基于spark的kmeans算法

from __future__ import print_function

import sys

import numpy as np
from pyspark.sql import SparkSession


def parseVector(line):
    return np.array([float(x) for x in line.split(' ')])


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)
        sys.exit(-1)

    spark = SparkSession\
        .builder\
        .appName("PythonKMeans")\
        .getOrCreate()

    lines = spark.read.text(sys.argv[1]).rdd.map(lambda r: r[0])
    data = lines.map(parseVector).cache()
    //聚类超参数K
    K = int(sys.argv[2]) 
    //收敛阈值
    convergeDist = float(sys.argv[3])
    //初始化K个中心点
    kPoints = data.takeSample(False, K, 1)
    tempDist = 1.0

    while tempDist > convergeDist:
        // map Key: 聚类中心点  Value: （当前点， 数量1）
        closest = data.map(
            lambda p: (closestPoint(p, kPoints), (p, 1)))
        // reduce Key：聚类中心点， 计算每个聚类中心点下的分布
        pointStats = closest.reduceByKey(
            lambda p1_c1, p2_c2: (p1_c1[0] + p2_c2[0], p1_c1[1] + p2_c2[1]))
        //map 计算新的中心点
        newPoints = pointStats.map(
            lambda st: (st[0], st[1][0] / st[1][1])).collect()

        tempDist = sum(np.sum((kPoints[iK] - p) ** 2) for (iK, p) in newPoints)

        for (iK, p) in newPoints:
            kPoints[iK] = p

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

spark.stop()