[SPARK-14301][EXAMPLES] Java examples code merge and clean up.

## What changes were proposed in this pull request?

This fix tries to remove duplicate Java code in examples/mllib and examples/ml. The following changes have been made:

```
deleted: ml/JavaCrossValidatorExample.java (duplicate of JavaModelSelectionViaCrossValidationExample.java)
deleted: ml/JavaTrainValidationSplitExample.java (duplicated of JavaModelSelectionViaTrainValidationSplitExample.java)
deleted: mllib/JavaFPGrowthExample.java (duplicate of JavaSimpleFPGrowth.java)
deleted: mllib/JavaLDAExample.java (duplicate of JavaLatentDirichletAllocationExample.java)
deleted: mllib/JavaKMeans.java (merged with JavaKMeansExample.java)
deleted: mllib/JavaLR.java (duplicate of JavaLinearRegressionWithSGDExample.java)
updated: mllib/JavaKMeansExample.java (merged with mllib/JavaKMeans.java)
```

## How was this patch tested?
Existing tests passed.

Author: Yong Tang <yong.tang.github@outlook.com>

Closes #12143 from yongtang/SPARK-14301.

8 files changed, 16 insertions, 534 deletions

diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java
deleted file mode 100644
index 07edeb3e52..0000000000
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaCrossValidatorExample.java
+++ /dev/null
@@ -1,127 +0,0 @@
-/*
- * 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;
-
-import java.util.List;
-
-import com.google.common.collect.Lists;
-
-import org.apache.spark.SparkConf;
-import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.ml.Pipeline;
-import org.apache.spark.ml.PipelineStage;
-import org.apache.spark.ml.classification.LogisticRegression;
-import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator;
-import org.apache.spark.ml.feature.HashingTF;
-import org.apache.spark.ml.feature.Tokenizer;
-import org.apache.spark.ml.param.ParamMap;
-import org.apache.spark.ml.tuning.CrossValidator;
-import org.apache.spark.ml.tuning.CrossValidatorModel;
-import org.apache.spark.ml.tuning.ParamGridBuilder;
-import org.apache.spark.sql.Dataset;
-import org.apache.spark.sql.Row;
-import org.apache.spark.sql.SQLContext;
-
-/**
- * A simple example demonstrating model selection using CrossValidator.
- * This example also demonstrates how Pipelines are Estimators.
- *
- * This example uses the Java bean classes {@link org.apache.spark.examples.ml.LabeledDocument} and
- * {@link org.apache.spark.examples.ml.Document} defined in the Scala example
- * {@link org.apache.spark.examples.ml.SimpleTextClassificationPipeline}.
- *
- * Run with
- * <pre>
- * bin/run-example ml.JavaCrossValidatorExample
- * </pre>
- */
-public class JavaCrossValidatorExample {
-
-  public static void main(String[] args) {
-    SparkConf conf = new SparkConf().setAppName("JavaCrossValidatorExample");
-    JavaSparkContext jsc = new JavaSparkContext(conf);
-    SQLContext jsql = new SQLContext(jsc);
-
-    // Prepare training documents, which are labeled.
-    List<LabeledDocument> localTraining = Lists.newArrayList(
-      new LabeledDocument(0L, "a b c d e spark", 1.0),
-      new LabeledDocument(1L, "b d", 0.0),
-      new LabeledDocument(2L, "spark f g h", 1.0),
-      new LabeledDocument(3L, "hadoop mapreduce", 0.0),
-      new LabeledDocument(4L, "b spark who", 1.0),
-      new LabeledDocument(5L, "g d a y", 0.0),
-      new LabeledDocument(6L, "spark fly", 1.0),
-      new LabeledDocument(7L, "was mapreduce", 0.0),
-      new LabeledDocument(8L, "e spark program", 1.0),
-      new LabeledDocument(9L, "a e c l", 0.0),
-      new LabeledDocument(10L, "spark compile", 1.0),
-      new LabeledDocument(11L, "hadoop software", 0.0));
-    Dataset<Row> training = jsql.createDataFrame(
-        jsc.parallelize(localTraining), LabeledDocument.class);
-
-    // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
-    Tokenizer tokenizer = new Tokenizer()
-      .setInputCol("text")
-      .setOutputCol("words");
-    HashingTF hashingTF = new HashingTF()
-      .setNumFeatures(1000)
-      .setInputCol(tokenizer.getOutputCol())
-      .setOutputCol("features");
-    LogisticRegression lr = new LogisticRegression()
-      .setMaxIter(10)
-      .setRegParam(0.01);
-    Pipeline pipeline = new Pipeline()
-      .setStages(new PipelineStage[] {tokenizer, hashingTF, lr});
-
-    // We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
-    // This will allow us to jointly choose parameters for all Pipeline stages.
-    // A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
-    CrossValidator crossval = new CrossValidator()
-        .setEstimator(pipeline)
-        .setEvaluator(new BinaryClassificationEvaluator());
-    // We use a ParamGridBuilder to construct a grid of parameters to search over.
-    // With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
-    // this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
-    ParamMap[] paramGrid = new ParamGridBuilder()
-        .addGrid(hashingTF.numFeatures(), new int[]{10, 100, 1000})
-        .addGrid(lr.regParam(), new double[]{0.1, 0.01})
-        .build();
-    crossval.setEstimatorParamMaps(paramGrid);
-    crossval.setNumFolds(2); // Use 3+ in practice
-
-    // Run cross-validation, and choose the best set of parameters.
-    CrossValidatorModel cvModel = crossval.fit(training);
-
-    // Prepare test documents, which are unlabeled.
-    List<Document> localTest = Lists.newArrayList(
-      new Document(4L, "spark i j k"),
-      new Document(5L, "l m n"),
-      new Document(6L, "mapreduce spark"),
-      new Document(7L, "apache hadoop"));
-    Dataset<Row> test = jsql.createDataFrame(jsc.parallelize(localTest), Document.class);
-
-    // Make predictions on test documents. cvModel uses the best model found (lrModel).
-    Dataset<Row> predictions = cvModel.transform(test);
-    for (Row r: predictions.select("id", "text", "probability", "prediction").collectAsList()) {
-      System.out.println("(" + r.get(0) + ", " + r.get(1) + ") --> prob=" + r.get(2)
-          + ", prediction=" + r.get(3));
-    }
-
-    jsc.stop();
-  }
-}
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java
index 6ac4aea3c4..4994f8f9fa 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java
@@ -32,7 +32,15 @@ import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SQLContext;
 
 /**
- * Java example for Model Selection via Train Validation Split.
+ * Java example demonstrating model selection using TrainValidationSplit.
+ *
+ * The example is based on {@link org.apache.spark.examples.ml.JavaSimpleParamsExample}
+ * using linear regression.
+ *
+ * Run with
+ * {{{
+ * bin/run-example ml.JavaModelSelectionViaTrainValidationSplitExample
+ * }}}
  */
 public class JavaModelSelectionViaTrainValidationSplitExample {
   public static void main(String[] args) {
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaTrainValidationSplitExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaTrainValidationSplitExample.java
deleted file mode 100644
index 09bbc39c01..0000000000
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaTrainValidationSplitExample.java
+++ /dev/null
@@ -1,87 +0,0 @@
-/*
- * 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;
-
-import org.apache.spark.SparkConf;
-import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.ml.evaluation.RegressionEvaluator;
-import org.apache.spark.ml.param.ParamMap;
-import org.apache.spark.ml.regression.LinearRegression;
-import org.apache.spark.ml.tuning.*;
-import org.apache.spark.sql.Dataset;
-import org.apache.spark.sql.Row;
-import org.apache.spark.sql.SQLContext;
-
-/**
- * A simple example demonstrating model selection using TrainValidationSplit.
- *
- * The example is based on {@link org.apache.spark.examples.ml.JavaSimpleParamsExample}
- * using linear regression.
- *
- * Run with
- * {{{
- * bin/run-example ml.JavaTrainValidationSplitExample
- * }}}
- */
-public class JavaTrainValidationSplitExample {
-
-  public static void main(String[] args) {
-    SparkConf conf = new SparkConf().setAppName("JavaTrainValidationSplitExample");
-    JavaSparkContext jsc = new JavaSparkContext(conf);
-    SQLContext jsql = new SQLContext(jsc);
-
-    Dataset<Row> data = jsql.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt");
-
-    // Prepare training and test data.
-    Dataset<Row>[] splits = data.randomSplit(new double [] {0.9, 0.1}, 12345);
-    Dataset<Row> training = splits[0];
-    Dataset<Row> test = splits[1];
-
-    LinearRegression lr = new LinearRegression();
-
-    // We use a ParamGridBuilder to construct a grid of parameters to search over.
-    // TrainValidationSplit will try all combinations of values and determine best model using
-    // the evaluator.
-    ParamMap[] paramGrid = new ParamGridBuilder()
-      .addGrid(lr.regParam(), new double[] {0.1, 0.01})
-      .addGrid(lr.fitIntercept())
-      .addGrid(lr.elasticNetParam(), new double[] {0.0, 0.5, 1.0})
-      .build();
-
-    // In this case the estimator is simply the linear regression.
-    // A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
-    TrainValidationSplit trainValidationSplit = new TrainValidationSplit()
-      .setEstimator(lr)
-      .setEvaluator(new RegressionEvaluator())
-      .setEstimatorParamMaps(paramGrid);
-
-    // 80% of the data will be used for training and the remaining 20% for validation.
-    trainValidationSplit.setTrainRatio(0.8);
-
-    // Run train validation split, and choose the best set of parameters.
-    TrainValidationSplitModel model = trainValidationSplit.fit(training);
-
-    // Make predictions on test data. model is the model with combination of parameters
-    // that performed best.
-    model.transform(test)
-      .select("features", "label", "prediction")
-      .show();
-
-    jsc.stop();
-  }
-}
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaFPGrowthExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaFPGrowthExample.java
deleted file mode 100644
index 36baf58687..0000000000
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaFPGrowthExample.java
+++ /dev/null
@@ -1,78 +0,0 @@
-/*
- * 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;
-
-import com.google.common.base.Joiner;
-import com.google.common.collect.Lists;
-
-import org.apache.spark.SparkConf;
-import org.apache.spark.api.java.JavaRDD;
-import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.api.java.function.Function;
-import org.apache.spark.mllib.fpm.FPGrowth;
-import org.apache.spark.mllib.fpm.FPGrowthModel;
-
-/**
- * Java example for mining frequent itemsets using FP-growth.
- * Example usage:  ./bin/run-example mllib.JavaFPGrowthExample ./data/mllib/sample_fpgrowth.txt
- */
-public class JavaFPGrowthExample {
-
-  public static void main(String[] args) {
-    String inputFile;
-    double minSupport = 0.3;
-    int numPartition = -1;
-    if (args.length < 1) {
-      System.err.println(
-        "Usage: JavaFPGrowth <input_file> [minSupport] [numPartition]");
-      System.exit(1);
-    }
-    inputFile = args[0];
-    if (args.length >= 2) {
-      minSupport = Double.parseDouble(args[1]);
-    }
-    if (args.length >= 3) {
-      numPartition = Integer.parseInt(args[2]);
-    }
-
-    SparkConf sparkConf = new SparkConf().setAppName("JavaFPGrowthExample");
-    JavaSparkContext sc = new JavaSparkContext(sparkConf);
-
-    JavaRDD<ArrayList<String>> transactions = sc.textFile(inputFile).map(
-      new Function<String, ArrayList<String>>() {
-        @Override
-        public ArrayList<String> call(String s) {
-          return Lists.newArrayList(s.split(" "));
-        }
-      }
-    );
-
-    FPGrowthModel<String> model = new FPGrowth()
-      .setMinSupport(minSupport)
-      .setNumPartitions(numPartition)
-      .run(transactions);
-
-    for (FPGrowth.FreqItemset<String> s: model.freqItemsets().toJavaRDD().collect()) {
-      System.out.println("[" + Joiner.on(",").join(s.javaItems()) + "], " + s.freq());
-    }
-
-    sc.stop();
-  }
-}
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java
deleted file mode 100644
index e575eedeb4..0000000000
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java
+++ /dev/null
@@ -1,82 +0,0 @@
-/*
- * 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.regex.Pattern;
-
-import org.apache.spark.SparkConf;
-import org.apache.spark.api.java.JavaRDD;
-import org.apache.spark.api.java.JavaSparkContext;
-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;
-
-/**
- * Example using MLlib KMeans from Java.
- */
-public final class JavaKMeans {
-
-  private static class ParsePoint implements Function<String, Vector> {
-    private static final Pattern SPACE = Pattern.compile(" ");
-
-    @Override
-    public Vector call(String line) {
-      String[] tok = SPACE.split(line);
-      double[] point = new double[tok.length];
-      for (int i = 0; i < tok.length; ++i) {
-        point[i] = Double.parseDouble(tok[i]);
-      }
-      return Vectors.dense(point);
-    }
-  }
-
-  public static void main(String[] args) {
-    if (args.length < 3) {
-      System.err.println(
-        "Usage: JavaKMeans <input_file> <k> <max_iterations> [<runs>]");
-      System.exit(1);
-    }
-    String inputFile = args[0];
-    int k = Integer.parseInt(args[1]);
-    int iterations = Integer.parseInt(args[2]);
-    int runs = 1;
-
-    if (args.length >= 4) {
-      runs = Integer.parseInt(args[3]);
-    }
-    SparkConf sparkConf = new SparkConf().setAppName("JavaKMeans");
-    JavaSparkContext sc = new JavaSparkContext(sparkConf);
-    JavaRDD<String> lines = sc.textFile(inputFile);
-
-    JavaRDD<Vector> points = lines.map(new ParsePoint());
-
-    KMeansModel model = KMeans.train(points.rdd(), k, iterations, runs, KMeans.K_MEANS_PARALLEL());
-
-    System.out.println("Cluster centers:");
-    for (Vector center : model.clusterCenters()) {
-      System.out.println(" " + center);
-    }
-    double cost = model.computeCost(points.rdd());
-    System.out.println("Cost: " + cost);
-
-    sc.stop();
-  }
-}
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeansExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeansExample.java
index 006d96d111..2d89c768fc 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeansExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeansExample.java
@@ -58,6 +58,13 @@ public class JavaKMeansExample {
     int numIterations = 20;
     KMeansModel clusters = KMeans.train(parsedData.rdd(), numClusters, numIterations);
 
+    System.out.println("Cluster centers:");
+    for (Vector center: clusters.clusterCenters()) {
+      System.out.println(" " + center);
+    }
+    double cost = clusters.computeCost(parsedData.rdd());
+    System.out.println("Cost: " + cost);
+
     // 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);
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLDAExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLDAExample.java
deleted file mode 100644
index de8e739ac9..0000000000
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLDAExample.java
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
- * 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 scala.Tuple2;
-
-import org.apache.spark.api.java.*;
-import org.apache.spark.api.java.function.Function;
-import org.apache.spark.mllib.clustering.DistributedLDAModel;
-import org.apache.spark.mllib.clustering.LDA;
-import org.apache.spark.mllib.linalg.Matrix;
-import org.apache.spark.mllib.linalg.Vector;
-import org.apache.spark.mllib.linalg.Vectors;
-import org.apache.spark.SparkConf;
-
-public class JavaLDAExample {
-  public static void main(String[] args) {
-    SparkConf conf = new SparkConf().setAppName("LDA Example");
-    JavaSparkContext sc = new JavaSparkContext(conf);
-
-    // Load and parse the data
-    String path = "data/mllib/sample_lda_data.txt";
-    JavaRDD<String> data = sc.textFile(path);
-    JavaRDD<Vector> parsedData = data.map(
-        new Function<String, Vector>() {
-          public Vector call(String s) {
-            String[] sarray = s.trim().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);
-          }
-        }
-    );
-    // Index documents with unique IDs
-    JavaPairRDD<Long, Vector> corpus = JavaPairRDD.fromJavaRDD(parsedData.zipWithIndex().map(
-        new Function<Tuple2<Vector, Long>, Tuple2<Long, Vector>>() {
-          public Tuple2<Long, Vector> call(Tuple2<Vector, Long> doc_id) {
-            return doc_id.swap();
-          }
-        }
-    ));
-    corpus.cache();
-
-    // Cluster the documents into three topics using LDA
-    DistributedLDAModel ldaModel = (DistributedLDAModel)new LDA().setK(3).run(corpus);
-
-    // Output topics. Each is a distribution over words (matching word count vectors)
-    System.out.println("Learned topics (as distributions over vocab of " + ldaModel.vocabSize()
-        + " words):");
-    Matrix topics = ldaModel.topicsMatrix();
-    for (int topic = 0; topic < 3; topic++) {
-      System.out.print("Topic " + topic + ":");
-      for (int word = 0; word < ldaModel.vocabSize(); word++) {
-        System.out.print(" " + topics.apply(word, topic));
-      }
-      System.out.println();
-    }
-    sc.stop();
-  }
-}
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLR.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLR.java
deleted file mode 100644
index eceb6927d5..0000000000
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLR.java
+++ /dev/null
@@ -1,82 +0,0 @@
-/*
- * 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.regex.Pattern;
-
-import org.apache.spark.SparkConf;
-import org.apache.spark.api.java.JavaRDD;
-import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.api.java.function.Function;
-
-import org.apache.spark.mllib.classification.LogisticRegressionWithSGD;
-import org.apache.spark.mllib.classification.LogisticRegressionModel;
-import org.apache.spark.mllib.linalg.Vectors;
-import org.apache.spark.mllib.regression.LabeledPoint;
-
-/**
- * Logistic regression based classification using ML Lib.
- */
-public final class JavaLR {
-
-  static class ParsePoint implements Function<String, LabeledPoint> {
-    private static final Pattern COMMA = Pattern.compile(",");
-    private static final Pattern SPACE = Pattern.compile(" ");
-
-    @Override
-    public LabeledPoint call(String line) {
-      String[] parts = COMMA.split(line);
-      double y = Double.parseDouble(parts[0]);
-      String[] tok = SPACE.split(parts[1]);
-      double[] x = new double[tok.length];
-      for (int i = 0; i < tok.length; ++i) {
-        x[i] = Double.parseDouble(tok[i]);
-      }
-      return new LabeledPoint(y, Vectors.dense(x));
-    }
-  }
-
-  public static void main(String[] args) {
-    if (args.length != 3) {
-      System.err.println("Usage: JavaLR <input_dir> <step_size> <niters>");
-      System.exit(1);
-    }
-    SparkConf sparkConf = new SparkConf().setAppName("JavaLR");
-    JavaSparkContext sc = new JavaSparkContext(sparkConf);
-    JavaRDD<String> lines = sc.textFile(args[0]);
-    JavaRDD<LabeledPoint> points = lines.map(new ParsePoint()).cache();
-    double stepSize = Double.parseDouble(args[1]);
-    int iterations = Integer.parseInt(args[2]);
-
-    // Another way to configure LogisticRegression
-    //
-    // LogisticRegressionWithSGD lr = new LogisticRegressionWithSGD();
-    // lr.optimizer().setNumIterations(iterations)
-    //               .setStepSize(stepSize)
-    //               .setMiniBatchFraction(1.0);
-    // lr.setIntercept(true);
-    // LogisticRegressionModel model = lr.train(points.rdd());
-
-    LogisticRegressionModel model = LogisticRegressionWithSGD.train(points.rdd(),
-      iterations, stepSize);
-
-    System.out.print("Final w: " + model.weights());
-
-    sc.stop();
-  }
-}