From 8beae59144827d81491eed385dc2aa6aedd6a7b4 Mon Sep 17 00:00:00 2001
From: Zheng RuiFeng <ruifengz@foxmail.com>
Date: Wed, 11 May 2016 10:01:43 +0200
Subject: [SPARK-15149][EXAMPLE][DOC] update kmeans example

## What changes were proposed in this pull request?
Python example for ml.kmeans already exists, but not included in user guide.
1,small changes like: `example_on` `example_off`
2,add it to user guide
3,update examples to directly read datafile

## How was this patch tested?
manual tests
`./bin/spark-submit examples/src/main/python/ml/kmeans_example.py

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #12925 from zhengruifeng/km_pe.
---
 .../spark/examples/ml/JavaKMeansExample.java       | 60 +++++-----------------
 1 file changed, 14 insertions(+), 46 deletions(-)

(limited to 'examples/src/main/java/org')

diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java
index 65e29ade29..2489a9b80b 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaKMeansExample.java
@@ -17,77 +17,45 @@
 
 package org.apache.spark.examples.ml;
 
-import java.util.regex.Pattern;
-
-import org.apache.spark.api.java.JavaRDD;
-import org.apache.spark.api.java.function.Function;
-import org.apache.spark.sql.Dataset;
-import org.apache.spark.sql.SparkSession;
-import org.apache.spark.sql.catalyst.expressions.GenericRow;
 // $example on$
 import org.apache.spark.ml.clustering.KMeansModel;
 import org.apache.spark.ml.clustering.KMeans;
 import org.apache.spark.mllib.linalg.Vector;
-import org.apache.spark.mllib.linalg.VectorUDT;
-import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.sql.Dataset;
 import org.apache.spark.sql.Row;
-import org.apache.spark.sql.types.Metadata;
-import org.apache.spark.sql.types.StructField;
-import org.apache.spark.sql.types.StructType;
 // $example off$
+import org.apache.spark.sql.SparkSession;
 
 
 /**
- * An example demonstrating a k-means clustering.
+ * An example demonstrating k-means clustering.
  * Run with
  * <pre>
- * bin/run-example ml.JavaKMeansExample <file> <k>
+ * bin/run-example ml.JavaKMeansExample
  * </pre>
  */
 public class JavaKMeansExample {
 
-  private static class ParsePoint implements Function<String, Row> {
-    private static final Pattern separator = Pattern.compile(" ");
-
-    @Override
-    public Row call(String line) {
-      String[] tok = separator.split(line);
-      double[] point = new double[tok.length];
-      for (int i = 0; i < tok.length; ++i) {
-        point[i] = Double.parseDouble(tok[i]);
-      }
-      Vector[] points = {Vectors.dense(point)};
-      return new GenericRow(points);
-    }
-  }
-
   public static void main(String[] args) {
-    if (args.length != 2) {
-      System.err.println("Usage: ml.JavaKMeansExample <file> <k>");
-      System.exit(1);
-    }
-    String inputFile = args[0];
-    int k = Integer.parseInt(args[1]);
-
-    // Parses the arguments
+    // Create a SparkSession.
     SparkSession spark = SparkSession
       .builder()
       .appName("JavaKMeansExample")
       .getOrCreate();
 
     // $example on$
-    // Loads data
-    JavaRDD<Row> points = spark.read().text(inputFile).javaRDD().map(new ParsePoint());
-    StructField[] fields = {new StructField("features", new VectorUDT(), false, Metadata.empty())};
-    StructType schema = new StructType(fields);
-    Dataset<Row> dataset = spark.createDataFrame(points, schema);
+    // Loads data.
+    Dataset<Row> dataset = spark.read().format("libsvm").load("data/mllib/sample_kmeans_data.txt");
 
-    // Trains a k-means model
-    KMeans kmeans = new KMeans()
-      .setK(k);
+    // Trains a k-means model.
+    KMeans kmeans = new KMeans().setK(2).setSeed(1L);
     KMeansModel model = kmeans.fit(dataset);
 
-    // Shows the result
+    // Evaluate clustering by computing Within Set Sum of Squared Errors.
+    double WSSSE = model.computeCost(dataset);
+    System.out.println("Within Set Sum of Squared Errors = " + WSSSE);
+
+    // Shows the result.
     Vector[] centers = model.clusterCenters();
     System.out.println("Cluster Centers: ");
     for (Vector center: centers) {
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