└── GradingDesent
    ├── LinearRegressionTest.py
    └── data.txt


/GradingDesent/LinearRegressionTest.py:
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 1 | import numpy as np
 2 | import pandas as pd
 3 | from matplotlib import pyplot as plt
 4 | 
 5 | path = 'C:\\Users\\Administrator\\Desktop\\data.txt'
 6 | data = pd.read_csv(path, header=None)
 7 | plt.scatter(data[:][0], data[:][1], marker='+')
 8 | data = np.array(data)
 9 | m = data.shape[0]
10 | theta = np.array([0, 0])
11 | data = np.hstack([np.ones([m, 1]), data])
12 | y = data[:, 2]
13 | data = data[:, :2]
14 | 
15 | 
16 | def cost_function(data, theta, y):
17 |     cost = np.sum((data.dot(theta) - y) ** 2)
18 |     return cost / (2 * m)
19 | 
20 | 
21 | def gradient(data, theta, y):
22 |     grad = np.empty(len(theta))
23 |     grad[0] = np.sum(data.dot(theta) - y)
24 |     for i in range(1, len(theta)):
25 |         grad[i] = (data.dot(theta) - y).dot(data[:, i])
26 |     return grad
27 | 
28 | 
29 | def gradient_descent(data, theta, y, eta):
30 |     while True:
31 |         last_theta = theta
32 |         grad = gradient(data, theta, y)
33 |         theta = theta - eta * grad
34 |         print(theta)
35 |         if abs(cost_function(data, last_theta, y) - cost_function(data, theta, y)) < 1e-15:
36 |             break
37 |     return theta
38 | 
39 | 
40 | res = gradient_descent(data, theta, y, 0.0001)
41 | X = np.arange(3, 25)
42 | Y = res[0] + res[1] * X
43 | plt.plot(X, Y, color='r')
44 | plt.show()
45 | 


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/GradingDesent/data.txt:
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 1 | 6.1101,17.592
 2 | 5.5277,9.1302
 3 | 8.5186,13.662
 4 | 7.0032,11.854
 5 | 5.8598,6.8233
 6 | 8.3829,11.886
 7 | 7.4764,4.3483
 8 | 8.5781,12
 9 | 6.4862,6.5987
10 | 5.0546,3.8166
11 | 5.7107,3.2522
12 | 14.164,15.505
13 | 5.734,3.1551
14 | 8.4084,7.2258
15 | 5.6407,0.71618
16 | 5.3794,3.5129
17 | 6.3654,5.3048
18 | 5.1301,0.56077
19 | 6.4296,3.6518
20 | 7.0708,5.3893
21 | 6.1891,3.1386
22 | 20.27,21.767
23 | 5.4901,4.263
24 | 6.3261,5.1875
25 | 5.5649,3.0825
26 | 18.945,22.638
27 | 12.828,13.501
28 | 10.957,7.0467
29 | 13.176,14.692
30 | 22.203,24.147
31 | 5.2524,-1.22
32 | 6.5894,5.9966
33 | 9.2482,12.134
34 | 5.8918,1.8495
35 | 8.2111,6.5426
36 | 7.9334,4.5623
37 | 8.0959,4.1164
38 | 5.6063,3.3928
39 | 12.836,10.117
40 | 6.3534,5.4974
41 | 5.4069,0.55657
42 | 6.8825,3.9115
43 | 11.708,5.3854
44 | 5.7737,2.4406
45 | 7.8247,6.7318
46 | 7.0931,1.0463
47 | 5.0702,5.1337
48 | 5.8014,1.844
49 | 11.7,8.0043
50 | 5.5416,1.0179
51 | 7.5402,6.7504
52 | 5.3077,1.8396
53 | 7.4239,4.2885
54 | 7.6031,4.9981
55 | 6.3328,1.4233
56 | 6.3589,-1.4211
57 | 6.2742,2.4756
58 | 5.6397,4.6042
59 | 9.3102,3.9624
60 | 9.4536,5.4141
61 | 8.8254,5.1694
62 | 5.1793,-0.74279
63 | 21.279,17.929
64 | 14.908,12.054
65 | 18.959,17.054
66 | 7.2182,4.8852
67 | 8.2951,5.7442
68 | 10.236,7.7754
69 | 5.4994,1.0173
70 | 20.341,20.992
71 | 10.136,6.6799
72 | 7.3345,4.0259
73 | 6.0062,1.2784
74 | 7.2259,3.3411
75 | 5.0269,-2.6807
76 | 6.5479,0.29678
77 | 7.5386,3.8845
78 | 5.0365,5.7014
79 | 10.274,6.7526
80 | 5.1077,2.0576
81 | 5.7292,0.47953
82 | 5.1884,0.20421
83 | 6.3557,0.67861
84 | 9.7687,7.5435
85 | 6.5159,5.3436
86 | 8.5172,4.2415
87 | 9.1802,6.7981
88 | 6.002,0.92695
89 | 5.5204,0.152
90 | 5.0594,2.8214
91 | 5.7077,1.8451
92 | 7.6366,4.2959
93 | 5.8707,7.2029
94 | 5.3054,1.9869
95 | 8.2934,0.14454
96 | 13.394,9.0551
97 | 5.4369,0.61705
98 | 


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