/*
    * 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.rng.sampling.distribution;
  
  import java.util.ArrayList;
  import java.util.Arrays;
  import java.util.List;
  import java.util.stream.Collectors;
  import org.junit.jupiter.api.Assertions;
  import org.junit.jupiter.params.ParameterizedTest;
  import org.junit.jupiter.params.provider.MethodSource;
  
  /**
   * Tests for random deviates generators.
   */
  class ContinuousSamplerParametricTest {
      private static Iterable<ContinuousSamplerTestData> getSamplerTestData() {
          return ContinuousSamplersList.list();
      }
  
      @ParameterizedTest
      @MethodSource("getSamplerTestData")
      void testSampling(ContinuousSamplerTestData data) {
          check(20000, data.getSampler(), data.getDeciles());
      }
  
      /**
       * Performs a chi-square test of homogeneity of the observed
       * distribution with the expected distribution.
       * Tests are performed at the 1% level and an average failure rate
       * higher than 5% causes the test case to fail.
       *
       * @param sampler Sampler.
       * @param sampleSize Number of random values to generate.
       * @param deciles Deciles.
       */
      private static void check(long sampleSize,
                                ContinuousSampler sampler,
                                double[] deciles) {
          final int numTests = 50;
  
          // Do not change (statistical test assumes that dof = 9).
          final int numBins = 10; // dof = numBins - 1
  
          // Run the tests.
          int numFailures = 0;
  
          final double[] expected = new double[numBins];
          Arrays.fill(expected, sampleSize / (double) numBins);
  
          final long[] observed = new long[numBins];
          // Chi-square critical value with 9 degrees of freedom
          // and 1% significance level.
          final double chi2CriticalValue = 21.665994333461924;
  
          // For storing chi2 larger than the critical value.
          final List<Double> failedStat = new ArrayList<>();
          try {
              final int lastDecileIndex = numBins - 1;
              for (int i = 0; i < numTests; i++) {
                  Arrays.fill(observed, 0);
                  SAMPLE: for (long j = 0; j < sampleSize; j++) {
                      final double value = sampler.sample();
  
                      for (int k = 0; k < lastDecileIndex; k++) {
                          if (value < deciles[k]) {
                              ++observed[k];
                              continue SAMPLE;
                          }
                      }
                      ++observed[lastDecileIndex];
                  }
  
                  // Compute chi-square.
                  double chi2 = 0;
                  for (int k = 0; k < numBins; k++) {
                      final double diff = observed[k] - expected[k];
                      chi2 += diff * diff / expected[k];
                  }
  
                  // Statistics check.
                  if (chi2 > chi2CriticalValue) {
                      failedStat.add(chi2);
                      ++numFailures;
                  }
             }
         } catch (Exception e) {
             // Should never happen.
             throw new RuntimeException("Unexpected", e);
         }
 
         // The expected number of failed tests can be modelled as a Binomial distribution
         // B(n, p) with n=50, p=0.01 (50 tests with a 1% significance level).
         // The cumulative probability of the number of failed tests (X) is:
         // x     P(X>x)
         // 1     0.0894
         // 2     0.0138
         // 3     0.0016
 
         if (numFailures > 3) { // Test will fail with 0.16% probability
             Assertions.fail(String.format(
                     "%s: Too many failures for sample size = %d " +
                     "(%d out of %d tests failed, chi2 > %.3f=%s)",
                     sampler, sampleSize, numFailures, numTests, chi2CriticalValue,
                     failedStat.stream().map(d -> String.format("%.3f", d))
                               .collect(Collectors.joining(", ", "[", "]"))));
         }
     }
 }