Toy example of 1D regression using linear, polynomial and RBF kernels.
1 2 3 4 5 | print(__doc__)import numpy as npfrom sklearn.svm import SVRimport matplotlib.pyplot as plt |
Generate sample data
1 2 | X = np.sort(5 * np.random.rand(40, 1), axis=0)y = np.sin(X).ravel() |
Add noise to targets
1 | y[::5] += 3 * (0.5 - np.random.rand(8)) |
Fit regression model
1 2 3 4 5 6 | svr_rbf = SVR(kernel='rbf', C=1e3, gamma=0.1)svr_lin = SVR(kernel='linear', C=1e3)svr_poly = SVR(kernel='poly', C=1e3, degree=2)y_rbf = svr_rbf.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
1 2 3 4 5 6 7 8 9 10 11 | lw = 2plt.scatter(X, y, color='darkorange', label='data')plt.hold('on')plt.plot(X, y_rbf, color='navy', lw=lw, label='RBF model')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() |
Total running time of the script: (0 minutes 0.769 seconds)
Download Python source code:
plot_svm_regression.py
Download IPython notebook:
plot_svm_regression.ipynb
Please login to continue.