/*
    * 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.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 {@link LogNormalDistribution}.
   */
  public class LogNormalDistributionTest extends RealDistributionAbstractTest {
  
      //-------------- Implementations for abstract methods -----------------------
  
      /** Creates the default real distribution instance to use in tests. */
      @Override
      public LogNormalDistribution makeDistribution() {
          return new LogNormalDistribution(2.1, 1.4);
      }
  
      /** Creates the default cumulative probability distribution test input values */
      @Override
      public double[] makeCumulativeTestPoints() {
          // quantiles computed using R
          return new double[] { -2.226325228634938, -1.156887023657177,
                                -0.643949578356075, -0.2027950777320613,
                                0.305827808237559, 6.42632522863494,
                                5.35688702365718, 4.843949578356074,
                                4.40279507773206, 3.89417219176244 };
      }
  
      /** Creates the default cumulative probability density test expected values */
      @Override
      public double[] makeCumulativeTestValues() {
          return new double[] { 0, 0, 0, 0, 0.00948199951485, 0.432056525076,
                                0.381648158697, 0.354555726206, 0.329513316888,
                                0.298422824228 };
      }
  
      /** Creates the default probability density test expected values */
      @Override
      public double[] makeDensityTestValues() {
          return new double[] { 0, 0, 0, 0, 0.0594218160072, 0.0436977691036,
                                0.0508364857798, 0.054873528325, 0.0587182664085,
                                0.0636229042785 };
      }
  
      /**
       * Creates the default inverse cumulative probability distribution test
       * input values.
       */
      @Override
      public double[] makeInverseCumulativeTestPoints() {
          // Exclude the test points less than zero, as they have cumulative
          // probability of zero, meaning the inverse returns zero, and not the
          // points less than zero.
          double[] points = makeCumulativeTestValues();
          double[] points2 = new double[points.length - 4];
          System.arraycopy(points, 4, points2, 0, points2.length - 4);
          return points2;
          //return Arrays.copyOfRange(points, 4, points.length - 4);
      }
  
      /**
       * Creates the default inverse cumulative probability test expected
       * values.
       */
      @Override
      public double[] makeInverseCumulativeTestValues() {
          // Exclude the test points less than zero, as they have cumulative
          // probability of zero, meaning the inverse returns zero, and not the
          // points less than zero.
          double[] points = makeCumulativeTestPoints();
          double[] points2 = new double[points.length - 4];
         System.arraycopy(points, 4, points2, 0, points2.length - 4);
         return points2;
         //return Arrays.copyOfRange(points, 1, points.length - 4);
     }
 
     // --------------------- Override tolerance  --------------
     @BeforeEach
     @Override
     public void setUp() {
         super.setUp();
         setTolerance(1e-9);
     }
 
     //---------------------------- Additional test cases -------------------------
 
     private void verifyQuantiles() {
         LogNormalDistribution distribution = (LogNormalDistribution)getDistribution();
         double mu = distribution.getLocation();
         double sigma = distribution.getShape();
         setCumulativeTestPoints( new double[] { mu - 2 *sigma, mu - sigma,
                                                 mu, mu + sigma, mu + 2 * sigma,
                                                 mu + 3 * sigma,mu + 4 * sigma,
                                                 mu + 5 * sigma });
         verifyCumulativeProbabilities();
     }
 
     @Test
     void testQuantiles() {
         setCumulativeTestValues(new double[] {0, 0.0396495152787,
                                               0.16601209243, 0.272533253269,
                                               0.357618409638, 0.426488363093,
                                               0.483255136841, 0.530823013877});
         setDensityTestValues(new double[] {0, 0.0873055825147, 0.0847676303432,
                                            0.0677935186237, 0.0544105523058,
                                            0.0444614628804, 0.0369750288945,
                                            0.0312206409653});
         verifyQuantiles();
         verifyDensities();
 
         setDistribution(new LogNormalDistribution(0, 1));
         setCumulativeTestValues(new double[] {0, 0, 0, 0.5, 0.755891404214,
                                               0.864031392359, 0.917171480998,
                                               0.946239689548});
         setDensityTestValues(new double[] {0, 0, 0, 0.398942280401,
                                            0.156874019279, 0.07272825614,
                                            0.0381534565119, 0.0218507148303});
         verifyQuantiles();
         verifyDensities();
 
         setDistribution(new LogNormalDistribution(0, 0.1));
         setCumulativeTestValues(new double[] {0, 0, 0, 1.28417563064e-117,
                                               1.39679883412e-58,
                                               1.09839325447e-33,
                                               2.52587961726e-20,
                                               2.0824223487e-12});
         setDensityTestValues(new double[] {0, 0, 0, 2.96247992535e-114,
                                            1.1283370232e-55, 4.43812313223e-31,
                                            5.85346445002e-18,
                                            2.9446618076e-10});
         verifyQuantiles();
         verifyDensities();
     }
 
     @Test
     void testInverseCumulativeProbabilityExtremes() {
         setInverseCumulativeTestPoints(new double[] {0, 1});
         setInverseCumulativeTestValues(
                 new double[] {0, Double.POSITIVE_INFINITY});
         verifyInverseCumulativeProbabilities();
     }
 
     @Test
     void testGetLocation() {
         LogNormalDistribution distribution = (LogNormalDistribution)getDistribution();
         assertEquals(2.1, distribution.getLocation(), 0);
     }
 
     @Test
     void testGetShape() {
         LogNormalDistribution distribution = (LogNormalDistribution)getDistribution();
         assertEquals(1.4, distribution.getShape(), 0);
     }
 
     @Test
     void testPreconditions() {
         assertThrows(MathIllegalArgumentException.class, () -> {
             new LogNormalDistribution(1, 0);
         });
     }
 
     @Test
     void testDensity() {
         double [] x = new double[]{-2, -1, 0, 1, 2};
         // R 2.13: print(dlnorm(c(-2,-1,0,1,2)), digits=10)
         checkDensity(0, 1, x, new double[] { 0.0000000000, 0.0000000000,
                                              0.0000000000, 0.3989422804,
                                              0.1568740193 });
         // R 2.13: print(dlnorm(c(-2,-1,0,1,2), mean=1.1), digits=10)
         checkDensity(1.1, 1, x, new double[] { 0.0000000000, 0.0000000000,
                                                0.0000000000, 0.2178521770,
                                                0.1836267118});
     }
 
     private void checkDensity(double scale, double shape, double[] x,
         double[] expected) {
         LogNormalDistribution d = new LogNormalDistribution(scale, shape);
         for (int i = 0; i < x.length; i++) {
             assertEquals(expected[i], d.density(x[i]), 1e-9);
         }
     }
 
     /**
      * Check to make sure top-coding of extreme values works correctly.
      * Verifies fixes for JIRA MATH-167, MATH-414
      */
     @Test
     void testExtremeValues() {
         LogNormalDistribution d = new LogNormalDistribution(0, 1);
         for (int i = 0; i < 1e5; i++) { // make sure no convergence exception
             double upperTail = d.cumulativeProbability(i);
             if (i <= 72) { // make sure not top-coded
                 assertTrue(upperTail < 1.0d);
             }
             else { // make sure top coding not reversed
                 assertTrue(upperTail > 0.99999);
             }
         }
 
         assertEquals(1, d.cumulativeProbability(Double.MAX_VALUE), 0);
         assertEquals(0, d.cumulativeProbability(-Double.MAX_VALUE), 0);
         assertEquals(1, d.cumulativeProbability(Double.POSITIVE_INFINITY), 0);
         assertEquals(0, d.cumulativeProbability(Double.NEGATIVE_INFINITY), 0);
     }
 
     @Test
     void testMeanVariance() {
         final double tol = 1e-9;
         LogNormalDistribution dist;
 
         dist = new LogNormalDistribution(0, 1);
         assertEquals(1.6487212707001282, dist.getNumericalMean(), tol);
         assertEquals(
             4.670774270471604, dist.getNumericalVariance(), tol);
 
         dist = new LogNormalDistribution(2.2, 1.4);
         assertEquals(24.046753552064498, dist.getNumericalMean(), tol);
         assertEquals(
             3526.913651880464, dist.getNumericalVariance(), tol);
 
         dist = new LogNormalDistribution(-2000.9, 10.4);
         assertEquals(0.0, dist.getNumericalMean(), tol);
         assertEquals(0.0, dist.getNumericalVariance(), tol);
     }
 
     @Test
     void testTinyVariance() {
         LogNormalDistribution dist = new LogNormalDistribution(0, 1e-9);
         double t = dist.getNumericalVariance();
         assertEquals(1e-18, t, 1e-20);
     }
 
 }