49 lines
1.3 KiB
Python
49 lines
1.3 KiB
Python
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"""
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=================================
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DP Mixture Model Ellipsoids
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=================================
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Plot the confidence ellipsoids of a mixture of two gaussians.
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"""
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import numpy as np
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from scikits.learn import mixture
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import itertools
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import pylab as pl
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import matplotlib as mpl
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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 = np.r_[np.dot(np.random.randn(n, 2), C),
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np.random.randn(n, 2) + np.array([3, 3])]
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for p,alpha in enumerate([0.01, 1., 100.]):
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clf = mixture.DPGMM(n_states=10, cvtype='full', alpha=alpha)
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clf.fit(X)
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splot = pl.subplot(310+p+1, aspect='equal')
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color_iter = itertools.cycle (['r', 'g', 'b', 'c'])
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Y_ = clf.predict(X)
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for i, (mean, covar, color) in enumerate(zip(clf.means, clf.covars, color_iter)):
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v, w = np.linalg.eigh(covar)
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u = w[0] / np.linalg.norm(w[0])
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if sum(Y_ == i) > 1:
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pl.scatter(X[Y_==i, 0], X[Y_==i, 1], .8, color=color)
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else:
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continue
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angle = np.arctan(u[1]/u[0])
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angle = 180 * angle / np.pi # convert to degrees
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ell = mpl.patches.Ellipse (mean, v[0], v[1], 180 + angle, color=color)
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ell.set_clip_box(splot.bbox)
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ell.set_alpha(0.5)
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splot.add_artist(ell)
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pl.show()
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