41 lines
972 B
Python
41 lines
972 B
Python
"""
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=============================================
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Density Estimation for a mixture of Gaussians
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=============================================
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Plot the density estimation of a mixture of two gaussians. Data is
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generated from two gaussians with different centers and covariance
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matrices.
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"""
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import itertools
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import numpy as np
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import pylab as pl
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from scikits.learn import gmm
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n, m = 300, 2
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# generate random sample, two components
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np.random.seed(0)
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C = np.array([[0., -0.7], [3.5, .7]])
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X_train = np.r_[np.dot(np.random.randn(n, 2), C),
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np.random.randn(n, 2) + np.array([20, 20])]
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clf = gmm.GMM(2, ndim=2, cvtype='full')
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clf.fit(X_train)
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x = np.linspace(-20.0, 30.0)
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y = np.linspace(-20.0, 40.0)
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X, Y = np.meshgrid(x, y)
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XX = np.c_[X.ravel(), Y.ravel()]
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Z = np.log(-clf.eval(XX)[0])
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Z = Z.reshape(X.shape)
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CS = pl.contour(X, Y, Z)
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CB = pl.colorbar(CS, shrink=0.8, extend='both')
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pl.scatter(X_train[:, 0], X_train[:, 1], .8)
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pl.axis('tight')
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pl.show()
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