线性逻辑回归与非线性逻辑回归pytorch+sklearn

 1 import matplotlib.pyplot as plt
 2 import numpy as np
 3 from sklearn.metrics import classification_report
 4 from sklearn import preprocessing
 5 
 6 # 载入数据
 7 data = np.genfromtxt("LR-testSet.csv", delimiter=",")
 8 x_data = data[:, :-1]
 9 y_data = data[:, -1]
10 
11 
12 def plot():
13     x0 = []
14     x1 = []
15     y0 = []
16     y1 = []
17     # 切分不同类别的数据
18     for i in range(len(x_data)):
19         if y_data[i] == 0:
20             x0.append(x_data[i, 0])
21             y0.append(x_data[i, 1])
22         else:
23             x1.append(x_data[i, 0])
24             y1.append(x_data[i, 1])
25 
26     # 画图
27     scatter0 = plt.scatter(x0, y0, c='b', marker='o')
28     scatter1 = plt.scatter(x1, y1, c='r', marker='x')
29     # 画图例
30     plt.legend(handles=[scatter0, scatter1], labels=['label0', 'label1'], loc='best')
31 
32 
33 plot()
34 plt.show()
35 
36 # 数据处理，添加偏置项
37 x_data = data[:,:-1]
38 y_data = data[:,-1,np.newaxis]
39 
40 print(np.mat(x_data).shape)
41 print(np.mat(y_data).shape)
42 # 给样本添加偏置项
43 X_data = np.concatenate((np.ones((100,1)),x_data),axis=1)
44 print(X_data.shape)
45 
46 
47 def sigmoid(x):
48     return 1.0 / (1 + np.exp(-x))
49 
50 
51 def cost(xMat, yMat, ws):
52     left = np.multiply(yMat, np.log(sigmoid(xMat * ws)))
53     right = np.multiply(1 - yMat, np.log(1 - sigmoid(xMat * ws)))
54     return np.sum(left + right) / -(len(xMat))
55 
56 
57 def gradAscent(xArr, yArr):
58     xMat = np.mat(xArr)
59     yMat = np.mat(yArr)
60 
61     lr = 0.001
62     epochs = 10000
63     costList = []
64     # 计算数据行列数
65     # 行代表数据个数，列代表权值个数
66     m, n = np.shape(xMat)
67     # 初始化权值
68     ws = np.mat(np.ones((n, 1)))
69 
70     for i in range(epochs + 1):
71         # xMat和weights矩阵相乘
72         h = sigmoid(xMat * ws)
73         # 计算误差
74         ws_grad = xMat.T * (h - yMat) / m
75         ws = ws - lr * ws_grad
76 
77         if i % 50 == 0:
78             costList.append(cost(xMat, yMat, ws))
79     return ws, costList
80 # 训练模型，得到权值和cost值的变化
81 ws,costList = gradAscent(X_data, y_data)
82 print(ws)
83 
84 plot()
85 x_test = [[-4], [3]]
86 y_test = (-ws[0] - x_test * ws[1]) / ws[2]
87 plt.plot(x_test, y_test, 'k')
88 plt.show()
89 
90 # 画图 loss值的变化
91 x = np.linspace(0,10000,201)
92 plt.plot(x, costList, c='r')
93 plt.title('Train')
94 plt.xlabel('Epochs')
95 plt.ylabel('Cost')
96 plt.show()

 1 import matplotlib.pyplot as plt
 2 import numpy as np
 3 from sklearn.metrics import classification_report
 4 from sklearn import preprocessing
 5 from sklearn.preprocessing import PolynomialFeatures
 6 
 7 # 载入数据
 8 data = np.genfromtxt("LR-testSet2.txt", delimiter=",")
 9 x_data = data[:, :-1]
10 y_data = data[:, -1, np.newaxis]
11 
12 
13 def plot():
14     x0 = []
15     x1 = []
16     y0 = []
17     y1 = []
18     # 切分不同类别的数据
19     for i in range(len(x_data)):
20         if y_data[i] == 0:
21             x0.append(x_data[i, 0])
22             y0.append(x_data[i, 1])
23         else:
24             x1.append(x_data[i, 0])
25             y1.append(x_data[i, 1])
26 
27     # 画图
28     scatter0 = plt.scatter(x0, y0, c='b', marker='o')
29     scatter1 = plt.scatter(x1, y1, c='r', marker='x')
30     # 画图例
31     plt.legend(handles=[scatter0, scatter1], labels=['label0', 'label1'], loc='best')
32 
33 
34 plot()
35 plt.show()
36 
37 # 定义多项式回归,degree的值可以调节多项式的特征
38 poly_reg  = PolynomialFeatures(degree=3)
39 # 特征处理
40 x_poly = poly_reg.fit_transform(x_data)
41 
42 
43 def sigmoid(x):
44     return 1.0 / (1 + np.exp(-x))
45 
46 
47 def cost(xMat, yMat, ws):
48     left = np.multiply(yMat, np.log(sigmoid(xMat * ws)))
49     right = np.multiply(1 - yMat, np.log(1 - sigmoid(xMat * ws)))
50     return np.sum(left + right) / -(len(xMat))
51 
52 
53 def gradAscent(xArr, yArr):
54     xMat = np.mat(xArr)
55     yMat = np.mat(yArr)
56 
57     lr = 0.03
58     epochs = 50000
59     costList = []
60     # 计算数据列数，有几列就有几个权值
61     m, n = np.shape(xMat)
62     # 初始化权值
63     ws = np.mat(np.ones((n, 1)))
64 
65     for i in range(epochs + 1):
66         # xMat和weights矩阵相乘
67         h = sigmoid(xMat * ws)
68         # 计算误差
69         ws_grad = xMat.T * (h - yMat) / m
70         ws = ws - lr * ws_grad
71 
72         if i % 50 == 0:
73             costList.append(cost(xMat, yMat, ws))
74     return ws, costList
75 
76 # 训练模型，得到权值和cost值的变化
77 ws,costList = gradAscent(x_poly, y_data)
78 print(ws)
79 
80 # 获取数据值所在的范围
81 x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1
82 y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1
83 
84 # 生成网格矩阵
85 xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
86                      np.arange(y_min, y_max, 0.02))
87 
88 z = sigmoid(poly_reg.fit_transform(np.c_[xx.ravel(), yy.ravel()]).dot(np.array(ws)))# ravel与flatten类似，多维数据转一维。flatten不会改变原始数据，ravel会改变原始数据
89 for i in range(len(z)):
90     if z[i] > 0.5:
91         z[i] = 1
92     else:
93         z[i] = 0
94 z = z.reshape(xx.shape)
95 
96 # 等高线图
97 cs = plt.contourf(xx, yy, z)
98 plot()
99 plt.show()

导入数据：1. LR-testSet.csv

-0.017612    14.053064    0
-1.395634    4.662541    1
-0.752157    6.53862    0
-1.322371    7.152853    0
0.423363    11.054677    0
0.406704    7.067335    1
0.667394    12.741452    0
-2.46015    6.866805    1
0.569411    9.548755    0
-0.026632    10.427743    0
0.850433    6.920334    1
1.347183    13.1755    0
1.176813    3.16702    1
-1.781871    9.097953    0
-0.566606    5.749003    1
0.931635    1.589505    1
-0.024205    6.151823    1
-0.036453    2.690988    1
-0.196949    0.444165    1
1.014459    5.754399    1
1.985298    3.230619    1
-1.693453    -0.55754    1
-0.576525    11.778922    0
-0.346811    -1.67873    1
-2.124484    2.672471    1
1.217916    9.597015    0
-0.733928    9.098687    0
-3.642001    -1.618087    1
0.315985    3.523953    1
1.416614    9.619232    0
-0.386323    3.989286    1
0.556921    8.294984    1
1.224863    11.58736    0
-1.347803    -2.406051    1
1.196604    4.951851    1
0.275221    9.543647    0
0.470575    9.332488    0
-1.889567    9.542662    0
-1.527893    12.150579    0
-1.185247    11.309318    0
-0.445678    3.297303    1
1.042222    6.105155    1
-0.618787    10.320986    0
1.152083    0.548467    1
0.828534    2.676045    1
-1.237728    10.549033    0
-0.683565    -2.166125    1
0.229456    5.921938    1
-0.959885    11.555336    0
0.492911    10.993324    0
0.184992    8.721488    0
-0.355715    10.325976    0
-0.397822    8.058397    0
0.824839    13.730343    0
1.507278    5.027866    1
0.099671    6.835839    1
-0.344008    10.717485    0
1.785928    7.718645    1
-0.918801    11.560217    0
-0.364009    4.7473    1
-0.841722    4.119083    1
0.490426    1.960539    1
-0.007194    9.075792    0
0.356107    12.447863    0
0.342578    12.281162    0
-0.810823    -1.466018    1
2.530777    6.476801    1
1.296683    11.607559    0
0.475487    12.040035    0
-0.783277    11.009725    0
0.074798    11.02365    0
-1.337472    0.468339    1
-0.102781    13.763651    0
-0.147324    2.874846    1
0.518389    9.887035    0
1.015399    7.571882    0
-1.658086    -0.027255    1
1.319944    2.171228    1
2.056216    5.019981    1
-0.851633    4.375691    1
-1.510047    6.061992    0
-1.076637    -3.181888    1
1.821096    10.28399    0
3.01015    8.401766    1
-1.099458    1.688274    1
-0.834872    -1.733869    1
-0.846637    3.849075    1
1.400102    12.628781    0
1.752842    5.468166    1
0.078557    0.059736    1
0.089392    -0.7153    1
1.825662    12.693808    0
0.197445    9.744638    0
0.126117    0.922311    1
-0.679797    1.22053    1
0.677983    2.556666    1
0.761349    10.693862    0
-2.168791    0.143632    1
1.38861    9.341997    0
0.317029    14.739025    0

      2.LR-testSet2.txt

0.051267,0.69956,1
-0.092742,0.68494,1
-0.21371,0.69225,1
-0.375,0.50219,1
-0.51325,0.46564,1
-0.52477,0.2098,1
-0.39804,0.034357,1
-0.30588,-0.19225,1
0.016705,-0.40424,1
0.13191,-0.51389,1
0.38537,-0.56506,1
0.52938,-0.5212,1
0.63882,-0.24342,1
0.73675,-0.18494,1
0.54666,0.48757,1
0.322,0.5826,1
0.16647,0.53874,1
-0.046659,0.81652,1
-0.17339,0.69956,1
-0.47869,0.63377,1
-0.60541,0.59722,1
-0.62846,0.33406,1
-0.59389,0.005117,1
-0.42108,-0.27266,1
-0.11578,-0.39693,1
0.20104,-0.60161,1
0.46601,-0.53582,1
0.67339,-0.53582,1
-0.13882,0.54605,1
-0.29435,0.77997,1
-0.26555,0.96272,1
-0.16187,0.8019,1
-0.17339,0.64839,1
-0.28283,0.47295,1
-0.36348,0.31213,1
-0.30012,0.027047,1
-0.23675,-0.21418,1
-0.06394,-0.18494,1
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0.22984,-0.41155,1
0.2932,-0.2288,1
0.48329,-0.18494,1
0.64459,-0.14108,1
0.46025,0.012427,1
0.6273,0.15863,1
0.57546,0.26827,1
0.72523,0.44371,1
0.22408,0.52412,1
0.44297,0.67032,1
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0.18376,0.93348,0
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0.28744,1.087,0
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