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提交第 120 天文章《机器学习算法之 K均值聚类》对应示例代码
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‎day-120/Iris.csv‎

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Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species
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1,5.1,3.5,1.4,0.2,Iris-setosa
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2,4.9,3.0,1.4,0.2,Iris-setosa
4+
3,4.7,3.2,1.3,0.2,Iris-setosa
5+
4,4.6,3.1,1.5,0.2,Iris-setosa
6+
5,5.0,3.6,1.4,0.2,Iris-setosa
7+
6,5.4,3.9,1.7,0.4,Iris-setosa
8+
7,4.6,3.4,1.4,0.3,Iris-setosa
9+
8,5.0,3.4,1.5,0.2,Iris-setosa
10+
9,4.4,2.9,1.4,0.2,Iris-setosa
11+
10,4.9,3.1,1.5,0.1,Iris-setosa
12+
11,5.4,3.7,1.5,0.2,Iris-setosa
13+
12,4.8,3.4,1.6,0.2,Iris-setosa
14+
13,4.8,3.0,1.4,0.1,Iris-setosa
15+
14,4.3,3.0,1.1,0.1,Iris-setosa
16+
15,5.8,4.0,1.2,0.2,Iris-setosa
17+
16,5.7,4.4,1.5,0.4,Iris-setosa
18+
17,5.4,3.9,1.3,0.4,Iris-setosa
19+
18,5.1,3.5,1.4,0.3,Iris-setosa
20+
19,5.7,3.8,1.7,0.3,Iris-setosa
21+
20,5.1,3.8,1.5,0.3,Iris-setosa
22+
21,5.4,3.4,1.7,0.2,Iris-setosa
23+
22,5.1,3.7,1.5,0.4,Iris-setosa
24+
23,4.6,3.6,1.0,0.2,Iris-setosa
25+
24,5.1,3.3,1.7,0.5,Iris-setosa
26+
25,4.8,3.4,1.9,0.2,Iris-setosa
27+
26,5.0,3.0,1.6,0.2,Iris-setosa
28+
27,5.0,3.4,1.6,0.4,Iris-setosa
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28,5.2,3.5,1.5,0.2,Iris-setosa
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29,5.2,3.4,1.4,0.2,Iris-setosa
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30,4.7,3.2,1.6,0.2,Iris-setosa
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31,4.8,3.1,1.6,0.2,Iris-setosa
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32,5.4,3.4,1.5,0.4,Iris-setosa
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33,5.2,4.1,1.5,0.1,Iris-setosa
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34,5.5,4.2,1.4,0.2,Iris-setosa
36+
35,4.9,3.1,1.5,0.1,Iris-setosa
37+
36,5.0,3.2,1.2,0.2,Iris-setosa
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37,5.5,3.5,1.3,0.2,Iris-setosa
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38,4.9,3.1,1.5,0.1,Iris-setosa
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39,4.4,3.0,1.3,0.2,Iris-setosa
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40,5.1,3.4,1.5,0.2,Iris-setosa
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41,5.0,3.5,1.3,0.3,Iris-setosa
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42,4.5,2.3,1.3,0.3,Iris-setosa
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43,4.4,3.2,1.3,0.2,Iris-setosa
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44,5.0,3.5,1.6,0.6,Iris-setosa
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45,5.1,3.8,1.9,0.4,Iris-setosa
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46,4.8,3.0,1.4,0.3,Iris-setosa
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47,5.1,3.8,1.6,0.2,Iris-setosa
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48,4.6,3.2,1.4,0.2,Iris-setosa
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49,5.3,3.7,1.5,0.2,Iris-setosa
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50,5.0,3.3,1.4,0.2,Iris-setosa
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51,7.0,3.2,4.7,1.4,Iris-versicolor
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52,6.4,3.2,4.5,1.5,Iris-versicolor
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53,6.9,3.1,4.9,1.5,Iris-versicolor
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54,5.5,2.3,4.0,1.3,Iris-versicolor
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55,6.5,2.8,4.6,1.5,Iris-versicolor
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56,5.7,2.8,4.5,1.3,Iris-versicolor
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57,6.3,3.3,4.7,1.6,Iris-versicolor
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58,4.9,2.4,3.3,1.0,Iris-versicolor
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59,6.6,2.9,4.6,1.3,Iris-versicolor
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60,5.2,2.7,3.9,1.4,Iris-versicolor
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61,5.0,2.0,3.5,1.0,Iris-versicolor
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62,5.9,3.0,4.2,1.5,Iris-versicolor
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63,6.0,2.2,4.0,1.0,Iris-versicolor
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64,6.1,2.9,4.7,1.4,Iris-versicolor
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65,5.6,2.9,3.6,1.3,Iris-versicolor
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66,6.7,3.1,4.4,1.4,Iris-versicolor
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67,5.6,3.0,4.5,1.5,Iris-versicolor
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68,5.8,2.7,4.1,1.0,Iris-versicolor
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69,6.2,2.2,4.5,1.5,Iris-versicolor
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70,5.6,2.5,3.9,1.1,Iris-versicolor
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71,5.9,3.2,4.8,1.8,Iris-versicolor
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72,6.1,2.8,4.0,1.3,Iris-versicolor
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73,6.3,2.5,4.9,1.5,Iris-versicolor
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74,6.1,2.8,4.7,1.2,Iris-versicolor
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75,6.4,2.9,4.3,1.3,Iris-versicolor
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76,6.6,3.0,4.4,1.4,Iris-versicolor
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77,6.8,2.8,4.8,1.4,Iris-versicolor
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78,6.7,3.0,5.0,1.7,Iris-versicolor
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79,6.0,2.9,4.5,1.5,Iris-versicolor
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80,5.7,2.6,3.5,1.0,Iris-versicolor
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81,5.5,2.4,3.8,1.1,Iris-versicolor
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82,5.5,2.4,3.7,1.0,Iris-versicolor
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83,5.8,2.7,3.9,1.2,Iris-versicolor
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84,6.0,2.7,5.1,1.6,Iris-versicolor
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85,5.4,3.0,4.5,1.5,Iris-versicolor
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86,6.0,3.4,4.5,1.6,Iris-versicolor
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87,6.7,3.1,4.7,1.5,Iris-versicolor
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88,6.3,2.3,4.4,1.3,Iris-versicolor
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89,5.6,3.0,4.1,1.3,Iris-versicolor
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90,5.5,2.5,4.0,1.3,Iris-versicolor
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91,5.5,2.6,4.4,1.2,Iris-versicolor
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92,6.1,3.0,4.6,1.4,Iris-versicolor
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93,5.8,2.6,4.0,1.2,Iris-versicolor
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94,5.0,2.3,3.3,1.0,Iris-versicolor
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95,5.6,2.7,4.2,1.3,Iris-versicolor
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96,5.7,3.0,4.2,1.2,Iris-versicolor
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97,5.7,2.9,4.2,1.3,Iris-versicolor
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98,6.2,2.9,4.3,1.3,Iris-versicolor
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99,5.1,2.5,3.0,1.1,Iris-versicolor
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100,5.7,2.8,4.1,1.3,Iris-versicolor
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101,6.3,3.3,6.0,2.5,Iris-virginica
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102,5.8,2.7,5.1,1.9,Iris-virginica
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103,7.1,3.0,5.9,2.1,Iris-virginica
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104,6.3,2.9,5.6,1.8,Iris-virginica
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105,6.5,3.0,5.8,2.2,Iris-virginica
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106,7.6,3.0,6.6,2.1,Iris-virginica
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107,4.9,2.5,4.5,1.7,Iris-virginica
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108,7.3,2.9,6.3,1.8,Iris-virginica
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109,6.7,2.5,5.8,1.8,Iris-virginica
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110,7.2,3.6,6.1,2.5,Iris-virginica
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111,6.5,3.2,5.1,2.0,Iris-virginica
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112,6.4,2.7,5.3,1.9,Iris-virginica
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113,6.8,3.0,5.5,2.1,Iris-virginica
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114,5.7,2.5,5.0,2.0,Iris-virginica
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115,5.8,2.8,5.1,2.4,Iris-virginica
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116,6.4,3.2,5.3,2.3,Iris-virginica
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117,6.5,3.0,5.5,1.8,Iris-virginica
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118,7.7,3.8,6.7,2.2,Iris-virginica
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119,7.7,2.6,6.9,2.3,Iris-virginica
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120,6.0,2.2,5.0,1.5,Iris-virginica
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121,6.9,3.2,5.7,2.3,Iris-virginica
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122,5.6,2.8,4.9,2.0,Iris-virginica
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123,7.7,2.8,6.7,2.0,Iris-virginica
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124,6.3,2.7,4.9,1.8,Iris-virginica
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125,6.7,3.3,5.7,2.1,Iris-virginica
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126,7.2,3.2,6.0,1.8,Iris-virginica
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127,6.2,2.8,4.8,1.8,Iris-virginica
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128,6.1,3.0,4.9,1.8,Iris-virginica
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129,6.4,2.8,5.6,2.1,Iris-virginica
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130,7.2,3.0,5.8,1.6,Iris-virginica
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131,7.4,2.8,6.1,1.9,Iris-virginica
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132,7.9,3.8,6.4,2.0,Iris-virginica
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133,6.4,2.8,5.6,2.2,Iris-virginica
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134,6.3,2.8,5.1,1.5,Iris-virginica
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135,6.1,2.6,5.6,1.4,Iris-virginica
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136,7.7,3.0,6.1,2.3,Iris-virginica
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137,6.3,3.4,5.6,2.4,Iris-virginica
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138,6.4,3.1,5.5,1.8,Iris-virginica
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139,6.0,3.0,4.8,1.8,Iris-virginica
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140,6.9,3.1,5.4,2.1,Iris-virginica
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141,6.7,3.1,5.6,2.4,Iris-virginica
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142,6.9,3.1,5.1,2.3,Iris-virginica
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143,5.8,2.7,5.1,1.9,Iris-virginica
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144,6.8,3.2,5.9,2.3,Iris-virginica
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145,6.7,3.3,5.7,2.5,Iris-virginica
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146,6.7,3.0,5.2,2.3,Iris-virginica
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147,6.3,2.5,5.0,1.9,Iris-virginica
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148,6.5,3.0,5.2,2.0,Iris-virginica
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149,6.2,3.4,5.4,2.3,Iris-virginica
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150,5.9,3.0,5.1,1.8,Iris-virginica

‎day-120/__init__.py‎

Whitespace-only changes.

‎day-120/clusters.py‎

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'''
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__author__ = justdopython.com
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'''
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import numpy as np
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import pandas as pd
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import random
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class Clusters():
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def __init__(self, train_data, K):
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'''
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:params train_data: ndarray. 训练数据.
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:params K: int. 要划分的簇的数量.
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:attr train_data:
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'''
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super().__init__()
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self.train_data = train_data
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self.K = K
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# 标记聚类是否完成。具体的真假，取决于是否还存在需要从一个簇移动到另一个簇的数据
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self.finished = False
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# 随机选取 K 个数据作为各个簇的中心点
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index = random.sample(range(len(self.train_data)), self.K)
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self.centroid = train_data[index, 1:5]
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# 将训练数据均匀分配到各个簇，以便以同一的形式适用于数据的分配
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self.clusters = []
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offset = len(train_data) // self.K
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for i in range(self.K):
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start = offset * i
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if i < self.K-1:
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self.clusters.append(train_data[start:start+offset,:])
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else:
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# 最后一个簇包含剩下的所有数据
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self.clusters.append(train_data[start:,:])
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# 加载所要用到的数据集
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@staticmethod
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def getData():
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'''
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获取数据，返回值类型为 ndarray
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'''
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train_data = pd.read_csv('iris.csv').to_numpy()
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return train_data
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# 将各数据分配到每个簇中去
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def assign(self):
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self.finished = True
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# data_index_list 和 target_index_list 分别记录“需要移动的数据在当前簇中的索引”以及“要移动到的目标簇索引”
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target_index_list = []
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data_index_list = []
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for i in range(self.K):
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target_index_list.append([])
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data_index_list.append([])
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for cluster_index in range(len(self.clusters)):
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for data_index in range(len(self.clusters[cluster_index])):
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diff = self.clusters[cluster_index][data_index, 1:5] - self.centroid
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distance_square = np.sum(diff * diff, axis=1)
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target_index = np.argmin(distance_square)
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if cluster_index != target_index:
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self.finished = False
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target_index_list[cluster_index].append(target_index)
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data_index_list[cluster_index].append(data_index)
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for cluster_index in range(self.K):
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for index in range(len(target_index_list[cluster_index])):
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target_index = target_index_list[cluster_index][index]
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data_index = data_index_list[cluster_index][index]
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self.clusters[target_index] = np.append(self.clusters[target_index],
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self.clusters[cluster_index][data_index, :]).reshape(-1, 6)
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for cluster_index in range(self.K):
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data_index = data_index_list[cluster_index]
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self.clusters[cluster_index] = np.delete(self.clusters[cluster_index], data_index, axis=0)
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# 更新各个簇的质心
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def update(self):
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for cluster_index in range(len(self.clusters)):
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self.centroid[cluster_index] = np.mean(self.clusters[cluster_index][:,1:5], axis=0)
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def train(self):
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'''
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进行聚类训练
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'''
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while not self.finished:
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self.assign()
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self.update()
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print('训练完成！！！')
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def printResult(self):
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'''
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打印聚类结果
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'''
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print('-'*80)
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print('*'*80)
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print('-'*80)
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print('*'*30, '聚类结果', '*'*30)
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print('-'*30,'各簇中心','-'*30)
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for i in range(self.K):
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print('第', str(i), '簇中心：', self.centroid[i])
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print('-'*80)
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print('-'*30,'各簇结果','-'*30)
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for i in range(self.K):
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print('-'*20, '第', str(i), '簇结果', '-'*20,)
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for d in self.clusters[i]:
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print(d[5])
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print('-'*80)
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print('*'*80)
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print('-'*80)
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if __name__ == '__main__':
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print('-'*80)
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K = int(input('请输入要划分的簇数（应为正整数）：'))
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data = Clusters.getData()
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clusters = Clusters(data, K)
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clusters.train()
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clusters.printResult()

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