text: /*
   * 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 java.util.Random;
  
  import org.junit.Assert;
  import org.junit.Test;
  
  import org.apache.spark.SharedSparkSession;
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.mllib.util.LinearDataGenerator;
  
  public class JavaRidgeRegressionSuite extends SharedSparkSession {
  
    private static double predictionError(List<LabeledPoint> validationData,
                                          RidgeRegressionModel model) {
      double errorSum = 0;
      for (LabeledPoint point : validationData) {
        Double prediction = model.predict(point.features());
        errorSum += (prediction - point.label()) * (prediction - point.label());
      }
      return errorSum / validationData.size();
    }
  
    private static List<LabeledPoint> generateRidgeData(int numPoints, int numFeatures, double std) {
      // Pick weights as random values distributed uniformly in [-0.5, 0.5]
      Random random = new Random(42);
      double[] w = new double[numFeatures];
      for (int i = 0; i < w.length; i++) {
        w[i] = random.nextDouble() - 0.5;
      }
      return LinearDataGenerator.generateLinearInputAsList(0.0, w, numPoints, 42, std);
    }
  
    @Test
    public void runRidgeRegressionUsingConstructor() {
      int numExamples = 50;
      int numFeatures = 20;
      List<LabeledPoint> data = generateRidgeData(2 * numExamples, numFeatures, 10.0);
  
      JavaRDD<LabeledPoint> testRDD = jsc.parallelize(data.subList(0, numExamples));
      List<LabeledPoint> validationData = data.subList(numExamples, 2 * numExamples);
  
      RidgeRegressionWithSGD ridgeSGDImpl = new RidgeRegressionWithSGD();
      ridgeSGDImpl.optimizer()
        .setStepSize(1.0)
        .setRegParam(0.0)
        .setNumIterations(200);
      RidgeRegressionModel model = ridgeSGDImpl.run(testRDD.rdd());
      double unRegularizedErr = predictionError(validationData, model);
  
      ridgeSGDImpl.optimizer().setRegParam(0.1);
      model = ridgeSGDImpl.run(testRDD.rdd());
      double regularizedErr = predictionError(validationData, model);
  
      Assert.assertTrue(regularizedErr < unRegularizedErr);
    }
  
    @Test
    public void runRidgeRegressionUsingStaticMethods() {
      int numExamples = 50;
      int numFeatures = 20;
      List<LabeledPoint> data = generateRidgeData(2 * numExamples, numFeatures, 10.0);
  
      JavaRDD<LabeledPoint> testRDD = jsc.parallelize(data.subList(0, numExamples));
      List<LabeledPoint> validationData = data.subList(numExamples, 2 * numExamples);
  
      RidgeRegressionModel model = RidgeRegressionWithSGD.train(testRDD.rdd(), 200, 1.0, 0.0);
      double unRegularizedErr = predictionError(validationData, model);
  
      model = RidgeRegressionWithSGD.train(testRDD.rdd(), 200, 1.0, 0.1);
      double regularizedErr = predictionError(validationData, model);
  
      Assert.assertTrue(regularizedErr < unRegularizedErr);
    }
  }
  