kmeans 算法，python

重点内容#################################################

kmeans: k-means cluster

Author : zouxy

Date : 2013-12-25

HomePage : http://blog.csdn.net/zouxy09

Email : zouxy09@qq.com

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from numpy import *

import time

import matplotlib.pyplot as plt

calculate Euclidean distance

def euclDistance(vector1, vector2):

return sqrt(sum(power(vector2 - vector1, 2)))

init centroids with random samples

def initCentroids(dataSet, k):

numSamples, dim = dataSet.shape

centroids = zeros((k, dim))

for i in range(k):

index = int(random.uniform(0, numSamples))

centroids[i, :] = dataSet[index, :]

return centroids

k-means cluster

def kmeans(dataSet, k):

numSamples = dataSet.shape[0]

# first column stores which cluster this sample belongs to,

# second column stores the error between this sample and its centroid

clusterAssment = mat(zeros((numSamples, 2)))

clusterChanged = True

## step 1: init centroids
centroids = initCentroids(dataSet, k)

while clusterChanged:
clusterChanged = False
## for each sample
for i in xrange(numSamples):
minDist  = 100000.0
minIndex = 0
## for each centroid
## step 2: find the centroid who is closest
for j in range(k):
distance = euclDistance(centroids[j, :], dataSet[i, :])
if distance < minDist:
minDist  = distance
minIndex = j

## step 3: update its cluster
if clusterAssment[i, 0] != minIndex:
clusterChanged = True
clusterAssment[i, :] = minIndex, minDist**2

## step 4: update centroids
for j in range(k):
pointsInCluster = dataSet[nonzero(clusterAssment[:, 0].A == j)[0]]
centroids[j, :] = mean(pointsInCluster, axis = 0)

print 'Congratulations, cluster complete!'
return centroids, clusterAssment

show your cluster only available with 2-D data

def showCluster(dataSet, k, centroids, clusterAssment):

numSamples, dim = dataSet.shape

if dim != 2:

print “Sorry! I can not draw because the dimension of your data is not 2!”

return 1

mark = ['or', 'ob', 'og', 'ok', '^r', '+r', 'sr', 'dr', '<r', 'pr']
if k > len(mark):
print "Sorry! Your k is too large! please contact Zouxy"
return 1

# draw all samples
for i in xrange(numSamples):
markIndex = int(clusterAssment[i, 0])
plt.plot(dataSet[i, 0], dataSet[i, 1], mark[markIndex])

mark = ['Dr', 'Db', 'Dg', 'Dk', '^b', '+b', 'sb', 'db', '<b', 'pb']
# draw the centroids
for i in range(k):
plt.plot(centroids[i, 0], centroids[i, 1], mark[i], markersize = 12)

plt.show()

step 1: load data

print “step 1: load data…”

dataSet = []

fileIn = open(‘E:/Dataset/testSet.txt’)

for line in fileIn.readlines():

lineArr = line.strip().split(‘\t’)

dataSet.append([float(lineArr[0]), float(lineArr[1])])

step 2: clustering…

print “step 2: clustering…”

dataSet = mat(dataSet)

k = 4

centroids, clusterAssment = kmeans(dataSet, k)

step 3: show the result

print “step 3: show the result…”

showCluster(dataSet, k, centroids, clusterAssment)