k均值算法

import matplotlib.pyplot as plt

import numpy as np

import time

from django.template.defaultfilters import center

def loadDataSet(fileName):

    dataMat=[]

    fr=open(fileName)

    for line in fr.readlines():

        curLine=line.strip().split(‘\t‘)

        fltLine=map(float,curLine)

        dataMat.append([i for i in fltLine])

    return dataMat

def distEclud(vecA,vecB):

    return np.sqrt(np.sum(np.power(vecA-vecB,2)))
def randCent(dataSet,k):

    n=np.shape(dataSet)[1]

    centroids=np.mat(np.zeros((k,n)))

    for j in range(n):

        minJ=min(dataSet[:,j])

        rangeJ=float(max(dataSet[:,j])-minJ)

        centroids[:,j]=minJ+rangeJ*np.random.rand(k,1)

    return centroids
def kMeans(dataSet,k):

    m=np.shape(dataSet)[0]

    clusterAssment=np.mat(np.zeros((m,2)))

    centroids=randCent(dataSet, k)

    clusterChanged=True

    while clusterChanged:

        clusterChanged=False

        for i in range(m):

            minDist=np.inf

            minIndex=-1

            for j in range(k):

                distJI=distEclud(centroids[j,:], dataSet[i,:])

                if distJI < minDist:

                    minDist=distJI;minIndex=j

            if  clusterAssment[i,0] != minIndex:

                clusterChanged=True

            clusterAssment[i,:]=minIndex,minDist**2

        for cent in range(k):

            ptsInClust=dataSet[np.nonzero(clusterAssment[:,0].A == cent)[0]]

            centroids[cent,:]=np.mean(ptsInClust, axis=0)

    return centroids,clusterAssment

def showImage(dataSet,center,label):

    c=[‘r‘,‘g‘,‘w‘,‘b‘]

    n=np.shape(dataSet)[0]

    for i in range(4):

        x=[],y=[]

        for j in range(n):

            if label[j]==i:

                x.append(dataSet[j,0])

                y.append(dataSet[j,1])

        plt.scatter(x,y,s=40,c=c[i])

    center=center.A

    plt.scatter(center[:,0],center[:,1],c=‘m‘,marker=‘p‘,s=200)

    plt.show()

if __name__ == ‘__main__‘:

    startTime=time.clock()

    dataSet=loadDataSet("testSet.txt")

    dataSet=np.array(dataSet)

    print(dataSet)

    center,cluster=kMeans(dataSet, 4)

    print(center)

    endTime=time.clock()

    print(endTime-startTime)

    showImage(dataSet, center, cluster[:,0])

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