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Commit 0a2e911

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2 parents 4b32c7c + b70f532 commit 0a2e911

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‎README.md‎

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- [第59天:PyQuery 详解](http://www.justdopython.com/2019/10/07/python-spider-PyQuery-059)
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- [第 62 天:HTTP 入门]( https://github.com/JustDoPython/python-100-day/tree/master/day-062 )
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- [第74天:Python newspaper 框架](http://www.justdopython.com/2019/11/24/python-newspaper-074)
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- [第 79 天:数据分析之 Numpy]( https://github.com/JustDoPython/python-100-day/tree/master/day-079 )
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- [第92天:Python多线程之 Event 事件](http://www.justdopython.com/2019/11/05/python-event-092)
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- [第 94 天:数据可视化之 pandas]( https://github.com/JustDoPython/python-100-day/tree/master/day-094 )
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- [第 106 天:机器学习概览]( https://github.com/JustDoPython/python-100-day/tree/master/day-106 )
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- [第 112 天:机器学习算法之蒙特卡洛方法]( https://github.com/JustDoPython/python-100-day/tree/master/day-112 )
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- [第 117 天:机器学习算法之 K 近邻]( https://github.com/JustDoPython/python-100-day/tree/master/day-117 )
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关注公众号:python技术,回复"python"一起学习交流
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‎day-079/__init__.py‎

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‎day-094/__init__.py‎

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‎day-106/__init__.py‎

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‎day-112/MonteCarlo.py‎

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import random
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def solve_pi(repeat = 20000) -> float:
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'''
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:param repeat: integral
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:return: float
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'''
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count = 0
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for i in range(repeat):
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x = random.random()
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y = random.random()
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if x*x + y*y < 1.0:
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count += 1
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ratio = count / repeat
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PI = 4 * ratio
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return PI
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if __name__ == "__main__":
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repeat = int(input("请输入实验次数:"))
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print(solve_pi(repeat))

‎day-112/MonteCarlo_integral.py‎

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import random
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def solve_integral(repeat = 20000) -> float:
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'''
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:param repeat: integral
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:return: float
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'''
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count = 0
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for i in range(repeat):
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x = random.random()
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y = random.random()
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if y > x*x:
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count += 1
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ratio = count / repeat
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integral = ratio * 1
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return integral
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if __name__ == "__main__":
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repeat = int(input("请输入实验次数:"))
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print(solve_integral(repeat))

‎day-112/__init__.py‎

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‎day-117/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
3+
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
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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
29+
28,5.2,3.5,1.5,0.2,Iris-setosa
30+
29,5.2,3.4,1.4,0.2,Iris-setosa
31+
30,4.7,3.2,1.6,0.2,Iris-setosa
32+
31,4.8,3.1,1.6,0.2,Iris-setosa
33+
32,5.4,3.4,1.5,0.4,Iris-setosa
34+
33,5.2,4.1,1.5,0.1,Iris-setosa
35+
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
38+
37,5.5,3.5,1.3,0.2,Iris-setosa
39+
38,4.9,3.1,1.5,0.1,Iris-setosa
40+
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
69+
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
86+
85,5.4,3.0,4.5,1.5,Iris-versicolor
87+
86,6.0,3.4,4.5,1.6,Iris-versicolor
88+
87,6.7,3.1,4.7,1.5,Iris-versicolor
89+
88,6.3,2.3,4.4,1.3,Iris-versicolor
90+
89,5.6,3.0,4.1,1.3,Iris-versicolor
91+
90,5.5,2.5,4.0,1.3,Iris-versicolor
92+
91,5.5,2.6,4.4,1.2,Iris-versicolor
93+
92,6.1,3.0,4.6,1.4,Iris-versicolor
94+
93,5.8,2.6,4.0,1.2,Iris-versicolor
95+
94,5.0,2.3,3.3,1.0,Iris-versicolor
96+
95,5.6,2.7,4.2,1.3,Iris-versicolor
97+
96,5.7,3.0,4.2,1.2,Iris-versicolor
98+
97,5.7,2.9,4.2,1.3,Iris-versicolor
99+
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
108+
107,4.9,2.5,4.5,1.7,Iris-virginica
109+
108,7.3,2.9,6.3,1.8,Iris-virginica
110+
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
112+
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
117+
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-117/KNN.py‎

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'''
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__author__ = justdopython.com
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'''
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import matplotlib.pyplot as plt
7+
import numpy as np
8+
import pandas as pd
9+
from collections import Counter
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# 生成所要用到的数据集
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def getData() -> (np.ndarray, np.ndarray, np.ndarray, np.ndarray):
14+
iris = pd.read_csv('iris.csv').to_numpy()
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train_data = [] # 训练数据,与测试数据按 4:1 比例划分
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test_data = [] # 测试数据
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train_target = [] # 训练数据对应标签
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test_target = [] # 测试数据对应标签
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# 原始数据集分为 3 个类别,分别是 0~49,50~99,100~149,各50个
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for i in range(3):
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offset = 50 * i
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data = iris[offset+0:offset+50, :]
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# data = np.random.shuffle(data)
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np.random.shuffle(data) # 就地随机打乱
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# try:
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# train_data.append(data[0:39, 1:5].tolist())
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# train_target.append(data[0:39, 5].tolist())
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# test_data.append(data[40:, 1:5].tolist())
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# test_target.append(data[40:, 5].tolist())
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# except NameError:
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# train_data = data[0:39, 1:5].tolist()
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# train_target = data[0:39, 5].tolist()
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# test_data = data[40:, 1:5].tolist()
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# test_target = data[40:, 5].tolist()
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train_data.extend(data[0:40, 1:5].tolist())
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train_target.extend(data[0:40, 5].tolist())
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test_data.extend(data[40:, 1:5].tolist())
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test_target.extend(data[40:, 5].tolist())
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train_data = np.array(train_data)
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test_data = np.array(test_data)
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train_target = np.array(train_target)
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test_target = np.array(test_target)
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return train_data, test_data, train_target, test_target
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# 计算距离
51+
def calculateDistance(test_data, train_data) -> list:
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distance = []
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for i in range(len(test_data)):
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sub_dist = []
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for j in range(len(train_data)):
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dif_array = test_data[i] - train_data[j]
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dist = np.sqrt(np.sum(dif_array * dif_array))
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sub_dist.append(dist)
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distance.append(sub_dist)
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distance = np.array(distance)
63+
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return distance
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# 求解结果
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def calculateResult(distance, K, train_target):
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results = []
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70+
for i in range(len(distance)):
71+
index = np.argsort(distance[i])[:K]
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result = train_target[index]
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# result = pd.value_counts(result)
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species = Counter(result).most_common(1)[0][0]
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results.append(species)
79+
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return results
81+
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# 评估结果
83+
def estimateResult(results, test_target):
84+
right = 0
85+
print('-'*80)
86+
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for i in range(len(results)):
88+
print('Right Species = ', test_target[i], \
89+
', \tReal Species = ', results[i])
90+
91+
if results[i] == test_target[i]:
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right += 1
93+
94+
right_rate = right / len(results)
95+
print('-'*80)
96+
print("Right Rate: ", right_rate)
97+
print('-'*80)
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return
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102+
if __name__ == '__main__':
103+
print('-'*80)
104+
K = int(input("请输入 K 值:"))
105+
106+
train_data, test_data, train_target, test_target = getData()
107+
distance = calculateDistance(test_data, train_data)
108+
results = calculateResult(distance, K, train_target)
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110+
estimateResult(results, test_target)
111+
112+
113+
114+

‎day-117/__init__.py‎

Whitespace-only changes.

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