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   * Licensed to the Apache Software Foundation (ASF) under one or more
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   * 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
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   *
   *    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,
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  package org.apache.spark.mllib.classification;
  
  import java.util.Arrays;
  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.linalg.Vectors;
  import org.apache.spark.mllib.regression.LabeledPoint;
  
  
  public class JavaNaiveBayesSuite extends SharedSparkSession {
  
    private static final List<LabeledPoint> POINTS = Arrays.asList(
      new LabeledPoint(0, Vectors.dense(1.0, 0.0, 0.0)),
      new LabeledPoint(0, Vectors.dense(2.0, 0.0, 0.0)),
      new LabeledPoint(1, Vectors.dense(0.0, 1.0, 0.0)),
      new LabeledPoint(1, Vectors.dense(0.0, 2.0, 0.0)),
      new LabeledPoint(2, Vectors.dense(0.0, 0.0, 1.0)),
      new LabeledPoint(2, Vectors.dense(0.0, 0.0, 2.0))
    );
  
    private int validatePrediction(List<LabeledPoint> points, NaiveBayesModel model) {
      int correct = 0;
      for (LabeledPoint p : points) {
        if (model.predict(p.features()) == p.label()) {
          correct += 1;
        }
      }
      return correct;
    }
  
    @Test
    public void runUsingConstructor() {
      JavaRDD<LabeledPoint> testRDD = jsc.parallelize(POINTS, 2).cache();
  
      NaiveBayes nb = new NaiveBayes().setLambda(1.0);
      NaiveBayesModel model = nb.run(testRDD.rdd());
  
      int numAccurate = validatePrediction(POINTS, model);
      Assert.assertEquals(POINTS.size(), numAccurate);
    }
  
    @Test
    public void runUsingStaticMethods() {
      JavaRDD<LabeledPoint> testRDD = jsc.parallelize(POINTS, 2).cache();
  
      NaiveBayesModel model1 = NaiveBayes.train(testRDD.rdd());
      int numAccurate1 = validatePrediction(POINTS, model1);
      Assert.assertEquals(POINTS.size(), numAccurate1);
  
      NaiveBayesModel model2 = NaiveBayes.train(testRDD.rdd(), 0.5);
      int numAccurate2 = validatePrediction(POINTS, model2);
      Assert.assertEquals(POINTS.size(), numAccurate2);
    }
  
    @Test
    public void testPredictJavaRDD() {
      JavaRDD<LabeledPoint> examples = jsc.parallelize(POINTS, 2).cache();
      NaiveBayesModel model = NaiveBayes.train(examples.rdd());
      JavaRDD<Vector> vectors = examples.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();
    }
  
    @Test
    public void testModelTypeSetters() {
      NaiveBayes nb = new NaiveBayes()
        .setModelType("bernoulli")
        .setModelType("multinomial");
    }
  }
  