/*
    * 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 java.math.BigDecimal;
  import org.junit.jupiter.api.Assertions;
  import org.junit.jupiter.api.Test;
  import org.junit.jupiter.params.ParameterizedTest;
  import org.junit.jupiter.params.provider.CsvFileSource;
  
  /**
   * Test cases for {@link ExponentialDistribution}.
   * Extends {@link BaseContinuousDistributionTest}. See javadoc of that class for details.
   */
  class ExponentialDistributionTest extends BaseContinuousDistributionTest {
      @Override
      ContinuousDistribution makeDistribution(Object... parameters) {
          final double mean = (Double) parameters[0];
          return ExponentialDistribution.of(mean);
      }
  
      @Override
      Object[][] makeInvalidParameters() {
          return new Object[][] {
              {0.0},
              {-0.1}
          };
      }
  
      @Override
      String[] getParameterNames() {
          return new String[] {"Mean"};
      }
  
      @Override
      protected double getRelativeTolerance() {
          // Tolerance is 8.881784197001252E-16
          return 4 * RELATIVE_EPS;
      }
  
      //------------ Additional tests -------------------------------------------
  
      @Test
      void testProbabilityRange() {
          final double actual = ExponentialDistribution.of(5).probability(0.25, 0.75);
          TestUtils.assertEquals(0.0905214480756562, actual, DoubleTolerances.ulps(1));
      }
  
      @Test
      void testAdditionalDensity() {
          final ExponentialDistribution d1 = ExponentialDistribution.of(1);
          Assertions.assertEquals(0.0, d1.density(-1e-9));
          Assertions.assertEquals(1.0, d1.density(0.0));
          Assertions.assertEquals(0.0, d1.density(1000.0));
          Assertions.assertEquals(Math.exp(-1), d1.density(1.0));
          Assertions.assertEquals(Math.exp(-2), d1.density(2.0));
  
          final ExponentialDistribution d2 = ExponentialDistribution.of(3);
          Assertions.assertEquals(1 / 3.0, d2.density(0.0));
  
          // computed using R 3.4.0: print(dexp(1, rate=1/3), digits=20)
          Assertions.assertEquals(0.23884377019126309949, d2.density(1.0), 1e-15);
  
          // computed using R 3.4.0: print(dexp(2, rate=1/3), digits=20)
          Assertions.assertEquals(0.17113903967753066326, d2.density(2.0), 1e-15);
      }
  
      @ParameterizedTest
      @CsvFileSource(resources = "exppdf.csv")
      void testPDF(double mean, double x, BigDecimal expected) {
          final double e = expected.doubleValue();
          final double a = ExponentialDistribution.of(mean).density(x);
          // Require high precision.
          // This has max error of 3 ulp if using exp(logDensity(x)).
          Assertions.assertEquals(e, a, 2 * Math.ulp(e),
              () -> "ULP error: " + expected.subtract(new BigDecimal(a)).doubleValue() / Math.ulp(e));
      }
  }