Improve the GMM PDF example.

Author: lpierron

examples/mixture/plot_gmm_pdf.py

@@ -9,30 +9,42 @@
 """
 
 import numpy as np
-import pylab as pl
+import matplotlib.pyplot as plt
+from matplotlib.colors import LogNorm
 from sklearn import mixture
 
 n_samples = 300
 
 # generate random sample, two components
 np.random.seed(0)
+
+# generate spherical data centered on (20, 20)
+shifted_gaussian = np.random.randn(n_samples, 2) + np.array([20, 20])
+
+# generate zero centered stretched gaussian data
 C = np.array([[0., -0.7], [3.5, .7]])
-X_train = np.r_[np.dot(np.random.randn(n_samples, 2), C),
-                np.random.randn(n_samples, 2) + np.array([20, 20])]
+stretched_gaussian = np.dot(np.random.randn(n_samples, 2), C)
+
+# concatenate the two datasets into the final training set
+X_train = np.vstack([shifted_gaussian, stretched_gaussian])
 
+# fit a Gaussian Mixture Model with two components 
 clf = mixture.GMM(n_components=2, covariance_type='full')
 clf.fit(X_train)
 
+# display predicted scores by the model as a contour plot
 x = np.linspace(-20.0, 30.0)
 y = np.linspace(-20.0, 40.0)
 X, Y = np.meshgrid(x, y)
-XX = np.c_[X.ravel(), Y.ravel()]
-Z = np.log(-clf.score_samples(XX)[0])
+XX = np.array([X.ravel(), Y.ravel()]).T
+Z = -clf.score_samples(XX)[0]
 Z = Z.reshape(X.shape)
 
-CS = pl.contour(X, Y, Z)
-CB = pl.colorbar(CS, shrink=0.8, extend='both')
-pl.scatter(X_train[:, 0], X_train[:, 1], .8)
+CS = plt.contour(X, Y, Z, norm=LogNorm(vmin=1.0,vmax=1000.0),
+                 levels=np.logspace(0, 3, 10))
+CB = plt.colorbar(CS, shrink=0.8, extend='both')
+plt.scatter(X_train[:, 0], X_train[:, 1], .8)
 
-pl.axis('tight')
-pl.show()
+plt.title('Predicted negative log-likelihood by a GMM')
+plt.axis('tight')
+plt.show()