/*
    * 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 org.hipparchus.UnitTestUtils;
  import org.hipparchus.exception.MathIllegalArgumentException;
  import org.junit.jupiter.api.BeforeEach;
  import org.junit.jupiter.api.Test;
  
  import static org.junit.jupiter.api.Assertions.assertEquals;
  import static org.junit.jupiter.api.Assertions.assertThrows;
  import static org.junit.jupiter.api.Assertions.assertTrue;
  
  /**
   * Test cases for TDistribution.
   */
  public class TDistributionTest extends RealDistributionAbstractTest {
  
  //-------------- Implementations for abstract methods -----------------------
  
      /** Creates the default continuous distribution instance to use in tests. */
      @Override
      public TDistribution makeDistribution() {
          return new TDistribution(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[] {-5.89342953136, -3.36492999891, -2.57058183564, -2.01504837333, -1.47588404882,
                  5.89342953136, 3.36492999891, 2.57058183564, 2.01504837333, 1.47588404882};
      }
  
      /** 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 probability density test expected values */
      @Override
      public double[] makeDensityTestValues() {
          return new double[] {0.000756494565517, 0.0109109752919, 0.0303377878006, 0.0637967988952, 0.128289492005,
                  0.000756494565517, 0.0109109752919, 0.0303377878006, 0.0637967988952, 0.128289492005};
      }
  
      // --------------------- Override tolerance  --------------
      @BeforeEach
      @Override
      public void setUp() {
          super.setUp();
          setTolerance(1E-9);
      }
  
      //---------------------------- Additional test cases -------------------------
      /**
       * @see <a href="http://issues.apache.org/bugzilla/show_bug.cgi?id=27243">
       *      Bug report that prompted this unit test.</a>
       */
      @Test
      void testCumulativeProbabilityAgainstStackOverflow() {
          TDistribution td = new TDistribution(5.);
          td.cumulativeProbability(.1);
          td.cumulativeProbability(.01);
      }
  
      @Test
      void testSmallDf() {
          setDistribution(new TDistribution(1d));
          // quantiles computed using R version 2.9.2
          setCumulativeTestPoints(new double[] {-318.308838986, -31.8205159538, -12.7062047362,
                  -6.31375151468, -3.07768353718, 318.308838986, 31.8205159538, 12.7062047362,
                   6.31375151468, 3.07768353718});
          setDensityTestValues(new double[] {3.14158231817e-06, 0.000314055924703, 0.00195946145194,
                  0.00778959736375, 0.0303958893917, 3.14158231817e-06, 0.000314055924703,
                  0.00195946145194, 0.00778959736375, 0.0303958893917});
          setInverseCumulativeTestValues(getCumulativeTestPoints());
          verifyCumulativeProbabilities();
         verifyInverseCumulativeProbabilities();
         verifyDensities();
     }
 
     @Test
     void testInverseCumulativeProbabilityExtremes() {
         setInverseCumulativeTestPoints(new double[] {0, 1});
         setInverseCumulativeTestValues(
                 new double[] {Double.NEGATIVE_INFINITY, Double.POSITIVE_INFINITY});
         verifyInverseCumulativeProbabilities();
     }
 
     @Test
     void testCumulativeProbablilityExtremes() {
         TDistribution dist;
         for (int i = 1; i < 11; i++) {
             dist = new TDistribution(i * 5);
             assertEquals(1,
                 dist.cumulativeProbability(Double.POSITIVE_INFINITY), Double.MIN_VALUE);
             assertEquals(0,
                 dist.cumulativeProbability(Double.NEGATIVE_INFINITY), Double.MIN_VALUE);
         }
     }
 
     @Test
     void testDfAccessors() {
         TDistribution dist = (TDistribution) getDistribution();
         assertEquals(5d, dist.getDegreesOfFreedom(), Double.MIN_VALUE);
     }
 
     @Test
     void testPreconditions() {
         assertThrows(MathIllegalArgumentException.class, () -> {
             new TDistribution(0);
         });
     }
 
     @Test
     void testMoments() {
         final double tol = 1e-9;
         TDistribution dist;
 
         dist = new TDistribution(1);
         assertTrue(Double.isNaN(dist.getNumericalMean()));
         assertTrue(Double.isNaN(dist.getNumericalVariance()));
 
         dist = new TDistribution(1.5);
         assertEquals(0, dist.getNumericalMean(), tol);
         assertTrue(Double.isInfinite(dist.getNumericalVariance()));
 
         dist = new TDistribution(5);
         assertEquals(0, dist.getNumericalMean(), tol);
         assertEquals(dist.getNumericalVariance(), 5d / (5d - 2d), tol);
     }
 
     /*
      * Adding this test to benchmark against tables published by NIST
      * http://itl.nist.gov/div898/handbook/eda/section3/eda3672.htm
      * Have chosen tabulated results for degrees of freedom 2,10,30,100
      * Have chosen problevels from 0.10 to 0.001
      */
     @Test
     void nistData(){
         double[] prob = new double[]{ 0.10,0.05,0.025,0.01,0.005,0.001};
         double[] args2 = new double[]{1.886,2.920,4.303,6.965,9.925,22.327};
         double[] args10 = new double[]{1.372,1.812,2.228,2.764,3.169,4.143};
         double[] args30 = new double[]{1.310,1.697,2.042,2.457,2.750,3.385};
         double[] args100= new double[]{1.290,1.660,1.984,2.364,2.626,3.174};
         UnitTestUtils.customAssertEquals(prob, makeNistResults(args2, 2), 1.0e-4);
         UnitTestUtils.customAssertEquals(prob, makeNistResults(args10, 10), 1.0e-4);
         UnitTestUtils.customAssertEquals(prob, makeNistResults(args30, 30), 1.0e-4);
         UnitTestUtils.customAssertEquals(prob, makeNistResults(args100, 100), 1.0e-4);
         return;
     }
     private double[] makeNistResults(double[] args, int df){
         TDistribution td =  new TDistribution(df);
         double[] res  = new double[ args.length ];
         for( int i = 0 ; i < res.length ; i++){
             res[i] = 1.0 - td.cumulativeProbability(args[i]);
         }
         return res;
     }
 }