Spark MLlib之K-Means聚类算法

0.0 0.0 0.0
0.1 0.1 0.1
0.2 0.2 0.2
9.0 9.0 9.0
9.1 9.1 9.1
9.2 9.2 9.2
15.1 16.1 17.0
18.0 17.0 19.0
20.0 21.0 22.0

private static final Pattern SPACE = Pattern.compile(" ");
 
public static void main(String[] args) {
    SparkConf sparkConf = new SparkConf()
          .setAppName("K-Means")
          .setMaster("local[2]");
    JavaSparkContext sc = new JavaSparkContext(sparkConf);
    JavaRDD<String> data = sc.textFile("/home/yurnom/data/kmeans_data.txt");
    JavaRDD<Vector> parsedData = data.map(s -> {
        double[] values = Arrays.asList(SPACE.split(s))
              .stream()
              .mapToDouble(Double::parseDouble)
              .toArray();
        return Vectors.dense(values);
    });
 
    int numClusters = 3; //预测分为3个簇类
    int numIterations = 20; //迭代20次
    int runs = 10; //运行10次，选出最优解
    KMeansModel clusters = KMeans.train(parsedData.rdd(), numClusters, numIterations, runs);
 
    //计算测试数据分别属于那个簇类
    print(parsedData.map(v -> v.toString()
          + " belong to cluster :" + clusters.predict(v)).collect());
    //计算cost
    double wssse = clusters.computeCost(parsedData.rdd());
    System.out.println("Within Set Sum of Squared Errors = " + wssse);
 
    //打印出中心点
    System.out.println("Cluster centers:");
    for (Vector center : clusters.clusterCenters()) {
        System.out.println(" " + center);
    }
    //进行一些预测
    System.out.println("Prediction of (1.1, 2.1, 3.1): "
          + clusters.predict(Vectors.dense(new double[]{1.1, 2.1, 3.1})));
    System.out.println("Prediction of (10.1, 9.1, 11.1): "
          + clusters.predict(Vectors.dense(new double[]{10.1, 9.1, 11.1})));
    System.out.println("Prediction of (21.1, 17.1, 16.1): "
          + clusters.predict(Vectors.dense(new double[]{21.1, 17.1, 16.1})));
}
 
public static <T> void print(Collection<T> c) {
    for(T t : c) {
        System.out.println(t.toString());
    }
}