/*
    * 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.
   */
  
  package org.apache.commons.statistics.distribution;
  
  import java.util.stream.Stream;
  import org.junit.jupiter.api.Assertions;
  import org.junit.jupiter.params.ParameterizedTest;
  import org.junit.jupiter.params.provider.Arguments;
  import org.junit.jupiter.params.provider.CsvSource;
  import org.junit.jupiter.params.provider.MethodSource;
  
  /**
   * Test cases for {@link LogUniformDistribution}.
   * Extends {@link BaseContinuousDistributionTest}. See javadoc of that class for details.
   */
  class LogUniformDistributionTest extends BaseContinuousDistributionTest {
      @Override
      ContinuousDistribution makeDistribution(Object... parameters) {
          final double a = (Double) parameters[0];
          final double b = (Double) parameters[1];
          return LogUniformDistribution.of(a, b);
      }
  
      @Override
      Object[][] makeInvalidParameters() {
          return new Object[][] {
              // lower >= upper
              {0.0, 0.0},
              {1.0, 0.5},
              // Range not finite
              {Double.NaN, 1.0},
              {0.5, Double.NaN},
              // lower <= 0
              {-1.0, 1.0},
              {0.0, 1.0},
          };
      }
  
      @Override
      String[] getParameterNames() {
          return new String[] {"SupportLowerBound", "SupportUpperBound"};
      }
  
      @Override
      protected double getRelativeTolerance() {
          return 5e-15;
      }
  
      //-------------------- Additional test cases -------------------------------
  
      /**
       * Test the moments using the canonical formulas (from the javadoc).
       */
      @ParameterizedTest
      @MethodSource
      void testAdditionalMoments(double a, double b) {
          final double diff = b - a;
          final double denom = Math.log(b / a);
          final double mean = diff / denom;
          // Note: b^2 - a^2 = (b-a)*(b+a)
          final double variance = mean * (b + a) / 2 - mean * mean;
          TestUtils.assertEquals(mean, LogUniformDistribution.of(a, b).getMean(),
              DoubleTolerances.relative(1e-14), "Mean");
          TestUtils.assertEquals(variance, LogUniformDistribution.of(a, b).getVariance(),
              DoubleTolerances.relative(1e-10), "Variance");
      }
  
      static Stream<Arguments> testAdditionalMoments() {
          final Stream.Builder<Arguments> builder = Stream.builder();
          for (final double a : new double[] {1, 10, 100}) {
              for (final double x : new double[] {10, 20, 40}) {
                  builder.add(Arguments.of(a, a + x));
              }
          }
          return builder.build();
      }
  
      /**
       * Test the survival function for extreme values is computed using high precision,
       * i.e. is not the default of 1 - CDF.
       */
      @ParameterizedTest
      @CsvSource({
          "1e-100, 1e100",
         "1e-10, 1e10",
     })
     void testExtremeSurvivalFunction(double a, double b) {
         final LogUniformDistribution d = LogUniformDistribution.of(a, b);
         // Find a small non-zero survival probability
         final double u = Math.ulp(b);
         int i = 1;
         double p;
         do {
             p = d.survivalProbability(b - i * u);
             i *= 2;
         } while (p == 0);
         Assertions.assertTrue(p < 0x1.0p-53, "sf is not small enough for high precision");
         Assertions.assertNotEquals(1 - d.cumulativeProbability(b - i * u), p, "sf is not high precision: sf == 1 - cdf");
     }
 }