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.JavaDoubleRDD;
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.api.java.function.Function;
  import org.apache.spark.mllib.linalg.Vectors;
  import org.apache.spark.mllib.regression.LabeledPoint;
  import org.apache.spark.mllib.regression.LinearRegressionModel;
  import org.apache.spark.mllib.regression.LinearRegressionWithSGD;
  // $example off$
  
  /**
   * Example for LinearRegressionWithSGD.
   */
  public class JavaLinearRegressionWithSGDExample {
    public static void main(String[] args) {
      SparkConf conf = new SparkConf().setAppName("JavaLinearRegressionWithSGDExample");
      JavaSparkContext sc = new JavaSparkContext(conf);
  
      // $example on$
      // Load and parse the data
      String path = "data/mllib/ridge-data/lpsa.data";
      JavaRDD<String> data = sc.textFile(path);
      JavaRDD<LabeledPoint> parsedData = data.map(
        new Function<String, LabeledPoint>() {
          public LabeledPoint call(String line) {
            String[] parts = line.split(",");
            String[] features = parts[1].split(" ");
            double[] v = new double[features.length];
            for (int i = 0; i < features.length - 1; i++) {
              v[i] = Double.parseDouble(features[i]);
            }
            return new LabeledPoint(Double.parseDouble(parts[0]), Vectors.dense(v));
          }
        }
      );
      parsedData.cache();
  
      // Building the model
      int numIterations = 100;
      double stepSize = 0.00000001;
      final LinearRegressionModel model =
        LinearRegressionWithSGD.train(JavaRDD.toRDD(parsedData), numIterations, stepSize);
  
      // Evaluate model on training examples and compute training error
      JavaRDD<Tuple2<Double, Double>> valuesAndPreds = parsedData.map(
        new Function<LabeledPoint, Tuple2<Double, Double>>() {
          public Tuple2<Double, Double> call(LabeledPoint point) {
            double prediction = model.predict(point.features());
            return new Tuple2<>(prediction, point.label());
          }
        }
      );
      double MSE = new JavaDoubleRDD(valuesAndPreds.map(
        new Function<Tuple2<Double, Double>, Object>() {
          public Object call(Tuple2<Double, Double> pair) {
            return Math.pow(pair._1() - pair._2(), 2.0);
          }
        }
      ).rdd()).mean();
      System.out.println("training Mean Squared Error = " + MSE);
  
      // Save and load model
      model.save(sc.sc(), "target/tmp/javaLinearRegressionWithSGDModel");
      LinearRegressionModel sameModel = LinearRegressionModel.load(sc.sc(),
        "target/tmp/javaLinearRegressionWithSGDModel");
      // $example off$
  
      sc.stop();
    }
  }
  