SVR(Python)的使用示例

# -*- coding: UTF-8 -*- import numpy as np from sklearn.svm import SVR import matplotlib.pyplot as plt ############################################################################### # Generate sample data #X = np.sort(5 * np.random.rand(40, 1), axis=0) #产生40组数据，每组一个数据，axis=0决定按列排列，=1表示行排列 #y =[0, 0, 0, 0, 0, 2, 1, 0, 3, 0, 1, 1, 4, 1, 0, 0, 4, 1, 5, 3, 3, 1] y = [0.5, 0.3333333333333333, 0.75, 1.0, 1.5, 1.5714285714285714, 3.0, 2.6666666666666665, 2.4, 2.1818181818181817, 2.0833333333333335, 1.9230769230769231, 1.7857142857142858, 1.7333333333333334, 2.6875, 2.8823529411764706, 3.611111111111111, 4.105263157894737, 4.2, 4.380952380952381] Y = [] X =[[1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20] #[21], #[22] ] #np.sin(X).ravel() #np.sin()输出的是列，和X对应，ravel表示转换成行 #X = np.sort(5 * np.random.rand(40, 1), axis=0) print(X) for i in y : Y.append(np.log(i)) y = Y ############################################################################### # Add noise to targets #y[::5] += 3 * (0.5 - np.random.rand(8)) ############################################################################### # Fit regression model svr_rbf10 = SVR(kernel='rbf',C=100, gamma=10.0) svr_rbf1 = SVR(kernel='rbf', C=100, gamma=0.1) svr_rbf1 = SVR(kernel='rbf', C=100, gamma=0.1) #svr_lin = SVR(kernel='linear', C=1e3) #svr_poly = SVR(kernel='poly', C=1e3, degree=3) y_rbf10 = svr_rbf10.fit(X, y).predict(X) y_rbf1 = svr_rbf1.fit(X, y).predict(X) #y_lin = svr_lin.fit(X, y).predict(X) #y_poly = svr_poly.fit(X, y).predict(X) ############################################################################### # look at the results lw = 2 #line width plt.scatter(X, y, color='darkorange', label='data') plt.hold('on') plt.plot(X, y_rbf10, color='navy', lw=lw, label='RBF gamma=10.0') plt.plot(X, y_rbf1, color='c', lw=lw, label='RBF gamma=1.0') #plt.plot(X, y_lin, color='c', lw=lw, label='Linear model') #plt.plot(X, y_poly, color='cornflowerblue', lw=lw, label='Polynomial model') plt.xlabel('data') plt.ylabel('target') plt.title('Support Vector Regression') plt.legend() plt.show()