spark JavaBisectingKMeansExample 源码

2022-10-20
浏览 (508)

spark JavaBisectingKMeansExample 代码

文件路径：/examples/src/main/java/org/apache/spark/examples/ml/JavaBisectingKMeansExample.java

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.spark.examples.ml;

// $example on$
import org.apache.spark.ml.clustering.BisectingKMeans;
import org.apache.spark.ml.clustering.BisectingKMeansModel;
import org.apache.spark.ml.evaluation.ClusteringEvaluator;
import org.apache.spark.ml.linalg.Vector;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
// $example off$
import org.apache.spark.sql.SparkSession;


/**
 * An example demonstrating bisecting k-means clustering.
 * Run with
 * <pre>
 * bin/run-example ml.JavaBisectingKMeansExample
 * </pre>
 */
public class JavaBisectingKMeansExample {

  public static void main(String[] args) {
    SparkSession spark = SparkSession
      .builder()
      .appName("JavaBisectingKMeansExample")
      .getOrCreate();

    // $example on$
    // Loads data.
    Dataset<Row> dataset = spark.read().format("libsvm").load("data/mllib/sample_kmeans_data.txt");

    // Trains a bisecting k-means model.
    BisectingKMeans bkm = new BisectingKMeans().setK(2).setSeed(1);
    BisectingKMeansModel model = bkm.fit(dataset);

    // Make predictions
    Dataset<Row> predictions = model.transform(dataset);

    // Evaluate clustering by computing Silhouette score
    ClusteringEvaluator evaluator = new ClusteringEvaluator();

    double silhouette = evaluator.evaluate(predictions);
    System.out.println("Silhouette with squared euclidean distance = " + silhouette);

    // Shows the result.
    System.out.println("Cluster Centers: ");
    Vector[] centers = model.clusterCenters();
    for (Vector center : centers) {
      System.out.println(center);
    }
    // $example off$

    spark.stop();
  }
}

相关信息

spark 源码目录

相关文章

spark JavaAFTSurvivalRegressionExample 源码

spark JavaALSExample 源码

spark JavaBinarizerExample 源码

spark JavaBucketedRandomProjectionLSHExample 源码

spark JavaBucketizerExample 源码

spark JavaChiSqSelectorExample 源码

spark JavaChiSquareTestExample 源码

spark JavaCorrelationExample 源码

spark JavaCountVectorizerExample 源码

spark JavaDCTExample 源码

0 赞

所属分类： 前端技术
本文标签： 大数据
版权声明： 原创文章如转载，请注明本文链接: https://www.seaxiang.com/blog/2c8ba159ece84b7fb0a6c5df178d7710

热门推荐

1、直接访问google.com
2、 - 优质文章
3、 gate.io
4、 harmony 鸿蒙hdc使用指导
5、 harmony 鸿蒙初识ArkTS语言
6、 harmony 鸿蒙ArkUI组件（ArkTS）开发常见问题
7、 flink kafka connector scan.startup.mode 的几个选项
8、 golang
9、 openharmony
10、 Vue中input框自动聚焦

Loading...