k-means基础实现

__author__ = 'Administrator'
from numpy import *
import time
import matplotlib.pyplot as plt
    # 计算距离（欧式）
def euclDistance(vector1, vector2):
    return sqrt(sum(power(vector2 - vector1, 2)))

    # 初始中心点(随机)
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
def loaddata(name):
    dataMat=[]
    fe=open(name,'r')
    for line in fe:
        strs=line.restrip().split(',')
        flt=map(float,strs)
        dataMat.append(flt)
    return dataMat
    # 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! notice ,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()


if __name__=='__main__':
    data=loaddata('data.txt')
    kmeans(data,5)

标签：return,means,实现,numSamples,centroids,dataSet,##,算法,clusterAssment
来源： https://blog.csdn.net/weixin_41442514/article/details/92141144