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author | Yu ISHIKAWA <yuu.ishikawa@gmail.com> | 2015-12-16 10:55:42 -0800 |
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committer | Joseph K. Bradley <joseph@databricks.com> | 2015-12-16 10:55:42 -0800 |
commit | 7b6dc29d0ebbfb3bb941130f8542120b6bc3e234 (patch) | |
tree | 94970c4bfb67f129f2e580542276f623426e8625 /examples/src | |
parent | ad8c1f0b840284d05da737fb2cc5ebf8848f4490 (diff) | |
download | spark-7b6dc29d0ebbfb3bb941130f8542120b6bc3e234.tar.gz spark-7b6dc29d0ebbfb3bb941130f8542120b6bc3e234.tar.bz2 spark-7b6dc29d0ebbfb3bb941130f8542120b6bc3e234.zip |
[SPARK-6518][MLLIB][EXAMPLE][DOC] Add example code and user guide for bisecting k-means
This PR includes only an example code in order to finish it quickly.
I'll send another PR for the docs soon.
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes #9952 from yu-iskw/SPARK-6518.
Diffstat (limited to 'examples/src')
-rw-r--r-- | examples/src/main/java/org/apache/spark/examples/mllib/JavaBisectingKMeansExample.java | 69 | ||||
-rw-r--r-- | examples/src/main/scala/org/apache/spark/examples/mllib/BisectingKMeansExample.scala | 60 |
2 files changed, 129 insertions, 0 deletions
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaBisectingKMeansExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaBisectingKMeansExample.java new file mode 100644 index 0000000000..0001500f4f --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaBisectingKMeansExample.java @@ -0,0 +1,69 @@ +/* + * 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.mllib; + +import java.util.ArrayList; + +// $example on$ +import com.google.common.collect.Lists; +// $example off$ +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaSparkContext; +// $example on$ +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.mllib.clustering.BisectingKMeans; +import org.apache.spark.mllib.clustering.BisectingKMeansModel; +import org.apache.spark.mllib.linalg.Vector; +import org.apache.spark.mllib.linalg.Vectors; +// $example off$ + +/** + * Java example for graph clustering using power iteration clustering (PIC). + */ +public class JavaBisectingKMeansExample { + public static void main(String[] args) { + SparkConf sparkConf = new SparkConf().setAppName("JavaBisectingKMeansExample"); + JavaSparkContext sc = new JavaSparkContext(sparkConf); + + // $example on$ + ArrayList<Vector> localData = Lists.newArrayList( + Vectors.dense(0.1, 0.1), Vectors.dense(0.3, 0.3), + Vectors.dense(10.1, 10.1), Vectors.dense(10.3, 10.3), + Vectors.dense(20.1, 20.1), Vectors.dense(20.3, 20.3), + Vectors.dense(30.1, 30.1), Vectors.dense(30.3, 30.3) + ); + JavaRDD<Vector> data = sc.parallelize(localData, 2); + + BisectingKMeans bkm = new BisectingKMeans() + .setK(4); + BisectingKMeansModel model = bkm.run(data); + + System.out.println("Compute Cost: " + model.computeCost(data)); + for (Vector center: model.clusterCenters()) { + System.out.println(""); + } + Vector[] clusterCenters = model.clusterCenters(); + for (int i = 0; i < clusterCenters.length; i++) { + Vector clusterCenter = clusterCenters[i]; + System.out.println("Cluster Center " + i + ": " + clusterCenter); + } + // $example off$ + + sc.stop(); + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/BisectingKMeansExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/BisectingKMeansExample.scala new file mode 100644 index 0000000000..3a596cccb8 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/BisectingKMeansExample.scala @@ -0,0 +1,60 @@ +/* + * 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.mllib + +// scalastyle:off println +// $example on$ +import org.apache.spark.mllib.clustering.BisectingKMeans +import org.apache.spark.mllib.linalg.{Vector, Vectors} +// $example off$ +import org.apache.spark.{SparkConf, SparkContext} + +/** + * An example demonstrating a bisecting k-means clustering in spark.mllib. + * + * Run with + * {{{ + * bin/run-example mllib.BisectingKMeansExample + * }}} + */ +object BisectingKMeansExample { + + def main(args: Array[String]) { + val sparkConf = new SparkConf().setAppName("mllib.BisectingKMeansExample") + val sc = new SparkContext(sparkConf) + + // $example on$ + // Loads and parses data + def parse(line: String): Vector = Vectors.dense(line.split(" ").map(_.toDouble)) + val data = sc.textFile("data/mllib/kmeans_data.txt").map(parse).cache() + + // Clustering the data into 6 clusters by BisectingKMeans. + val bkm = new BisectingKMeans().setK(6) + val model = bkm.run(data) + + // Show the compute cost and the cluster centers + println(s"Compute Cost: ${model.computeCost(data)}") + model.clusterCenters.zipWithIndex.foreach { case (center, idx) => + println(s"Cluster Center ${idx}: ${center}") + } + // $example off$ + + sc.stop() + } +} +// scalastyle:on println |