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.mllib.examples;
  
  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.recommendation.ALS;
  import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;
  import org.apache.spark.mllib.recommendation.Rating;
  
  import java.util.Arrays;
  import java.util.regex.Pattern;
  
  import scala.Tuple2;
  
  /**
   * Example using MLLib ALS from Java.
   */
  public final class JavaALS {
  
    static class ParseRating implements Function<String, Rating> {
      private static final Pattern COMMA = Pattern.compile(",");
  
      @Override
      public Rating call(String line) {
        String[] tok = COMMA.split(line);
        int x = Integer.parseInt(tok[0]);
        int y = Integer.parseInt(tok[1]);
        double rating = Double.parseDouble(tok[2]);
        return new Rating(x, y, rating);
      }
    }
  
    static class FeaturesToString implements Function<Tuple2<Object, double[]>, String> {
      @Override
      public String call(Tuple2<Object, double[]> element) {
        return element._1() + "," + Arrays.toString(element._2());
      }
    }
  
    public static void main(String[] args) {
  
      if (args.length != 5 && args.length != 6) {
        System.err.println(
            "Usage: JavaALS <master> <ratings_file> <rank> <iterations> <output_dir> [<blocks>]");
        System.exit(1);
      }
  
      int rank = Integer.parseInt(args[2]);
      int iterations = Integer.parseInt(args[3]);
      String outputDir = args[4];
      int blocks = -1;
      if (args.length == 6) {
        blocks = Integer.parseInt(args[5]);
      }
  
      JavaSparkContext sc = new JavaSparkContext(args[0], "JavaALS",
          System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaALS.class));
      JavaRDD<String> lines = sc.textFile(args[1]);
  
      JavaRDD<Rating> ratings = lines.map(new ParseRating());
  
      MatrixFactorizationModel model = ALS.train(ratings.rdd(), rank, iterations, 0.01, blocks);
  
      model.userFeatures().toJavaRDD().map(new FeaturesToString()).saveAsTextFile(
          outputDir + "/userFeatures");
      model.productFeatures().toJavaRDD().map(new FeaturesToString()).saveAsTextFile(
          outputDir + "/productFeatures");
      System.out.println("Final user/product features written to " + outputDir);
  
      System.exit(0);
    }
  }
  