分析一下自带的kmeans代码，这个要稍微复杂一些

import numpy as np
from pyspark import SparkContext

#这个函数的目的就是把读入的数据都转化为float类型的数据
def parseVector(line):
    return np.array([float(x) for x in line.split(' ')])

#此函数的目的就是求取该点应该分到哪个点集中去，返回的是序号
def closestPoint(p, centers):
    bestIndex = 0
    closest = float("+inf")
    for i in range(len(centers)):
        tempDist = np.sum((p - centers[i]) ** 2)
        if tempDist < closest:
            closest = tempDist
            bestIndex = i
    return bestIndex


if __name__ == "__main__":

    if len(sys.argv) != 4:
        print("Usage: kmeans <file> <k> <convergeDist>", file=sys.stderr)
        exit(-1)

    print("""WARN: This is a naive implementation of KMeans Clustering and is given
       as an example! Please refer to examples/src/main/python/mllib/kmeans.py for an example on
       how to use MLlib's KMeans implementation.""", file=sys.stderr)

    sc = SparkContext(appName="PythonKMeans")
    lines = sc.textFile(sys.argv[1])
    #此处调用了rdd的map函数把所有的数据都转换为float类型
    data = lines.map(parseVector).cache()
    #这里的K就是设置的中心点数
    K = int(sys.argv[2])
    #设置的阈值，如果两次之间的距离小于该阈值的话则停止迭代
    convergeDist = float(sys.argv[3])
    #从点集中用采样的方式来抽取K个值
    kPoints = data.takeSample(False, K, 1)
    #中心点调整后的距离差
    tempDist = 1.0
    
    #如果距离差大于阈值则执行
    while tempDist > convergeDist:
        #对所有数据执行map过程，最终生成的是(index, (point, 1))的rdd
        closest = data.map(
            lambda p: (closestPoint(p, kPoints), (p, 1)))
        #执行reduce过程，该过程的目的是重新求取中心点，生成的也是rdd
        pointStats = closest.reduceByKey(
            lambda p1_c1, p2_c2: (p1_c1[0] + p2_c2[0], p1_c1[1] + p2_c2[1]))
        #生成新的中心点
        newPoints = pointStats.map(
            lambda st: (st[0], st[1][0] / st[1][1])).collect()
        #计算一下新旧中心点的距离差
        tempDist = sum(np.sum((kPoints[iK] - p) ** 2) for (iK, p) in newPoints)
        
        #设置新的中心点
        for (iK, p) in newPoints:
            kPoints[iK] = p

    print("Final centers: " + str(kPoints))

    sc.stop()

这里就是整个过程了，说起来结合了numpy来使用还比较简单