content: /*
   * 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 org.apache.spark.SparkConf;
  import org.apache.spark.api.java.JavaSparkContext;
  
  // $example on$
  import scala.Tuple2;
  
  import org.apache.spark.api.java.JavaPairRDD;
  import org.apache.spark.api.java.JavaRDD;
  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.clustering.LDAModel;
  import org.apache.spark.mllib.linalg.Matrix;
  import org.apache.spark.mllib.linalg.Vector;
  import org.apache.spark.mllib.linalg.Vectors;
  // $example off$
  
  public class JavaLatentDirichletAllocationExample {
    public static void main(String[] args) {
  
      SparkConf conf = new SparkConf().setAppName("JavaKLatentDirichletAllocationExample");
      JavaSparkContext jsc = new JavaSparkContext(conf);
  
      // $example on$
      // Load and parse the data
      String path = "data/mllib/sample_lda_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.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
      LDAModel ldaModel = 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();
      }
  
      ldaModel.save(jsc.sc(),
        "target/org/apache/spark/JavaLatentDirichletAllocationExample/LDAModel");
      DistributedLDAModel sameModel = DistributedLDAModel.load(jsc.sc(),
        "target/org/apache/spark/JavaLatentDirichletAllocationExample/LDAModel");
      // $example off$
  
      jsc.stop();
    }
  }
  