/*
    * 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.numbers.gamma;
  
  import org.junit.jupiter.api.Assertions;
  import org.junit.jupiter.params.ParameterizedTest;
  import org.junit.jupiter.params.provider.CsvFileSource;
  import org.junit.jupiter.params.provider.CsvSource;
  import org.junit.jupiter.params.provider.ValueSource;
  
  /**
   * Tests for {@link RegularizedGamma}.
   */
  class RegularizedGammaTest {
      private static final double EPS = Math.ulp(1.0);
  
      /**
       * Test argument A cannot be NaN, negative or zero.
       *
       * @param a Argument a
       */
      @ParameterizedTest
      @ValueSource(doubles = {Double.NaN, 0, -1})
      void testInvalidArgumentA(double a) {
          // No exception thrown. The result is NaN.
          assertRegularizedGamma(a, 1.0, Double.NaN);
      }
  
      /**
       * Test argument X cannot be NaN or negative.
       *
       * @param x Argument x
       */
      @ParameterizedTest
      @ValueSource(doubles = {Double.NaN, -1})
      void testInvalidArgumentX(double x) {
          // No exception thrown. The result is NaN.
          assertRegularizedGamma(1.0, x, Double.NaN);
      }
  
      @ParameterizedTest
      @CsvSource({
          // Generated using matlab's gammainc function
          "0.250, 0.0, 0, 1",
          "0.250, 0.250, 0.74367794473146098, 0.25632205526853896",
          "0.250, 0.50, 0.84648640419167753, 0.15351359580832244",
          "0.250, 1.0, 0.93207886798989104, 0.067921132010108964",
          "0.250, 1.50, 0.96658355584102096, 0.033416444158979083",
          "0.250, 2.0, 0.98271398814048316, 0.017286011859516792",
          "0.250, 4.0, 0.99845731780336078, 0.0015426821966391837",
          "0.250, 10.0, 0.99999791696959139, 2.0830304086494328e-06",
          "0.50, 0.0, 0, 1",
          "0.50, 0.250, 0.52049987781304641, 0.47950012218695348",
          "0.50, 0.50, 0.68268949213708585, 0.31731050786291409",
          "0.50, 1.0, 0.84270079294971489, 0.15729920705028508",
          "0.50, 1.50, 0.91673548333644961, 0.083264516663550392",
          "0.50, 2.0, 0.95449973610364158, 0.045500263896358438",
          "0.50, 4.0, 0.99532226501895271, 0.0046777349810472628",
          "0.50, 10.0, 0.99999225578356898, 7.7442164310441028e-06",
          "1.0, 0.0, 0, 1",
          "1.0, 0.250, 0.22119921692859515, 0.77880078307140488",
          "1.0, 0.50, 0.39346934028736663, 0.60653065971263342",
          "1.0, 1.0, 0.63212055882855767, 0.36787944117144233",
          "1.0, 1.50, 0.77686983985157021, 0.22313016014842982",
          "1.0, 2.0, 0.8646647167633873, 0.1353352832366127",
          "1.0, 4.0, 0.98168436111126578, 0.018315638888734182",
          "1.0, 10.0, 0.99995460007023751, 4.5399929762484827e-05",
          "1.50, 0.0, 0, 1",
          "1.50, 0.250, 0.081108588345324126, 0.91889141165467592",
          "1.50, 0.50, 0.19874804309879915, 0.80125195690120088",
          "1.50, 1.0, 0.42759329552912018, 0.57240670447087982",
          "1.50, 1.50, 0.60837482372891105, 0.39162517627108895",
          "1.50, 2.0, 0.73853587005088939, 0.26146412994911061",
          "1.50, 4.0, 0.95398829431076859, 0.046011705689231366",
          "1.50, 10.0, 0.99983025756444721, 0.00016974243555282646",
          "2.0, 0.0, 0, 1",
          "2.0, 0.250, 0.026499021160743926, 0.97350097883925613",
          "2.0, 0.50, 0.090204010431049877, 0.90979598956895014",
          "2.0, 1.0, 0.26424111765711533, 0.73575888234288467",
          "2.0, 1.50, 0.44217459962892558, 0.55782540037107442",
          "2.0, 2.0, 0.59399415029016189, 0.40600584970983811",
          "2.0, 4.0, 0.90842180555632912, 0.091578194443670935",
          "2.0, 10.0, 0.99950060077261271, 0.00049939922738733366",
          "4.0, 0.0, 0, 1",
          "4.0, 0.250, 0.00013336965051406234, 0.99986663034948597",
         "4.0, 0.50, 0.001751622556290825, 0.9982483774437092",
         "4.0, 1.0, 0.018988156876153815, 0.98101184312384615",
         "4.0, 1.50, 0.065642454378450107, 0.93435754562154993",
         "4.0, 2.0, 0.1428765395014529, 0.85712346049854715",
         "4.0, 4.0, 0.56652987963329116, 0.4334701203667089",
         "4.0, 10.0, 0.98966394932407431, 0.010336050675925723",
         "10.0, 0.0, 0, 1",
         "10.0, 0.250, 2.0942485399973598e-13, 0.99999999999979061",
         "10.0, 0.50, 1.7096700293489097e-10, 0.99999999982903298",
         "10.0, 1.0, 1.1142547833872003e-07, 0.99999988857452171",
         "10.0, 1.50, 4.0975009763948422e-06, 0.99999590249902359",
         "10.0, 2.0, 4.6498075017263825e-05, 0.99995350192498278",
         "10.0, 4.0, 0.0081322427969338588, 0.99186775720306619",
         "10.0, 10.0, 0.54207028552814784, 0.4579297144718521",
     })
     void testRegularizedGamma(double a, double x, double p, double q) {
         final double actualP = RegularizedGamma.P.value(a, x);
         final double actualQ = RegularizedGamma.Q.value(a, x);
         final double eps = 1e-14;
         Assertions.assertEquals(p, actualP, p * eps);
         Assertions.assertEquals(q, actualQ, q * eps);
     }
 
     /**
      * Test the incomplete gamma function uses the policy containing the epsilon and
      * maximum iterations for series evaluations. The data targets each method computed
      * using a series component to check the policy is not ignored.
      *
      * @see BoostGammaTest#testIGammaPolicy(double, double, double, double, double, double)
      */
     @ParameterizedTest
     @CsvSource(value = {
         // Method 2: x > 1.1, x - (1 / (3 * x)) < a
         "5.0,2.5,21.38827245393963,0.8911780189141513,2.6117275460603704,0.10882198108584876",
         // Method 4: a < 20, x > 1.1, x - (1 / (3 * x)) > a
         "19.24400520324707,21.168405532836914,4.0308280447358675E15,0.3084240508178698,9.038282597080282E15,0.6915759491821302",
         // Method 7: (x > 1000) && (a < x * 0.75f)
         "664.0791015625,1328.158203125,Infinity,4.90100553385586E-91,Infinity,1.0",
         // Method 2: 0.5 < x < 1.1, x * 0.75f < a
         "0.9759566783905029,1.0735523700714111,0.33659577343416824,0.33179703084688433,0.6778671124302277,0.6682029691531157",
         // Method 3: 0.5 < x < 1.1, x * 0.75f > a
         "0.4912221431732178,0.9824442863464355,0.2840949896471149,0.1575143024618326,1.519518937513272,0.8424856975381674",
     })
     void testIGammaPolicy(double a, double x, double upper, double q, double lower, double p) {
         // Low iterations should fail to converge
         Assertions.assertThrows(ArithmeticException.class, () -> RegularizedGamma.P.value(a, x, EPS, 1), "p");
         Assertions.assertThrows(ArithmeticException.class, () -> RegularizedGamma.Q.value(a, x, EPS, 1), "q");
 
         // Low epsilon should not be as accurate
 
         // Ignore 0 or 1
         if ((int) p != p) {
             final double p1 = RegularizedGamma.P.value(a, x);
             final double p2 = RegularizedGamma.P.value(a, x, 1e-3, Integer.MAX_VALUE);
             assertCloser("p", p, p1, p2);
         }
         if ((int) q != q) {
             final double q1 = RegularizedGamma.Q.value(a, x);
             final double q2 = RegularizedGamma.Q.value(a, x, 1e-3, Integer.MAX_VALUE);
             assertCloser("q", q, q1, q2);
         }
     }
 
     @ParameterizedTest
     @CsvFileSource(resources = "igamma_med_data_p_derivative.csv")
     void testGammaPDerivative(double a, double x, double expected) {
         final double actual = RegularizedGamma.P.derivative(a, x);
         TestUtils.assertEquals(expected, actual, 5);
         Assertions.assertEquals(-actual, RegularizedGamma.Q.derivative(a, x));
     }
 
     /**
      * Assert the regularized gamma exactly matches value p. q is tested as 1-p.
      *
      * @param a Argument a
      * @param x Argument x
      * @param p Expected p
      */
     private static void assertRegularizedGamma(double a, double x, double p) {
         final double actualP = RegularizedGamma.P.value(a, x);
         final double actualQ = RegularizedGamma.Q.value(a, x);
         Assertions.assertEquals(p, actualP);
         Assertions.assertEquals(1 - p, actualQ);
     }
 
     /**
      * Assert x is closer to the expected result than y.
      */
     private static void assertCloser(String msg, double expected, double x, double y) {
         final double dx = Math.abs(expected - x);
         final double dy = Math.abs(expected - y);
         Assertions.assertTrue(dx < dy,
             () -> String.format("%s %s : %s (%s) : %s (%s)", msg, expected, x, dx, y, dy));
     }
 }