/*
    * 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.distribution.continuous;
  
  import static org.junit.Assert.assertEquals;
  
  import org.junit.Test;
  
  /**
   * Test cases for {@link ChiSquaredDistribution}.
   *
   * @see RealDistributionAbstractTest
   */
  public class ChiSquaredDistributionTest extends RealDistributionAbstractTest {
  
      //-------------- Implementations for abstract methods -----------------------
  
      /** Creates the default continuous distribution instance to use in tests. */
      @Override
      public ChiSquaredDistribution makeDistribution() {
          return new ChiSquaredDistribution(5.0);
      }
  
      /** Creates the default cumulative probability distribution test input values */
      @Override
      public double[] makeCumulativeTestPoints() {
          // quantiles computed using R version 2.9.2
          return new double[] {
              0.210212602629, 0.554298076728, 0.831211613487, 1.14547622606, 1.61030798696,
              20.5150056524, 15.0862724694, 12.8325019940, 11.0704976935, 9.23635689978
          };
      }
  
      /** Creates the default cumulative probability density test expected values */
      @Override
      public double[] makeCumulativeTestValues() {
          return new double[] { 0.001, 0.01, 0.025, 0.05, 0.1, 0.999, 0.990, 0.975, 0.950, 0.900 };
      }
  
      /** Creates the default inverse cumulative probability test input values */
      @Override
      public double[] makeInverseCumulativeTestPoints() {
          return new double[] { 0, 0.001d, 0.01d, 0.025d, 0.05d, 0.1d, 0.999d, 0.990d, 0.975d, 0.950d, 0.900d, 1 };
      }
  
      /** Creates the default inverse cumulative probability density test expected values */
      @Override
      public double[] makeInverseCumulativeTestValues() {
          return new double[] {
              0, 0.210212602629, 0.554298076728, 0.831211613487, 1.14547622606, 1.61030798696, 20.5150056524,
              15.0862724694, 12.8325019940, 11.0704976935, 9.23635689978, Double.POSITIVE_INFINITY
          };
      }
  
      /** Creates the default probability density test expected values */
      @Override
      public double[] makeDensityTestValues() {
          return new double[] {
              0.0115379817652, 0.0415948507811, 0.0665060119842, 0.0919455953114, 0.121472591024,
              0.000433630076361, 0.00412780610309, 0.00999340341045, 0.0193246438937, 0.0368460089216
          };
      }
  
   // --------------------- Override tolerance  --------------
      @Override
      public void setUp() {
          super.setUp();
          setTolerance(1e-9);
      }
  
   //---------------------------- Additional test cases -------------------------
  
      @Test
      public void testSmallDf() {
          setDistribution(new ChiSquaredDistribution(0.1d));
          setTolerance(1E-4);
          // quantiles computed using R version 1.8.1 (linux version)
          setCumulativeTestPoints(new double[] {
              1.168926E-60, 1.168926E-40, 1.063132E-32, 1.144775E-26, 1.168926E-20,
             5.472917, 2.175255, 1.13438, 0.5318646, 0.1526342
         });
         setInverseCumulativeTestValues(getCumulativeTestPoints());
         setInverseCumulativeTestPoints(getCumulativeTestValues());
         verifyCumulativeProbabilities();
         verifyInverseCumulativeProbabilities();
     }
 
     @Test
     public void testDfAccessors() {
         ChiSquaredDistribution distribution = (ChiSquaredDistribution) getDistribution();
         assertEquals(5d, distribution.getDegreesOfFreedom(), Double.MIN_VALUE);
     }
 
     @Test
     public void testDensity() {
         double[] x = new double[]{-0.1, 1e-6, 0.5, 1, 2, 5};
         //R 2.5: print(dchisq(x, df=1), digits=10)
         checkDensity(1, x, new double[] {
             0.00000000000, 398.94208093034, 0.43939128947, 0.24197072452, 0.10377687436, 0.01464498256
         });
         //R 2.5: print(dchisq(x, df=0.1), digits=10)
         checkDensity(0.1, x, new double[] {
             0.000000000e+00, 2.486453997e+04, 7.464238732e-02, 3.009077718e-02, 9.447299159e-03, 8.827199396e-04
         });
         //R 2.5: print(dchisq(x, df=2), digits=10)
         checkDensity(2, x, new double[] {
             0.00000000000, 0.49999975000, 0.38940039154, 0.30326532986, 0.18393972059, 0.04104249931
         });
         //R 2.5: print(dchisq(x, df=10), digits=10)
         checkDensity(10, x, new double[] {
             0.000000000e+00, 1.302082682e-27, 6.337896998e-05, 7.897534632e-04, 7.664155024e-03, 6.680094289e-02
         });
     }
 
     private void checkDensity(double df, double[] x, double[] expected) {
         ChiSquaredDistribution d = new ChiSquaredDistribution(df);
         for (int i = 0; i < x.length; i++) {
             assertEquals(expected[i], d.density(x[i]), 1e-5);
         }
     }
 
     @Test
     public void testMoments() {
         final double tol = 1e-9;
         ChiSquaredDistribution dist;
 
         dist = new ChiSquaredDistribution(1500);
         assertEquals(dist.getNumericalMean(), 1500, tol);
         assertEquals(dist.getNumericalVariance(), 3000, tol);
 
         dist = new ChiSquaredDistribution(1.12);
         assertEquals(dist.getNumericalMean(), 1.12, tol);
         assertEquals(dist.getNumericalVariance(), 2.24, tol);
     }
 }