clf = best_gmm
bars = []
# Plot the BIC scores
plt.figure(figsize=(8, 6))
spl = plt.subplot(2, 1, 1)
for i, (cv_type, color) in enumerate(zip(cv_types, color_iter)):
xpos = np.array(n_components_range) + .2 * (i - 2)
bars.append(
plt.bar(xpos,
bic[i * len(n_components_range):(i + 1) *
len(n_components_range)],
width=.2,
color=color))
plt.xticks(n_components_range)
plt.ylim([bic.min() * 1.01 - .01 * bic.max(), bic.max()])
plt.title('BIC score per model')
xpos = np.mod(bic.argmin(), len(n_components_range)) + .65 +\
.2 * np.floor(bic.argmin() / len(n_components_range))
plt.text(xpos, bic.min() * 0.97 + .03 * bic.max(), '*', fontsize=14)
spl.set_xlabel('Number of components')
spl.legend([b[0] for b in bars], cv_types)
plt.savefig('charts/expectation-max.ICA.creditcards.png')
results = clf.predict(X_train)
uniqueValues, occurCount = np.unique(results, return_counts=True)
print("Clusters : ", uniqueValues)
print("Cluster memberships : ", occurCount)
