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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.regression;
  
  import java.util.List;
  
  import org.junit.Assert;
  import org.junit.Test;
  
  import org.apache.spark.SharedSparkSession;
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.api.java.function.Function;
  import org.apache.spark.mllib.linalg.Vector;
  import org.apache.spark.mllib.util.LinearDataGenerator;
  
  public class JavaLinearRegressionSuite extends SharedSparkSession {
  
    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 = jsc.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();
      linSGDImpl.setIntercept(true);
      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 = 0.0;
      double[] weights = {10, 10};
  
      JavaRDD<LabeledPoint> testRDD = jsc.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);
    }
  
    @Test
    public void testPredictJavaRDD() {
      int nPoints = 100;
      double A = 0.0;
      double[] weights = {10, 10};
      JavaRDD<LabeledPoint> testRDD = jsc.parallelize(
        LinearDataGenerator.generateLinearInputAsList(A, weights, nPoints, 42, 0.1), 2).cache();
      LinearRegressionWithSGD linSGDImpl = new LinearRegressionWithSGD();
      LinearRegressionModel model = linSGDImpl.run(testRDD.rdd());
      JavaRDD<Vector> vectors = testRDD.map(new Function<LabeledPoint, Vector>() {
        @Override
        public Vector call(LabeledPoint v) throws Exception {
          return v.features();
        }
      });
      JavaRDD<Double> predictions = model.predict(vectors);
      // Should be able to get the first prediction.
      predictions.first();
    }
  }
  