K均值Kmeans聚类算法
Kmeans聚类算法——K均值聚类算法百度网盘链接：
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使用环境：Win7-32bit-Anaconda2-4.3.1-Windows-x86.exe具体的代码如下：Kmeans_function.py子函数文件：
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 08 22:19:29 2017
@author: ysw
"""
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 = dataSet
return centroids
# k-means cluster
def kmeans(dataSet, k):
numSamples = dataSet.shape
# 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= 100000000.0
minIndex = 0
## for each centroid
## step 2: find the centroid who is closest
for j in range(k):
distance = euclDistance(centroids, dataSet)
if distance < minDist:
minDist= distance
minIndex = j
## step 3: update its cluster
if clusterAssment != minIndex:
clusterChanged = True
clusterAssment = minIndex, minDist**2
## step 4: update centroids
for j in range(k):
pointsInCluster = dataSet.A == j)]
centroids = 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!"
return 1
# draw all samples
for i in xrange(numSamples):
markIndex = int(clusterAssment)
plt.plot(dataSet, dataSet, mark)
mark = ['Dr', 'Db', 'Dg', 'Dk', '^b', '+b', 'sb', 'db', '<b', 'pb']
# draw the centroids
for i in range(k):
plt.plot(centroids, centroids, mark, markersize = 12)
plt.show()Kmeans_main.py主函数程序：
from numpy import *
#import time
#import matplotlib.pyplot as plt
import Kmeans_function
## step 1: load data
print "step 1: load data..."
dataSet = []
fileIn = open(r'C:\Users\ysw\Desktop\Python(x,y)2.7.10\testSet.txt')
for line in fileIn.readlines():
lineArr = line.strip().split()
dataSet.append(), float(lineArr)])
## step 2: clustering...
print "step 2: clustering..."
dataSet = mat(dataSet)
k = 4
centroids, clusterAssment = Kmeans_function.kmeans(dataSet, k)
## step 3: show the result
print "step 3: show the result..."
Kmeans_function.showCluster(dataSet, k, centroids, clusterAssment)
参考链接：http://blog.csdn.net/zouxy09/article/details/17589329
http://halcom.cn/forum.php?mod=viewthread&tid=2770&extra=
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