Github user yinxusen commented on a diff in the pull request:
https://github.com/apache/spark/pull/11116#discussion_r53394148
--- Diff:
examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeansExample.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;
+
+// $example on$
+import org.apache.spark.api.java.*;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.mllib.clustering.KMeans;
+import org.apache.spark.mllib.clustering.KMeansModel;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.SparkConf;
+// $example off$
+
+public class JavaKMeansExample {
+ public static void main(String[] args) {
+
+ SparkConf conf = new SparkConf().setAppName("JavaKMeansExample");
+ JavaSparkContext jsc = new JavaSparkContext(conf);
+
+ // $example on$
+ // Load and parse data
+ String path = "data/mllib/kmeans_data.txt";
+ JavaRDD<String> data = jsc.textFile(path);
+ JavaRDD<Vector> parsedData = data.map(
+ new Function<String, Vector>() {
+ public Vector call(String s) {
+ String[] sarray = s.split(" ");
+ double[] values = new double[sarray.length];
+ for (int i = 0; i < sarray.length; i++)
+ values[i] = Double.parseDouble(sarray[i]);
+ return Vectors.dense(values);
+ }
+ }
+ );
+ parsedData.cache();
+
+ // Cluster the data into two classes using KMeans
+ int numClusters = 2;
+ int numIterations = 20;
+ KMeansModel clusters = KMeans.train(parsedData.rdd(), numClusters,
numIterations);
+
+ // Evaluate clustering by computing Within Set Sum of Squared Errors
+ double WSSSE = clusters.computeCost(parsedData.rdd());
+ System.out.println("Within Set Sum of Squared Errors = " + WSSSE);
+
+ // Save and load model
+ clusters.save(jsc.sc(), "myModelPath");
+ KMeansModel sameModel = KMeansModel.load(jsc.sc(), "myModelPath");
+ // $example off$
+
+ jsc.stop();
+}
--- End diff --
2-indent
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