/*
    * 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.commons.math4.legacy.stat.descriptive.moment;
  
  import org.apache.commons.math4.legacy.stat.descriptive.StorelessUnivariateStatisticAbstractTest;
  import org.apache.commons.math4.legacy.stat.descriptive.UnivariateStatistic;
  import org.apache.commons.math4.legacy.core.MathArrays;
  import org.apache.commons.math4.legacy.exception.MathIllegalArgumentException;
  import org.junit.Assert;
  import org.junit.Test;
  import org.junit.jupiter.api.Assertions;
  
  /**
   * Test cases for the {@link UnivariateStatistic} class.
   *
   */
  public class VarianceTest extends StorelessUnivariateStatisticAbstractTest{
  
      protected Variance stat;
  
      /**
       * {@inheritDoc}
       */
      @Override
      public UnivariateStatistic getUnivariateStatistic() {
          return new Variance();
      }
  
      /**
       * {@inheritDoc}
       */
      @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() {
          StandardDeviation std = new StandardDeviation();
          Assert.assertTrue(Double.isNaN(std.getResult()));
          std.increment(1d);
          Assert.assertEquals(0d, std.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.setBiasCorrected(false);
          Assert.assertEquals(populationVariance(values), v1.evaluate(values), 1E-14);
          v1.incrementAll(values);
          Assert.assertEquals(populationVariance(values), v1.getResult(), 1E-14);
          v1 = new Variance(false, m);
          Assert.assertEquals(populationVariance(values), v1.getResult(), 1E-14);
          v1 = new Variance(false);
          Assert.assertEquals(populationVariance(values), v1.evaluate(values), 1E-14);
          v1.incrementAll(values);
          Assert.assertEquals(populationVariance(values), v1.getResult(), 1E-14);
      }
  
      /**
       * Definitional formula for population variance
       */
      protected double populationVariance(double[] v) {
          double mean = new Mean().evaluate(v);
          double sum = 0;
          for (int i = 0; i < v.length; i++) {
             sum += (v[i] - mean) * (v[i] - mean);
          }
          return sum / v.length;
      }
 
     @Test
     public void testWeightedVariance() {
         Variance variance = new Variance();
         Assert.assertEquals(expectedWeightedValue(),
                 variance.evaluate(testArray, testWeightsArray, 0, testArray.length), getTolerance());
 
         // All weights = 1 -> weighted variance = unweighted variance
         Assert.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
         Assert.assertEquals(expectedValue(),
                 variance.evaluate(testArray, MathArrays.normalizeArray(identicalWeightsArray, testArray.length),
                         0, testArray.length), getTolerance());
     }
 
     @Test
     public void testZeroWeights() {
         Variance variance = new Variance();
         final double[] values = {1, 2, 3, 4};
         final double[] weights = new double[values.length];
 
         // No weights
         Assertions.assertThrows(MathIllegalArgumentException.class, () -> {
             variance.evaluate(values, weights);
         });
 
         // No length
         final int begin = 1;
         final int zeroLength = 0;
         Assertions.assertEquals(Double.NaN, variance.evaluate(values, weights, begin, zeroLength));
 
         // One weight (must be non-zero)
         Assertions.assertThrows(MathIllegalArgumentException.class, () -> {
             variance.evaluate(values, weights, begin, zeroLength + 1);
         });
 
         weights[begin] = Double.MIN_VALUE;
         Assertions.assertEquals(0.0, variance.evaluate(values, weights, begin, zeroLength + 1));
     }
 }