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.examples.mllib;
  
  // $example on$
  import scala.Tuple2;
  import org.apache.spark.api.java.function.Function;
  import org.apache.spark.api.java.function.PairFunction;
  import org.apache.spark.api.java.JavaPairRDD;
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.api.java.JavaSparkContext;
  import org.apache.spark.mllib.classification.NaiveBayes;
  import org.apache.spark.mllib.classification.NaiveBayesModel;
  import org.apache.spark.mllib.regression.LabeledPoint;
  import org.apache.spark.mllib.util.MLUtils;
  // $example off$
  import org.apache.spark.SparkConf;
  
  public class JavaNaiveBayesExample {
    public static void main(String[] args) {
      SparkConf sparkConf = new SparkConf().setAppName("JavaNaiveBayesExample");
      JavaSparkContext jsc = new JavaSparkContext(sparkConf);
      // $example on$
      String path = "data/mllib/sample_libsvm_data.txt";
      JavaRDD<LabeledPoint> inputData = MLUtils.loadLibSVMFile(jsc.sc(), path).toJavaRDD();
      JavaRDD<LabeledPoint>[] tmp = inputData.randomSplit(new double[]{0.6, 0.4});
      JavaRDD<LabeledPoint> training = tmp[0]; // training set
      JavaRDD<LabeledPoint> test = tmp[1]; // test set
      final NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0);
      JavaPairRDD<Double, Double> predictionAndLabel =
        test.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
          @Override
          public Tuple2<Double, Double> call(LabeledPoint p) {
            return new Tuple2<>(model.predict(p.features()), p.label());
          }
        });
      double accuracy = predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
        @Override
        public Boolean call(Tuple2<Double, Double> pl) {
          return pl._1().equals(pl._2());
        }
      }).count() / (double) test.count();
  
      // Save and load model
      model.save(jsc.sc(), "target/tmp/myNaiveBayesModel");
      NaiveBayesModel sameModel = NaiveBayesModel.load(jsc.sc(), "target/tmp/myNaiveBayesModel");
      // $example off$
  
      jsc.stop();
    }
  }
  