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.classification;
  
  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.mllib.regression.LabeledPoint;
  
  public class JavaLogisticRegressionSuite extends SharedSparkSession {
  
    int validatePrediction(List<LabeledPoint> validationData, LogisticRegressionModel model) {
      int numAccurate = 0;
      for (LabeledPoint point : validationData) {
        Double prediction = model.predict(point.features());
        if (prediction == point.label()) {
          numAccurate++;
        }
      }
      return numAccurate;
    }
  
    @Test
    public void runLRUsingConstructor() {
      int nPoints = 10000;
      double A = 2.0;
      double B = -1.5;
  
      JavaRDD<LabeledPoint> testRDD = jsc.parallelize(
        LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache();
      List<LabeledPoint> validationData =
        LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 17);
  
      LogisticRegressionWithSGD lrImpl = new LogisticRegressionWithSGD();
      lrImpl.setIntercept(true);
      lrImpl.optimizer().setStepSize(1.0)
        .setRegParam(1.0)
        .setNumIterations(100);
      LogisticRegressionModel model = lrImpl.run(testRDD.rdd());
  
      int numAccurate = validatePrediction(validationData, model);
      Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
    }
  
    @Test
    public void runLRUsingStaticMethods() {
      int nPoints = 10000;
      double A = 0.0;
      double B = -2.5;
  
      JavaRDD<LabeledPoint> testRDD = jsc.parallelize(
        LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache();
      List<LabeledPoint> validationData =
        LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 17);
  
      LogisticRegressionModel model = LogisticRegressionWithSGD.train(
        testRDD.rdd(), 100, 1.0, 1.0);
  
      int numAccurate = validatePrediction(validationData, model);
      Assert.assertTrue(numAccurate > nPoints * 4.0 / 5.0);
    }
  }
  