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January 16, 2013 13:24
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| import itertools | |
| import numpy as np | |
| from scipy import linalg | |
| import pylab as pl | |
| import matplotlib as mpl | |
| from sklearn import mixture | |
| data_set = 'fig' | |
| if data_set == 'fig': | |
| X = np.genfromtxt('figexampledata.txt') | |
| n_components = 4 | |
| elif data_set == 'oldfaithful': | |
| X = np.genfromtxt('oldfaithful.txt') | |
| n_components = 2 | |
| # n_components stores the correct number of components. | |
| # Normalise X | |
| X_norm = (X - X.mean(axis=0)) / X.std(axis=0) | |
| X = X_norm | |
| # Fit a mixture of gaussians with EM using five components | |
| gmm = mixture.GMM(n_components=n_components, | |
| covariance_type='full') | |
| gmm.fit(X) | |
| # Fit a variational bayes process mixture of gaussians using five components | |
| vbgmm = mixture.VBGMM(n_components=n_components, | |
| covariance_type='full') | |
| vbgmm.fit(X) | |
| # Fit a variational bayes process mixture of gaussians using five components | |
| dpgmm = mixture.DPGMM(n_components=n_components, | |
| covariance_type='full') | |
| dpgmm.fit(X) | |
| color_iter = itertools.cycle(['r', 'g', 'b', 'c', 'm']) | |
| for i, (clf, title) in enumerate([(gmm, 'GMM'), | |
| (vbgmm, 'Variational Bayes GMM'), | |
| (dpgmm, 'Dirichlet Process GMM')]): | |
| splot = pl.subplot(3, 1, 1+i) | |
| Y_ = clf.predict(X) | |
| for i, (mean, covar, color) in enumerate(zip(clf.means_, clf._get_covars(), | |
| color_iter)): | |
| v, w = linalg.eigh(covar) | |
| u = w[0] / linalg.norm(w[0]) | |
| # as the DP will not use every component it has access to | |
| # unless it needs it, we shouldn't plot the redundant | |
| # components. | |
| if not np.any(Y_ == i): | |
| continue | |
| pl.scatter(X[Y_ == i, 0], X[Y_ == i, 1], .8, color=color) | |
| # Plot an ellipse to show the Gaussian component | |
| angle = np.arctan(u[1]/u[0]) | |
| angle = 180 * angle / np.pi # convert to degrees | |
| ell = mpl.patches.Ellipse(mean, v[0], v[1], 180 + angle, color=color) | |
| ell.set_clip_box(splot.bbox) | |
| ell.set_alpha(0.5) | |
| splot.add_artist(ell) | |
| paddingX = (X[:, 0].max() - X[:, 0].min()) * 0.2 / 2 | |
| paddingY = (X[:, 1].max() - X[:, 1].min()) * 0.2 / 2 | |
| pl.xlim(X[:, 0].min() - paddingX, X[:, 0].max() + paddingX) | |
| pl.ylim(X[:, 1].min() - paddingY, X[:, 1].max() + paddingY) | |
| pl.xticks(()) | |
| pl.yticks(()) | |
| pl.title(title) | |
| print "{}: {}".format(title, clf.weights_) | |
| pl.show() |
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