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;
  
  import org.apache.spark.SparkConf;
  import org.apache.spark.api.java.JavaSparkContext;
  
  // $example on$
  import java.util.Arrays;
  
  import org.apache.spark.api.java.JavaRDD;
  import org.apache.spark.mllib.linalg.Matrices;
  import org.apache.spark.mllib.linalg.Matrix;
  import org.apache.spark.mllib.linalg.Vector;
  import org.apache.spark.mllib.linalg.Vectors;
  import org.apache.spark.mllib.regression.LabeledPoint;
  import org.apache.spark.mllib.stat.Statistics;
  import org.apache.spark.mllib.stat.test.ChiSqTestResult;
  // $example off$
  
  public class JavaHypothesisTestingExample {
    public static void main(String[] args) {
  
      SparkConf conf = new SparkConf().setAppName("JavaHypothesisTestingExample");
      JavaSparkContext jsc = new JavaSparkContext(conf);
  
      // $example on$
      // a vector composed of the frequencies of events
      Vector vec = Vectors.dense(0.1, 0.15, 0.2, 0.3, 0.25);
  
      // compute the goodness of fit. If a second vector to test against is not supplied
      // as a parameter, the test runs against a uniform distribution.
      ChiSqTestResult goodnessOfFitTestResult = Statistics.chiSqTest(vec);
      // summary of the test including the p-value, degrees of freedom, test statistic,
      // the method used, and the null hypothesis.
      System.out.println(goodnessOfFitTestResult + "\n");
  
      // Create a contingency matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
      Matrix mat = Matrices.dense(3, 2, new double[]{1.0, 3.0, 5.0, 2.0, 4.0, 6.0});
  
      // conduct Pearson's independence test on the input contingency matrix
      ChiSqTestResult independenceTestResult = Statistics.chiSqTest(mat);
      // summary of the test including the p-value, degrees of freedom...
      System.out.println(independenceTestResult + "\n");
  
      // an RDD of labeled points
      JavaRDD<LabeledPoint> obs = jsc.parallelize(
        Arrays.asList(
          new LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 3.0)),
          new LabeledPoint(1.0, Vectors.dense(1.0, 2.0, 0.0)),
          new LabeledPoint(-1.0, Vectors.dense(-1.0, 0.0, -0.5))
        )
      );
  
      // The contingency table is constructed from the raw (feature, label) pairs and used to conduct
      // the independence test. Returns an array containing the ChiSquaredTestResult for every feature
      // against the label.
      ChiSqTestResult[] featureTestResults = Statistics.chiSqTest(obs.rdd());
      int i = 1;
      for (ChiSqTestResult result : featureTestResults) {
        System.out.println("Column " + i + ":");
        System.out.println(result + "\n");  // summary of the test
        i++;
      }
      // $example off$
  
      jsc.stop();
    }
  }
  