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.regression;
  
  import java.io.Serializable;
  import java.util.List;
  
  import org.junit.After;
  import org.junit.Assert;
  import org.junit.Before;
  import org.junit.Test;
  
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.api.java.JavaSparkContext;
  import org.apache.spark.mllib.util.LinearDataGenerator;
  
  public class JavaLinearRegressionSuite implements Serializable {
    private transient JavaSparkContext sc;
  
    @Before
    public void setUp() {
      sc = new JavaSparkContext("local", "JavaLinearRegressionSuite");
    }
  
    @After
    public void tearDown() {
      sc.stop();
      sc = null;
      System.clearProperty("spark.driver.port");
    }
  
    int validatePrediction(List<LabeledPoint> validationData, LinearRegressionModel model) {
      int numAccurate = 0;
      for (LabeledPoint point: validationData) {
          Double prediction = model.predict(point.features());
          // A prediction is off if the prediction is more than 0.5 away from expected value.
          if (Math.abs(prediction - point.label()) <= 0.5) {
              numAccurate++;
          }
      }
      return numAccurate;
    }
  
    @Test
    public void runLinearRegressionUsingConstructor() {
      int nPoints = 100;
      double A = 3.0;
      double[] weights = {10, 10};
  
      JavaRDD<LabeledPoint> testRDD = sc.parallelize(
          LinearDataGenerator.generateLinearInputAsList(A, weights, nPoints, 42, 0.1), 2).cache();
      List<LabeledPoint> validationData =
              LinearDataGenerator.generateLinearInputAsList(A, weights, nPoints, 17, 0.1);
  
      LinearRegressionWithSGD linSGDImpl = new LinearRegressionWithSGD();
      LinearRegressionModel model = linSGDImpl.run(testRDD.rdd());
  
      int numAccurate = validatePrediction(validationData, model);
      Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
    }
  
    @Test
    public void runLinearRegressionUsingStaticMethods() {
      int nPoints = 100;
      double A = 3.0;
      double[] weights = {10, 10};
  
      JavaRDD<LabeledPoint> testRDD = sc.parallelize(
          LinearDataGenerator.generateLinearInputAsList(A, weights, nPoints, 42, 0.1), 2).cache();
      List<LabeledPoint> validationData =
              LinearDataGenerator.generateLinearInputAsList(A, weights, nPoints, 17, 0.1);
  
      LinearRegressionModel model = LinearRegressionWithSGD.train(testRDD.rdd(), 100);
  
      int numAccurate = validatePrediction(validationData, model);
      Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
    }
  
  }
  