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Added: dev/commons/statistics/1.0-RC1/site/xref-test/org/apache/commons/statistics/distribution/BetaDistributionTest.html
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--- dev/commons/statistics/1.0-RC1/site/xref-test/org/apache/commons/statistics/distribution/BetaDistributionTest.html (added)
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+<title>BetaDistributionTest xref</title>
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+<div id="overview"><a href="../../../../../../testapidocs/org/apache/commons/statistics/distribution/BetaDistributionTest.html">View Javadoc</a></div><pre>
+<a class="jxr_linenumber" name="L1" href="#L1">1</a> <em class="jxr_comment">/*</em>
+<a class="jxr_linenumber" name="L2" href="#L2">2</a> <em class="jxr_comment"> * Licensed to the Apache Software Foundation (ASF) under one or more</em>
+<a class="jxr_linenumber" name="L3" href="#L3">3</a> <em class="jxr_comment"> * contributor license agreements. See the NOTICE file distributed with</em>
+<a class="jxr_linenumber" name="L4" href="#L4">4</a> <em class="jxr_comment"> * this work for additional information regarding copyright ownership.</em>
+<a class="jxr_linenumber" name="L5" href="#L5">5</a> <em class="jxr_comment"> * The ASF licenses this file to You under the Apache License, Version 2.0</em>
+<a class="jxr_linenumber" name="L6" href="#L6">6</a> <em class="jxr_comment"> * (the "License"); you may not use this file except in compliance with</em>
+<a class="jxr_linenumber" name="L7" href="#L7">7</a> <em class="jxr_comment"> * the License. You may obtain a copy of the License at</em>
+<a class="jxr_linenumber" name="L8" href="#L8">8</a> <em class="jxr_comment"> *</em>
+<a class="jxr_linenumber" name="L9" href="#L9">9</a> <em class="jxr_comment"> * <a href="http://www.apache.org/licenses/LICENSE-2.0" target="alexandria_uri">http://www.apache.org/licenses/LICENSE-2.0</a></em>
+<a class="jxr_linenumber" name="L10" href="#L10">10</a> <em class="jxr_comment"> *</em>
+<a class="jxr_linenumber" name="L11" href="#L11">11</a> <em class="jxr_comment"> * Unless required by applicable law or agreed to in writing, software</em>
+<a class="jxr_linenumber" name="L12" href="#L12">12</a> <em class="jxr_comment"> * distributed under the License is distributed on an "AS IS" BASIS,</em>
+<a class="jxr_linenumber" name="L13" href="#L13">13</a> <em class="jxr_comment"> * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</em>
+<a class="jxr_linenumber" name="L14" href="#L14">14</a> <em class="jxr_comment"> * See the License for the specific language governing permissions and</em>
+<a class="jxr_linenumber" name="L15" href="#L15">15</a> <em class="jxr_comment"> * limitations under the License.</em>
+<a class="jxr_linenumber" name="L16" href="#L16">16</a> <em class="jxr_comment"> */</em>
+<a class="jxr_linenumber" name="L17" href="#L17">17</a> <strong class="jxr_keyword">package</strong> org.apache.commons.statistics.distribution;
+<a class="jxr_linenumber" name="L18" href="#L18">18</a>
+<a class="jxr_linenumber" name="L19" href="#L19">19</a> <strong class="jxr_keyword">import</strong> java.util.Arrays;
+<a class="jxr_linenumber" name="L20" href="#L20">20</a>
+<a class="jxr_linenumber" name="L21" href="#L21">21</a> <strong class="jxr_keyword">import</strong> org.apache.commons.rng.simple.RandomSource;
+<a class="jxr_linenumber" name="L22" href="#L22">22</a> <strong class="jxr_keyword">import</strong> org.apache.commons.rng.UniformRandomProvider;
+<a class="jxr_linenumber" name="L23" href="#L23">23</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.stat.StatUtils;
+<a class="jxr_linenumber" name="L24" href="#L24">24</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.stat.inference.GTest;
+<a class="jxr_linenumber" name="L25" href="#L25">25</a> <strong class="jxr_keyword">import</strong> org.junit.jupiter.api.Assertions;
+<a class="jxr_linenumber" name="L26" href="#L26">26</a> <strong class="jxr_keyword">import</strong> org.junit.jupiter.api.Test;
+<a class="jxr_linenumber" name="L27" href="#L27">27</a> <strong class="jxr_keyword">import</strong> org.junit.jupiter.params.ParameterizedTest;
+<a class="jxr_linenumber" name="L28" href="#L28">28</a> <strong class="jxr_keyword">import</strong> org.junit.jupiter.params.provider.CsvSource;
+<a class="jxr_linenumber" name="L29" href="#L29">29</a>
+<a class="jxr_linenumber" name="L30" href="#L30">30</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L31" href="#L31">31</a> <em class="jxr_javadoccomment"> * Test cases for {@link BetaDistribution}.</em>
+<a class="jxr_linenumber" name="L32" href="#L32">32</a> <em class="jxr_javadoccomment"> * Extends {@link BaseContinuousDistributionTest}. See javadoc of that class for details.</em>
+<a class="jxr_linenumber" name="L33" href="#L33">33</a> <em class="jxr_javadoccomment"> *</em>
+<a class="jxr_linenumber" name="L34" href="#L34">34</a> <em class="jxr_javadoccomment"> * <p>The properties files contain test cases for</em>
+<a class="jxr_linenumber" name="L35" href="#L35">35</a> <em class="jxr_javadoccomment"> * alpha and beta in [0.1, 0.5, 1.0, 2.0, 4.0] (25 cases).</em>
+<a class="jxr_linenumber" name="L36" href="#L36">36</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="L37" href="#L37">37</a> <strong class="jxr_keyword">class</strong> <a name="BetaDistributionTest" href="../../../../../org/apache/commons/statistics/distribution/BetaDistributionTest.html#BetaDistributionTest">BetaDistributionTest</a> <strong class="jxr_keyword">extends</strong> <a name="BaseContinuousDistributionTest" href="../../../../../org/apache/commons/statistics/distribution/BaseContinuousDistributionTest.html#BaseContinuousDistributionTest">BaseContinuousDistributionTest</a> {
+<a class="jxr_linenumber" name="L38" href="#L38">38</a> <em class="jxr_javadoccomment">/** Alpha/Beta values for extended test of the sampling. */</em>
+<a class="jxr_linenumber" name="L39" href="#L39">39</a> <strong class="jxr_keyword">static</strong> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] ALPHA_BETAS = {0.1, 1, 10, 100, 1000};
+<a class="jxr_linenumber" name="L40" href="#L40">40</a> <em class="jxr_javadoccomment">/** Epsilon value for extended test of the sampling. */</em>
+<a class="jxr_linenumber" name="L41" href="#L41">41</a> <strong class="jxr_keyword">static</strong> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> EPSILON = StatUtils.min(ALPHA_BETAS);
+<a class="jxr_linenumber" name="L42" href="#L42">42</a>
+<a class="jxr_linenumber" name="L43" href="#L43">43</a> @Override
+<a class="jxr_linenumber" name="L44" href="#L44">44</a> ContinuousDistribution makeDistribution(Object... parameters) {
+<a class="jxr_linenumber" name="L45" href="#L45">45</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> alpha = (Double) parameters[0];
+<a class="jxr_linenumber" name="L46" href="#L46">46</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> beta = (Double) parameters[1];
+<a class="jxr_linenumber" name="L47" href="#L47">47</a> <strong class="jxr_keyword">return</strong> BetaDistribution.of(alpha, beta);
+<a class="jxr_linenumber" name="L48" href="#L48">48</a> }
+<a class="jxr_linenumber" name="L49" href="#L49">49</a>
+<a class="jxr_linenumber" name="L50" href="#L50">50</a> @Override
+<a class="jxr_linenumber" name="L51" href="#L51">51</a> Object[][] makeInvalidParameters() {
+<a class="jxr_linenumber" name="L52" href="#L52">52</a> <strong class="jxr_keyword">return</strong> <strong class="jxr_keyword">new</strong> Object[][] {
+<a class="jxr_linenumber" name="L53" href="#L53">53</a> {0.0, 1.0},
+<a class="jxr_linenumber" name="L54" href="#L54">54</a> {-0.1, 1.0},
+<a class="jxr_linenumber" name="L55" href="#L55">55</a> {0.5, 0.0},
+<a class="jxr_linenumber" name="L56" href="#L56">56</a> {0.5, -0.1}
+<a class="jxr_linenumber" name="L57" href="#L57">57</a> };
+<a class="jxr_linenumber" name="L58" href="#L58">58</a> }
+<a class="jxr_linenumber" name="L59" href="#L59">59</a>
+<a class="jxr_linenumber" name="L60" href="#L60">60</a> @Override
+<a class="jxr_linenumber" name="L61" href="#L61">61</a> String[] getParameterNames() {
+<a class="jxr_linenumber" name="L62" href="#L62">62</a> <strong class="jxr_keyword">return</strong> <strong class="jxr_keyword">new</strong> String[] {<span class="jxr_string">"Alpha"</span>, <span class="jxr_string">"Beta"</span>};
+<a class="jxr_linenumber" name="L63" href="#L63">63</a> }
+<a class="jxr_linenumber" name="L64" href="#L64">64</a>
+<a class="jxr_linenumber" name="L65" href="#L65">65</a> @Override
+<a class="jxr_linenumber" name="L66" href="#L66">66</a> <strong class="jxr_keyword">protected</strong> <strong class="jxr_keyword">double</strong> getRelativeTolerance() {
+<a class="jxr_linenumber" name="L67" href="#L67">67</a> <strong class="jxr_keyword">return</strong> 8e-15;
+<a class="jxr_linenumber" name="L68" href="#L68">68</a> }
+<a class="jxr_linenumber" name="L69" href="#L69">69</a>
+<a class="jxr_linenumber" name="L70" href="#L70">70</a> <em class="jxr_comment">//-------------------- Additional test cases -------------------------------</em>
+<a class="jxr_linenumber" name="L71" href="#L71">71</a>
+<a class="jxr_linenumber" name="L72" href="#L72">72</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L73" href="#L73">73</a> <em class="jxr_javadoccomment"> * Precision tests for verifying that CDF calculates accurately in cases</em>
+<a class="jxr_linenumber" name="L74" href="#L74">74</a> <em class="jxr_javadoccomment"> * where 1-cdf(x) is inaccurately 1.</em>
+<a class="jxr_linenumber" name="L75" href="#L75">75</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="L76" href="#L76">76</a> @ParameterizedTest
+<a class="jxr_linenumber" name="L77" href="#L77">77</a> @CsvSource({
+<a class="jxr_linenumber" name="L78" href="#L78">78</a> <em class="jxr_comment">// Calculated using WolframAlpha</em>
+<a class="jxr_linenumber" name="L79" href="#L79">79</a> <span class="jxr_string">"5.0, 5.0, 0.0001, 1.2595800539968654e-18"</span>,
+<a class="jxr_linenumber" name="L80" href="#L80">80</a> <span class="jxr_string">"4.0, 5.0, 0.00001, 6.999776002800025e-19"</span>,
+<a class="jxr_linenumber" name="L81" href="#L81">81</a> <span class="jxr_string">"5.0, 4.0, 0.0001, 5.598600119996539e-19"</span>,
+<a class="jxr_linenumber" name="L82" href="#L82">82</a> <span class="jxr_string">"6.0, 2.0, 0.001, 6.994000000000028e-18"</span>,
+<a class="jxr_linenumber" name="L83" href="#L83">83</a> <span class="jxr_string">"2.0, 6.0, 1e-9, 2.0999999930000014e-17"</span>,
+<a class="jxr_linenumber" name="L84" href="#L84">84</a> })
+<a class="jxr_linenumber" name="L85" href="#L85">85</a> <strong class="jxr_keyword">void</strong> testCumulativePrecision(<strong class="jxr_keyword">double</strong> alpha, <strong class="jxr_keyword">double</strong> beta, <strong class="jxr_keyword">double</strong> value, <strong class="jxr_keyword">double</strong> expected) {
+<a class="jxr_linenumber" name="L86" href="#L86">86</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> tolerance = 1e-22;
+<a class="jxr_linenumber" name="L87" href="#L87">87</a> <strong class="jxr_keyword">final</strong> BetaDistribution dist = BetaDistribution.of(alpha, beta);
+<a class="jxr_linenumber" name="L88" href="#L88">88</a> Assertions.assertEquals(
+<a class="jxr_linenumber" name="L89" href="#L89">89</a> expected,
+<a class="jxr_linenumber" name="L90" href="#L90">90</a> dist.cumulativeProbability(value),
+<a class="jxr_linenumber" name="L91" href="#L91">91</a> tolerance,
+<a class="jxr_linenumber" name="L92" href="#L92">92</a> () -> <span class="jxr_string">"cumulative probability not precise at "</span> + value + <span class="jxr_string">" for a="</span> + alpha + <span class="jxr_string">" & b="</span> + beta);
+<a class="jxr_linenumber" name="L93" href="#L93">93</a> }
+<a class="jxr_linenumber" name="L94" href="#L94">94</a>
+<a class="jxr_linenumber" name="L95" href="#L95">95</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L96" href="#L96">96</a> <em class="jxr_javadoccomment"> * Precision tests for verifying that survival function calculates accurately in cases</em>
+<a class="jxr_linenumber" name="L97" href="#L97">97</a> <em class="jxr_javadoccomment"> * where 1-sf(x) is inaccurately 1.</em>
+<a class="jxr_linenumber" name="L98" href="#L98">98</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="L99" href="#L99">99</a> @ParameterizedTest
+<a class="jxr_linenumber" name="L100" href="#L100">100</a> @CsvSource({
+<a class="jxr_linenumber" name="L101" href="#L101">101</a> <em class="jxr_comment">// Calculated using WolframAlpha</em>
+<a class="jxr_linenumber" name="L102" href="#L102">102</a> <span class="jxr_string">"5.0, 5.0, 0.9999, 1.2595800539961496e-18"</span>,
+<a class="jxr_linenumber" name="L103" href="#L103">103</a> <span class="jxr_string">"4.0, 5.0, 0.9999, 5.598600119993397e-19"</span>,
+<a class="jxr_linenumber" name="L104" href="#L104">104</a> <span class="jxr_string">"5.0, 4.0, 0.99998, 1.1199283217964632e-17"</span>,
+<a class="jxr_linenumber" name="L105" href="#L105">105</a> <span class="jxr_string">"6.0, 2.0, 0.999999999, 2.0999998742158932e-17"</span>,
+<a class="jxr_linenumber" name="L106" href="#L106">106</a> <span class="jxr_string">"2.0, 6.0, 0.999, 6.994000000000077e-18"</span>,
+<a class="jxr_linenumber" name="L107" href="#L107">107</a> })
+<a class="jxr_linenumber" name="L108" href="#L108">108</a> <strong class="jxr_keyword">void</strong> testSurvivalPrecision(<strong class="jxr_keyword">double</strong> alpha, <strong class="jxr_keyword">double</strong> beta, <strong class="jxr_keyword">double</strong> value, <strong class="jxr_keyword">double</strong> expected) {
+<a class="jxr_linenumber" name="L109" href="#L109">109</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> tolerance = 1e-22;
+<a class="jxr_linenumber" name="L110" href="#L110">110</a> <strong class="jxr_keyword">final</strong> BetaDistribution dist = BetaDistribution.of(alpha, beta);
+<a class="jxr_linenumber" name="L111" href="#L111">111</a> Assertions.assertEquals(
+<a class="jxr_linenumber" name="L112" href="#L112">112</a> expected,
+<a class="jxr_linenumber" name="L113" href="#L113">113</a> dist.survivalProbability(value),
+<a class="jxr_linenumber" name="L114" href="#L114">114</a> tolerance,
+<a class="jxr_linenumber" name="L115" href="#L115">115</a> () -> <span class="jxr_string">"survival function not precise at "</span> + value + <span class="jxr_string">" for a="</span> + alpha + <span class="jxr_string">" & b="</span> + beta);
+<a class="jxr_linenumber" name="L116" href="#L116">116</a> }
+<a class="jxr_linenumber" name="L117" href="#L117">117</a>
+<a class="jxr_linenumber" name="L118" href="#L118">118</a> @ParameterizedTest
+<a class="jxr_linenumber" name="L119" href="#L119">119</a> @CsvSource({
+<a class="jxr_linenumber" name="L120" href="#L120">120</a> <span class="jxr_string">"0.5, 3, 0, Infinity"</span>,
+<a class="jxr_linenumber" name="L121" href="#L121">121</a> <span class="jxr_string">"2, 0.5, 1, Infinity"</span>,
+<a class="jxr_linenumber" name="L122" href="#L122">122</a> })
+<a class="jxr_linenumber" name="L123" href="#L123">123</a> <strong class="jxr_keyword">void</strong> testLogDensityPrecondition(<strong class="jxr_keyword">double</strong> a, <strong class="jxr_keyword">double</strong> b, <strong class="jxr_keyword">double</strong> x, <strong class="jxr_keyword">double</strong> expected) {
+<a class="jxr_linenumber" name="L124" href="#L124">124</a> <strong class="jxr_keyword">final</strong> BetaDistribution dist = BetaDistribution.of(a, b);
+<a class="jxr_linenumber" name="L125" href="#L125">125</a> Assertions.assertEquals(expected, dist.density(x));
+<a class="jxr_linenumber" name="L126" href="#L126">126</a> Assertions.assertEquals(Math.log(expected), dist.logDensity(x));
+<a class="jxr_linenumber" name="L127" href="#L127">127</a> }
+<a class="jxr_linenumber" name="L128" href="#L128">128</a>
+<a class="jxr_linenumber" name="L129" href="#L129">129</a> @Test
+<a class="jxr_linenumber" name="L130" href="#L130">130</a> <strong class="jxr_keyword">void</strong> testMomentsSampling() {
+<a class="jxr_linenumber" name="L131" href="#L131">131</a> <strong class="jxr_keyword">final</strong> UniformRandomProvider rng = RandomSource.XO_SHI_RO_256_PP.create(123456789L);
+<a class="jxr_linenumber" name="L132" href="#L132">132</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> numSamples = 1000;
+<a class="jxr_linenumber" name="L133" href="#L133">133</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> alpha : ALPHA_BETAS) {
+<a class="jxr_linenumber" name="L134" href="#L134">134</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> beta : ALPHA_BETAS) {
+<a class="jxr_linenumber" name="L135" href="#L135">135</a> <strong class="jxr_keyword">final</strong> BetaDistribution betaDistribution = BetaDistribution.of(alpha, beta);
+<a class="jxr_linenumber" name="L136" href="#L136">136</a>
+<a class="jxr_linenumber" name="L137" href="#L137">137</a> <strong class="jxr_keyword">final</strong> ContinuousDistribution.Sampler sampler = betaDistribution.createSampler(rng);
+<a class="jxr_linenumber" name="L138" href="#L138">138</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] observed = TestUtils.sample(numSamples, sampler);
+<a class="jxr_linenumber" name="L139" href="#L139">139</a> Arrays.sort(observed);
+<a class="jxr_linenumber" name="L140" href="#L140">140</a>
+<a class="jxr_linenumber" name="L141" href="#L141">141</a> Assertions.assertEquals(betaDistribution.getMean(), StatUtils.mean(observed), EPSILON,
+<a class="jxr_linenumber" name="L142" href="#L142">142</a> () -> String.format(<span class="jxr_string">"E[Beta(%.2f, %.2f)]"</span>, alpha, beta));
+<a class="jxr_linenumber" name="L143" href="#L143">143</a> Assertions.assertEquals(betaDistribution.getVariance(), StatUtils.variance(observed), EPSILON,
+<a class="jxr_linenumber" name="L144" href="#L144">144</a> () -> String.format(<span class="jxr_string">"Var[Beta(%.2f, %.2f)]"</span>, alpha, beta));
+<a class="jxr_linenumber" name="L145" href="#L145">145</a> }
+<a class="jxr_linenumber" name="L146" href="#L146">146</a> }
+<a class="jxr_linenumber" name="L147" href="#L147">147</a> }
+<a class="jxr_linenumber" name="L148" href="#L148">148</a>
+<a class="jxr_linenumber" name="L149" href="#L149">149</a> @Test
+<a class="jxr_linenumber" name="L150" href="#L150">150</a> <strong class="jxr_keyword">void</strong> testGoodnessOfFit() {
+<a class="jxr_linenumber" name="L151" href="#L151">151</a> <strong class="jxr_keyword">final</strong> UniformRandomProvider rng = RandomSource.XO_SHI_RO_256_PP.create(123456789L);
+<a class="jxr_linenumber" name="L152" href="#L152">152</a>
+<a class="jxr_linenumber" name="L153" href="#L153">153</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> numSamples = 1000;
+<a class="jxr_linenumber" name="L154" href="#L154">154</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> level = 0.01;
+<a class="jxr_linenumber" name="L155" href="#L155">155</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> alpha : ALPHA_BETAS) {
+<a class="jxr_linenumber" name="L156" href="#L156">156</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> beta : ALPHA_BETAS) {
+<a class="jxr_linenumber" name="L157" href="#L157">157</a> <strong class="jxr_keyword">final</strong> BetaDistribution betaDistribution = BetaDistribution.of(alpha, beta);
+<a class="jxr_linenumber" name="L158" href="#L158">158</a>
+<a class="jxr_linenumber" name="L159" href="#L159">159</a> <strong class="jxr_keyword">final</strong> ContinuousDistribution.Sampler sampler = betaDistribution.createSampler(rng);
+<a class="jxr_linenumber" name="L160" href="#L160">160</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] observed = TestUtils.sample(numSamples, sampler);
+<a class="jxr_linenumber" name="L161" href="#L161">161</a>
+<a class="jxr_linenumber" name="L162" href="#L162">162</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> gT = gTest(betaDistribution, observed);
+<a class="jxr_linenumber" name="L163" href="#L163">163</a> Assertions.assertFalse(gT < level,
+<a class="jxr_linenumber" name="L164" href="#L164">164</a> () -> String.format(<span class="jxr_string">"Beta(%s, %s): G goodness-of-fit (%s) test rejected null at alpha = %s"</span>,
+<a class="jxr_linenumber" name="L165" href="#L165">165</a> alpha, beta, gT, level));
+<a class="jxr_linenumber" name="L166" href="#L166">166</a> }
+<a class="jxr_linenumber" name="L167" href="#L167">167</a> }
+<a class="jxr_linenumber" name="L168" href="#L168">168</a> }
+<a class="jxr_linenumber" name="L169" href="#L169">169</a>
+<a class="jxr_linenumber" name="L170" href="#L170">170</a> <strong class="jxr_keyword">private</strong> <strong class="jxr_keyword">static</strong> <strong class="jxr_keyword">double</strong> gTest(<strong class="jxr_keyword">final</strong> ContinuousDistribution expectedDistribution, <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] values) {
+<a class="jxr_linenumber" name="L171" href="#L171">171</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> numBins = values.length / 30;
+<a class="jxr_linenumber" name="L172" href="#L172">172</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] breaks = <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[numBins];
+<a class="jxr_linenumber" name="L173" href="#L173">173</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> b = 0; b < numBins; b++) {
+<a class="jxr_linenumber" name="L174" href="#L174">174</a> breaks[b] = expectedDistribution.inverseCumulativeProbability((<strong class="jxr_keyword">double</strong>) (b + 1) / numBins);
+<a class="jxr_linenumber" name="L175" href="#L175">175</a> }
+<a class="jxr_linenumber" name="L176" href="#L176">176</a>
+<a class="jxr_linenumber" name="L177" href="#L177">177</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">long</strong>[] observed = <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">long</strong>[numBins];
+<a class="jxr_linenumber" name="L178" href="#L178">178</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> value : values) {
+<a class="jxr_linenumber" name="L179" href="#L179">179</a> <strong class="jxr_keyword">int</strong> b = Arrays.binarySearch(breaks, value);
+<a class="jxr_linenumber" name="L180" href="#L180">180</a> <strong class="jxr_keyword">if</strong> (b < 0) {
+<a class="jxr_linenumber" name="L181" href="#L181">181</a> b = -(b + 1);
+<a class="jxr_linenumber" name="L182" href="#L182">182</a> }
+<a class="jxr_linenumber" name="L183" href="#L183">183</a> observed[b]++;
+<a class="jxr_linenumber" name="L184" href="#L184">184</a> }
+<a class="jxr_linenumber" name="L185" href="#L185">185</a>
+<a class="jxr_linenumber" name="L186" href="#L186">186</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] expected = <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[numBins];
+<a class="jxr_linenumber" name="L187" href="#L187">187</a> <em class="jxr_comment">// This is not uniform for extreme parameterisations.</em>
+<a class="jxr_linenumber" name="L188" href="#L188">188</a> <em class="jxr_comment">// E.g. beta(1000, 0.1).cdf(0.9999999999999999) = 0.94676.</em>
+<a class="jxr_linenumber" name="L189" href="#L189">189</a> <em class="jxr_comment">// This is below the 29/30 = 0.96667 for the penultimate bin.</em>
+<a class="jxr_linenumber" name="L190" href="#L190">190</a> <em class="jxr_comment">// So fill the expected using the CDF.</em>
+<a class="jxr_linenumber" name="L191" href="#L191">191</a> <strong class="jxr_keyword">double</strong> x0 = 0;
+<a class="jxr_linenumber" name="L192" href="#L192">192</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> b = 0; b < numBins; b++) {
+<a class="jxr_linenumber" name="L193" href="#L193">193</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> x1 = breaks[b];
+<a class="jxr_linenumber" name="L194" href="#L194">194</a> expected[b] = expectedDistribution.probability(x0, x1);
+<a class="jxr_linenumber" name="L195" href="#L195">195</a> x0 = x1;
+<a class="jxr_linenumber" name="L196" href="#L196">196</a> }
+<a class="jxr_linenumber" name="L197" href="#L197">197</a>
+<a class="jxr_linenumber" name="L198" href="#L198">198</a> <strong class="jxr_keyword">return</strong> <strong class="jxr_keyword">new</strong> GTest().gTest(expected, observed);
+<a class="jxr_linenumber" name="L199" href="#L199">199</a> }
+<a class="jxr_linenumber" name="L200" href="#L200">200</a> }
+</pre>
+<hr/>
+<div id="footer">Copyright © 2018–2022 <a href="https://www.apache.org/">The Apache Software Foundation</a>. All rights reserved.</div>
+</body>
+</html>
Added: dev/commons/statistics/1.0-RC1/site/xref-test/org/apache/commons/statistics/distribution/BinomialDistributionTest.html
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+++ dev/commons/statistics/1.0-RC1/site/xref-test/org/apache/commons/statistics/distribution/BinomialDistributionTest.html Thu Dec 1 16:47:12 2022
@@ -0,0 +1,291 @@
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+<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
+<head><meta http-equiv="content-type" content="text/html; charset=UTF-8" />
+<title>BinomialDistributionTest xref</title>
+<link type="text/css" rel="stylesheet" href="../../../../../stylesheet.css" />
+</head>
+<body>
+<div id="overview"><a href="../../../../../../testapidocs/org/apache/commons/statistics/distribution/BinomialDistributionTest.html">View Javadoc</a></div><pre>
+<a class="jxr_linenumber" name="L1" href="#L1">1</a> <em class="jxr_comment">/*</em>
+<a class="jxr_linenumber" name="L2" href="#L2">2</a> <em class="jxr_comment"> * Licensed to the Apache Software Foundation (ASF) under one or more</em>
+<a class="jxr_linenumber" name="L3" href="#L3">3</a> <em class="jxr_comment"> * contributor license agreements. See the NOTICE file distributed with</em>
+<a class="jxr_linenumber" name="L4" href="#L4">4</a> <em class="jxr_comment"> * this work for additional information regarding copyright ownership.</em>
+<a class="jxr_linenumber" name="L5" href="#L5">5</a> <em class="jxr_comment"> * The ASF licenses this file to You under the Apache License, Version 2.0</em>
+<a class="jxr_linenumber" name="L6" href="#L6">6</a> <em class="jxr_comment"> * (the "License"); you may not use this file except in compliance with</em>
+<a class="jxr_linenumber" name="L7" href="#L7">7</a> <em class="jxr_comment"> * the License. You may obtain a copy of the License at</em>
+<a class="jxr_linenumber" name="L8" href="#L8">8</a> <em class="jxr_comment"> *</em>
+<a class="jxr_linenumber" name="L9" href="#L9">9</a> <em class="jxr_comment"> * <a href="http://www.apache.org/licenses/LICENSE-2.0" target="alexandria_uri">http://www.apache.org/licenses/LICENSE-2.0</a></em>
+<a class="jxr_linenumber" name="L10" href="#L10">10</a> <em class="jxr_comment"> *</em>
+<a class="jxr_linenumber" name="L11" href="#L11">11</a> <em class="jxr_comment"> * Unless required by applicable law or agreed to in writing, software</em>
+<a class="jxr_linenumber" name="L12" href="#L12">12</a> <em class="jxr_comment"> * distributed under the License is distributed on an "AS IS" BASIS,</em>
+<a class="jxr_linenumber" name="L13" href="#L13">13</a> <em class="jxr_comment"> * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</em>
+<a class="jxr_linenumber" name="L14" href="#L14">14</a> <em class="jxr_comment"> * See the License for the specific language governing permissions and</em>
+<a class="jxr_linenumber" name="L15" href="#L15">15</a> <em class="jxr_comment"> * limitations under the License.</em>
+<a class="jxr_linenumber" name="L16" href="#L16">16</a> <em class="jxr_comment"> */</em>
+<a class="jxr_linenumber" name="L17" href="#L17">17</a> <strong class="jxr_keyword">package</strong> org.apache.commons.statistics.distribution;
+<a class="jxr_linenumber" name="L18" href="#L18">18</a>
+<a class="jxr_linenumber" name="L19" href="#L19">19</a> <strong class="jxr_keyword">import</strong> java.math.BigDecimal;
+<a class="jxr_linenumber" name="L20" href="#L20">20</a> <strong class="jxr_keyword">import</strong> java.math.BigInteger;
+<a class="jxr_linenumber" name="L21" href="#L21">21</a> <strong class="jxr_keyword">import</strong> org.junit.jupiter.api.Assertions;
+<a class="jxr_linenumber" name="L22" href="#L22">22</a> <strong class="jxr_keyword">import</strong> org.junit.jupiter.api.Test;
+<a class="jxr_linenumber" name="L23" href="#L23">23</a> <strong class="jxr_keyword">import</strong> org.junit.jupiter.params.ParameterizedTest;
+<a class="jxr_linenumber" name="L24" href="#L24">24</a> <strong class="jxr_keyword">import</strong> org.junit.jupiter.params.provider.CsvSource;
+<a class="jxr_linenumber" name="L25" href="#L25">25</a> <strong class="jxr_keyword">import</strong> org.junit.jupiter.params.provider.ValueSource;
+<a class="jxr_linenumber" name="L26" href="#L26">26</a>
+<a class="jxr_linenumber" name="L27" href="#L27">27</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L28" href="#L28">28</a> <em class="jxr_javadoccomment"> * Test cases for {@link BinomialDistribution}.</em>
+<a class="jxr_linenumber" name="L29" href="#L29">29</a> <em class="jxr_javadoccomment"> * Extends {@link BaseDiscreteDistributionTest}. See javadoc of that class for details.</em>
+<a class="jxr_linenumber" name="L30" href="#L30">30</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="L31" href="#L31">31</a> <strong class="jxr_keyword">class</strong> <a name="BinomialDistributionTest" href="../../../../../org/apache/commons/statistics/distribution/BinomialDistributionTest.html#BinomialDistributionTest">BinomialDistributionTest</a> <strong class="jxr_keyword">extends</strong> <a name="BaseDiscreteDistributionTest" href="../../../../../org/apache/commons/statistics/distribution/BaseDiscreteDistributionTest.html#BaseDiscreteDistributionTest">BaseDiscreteDistributionTest</a> {
+<a class="jxr_linenumber" name="L32" href="#L32">32</a> @Override
+<a class="jxr_linenumber" name="L33" href="#L33">33</a> DiscreteDistribution makeDistribution(Object... parameters) {
+<a class="jxr_linenumber" name="L34" href="#L34">34</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> n = (Integer) parameters[0];
+<a class="jxr_linenumber" name="L35" href="#L35">35</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> p = (Double) parameters[1];
+<a class="jxr_linenumber" name="L36" href="#L36">36</a> <strong class="jxr_keyword">return</strong> BinomialDistribution.of(n, p);
+<a class="jxr_linenumber" name="L37" href="#L37">37</a> }
+<a class="jxr_linenumber" name="L38" href="#L38">38</a>
+<a class="jxr_linenumber" name="L39" href="#L39">39</a>
+<a class="jxr_linenumber" name="L40" href="#L40">40</a> @Override
+<a class="jxr_linenumber" name="L41" href="#L41">41</a> Object[][] makeInvalidParameters() {
+<a class="jxr_linenumber" name="L42" href="#L42">42</a> <strong class="jxr_keyword">return</strong> <strong class="jxr_keyword">new</strong> Object[][] {
+<a class="jxr_linenumber" name="L43" href="#L43">43</a> {-1, 0.1},
+<a class="jxr_linenumber" name="L44" href="#L44">44</a> {10, -0.1},
+<a class="jxr_linenumber" name="L45" href="#L45">45</a> {10, 1.1},
+<a class="jxr_linenumber" name="L46" href="#L46">46</a> };
+<a class="jxr_linenumber" name="L47" href="#L47">47</a> }
+<a class="jxr_linenumber" name="L48" href="#L48">48</a>
+<a class="jxr_linenumber" name="L49" href="#L49">49</a> @Override
+<a class="jxr_linenumber" name="L50" href="#L50">50</a> String[] getParameterNames() {
+<a class="jxr_linenumber" name="L51" href="#L51">51</a> <strong class="jxr_keyword">return</strong> <strong class="jxr_keyword">new</strong> String[] {<span class="jxr_string">"NumberOfTrials"</span>, <span class="jxr_string">"ProbabilityOfSuccess"</span>};
+<a class="jxr_linenumber" name="L52" href="#L52">52</a> }
+<a class="jxr_linenumber" name="L53" href="#L53">53</a>
+<a class="jxr_linenumber" name="L54" href="#L54">54</a> @Override
+<a class="jxr_linenumber" name="L55" href="#L55">55</a> <strong class="jxr_keyword">protected</strong> <strong class="jxr_keyword">double</strong> getRelativeTolerance() {
+<a class="jxr_linenumber" name="L56" href="#L56">56</a> <em class="jxr_comment">// Tolerance is 8.881784197001252E-16</em>
+<a class="jxr_linenumber" name="L57" href="#L57">57</a> <strong class="jxr_keyword">return</strong> 4 * RELATIVE_EPS;
+<a class="jxr_linenumber" name="L58" href="#L58">58</a> }
+<a class="jxr_linenumber" name="L59" href="#L59">59</a>
+<a class="jxr_linenumber" name="L60" href="#L60">60</a> <em class="jxr_comment">//-------------------- Additional test cases -------------------------------</em>
+<a class="jxr_linenumber" name="L61" href="#L61">61</a>
+<a class="jxr_linenumber" name="L62" href="#L62">62</a> @Test
+<a class="jxr_linenumber" name="L63" href="#L63">63</a> <strong class="jxr_keyword">void</strong> testMath718() {
+<a class="jxr_linenumber" name="L64" href="#L64">64</a> <em class="jxr_comment">// For large trials the evaluation of ContinuedFraction was inaccurate.</em>
+<a class="jxr_linenumber" name="L65" href="#L65">65</a> <em class="jxr_comment">// Do a sweep over several large trials to test if the current implementation is</em>
+<a class="jxr_linenumber" name="L66" href="#L66">66</a> <em class="jxr_comment">// numerically stable.</em>
+<a class="jxr_linenumber" name="L67" href="#L67">67</a>
+<a class="jxr_linenumber" name="L68" href="#L68">68</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> trials = 500000; trials < 20000000; trials += 100000) {
+<a class="jxr_linenumber" name="L69" href="#L69">69</a> <strong class="jxr_keyword">final</strong> BinomialDistribution dist = BinomialDistribution.of(trials, 0.5);
+<a class="jxr_linenumber" name="L70" href="#L70">70</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> p = dist.inverseCumulativeProbability(0.5);
+<a class="jxr_linenumber" name="L71" href="#L71">71</a> Assertions.assertEquals(trials / 2, p);
+<a class="jxr_linenumber" name="L72" href="#L72">72</a> }
+<a class="jxr_linenumber" name="L73" href="#L73">73</a> }
+<a class="jxr_linenumber" name="L74" href="#L74">74</a>
+<a class="jxr_linenumber" name="L75" href="#L75">75</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L76" href="#L76">76</a> <em class="jxr_javadoccomment"> * Test special case of probability of success 0.0.</em>
+<a class="jxr_linenumber" name="L77" href="#L77">77</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="L78" href="#L78">78</a> @ParameterizedTest
+<a class="jxr_linenumber" name="L79" href="#L79">79</a> @ValueSource(ints = {0, 1, 2, 3, 10})
+<a class="jxr_linenumber" name="L80" href="#L80">80</a> <strong class="jxr_keyword">void</strong> testProbabilityOfSuccess0(<strong class="jxr_keyword">int</strong> n) {
+<a class="jxr_linenumber" name="L81" href="#L81">81</a> <em class="jxr_comment">// The sign of p should not matter.</em>
+<a class="jxr_linenumber" name="L82" href="#L82">82</a> <em class="jxr_comment">// Exact equality checks no -0.0 values are generated.</em>
+<a class="jxr_linenumber" name="L83" href="#L83">83</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> p : <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[] {-0.0, 0.0}) {
+<a class="jxr_linenumber" name="L84" href="#L84">84</a> <strong class="jxr_keyword">final</strong> BinomialDistribution dist = BinomialDistribution.of(n, p);
+<a class="jxr_linenumber" name="L85" href="#L85">85</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> k = -1; k <= n + 1; k++) {
+<a class="jxr_linenumber" name="L86" href="#L86">86</a> Assertions.assertEquals(k == 0 ? 1.0 : 0.0, dist.probability(k));
+<a class="jxr_linenumber" name="L87" href="#L87">87</a> Assertions.assertEquals(k == 0 ? 0.0 : Double.NEGATIVE_INFINITY, dist.logProbability(k));
+<a class="jxr_linenumber" name="L88" href="#L88">88</a> Assertions.assertEquals(k >= 0 ? 1.0 : 0.0, dist.cumulativeProbability(k));
+<a class="jxr_linenumber" name="L89" href="#L89">89</a> Assertions.assertEquals(k >= 0 ? 0.0 : 1.0, dist.survivalProbability(k));
+<a class="jxr_linenumber" name="L90" href="#L90">90</a> }
+<a class="jxr_linenumber" name="L91" href="#L91">91</a> }
+<a class="jxr_linenumber" name="L92" href="#L92">92</a> }
+<a class="jxr_linenumber" name="L93" href="#L93">93</a>
+<a class="jxr_linenumber" name="L94" href="#L94">94</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L95" href="#L95">95</a> <em class="jxr_javadoccomment"> * Test special case of probability of success 1.0.</em>
+<a class="jxr_linenumber" name="L96" href="#L96">96</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="L97" href="#L97">97</a> @ParameterizedTest
+<a class="jxr_linenumber" name="L98" href="#L98">98</a> @ValueSource(ints = {0, 1, 2, 3, 10})
+<a class="jxr_linenumber" name="L99" href="#L99">99</a> <strong class="jxr_keyword">void</strong> testProbabilityOfSuccess1(<strong class="jxr_keyword">int</strong> n) {
+<a class="jxr_linenumber" name="L100" href="#L100">100</a> <strong class="jxr_keyword">final</strong> BinomialDistribution dist = BinomialDistribution.of(n, 1);
+<a class="jxr_linenumber" name="L101" href="#L101">101</a> <em class="jxr_comment">// Exact equality checks no -0.0 values are generated.</em>
+<a class="jxr_linenumber" name="L102" href="#L102">102</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> k = -1; k <= n + 1; k++) {
+<a class="jxr_linenumber" name="L103" href="#L103">103</a> Assertions.assertEquals(k == n ? 1.0 : 0.0, dist.probability(k));
+<a class="jxr_linenumber" name="L104" href="#L104">104</a> Assertions.assertEquals(k == n ? 0.0 : Double.NEGATIVE_INFINITY, dist.logProbability(k));
+<a class="jxr_linenumber" name="L105" href="#L105">105</a> Assertions.assertEquals(k >= n ? 1.0 : 0.0, dist.cumulativeProbability(k));
+<a class="jxr_linenumber" name="L106" href="#L106">106</a> Assertions.assertEquals(k >= n ? 0.0 : 1.0, dist.survivalProbability(k));
+<a class="jxr_linenumber" name="L107" href="#L107">107</a> }
+<a class="jxr_linenumber" name="L108" href="#L108">108</a> }
+<a class="jxr_linenumber" name="L109" href="#L109">109</a>
+<a class="jxr_linenumber" name="L110" href="#L110">110</a> @ParameterizedTest
+<a class="jxr_linenumber" name="L111" href="#L111">111</a> @ValueSource(doubles = {0, 1, 0.01, 0.99, 1e-17, 0.3645257e-8, 0.123415276368128, 0.67834532657232434})
+<a class="jxr_linenumber" name="L112" href="#L112">112</a> <strong class="jxr_keyword">void</strong> testNumberOfTrials0(<strong class="jxr_keyword">double</strong> p) {
+<a class="jxr_linenumber" name="L113" href="#L113">113</a> <strong class="jxr_keyword">final</strong> BinomialDistribution dist = BinomialDistribution.of(0, p);
+<a class="jxr_linenumber" name="L114" href="#L114">114</a> <em class="jxr_comment">// Edge case where the probability is ignored when computing the result</em>
+<a class="jxr_linenumber" name="L115" href="#L115">115</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> k = -1; k <= 2; k++) {
+<a class="jxr_linenumber" name="L116" href="#L116">116</a> Assertions.assertEquals(k == 0 ? 1.0 : 0.0, dist.probability(k));
+<a class="jxr_linenumber" name="L117" href="#L117">117</a> Assertions.assertEquals(k == 0 ? 0.0 : Double.NEGATIVE_INFINITY, dist.logProbability(k));
+<a class="jxr_linenumber" name="L118" href="#L118">118</a> Assertions.assertEquals(k >= 0 ? 1.0 : 0.0, dist.cumulativeProbability(k));
+<a class="jxr_linenumber" name="L119" href="#L119">119</a> Assertions.assertEquals(k >= 0 ? 0.0 : 1.0, dist.survivalProbability(k));
+<a class="jxr_linenumber" name="L120" href="#L120">120</a> }
+<a class="jxr_linenumber" name="L121" href="#L121">121</a> }
+<a class="jxr_linenumber" name="L122" href="#L122">122</a>
+<a class="jxr_linenumber" name="L123" href="#L123">123</a> @ParameterizedTest
+<a class="jxr_linenumber" name="L124" href="#L124">124</a> @ValueSource(doubles = {0, 1, 0.01, 0.99, 1e-17, 0.3645257e-8, 0.123415276368128, 0.67834532657232434})
+<a class="jxr_linenumber" name="L125" href="#L125">125</a> <strong class="jxr_keyword">void</strong> testNumberOfTrials1(<strong class="jxr_keyword">double</strong> p) {
+<a class="jxr_linenumber" name="L126" href="#L126">126</a> <strong class="jxr_keyword">final</strong> BinomialDistribution dist = BinomialDistribution.of(1, p);
+<a class="jxr_linenumber" name="L127" href="#L127">127</a> <em class="jxr_comment">// Edge case where the probability should be exact</em>
+<a class="jxr_linenumber" name="L128" href="#L128">128</a> Assertions.assertEquals(0.0, dist.probability(-1));
+<a class="jxr_linenumber" name="L129" href="#L129">129</a> Assertions.assertEquals(1 - p, dist.probability(0));
+<a class="jxr_linenumber" name="L130" href="#L130">130</a> Assertions.assertEquals(p, dist.probability(1));
+<a class="jxr_linenumber" name="L131" href="#L131">131</a> Assertions.assertEquals(0.0, dist.probability(2));
+<a class="jxr_linenumber" name="L132" href="#L132">132</a> Assertions.assertEquals(Double.NEGATIVE_INFINITY, dist.logProbability(-1));
+<a class="jxr_linenumber" name="L133" href="#L133">133</a> <em class="jxr_comment">// Current implementation does not use log1p so allow an error tolerance</em>
+<a class="jxr_linenumber" name="L134" href="#L134">134</a> TestUtils.assertEquals(Math.log1p(-p), dist.logProbability(0), DoubleTolerances.ulps(1));
+<a class="jxr_linenumber" name="L135" href="#L135">135</a> Assertions.assertEquals(Math.log(p), dist.logProbability(1));
+<a class="jxr_linenumber" name="L136" href="#L136">136</a> Assertions.assertEquals(Double.NEGATIVE_INFINITY, dist.logProbability(2));
+<a class="jxr_linenumber" name="L137" href="#L137">137</a> Assertions.assertEquals(0.0, dist.cumulativeProbability(-1));
+<a class="jxr_linenumber" name="L138" href="#L138">138</a> Assertions.assertEquals(1 - p, dist.cumulativeProbability(0));
+<a class="jxr_linenumber" name="L139" href="#L139">139</a> Assertions.assertEquals(1.0, dist.cumulativeProbability(1));
+<a class="jxr_linenumber" name="L140" href="#L140">140</a> Assertions.assertEquals(1.0, dist.cumulativeProbability(2));
+<a class="jxr_linenumber" name="L141" href="#L141">141</a> Assertions.assertEquals(1.0, dist.survivalProbability(-1));
+<a class="jxr_linenumber" name="L142" href="#L142">142</a> Assertions.assertEquals(p, dist.survivalProbability(0));
+<a class="jxr_linenumber" name="L143" href="#L143">143</a> Assertions.assertEquals(0.0, dist.survivalProbability(1));
+<a class="jxr_linenumber" name="L144" href="#L144">144</a> Assertions.assertEquals(0.0, dist.survivalProbability(2));
+<a class="jxr_linenumber" name="L145" href="#L145">145</a> }
+<a class="jxr_linenumber" name="L146" href="#L146">146</a>
+<a class="jxr_linenumber" name="L147" href="#L147">147</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L148" href="#L148">148</a> <em class="jxr_javadoccomment"> * Special case for x=0.</em>
+<a class="jxr_linenumber" name="L149" href="#L149">149</a> <em class="jxr_javadoccomment"> * This hits cases where the SaddlePointExpansionUtils are not used for</em>
+<a class="jxr_linenumber" name="L150" href="#L150">150</a> <em class="jxr_javadoccomment"> * probability functions. It ensures the edge case handling in BinomialDistribution</em>
+<a class="jxr_linenumber" name="L151" href="#L151">151</a> <em class="jxr_javadoccomment"> * matches the original logic in the saddle point expansion. This x=0 logic is used</em>
+<a class="jxr_linenumber" name="L152" href="#L152">152</a> <em class="jxr_javadoccomment"> * by the related hypergeometric distribution and covered by test cases for that</em>
+<a class="jxr_linenumber" name="L153" href="#L153">153</a> <em class="jxr_javadoccomment"> * distribution ensuring it is correct.</em>
+<a class="jxr_linenumber" name="L154" href="#L154">154</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="L155" href="#L155">155</a> @ParameterizedTest
+<a class="jxr_linenumber" name="L156" href="#L156">156</a> @ValueSource(doubles = {0, 1, 0.01, 0.99, 1e-17, 0.3645257e-8, 0.123415276368128, 0.67834532657232434})
+<a class="jxr_linenumber" name="L157" href="#L157">157</a> <strong class="jxr_keyword">void</strong> testX0(<strong class="jxr_keyword">double</strong> p) {
+<a class="jxr_linenumber" name="L158" href="#L158">158</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> n : <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">int</strong>[] {0, 1, 10}) {
+<a class="jxr_linenumber" name="L159" href="#L159">159</a> <strong class="jxr_keyword">final</strong> BinomialDistribution dist = BinomialDistribution.of(n, p);
+<a class="jxr_linenumber" name="L160" href="#L160">160</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> expected = SaddlePointExpansionUtils.logBinomialProbability(0, n, p, 1 - p);
+<a class="jxr_linenumber" name="L161" href="#L161">161</a> Assertions.assertEquals(expected, dist.logProbability(0));
+<a class="jxr_linenumber" name="L162" href="#L162">162</a> }
+<a class="jxr_linenumber" name="L163" href="#L163">163</a> }
+<a class="jxr_linenumber" name="L164" href="#L164">164</a>
+<a class="jxr_linenumber" name="L165" href="#L165">165</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L166" href="#L166">166</a> <em class="jxr_javadoccomment"> * Test the probability functions at the lower and upper bounds when the p-values</em>
+<a class="jxr_linenumber" name="L167" href="#L167">167</a> <em class="jxr_javadoccomment"> * are very small. The expected results should be within 1 ULP of an exact</em>
+<a class="jxr_linenumber" name="L168" href="#L168">168</a> <em class="jxr_javadoccomment"> * computation for pmf(x=0) and pmf(x=n). These values can be computed using</em>
+<a class="jxr_linenumber" name="L169" href="#L169">169</a> <em class="jxr_javadoccomment"> * java.lang.Math functions.</em>
+<a class="jxr_linenumber" name="L170" href="#L170">170</a> <em class="jxr_javadoccomment"> *</em>
+<a class="jxr_linenumber" name="L171" href="#L171">171</a> <em class="jxr_javadoccomment"> * <p>The next value, e.g. pmf(x=1) and cdf(x=1), is asserted to the specified</em>
+<a class="jxr_linenumber" name="L172" href="#L172">172</a> <em class="jxr_javadoccomment"> * relative error tolerance. These values require computations using p and 1-p</em>
+<a class="jxr_linenumber" name="L173" href="#L173">173</a> <em class="jxr_javadoccomment"> * and are less exact.</em>
+<a class="jxr_linenumber" name="L174" href="#L174">174</a> <em class="jxr_javadoccomment"> *</em>
+<a class="jxr_linenumber" name="L175" href="#L175">175</a> <em class="jxr_javadoccomment"> * @param n Number of trials</em>
+<a class="jxr_linenumber" name="L176" href="#L176">176</a> <em class="jxr_javadoccomment"> * @param p Probability of success</em>
+<a class="jxr_linenumber" name="L177" href="#L177">177</a> <em class="jxr_javadoccomment"> * @param eps1 Relative error tolerance for pmf(x=1)</em>
+<a class="jxr_linenumber" name="L178" href="#L178">178</a> <em class="jxr_javadoccomment"> * @param epsn1 Relative error tolerance for pmf(x=n-1)</em>
+<a class="jxr_linenumber" name="L179" href="#L179">179</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="L180" href="#L180">180</a> @ParameterizedTest
+<a class="jxr_linenumber" name="L181" href="#L181">181</a> @CsvSource({
+<a class="jxr_linenumber" name="L182" href="#L182">182</a> <em class="jxr_comment">// Min p-value is shown for reference.</em>
+<a class="jxr_linenumber" name="L183" href="#L183">183</a> <span class="jxr_string">"100, 0.50, 8e-15, 8e-15"</span>, <em class="jxr_comment">// 7.888609052210118E-31</em>
+<a class="jxr_linenumber" name="L184" href="#L184">184</a> <span class="jxr_string">"100, 0.01, 1e-15, 3e-14"</span>, <em class="jxr_comment">// 1.000000000000002E-200</em>
+<a class="jxr_linenumber" name="L185" href="#L185">185</a> <span class="jxr_string">"100, 0.99, 4e-15, 1e-15"</span>, <em class="jxr_comment">// 1.0000000000000887E-200</em>
+<a class="jxr_linenumber" name="L186" href="#L186">186</a> <span class="jxr_string">"140, 0.01, 1e-15, 2e-13"</span>, <em class="jxr_comment">// 1.0000000000000029E-280</em>
+<a class="jxr_linenumber" name="L187" href="#L187">187</a> <span class="jxr_string">"140, 0.99, 2e-13, 1e-15"</span>, <em class="jxr_comment">// 1.0000000000001244E-280</em>
+<a class="jxr_linenumber" name="L188" href="#L188">188</a> <span class="jxr_string">"50, 0.001, 1e-15, 5e-14"</span>, <em class="jxr_comment">// 1.0000000000000011E-150</em>
+<a class="jxr_linenumber" name="L189" href="#L189">189</a> <span class="jxr_string">"50, 0.999, 3e-14, 1e-15"</span>, <em class="jxr_comment">// 1.0000000000000444E-150</em>
+<a class="jxr_linenumber" name="L190" href="#L190">190</a> })
+<a class="jxr_linenumber" name="L191" href="#L191">191</a> <strong class="jxr_keyword">void</strong> testBounds(<strong class="jxr_keyword">int</strong> n, <strong class="jxr_keyword">double</strong> p, <strong class="jxr_keyword">double</strong> eps1, <strong class="jxr_keyword">double</strong> epsn1) {
+<a class="jxr_linenumber" name="L192" href="#L192">192</a> <strong class="jxr_keyword">final</strong> BinomialDistribution dist = BinomialDistribution.of(n, p);
+<a class="jxr_linenumber" name="L193" href="#L193">193</a> <strong class="jxr_keyword">final</strong> BigDecimal prob0 = binomialProbability(n, p, 0);
+<a class="jxr_linenumber" name="L194" href="#L194">194</a> <strong class="jxr_keyword">final</strong> BigDecimal probn = binomialProbability(n, p, n);
+<a class="jxr_linenumber" name="L195" href="#L195">195</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> p0 = prob0.doubleValue();
+<a class="jxr_linenumber" name="L196" href="#L196">196</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> pn = probn.doubleValue();
+<a class="jxr_linenumber" name="L197" href="#L197">197</a>
+<a class="jxr_linenumber" name="L198" href="#L198">198</a> <em class="jxr_comment">// Require very small non-zero probabilities to make the test difficult.</em>
+<a class="jxr_linenumber" name="L199" href="#L199">199</a> <em class="jxr_comment">// Check using 2^-53 so that at least one p-value satisfies 1 - p == 1.</em>
+<a class="jxr_linenumber" name="L200" href="#L200">200</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> minp = Math.min(p0, pn);
+<a class="jxr_linenumber" name="L201" href="#L201">201</a> Assertions.assertTrue(minp < 0x1.0p-53, () -> <span class="jxr_string">"Test should target small p-values: "</span> + minp);
+<a class="jxr_linenumber" name="L202" href="#L202">202</a> Assertions.assertTrue(minp > Double.MIN_NORMAL, () -> <span class="jxr_string">"Minimum P-value should not be sub-normal: "</span> + minp);
+<a class="jxr_linenumber" name="L203" href="#L203">203</a>
+<a class="jxr_linenumber" name="L204" href="#L204">204</a> <em class="jxr_comment">// Almost exact at the bounds</em>
+<a class="jxr_linenumber" name="L205" href="#L205">205</a> <strong class="jxr_keyword">final</strong> <a name="DoubleTolerance" href="../../../../../org/apache/commons/statistics/distribution/DoubleTolerance.html#DoubleTolerance">DoubleTolerance</a> tol1 = DoubleTolerances.ulps(1);
+<a class="jxr_linenumber" name="L206" href="#L206">206</a> TestUtils.assertEquals(p0, dist.probability(0), tol1, <span class="jxr_string">"pmf(0)"</span>);
+<a class="jxr_linenumber" name="L207" href="#L207">207</a> TestUtils.assertEquals(pn, dist.probability(n), tol1, <span class="jxr_string">"pmf(n)"</span>);
+<a class="jxr_linenumber" name="L208" href="#L208">208</a> <em class="jxr_comment">// Consistent at the bounds</em>
+<a class="jxr_linenumber" name="L209" href="#L209">209</a> Assertions.assertEquals(dist.probability(0), dist.cumulativeProbability(0), <span class="jxr_string">"pmf(0) != cdf(0)"</span>);
+<a class="jxr_linenumber" name="L210" href="#L210">210</a> Assertions.assertEquals(dist.probability(n), dist.survivalProbability(n - 1), <span class="jxr_string">"pmf(n) != sf(n-1)"</span>);
+<a class="jxr_linenumber" name="L211" href="#L211">211</a>
+<a class="jxr_linenumber" name="L212" href="#L212">212</a> <em class="jxr_comment">// Test probability and log probability are consistent.</em>
+<a class="jxr_linenumber" name="L213" href="#L213">213</a> <em class="jxr_comment">// Avoid log when p-value is close to 1.</em>
+<a class="jxr_linenumber" name="L214" href="#L214">214</a> <strong class="jxr_keyword">if</strong> (p0 < 0.9) {
+<a class="jxr_linenumber" name="L215" href="#L215">215</a> TestUtils.assertEquals(Math.log(p0), dist.logProbability(0), tol1, <span class="jxr_string">"log(pmf(0)) != logpmf(0)"</span>);
+<a class="jxr_linenumber" name="L216" href="#L216">216</a> } <strong class="jxr_keyword">else</strong> {
+<a class="jxr_linenumber" name="L217" href="#L217">217</a> TestUtils.assertEquals(p0, Math.exp(dist.logProbability(0)), tol1, <span class="jxr_string">"pmf(0) != exp(logpmf(0))"</span>);
+<a class="jxr_linenumber" name="L218" href="#L218">218</a> }
+<a class="jxr_linenumber" name="L219" href="#L219">219</a> <strong class="jxr_keyword">if</strong> (pn < 0.9) {
+<a class="jxr_linenumber" name="L220" href="#L220">220</a> TestUtils.assertEquals(Math.log(pn), dist.logProbability(n), tol1, <span class="jxr_string">"log(pmf(n)) != logpmf(n)"</span>);
+<a class="jxr_linenumber" name="L221" href="#L221">221</a> } <strong class="jxr_keyword">else</strong> {
+<a class="jxr_linenumber" name="L222" href="#L222">222</a> TestUtils.assertEquals(pn, Math.exp(dist.logProbability(n)), tol1, <span class="jxr_string">"pmf(n) != exp(logpmf(n))"</span>);
+<a class="jxr_linenumber" name="L223" href="#L223">223</a> }
+<a class="jxr_linenumber" name="L224" href="#L224">224</a>
+<a class="jxr_linenumber" name="L225" href="#L225">225</a> <em class="jxr_comment">// The next probability is accurate to the specified tolerance.</em>
+<a class="jxr_linenumber" name="L226" href="#L226">226</a> <strong class="jxr_keyword">final</strong> BigDecimal prob1 = binomialProbability(n, p, 1);
+<a class="jxr_linenumber" name="L227" href="#L227">227</a> <strong class="jxr_keyword">final</strong> BigDecimal probn1 = binomialProbability(n, p, n - 1);
+<a class="jxr_linenumber" name="L228" href="#L228">228</a> TestUtils.assertEquals(prob1.doubleValue(), dist.probability(1), createRelTolerance(eps1), <span class="jxr_string">"pmf(1)"</span>);
+<a class="jxr_linenumber" name="L229" href="#L229">229</a> TestUtils.assertEquals(probn1.doubleValue(), dist.probability(n - 1), createRelTolerance(epsn1), <span class="jxr_string">"pmf(n-1)"</span>);
+<a class="jxr_linenumber" name="L230" href="#L230">230</a>
+<a class="jxr_linenumber" name="L231" href="#L231">231</a> <em class="jxr_comment">// Check the cumulative functions</em>
+<a class="jxr_linenumber" name="L232" href="#L232">232</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> cdf1 = prob0.add(prob1).doubleValue();
+<a class="jxr_linenumber" name="L233" href="#L233">233</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> sfn2 = probn.add(probn1).doubleValue();
+<a class="jxr_linenumber" name="L234" href="#L234">234</a> TestUtils.assertEquals(cdf1, dist.cumulativeProbability(1), createRelTolerance(eps1), <span class="jxr_string">"cmf(1)"</span>);
+<a class="jxr_linenumber" name="L235" href="#L235">235</a> TestUtils.assertEquals(sfn2, dist.survivalProbability(n - 2), createRelTolerance(epsn1), <span class="jxr_string">"sf(n-2)"</span>);
+<a class="jxr_linenumber" name="L236" href="#L236">236</a> }
+<a class="jxr_linenumber" name="L237" href="#L237">237</a>
+<a class="jxr_linenumber" name="L238" href="#L238">238</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L239" href="#L239">239</a> <em class="jxr_javadoccomment"> * Compute the binomial distribution probability mass function using exact</em>
+<a class="jxr_linenumber" name="L240" href="#L240">240</a> <em class="jxr_javadoccomment"> * arithmetic.</em>
+<a class="jxr_linenumber" name="L241" href="#L241">241</a> <em class="jxr_javadoccomment"> *</em>
+<a class="jxr_linenumber" name="L242" href="#L242">242</a> <em class="jxr_javadoccomment"> * <p>This has no error handling for invalid arguments.</em>
+<a class="jxr_linenumber" name="L243" href="#L243">243</a> <em class="jxr_javadoccomment"> *</em>
+<a class="jxr_linenumber" name="L244" href="#L244">244</a> <em class="jxr_javadoccomment"> * <p>Warning: BigDecimal has a limit on the size of the exponent for the power</em>
+<a class="jxr_linenumber" name="L245" href="#L245">245</a> <em class="jxr_javadoccomment"> * function. This method has not been extensively tested with very small</em>
+<a class="jxr_linenumber" name="L246" href="#L246">246</a> <em class="jxr_javadoccomment"> * p-values, large n or large k. Use of a MathContext to round intermediates may be</em>
+<a class="jxr_linenumber" name="L247" href="#L247">247</a> <em class="jxr_javadoccomment"> * required to reduce memory consumption. The binomial coefficient may not</em>
+<a class="jxr_linenumber" name="L248" href="#L248">248</a> <em class="jxr_javadoccomment"> * compute for large n and k ~ n/2.</em>
+<a class="jxr_linenumber" name="L249" href="#L249">249</a> <em class="jxr_javadoccomment"> *</em>
+<a class="jxr_linenumber" name="L250" href="#L250">250</a> <em class="jxr_javadoccomment"> * @param n Number of trials (must be positive)</em>
+<a class="jxr_linenumber" name="L251" href="#L251">251</a> <em class="jxr_javadoccomment"> * @param p Probability of success (in [0, 1])</em>
+<a class="jxr_linenumber" name="L252" href="#L252">252</a> <em class="jxr_javadoccomment"> * @param k Number of successes (must be positive)</em>
+<a class="jxr_linenumber" name="L253" href="#L253">253</a> <em class="jxr_javadoccomment"> * @return pmf(X=k)</em>
+<a class="jxr_linenumber" name="L254" href="#L254">254</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="L255" href="#L255">255</a> <strong class="jxr_keyword">private</strong> <strong class="jxr_keyword">static</strong> BigDecimal binomialProbability(<strong class="jxr_keyword">int</strong> n, <strong class="jxr_keyword">double</strong> p, <strong class="jxr_keyword">int</strong> k) {
+<a class="jxr_linenumber" name="L256" href="#L256">256</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> nmk = n - k;
+<a class="jxr_linenumber" name="L257" href="#L257">257</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> m = Math.min(k, nmk);
+<a class="jxr_linenumber" name="L258" href="#L258">258</a> <em class="jxr_comment">// Probability component: p^k * (1-p)^(n-k)</em>
+<a class="jxr_linenumber" name="L259" href="#L259">259</a> <strong class="jxr_keyword">final</strong> BigDecimal bp = <strong class="jxr_keyword">new</strong> BigDecimal(p);
+<a class="jxr_linenumber" name="L260" href="#L260">260</a> <strong class="jxr_keyword">final</strong> BigDecimal result = bp.pow(k).multiply(
+<a class="jxr_linenumber" name="L261" href="#L261">261</a> BigDecimal.ONE.subtract(bp).pow(nmk));
+<a class="jxr_linenumber" name="L262" href="#L262">262</a> <em class="jxr_comment">// Compute the binomial coefficient</em>
+<a class="jxr_linenumber" name="L263" href="#L263">263</a> <em class="jxr_comment">// Simple edge cases first.</em>
+<a class="jxr_linenumber" name="L264" href="#L264">264</a> <strong class="jxr_keyword">if</strong> (m == 0) {
+<a class="jxr_linenumber" name="L265" href="#L265">265</a> <strong class="jxr_keyword">return</strong> result;
+<a class="jxr_linenumber" name="L266" href="#L266">266</a> } <strong class="jxr_keyword">else</strong> <strong class="jxr_keyword">if</strong> (m == 1) {
+<a class="jxr_linenumber" name="L267" href="#L267">267</a> <strong class="jxr_keyword">return</strong> result.multiply(<strong class="jxr_keyword">new</strong> BigDecimal(n));
+<a class="jxr_linenumber" name="L268" href="#L268">268</a> }
+<a class="jxr_linenumber" name="L269" href="#L269">269</a> <em class="jxr_comment">// See org.apache.commons.numbers.combinatorics.BinomialCoefficient</em>
+<a class="jxr_linenumber" name="L270" href="#L270">270</a> BigInteger nCk = BigInteger.ONE;
+<a class="jxr_linenumber" name="L271" href="#L271">271</a> <strong class="jxr_keyword">int</strong> i = n - m + 1;
+<a class="jxr_linenumber" name="L272" href="#L272">272</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> j = 1; j <= m; j++) {
+<a class="jxr_linenumber" name="L273" href="#L273">273</a> nCk = nCk.multiply(BigInteger.valueOf(i)).divide(BigInteger.valueOf(j));
+<a class="jxr_linenumber" name="L274" href="#L274">274</a> i++;
+<a class="jxr_linenumber" name="L275" href="#L275">275</a> }
+<a class="jxr_linenumber" name="L276" href="#L276">276</a> <strong class="jxr_keyword">return</strong> <strong class="jxr_keyword">new</strong> BigDecimal(nCk).multiply(result);
+<a class="jxr_linenumber" name="L277" href="#L277">277</a> }
+<a class="jxr_linenumber" name="L278" href="#L278">278</a> }
+</pre>
+<hr/>
+<div id="footer">Copyright © 2018–2022 <a href="https://www.apache.org/">The Apache Software Foundation</a>. All rights reserved.</div>
+</body>
+</html>
Added: dev/commons/statistics/1.0-RC1/site/xref-test/org/apache/commons/statistics/distribution/CauchyDistributionTest.html
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--- dev/commons/statistics/1.0-RC1/site/xref-test/org/apache/commons/statistics/distribution/CauchyDistributionTest.html (added)
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@@ -0,0 +1,62 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
+<head><meta http-equiv="content-type" content="text/html; charset=UTF-8" />
+<title>CauchyDistributionTest xref</title>
+<link type="text/css" rel="stylesheet" href="../../../../../stylesheet.css" />
+</head>
+<body>
+<div id="overview"><a href="../../../../../../testapidocs/org/apache/commons/statistics/distribution/CauchyDistributionTest.html">View Javadoc</a></div><pre>
+<a class="jxr_linenumber" name="L1" href="#L1">1</a> <em class="jxr_comment">/*</em>
+<a class="jxr_linenumber" name="L2" href="#L2">2</a> <em class="jxr_comment"> * Licensed to the Apache Software Foundation (ASF) under one or more</em>
+<a class="jxr_linenumber" name="L3" href="#L3">3</a> <em class="jxr_comment"> * contributor license agreements. See the NOTICE file distributed with</em>
+<a class="jxr_linenumber" name="L4" href="#L4">4</a> <em class="jxr_comment"> * this work for additional information regarding copyright ownership.</em>
+<a class="jxr_linenumber" name="L5" href="#L5">5</a> <em class="jxr_comment"> * The ASF licenses this file to You under the Apache License, Version 2.0</em>
+<a class="jxr_linenumber" name="L6" href="#L6">6</a> <em class="jxr_comment"> * (the "License"); you may not use this file except in compliance with</em>
+<a class="jxr_linenumber" name="L7" href="#L7">7</a> <em class="jxr_comment"> * the License. You may obtain a copy of the License at</em>
+<a class="jxr_linenumber" name="L8" href="#L8">8</a> <em class="jxr_comment"> *</em>
+<a class="jxr_linenumber" name="L9" href="#L9">9</a> <em class="jxr_comment"> * <a href="http://www.apache.org/licenses/LICENSE-2.0" target="alexandria_uri">http://www.apache.org/licenses/LICENSE-2.0</a></em>
+<a class="jxr_linenumber" name="L10" href="#L10">10</a> <em class="jxr_comment"> *</em>
+<a class="jxr_linenumber" name="L11" href="#L11">11</a> <em class="jxr_comment"> * Unless required by applicable law or agreed to in writing, software</em>
+<a class="jxr_linenumber" name="L12" href="#L12">12</a> <em class="jxr_comment"> * distributed under the License is distributed on an "AS IS" BASIS,</em>
+<a class="jxr_linenumber" name="L13" href="#L13">13</a> <em class="jxr_comment"> * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</em>
+<a class="jxr_linenumber" name="L14" href="#L14">14</a> <em class="jxr_comment"> * See the License for the specific language governing permissions and</em>
+<a class="jxr_linenumber" name="L15" href="#L15">15</a> <em class="jxr_comment"> * limitations under the License.</em>
+<a class="jxr_linenumber" name="L16" href="#L16">16</a> <em class="jxr_comment"> */</em>
+<a class="jxr_linenumber" name="L17" href="#L17">17</a>
+<a class="jxr_linenumber" name="L18" href="#L18">18</a> <strong class="jxr_keyword">package</strong> org.apache.commons.statistics.distribution;
+<a class="jxr_linenumber" name="L19" href="#L19">19</a>
+<a class="jxr_linenumber" name="L20" href="#L20">20</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L21" href="#L21">21</a> <em class="jxr_javadoccomment"> * Test cases for {@link CauchyDistribution}.</em>
+<a class="jxr_linenumber" name="L22" href="#L22">22</a> <em class="jxr_javadoccomment"> * Extends {@link BaseContinuousDistributionTest}. See javadoc of that class for details.</em>
+<a class="jxr_linenumber" name="L23" href="#L23">23</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="L24" href="#L24">24</a> <strong class="jxr_keyword">class</strong> <a name="CauchyDistributionTest" href="../../../../../org/apache/commons/statistics/distribution/CauchyDistributionTest.html#CauchyDistributionTest">CauchyDistributionTest</a> <strong class="jxr_keyword">extends</strong> <a name="BaseContinuousDistributionTest" href="../../../../../org/apache/commons/statistics/distribution/BaseContinuousDistributionTest.html#BaseContinuousDistributionTest">BaseContinuousDistributionTest</a> {
+<a class="jxr_linenumber" name="L25" href="#L25">25</a> @Override
+<a class="jxr_linenumber" name="L26" href="#L26">26</a> ContinuousDistribution makeDistribution(Object... parameters) {
+<a class="jxr_linenumber" name="L27" href="#L27">27</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> location = (Double) parameters[0];
+<a class="jxr_linenumber" name="L28" href="#L28">28</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> scale = (Double) parameters[1];
+<a class="jxr_linenumber" name="L29" href="#L29">29</a> <strong class="jxr_keyword">return</strong> CauchyDistribution.of(location, scale);
+<a class="jxr_linenumber" name="L30" href="#L30">30</a> }
+<a class="jxr_linenumber" name="L31" href="#L31">31</a>
+<a class="jxr_linenumber" name="L32" href="#L32">32</a> @Override
+<a class="jxr_linenumber" name="L33" href="#L33">33</a> Object[][] makeInvalidParameters() {
+<a class="jxr_linenumber" name="L34" href="#L34">34</a> <strong class="jxr_keyword">return</strong> <strong class="jxr_keyword">new</strong> Object[][] {
+<a class="jxr_linenumber" name="L35" href="#L35">35</a> {0.0, 0.0},
+<a class="jxr_linenumber" name="L36" href="#L36">36</a> {0.0, -0.1}
+<a class="jxr_linenumber" name="L37" href="#L37">37</a> };
+<a class="jxr_linenumber" name="L38" href="#L38">38</a> }
+<a class="jxr_linenumber" name="L39" href="#L39">39</a>
+<a class="jxr_linenumber" name="L40" href="#L40">40</a> @Override
+<a class="jxr_linenumber" name="L41" href="#L41">41</a> String[] getParameterNames() {
+<a class="jxr_linenumber" name="L42" href="#L42">42</a> <strong class="jxr_keyword">return</strong> <strong class="jxr_keyword">new</strong> String[] {<span class="jxr_string">"Location"</span>, <span class="jxr_string">"Scale"</span>};
+<a class="jxr_linenumber" name="L43" href="#L43">43</a> }
+<a class="jxr_linenumber" name="L44" href="#L44">44</a>
+<a class="jxr_linenumber" name="L45" href="#L45">45</a> @Override
+<a class="jxr_linenumber" name="L46" href="#L46">46</a> <strong class="jxr_keyword">protected</strong> <strong class="jxr_keyword">double</strong> getRelativeTolerance() {
+<a class="jxr_linenumber" name="L47" href="#L47">47</a> <strong class="jxr_keyword">return</strong> 5e-15;
+<a class="jxr_linenumber" name="L48" href="#L48">48</a> }
+<a class="jxr_linenumber" name="L49" href="#L49">49</a> }
+</pre>
+<hr/>
+<div id="footer">Copyright © 2018–2022 <a href="https://www.apache.org/">The Apache Software Foundation</a>. All rights reserved.</div>
+</body>
+</html>