57 lines
2.2 KiB
Python
57 lines
2.2 KiB
Python
"""
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===================================================================
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Support Vector Regression (SVR) using linear and non-linear kernels
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===================================================================
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Toy example of 1D regression using linear, polynomial and RBF kernels.
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"""
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print(__doc__)
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import numpy as np
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from sklearn.svm import SVR
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import matplotlib.pyplot as plt
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# #############################################################################
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# Generate sample data
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X = np.sort(5 * np.random.rand(40, 1), axis=0)
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y = np.sin(X).ravel()
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# #############################################################################
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# Add noise to targets
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y[::5] += 3 * (0.5 - np.random.rand(8))
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# #############################################################################
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# Fit regression model
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svr_rbf = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1)
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svr_lin = SVR(kernel='linear', C=100, gamma='auto')
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svr_poly = SVR(kernel='poly', C=100, gamma='auto', degree=3, epsilon=.1,
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coef0=1)
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# #############################################################################
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# Look at the results
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lw = 2
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svrs = [svr_rbf, svr_lin, svr_poly]
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kernel_label = ['RBF', 'Linear', 'Polynomial']
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model_color = ['m', 'c', 'g']
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fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 10), sharey=True)
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for ix, svr in enumerate(svrs):
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axes[ix].plot(X, svr.fit(X, y).predict(X), color=model_color[ix], lw=lw,
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label='{} model'.format(kernel_label[ix]))
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axes[ix].scatter(X[svr.support_], y[svr.support_], facecolor="none",
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edgecolor=model_color[ix], s=50,
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label='{} support vectors'.format(kernel_label[ix]))
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axes[ix].scatter(X[np.setdiff1d(np.arange(len(X)), svr.support_)],
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y[np.setdiff1d(np.arange(len(X)), svr.support_)],
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facecolor="none", edgecolor="k", s=50,
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label='other training data')
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axes[ix].legend(loc='upper center', bbox_to_anchor=(0.5, 1.1),
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ncol=1, fancybox=True, shadow=True)
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fig.text(0.5, 0.04, 'data', ha='center', va='center')
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fig.text(0.06, 0.5, 'target', ha='center', va='center', rotation='vertical')
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fig.suptitle("Support Vector Regression", fontsize=14)
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plt.show()
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