) # generate dataset consisting of two Gaussian clusters" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Take a look at how large the data is and what the labels look like:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X.shape: (100, 2)\n", - "y: [1 0 0 1 1 0 1 0 0 0 1 0 1 1 1 0 0 1 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 0 0\n", - " 0 1 0 0 1 0 1 0 1 1 0 0 1 1 0 1 1 1 1 0 1 0 1 1 0 1 1 0 0 0 1 0 1 1 0 1 0\n", - " 1 0 0 1 1 1 0 0 0 0 1 0 0 0 1 0 0 0 1 1 0 1 0 1 0 0]\n" - ] - } - ], - "source": [ - "print \"X.shape:\", X.shape \n", - "print \"y: \", y " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As the data is two-dimensional, we can easily visualize it:" - ] - }, - { - "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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TM/licfEx/OrVqzPu6fFsiHTnp6GuJLR0Z+jgx6hXr16J9m/K66/y6DN/J8H/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\niB1TyIuI2DGFvIiIHVPIi4jYsf8DukTFtk9nJv0AAAAASUVORK5CYII=\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.prism()\n", - "plt.scatter(X[:, 0], X[:, 1], c=y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import logistic regression from scikit-learn and generate a classification object." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.linear_model import LogisticRegression\n", - "logreg = LogisticRegression()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Split the dataset into a training set and a test set." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "X_train = X[:50]\n", - "y_train = y[:50]\n", - "X_test = X[50:]\n", - "y_test = y[50:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To get an idea of how hard the task is, let us visualize the data again, this time only labeling the training points.\n", - "\n", - "The test points are plottet as white triangles.\n", - "Can we see which class the test points belong to?" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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PVaJ+/fpVOteVK1ciKyurSm3QU2hlhX4F6ahboudSdna2eHfkW8LK2lLMmjVL\nGBsbi6CgIKHRaIS/v78YM2aMMLMyEQ3bGYsPPhlXdtw777wjpkyZIgYMGCBMTU2Fo6OjmDNnzhP7\nWbp0qejVq5eYPHmyGDFixGNfM2X6l8LGxUx4jjARdd3NxEefTajSuV24cEHIZDIxYULV2nlRVCY7\n+cYrUS138OBBTJz8EfQ1hjh18hTs7OzwzTffoH379lAoFJg/fz66deuGkzEncTL6JORyOVJSUhAU\nFISUlBRkZGQgKCgIrVu3RkJCAq5cuQIzM7NyfRQXF6NJkybYtm0b3Nzc4OHhgdOnT8PV1fWReo4f\nP46kpCR4enri5ZdfrtK5DRo0CPb29lizZg2SkpLQsGHDKrUndVwnTyQxhw8fxqtv9oGeSo4pX0zF\nkSNHYG5ujk8++QR6enowMjKCsbExbG1tkZiQiFu3bqFRo0aYNWsWHBwcMG/ePMhkMpibmyMmJgaB\ngYEYO3Ys+vXrhy5duqBOnToAgNWrV0OtViMmJgYxMTFo7NYYHV5uh48/mogJ4yaUuxdNhw4d0KFD\nhyqfW2JiIhQKBa5cuQIA+OabbzB//vwqt0vlcSRPVIv1GdQL5+T7cGenKUxs5NAzFlDekUFADY1K\noF3b9rC2tgZQ+u9q+vTpcHNzw65duzBlyhSkpKTAzMwM+vr6yM3NhampKXLzc2DpL6C5Vgcz/jsL\nenp6+OuvvzB3/hzomwkU3iuBqTNg1kwF5R9GmDD0C0ya+B+tn9vfo/jx48fj5s2b6N69O/744w+O\n5p+CtzUgkpi+r/dC1MkoLP1mBUaOHQ63KYXI2Ad02Af8FQmkhNdFRnrWI3d93Lx5M4QQSE1NRUpK\nCoyMjPBbbDQy612Ax0SgfhfgeIgM+eeMYN9bH6k7CuAaLnBrPwAZUL9z6bp8u1BAc7gRriZd1/q5\nBQQE4Pbt2+X2LV++HN26ddN6X1LB6RoiiWnexB/pl//C4MGDcfpsHL77cilabi79BKp9KHB60H3k\n5ubCwsKi7JiEhAQMGTIE5vWMoVe/GPnpAhb1TaDM0aDZf0oDXl0IFJw3hrpQwC0iH7avAucmANAA\n3S8B+kaAx8fAfg/ApXH1LMKLi4urlnapPC6hJKql1Go1tm3bhrZt22HTpk1wcnCGMl8Fowalz2fs\nAyysLWBubg4A2LJlC65cuYIR743AsLBh0KgFOh7TwOMzAcOmBbDprETCZ6UBf2WJDEGt2mP8Bx/g\nykxjWLfI9jJiAAAMu0lEQVQEim6VfuBK36i0fWN7QGYAjBk+QTcXgLSC0zVEtZRSqUR4eDgKCgrK\n9l26fAnJKYmwdjNB/nUNIrfvRVpaGvz8/NC6dWt07NgRMTExSE9PR/iH7+Oc/RbYvapC7FDA+0vg\n7DigJAcw1jdB9IloODs7w6WJI+p2UyJjv0CJSo3W60qna1IWAbeWWeFW+h3eQriWqNHpmq1btyIi\nIgLJycmIi4tDixYtyp5btGgRFi9eDLlcju+//77c3fOI6N+pVCps27YNnp6eCAoKKreaJT09HTdv\n3oSnpyfu3LmD4OBg+Pv744033sDPP/+M/v37Q61W48Pwj9ExZAfyM1Ww9AGurdZDg64a2PjLkLnA\nAkePHgUANHHxwt24O7BvpIH1h9eRNBmITQNMnYBXOndjwD/nKj2ST05Ohp6eHkaNGoV58+aVhXxm\nZiY6duyIgwcPIjU1FR9++CHi4+PLd8qRPNETqdVq9OrfHUl3TsEiUImb2+SYNulrjAt/9Ha/YWFh\nMDExwerVq3Hs2DFERUVh+vTpKCkpgVqthkZoIAxKYGxmDCsrK5i9koX6PdW4vV+Ou7/poX5RE3QJ\nDoG/vz8W//A/GIw7j0YDS9tOGGeAARYf4+sZs2r4CtCT1OhI3svL67H7Y2Ji0KNHDzg5OcHJyQlC\nCOTm5pbNGxLR0x05cgQJ6TFoezofegaAy7gSTGw+EaNHhpe7ve+VK1ewe/dupKSkwNjYGIsWLcKk\nSZMwffp0/Pbbb6hfvz4KCgqg0Wggl8thZGSEoI6tkH4hF5ABhll1cOD4wbIli87Ozgh9rRfuxRTh\nXqwM+cl6uP1qFjIyMmBnZ6ery0FVpPXVNbGxsfD29i7b9vT0RGxsLLp06VLudREREWV/Dg4ORnBw\nsLZLIXou3blzB3WayKD34F+nqQugEWq8OqQvDh84CjMLU7zzRhhuZ93G66+/jtzcXAwdOhRt27ZF\nZORuqNUahPTsjF1bd6Ndu3bl2k6MT8bhw4ehUPyKjUnr4Obpil6hPfHj8nXo1KkToo+ewoSPxyEz\n6yS8Zilx4sJatGq7FxfO/F62Hp9qjkKhgEKhqFIbTw35rl27IiMj45H9M2fORGho6GOPedyvEo/7\n5vZ/hjwRPdS2bVtkjtcgYz9Qtw2QMlcf5nXNkCQ/gi5Xi3H9pyLMHTMXjRs3hlKpRGRkJABAbiiH\nZa88+H+nQeavQHD3DjA0MELP3j2wetlamJubw9zcHLa2tlizbSVa7S6AqQtwduw+jBw3HBtX/4Tm\nzZvjVEwMgi8oYeoEACrEp+dg586dCAsL0+VleSH9/wHw31/8UhFPDflDhw5VuMHAwEAcPny4bDs5\nORkBAQEVbofoReXs7IxdWyIx7P23cPrPLLRq0wI3ihPh9U0xDG2A25vrYPDgV/Dbb78hPT0dMpkM\nmZmZaOzthIANGshkpbcBvtpBwHFwEc4d2oew0e9g6/odAIBDhw/BPqwQ1i1L+/OcVYyDbQ+W9a8q\nUcOgzsN69M0FlEplTV4C0iKtvG3+z9F769atceDAAaSnp0OhUEBPT4/z8UQV9PLLLyPt0g0U5hXj\n+KGTsLG1Rm4ykPUroHfLEuvWrYO5uTl+/vlnAIC5uTlURRoU/ll6vEYJFFwDzFwB73nFOLDvYYjX\ntamLomSjsu3cZMCmrhWA0t+6h749BGffMEHWceDqUhmy9hmgd+/eNXfypF2VveXljh07RKNGjYSx\nsbFo0KCB6NGjR9lzCxYsEG5ubsLb21tERUU9cmwVuiV6IUVGRoo6tsairqu5WLdunRBCiF27dgk/\nPz+h0WiEEELMmvO1sHExFZ4T9IWVH4TDaxCvqiE6RUE4uNmVtZWTkyM8X3IXLqGmwmu8obCoZyr2\n7dtX9rxSqRSfTv5E+LZpKrr16SwSEhJq9mTpiSqTnfwwFNFz4uDBg+jZsydkMlnZvyEhBC5evAgP\nDw8AwNGjRxEdHY3lq5bCwOc+TD1K8Oc6OX5ctgH9+/cvaysvLw+bNm1CTk4OunXrBh8fH12dFlUA\nb1BGJHEqlarcvx2ZTFZuWeXf8vPzsX79emRnZyMkJAQtW7asyTKpmjDkiYgkrDLZyc8rExFJGEOe\niB5r3bp1+OGHH3RdBlURp2uI6BH5+flwc3ODWq1GSkoKLC0tdV0SgdM1RKQly5YtQ4cOHdCzZ08s\nXrxY1+VQFXAkT0Tl/D2KP3ToEIyMjNCuXTuO5msJjuSJqMqWLVsGa2trJCUlIT4+Hg0bNuRo/jnG\n73glonJsbGzg6+uLXbt2QQiBO3fuIDExUddlUSVxuoaInujAgQMYPnw41Go1rly5AlNTU12X9ELj\ndA0RaY0QAhEREZg3bx7atm2L5cuX67okqgRO1xDRYx08eBA5OTl47bXX4OXlhR49emDUqFEczT9n\nGPJE9FizZs2CgYFB2ZeFFBQUYM2aNRg9erSOK6OK4Jw8ET3W0aNHcf369XL72rdvDzc3Nx1VRLxB\nGRGRhPGNVyIiKochT0QkYQx5IiIJY8gTEUkYQ56ISMIY8kREEsaQJyKSMIY8EZGEMeSJiCSMIU9E\nJGEMeSIiCWPIExFJGEOeiEjCGPJERBLGkCcikjCGPBGRhDHkiYgkjCFPRCRhDHkiIgljyBMRSRhD\nXscUCoWuS6g1eC0e4rV4iNeiaiod8lu3bkWzZs2gr6+P+Pj4sv1paWkwMTGBv78//P39ER4erpVC\npYo/wA/xWjzEa/EQr0XVGFT2QB8fH+zcuROjRo165Dl3d3ecPXu2SoUREVHVVTrkvby8tFkHERFV\nB1FFwcHB4syZM2XbqampwszMTPj6+oqRI0eKc+fOPXIMAD744IMPPirxqKinjuS7du2KjIyMR/bP\nnDkToaGhjz3G3t4e169fh7W1Nfbt24e33noLCQkJ5V5TmvNERFTdnhryhw4dqnCDhoaGMDQ0BAD0\n7NkTkydPRkpKCtzd3StXIRERVZpWllD+c2R++/ZtqNVqAEB8fDwKCwsZ8EREOlLpkN+5cyccHR1x\n6tQp9O7dGz179gQAHDt2DL6+vvDz88PMmTOxfPlyrRVLREQVVIX3XCtsy5YtomnTpkJPT6/cm7VC\nCLFw4ULh7u4uvL29xfHjx2uyrFphypQpwsHBQfj5+Qk/Pz+xb98+XZdUo44dOya8vLyEu7u7WLRo\nka7L0TlnZ2fh4+Mj/Pz8REBAgK7LqTHDhg0T9evXF82bNy/bl5OTI/r06SMcHR1F3759RW5urg4r\nrDmPuxaVyYkaDfmLFy+KS5cuPbIi59atW8LT01Ncu3ZNKBQK4e/vX5Nl1QoRERFi3rx5ui5DZ/z8\n/MSxY8dEWlqa8PT0FFlZWbouSadcXFzEnTt3dF1GjYuKihLx8fHlgm327Nli7NixoqioSIwZM0bM\nmTNHhxXWnMddi8rkRI3e1sDLywseHh6P7I+JiUGPHj3g5OSETp06QQiB3NzcmiytVhAv6Kqj+/fv\nAwA6duwIZ2dndOvWDTExMTquSvdexJ+HDh06wNrauty+2NhYDB8+HEZGRggLC3thfjYedy2Aiv9c\n1Ip718TGxsLb27ts29PTE7GxsTqsSDcWL16MoKAgzJ49+4X6Ty4uLq7ch+uaNm2KU6dO6bAi3ZPJ\nZOjcuTP69euH3bt367ocnfrnz4eXl9cLmQ3/VNGc0HrId+3aFT4+Po88IiMjn3jM4/5nkslk2i5N\n5550bXbv3o3Ro0cjNTUVBw4cwJUrV/iG9QsuOjoa58+fx9dff42PPvrosZ9XeVG8iL/RPEllcqLS\ntzV4ksqsrQ8MDMThw4fLtpOTkxEQEKDNsmqFZ7k2lpaWGDNmDMLDw/HJJ5/UQFW6FxAQgIkTJ5Zt\nJyUloUePHjqsSPcaNmwIAPD29kafPn0QGRmJ9957T8dV6UZAQAAuXrwIf39/XLx4UZLZ8Kzq168P\noGI5obPpmn/+79y6dWscOHAA6enpUCgU0NPTg7m5ua5K04mbN28CAFQqFTZu3IhevXrpuKKaY2lp\nCQCIiopCWloaDh06hMDAQB1XpTsFBQVlv4ZnZWXhwIEDL/R/eoGBgVi1ahUKCwuxatUqBAUF6bok\nnalUTmjtreBnsGPHDtGoUSNhbGwsGjRoIHr06FH23IIFC4Sbm5vw9vYWUVFRNVlWrfDWW28JHx8f\n0bJlS/Hhhx++cCsrFAqF8PLyEm5ubmLhwoW6Lkenrl69Knx9fYWvr6/o3LmzWLlypa5LqjGDBw8W\nDRs2FIaGhqJRo0Zi1apVL+wSyr+vhVwuF40aNRIrV66sVE7IhOCEFxGRVNWK1TVERFQ9GPJERBLG\nkCcikjCGPBGRhDHkiYgkjCFPRCRh/wec8kSBPkpGngAAAABJRU5ErkJggg==\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.prism()\n", - "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train)\n", - "plt.scatter(X_test[:, 0], X_test[:, 1], c='white', marker='^')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That doesn't look too hard.\n", - "\n", - "Now let's fit the logistic regression model to the training data, to see if we can do it automatically:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", - " intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logreg.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we have a look at how logistic regression did on the training set.\n", - "\n", - "We do this by now setting colors using the predicted class." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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mKCiIK6+8klq1alGmTBmuv/561q/czNM936BJ9TYsWrCI0pFhvDD6eYwxPPX4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\nNlLoiojY6P8B89y1HqfLjHwAAAAASUVORK5CYII=\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from utility import plot_decision_boundary\n", - "y_pred_train = logreg.predict(X_train)\n", - "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_pred_train)\n", - "plot_decision_boundary(logreg, X)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That looks pretty good. It is not always that easy to inspect the result visually,\n", - "so we can also use the logistic regression object to calculate a core for us:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy on training set: 1.0\n" - ] - } - ], - "source": [ - "print \"Accuracy on training set:\", logreg.score(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1.0 means an accuracy of 100%, just as it looked like.\n", - "\n", - "Now let us have a look at how the algorithm generalizes to the test data." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy on test set: 1.0\n" - ] - }, - { - "data": { - "image/png": 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IwORlIivdxak9qezbt59adWtQf1kKxarBlv/a+L9K/Xl90Bt06daZbaUWcPw7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\nQ1cppdzo/wEzbZF7AN/ugwAAAABJRU5ErkJggg==\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_pred_test = logreg.predict(X_test)\n", - "plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred_test, marker='^')\n", - "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_pred_train)\n", - "plot_decision_boundary(logreg, X)\n", - "print \"Accuracy on test set:\", logreg.score(X_test, y_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also take a look at the coefficients, but they probably don't tell you much." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0.42842754, -0.62076837]])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logreg.coef_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "MNIST DIGITS\n", - "-------------\n", - "Let's go back to the MNIST example of before.\n", - "This dataset is a bit more exciting since it is much higher dimensional and not as easy to separate.\n", - "First we load the data again" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.datasets import fetch_mldata\n", - "mnist = fetch_mldata(\"MNIST original\")\n", - "X_digits, y_digits = mnist.data, mnist.target" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Remember what the dataset looks like:\n", - "\n", - "- 70000 examples, 784 dimensional\n", - "- ten classes, 0-9\n", - "- each 784 vector is a 28 * 28 digit" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X_digits.shape: (70000, 784)\n", - "Unique entries of y_digits: [ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]\n" - ] - } - ], - "source": [ - "print \"X_digits.shape:\", X_digits.shape\n", - "print \"Unique entries of y_digits:\", np.unique(y_digits)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(y_digits[0])\n", - "plt.rc(\"image\", cmap=\"binary\")\n", - "plt.matshow(X_digits[0].reshape(28, 28))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We could try to learn all ten classes with logistic regression, but let us keep with two classes for the moment.\n", - "We will use two classes that are quite hard to distinguish: seven and nine.\n", - "\n", - "To create a dataset only consisting of the classes zero and one, we need a new numpy trick!\n", - "\n", - "We can not only slice our data using ranges, like ``X[5:10]``, we can also select elements using conditions, like so:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zeros.shape: (6903, 784)\n", - "ones.shape: (7877, 784)\n" - ] - } - ], - "source": [ - "zeros = X_digits[y_digits==0] # select all the rows of X where y is zero (i.e. the zeros)\n", - "ones = X_digits[y_digits==1] # select all the rows of X where y is one (i.e. the ones)\n", - "print \"zeros.shape: \", zeros.shape\n", - "print \"ones.shape: \", ones.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lets have a quick look to see that we did it right." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.matshow(ones[0].reshape(28, 28)) # change the 0 to another number to see some more zeros. Or try looking at some ones." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we generate some new data set.\n", - "\n", - "For the training labels y, we write as many 0's as the ``zeros`` array is long, the asame for ``ones``." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X_new.shape: (14780, 784)\n", - "y_new.shape: (14780,)\n", - "y_new: [0 0 0 ..., 1 1 1]\n" - ] - } - ], - "source": [ - "X_new = np.vstack([zeros, ones]) # this \"stacks\" sevens and nines vertically\n", - "print \"X_new.shape: \", X_new.shape\n", - "y_new = np.hstack([np.repeat(0, zeros.shape[0]), np.repeat(1, ones.shape[0])])\n", - "print \"y_new.shape: \", y_new.shape\n", - "print \"y_new: \", y_new" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we shuffle them around and create a training and test dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.utils import shuffle\n", - "X_new, y_new = shuffle(X_new, y_new)\n", - "X_mnist_train = X_new[:5000]\n", - "y_mnist_train = y_new[:5000]\n", - "X_mnist_test = X_new[5000:]\n", - "y_mnist_test = y_new[5000:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let us learn a logistic regression model." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", - " intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "logreg.fit(X_mnist_train, y_mnist_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "... and visualize the coefficients.\n", - "\n", - "We can see that the middle is dark, corresponding to high positive values for where\n", - "a 1 would be. Around it is a lighter circle, corresponding to the position of a 0." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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sHOFQQLFarVi3bh127NiBKVOmONSpKS0tzeXGEeJpg7/8Bn4xOsLXA8qwOa7e\n3l6sWbMGmZmZSE9PB8AfsIUQX+fqINXeTvIMhTGG7OxszJs3D48//rhQbhuw5dVXX7UbsGU08LI8\nUh3deIPI8IYTlPrWmTVrlmh5UFAQt05ISIhoudTkYLyJy3jZnKioKO66nNlP3lCTUtm0L7/8UrTc\nNpDQYFJt5u2/1NkyL2sk1XHUVXSGIuH48eMoKirCxx9/jLi4OMTFxeHw4cPIyclBa2sr1Go12tvb\n8cgjj3iqvYTc1OimrITExETuswTvvfeeWxpEiDcby8HCEfSkLCEyooBCCJENBRRCiGwooBBCZDOW\nU8KO8PqAwpvVT6pD35133ilaHhwcLFouNbL7iRMnRMv9/f25dXgj0t9xxx3cOrxvvhkzZoiW88bH\nBfgpWKkxXXlz3kp1zouMjBQt57VZ6pjx0sMBAQHcOqOBzlAIIbKhgEIIkY2vBxTfvuAjRGbuerDN\narUiPT0d4eHhWLVqFXfsGrPZDK1Wi+joaOTn5w9b/+rVq/j5z3+OqVOn4rHHHrNb1z333AONRiM8\n1Cr19LkNBRRCZOSugOLoGERbtmxBQUEBjhw5gj179qCrq0uy/sSJE/HHP/4Rf/7zn0X3pbi4GLW1\ntaitrXVo9koKKITIiNcZ8MyZM/jb3/4mvEbKkTGIbJPOJSUlISIiAkuXLkVVVZVk/VtuuQWLFy/m\nTjI30ulSvf4eCq+jmVTGgDec4OXLl0XLpTJGvI52vE6LAD/LJDW3Ma8NvLmdpWY75B0bqVn4eJ0d\npeYj5uENtSnFW+5N8Nppu2yweeutt0a03pqammHHIBq4DADodDpUVVUhNTV12Pq8dj/00EMICQnB\n+vXr8dBDDw3bTq8PKITcTFwJfPfff7/oBPMvvPCC0xOr29rjTP133nkHs2bNwldffYVf/vKXiImJ\nQUJCgmQdCiiEyMiVgFJeXs597+2330Z9fT3i4uK4YxDp9Xps27ZN+L2urg4pKSnCe8PVH8w2NEdE\nRATWr1+PkpKSYQMK3UMhREbuuilrG4Oop6eHOwaR7fLfbDajpaUF5eXlMBqNDtUffAZz48YNIavT\n3d2NkpISrFixYth2UkAhREbuCii8MYi+/vprpKamCsvt3LkTJpMJ9913HzZt2iRkZqTGMIqMjMST\nTz6Jt956C2FhYWhoaMD169eRkpKCBQsWIDU1FcuXL8fixYuHbSdd8hAiI3fdPJ46daroGESzZs3C\nhx9+KPxqwRt2AAAF2UlEQVSenJwsDM/qSH0AaGlpES3ndSuR4vUBhZfl4Q2zCEj3PxFz6623ct/j\n5eZ5mSQAOHXqlGh5c3Mztw5vqMX58+eLlks9hDR79mzRct5gWgC/09v48V7/EZIVdQ4khMjGW9Lb\n7kIBhRAZUUAhhMiGAgohRDYUUAghsqGAQgiRDQUUCW1tbXjwwQdx5coVBAYG4uGHH8YDDzyA559/\nHvv370dgYCAA4KWXXhIe8b1ZSHXO4+HNjieVZualWqXSxrxOeGfOnOHW4c12xxsXQ2r7vOEceUND\nAs71BfFFlDaW4O/vjx07diA2NhadnZ0wGAxYuXIlFAoFtm7diq1bt3qqnYR4BTpDkRAcHCwM3Dxz\n5kzExMSgpqYGAH1jESKGAoqDzp8/j7q6OhiNRlRWViI/Px/vvvsuVq9ejU2bNg2ZgLy0tFT4Wa1W\nQ61Wy9dqQtyksbERjY2NTtengOIAq9WKdevWYceOHZg8eTJycnLw7LPPoru7G9u2bUNBQQHy8vLs\n6qSlpbmlwYS40+Avv4FfjI7w9YAy7B2k3t5erFmzBpmZmUhPTwfQP3qXQqHAtGnTsHnzZpSUlLi9\noYR4A3f1NvYWkmcojDFkZ2dj3rx5ePzxx4XyixcvIiQkBH19fSguLnZonARv4KlJo3hDMN57770j\nXldPT49o+cSJE0e8LuK6sRwsHCEZUI4fP46ioiLceeedwniYL774Ig4cOIBTp04hICAASUlJyMnJ\n8UhjCbnZUdpYQmJiouhzFsuXL3dbgwjxZnSGQgiRDQUUQohsKKAQQmRDAYUQIhsKKMSr8Tr0SXX0\nI+7j6wHFIzkuVx5l9vbt+/K+++L2eXMbD36NVRRQxvC2afue376vPyk7dkMlIaPAXQHFarUiPT0d\n4eHhWLVqFXccHLPZDK1Wi+joaOTn5w9b32KxIC4uDrGxsbj33ntx+PBhoU59fT0WLlwIlUqFZ555\nxqF2UkAhREbuCih79+5FeHg4zp07h9DQUOzbt090uS1btqCgoABHjhzBnj170NXVJVl//vz5+PTT\nT3Hq1CkUFBQgNzdXGJrkySefxNNPP42amhocPXrUsYm/mBsAoBe9xsxrJJ/7CxcuOPQayXoZY2zN\nmjWstraWMcbYp59+ytauXTtkmW+//ZbFxsYKvz/22GPsgw8+cLj+yZMnmUajEX5XqVTCz6+99hr7\n61//Omw73ZLlYTT4EvFRvLOPTz75BP/973+dXm9NTQ00Gg0AQKPRwGKxSC4DADqdDlVVVUhNTZWs\nb7FY8Jvf/AYXLlxAZWUlgP7xj4KCguzW9c4772Dz5s2S7aS0MSEy4gWUxYsX2002/pe//GXIMvff\nfz8uXbo0pPyFF15w+kva1h6p+gaDAefOncOxY8ewcuVKtLe3D1ne0e1TQCFERq6khMvLy7nvvf32\n26ivr0dcXBzq6+uh1+uHLKPX67Ft2zbh97q6OmHweL1eP2z9xMREzJ49G+fOnYNarcbly5eF986e\nPYtFixYNuw90U5YQGbnrpqzRaERhYSF6enpQWFgo+s89bdo0AP2ZnpaWFpSXl8NoNErWb2lpEWZh\nOHPmDK5fvy6MWKfRaPCPf/wDnZ2dKCkpEdYlafjbQYQQRwBgV65cceg10n+97u5ulpaWxsLCwlh6\nejqzWq2MMcba29vZihUrhOUqKiqYRqNhc+fOZbt27Rq2/t///ncWExPDYmNj2bp161hlZaVQp66u\njsXFxbHIyEj2u9/9zqF2Kv7/gSCEuEihUKCjo8OhZQMDA8dk8oLuoRAio7H8FKwjKKAQIiMKKIQQ\n2Yzljn+OoIBCiIzoDIUQIhsKKIQQ2VBAIYTIhgIKIUQ2FFAIIbKhgEIIkQ2ljQkhsqEzFEKIbCig\nEEJkQwGFECIbCiiEENlQQCGEyIYCCiFENuPGjXNouRkzZri5JaODAgohMhmLI7CNlG8/hUMIkRUF\nFEKIbCigEEJkQwGFECIbCiiEENlQQCGEyOb/AX+QUIg/zZM4AAAAAElFTkSuQmCC\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.matshow(logreg.coef_.reshape(28, 28))\n", - "plt.colorbar()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let as look at the accuracy on training and test set:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy training set: 1.0\n", - "Accuracy test set: 0.998466257669\n" - ] - } - ], - "source": [ - "print \"Accuracy training set:\", logreg.score(X_mnist_train, y_mnist_train)\n", - "print \"Accuracy test set:\", logreg.score(X_mnist_test, y_mnist_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Playing around with this notebook\n", - "=================================\n", - "1. What would be the accuracy of a completely random predictor on the first dataset?\n", - "\n", - "2. Can you still pick out the class yourself if you set the ``cluster_std`` to ``10`` in ``make_blobs``? Can logistic regression do it?\n", - "\n", - "3. Try to separate some other digit classes from MNIST. Which are hard, which are not?\n", - "\n", - "4. Visualize the classes 0 and 1 using PCA down to two dimensions. Use a scatter plot as above. Would you expect they can be separated with a linear classifier?" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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_logistic_regression/demo1/data/artifical_lin.txt b/1_logistic_regression/demo1/data/artifical_lin.txt deleted file mode 100644 index fc158e3..0000000 --- a/1_logistic_regression/demo1/data/artifical_lin.txt +++ /dev/null @@ -1,500 +0,0 @@ -7.475811838082768723e-03 4.320836189610323119e-01 1.450549175723391038e+00 -4.991058415580543750e-01 2.094374801667562291e-01 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port -c.NotebookApp.port = 9999 -#c.NotebookApp.password = u'sha1:60e7d2645fb4:97064d28bad2a4a12950055c830d9637b652c9ec' diff --git a/1_logistic_regression/demo1/utility.py b/1_logistic_regression/demo1/utility.py deleted file mode 100644 index 213a11f..0000000 --- a/1_logistic_regression/demo1/utility.py +++ /dev/null @@ -1,25 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt - - -def plot_decision_boundary(clf, X): - w = clf.coef_.ravel() - a = -w[0] / w[1] - xx = np.linspace(np.min(X[:, 0]), np.max(X[:, 0])) - yy = a * xx - clf.intercept_ / w[1] - plt.plot(xx, yy) - plt.xticks(()) - plt.yticks(()) - -def plotEllipse(pos,P,edge='k',face='none',line_width='.1'): - from numpy.linalg import svd - from matplotlib.patches import Ellipse - import math - from numpy import pi - from matplotlib.pyplot import gca - U, s , Vh = svd(P) - orient = math.atan2(U[1,0],U[0,0])*180/pi - ellipsePlot = Ellipse(xy=pos, width=2.0*math.sqrt(s[0]), height=2.0*math.sqrt(s[1]), angle=orient,facecolor=face, edgecolor=edge, lw=line_width) - ax = gca() - ax.add_patch(ellipsePlot); - return ellipsePlot diff --git a/References_notes.md b/References_notes.md index 50eb9d0..f2d3e6f 100644 --- a/References_notes.md +++ b/References_notes.md @@ -11,4 +11,19 @@ MachineLearningNotebooks/08.%20Practical_NeuralNets.ipynb http://sofasofa.io/competitions.php?type=practice https://www.kaggle.com/competitions -https://github.com/wmpscc/DataMiningNotesAndPractice/blob/master/2.KMeans%E7%AE%97%E6%B3%95%E4%B8%8E%E4%BA%A4%E9%80%9A%E4%BA%8B%E6%95%85%E7%90%86%E8%B5%94%E5%AE%A1%E6%A0%B8%E9%A2%84%E6%B5%8B.md \ No newline at end of file +https://github.com/wmpscc/DataMiningNotesAndPractice/blob/master/2.KMeans%E7%AE%97%E6%B3%95%E4%B8%8E%E4%BA%A4%E9%80%9A%E4%BA%8B%E6%95%85%E7%90%86%E8%B5%94%E5%AE%A1%E6%A0%B8%E9%A2%84%E6%B5%8B.md + +evaluation metrics +http://localhost:8889/notebooks/machineLearning/10_digits_classification.ipynb + +model selection and assessment +http://localhost:8889/notebooks/machineLearning/notebooks/01%20-%20Model%20Selection%20and%20Assessment.ipynb + +Programming Multiclass Logistic Regression +http://localhost:8889/notebooks/MachineLearningNotebooks/05.%20Logistic%20Regression.ipynb + +Equation for MLP +http://localhost:8889/notebooks/MachineLearningNotebooks/07.%20MLP%20Neural%20Networks.ipynb + +Optimization methods +http://localhost:8889/notebooks/MachineLearningNotebooks/06.%20Optimization.ipynb \ No newline at end of file diff --git a/exercise/1_python.ipynb b/exercise/1_basic/1_python.ipynb similarity index 100% rename from exercise/1_python.ipynb rename to exercise/1_basic/1_python.ipynb diff --git a/exercise/1_python.py b/exercise/1_basic/1_python.py similarity index 100% rename from exercise/1_python.py rename to exercise/1_basic/1_python.py diff --git a/exercise/2_numpy.ipynb b/exercise/1_basic/2_numpy.ipynb similarity index 100% rename from exercise/2_numpy.ipynb rename to exercise/1_basic/2_numpy.ipynb diff --git a/exercise/2_numpy.py b/exercise/1_basic/2_numpy.py similarity index 100% rename from exercise/2_numpy.py rename to exercise/1_basic/2_numpy.py diff --git a/exercise/3_matplotlib.ipynb b/exercise/1_basic/3_matplotlib.ipynb similarity index 100% rename from exercise/3_matplotlib.ipynb rename to exercise/1_basic/3_matplotlib.ipynb diff --git a/exercise/3_matplotlib.py b/exercise/1_basic/3_matplotlib.py similarity index 100% rename from exercise/3_matplotlib.py rename to exercise/1_basic/3_matplotlib.py diff --git a/exercise/2_交通事故理赔审核预测/Exercise - 交通事故理赔审核预测.ipynb b/exercise/2_交通事故理赔审核预测/Exercise - 交通事故理赔审核预测.ipynb new file mode 100644 index 0000000..ca68092 --- /dev/null +++ b/exercise/2_交通事故理赔审核预测/Exercise - 交通事故理赔审核预测.ipynb @@ -0,0 +1,931 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercise - 交通事故理赔审核预测\n", + "\n", + "\n", + "这个比赛的链接：http://sofasofa.io/competition.php?id=2\n", + "\n", + "\n", + "* 任务类型：二元分类\n", + "\n", + "* 背景介绍：在交通摩擦（事故）发生后，理赔员会前往现场勘察、采集信息，这些信息往往影响着车主是否能够得到保险公司的理赔。训练集数据包括理赔人员在现场对该事故方采集的36条信息，信息已经被编码，以及该事故方最终是否获得理赔。我们的任务是根据这36条信息预测该事故方没有被理赔的概率。\n", + "\n", + "* 数据介绍：训练集中共有200000条样本，预测集中有80000条样本。 \n", + "![data_description](images/data_description.png)\n", + "\n", + "* 评价方法：Precision-Recall AUC\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Demo code\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# read data\n", + "homePath = \"data\"\n", + "trainPath = os.path.join(homePath, \"train.csv\")\n", + "testPath = os.path.join(homePath, \"test.csv\")\n", + "submitPath = os.path.join(homePath, \"sample_submit.csv\")\n", + "trainData = pd.read_csv(trainPath)\n", + "testData = pd.read_csv(testPath)\n", + "submitData = pd.read_csv(submitPath)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "参照数据说明，CaseID这列是没有意义的编号，因此这里将他丢弃。\n", + "\n", + "~drop()函数：axis指沿着哪个轴，0为行，1为列；inplace指是否在原数据上直接操作\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# 去掉没有意义的一列\n", + "trainData.drop(\"CaseId\", axis=1, inplace=True)\n", + "testData.drop(\"CaseId\", axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 快速了解数据\n", + "\n", + "~head()：默认显示前5行数据，可指定显示多行，例如.head(15)显示前15行\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"~hist():绘制直方图，参数figsize可指定输出图片的尺寸。\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " [,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]],\n", + " dtype=object)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "trainData.hist(figsize=(20, 20))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "想要了解特征之间的相关性，可计算相关系数矩阵。然后可对某个特征来排序。\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Evaluation 1.000000\n", + "Q28 0.410700\n", + "Q30 0.324421\n", + "Q36 0.302709\n", + "Q35 0.224996\n", + "Q34 0.152743\n", + "Q32 0.049397\n", + "Q21 0.034897\n", + "Q33 0.032248\n", + "Q13 0.023603\n", + "Q8 0.021922\n", + "Q19 0.019694\n", + "Q20 0.013903\n", + "Q4 0.011626\n", + "Q27 0.004262\n", + "Q23 0.002898\n", + "Q7 0.001143\n", + "Q31 -0.000036\n", + "Q14 -0.000669\n", + "Q29 -0.002014\n", + "Q10 -0.002711\n", + "Q12 -0.005287\n", + "Q1 -0.006511\n", + "Q16 -0.007184\n", + "Q18 -0.007643\n", + "Q26 -0.008188\n", + "Q11 -0.009252\n", + "Q24 -0.010891\n", + "Q22 -0.011821\n", + "Q25 -0.012660\n", + "Q6 -0.016072\n", + "Q2 -0.018307\n", + "Q15 -0.019570\n", + "Q9 -0.021261\n", + "Q5 -0.023893\n", + "Q3 -0.026349\n", + "Q17 -0.028461\n", + "Name: Evaluation, dtype: float64" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "corr_matrix = trainData.corr()\n", + "corr_matrix[\"Evaluation\"].sort_values(ascending=False) # ascending=False 降序排列" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "从训练集中分离标签" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "y = trainData['Evaluation']\n", + "trainData.drop(\"Evaluation\", axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "使用K-Means训练模型\n", + "\n", + "KMeans()：\n", + "* `n_clusters`指要预测的有几个类；\n", + "* `init`指初始化中心的方法，默认使用的是`k-means++`方法，而非经典的K-means方法的随机采样初始化，当然你可以设置为random使用随机初始化；\n", + "* `n_jobs`指定使用CPU核心数，-1为使用全部CPU。" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# do k-means\n", + "from sklearn.cluster import KMeans\n", + "est = KMeans(n_clusters=2, init=\"k-means++\", n_jobs=-1)\n", + "est.fit(trainData, y)\n", + "\n", + "y_train = est.predict(trainData)\n", + "y_pred = est.predict(testData)\n", + "\n", + "# 保存预测的结果\n", + "submitData['Evaluation'] = y_pred\n", + "submitData.to_csv(\"submit_data.csv\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "acc_train = 0.682140\n" + ] + } + ], + "source": [ + "# calculate accuracy\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "acc_train = accuracy_score(y, y_train)\n", + "print(\"acc_train = %f\" % (acc_train))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 随机森林\n", + "\n", + "使用K-means可能得到的结果没那么理想。在官网上，举办方给出了两个标杆模型，效果最好的是随机森林。以下是代码，读者可以自己测试。\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# 读取数据\n", + "train = pd.read_csv(\"data/train.csv\")\n", + "test = pd.read_csv(\"data/test.csv\")\n", + "submit = pd.read_csv(\"data/sample_submit.csv\")\n", + "\n", + "# 删除id\n", + "train.drop('CaseId', axis=1, inplace=True)\n", + "test.drop('CaseId', axis=1, inplace=True)\n", + "\n", + "# 取出训练集的y\n", + "y_train = train.pop('Evaluation')\n", + "\n", + "# 建立随机森林模型\n", + "clf = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "clf.fit(train, y_train)\n", + "y_pred = clf.predict_proba(test)[:, 1]\n", + "\n", + "# 输出预测结果至my_RF_prediction.csv\n", + "submit['Evaluation'] = y_pred\n", + "submit.to_csv('my_RF_prediction.csv', index=False)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.00177294 0.00207449 0.00187096 0.00471492 0.00443815 0.0029538\n", + " 0.00364967 0.00652341 0.00235713 0.00739511 0.00245106 0.00106103\n", + " 0.0007513 0.00090631 0.00150727 0.0037793 0.00183821 0.00196833\n", + " 0.00209665 0.00726069 0.00816243 0.00107563 0.00559247 0.00766561\n", + " 0.00760666 0.00028462 0.00025573 0.18472067 0.25559838 0.21436631\n", + " 0.0425301 0.00662325 0.00297955 0.03148822 0.03907383 0.13060584]\n", + "[0 0 0 ... 0 0 0]\n", + "acc_train = 0.931525\n" + ] + } + ], + "source": [ + "# freature importances\n", + "print(clf.feature_importances_)\n", + "\n", + "# Train accuracy\n", + "from sklearn.metrics import accuracy_score\n", + "y_train_pred = clf.predict(train)\n", + "print(y_train_pred)\n", + "\n", + "acc_train = accuracy_score(y_train, y_train_pred)\n", + "print(\"acc_train = %f\" % (acc_train))" + ] + } + ], + "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" + }, + "main_language": "python" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/exercise/2_交通事故理赔审核预测/Exercise - 交通事故理赔审核预测.py b/exercise/2_交通事故理赔审核预测/Exercise - 交通事故理赔审核预测.py new file mode 100644 index 0000000..2933e1a --- /dev/null +++ b/exercise/2_交通事故理赔审核预测/Exercise - 交通事故理赔审核预测.py @@ -0,0 +1,169 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext_format_version: '1.2' +# 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 +# --- + +# # Exercise - 交通事故理赔审核预测 +# +# +# 这个比赛的链接：http://sofasofa.io/competition.php?id=2 +# +# +# * 任务类型：二元分类 +# +# * 背景介绍：在交通摩擦（事故）发生后，理赔员会前往现场勘察、采集信息，这些信息往往影响着车主是否能够得到保险公司的理赔。训练集数据包括理赔人员在现场对该事故方采集的36条信息，信息已经被编码，以及该事故方最终是否获得理赔。我们的任务是根据这36条信息预测该事故方没有被理赔的概率。 +# +# * 数据介绍：训练集中共有200000条样本，预测集中有80000条样本。 +# ![data_description](images/data_description.png) +# +# * 评价方法：Precision-Recall AUC +# + +# ## Demo code +# + +import pandas as pd +import numpy as np +import os +import matplotlib.pyplot as plt +# %matplotlib inline + +# read data +homePath = "data" +trainPath = os.path.join(homePath, "train.csv") +testPath = os.path.join(homePath, "test.csv") +submitPath = os.path.join(homePath, "sample_submit.csv") +trainData = pd.read_csv(trainPath) +testData = pd.read_csv(testPath) +submitData = pd.read_csv(submitPath) + +# 参照数据说明，CaseID这列是没有意义的编号，因此这里将他丢弃。 +# +# ~drop()函数：axis指沿着哪个轴，0为行，1为列；inplace指是否在原数据上直接操作 +# + +# 去掉没有意义的一列 +trainData.drop("CaseId", axis=1, inplace=True) +testData.drop("CaseId", axis=1, inplace=True) + +# # 快速了解数据 +# +# ~head()：默认显示前5行数据，可指定显示多行，例如.head(15)显示前15行 +# + +trainData.head(15) + + +# 显示数据简略信息，可以每列有多少非空的值，以及每列数据对应的数据类型。 +# +# + +trainData.info() + + +# ~hist():绘制直方图，参数figsize可指定输出图片的尺寸。 +# + +trainData.hist(figsize=(20, 20)) + + +# 想要了解特征之间的相关性，可计算相关系数矩阵。然后可对某个特征来排序。 +# +# + +corr_matrix = trainData.corr() +corr_matrix["Evaluation"].sort_values(ascending=False) # ascending=False 降序排列 + +# 从训练集中分离标签 + +y = trainData['Evaluation'] +trainData.drop("Evaluation", axis=1, inplace=True) + +# 使用K-Means训练模型 +# +# KMeans()： +# * `n_clusters`指要预测的有几个类； +# * `init`指初始化中心的方法，默认使用的是`k-means++`方法，而非经典的K-means方法的随机采样初始化，当然你可以设置为random使用随机初始化； +# * `n_jobs`指定使用CPU核心数，-1为使用全部CPU。 + +# + +# do k-means +from sklearn.cluster import KMeans +est = KMeans(n_clusters=2, init="k-means++", n_jobs=-1) +est.fit(trainData, y) + +y_train = est.predict(trainData) +y_pred = est.predict(testData) + +# 保存预测的结果 +submitData['Evaluation'] = y_pred +submitData.to_csv("submit_data.csv", index=False) + +# + +# calculate accuracy +from sklearn.metrics import accuracy_score + +acc_train = accuracy_score(y, y_train) +print("acc_train = %f" % (acc_train)) +# - + +# ## 随机森林 +# +# 使用K-means可能得到的结果没那么理想。在官网上，举办方给出了两个标杆模型，效果最好的是随机森林。以下是代码，读者可以自己测试。 +# +# + +# + +import pandas as pd +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score + +# 读取数据 +train = pd.read_csv("data/train.csv") +test = pd.read_csv("data/test.csv") +submit = pd.read_csv("data/sample_submit.csv") + +# 删除id +train.drop('CaseId', axis=1, inplace=True) +test.drop('CaseId', axis=1, inplace=True) + +# 取出训练集的y +y_train = train.pop('Evaluation') + +# 建立随机森林模型 +clf = RandomForestClassifier(n_estimators=100, random_state=0) +clf.fit(train, y_train) +y_pred = clf.predict_proba(test)[:, 1] + +# 输出预测结果至my_RF_prediction.csv +submit['Evaluation'] = y_pred +submit.to_csv('my_RF_prediction.csv', index=False) + + + +# + +# freature importances +print(clf.feature_importances_) + +# Train accuracy +from sklearn.metrics import accuracy_score +y_train_pred = clf.predict(train) +print(y_train_pred) + +acc_train = accuracy_score(y_train, y_train_pred) +print("acc_train = %f" % (acc_train)) diff --git a/exercise/2_交通事故理赔审核预测/data.zip b/exercise/2_交通事故理赔审核预测/data.zip new file mode 100644 index 0000000..33d0f49 Binary files /dev/null and b/exercise/2_交通事故理赔审核预测/data.zip differ diff --git a/exercise/2_交通事故理赔审核预测/data/sample_submit.csv b/exercise/2_交通事故理赔审核预测/data/sample_submit.csv new file mode 100644 index 0000000..0b65cc4 --- /dev/null +++ b/exercise/2_交通事故理赔审核预测/data/sample_submit.csv @@ -0,0 +1,80001 @@ +CaseId,Evaluation +200001,0.3141516 +200002,0.3141516 +200003,0.3141516 +200004,0.3141516 +200005,0.3141516 +200006,0.3141516 +200007,0.3141516 +200008,0.3141516 +200009,0.3141516 +200010,0.3141516 +200011,0.3141516 +200012,0.3141516 +200013,0.3141516 +200014,0.3141516 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On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.\n", + "\n", + "One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.\n", + "\n", + "In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.\n", + "\n", + "## Practice Skills\n", + "* Binary classification\n", + "* Python & SKLearn\n", + "\n", + "## Data\n", + "The data has been split into two groups:\n", + "\n", + "* training set (train.csv)\n", + "* test set (test.csv)\n", + "\n", + "The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the `ground truth`) for each passenger. Your model will be based on `features` like passengers' gender and class. You can also use feature engineering to create new features.\n", + "\n", + "The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic.\n", + "\n", + "We also include `gender_submission.csv`, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like.\n", + "\n", + "### Data description\n", + "![data description1](images/data_description1.png)\n", + "![data description2](images/data_description2.png)\n", + "\n", + "\n", + "### Variable Notes\n", + "pclass: A proxy for socio-economic status (SES)\n", + "* 1st = Upper\n", + "* 2nd = Middle\n", + "* 3rd = Lower\n", + "\n", + "\n", + "## Links\n", + "* [Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic)" + ] + } + ], + "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" + }, + "main_language": "python" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/exercise/4_Titanic/Titanic.py b/exercise/4_Titanic/Titanic.py new file mode 100644 index 0000000..b66aa59 --- /dev/null +++ b/exercise/4_Titanic/Titanic.py @@ -0,0 +1,58 @@ +# --- +# jupyter: +# jupytext_format_version: '1.2' +# 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 +# --- + +# # Titanic +# +# ## Competition Description +# The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. +# +# One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. +# +# In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy. +# +# ## Practice Skills +# * Binary classification +# * Python & SKLearn +# +# ## Data +# The data has been split into two groups: +# +# * training set (train.csv) +# * test set (test.csv) +# +# The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the `ground truth`) for each passenger. Your model will be based on `features` like passengers' gender and class. You can also use feature engineering to create new features. +# +# The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic. +# +# We also include `gender_submission.csv`, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like. +# +# ### Data description +# ![data description1](images/data_description1.png) +# ![data description2](images/data_description2.png) +# +# +# ### Variable Notes +# pclass: A proxy for socio-economic status (SES) +# * 1st = Upper +# * 2nd = Middle +# * 3rd = Lower +# +# +# ## Links +# * [Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic) diff --git a/exercise/4_Titanic/data.zip b/exercise/4_Titanic/data.zip new file mode 100644 index 0000000..5303bf4 Binary files /dev/null and b/exercise/4_Titanic/data.zip differ diff --git a/exercise/4_Titanic/images/data_description1.png b/exercise/4_Titanic/images/data_description1.png new file mode 100644 index 0000000..414bec1 Binary files /dev/null and b/exercise/4_Titanic/images/data_description1.png differ diff --git a/exercise/4_Titanic/images/data_description2.png b/exercise/4_Titanic/images/data_description2.png new file mode 100644 index 0000000..24f6b56 Binary files /dev/null and b/exercise/4_Titanic/images/data_description2.png differ diff --git a/metric/confusion matrix.ipynb b/metric/confusion matrix.ipynb new file mode 100644 index 0000000..0202d17 --- /dev/null +++ b/metric/confusion matrix.ipynb @@ -0,0 +1,98 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 混淆矩阵(confusion matrix)\n", + "\n", + "混淆矩阵是用来总结一个分类器结果的矩阵。对于k元分类，其实它就是一个$k \\times k$的表格，用来记录分类器的预测结果。\n", + "\n", + "对于最常见的二元分类来说，它的混淆矩阵是2乘2的，如下\n", + "![confusion_matrix1](images/confusion_matrix1.png)\n", + "\n", + "* `TP` = True Postive = 真阳性\n", + "* `FP` = False Positive = 假阳性\n", + "* `FN` = False Negative = 假阴性\n", + "* `TN` = True Negative = 真阴性\n", + "\n", + "你要的例子来了。。。比如我们一个模型对15个样本进行预测，然后结果如下。\n", + "\n", + "* 预测值：1 1 1 1 1 0 0 0 0 0 1 1 1 0 1\n", + "* 真实值：0 1 1 0 1 1 0 0 1 0 1 0 1 0 0\n", + "\n", + "![confusion_matrix2](images/confusion_matrix2.png)\n", + "\n", + "\n", + "这个就是混淆矩阵。混淆矩阵中的这四个数值，经常被用来定义其他一些度量。\n", + "\n", + "\n", + "### 准确度\n", + "```\n", + "Accuracy = (TP+TN) / (TP+TN+FN+TN)\n", + "```\n", + "\n", + "在上面的例子中，准确度 = (5+4) / 15 = 0.6\n", + "\n", + "\n", + "\n", + "### 精度(precision, 或者PPV, positive predictive value) \n", + "```\n", + "precision = TP / (TP + FP)\n", + "```\n", + "在上面的例子中，精度 = 5 / (5+4) = 0.556\n", + "\n", + "\n", + "\n", + "### 召回(recall, 或者敏感度，sensitivity，真阳性率，TPR，True Positive Rate) \n", + "\n", + "```\n", + "recall = TP / (TP + FN)\n", + "```\n", + "\n", + "在上面的例子中，召回 = 5 / (5+2) = 0.714\n", + "\n", + "\n", + "\n", + "### 特异度(specificity，或者真阴性率，TNR，True Negative Rate)\n", + "```\n", + "specificity = TN / (TN + FP)\n", + "```\n", + "\n", + "在上面的例子中，特异度 = 4 / (4+2) = 0.667\n", + "\n", + "\n", + "\n", + "### F1-值(F1-score) \n", + "```\n", + "F1 = 2*TP / (2*TP+FP+FN) \n", + "```\n", + "在上面的例子中，F1-值 = 2*5 / (2*5+4+2) = 0.625\n", + "\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" + }, + "main_language": "python" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/metric/confusion matrix.py b/metric/confusion matrix.py new file mode 100644 index 0000000..4991b70 --- /dev/null +++ b/metric/confusion matrix.py @@ -0,0 +1,87 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext_format_version: '1.2' +# 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 +# --- + +# # 混淆矩阵(confusion matrix) +# +# 混淆矩阵是用来总结一个分类器结果的矩阵。对于k元分类，其实它就是一个$k \times k$的表格，用来记录分类器的预测结果。 +# +# 对于最常见的二元分类来说，它的混淆矩阵是2乘2的，如下 +# ![confusion_matrix1](images/confusion_matrix1.png) +# +# * `TP` = True Postive = 真阳性 +# * `FP` = False Positive = 假阳性 +# * `FN` = False Negative = 假阴性 +# * `TN` = True Negative = 真阴性 +# +# 你要的例子来了。。。比如我们一个模型对15个样本进行预测，然后结果如下。 +# +# * 预测值：1 1 1 1 1 0 0 0 0 0 1 1 1 0 1 +# * 真实值：0 1 1 0 1 1 0 0 1 0 1 0 1 0 0 +# +# ![confusion_matrix2](images/confusion_matrix2.png) +# +# +# 这个就是混淆矩阵。混淆矩阵中的这四个数值，经常被用来定义其他一些度量。 +# +# +# ### 准确度 +# ``` +# Accuracy = (TP+TN) / (TP+TN+FN+TN) +# ``` +# +# 在上面的例子中，准确度 = (5+4) / 15 = 0.6 +# +# +# +# ### 精度(precision, 或者PPV, positive predictive value) +# ``` +# precision = TP / (TP + FP) +# ``` +# 在上面的例子中，精度 = 5 / (5+4) = 0.556 +# +# +# +# ### 召回(recall, 或者敏感度，sensitivity，真阳性率，TPR，True Positive Rate) +# +# ``` +# recall = TP / (TP + FN) +# ``` +# +# 在上面的例子中，召回 = 5 / (5+2) = 0.714 +# +# +# +# ### 特异度(specificity，或者真阴性率，TNR，True Negative Rate) +# ``` +# specificity = TN / (TN + FP) +# ``` +# +# 在上面的例子中，特异度 = 4 / (4+2) = 0.667 +# +# +# +# ### F1-值(F1-score) +# ``` +# F1 = 2*TP / (2*TP+FP+FN) +# ``` +# 在上面的例子中，F1-值 = 2*5 / (2*5+4+2) = 0.625 +# +# +# diff --git a/metric/images/confusion_matrix1.png b/metric/images/confusion_matrix1.png new file mode 100644 index 0000000..717fb53 Binary files /dev/null and b/metric/images/confusion_matrix1.png differ diff --git a/metric/images/confusion_matrix2.png b/metric/images/confusion_matrix2.png new file mode 100644 index 0000000..f923507 Binary files /dev/null and b/metric/images/confusion_matrix2.png differ