classifier.py

from time import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA

def calculateCluster( data, labels ):

	digits = labels
	data = data

	n_samples, n_features = data.shape
	n_digits = len(np.unique(digits))
	labels = digits

	print("n_digits: %d, \t n_samples %d, \t n_features %d"
	      % (n_digits, n_samples, n_features))

	###############################################################################
	# Visualize the results on PCA-reduced data

	reduced_data = PCA(n_components=2).fit_transform(data)
	kmeans = KMeans(init='k-means++', n_clusters=n_digits, n_init=10)
	kmeans.fit(reduced_data)

	# Step size of the mesh. Decrease to increase the quality of the VQ.
	h = .02     # point in the mesh [x_min, m_max]x[y_min, y_max].

	# Plot the decision boundary. For that, we will assign a color to each
	x_min, x_max = reduced_data[:, 0].min() + 1, reduced_data[:, 0].max() - 1
	y_min, y_max = reduced_data[:, 1].min() + 1, reduced_data[:, 1].max() - 1
	xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))

	# Obtain labels for each point in mesh. Use last trained model.
	Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])

	# Put the result into a color plot
	Z = Z.reshape(xx.shape)
	plt.figure(1)
	plt.clf()
	plt.imshow(Z, interpolation='nearest',
	           extent=(xx.min(), xx.max(), yy.min(), yy.max()),
	           cmap=plt.cm.Paired,
	           aspect='auto', origin='lower')

	plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)
	# Plot the centroids as a white X
	centroids = kmeans.cluster_centers_
	plt.scatter(centroids[:, 0], centroids[:, 1],
	            marker='x', s=169, linewidths=3,
	            color='w', zorder=10)
	plt.title('K-means clustering on paintings (PCA-reduced data)\n'
	          'Centroids are marked with white cross')
	plt.xlim(x_min, x_max)
	plt.ylim(y_min, y_max)
	plt.xticks(())
	plt.yticks(())
	plt.show()