/*
    * 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.statistics.distribution;
  
  import org.junit.jupiter.api.Assertions;
  import org.junit.jupiter.api.Test;
  
  /**
   * Test cases for {@link LogNormalDistribution}.
   * Extends {@link BaseContinuousDistributionTest}. See javadoc of that class for details.
   */
  class LogNormalDistributionTest extends BaseContinuousDistributionTest {
      @Override
      ContinuousDistribution makeDistribution(Object... parameters) {
          final double mu = (Double) parameters[0];
          final double sigma = (Double) parameters[1];
          return LogNormalDistribution.of(mu, sigma);
      }
  
      @Override
      Object[][] makeInvalidParameters() {
          return new Object[][] {
              {0.0, 0.0},
              {0.0, -0.1}
          };
      }
  
      @Override
      String[] getParameterNames() {
          return new String[] {"Mu", "Sigma"};
      }
  
      @Override
      protected double getRelativeTolerance() {
          return 5e-15;
      }
  
      //-------------------- Additional test cases -------------------------------
  
      @Test
      void testCumulativeProbabilityExtremes() {
          // Use a small shape parameter so that we can exceed 40 * shape
          testCumulativeProbability(LogNormalDistribution.of(1, 0.0001),
                                    new double[] {0.5, 10},
                                    new double[] {0, 1.0},
                                    DoubleTolerances.equals());
      }
  
      @Test
      void testSurvivalProbabilityExtremes() {
          // Use a small shape parameter so that we can exceed 40 * shape
          testSurvivalProbability(LogNormalDistribution.of(1, 0.0001),
                                  new double[] {0.5, 10},
                                  new double[] {1.0, 0.0},
                                  DoubleTolerances.equals());
      }
  
      /**
       * Check to make sure top-coding of extreme values works correctly.
       * Verifies fixes for JIRA MATH-167, MATH-414
       */
      @Test
      void testExtremeValues() {
          final LogNormalDistribution dist = LogNormalDistribution.of(0, 1);
          for (int i = 0; i < 1e5; i++) { // make sure no convergence exception
              final double upperTail = dist.cumulativeProbability(i);
              if (i <= 72) { // make sure not top-coded
                  Assertions.assertTrue(upperTail < 1.0d);
              } else { // make sure top coding not reversed
                  Assertions.assertTrue(upperTail > 0.99999);
              }
          }
  
          Assertions.assertEquals(1, dist.cumulativeProbability(Double.MAX_VALUE));
          Assertions.assertEquals(0, dist.cumulativeProbability(-Double.MAX_VALUE));
          Assertions.assertEquals(1, dist.cumulativeProbability(Double.POSITIVE_INFINITY));
          Assertions.assertEquals(0, dist.cumulativeProbability(Double.NEGATIVE_INFINITY));
      }
  
      @Test
      void testTinyVariance() {
          final LogNormalDistribution dist = LogNormalDistribution.of(0, 1e-9);
          final double t = dist.getVariance();
          Assertions.assertEquals(1e-18, t, 1e-20);
     }
 }