K-Means--机器学习实战-Peter Harrington

#Machine Learning in Action: 机器学习实战, [美]Perer Harrington，K-均值聚类算法
from numpy import *

def loadDataSet(fileName):
    dataMat = []
    fr = open(fileName)
    for line in fr.readlines():
        curLine = line.strip().split('\t')    #strip()--移除字符串头尾指定的字符（默认为空格）或字符序列。split('\t') --指定分隔符对字符串进行切片
        fltLine = list(map(float, curLine))
        dataMat.append(fltLine)
        # print("aaa:",list(fltLine))
    return dataMat
def distEclud(vecA, vecB):
    return sqrt(sum(power(vecA - vecB, 2)))

def randCent(dataSet, k):
    n = shape(dataSet)[1]              #shape:(80,2)
    centroids = mat(zeros((k, n)))     #构建质心
    for j in range(n):
        minJ = min(dataSet[:,j])
        rangeJ = float(max(dataSet[:,j]) - minJ)
        centroids[:,j] = minJ + rangeJ * random.rand(k,1)    #rand()--生成随机数[0,1), k行1列 ,centroids--质心、中心
    # print("ccc:\n", centroids)
    return centroids

def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):
    m = shape(dataSet)[0]        #80
    clusterAssment = mat(zeros((m,2)))     #一列记录簇索引值，一列记录存储误差
    centroids = createCent(dataSet, k)     #随机初始化聚类中心

    clusterChanged = True         #标定值
    while clusterChanged:
        clusterChanged = False
        for i in range(m):
            minDist = inf        #比较大的值
            minIndex = -1
            for j in range(k):
                distJI = distMeas(centroids[j,:],dataSet[i,:])     #distEclud--计算每个点到中心的距离
                if distJI < minDist:
                    minDist = distJI
                    minIndex = j
            if clusterAssment[i,0] != minIndex:
                clusterChanged = True
            clusterAssment[i,:] = minIndex, minDist**2
        print(centroids)
        for cent in range(k):     #更新质心的位置
            ptsInClust = dataSet[nonzero(clusterAssment[:,0].A==cent)[0]]   #nonzero--返回非零元素的索引，A==cent将数字变为布尔值：True、False
            centroids[cent,:] = mean(ptsInClust, axis=0)     #按列平均，[4, 2]
            # print("mmm:\n",  (clusterAssment[:,0].A==cent))
    return centroids, clusterAssment




if __name__ == '__main__':
    datMat = mat(loadDataSet('testSet.txt'))     #mat: matrix
    randCent(datMat, 2)
    distance = distEclud(datMat[0],datMat[1])

    centroids, clustAssment = kMeans(datMat, 4)
    # print("ddd:", centroids)