50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
"""
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==============================
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k-Nearest Neighbors regression
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==============================
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Demonstrate the resolution of a regression problem
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using a k-Nearest Neighbor and the interpolation of the
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target using both barycenter and constant weights.
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"""
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print __doc__
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# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# Fabian Pedregosa <fabian.pedregosa@inria.fr>
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#
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# License: BSD, (C) INRIA
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###############################################################################
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# Generate sample data
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import numpy as np
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import pylab as pl
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from scikits.learn import neighbors
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np.random.seed(0)
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X = np.sort(5*np.random.rand(40, 1), axis=0)
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T = np.linspace(0, 5, 500)[:, np.newaxis]
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y = np.sin(X).ravel()
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# Add noise to targets
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y[::5] += 1*(0.5 - np.random.rand(8))
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###############################################################################
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# Fit regression model
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for i, mode in enumerate(('mean', 'barycenter')):
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knn = neighbors.NeighborsRegressor(n_neighbors=4, mode=mode)
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y_ = knn.fit(X, y).predict(T)
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pl.subplot(2, 1, 1 + i)
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pl.scatter(X, y, c='k', label='data')
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pl.plot(T, y_, c='g', label='prediction')
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pl.axis('tight')
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pl.legend()
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pl.title('NeighborsRegressor with %s weights' % mode)
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pl.subplots_adjust(0.1, 0.04, 0.95, 0.94, 0.3, 0.28)
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pl.show()
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