Toy example of 1D regression using linear, polynomial and RBF kernels.
print(__doc__) 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) y = np.sin(X).ravel()
Add noise to targets
y[::5] += 3 * (0.5 - np.random.rand(8))
Fit regression model
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
lw = 2 plt.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
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