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
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2.LR-testSet2.txt
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