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/*
 * 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.io.Serializable;
import java.util.Arrays;
import java.util.StringTokenizer;

import scala.Tuple2;

/**
 * Example using MLLib ALS from Java.
 */
public class  JavaALS {

  static class ParseRating extends Function {
    public Rating call(String line) {
      StringTokenizer tok = new StringTokenizer(line, ",");
      int x = Integer.parseInt(tok.nextToken());
      int y = Integer.parseInt(tok.nextToken());
      double rating = Double.parseDouble(tok.nextToken());
      return new Rating(x, y, rating);
    }
  }

  static class FeaturesToString extends Function, String> {
    public String call(Tuple2 element) {
      return element._1().toString() + "," + Arrays.toString(element._2());
    }
  }

  public static void main(String[] args) {

    if (args.length != 5 && args.length != 6) {
      System.err.println(
          "Usage: JavaALS      []");
      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"), System.getenv("SPARK_EXAMPLES_JAR"));
    JavaRDD lines = sc.textFile(args[1]);

    JavaRDD 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);
  }
}