From 94c90759c27fa9f7243640be448f840fb4584831 Mon Sep 17 00:00:00 2001 From: bushuhui Date: Wed, 23 Sep 2020 08:39:13 +0800 Subject: [PATCH] Fix some type errors --- 0_python/5_Control_Flow.ipynb | 10 +- 0_python/6_Function.ipynb | 74 ++--- 0_python/7_Class.ipynb | 163 +++++----- .../1-numpy_tutorial.ipynb | 348 ++++++++++----------- 1_numpy_matplotlib_scipy_sympy/random-matrix.csv | 6 +- 1_numpy_matplotlib_scipy_sympy/random-matrix.npy | Bin 200 -> 200 bytes README.md | 8 +- tips/InstallPython.md | 6 +- 8 files changed, 308 insertions(+), 307 deletions(-) diff --git a/0_python/5_Control_Flow.ipynb b/0_python/5_Control_Flow.ipynb index 4d44307..2109060 100644 --- a/0_python/5_Control_Flow.ipynb +++ b/0_python/5_Control_Flow.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -38,9 +38,9 @@ ], "source": [ "x = 4\n", - "if x >10:\n", + "if x >10: \n", " print(\"Hello\")\n", - "else:\n", + "else: \n", " print(\"Welcome!\")" ] }, @@ -194,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -216,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { diff --git a/0_python/6_Function.ipynb b/0_python/6_Function.ipynb index 410eeb7..ea80f1f 100644 --- a/0_python/6_Function.ipynb +++ b/0_python/6_Function.ipynb @@ -78,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -118,14 +118,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Please enter your name : Willam\n" + "Please enter your name : Jack\n" ] } ], @@ -142,15 +142,15 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Hey Willam!\n", - "Willam, How do you do?\n" + "Hey Jack!\n", + "Jack, How do you do?\n" ] } ], @@ -167,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -214,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -232,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -264,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -275,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -300,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -321,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -337,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -346,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -367,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -385,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -650,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -666,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -686,7 +686,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -732,7 +732,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -761,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -770,7 +770,7 @@ "(6, 8)" ] }, - "execution_count": 50, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -782,7 +782,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -791,7 +791,7 @@ "function" ] }, - "execution_count": 51, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -802,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -811,7 +811,7 @@ "function" ] }, - "execution_count": 52, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -839,7 +839,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -848,7 +848,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -866,7 +866,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -900,7 +900,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -913,7 +913,7 @@ ], "source": [ "eg2 = map(lambda x,y:x+y, list1,list2)\n", - "print(eg2)" + "print(list(eg2))" ] }, { @@ -925,14 +925,14 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['10', '10', '10', '10', '10', '10', '10', '10', '10']\n" + "\n" ] } ], @@ -957,7 +957,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -973,7 +973,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 41, "metadata": {}, "outputs": [ { diff --git a/0_python/7_Class.ipynb b/0_python/7_Class.ipynb index 24c42f4..817af0a 100644 --- a/0_python/7_Class.ipynb +++ b/0_python/7_Class.ipynb @@ -25,9 +25,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "```\n", "class class_name:\n", "\n", - " Functions" + " Functions\n", + "```" ] }, { @@ -36,6 +38,7 @@ "metadata": {}, "outputs": [], "source": [ + "# 一个最简单的类\n", "class FirstClass:\n", " pass\n" ] @@ -128,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -136,9 +139,9 @@ "evalue": "'FirstClass' object has no attribute 'init'", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0meg0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFirstClass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0meg0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31m-----------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0meg0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFirstClass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0meg0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m: 'FirstClass' object has no attribute 'init'" ] } @@ -166,12 +169,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "class FirstClass:\n", " \"\"\"My first class\"\"\"\n", + " class_var = 10\n", " def __init__(self,name,symbol):\n", " self.name = name\n", " self.symbol = symbol" @@ -186,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -196,14 +200,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "onex 11\n", + "one 1\n", "two 2\n", "My first class\n" ] @@ -224,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": { "scrolled": false }, @@ -244,6 +248,7 @@ " '__gt__',\n", " '__hash__',\n", " '__init__',\n", + " '__init_subclass__',\n", " '__le__',\n", " '__lt__',\n", " '__module__',\n", @@ -256,10 +261,11 @@ " '__sizeof__',\n", " '__str__',\n", " '__subclasshook__',\n", - " '__weakref__']" + " '__weakref__',\n", + " 'class_var']" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -270,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -279,7 +285,7 @@ "'My first class'" ] }, - "execution_count": 24, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -350,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -369,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -379,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -387,9 +393,9 @@ "evalue": "'FirstClass' object has no attribute 'name'", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meg2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meg2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31m-----------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meg1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meg2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meg2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'FirstClass' object has no attribute 'name'" ] } @@ -408,7 +414,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -426,6 +432,7 @@ " '__gt__',\n", " '__hash__',\n", " '__init__',\n", + " '__init_subclass__',\n", " '__le__',\n", " '__lt__',\n", " '__module__',\n", @@ -443,7 +450,7 @@ " 's']" ] }, - "execution_count": 24, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -454,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -486,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -498,10 +505,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": true - }, + "execution_count": 21, + "metadata": {}, "outputs": [], "source": [ "eg1 = FirstClass('one',1)\n", @@ -510,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -536,7 +541,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -546,7 +551,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -564,6 +569,7 @@ " '__gt__',\n", " '__hash__',\n", " '__init__',\n", + " '__init_subclass__',\n", " '__le__',\n", " '__lt__',\n", " '__module__',\n", @@ -582,7 +588,7 @@ " 's']" ] }, - "execution_count": 26, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -606,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -626,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -646,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -670,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -688,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -697,7 +703,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -716,7 +722,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -725,7 +731,7 @@ "10" ] }, - "execution_count": 27, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -743,19 +749,18 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 34, "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "type object 'FirstClass' has no attribute 'multiply'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mFirstClass\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meg4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m: type object 'FirstClass' has no attribute 'multiply'" - ] + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -785,7 +790,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -800,7 +805,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -809,7 +814,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -826,7 +831,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -861,7 +866,7 @@ " 'salary']" ] }, - "execution_count": 5, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -879,7 +884,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -897,7 +902,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -906,7 +911,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -925,7 +930,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -943,6 +948,7 @@ " '__gt__',\n", " '__hash__',\n", " '__init__',\n", + " '__init_subclass__',\n", " '__le__',\n", " '__lt__',\n", " '__module__',\n", @@ -957,10 +963,10 @@ " '__subclasshook__',\n", " '__weakref__',\n", " 'artform',\n", - " 'money']" + " 'salary']" ] }, - "execution_count": 45, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -978,7 +984,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -990,7 +996,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -999,7 +1005,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1017,6 +1023,7 @@ " '__gt__',\n", " '__hash__',\n", " '__init__',\n", + " '__init_subclass__',\n", " '__le__',\n", " '__lt__',\n", " '__module__',\n", @@ -1034,7 +1041,7 @@ " 'salary']" ] }, - "execution_count": 48, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1045,7 +1052,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1071,7 +1078,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1087,7 +1094,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -1096,7 +1103,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -1123,7 +1130,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -1140,7 +1147,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -1149,7 +1156,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -1266,22 +1273,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "单独练习可以帮助你掌握python的窍门。给自己一个问题陈述并解决它们。您还可以在任何竞争的编码平台上提交问题求解。你编写的代码越多，你发现的越多，你就越开始欣赏这门语言。\n", + "找各个方面的练习题，并独立完成能帮助你掌握Python的窍门，例如给自己一个问题并解决它们，你还可以在任何编程竞赛平台上提交问题求解。你编写的代码越多，你发现的越多，你就越开始欣赏这门语言。强烈建议把[Python作业](https://gitee.com/pi-lab/machinelearning_homework/blob/master/homework_01_python/README.md)完成，并在[其他编程练习](https://gitee.com/pi-lab/machinelearning_homework/blob/master/homework_01_python/README.md#references)里面找一些练习题或者项目做一下。\n", "\n", - "现在已经向您介绍了python，您可以尝试您感兴趣的领域中的不同python库。我强烈建议您查看这个Python框架、库和软件列表http://awesome-python.com\n", + "现在已经向你介绍了Python，您可以尝试您感兴趣的领域中的不同Python库。我强烈建议您查看这个Python框架、库和软件列表 http://awesome-python.com\n", "\n", "\n", - "python官方文档: https://docs.python.org/3/\n", - "\n", "Pyton 教程:\n", "* [Python tutorial (廖雪峰）](https://www.liaoxuefeng.com/wiki/1016959663602400)\n", "* [Python基础教程](https://www.runoob.com/python/python-tutorial.html)\n", "* [Python官方教程（中文版)](https://docs.python.org/zh-cn/3/tutorial/index.html)\n", - "\n", - "你也可以参考一些Python的练习程序，是由 Kartik Kannapur所写的。 Github Repo : https://github.com/rajathkumarmp/Python-Lectures \n", + "* Python官方文档: https://docs.python.org/3/\n", + "* 本教程来源于：https://github.com/rajathkumarmp/Python-Lectures \n", "\n", "\n", - "享受解决问题陈述，因为生命短暂，你需要python!" + "**最后，享受解决问题的快乐！因为生命短暂，你需要Python!**" ] }, { diff --git a/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb b/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb index 27a3c05..2182441 100644 --- a/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb +++ b/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -107,7 +107,7 @@ "array([1, 2, 3, 4])" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -123,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -154,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -163,7 +163,7 @@ "(numpy.ndarray, numpy.ndarray)" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -181,7 +181,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -190,7 +190,7 @@ "(4,)" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -201,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -210,7 +210,7 @@ "(3, 2)" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -228,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -237,7 +237,7 @@ "6" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -255,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -264,7 +264,7 @@ "(3, 2)" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -275,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -284,7 +284,7 @@ "6" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -311,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -320,7 +320,7 @@ "dtype('int64')" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -338,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -346,9 +346,9 @@ "evalue": "invalid literal for int() with base 10: 'hello'", "output_type": "error", "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"hello\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31m-----------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"hello\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'hello'" ] } @@ -366,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -376,7 +376,7 @@ " [3.+0.j, 4.+0.j]])" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -419,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -442,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -455,7 +455,7 @@ " 6.00000000e-01, 7.00000000e-01, 8.00000000e-01, 9.00000000e-01])" ] }, - "execution_count": 15, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -475,32 +475,28 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 0. , 0.41666667, 0.83333333, 1.25 , 1.66666667,\n", - " 2.08333333, 2.5 , 2.91666667, 3.33333333, 3.75 ,\n", - " 4.16666667, 4.58333333, 5. , 5.41666667, 5.83333333,\n", - " 6.25 , 6.66666667, 7.08333333, 7.5 , 7.91666667,\n", - " 8.33333333, 8.75 , 9.16666667, 9.58333333, 10. ])" + "array([ 0. , 2.5, 5. , 7.5, 10. ])" ] }, - "execution_count": 16, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 使用linspace两边的端点也被包含进去\n", - "np.linspace(0, 10, 25)" + "np.linspace(0, 10, 5)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -511,7 +507,7 @@ " 7.25095809e+03, 2.20264658e+04])" ] }, - "execution_count": 17, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -529,7 +525,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -538,7 +534,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -551,7 +547,7 @@ " [4, 4, 4, 4, 4]])" ] }, - "execution_count": 19, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -562,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -575,7 +571,7 @@ " [0, 1, 2, 3, 4]])" ] }, - "execution_count": 20, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -593,7 +589,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -602,20 +598,20 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.31850549, 0.64755869, 0.93737096, 0.06141188, 0.17055487],\n", - " [0.95771684, 0.88466718, 0.81119863, 0.95268744, 0.73734857],\n", - " [0.51036326, 0.8779331 , 0.41560197, 0.300393 , 0.42244209],\n", - " [0.50866631, 0.84322931, 0.34459543, 0.47379641, 0.03312725],\n", - " [0.96519922, 0.20557788, 0.38343937, 0.21493144, 0.27541461]])" + "array([[0.77849722, 0.80418995, 0.05675561, 0.70158519, 0.25432473],\n", + " [0.26593179, 0.68124455, 0.75827058, 0.54821965, 0.65368682],\n", + " [0.10501453, 0.61381473, 0.32029867, 0.05271199, 0.14810179],\n", + " [0.81571699, 0.311358 , 0.00545839, 0.81465233, 0.55005373],\n", + " [0.64861977, 0.50134439, 0.11211157, 0.97227545, 0.52994903]])" ] }, - "execution_count": 22, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -627,20 +623,20 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 1.12204579, 2.90667688, -1.06379302, 1.52801804, 1.34553205],\n", - " [ 2.22610261, -0.18597008, 1.12948162, -1.44339033, 0.14366645],\n", - " [ 0.12767746, -0.04534549, 0.1536468 , 0.7333602 , 0.96510913],\n", - " [ 0.30848743, -2.31710677, 0.37803085, -0.52433003, 1.39883453],\n", - " [-0.52307504, 0.40612781, 0.48341866, -1.96277249, 1.1671546 ]])" + "array([[-0.09235676, -0.71023602, 0.61363172, 0.49120177, 1.00102961],\n", + " [ 0.70097434, 1.98685481, -0.48047899, -0.83134067, 1.17453105],\n", + " [ 0.50057823, -0.23609257, 1.08942973, 0.03857935, -2.00169139],\n", + " [-0.09077163, 1.08568903, 0.53531071, 0.30819683, 0.40767628],\n", + " [-0.24485242, -0.15219474, 0.29362566, -0.37050405, 0.17776159]])" ] }, - "execution_count": 23, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -659,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -670,7 +666,7 @@ " [0, 0, 3]])" ] }, - "execution_count": 24, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -682,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -694,7 +690,7 @@ " [0, 0, 0, 0]])" ] }, - "execution_count": 25, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -713,7 +709,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -724,7 +720,7 @@ " [0., 0., 0.]])" ] }, - "execution_count": 26, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -735,7 +731,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -746,7 +742,7 @@ " [1., 1., 1.]])" ] }, - "execution_count": 27, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -778,7 +774,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -804,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -814,7 +810,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -823,7 +819,7 @@ "(77431, 7)" ] }, - "execution_count": 30, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -834,12 +830,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -871,18 +867,18 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.73171836, 0.46544202, 0.72372739],\n", - " [0.32390603, 0.09679475, 0.95467059],\n", - " [0.36051701, 0.78361037, 0.00716923]])" + "array([[0.14040248, 0.96924573, 0.53434945],\n", + " [0.77573698, 0.21286524, 0.68518057],\n", + " [0.32862765, 0.70297393, 0.39513101]])" ] }, - "execution_count": 32, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -895,7 +891,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -904,16 +900,16 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "7.317183558113176112e-01 4.654420244898096470e-01 7.237273924754552556e-01\r\n", - "3.239060308567449642e-01 9.679474636543183852e-02 9.546705930168928322e-01\r\n", - "3.605170063363589694e-01 7.836103655978251536e-01 7.169228636445423852e-03\r\n" + "1.404024772095778806e-01 9.692457261060815066e-01 5.343494544483793351e-01\r\n", + "7.757369846310477879e-01 2.128652371287943490e-01 6.851805738917894351e-01\r\n", + "3.286276500132384593e-01 7.029739262669426614e-01 3.951310081778761640e-01\r\n" ] } ], @@ -923,16 +919,16 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.73172 0.46544 0.72373\r\n", - "0.32391 0.09679 0.95467\r\n", - "0.36052 0.78361 0.00717\r\n" + "0.14040 0.96925 0.53435\r\n", + "0.77574 0.21287 0.68518\r\n", + "0.32863 0.70297 0.39513\r\n" ] } ], @@ -958,7 +954,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -977,18 +973,18 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.73171836, 0.46544202, 0.72372739],\n", - " [0.32390603, 0.09679475, 0.95467059],\n", - " [0.36051701, 0.78361037, 0.00716923]])" + "array([[0.14040248, 0.96924573, 0.53434945],\n", + " [0.77573698, 0.21286524, 0.68518057],\n", + " [0.32862765, 0.70297393, 0.39513101]])" ] }, - "execution_count": 37, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1006,7 +1002,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1086,7 +1082,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -1095,7 +1091,7 @@ "1" ] }, - "execution_count": 41, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -1108,16 +1104,16 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.09679474636543184\n", - "0.09679474636543184\n", - "[0.32390603 0.09679475 0.95467059]\n" + "0.21286523712879435\n", + "0.21286523712879435\n", + "[0.77573698 0.21286524 0.68518057]\n" ] } ], @@ -1160,16 +1156,16 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.32390603, 0.09679475, 0.95467059])" + "array([0.77573698, 0.21286524, 0.68518057])" ] }, - "execution_count": 44, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1187,16 +1183,16 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.32390603, 0.09679475, 0.95467059])" + "array([0.77573698, 0.21286524, 0.68518057])" ] }, - "execution_count": 45, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -1207,16 +1203,16 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0.46544202, 0.09679475, 0.78361037])" + "array([0.96924573, 0.21286524, 0.70297393])" ] }, - "execution_count": 46, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1234,7 +1230,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1243,18 +1239,18 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[1. , 0.46544202, 0.72372739],\n", - " [0.32390603, 0.09679475, 0.95467059],\n", - " [0.36051701, 0.78361037, 0.00716923]])" + "array([[1. , 0.96924573, 0.53434945],\n", + " [0.77573698, 0.21286524, 0.68518057],\n", + " [0.32862765, 0.70297393, 0.39513101]])" ] }, - "execution_count": 48, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1265,7 +1261,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -1276,18 +1272,18 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 1. , 0.46544202, -1. ],\n", + "array([[ 1. , 0.96924573, -1. ],\n", " [ 0. , 0. , -1. ],\n", - " [ 0.36051701, 0.78361037, -1. ]])" + " [ 0.32862765, 0.70297393, -1. ]])" ] }, - "execution_count": 50, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -1312,7 +1308,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -1321,7 +1317,7 @@ "array([1, 2, 3, 4, 5])" ] }, - "execution_count": 51, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -1333,7 +1329,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -1342,7 +1338,7 @@ "array([2, 3])" ] }, - "execution_count": 52, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -1360,7 +1356,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -1369,7 +1365,7 @@ "array([ 1, -2, -3, 4, 5])" ] }, - "execution_count": 53, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -1391,7 +1387,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -1400,7 +1396,7 @@ "array([ 1, -2, -3, 4, 5])" ] }, - "execution_count": 54, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -1411,7 +1407,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -1420,7 +1416,7 @@ "array([ 1, -2, -3, 4, 5])" ] }, - "execution_count": 55, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -1431,7 +1427,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -1440,7 +1436,7 @@ "array([ 1, -3, 5])" ] }, - "execution_count": 56, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -1451,7 +1447,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 59, "metadata": {}, "outputs": [ { @@ -1460,7 +1456,7 @@ "array([ 1, -2, -3])" ] }, - "execution_count": 57, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } @@ -1471,7 +1467,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -1480,7 +1476,7 @@ "array([4, 5])" ] }, - "execution_count": 58, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -1498,7 +1494,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -1507,7 +1503,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -1516,7 +1512,7 @@ "5" ] }, - "execution_count": 60, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } @@ -1527,7 +1523,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -1536,7 +1532,7 @@ "array([3, 4, 5])" ] }, - "execution_count": 61, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -1554,7 +1550,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -1567,7 +1563,7 @@ " [40, 41, 42, 43, 44]])" ] }, - "execution_count": 62, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -1580,7 +1576,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -1591,7 +1587,7 @@ " [31, 32, 33]])" ] }, - "execution_count": 63, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } @@ -1603,7 +1599,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -1614,7 +1610,7 @@ " [40, 42, 44]])" ] }, - "execution_count": 64, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -1640,7 +1636,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -1666,7 +1662,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -1675,7 +1671,7 @@ "array([11, 22, 34])" ] }, - "execution_count": 66, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -1694,7 +1690,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -1703,7 +1699,7 @@ "array([0, 1, 2, 3, 4])" ] }, - "execution_count": 67, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -1715,7 +1711,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -1724,7 +1720,7 @@ "array([0, 2])" ] }, - "execution_count": 68, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -1736,7 +1732,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -1745,7 +1741,7 @@ "array([0, 2])" ] }, - "execution_count": 69, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -1765,7 +1761,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -1775,7 +1771,7 @@ " 6.5, 7. , 7.5, 8. , 8.5, 9. , 9.5])" ] }, - "execution_count": 70, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -1787,7 +1783,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -1798,7 +1794,7 @@ " False, False])" ] }, - "execution_count": 71, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -1831,7 +1827,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -1840,7 +1836,7 @@ "array([3.5, 4. , 4.5, 5. , 5.5])" ] }, - "execution_count": 73, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -1894,7 +1890,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -1903,7 +1899,7 @@ "array([5.5, 6. , 6.5, 7. ])" ] }, - "execution_count": 75, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -1928,7 +1924,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -1937,7 +1933,7 @@ "array([ 0, 11, 22, 33, 44])" ] }, - "execution_count": 74, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -1948,7 +1944,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -1957,7 +1953,7 @@ "array([10, 21, 32, 43])" ] }, - "execution_count": 75, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -1982,7 +1978,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -1991,7 +1987,7 @@ "array([-3, -2, -1, 0, 1, 2])" ] }, - "execution_count": 76, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } @@ -2003,7 +1999,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -2012,7 +2008,7 @@ "array([-2, 0, 2])" ] }, - "execution_count": 77, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -2024,7 +2020,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -2033,7 +2029,7 @@ "array([-2, 0, 2])" ] }, - "execution_count": 78, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -2051,7 +2047,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -2060,7 +2056,7 @@ "array([-2, 0, 2])" ] }, - "execution_count": 79, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } @@ -2085,7 +2081,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -2094,7 +2090,7 @@ "array([ 5, -2, 5, -2])" ] }, - "execution_count": 49, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } @@ -4882,7 +4878,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/1_numpy_matplotlib_scipy_sympy/random-matrix.csv b/1_numpy_matplotlib_scipy_sympy/random-matrix.csv index 7451061..2577af0 100644 --- a/1_numpy_matplotlib_scipy_sympy/random-matrix.csv +++ b/1_numpy_matplotlib_scipy_sympy/random-matrix.csv @@ -1,3 +1,3 @@ -0.73172 0.46544 0.72373 -0.32391 0.09679 0.95467 -0.36052 0.78361 0.00717 +0.14040 0.96925 0.53435 +0.77574 0.21287 0.68518 +0.32863 0.70297 0.39513 diff --git a/1_numpy_matplotlib_scipy_sympy/random-matrix.npy b/1_numpy_matplotlib_scipy_sympy/random-matrix.npy index 60f92b3da18cdb7f200abaa5fedb84e7f5ad7cfa..637693f39a478edbb5accfb6160e8c5f001bd8ff 100644 GIT binary patch delta 79 zcmV-V0I>ha0muQ6fJiJp@>jL^!9RsM?VJw;?>{QI?*?QU;XhV4Xha0muQ6fJnsk0-Zc+=Ra_XS6I!--9M7pJXyvl=ReeNU|it3)IZ2;5MGMM lxIaR??pLXd?mvWd_v^J1*FTSwp$k?M=|6x?e<*uheLq*IC)EG| diff --git a/README.md b/README.md index a6214f5..bc8ce15 100644 --- a/README.md +++ b/README.md @@ -69,11 +69,11 @@ ## 2. 学习的建议 -1. 为了更好的学习本课程，需要大家把Python编程能力培养好，通过做一定数量的练习题、小项目培养Python编程思维，这样后续的机器学习理论与实践才能学的比较扎实。 -2. 每个课程前半部分是理论基础，后半部分是代码实现。如果想学的更扎实，可以自己把各个方法的代码亲自实现一下。做的过程尽可能自己想解决办法，因为最重要的目标不是代码本身，而是学会分析问题、解决问题的能力。 +1. 为了更好的学习本课程，需要大家把Python编程能力培养好，通过一定数量的练习题、小项目培养Python编程思维，为后续的机器学习理论与实践打好坚实的基础。 +2. 每个课程前半部分是理论基础，后半部分是代码实现。如果想学的更扎实，可以自己把各个方法的代码亲自实现一下。做的过程如果遇到问题尽可能自己想解决办法，因为最重要的目标不是代码本身，而是学会分析问题、解决问题的能力。 3. **不能直接抄已有的程序，或者抄别人的程序**，如果自己不会要自己去想，去找解决方法，或者去问。如果直接抄别人的代码，这样的练习一点意义都没有。**如果感觉太难，可以做的慢一些，但是坚持自己思考、自己编写练习代码**。。 4. **请先遍历一遍所有的文件夹，了解有什么内容，资料**。各个目录里有很多说明文档，如果不会先找找有没有文档，如果找不到合适的文档就去网上找找。通过这个过程锻炼自己搜索文献、资料的能力。 -5. 本课程的练习题最好使用[Linux](https://gitee.com/pi-lab/learn_programming/blob/master/6_tools/linux)以及Linux下的工具来做。逼迫自己使用[Linux](https://gitee.com/pi-lab/learn_programming/blob/master/6_tools/linux)，只有多练、多用才能快速进步。如果实在太难，先在虚拟机里装一个Linux（例如Ubuntu，或者LinuxMint等），先熟悉一下。但是最终需要学会使用Linux。 +5. 本课程的练习题最好使用[Linux](https://gitee.com/pi-lab/learn_programming/blob/master/6_tools/linux)以及Linux下的工具来做。逼迫自己使用[Linux](https://gitee.com/pi-lab/learn_programming/blob/master/6_tools/linux)，只有多练、多用才能快速进步。如果实在太难，先在虚拟机（建议VirtualBox）里装一个Linux（例如Ubuntu，或者LinuxMint等），先熟悉一下。但是最终需要学会使用Linux。 @@ -103,7 +103,7 @@ -## 4. 相关学习资料与参考 +## 4. 更进一步学习 在上述内容学习完成之后，可以进行更进一步机器学习、计算机视觉方面的学习与研究，具体的资料可以参考： 1. 编程是机器学习研究、实现过程非常重要的能力，编程能力弱则无法快速试错，导致研究进度缓慢；如果编程能力强，则可以快速试错，快速编写实验代码等。强烈建议大家在学习本课程之后或之中，好好把数据结构、算法等基本功锻炼一下。具体的教程可以参考[《一步一步学编程》](https://gitee.com/pi-lab/learn_programming) diff --git a/tips/InstallPython.md b/tips/InstallPython.md index af7f7a6..bdde019 100644 --- a/tips/InstallPython.md +++ b/tips/InstallPython.md @@ -1,8 +1,8 @@ -# Installing Python Environments +# 按照Python环境 -由于Python的库比较多，并且依赖关系比较复杂，所以请仔细阅读下面的说明，使用下面的说明来安装能够减少问题的可能。*不过所列的安装方法，里面存在较多的细节，也许和你的系统并不适配，所以会遇到问题。如果遇到问题请通过搜索引擎去查找解决的办法*，通过这个方式锻炼自己解决问题的能力。 +由于Python的库比较多，并且依赖关系比较复杂，所以请仔细阅读下面的说明，并按下面的说明来操作，减少问题出现的可能。 **但是所列的安装方法说明里有较多的细节，也许和你的系统并不适配，所以会遇到问题。如果遇到问题请通过搜索引擎去查找解决的办法**，通过这个方式锻炼自己解决问题的能力。 -可以参考后面所列的`1.Winodws`或者`2.Linux`章节所列的将Python环境安装到计算机里。如果想一次性把所有的所需要的软件都安装到机器上，可以在本项目的根目录下执行下面的命令，需要Python 3.5版本，如果出现问题，则可以参考`requirements.txt`里面所列的软件包名字，手动一个一个安装。 +可以参考后面所列的`1.Winodws`或者`2.Linux`章节所列的将Python环境安装到计算机里。如果想一次性把所有的所需要的软件都安装到机器上，可以在本项目的根目录下执行下面的命令，需要Python 3.5以上的版本，如果出现问题，则可以参考`requirements.txt`里面所列的软件包名字，手动一个一个安装。 ``` pip install -r requirements.txt ```