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KMeans Clustering Implemented in python with numpy
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'''Implementation and of K Means Clustering | |
Requires : python 2.7.x, Numpy 1.7.1+''' | |
import numpy as np | |
def kMeans(X, K, maxIters = 10, plot_progress = None): | |
centroids = X[np.random.choice(np.arange(len(X)), K), :] | |
for i in range(maxIters): | |
# Cluster Assignment step | |
C = np.array([np.argmin([np.dot(x_i-y_k, x_i-y_k) for y_k in centroids]) for x_i in X]) | |
# Move centroids step | |
centroids = [X[C == k].mean(axis = 0) for k in range(K)] | |
if plot_progress != None: plot_progress(X, C, np.array(centroids)) | |
return np.array(centroids) , C |
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'''dEMONSTRATION of K Means Clustering | |
Requires : python 2.7.x, Numpy 1.7.1+, Matplotlib, 1.2.1+''' | |
import sys | |
import pylab as plt | |
import numpy as np | |
plt.ion() | |
def show(X, C, centroids, keep = False): | |
import time | |
time.sleep(0.5) | |
plt.cla() | |
plt.plot(X[C == 0, 0], X[C == 0, 1], '*b', | |
X[C == 1, 0], X[C == 1, 1], '*r', | |
X[C == 2, 0], X[C == 2, 1], '*g') | |
plt.plot(centroids[:,0],centroids[:,1],'*m',markersize=20) | |
plt.draw() | |
if keep : | |
plt.ioff() | |
plt.show() | |
# generate 3 cluster data | |
# data = np.genfromtxt('data1.csv', delimiter=',') | |
m1, cov1 = [9, 8], [[1.5, 2], [1, 2]] | |
m2, cov2 = [5, 13], [[2.5, -1.5], [-1.5, 1.5]] | |
m3, cov3 = [3, 7], [[0.25, 0.5], [-0.1, 0.5]] | |
data1 = np.random.multivariate_normal(m1, cov1, 250) | |
data2 = np.random.multivariate_normal(m2, cov2, 180) | |
data3 = np.random.multivariate_normal(m3, cov3, 100) | |
X = np.vstack((data1,np.vstack((data2,data3)))) | |
np.random.shuffle(X) | |
from kMeans import kMeans | |
centroids, C = kMeans(X, K = 3, plot_progress = show) | |
show(X, C, centroids, True) |
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