45 lines
1.1 KiB
Python
45 lines
1.1 KiB
Python
import pylab as pl
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import numpy as np
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from mpl_toolkits.mplot3d import Axes3D
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from scikits.learn import datasets, linear_model
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diabetes = datasets.load_diabetes()
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indices = (0, 1)
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X_train = diabetes.data[:-20, indices]
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X_test = diabetes.data[-20:, indices]
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y_train = diabetes.target[:-20]
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y_test = diabetes.target[-20:]
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ols = linear_model.LinearRegression()
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ols.fit(X_train, y_train)
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fig = pl.figure(1, figsize=(4, 3))
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pl.clf()
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ax = Axes3D(fig, elev=43.5, azim=-110)
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n = 100
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ax.scatter(X_train[:, 0], X_train[:, 1], y_train, c='k', marker='+')
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ax.plot_surface(np.array([[-.1, -.1], [.15, .15]]),
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np.array([[-.1, .15], [-.1, .15]]),
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ols.predict(np.array([[-.1, -.1, .15, .15],
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[-.1, .15, -.1, .15]]).T
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).reshape((2, 2)),
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alpha=.5)
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ax.set_xlabel('X_1')
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ax.set_ylabel('X_2')
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ax.set_zlabel('Y')
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ax.set_xticks(())
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ax.set_yticks(())
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ax.set_zticks(())
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#pl.savefig('diabetes_ols_diag.png')
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ax.elev = -.5
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ax.azim = 0
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#pl.savefig('diabetes_ols_x1.png')
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ax.elev = -.5
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ax.azim = 90
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#pl.savefig('diabetes_ols_x2.png')
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