d485c08
joyvan 6 years ago
3 changed file(s) with 153 addition(s) and 135 deletion(s). Raw diff Collapse all Expand all
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01.03 机器学习常用的包.ipynb less more
952952 "cell_type": "markdown",
953953 "metadata": {},
954954 "source": [
955 "### 广播机制"
956 ]
957 },
958 {
959 "cell_type": "markdown",
960 "metadata": {},
961 "source": [
962 "正常的加法："
963 ]
964 },
965 {
966 "cell_type": "code",
967 "execution_count": null,
968 "metadata": {},
969 "outputs": [],
970 "source": [
971 "a = np.array([[ 0, 0, 0],\n",
972 " [10,10,10],\n",
973 " [20,20,20],\n",
974 " [30,30,30]])\n",
975 "b = np.array([[ 0, 1, 2],\n",
976 " [ 0, 1, 2],\n",
977 " [ 0, 1, 2],\n",
978 " [ 0, 1, 2]])\n",
979 "a + b\n"
980 ]
981 },
982 {
983 "cell_type": "markdown",
984 "metadata": {},
985 "source": [
986 "将 `b` 的值变成二维的 `[0,1,2]` 之后的加法："
987 ]
988 },
989 {
990 "cell_type": "code",
991 "execution_count": null,
992 "metadata": {},
993 "outputs": [],
994 "source": [
995 "b = np.array([[0,1,2],[0,1,2]])\n",
996 "print(b.shape)\n",
997 "a + b\n"
998 ]
999 },
1000 {
1001 "cell_type": "markdown",
1002 "metadata": {},
1003 "source": [
1004 "两个 `ndarray` 执行的是对应元素的的运算，广播机制的功能是为了方便不同形状的`ndarray`（`NumPy` 库的核心数据结构）进行数学运算。\n",
1005 " \n",
1006 " 当操作两个数组时，`NumPy` 会逐个比较它们的`shape`（构成的元组`tuple`），只有在下述情况下，两个数组才能够进行数组与数组的运算。\n",
1007 "\n",
1008 "+ 维度相等\n",
1009 "+ `shape`（其中相对应的一个地方为 `1`）"
1010 ]
1011 },
1012 {
1013 "cell_type": "code",
1014 "execution_count": null,
1015 "metadata": {},
1016 "outputs": [],
1017 "source": [
1018 "# 将 `b` 的值变成一维的 `[0,1,2]` 之后的加法：\n",
1019 "b = np.array([0,1,2])\n",
1020 "\n",
1021 "a + b\n"
1022 ]
1023 },
1024 {
1025 "cell_type": "markdown",
1026 "metadata": {},
1027 "source": [
1028 "结果一样，虽然两个数组的维数不一样，但是 **Numpy** 检测到 `b` 的维度与 `a` 的维度匹配，所以将 `b` 扩展为之前的形式，得到相同的形状。\n",
1029 "\n",
1030 "对于更高维度，这样的扩展依然有效。 \n",
1031 "\n",
1032 "如果我们再将 `a` 变成一个列向量呢？"
1033 ]
1034 },
1035 {
1036 "cell_type": "code",
1037 "execution_count": null,
1038 "metadata": {},
1039 "outputs": [],
1040 "source": [
1041 "a = np.array([0,10,20,30])\n",
1042 "a.shape = 4,1\n",
1043 "a\n"
1044 ]
1045 },
1046 {
1047 "cell_type": "code",
1048 "execution_count": null,
1049 "metadata": {},
1050 "outputs": [],
1051 "source": [
1052 "a + b\n"
1053 ]
1054 },
1055 {
1056 "cell_type": "code",
1057 "execution_count": null,
1058 "metadata": {},
1059 "outputs": [],
1060 "source": [
1061 "# 此时a，b的维度分别为\n",
1062 "a.shape, b.shape\n"
1063 ]
1064 },
1065 {
1066 "cell_type": "markdown",
1067 "metadata": {},
1068 "source": [
1069 "可以看到，虽然两者的维度并不相同，但是 `Numpy` 还是根据两者的维度，自动将它们进行扩展然后进行计算。\n",
1070 "\n",
1071 "匹配会从最后一维开始进行，直到某一个的维度全部匹配为止，因此对于以下情况，`Numpy` 都会进行相应的匹配：\n",
1072 "\n",
1073 "A|B|Result\n",
1074 "---|---|---\n",
1075 "3d array: 256 x 256 x 3 | 1d array: 3 | 3d array: 256 x 256 x 3\n",
1076 "4d array: 8 x 1 x 6 x 1 | 3d array: 7 x 1 x 5 | 3d array: 8 x 7 x 6 x 5\n",
1077 "3d array: 5 x 4 x 3 | 1d array: 1 | 3d array: 5 x 4 x 3\n",
1078 "3d array: 15 x 4 x 13 | 1d array: 15 x 1 x 13 | 3d array: 15 x 4 x 13\n",
1079 "2d array: 4 x 1 | 1d array: 3 | 2d array: 4 x 3\n",
1080 "\n",
1081 "匹配成功后，`Numpy` 会进行运算得到相应的结果。\n",
1082 "\n",
1083 "当然，如果相应的维度不匹配，那么 `Numpy` 会报错：`ValueError`。"
1084 ]
1085 },
1086 {
1087 "cell_type": "markdown",
1088 "metadata": {},
1089 "source": [
1090955 "### `IO` 操作"
1091956 ]
1092957 },
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02.03 回归分析(学生版).ipynb less more
5858 "import numpy as np\n",
5959 "import matplotlib.pyplot as plt\n",
6060 "%matplotlib inline\n",
61 "!mkdir -p ~/.keras/datasets\n",
62 "!cp ./mnist.npz ~/.keras/datasets/mnist.npz\n",
6163 "\n",
6264 "x = np.array([1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005])\n",
6365 "y = np.array([325.68, 331.15, 338.69, 345.90, 354.19, 360.88, 369.48, 379.67])\n",
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iris.csv less more
0 sepal_length,sepal_width,petal_length,petal_width,species
1 5.1,3.5,1.4,0.2,setosa
2 4.9,3.0,1.4,0.2,setosa
3 4.7,3.2,1.3,0.2,setosa
4 4.6,3.1,1.5,0.2,setosa
5 5.0,3.6,1.4,0.2,setosa
6 5.4,3.9,1.7,0.4,setosa
7 4.6,3.4,1.4,0.3,setosa
8 5.0,3.4,1.5,0.2,setosa
9 4.4,2.9,1.4,0.2,setosa
10 4.9,3.1,1.5,0.1,setosa
11 5.4,3.7,1.5,0.2,setosa
12 4.8,3.4,1.6,0.2,setosa
13 4.8,3.0,1.4,0.1,setosa
14 4.3,3.0,1.1,0.1,setosa
15 5.8,4.0,1.2,0.2,setosa
16 5.7,4.4,1.5,0.4,setosa
17 5.4,3.9,1.3,0.4,setosa
18 5.1,3.5,1.4,0.3,setosa
19 5.7,3.8,1.7,0.3,setosa
20 5.1,3.8,1.5,0.3,setosa
21 5.4,3.4,1.7,0.2,setosa
22 5.1,3.7,1.5,0.4,setosa
23 4.6,3.6,1.0,0.2,setosa
24 5.1,3.3,1.7,0.5,setosa
25 4.8,3.4,1.9,0.2,setosa
26 5.0,3.0,1.6,0.2,setosa
27 5.0,3.4,1.6,0.4,setosa
28 5.2,3.5,1.5,0.2,setosa
29 5.2,3.4,1.4,0.2,setosa
30 4.7,3.2,1.6,0.2,setosa
31 4.8,3.1,1.6,0.2,setosa
32 5.4,3.4,1.5,0.4,setosa
33 5.2,4.1,1.5,0.1,setosa
34 5.5,4.2,1.4,0.2,setosa
35 4.9,3.1,1.5,0.2,setosa
36 5.0,3.2,1.2,0.2,setosa
37 5.5,3.5,1.3,0.2,setosa
38 4.9,3.6,1.4,0.1,setosa
39 4.4,3.0,1.3,0.2,setosa
40 5.1,3.4,1.5,0.2,setosa
41 5.0,3.5,1.3,0.3,setosa
42 4.5,2.3,1.3,0.3,setosa
43 4.4,3.2,1.3,0.2,setosa
44 5.0,3.5,1.6,0.6,setosa
45 5.1,3.8,1.9,0.4,setosa
46 4.8,3.0,1.4,0.3,setosa
47 5.1,3.8,1.6,0.2,setosa
48 4.6,3.2,1.4,0.2,setosa
49 5.3,3.7,1.5,0.2,setosa
50 5.0,3.3,1.4,0.2,setosa
51 7.0,3.2,4.7,1.4,versicolor
52 6.4,3.2,4.5,1.5,versicolor
53 6.9,3.1,4.9,1.5,versicolor
54 5.5,2.3,4.0,1.3,versicolor
55 6.5,2.8,4.6,1.5,versicolor
56 5.7,2.8,4.5,1.3,versicolor
57 6.3,3.3,4.7,1.6,versicolor
58 4.9,2.4,3.3,1.0,versicolor
59 6.6,2.9,4.6,1.3,versicolor
60 5.2,2.7,3.9,1.4,versicolor
61 5.0,2.0,3.5,1.0,versicolor
62 5.9,3.0,4.2,1.5,versicolor
63 6.0,2.2,4.0,1.0,versicolor
64 6.1,2.9,4.7,1.4,versicolor
65 5.6,2.9,3.6,1.3,versicolor
66 6.7,3.1,4.4,1.4,versicolor
67 5.6,3.0,4.5,1.5,versicolor
68 5.8,2.7,4.1,1.0,versicolor
69 6.2,2.2,4.5,1.5,versicolor
70 5.6,2.5,3.9,1.1,versicolor
71 5.9,3.2,4.8,1.8,versicolor
72 6.1,2.8,4.0,1.3,versicolor
73 6.3,2.5,4.9,1.5,versicolor
74 6.1,2.8,4.7,1.2,versicolor
75 6.4,2.9,4.3,1.3,versicolor
76 6.6,3.0,4.4,1.4,versicolor
77 6.8,2.8,4.8,1.4,versicolor
78 6.7,3.0,5.0,1.7,versicolor
79 6.0,2.9,4.5,1.5,versicolor
80 5.7,2.6,3.5,1.0,versicolor
81 5.5,2.4,3.8,1.1,versicolor
82 5.5,2.4,3.7,1.0,versicolor
83 5.8,2.7,3.9,1.2,versicolor
84 6.0,2.7,5.1,1.6,versicolor
85 5.4,3.0,4.5,1.5,versicolor
86 6.0,3.4,4.5,1.6,versicolor
87 6.7,3.1,4.7,1.5,versicolor
88 6.3,2.3,4.4,1.3,versicolor
89 5.6,3.0,4.1,1.3,versicolor
90 5.5,2.5,4.0,1.3,versicolor
91 5.5,2.6,4.4,1.2,versicolor
92 6.1,3.0,4.6,1.4,versicolor
93 5.8,2.6,4.0,1.2,versicolor
94 5.0,2.3,3.3,1.0,versicolor
95 5.6,2.7,4.2,1.3,versicolor
96 5.7,3.0,4.2,1.2,versicolor
97 5.7,2.9,4.2,1.3,versicolor
98 6.2,2.9,4.3,1.3,versicolor
99 5.1,2.5,3.0,1.1,versicolor
100 5.7,2.8,4.1,1.3,versicolor
101 6.3,3.3,6.0,2.5,virginica
102 5.8,2.7,5.1,1.9,virginica
103 7.1,3.0,5.9,2.1,virginica
104 6.3,2.9,5.6,1.8,virginica
105 6.5,3.0,5.8,2.2,virginica
106 7.6,3.0,6.6,2.1,virginica
107 4.9,2.5,4.5,1.7,virginica
108 7.3,2.9,6.3,1.8,virginica
109 6.7,2.5,5.8,1.8,virginica
110 7.2,3.6,6.1,2.5,virginica
111 6.5,3.2,5.1,2.0,virginica
112 6.4,2.7,5.3,1.9,virginica
113 6.8,3.0,5.5,2.1,virginica
114 5.7,2.5,5.0,2.0,virginica
115 5.8,2.8,5.1,2.4,virginica
116 6.4,3.2,5.3,2.3,virginica
117 6.5,3.0,5.5,1.8,virginica
118 7.7,3.8,6.7,2.2,virginica
119 7.7,2.6,6.9,2.3,virginica
120 6.0,2.2,5.0,1.5,virginica
121 6.9,3.2,5.7,2.3,virginica
122 5.6,2.8,4.9,2.0,virginica
123 7.7,2.8,6.7,2.0,virginica
124 6.3,2.7,4.9,1.8,virginica
125 6.7,3.3,5.7,2.1,virginica
126 7.2,3.2,6.0,1.8,virginica
127 6.2,2.8,4.8,1.8,virginica
128 6.1,3.0,4.9,1.8,virginica
129 6.4,2.8,5.6,2.1,virginica
130 7.2,3.0,5.8,1.6,virginica
131 7.4,2.8,6.1,1.9,virginica
132 7.9,3.8,6.4,2.0,virginica
133 6.4,2.8,5.6,2.2,virginica
134 6.3,2.8,5.1,1.5,virginica
135 6.1,2.6,5.6,1.4,virginica
136 7.7,3.0,6.1,2.3,virginica
137 6.3,3.4,5.6,2.4,virginica
138 6.4,3.1,5.5,1.8,virginica
139 6.0,3.0,4.8,1.8,virginica
140 6.9,3.1,5.4,2.1,virginica
141 6.7,3.1,5.6,2.4,virginica
142 6.9,3.1,5.1,2.3,virginica
143 5.8,2.7,5.1,1.9,virginica
144 6.8,3.2,5.9,2.3,virginica
145 6.7,3.3,5.7,2.5,virginica
146 6.7,3.0,5.2,2.3,virginica
147 6.3,2.5,5.0,1.9,virginica
148 6.5,3.0,5.2,2.0,virginica
149 6.2,3.4,5.4,2.3,virginica
150 5.9,3.0,5.1,1.8,virginica