/*
    * 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
    *
    *      https://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.
   */
  
  /*
   * This is not the original file distributed by the Apache Software Foundation
   * It has been modified by the Hipparchus project
   */
  package org.hipparchus.stat.descriptive.moment;
  
  import static org.junit.Assert.assertEquals;
  import static org.junit.Assert.assertTrue;
  
  import org.hipparchus.stat.StatUtils;
  import org.hipparchus.stat.descriptive.StorelessUnivariateStatisticAbstractTest;
  import org.hipparchus.util.MathArrays;
  import org.junit.Test;
  
  /**
   * Test cases for the {@link Variance} class.
   */
  public class VarianceTest extends StorelessUnivariateStatisticAbstractTest{
  
      @Override
      public Variance getUnivariateStatistic() {
          return new Variance();
      }
  
      @Override
      public double expectedValue() {
          return this.var;
      }
  
      /** Expected value for  the testArray defined in UnivariateStatisticAbstractTest */
      public double expectedWeightedValue() {
          return this.weightedVar;
      }
  
      /**
       * Make sure Double.NaN is returned iff n = 0
       */
      @Test
      public void testNaN() {
          Variance var = getUnivariateStatistic();
          assertTrue(Double.isNaN(var.getResult()));
          var.increment(1d);
          assertEquals(0d, var.getResult(), 0);
      }
  
      /**
       * Test population version of variance
       */
      @Test
      public void testPopulation() {
          double[] values = { -1.0d, 3.1d, 4.0d, -2.1d, 22d, 11.7d, 3d, 14d };
          SecondMoment m = new SecondMoment();
          m.incrementAll(values);  // side effect is to add values
          Variance v1 = new Variance();
          v1 = v1.withBiasCorrection(false);
          assertEquals(populationVariance(values), v1.evaluate(values), 1E-14);
          v1.incrementAll(values);
          assertEquals(populationVariance(values), v1.getResult(), 1E-14);
          v1 = new Variance(false, m);
          assertEquals(populationVariance(values), v1.getResult(), 1E-14);
          v1 = new Variance(false);
          assertEquals(populationVariance(values), v1.evaluate(values), 1E-14);
          v1.incrementAll(values);
          assertEquals(populationVariance(values), v1.getResult(), 1E-14);
      }
  
      /**
       * Definitional formula for population variance
       */
      protected double populationVariance(double[] v) {
          double mean = StatUtils.mean(v);
          double sum = 0;
          for (double val : v) {
             sum += (val - mean) * (val - mean);
          }
          return sum / v.length;
      }
  
      @Test
      public void testWeightedVariance() {
          Variance variance = getUnivariateStatistic();
          assertEquals(expectedWeightedValue(),
                      variance.evaluate(testArray, testWeightsArray, 0, testArray.length),
                      getTolerance());
 
         // All weights = 1 -> weighted variance = unweighted variance
         assertEquals(expectedValue(),
                      variance.evaluate(testArray, unitWeightsArray, 0, testArray.length),
                      getTolerance());
 
         // All weights the same -> when weights are normalized to sum to the length of the values array,
         // weighted variance = unweighted value
         assertEquals(expectedValue(),
                      variance.evaluate(testArray,
                                        MathArrays.normalizeArray(identicalWeightsArray, testArray.length),
                                        0, testArray.length),
                      getTolerance());
 
     }
 
 }