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 java.util.regex.Pattern;
  
  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 != 4) {
        System.err.println("Usage: JavaLR <master> <input_dir> <step_size> <niters>");
        System.exit(1);
      }
  
      JavaSparkContext sc = new JavaSparkContext(args[0], "JavaLR",
          System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaLR.class));
      JavaRDD<String> lines = sc.textFile(args[1]);
      JavaRDD<LabeledPoint> points = lines.map(new ParsePoint()).cache();
      double stepSize = Double.parseDouble(args[2]);
      int iterations = Integer.parseInt(args[3]);
  
      // 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());
  
      System.exit(0);
    }
  }
  