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Added: websites/production/commons/content/proper/commons-math/xref-test/org/apache/commons/math3/distribution/LevyDistributionTest.html
==============================================================================
--- websites/production/commons/content/proper/commons-math/xref-test/org/apache/commons/math3/distribution/LevyDistributionTest.html (added)
+++ websites/production/commons/content/proper/commons-math/xref-test/org/apache/commons/math3/distribution/LevyDistributionTest.html Sun Apr 7 07:52:05 2013
@@ -0,0 +1,81 @@
+<!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>LevyDistributionTest xref</title>
+<link type="text/css" rel="stylesheet" href="../../../../../stylesheet.css" />
+</head>
+<body>
+<div id="overview"><a href="../../../../../../testapidocs/org/apache/commons/math3/distribution/LevyDistributionTest.html">View Javadoc</a></div><pre>
+
+<a class="jxr_linenumber" name="1" href="#1">1</a> <em class="jxr_comment">/*</em>
+<a class="jxr_linenumber" name="2" href="#2">2</a> <em class="jxr_comment"> * Licensed to the Apache Software Foundation (ASF) under one or more</em>
+<a class="jxr_linenumber" name="3" href="#3">3</a> <em class="jxr_comment"> * contributor license agreements. See the NOTICE file distributed with</em>
+<a class="jxr_linenumber" name="4" href="#4">4</a> <em class="jxr_comment"> * this work for additional information regarding copyright ownership.</em>
+<a class="jxr_linenumber" name="5" href="#5">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="6" href="#6">6</a> <em class="jxr_comment"> * (the "License"); you may not use this file except in compliance with</em>
+<a class="jxr_linenumber" name="7" href="#7">7</a> <em class="jxr_comment"> * the License. You may obtain a copy of the License at</em>
+<a class="jxr_linenumber" name="8" href="#8">8</a> <em class="jxr_comment"> *</em>
+<a class="jxr_linenumber" name="9" href="#9">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="10" href="#10">10</a> <em class="jxr_comment"> *</em>
+<a class="jxr_linenumber" name="11" href="#11">11</a> <em class="jxr_comment"> * Unless required by applicable law or agreed to in writing, software</em>
+<a class="jxr_linenumber" name="12" href="#12">12</a> <em class="jxr_comment"> * distributed under the License is distributed on an "AS IS" BASIS,</em>
+<a class="jxr_linenumber" name="13" href="#13">13</a> <em class="jxr_comment"> * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</em>
+<a class="jxr_linenumber" name="14" href="#14">14</a> <em class="jxr_comment"> * See the License for the specific language governing permissions and</em>
+<a class="jxr_linenumber" name="15" href="#15">15</a> <em class="jxr_comment"> * limitations under the License.</em>
+<a class="jxr_linenumber" name="16" href="#16">16</a> <em class="jxr_comment"> */</em>
+<a class="jxr_linenumber" name="17" href="#17">17</a> <strong class="jxr_keyword">package</strong> org.apache.commons.math3.distribution;
+<a class="jxr_linenumber" name="18" href="#18">18</a>
+<a class="jxr_linenumber" name="19" href="#19">19</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.random.Well19937a;
+<a class="jxr_linenumber" name="20" href="#20">20</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.util.Precision;
+<a class="jxr_linenumber" name="21" href="#21">21</a> <strong class="jxr_keyword">import</strong> org.junit.Assert;
+<a class="jxr_linenumber" name="22" href="#22">22</a> <strong class="jxr_keyword">import</strong> org.junit.Test;
+<a class="jxr_linenumber" name="23" href="#23">23</a>
+<a class="jxr_linenumber" name="24" href="#24">24</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">class</strong> <a href="../../../../../org/apache/commons/math3/distribution/LevyDistributionTest.html">LevyDistributionTest</a> <strong class="jxr_keyword">extends</strong> <a href="../../../../../org/apache/commons/math3/distribution/RealDistributionAbstractTest.html">RealDistributionAbstractTest</a> {
+<a class="jxr_linenumber" name="25" href="#25">25</a>
+<a class="jxr_linenumber" name="26" href="#26">26</a> @Test
+<a class="jxr_linenumber" name="27" href="#27">27</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testParameters() {
+<a class="jxr_linenumber" name="28" href="#28">28</a> LevyDistribution d = makeDistribution();
+<a class="jxr_linenumber" name="29" href="#29">29</a> Assert.assertEquals(1.2, d.getLocation(), Precision.EPSILON);
+<a class="jxr_linenumber" name="30" href="#30">30</a> Assert.assertEquals(0.4, d.getScale(), Precision.EPSILON);
+<a class="jxr_linenumber" name="31" href="#31">31</a> }
+<a class="jxr_linenumber" name="32" href="#32">32</a>
+<a class="jxr_linenumber" name="33" href="#33">33</a> @Test
+<a class="jxr_linenumber" name="34" href="#34">34</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testSupport() {
+<a class="jxr_linenumber" name="35" href="#35">35</a> LevyDistribution d = makeDistribution();
+<a class="jxr_linenumber" name="36" href="#36">36</a> Assert.assertEquals(d.getLocation(), d.getSupportLowerBound(), Precision.EPSILON);
+<a class="jxr_linenumber" name="37" href="#37">37</a> Assert.assertTrue(Double.isInfinite(d.getSupportUpperBound()));
+<a class="jxr_linenumber" name="38" href="#38">38</a> Assert.assertTrue(d.isSupportConnected());
+<a class="jxr_linenumber" name="39" href="#39">39</a> }
+<a class="jxr_linenumber" name="40" href="#40">40</a>
+<a class="jxr_linenumber" name="41" href="#41">41</a> <strong class="jxr_keyword">public</strong> LevyDistribution makeDistribution() {
+<a class="jxr_linenumber" name="42" href="#42">42</a> <strong class="jxr_keyword">return</strong> <strong class="jxr_keyword">new</strong> LevyDistribution(<strong class="jxr_keyword">new</strong> Well19937a(0xc5a5506bbb17e57al), 1.2, 0.4);
+<a class="jxr_linenumber" name="43" href="#43">43</a> }
+<a class="jxr_linenumber" name="44" href="#44">44</a>
+<a class="jxr_linenumber" name="45" href="#45">45</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">double</strong>[] makeCumulativeTestPoints() {
+<a class="jxr_linenumber" name="46" href="#46">46</a> <strong class="jxr_keyword">return</strong> <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[] {
+<a class="jxr_linenumber" name="47" href="#47">47</a> 1.2001, 1.21, 1.225, 1.25, 1.3, 1.9, 3.4, 5.6
+<a class="jxr_linenumber" name="48" href="#48">48</a> };
+<a class="jxr_linenumber" name="49" href="#49">49</a> }
+<a class="jxr_linenumber" name="50" href="#50">50</a>
+<a class="jxr_linenumber" name="51" href="#51">51</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">double</strong>[] makeCumulativeTestValues() {
+<a class="jxr_linenumber" name="52" href="#52">52</a> <em class="jxr_comment">// values computed with R and function plevy from rmutil package</em>
+<a class="jxr_linenumber" name="53" href="#53">53</a> <strong class="jxr_keyword">return</strong> <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[] {
+<a class="jxr_linenumber" name="54" href="#54">54</a> 0, 2.53962850749e-10, 6.33424836662e-05, 0.00467773498105,
+<a class="jxr_linenumber" name="55" href="#55">55</a> 0.0455002638964, 0.449691797969, 0.669815357599, 0.763024600553
+<a class="jxr_linenumber" name="56" href="#56">56</a> };
+<a class="jxr_linenumber" name="57" href="#57">57</a> }
+<a class="jxr_linenumber" name="58" href="#58">58</a>
+<a class="jxr_linenumber" name="59" href="#59">59</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">double</strong>[] makeDensityTestValues() {
+<a class="jxr_linenumber" name="60" href="#60">60</a> <em class="jxr_comment">// values computed with R and function dlevy from rmutil package</em>
+<a class="jxr_linenumber" name="61" href="#61">61</a> <strong class="jxr_keyword">return</strong> <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[] {
+<a class="jxr_linenumber" name="62" href="#62">62</a> 0, 5.20056373765e-07, 0.0214128361224, 0.413339707082, 1.07981933026,
+<a class="jxr_linenumber" name="63" href="#63">63</a> 0.323749319161, 0.0706032550094, 0.026122839884
+<a class="jxr_linenumber" name="64" href="#64">64</a> };
+<a class="jxr_linenumber" name="65" href="#65">65</a> }
+<a class="jxr_linenumber" name="66" href="#66">66</a>
+<a class="jxr_linenumber" name="67" href="#67">67</a> }
+</pre>
+<hr/><div id="footer">This page was automatically generated by <a href="http://maven.apache.org/">Maven</a></div></body>
+</html>
+
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Modified: websites/production/commons/content/proper/commons-math/xref-test/org/apache/commons/math3/distribution/MultivariateNormalDistributionTest.html
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--- websites/production/commons/content/proper/commons-math/xref-test/org/apache/commons/math3/distribution/MultivariateNormalDistributionTest.html (original)
+++ websites/production/commons/content/proper/commons-math/xref-test/org/apache/commons/math3/distribution/MultivariateNormalDistributionTest.html Sun Apr 7 07:52:05 2013
@@ -30,117 +30,138 @@
<a class="jxr_linenumber" name="20" href="#20">20</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.stat.correlation.Covariance;
<a class="jxr_linenumber" name="21" href="#21">21</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.linear.RealMatrix;
<a class="jxr_linenumber" name="22" href="#22">22</a>
-<a class="jxr_linenumber" name="23" href="#23">23</a> <strong class="jxr_keyword">import</strong> org.junit.After;
-<a class="jxr_linenumber" name="24" href="#24">24</a> <strong class="jxr_keyword">import</strong> org.junit.Assert;
-<a class="jxr_linenumber" name="25" href="#25">25</a> <strong class="jxr_keyword">import</strong> org.junit.Before;
-<a class="jxr_linenumber" name="26" href="#26">26</a> <strong class="jxr_keyword">import</strong> org.junit.Test;
-<a class="jxr_linenumber" name="27" href="#27">27</a>
-<a class="jxr_linenumber" name="28" href="#28">28</a> <em class="jxr_javadoccomment">/**</em>
-<a class="jxr_linenumber" name="29" href="#29">29</a> <em class="jxr_javadoccomment"> * Test cases for {@link MultivariateNormalDistribution}.</em>
-<a class="jxr_linenumber" name="30" href="#30">30</a> <em class="jxr_javadoccomment"> */</em>
-<a class="jxr_linenumber" name="31" href="#31">31</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">class</strong> <a href="../../../../../org/apache/commons/math3/distribution/MultivariateNormalDistributionTest.html">MultivariateNormalDistributionTest</a> {
-<a class="jxr_linenumber" name="32" href="#32">32</a> <em class="jxr_javadoccomment">/**</em>
-<a class="jxr_linenumber" name="33" href="#33">33</a> <em class="jxr_javadoccomment"> * Test the ability of the distribution to report its mean value parameter.</em>
-<a class="jxr_linenumber" name="34" href="#34">34</a> <em class="jxr_javadoccomment"> */</em>
-<a class="jxr_linenumber" name="35" href="#35">35</a> @Test
-<a class="jxr_linenumber" name="36" href="#36">36</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testGetMean() {
-<a class="jxr_linenumber" name="37" href="#37">37</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] mu = { -1.5, 2 };
-<a class="jxr_linenumber" name="38" href="#38">38</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] sigma = { { 2, -1.1 },
-<a class="jxr_linenumber" name="39" href="#39">39</a> { -1.1, 2 } };
-<a class="jxr_linenumber" name="40" href="#40">40</a> <strong class="jxr_keyword">final</strong> MultivariateNormalDistribution d = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution(mu, sigma);
-<a class="jxr_linenumber" name="41" href="#41">41</a>
-<a class="jxr_linenumber" name="42" href="#42">42</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] m = d.getMeans();
-<a class="jxr_linenumber" name="43" href="#43">43</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> i = 0; i < m.length; i++) {
-<a class="jxr_linenumber" name="44" href="#44">44</a> Assert.assertEquals(mu[i], m[i], 0);
-<a class="jxr_linenumber" name="45" href="#45">45</a> }
-<a class="jxr_linenumber" name="46" href="#46">46</a> }
-<a class="jxr_linenumber" name="47" href="#47">47</a>
-<a class="jxr_linenumber" name="48" href="#48">48</a> <em class="jxr_javadoccomment">/**</em>
-<a class="jxr_linenumber" name="49" href="#49">49</a> <em class="jxr_javadoccomment"> * Test the ability of the distribution to report its covariance matrix parameter.</em>
-<a class="jxr_linenumber" name="50" href="#50">50</a> <em class="jxr_javadoccomment"> */</em>
-<a class="jxr_linenumber" name="51" href="#51">51</a> @Test
-<a class="jxr_linenumber" name="52" href="#52">52</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testGetCovarianceMatrix() {
-<a class="jxr_linenumber" name="53" href="#53">53</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] mu = { -1.5, 2 };
-<a class="jxr_linenumber" name="54" href="#54">54</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] sigma = { { 2, -1.1 },
-<a class="jxr_linenumber" name="55" href="#55">55</a> { -1.1, 2 } };
-<a class="jxr_linenumber" name="56" href="#56">56</a> <strong class="jxr_keyword">final</strong> MultivariateNormalDistribution d = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution(mu, sigma);
-<a class="jxr_linenumber" name="57" href="#57">57</a>
-<a class="jxr_linenumber" name="58" href="#58">58</a> <strong class="jxr_keyword">final</strong> RealMatrix s = d.getCovariances();
-<a class="jxr_linenumber" name="59" href="#59">59</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> dim = d.getDimension();
-<a class="jxr_linenumber" name="60" href="#60">60</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> i = 0; i < dim; i++) {
-<a class="jxr_linenumber" name="61" href="#61">61</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> j = 0; j < dim; j++) {
-<a class="jxr_linenumber" name="62" href="#62">62</a> Assert.assertEquals(sigma[i][j], s.getEntry(i, j), 0);
-<a class="jxr_linenumber" name="63" href="#63">63</a> }
-<a class="jxr_linenumber" name="64" href="#64">64</a> }
-<a class="jxr_linenumber" name="65" href="#65">65</a> }
-<a class="jxr_linenumber" name="66" href="#66">66</a>
-<a class="jxr_linenumber" name="67" href="#67">67</a> <em class="jxr_javadoccomment">/**</em>
-<a class="jxr_linenumber" name="68" href="#68">68</a> <em class="jxr_javadoccomment"> * Test the accuracy of sampling from the distribution.</em>
-<a class="jxr_linenumber" name="69" href="#69">69</a> <em class="jxr_javadoccomment"> */</em>
-<a class="jxr_linenumber" name="70" href="#70">70</a> @Test
-<a class="jxr_linenumber" name="71" href="#71">71</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testSampling() {
-<a class="jxr_linenumber" name="72" href="#72">72</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] mu = { -1.5, 2 };
-<a class="jxr_linenumber" name="73" href="#73">73</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] sigma = { { 2, -1.1 },
-<a class="jxr_linenumber" name="74" href="#74">74</a> { -1.1, 2 } };
-<a class="jxr_linenumber" name="75" href="#75">75</a> <strong class="jxr_keyword">final</strong> MultivariateNormalDistribution d = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution(mu, sigma);
-<a class="jxr_linenumber" name="76" href="#76">76</a> d.reseedRandomGenerator(50);
-<a class="jxr_linenumber" name="77" href="#77">77</a>
-<a class="jxr_linenumber" name="78" href="#78">78</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> n = 500000;
-<a class="jxr_linenumber" name="79" href="#79">79</a>
-<a class="jxr_linenumber" name="80" href="#80">80</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] samples = d.sample(n);
-<a class="jxr_linenumber" name="81" href="#81">81</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> dim = d.getDimension();
-<a class="jxr_linenumber" name="82" href="#82">82</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] sampleMeans = <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[dim];
-<a class="jxr_linenumber" name="83" href="#83">83</a>
-<a class="jxr_linenumber" name="84" href="#84">84</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> i = 0; i < samples.length; i++) {
-<a class="jxr_linenumber" name="85" href="#85">85</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> j = 0; j < dim; j++) {
-<a class="jxr_linenumber" name="86" href="#86">86</a> sampleMeans[j] += samples[i][j];
-<a class="jxr_linenumber" name="87" href="#87">87</a> }
-<a class="jxr_linenumber" name="88" href="#88">88</a> }
-<a class="jxr_linenumber" name="89" href="#89">89</a>
-<a class="jxr_linenumber" name="90" href="#90">90</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> sampledValueTolerance = 1e-2;
-<a class="jxr_linenumber" name="91" href="#91">91</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> j = 0; j < dim; j++) {
-<a class="jxr_linenumber" name="92" href="#92">92</a> sampleMeans[j] /= samples.length;
-<a class="jxr_linenumber" name="93" href="#93">93</a> Assert.assertEquals(mu[j], sampleMeans[j], sampledValueTolerance);
-<a class="jxr_linenumber" name="94" href="#94">94</a> }
-<a class="jxr_linenumber" name="95" href="#95">95</a>
-<a class="jxr_linenumber" name="96" href="#96">96</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] sampleSigma = <strong class="jxr_keyword">new</strong> Covariance(samples).getCovarianceMatrix().getData();
-<a class="jxr_linenumber" name="97" href="#97">97</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> i = 0; i < dim; i++) {
-<a class="jxr_linenumber" name="98" href="#98">98</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> j = 0; j < dim; j++) {
-<a class="jxr_linenumber" name="99" href="#99">99</a> Assert.assertEquals(sigma[i][j], sampleSigma[i][j], sampledValueTolerance);
-<a class="jxr_linenumber" name="100" href="#100">100</a> }
-<a class="jxr_linenumber" name="101" href="#101">101</a> }
-<a class="jxr_linenumber" name="102" href="#102">102</a> }
-<a class="jxr_linenumber" name="103" href="#103">103</a>
-<a class="jxr_linenumber" name="104" href="#104">104</a> <em class="jxr_javadoccomment">/**</em>
-<a class="jxr_linenumber" name="105" href="#105">105</a> <em class="jxr_javadoccomment"> * Test the accuracy of the distribution when calculating densities.</em>
-<a class="jxr_linenumber" name="106" href="#106">106</a> <em class="jxr_javadoccomment"> */</em>
-<a class="jxr_linenumber" name="107" href="#107">107</a> @Test
-<a class="jxr_linenumber" name="108" href="#108">108</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testDensities() {
-<a class="jxr_linenumber" name="109" href="#109">109</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] mu = { -1.5, 2 };
-<a class="jxr_linenumber" name="110" href="#110">110</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] sigma = { { 2, -1.1 },
-<a class="jxr_linenumber" name="111" href="#111">111</a> { -1.1, 2 } };
-<a class="jxr_linenumber" name="112" href="#112">112</a> <strong class="jxr_keyword">final</strong> MultivariateNormalDistribution d = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution(mu, sigma);
-<a class="jxr_linenumber" name="113" href="#113">113</a>
-<a class="jxr_linenumber" name="114" href="#114">114</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] testValues = { { -1.5, 2 },
-<a class="jxr_linenumber" name="115" href="#115">115</a> { 4, 4 },
-<a class="jxr_linenumber" name="116" href="#116">116</a> { 1.5, -2 },
-<a class="jxr_linenumber" name="117" href="#117">117</a> { 0, 0 } };
-<a class="jxr_linenumber" name="118" href="#118">118</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] densities = <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[testValues.length];
-<a class="jxr_linenumber" name="119" href="#119">119</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> i = 0; i < densities.length; i++) {
-<a class="jxr_linenumber" name="120" href="#120">120</a> densities[i] = d.density(testValues[i]);
-<a class="jxr_linenumber" name="121" href="#121">121</a> }
-<a class="jxr_linenumber" name="122" href="#122">122</a>
-<a class="jxr_linenumber" name="123" href="#123">123</a> <em class="jxr_comment">// From dmvnorm function in R 2.15 CRAN package Mixtools v0.4.5</em>
-<a class="jxr_linenumber" name="124" href="#124">124</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] correctDensities = { 0.09528357207691344,
-<a class="jxr_linenumber" name="125" href="#125">125</a> 5.80932710124009e-09,
-<a class="jxr_linenumber" name="126" href="#126">126</a> 0.001387448895173267,
-<a class="jxr_linenumber" name="127" href="#127">127</a> 0.03309922090210541 };
-<a class="jxr_linenumber" name="128" href="#128">128</a>
-<a class="jxr_linenumber" name="129" href="#129">129</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> i = 0; i < testValues.length; i++) {
-<a class="jxr_linenumber" name="130" href="#130">130</a> Assert.assertEquals(correctDensities[i], densities[i], 1e-16);
-<a class="jxr_linenumber" name="131" href="#131">131</a> }
-<a class="jxr_linenumber" name="132" href="#132">132</a> }
-<a class="jxr_linenumber" name="133" href="#133">133</a> }
+<a class="jxr_linenumber" name="23" href="#23">23</a> <strong class="jxr_keyword">import</strong> java.util.Random;
+<a class="jxr_linenumber" name="24" href="#24">24</a> <strong class="jxr_keyword">import</strong> org.junit.After;
+<a class="jxr_linenumber" name="25" href="#25">25</a> <strong class="jxr_keyword">import</strong> org.junit.Assert;
+<a class="jxr_linenumber" name="26" href="#26">26</a> <strong class="jxr_keyword">import</strong> org.junit.Before;
+<a class="jxr_linenumber" name="27" href="#27">27</a> <strong class="jxr_keyword">import</strong> org.junit.Test;
+<a class="jxr_linenumber" name="28" href="#28">28</a>
+<a class="jxr_linenumber" name="29" href="#29">29</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="30" href="#30">30</a> <em class="jxr_javadoccomment"> * Test cases for {@link MultivariateNormalDistribution}.</em>
+<a class="jxr_linenumber" name="31" href="#31">31</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="32" href="#32">32</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">class</strong> <a href="../../../../../org/apache/commons/math3/distribution/MultivariateNormalDistributionTest.html">MultivariateNormalDistributionTest</a> {
+<a class="jxr_linenumber" name="33" href="#33">33</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="34" href="#34">34</a> <em class="jxr_javadoccomment"> * Test the ability of the distribution to report its mean value parameter.</em>
+<a class="jxr_linenumber" name="35" href="#35">35</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="36" href="#36">36</a> @Test
+<a class="jxr_linenumber" name="37" href="#37">37</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testGetMean() {
+<a class="jxr_linenumber" name="38" href="#38">38</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] mu = { -1.5, 2 };
+<a class="jxr_linenumber" name="39" href="#39">39</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] sigma = { { 2, -1.1 },
+<a class="jxr_linenumber" name="40" href="#40">40</a> { -1.1, 2 } };
+<a class="jxr_linenumber" name="41" href="#41">41</a> <strong class="jxr_keyword">final</strong> MultivariateNormalDistribution d = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution(mu, sigma);
+<a class="jxr_linenumber" name="42" href="#42">42</a>
+<a class="jxr_linenumber" name="43" href="#43">43</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] m = d.getMeans();
+<a class="jxr_linenumber" name="44" href="#44">44</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> i = 0; i < m.length; i++) {
+<a class="jxr_linenumber" name="45" href="#45">45</a> Assert.assertEquals(mu[i], m[i], 0);
+<a class="jxr_linenumber" name="46" href="#46">46</a> }
+<a class="jxr_linenumber" name="47" href="#47">47</a> }
+<a class="jxr_linenumber" name="48" href="#48">48</a>
+<a class="jxr_linenumber" name="49" href="#49">49</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="50" href="#50">50</a> <em class="jxr_javadoccomment"> * Test the ability of the distribution to report its covariance matrix parameter.</em>
+<a class="jxr_linenumber" name="51" href="#51">51</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="52" href="#52">52</a> @Test
+<a class="jxr_linenumber" name="53" href="#53">53</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testGetCovarianceMatrix() {
+<a class="jxr_linenumber" name="54" href="#54">54</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] mu = { -1.5, 2 };
+<a class="jxr_linenumber" name="55" href="#55">55</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] sigma = { { 2, -1.1 },
+<a class="jxr_linenumber" name="56" href="#56">56</a> { -1.1, 2 } };
+<a class="jxr_linenumber" name="57" href="#57">57</a> <strong class="jxr_keyword">final</strong> MultivariateNormalDistribution d = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution(mu, sigma);
+<a class="jxr_linenumber" name="58" href="#58">58</a>
+<a class="jxr_linenumber" name="59" href="#59">59</a> <strong class="jxr_keyword">final</strong> RealMatrix s = d.getCovariances();
+<a class="jxr_linenumber" name="60" href="#60">60</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> dim = d.getDimension();
+<a class="jxr_linenumber" name="61" href="#61">61</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> i = 0; i < dim; i++) {
+<a class="jxr_linenumber" name="62" href="#62">62</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> j = 0; j < dim; j++) {
+<a class="jxr_linenumber" name="63" href="#63">63</a> Assert.assertEquals(sigma[i][j], s.getEntry(i, j), 0);
+<a class="jxr_linenumber" name="64" href="#64">64</a> }
+<a class="jxr_linenumber" name="65" href="#65">65</a> }
+<a class="jxr_linenumber" name="66" href="#66">66</a> }
+<a class="jxr_linenumber" name="67" href="#67">67</a>
+<a class="jxr_linenumber" name="68" href="#68">68</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="69" href="#69">69</a> <em class="jxr_javadoccomment"> * Test the accuracy of sampling from the distribution.</em>
+<a class="jxr_linenumber" name="70" href="#70">70</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="71" href="#71">71</a> @Test
+<a class="jxr_linenumber" name="72" href="#72">72</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testSampling() {
+<a class="jxr_linenumber" name="73" href="#73">73</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] mu = { -1.5, 2 };
+<a class="jxr_linenumber" name="74" href="#74">74</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] sigma = { { 2, -1.1 },
+<a class="jxr_linenumber" name="75" href="#75">75</a> { -1.1, 2 } };
+<a class="jxr_linenumber" name="76" href="#76">76</a> <strong class="jxr_keyword">final</strong> MultivariateNormalDistribution d = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution(mu, sigma);
+<a class="jxr_linenumber" name="77" href="#77">77</a> d.reseedRandomGenerator(50);
+<a class="jxr_linenumber" name="78" href="#78">78</a>
+<a class="jxr_linenumber" name="79" href="#79">79</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> n = 500000;
+<a class="jxr_linenumber" name="80" href="#80">80</a>
+<a class="jxr_linenumber" name="81" href="#81">81</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] samples = d.sample(n);
+<a class="jxr_linenumber" name="82" href="#82">82</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> dim = d.getDimension();
+<a class="jxr_linenumber" name="83" href="#83">83</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] sampleMeans = <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[dim];
+<a class="jxr_linenumber" name="84" href="#84">84</a>
+<a class="jxr_linenumber" name="85" href="#85">85</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> i = 0; i < samples.length; i++) {
+<a class="jxr_linenumber" name="86" href="#86">86</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> j = 0; j < dim; j++) {
+<a class="jxr_linenumber" name="87" href="#87">87</a> sampleMeans[j] += samples[i][j];
+<a class="jxr_linenumber" name="88" href="#88">88</a> }
+<a class="jxr_linenumber" name="89" href="#89">89</a> }
+<a class="jxr_linenumber" name="90" href="#90">90</a>
+<a class="jxr_linenumber" name="91" href="#91">91</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> sampledValueTolerance = 1e-2;
+<a class="jxr_linenumber" name="92" href="#92">92</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> j = 0; j < dim; j++) {
+<a class="jxr_linenumber" name="93" href="#93">93</a> sampleMeans[j] /= samples.length;
+<a class="jxr_linenumber" name="94" href="#94">94</a> Assert.assertEquals(mu[j], sampleMeans[j], sampledValueTolerance);
+<a class="jxr_linenumber" name="95" href="#95">95</a> }
+<a class="jxr_linenumber" name="96" href="#96">96</a>
+<a class="jxr_linenumber" name="97" href="#97">97</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] sampleSigma = <strong class="jxr_keyword">new</strong> Covariance(samples).getCovarianceMatrix().getData();
+<a class="jxr_linenumber" name="98" href="#98">98</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> i = 0; i < dim; i++) {
+<a class="jxr_linenumber" name="99" href="#99">99</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> j = 0; j < dim; j++) {
+<a class="jxr_linenumber" name="100" href="#100">100</a> Assert.assertEquals(sigma[i][j], sampleSigma[i][j], sampledValueTolerance);
+<a class="jxr_linenumber" name="101" href="#101">101</a> }
+<a class="jxr_linenumber" name="102" href="#102">102</a> }
+<a class="jxr_linenumber" name="103" href="#103">103</a> }
+<a class="jxr_linenumber" name="104" href="#104">104</a>
+<a class="jxr_linenumber" name="105" href="#105">105</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="106" href="#106">106</a> <em class="jxr_javadoccomment"> * Test the accuracy of the distribution when calculating densities.</em>
+<a class="jxr_linenumber" name="107" href="#107">107</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="108" href="#108">108</a> @Test
+<a class="jxr_linenumber" name="109" href="#109">109</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testDensities() {
+<a class="jxr_linenumber" name="110" href="#110">110</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] mu = { -1.5, 2 };
+<a class="jxr_linenumber" name="111" href="#111">111</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] sigma = { { 2, -1.1 },
+<a class="jxr_linenumber" name="112" href="#112">112</a> { -1.1, 2 } };
+<a class="jxr_linenumber" name="113" href="#113">113</a> <strong class="jxr_keyword">final</strong> MultivariateNormalDistribution d = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution(mu, sigma);
+<a class="jxr_linenumber" name="114" href="#114">114</a>
+<a class="jxr_linenumber" name="115" href="#115">115</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] testValues = { { -1.5, 2 },
+<a class="jxr_linenumber" name="116" href="#116">116</a> { 4, 4 },
+<a class="jxr_linenumber" name="117" href="#117">117</a> { 1.5, -2 },
+<a class="jxr_linenumber" name="118" href="#118">118</a> { 0, 0 } };
+<a class="jxr_linenumber" name="119" href="#119">119</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] densities = <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[testValues.length];
+<a class="jxr_linenumber" name="120" href="#120">120</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> i = 0; i < densities.length; i++) {
+<a class="jxr_linenumber" name="121" href="#121">121</a> densities[i] = d.density(testValues[i]);
+<a class="jxr_linenumber" name="122" href="#122">122</a> }
+<a class="jxr_linenumber" name="123" href="#123">123</a>
+<a class="jxr_linenumber" name="124" href="#124">124</a> <em class="jxr_comment">// From dmvnorm function in R 2.15 CRAN package Mixtools v0.4.5</em>
+<a class="jxr_linenumber" name="125" href="#125">125</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] correctDensities = { 0.09528357207691344,
+<a class="jxr_linenumber" name="126" href="#126">126</a> 5.80932710124009e-09,
+<a class="jxr_linenumber" name="127" href="#127">127</a> 0.001387448895173267,
+<a class="jxr_linenumber" name="128" href="#128">128</a> 0.03309922090210541 };
+<a class="jxr_linenumber" name="129" href="#129">129</a>
+<a class="jxr_linenumber" name="130" href="#130">130</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> i = 0; i < testValues.length; i++) {
+<a class="jxr_linenumber" name="131" href="#131">131</a> Assert.assertEquals(correctDensities[i], densities[i], 1e-16);
+<a class="jxr_linenumber" name="132" href="#132">132</a> }
+<a class="jxr_linenumber" name="133" href="#133">133</a> }
+<a class="jxr_linenumber" name="134" href="#134">134</a>
+<a class="jxr_linenumber" name="135" href="#135">135</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="136" href="#136">136</a> <em class="jxr_javadoccomment"> * Test the accuracy of the distribution when calculating densities.</em>
+<a class="jxr_linenumber" name="137" href="#137">137</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="138" href="#138">138</a> @Test
+<a class="jxr_linenumber" name="139" href="#139">139</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testUnivariateDistribution() {
+<a class="jxr_linenumber" name="140" href="#140">140</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] mu = { -1.5 };
+<a class="jxr_linenumber" name="141" href="#141">141</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] sigma = { { 1 } };
+<a class="jxr_linenumber" name="142" href="#142">142</a>
+<a class="jxr_linenumber" name="143" href="#143">143</a> <strong class="jxr_keyword">final</strong> MultivariateNormalDistribution multi = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution(mu, sigma);
+<a class="jxr_linenumber" name="144" href="#144">144</a>
+<a class="jxr_linenumber" name="145" href="#145">145</a> <strong class="jxr_keyword">final</strong> NormalDistribution uni = <strong class="jxr_keyword">new</strong> NormalDistribution(mu[0], sigma[0][0]);
+<a class="jxr_linenumber" name="146" href="#146">146</a> <strong class="jxr_keyword">final</strong> Random rng = <strong class="jxr_keyword">new</strong> Random();
+<a class="jxr_linenumber" name="147" href="#147">147</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> numCases = 100;
+<a class="jxr_linenumber" name="148" href="#148">148</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> tol = Math.ulp(1d);
+<a class="jxr_linenumber" name="149" href="#149">149</a> <strong class="jxr_keyword">for</strong> (<strong class="jxr_keyword">int</strong> i = 0; i < numCases; i++) {
+<a class="jxr_linenumber" name="150" href="#150">150</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> v = rng.nextDouble() * 10 - 5;
+<a class="jxr_linenumber" name="151" href="#151">151</a> Assert.assertEquals(uni.density(v), multi.density(<strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[] { v }), tol);
+<a class="jxr_linenumber" name="152" href="#152">152</a> }
+<a class="jxr_linenumber" name="153" href="#153">153</a> }
+<a class="jxr_linenumber" name="154" href="#154">154</a> }
</pre>
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Modified: websites/production/commons/content/proper/commons-math/xref-test/org/apache/commons/math3/distribution/UniformRealDistributionTest.html
==============================================================================
--- websites/production/commons/content/proper/commons-math/xref-test/org/apache/commons/math3/distribution/UniformRealDistributionTest.html (original)
+++ websites/production/commons/content/proper/commons-math/xref-test/org/apache/commons/math3/distribution/UniformRealDistributionTest.html Sun Apr 7 07:52:05 2013
@@ -120,7 +120,18 @@
<a class="jxr_linenumber" name="110" href="#110">110</a> Assert.assertEquals(dist.getNumericalMean(), 0.375, 0);
<a class="jxr_linenumber" name="111" href="#111">111</a> Assert.assertEquals(dist.getNumericalVariance(), 0.2552083333333333, 0);
<a class="jxr_linenumber" name="112" href="#112">112</a> }
-<a class="jxr_linenumber" name="113" href="#113">113</a> }
+<a class="jxr_linenumber" name="113" href="#113">113</a>
+<a class="jxr_linenumber" name="114" href="#114">114</a> <em class="jxr_javadoccomment">/**</em><em class="jxr_javadoccomment"> </em>
+<a class="jxr_linenumber" name="115" href="#115">115</a> <em class="jxr_javadoccomment"> * Check accuracy of analytical inverse CDF. Fails if a solver is used </em>
+<a class="jxr_linenumber" name="116" href="#116">116</a> <em class="jxr_javadoccomment"> * with the default accuracy. </em>
+<a class="jxr_linenumber" name="117" href="#117">117</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="118" href="#118">118</a> @Test
+<a class="jxr_linenumber" name="119" href="#119">119</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testInverseCumulativeDistribution() {
+<a class="jxr_linenumber" name="120" href="#120">120</a> UniformRealDistribution dist = <strong class="jxr_keyword">new</strong> UniformRealDistribution(0, 1e-9);
+<a class="jxr_linenumber" name="121" href="#121">121</a>
+<a class="jxr_linenumber" name="122" href="#122">122</a> Assert.assertEquals(2.5e-10, dist.inverseCumulativeProbability(0.25), 0);
+<a class="jxr_linenumber" name="123" href="#123">123</a> }
+<a class="jxr_linenumber" name="124" href="#124">124</a> }
</pre>
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+<meta http-equiv="content-type" content="text/html; charset=UTF-8" />
+<title>MultivariateNormalMixtureExpectationMaximizationTest xref</title>
+<link type="text/css" rel="stylesheet" href="../../../../../../stylesheet.css" />
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+<div id="overview"><a href="../../../../../../../testapidocs/org/apache/commons/math3/distribution/fitting/MultivariateNormalMixtureExpectationMaximizationTest.html">View Javadoc</a></div><pre>
+
+<a class="jxr_linenumber" name="1" href="#1">1</a> <em class="jxr_comment">/*</em>
+<a class="jxr_linenumber" name="2" href="#2">2</a> <em class="jxr_comment"> * Licensed to the Apache Software Foundation (ASF) under one or more</em>
+<a class="jxr_linenumber" name="3" href="#3">3</a> <em class="jxr_comment"> * contributor license agreements. See the NOTICE file distributed with this</em>
+<a class="jxr_linenumber" name="4" href="#4">4</a> <em class="jxr_comment"> * work for additional information regarding copyright ownership. The ASF</em>
+<a class="jxr_linenumber" name="5" href="#5">5</a> <em class="jxr_comment"> * licenses this file to You under the Apache License, Version 2.0 (the</em>
+<a class="jxr_linenumber" name="6" href="#6">6</a> <em class="jxr_comment"> * "License"); you may not use this file except in compliance with the License.</em>
+<a class="jxr_linenumber" name="7" href="#7">7</a> <em class="jxr_comment"> * You may obtain a copy of the License at</em>
+<a class="jxr_linenumber" name="8" href="#8">8</a> <em class="jxr_comment"> *</em>
+<a class="jxr_linenumber" name="9" href="#9">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="10" href="#10">10</a> <em class="jxr_comment"> *</em>
+<a class="jxr_linenumber" name="11" href="#11">11</a> <em class="jxr_comment"> * Unless required by applicable law or agreed to in writing, software</em>
+<a class="jxr_linenumber" name="12" href="#12">12</a> <em class="jxr_comment"> * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT</em>
+<a class="jxr_linenumber" name="13" href="#13">13</a> <em class="jxr_comment"> * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the</em>
+<a class="jxr_linenumber" name="14" href="#14">14</a> <em class="jxr_comment"> * License for the specific language governing permissions and limitations under</em>
+<a class="jxr_linenumber" name="15" href="#15">15</a> <em class="jxr_comment"> * the License.</em>
+<a class="jxr_linenumber" name="16" href="#16">16</a> <em class="jxr_comment"> */</em>
+<a class="jxr_linenumber" name="17" href="#17">17</a> <strong class="jxr_keyword">package</strong> org.apache.commons.math3.distribution.fitting;
+<a class="jxr_linenumber" name="18" href="#18">18</a>
+<a class="jxr_linenumber" name="19" href="#19">19</a> <strong class="jxr_keyword">import</strong> java.util.ArrayList;
+<a class="jxr_linenumber" name="20" href="#20">20</a> <strong class="jxr_keyword">import</strong> java.util.Arrays;
+<a class="jxr_linenumber" name="21" href="#21">21</a> <strong class="jxr_keyword">import</strong> java.util.List;
+<a class="jxr_linenumber" name="22" href="#22">22</a>
+<a class="jxr_linenumber" name="23" href="#23">23</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.distribution.MixtureMultivariateNormalDistribution;
+<a class="jxr_linenumber" name="24" href="#24">24</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.distribution.MultivariateNormalDistribution;
+<a class="jxr_linenumber" name="25" href="#25">25</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.exception.ConvergenceException;
+<a class="jxr_linenumber" name="26" href="#26">26</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.exception.DimensionMismatchException;
+<a class="jxr_linenumber" name="27" href="#27">27</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.exception.NotStrictlyPositiveException;
+<a class="jxr_linenumber" name="28" href="#28">28</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.exception.NumberIsTooSmallException;
+<a class="jxr_linenumber" name="29" href="#29">29</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.linear.Array2DRowRealMatrix;
+<a class="jxr_linenumber" name="30" href="#30">30</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.linear.RealMatrix;
+<a class="jxr_linenumber" name="31" href="#31">31</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.util.Pair;
+<a class="jxr_linenumber" name="32" href="#32">32</a> <strong class="jxr_keyword">import</strong> org.junit.Assert;
+<a class="jxr_linenumber" name="33" href="#33">33</a> <strong class="jxr_keyword">import</strong> org.junit.Test;
+<a class="jxr_linenumber" name="34" href="#34">34</a>
+<a class="jxr_linenumber" name="35" href="#35">35</a> <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="36" href="#36">36</a> <em class="jxr_javadoccomment"> * Test that demonstrates the use of</em>
+<a class="jxr_linenumber" name="37" href="#37">37</a> <em class="jxr_javadoccomment"> * {@link MultivariateNormalMixtureExpectationMaximization}.</em>
+<a class="jxr_linenumber" name="38" href="#38">38</a> <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="39" href="#39">39</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">class</strong> <a href="../../../../../../org/apache/commons/math3/distribution/fitting/MultivariateNormalMixtureExpectationMaximizationTest.html">MultivariateNormalMixtureExpectationMaximizationTest</a> {
+<a class="jxr_linenumber" name="40" href="#40">40</a>
+<a class="jxr_linenumber" name="41" href="#41">41</a> @Test(expected = NotStrictlyPositiveException.<strong class="jxr_keyword">class</strong>)
+<a class="jxr_linenumber" name="42" href="#42">42</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testNonEmptyData() {
+<a class="jxr_linenumber" name="43" href="#43">43</a> <em class="jxr_comment">// Should not accept empty data</em>
+<a class="jxr_linenumber" name="44" href="#44">44</a> <strong class="jxr_keyword">new</strong> MultivariateNormalMixtureExpectationMaximization(<strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[][] {});
+<a class="jxr_linenumber" name="45" href="#45">45</a> }
+<a class="jxr_linenumber" name="46" href="#46">46</a>
+<a class="jxr_linenumber" name="47" href="#47">47</a> @Test(expected = DimensionMismatchException.<strong class="jxr_keyword">class</strong>)
+<a class="jxr_linenumber" name="48" href="#48">48</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testNonJaggedData() {
+<a class="jxr_linenumber" name="49" href="#49">49</a> <em class="jxr_comment">// Reject data with nonconstant numbers of columns</em>
+<a class="jxr_linenumber" name="50" href="#50">50</a> <strong class="jxr_keyword">double</strong>[][] data = <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[][] {
+<a class="jxr_linenumber" name="51" href="#51">51</a> { 1, 2, 3 },
+<a class="jxr_linenumber" name="52" href="#52">52</a> { 4, 5, 6, 7 },
+<a class="jxr_linenumber" name="53" href="#53">53</a> };
+<a class="jxr_linenumber" name="54" href="#54">54</a> <strong class="jxr_keyword">new</strong> MultivariateNormalMixtureExpectationMaximization(data);
+<a class="jxr_linenumber" name="55" href="#55">55</a> }
+<a class="jxr_linenumber" name="56" href="#56">56</a>
+<a class="jxr_linenumber" name="57" href="#57">57</a> @Test(expected = NumberIsTooSmallException.<strong class="jxr_keyword">class</strong>)
+<a class="jxr_linenumber" name="58" href="#58">58</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testMultipleColumnsRequired() {
+<a class="jxr_linenumber" name="59" href="#59">59</a> <em class="jxr_comment">// Data should have at least 2 columns</em>
+<a class="jxr_linenumber" name="60" href="#60">60</a> <strong class="jxr_keyword">double</strong>[][] data = <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[][] {
+<a class="jxr_linenumber" name="61" href="#61">61</a> { 1 }, { 2 }
+<a class="jxr_linenumber" name="62" href="#62">62</a> };
+<a class="jxr_linenumber" name="63" href="#63">63</a> <strong class="jxr_keyword">new</strong> MultivariateNormalMixtureExpectationMaximization(data);
+<a class="jxr_linenumber" name="64" href="#64">64</a> }
+<a class="jxr_linenumber" name="65" href="#65">65</a>
+<a class="jxr_linenumber" name="66" href="#66">66</a> @Test(expected = NotStrictlyPositiveException.<strong class="jxr_keyword">class</strong>)
+<a class="jxr_linenumber" name="67" href="#67">67</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testMaxIterationsPositive() {
+<a class="jxr_linenumber" name="68" href="#68">68</a> <em class="jxr_comment">// Maximum iterations for fit must be positive integer</em>
+<a class="jxr_linenumber" name="69" href="#69">69</a> <strong class="jxr_keyword">double</strong>[][] data = getTestSamples();
+<a class="jxr_linenumber" name="70" href="#70">70</a> MultivariateNormalMixtureExpectationMaximization fitter =
+<a class="jxr_linenumber" name="71" href="#71">71</a> <strong class="jxr_keyword">new</strong> MultivariateNormalMixtureExpectationMaximization(data);
+<a class="jxr_linenumber" name="72" href="#72">72</a>
+<a class="jxr_linenumber" name="73" href="#73">73</a> MixtureMultivariateNormalDistribution
+<a class="jxr_linenumber" name="74" href="#74">74</a> initialMix = MultivariateNormalMixtureExpectationMaximization.estimate(data, 2);
+<a class="jxr_linenumber" name="75" href="#75">75</a>
+<a class="jxr_linenumber" name="76" href="#76">76</a> fitter.fit(initialMix, 0, 1E-5);
+<a class="jxr_linenumber" name="77" href="#77">77</a> }
+<a class="jxr_linenumber" name="78" href="#78">78</a>
+<a class="jxr_linenumber" name="79" href="#79">79</a> @Test(expected = NotStrictlyPositiveException.<strong class="jxr_keyword">class</strong>)
+<a class="jxr_linenumber" name="80" href="#80">80</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testThresholdPositive() {
+<a class="jxr_linenumber" name="81" href="#81">81</a> <em class="jxr_comment">// Maximum iterations for fit must be positive</em>
+<a class="jxr_linenumber" name="82" href="#82">82</a> <strong class="jxr_keyword">double</strong>[][] data = getTestSamples();
+<a class="jxr_linenumber" name="83" href="#83">83</a> MultivariateNormalMixtureExpectationMaximization fitter =
+<a class="jxr_linenumber" name="84" href="#84">84</a> <strong class="jxr_keyword">new</strong> MultivariateNormalMixtureExpectationMaximization(
+<a class="jxr_linenumber" name="85" href="#85">85</a> data);
+<a class="jxr_linenumber" name="86" href="#86">86</a>
+<a class="jxr_linenumber" name="87" href="#87">87</a> MixtureMultivariateNormalDistribution
+<a class="jxr_linenumber" name="88" href="#88">88</a> initialMix = MultivariateNormalMixtureExpectationMaximization.estimate(data, 2);
+<a class="jxr_linenumber" name="89" href="#89">89</a>
+<a class="jxr_linenumber" name="90" href="#90">90</a> fitter.fit(initialMix, 1000, 0);
+<a class="jxr_linenumber" name="91" href="#91">91</a> }
+<a class="jxr_linenumber" name="92" href="#92">92</a>
+<a class="jxr_linenumber" name="93" href="#93">93</a> @Test(expected = ConvergenceException.<strong class="jxr_keyword">class</strong>)
+<a class="jxr_linenumber" name="94" href="#94">94</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testConvergenceException() {
+<a class="jxr_linenumber" name="95" href="#95">95</a> <em class="jxr_comment">// ConvergenceException thrown if fit terminates before threshold met</em>
+<a class="jxr_linenumber" name="96" href="#96">96</a> <strong class="jxr_keyword">double</strong>[][] data = getTestSamples();
+<a class="jxr_linenumber" name="97" href="#97">97</a> MultivariateNormalMixtureExpectationMaximization fitter
+<a class="jxr_linenumber" name="98" href="#98">98</a> = <strong class="jxr_keyword">new</strong> MultivariateNormalMixtureExpectationMaximization(data);
+<a class="jxr_linenumber" name="99" href="#99">99</a>
+<a class="jxr_linenumber" name="100" href="#100">100</a> MixtureMultivariateNormalDistribution
+<a class="jxr_linenumber" name="101" href="#101">101</a> initialMix = MultivariateNormalMixtureExpectationMaximization.estimate(data, 2);
+<a class="jxr_linenumber" name="102" href="#102">102</a>
+<a class="jxr_linenumber" name="103" href="#103">103</a> <em class="jxr_comment">// 5 iterations not enough to meet convergence threshold</em>
+<a class="jxr_linenumber" name="104" href="#104">104</a> fitter.fit(initialMix, 5, 1E-5);
+<a class="jxr_linenumber" name="105" href="#105">105</a> }
+<a class="jxr_linenumber" name="106" href="#106">106</a>
+<a class="jxr_linenumber" name="107" href="#107">107</a> @Test(expected = DimensionMismatchException.<strong class="jxr_keyword">class</strong>)
+<a class="jxr_linenumber" name="108" href="#108">108</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testIncompatibleIntialMixture() {
+<a class="jxr_linenumber" name="109" href="#109">109</a> <em class="jxr_comment">// Data has 3 columns</em>
+<a class="jxr_linenumber" name="110" href="#110">110</a> <strong class="jxr_keyword">double</strong>[][] data = <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[][] {
+<a class="jxr_linenumber" name="111" href="#111">111</a> { 1, 2, 3 }, { 4, 5, 6 }, { 7, 8, 9 }
+<a class="jxr_linenumber" name="112" href="#112">112</a> };
+<a class="jxr_linenumber" name="113" href="#113">113</a> <strong class="jxr_keyword">double</strong>[] weights = <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[] { 0.5, 0.5 };
+<a class="jxr_linenumber" name="114" href="#114">114</a>
+<a class="jxr_linenumber" name="115" href="#115">115</a> <em class="jxr_comment">// These distributions are compatible with 2-column data, not 3-column</em>
+<a class="jxr_linenumber" name="116" href="#116">116</a> <em class="jxr_comment">// data</em>
+<a class="jxr_linenumber" name="117" href="#117">117</a> MultivariateNormalDistribution[] mvns = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution[2];
+<a class="jxr_linenumber" name="118" href="#118">118</a>
+<a class="jxr_linenumber" name="119" href="#119">119</a> mvns[0] = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution(<strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[] {
+<a class="jxr_linenumber" name="120" href="#120">120</a> -0.0021722935000328823, 3.5432892936887908 },
+<a class="jxr_linenumber" name="121" href="#121">121</a> <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[][] {
+<a class="jxr_linenumber" name="122" href="#122">122</a> { 4.537422569229048, 3.5266152281729304 },
+<a class="jxr_linenumber" name="123" href="#123">123</a> { 3.5266152281729304, 6.175448814169779 } });
+<a class="jxr_linenumber" name="124" href="#124">124</a> mvns[1] = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution(<strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[] {
+<a class="jxr_linenumber" name="125" href="#125">125</a> 5.090902706507635, 8.68540656355283 }, <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[][] {
+<a class="jxr_linenumber" name="126" href="#126">126</a> { 2.886778573963039, 1.5257474543463154 },
+<a class="jxr_linenumber" name="127" href="#127">127</a> { 1.5257474543463154, 3.3794567673616918 } });
+<a class="jxr_linenumber" name="128" href="#128">128</a>
+<a class="jxr_linenumber" name="129" href="#129">129</a> <em class="jxr_comment">// Create components and mixture</em>
+<a class="jxr_linenumber" name="130" href="#130">130</a> List<Pair<Double, MultivariateNormalDistribution>> components =
+<a class="jxr_linenumber" name="131" href="#131">131</a> <strong class="jxr_keyword">new</strong> ArrayList<Pair<Double, MultivariateNormalDistribution>>();
+<a class="jxr_linenumber" name="132" href="#132">132</a> components.add(<strong class="jxr_keyword">new</strong> Pair<Double, MultivariateNormalDistribution>(
+<a class="jxr_linenumber" name="133" href="#133">133</a> weights[0], mvns[0]));
+<a class="jxr_linenumber" name="134" href="#134">134</a> components.add(<strong class="jxr_keyword">new</strong> Pair<Double, MultivariateNormalDistribution>(
+<a class="jxr_linenumber" name="135" href="#135">135</a> weights[1], mvns[1]));
+<a class="jxr_linenumber" name="136" href="#136">136</a>
+<a class="jxr_linenumber" name="137" href="#137">137</a> MixtureMultivariateNormalDistribution badInitialMix
+<a class="jxr_linenumber" name="138" href="#138">138</a> = <strong class="jxr_keyword">new</strong> MixtureMultivariateNormalDistribution(components);
+<a class="jxr_linenumber" name="139" href="#139">139</a>
+<a class="jxr_linenumber" name="140" href="#140">140</a> MultivariateNormalMixtureExpectationMaximization fitter
+<a class="jxr_linenumber" name="141" href="#141">141</a> = <strong class="jxr_keyword">new</strong> MultivariateNormalMixtureExpectationMaximization(data);
+<a class="jxr_linenumber" name="142" href="#142">142</a>
+<a class="jxr_linenumber" name="143" href="#143">143</a> fitter.fit(badInitialMix);
+<a class="jxr_linenumber" name="144" href="#144">144</a> }
+<a class="jxr_linenumber" name="145" href="#145">145</a>
+<a class="jxr_linenumber" name="146" href="#146">146</a> @Test
+<a class="jxr_linenumber" name="147" href="#147">147</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testInitialMixture() {
+<a class="jxr_linenumber" name="148" href="#148">148</a> <em class="jxr_comment">// Testing initial mixture estimated from data</em>
+<a class="jxr_linenumber" name="149" href="#149">149</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] correctWeights = <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[] { 0.5, 0.5 };
+<a class="jxr_linenumber" name="150" href="#150">150</a>
+<a class="jxr_linenumber" name="151" href="#151">151</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] correctMeans = <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[][] {
+<a class="jxr_linenumber" name="152" href="#152">152</a> {-0.0021722935000328823, 3.5432892936887908},
+<a class="jxr_linenumber" name="153" href="#153">153</a> {5.090902706507635, 8.68540656355283},
+<a class="jxr_linenumber" name="154" href="#154">154</a> };
+<a class="jxr_linenumber" name="155" href="#155">155</a>
+<a class="jxr_linenumber" name="156" href="#156">156</a> <strong class="jxr_keyword">final</strong> RealMatrix[] correctCovMats = <strong class="jxr_keyword">new</strong> Array2DRowRealMatrix[2];
+<a class="jxr_linenumber" name="157" href="#157">157</a>
+<a class="jxr_linenumber" name="158" href="#158">158</a> correctCovMats[0] = <strong class="jxr_keyword">new</strong> Array2DRowRealMatrix(<strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[][] {
+<a class="jxr_linenumber" name="159" href="#159">159</a> { 4.537422569229048, 3.5266152281729304 },
+<a class="jxr_linenumber" name="160" href="#160">160</a> { 3.5266152281729304, 6.175448814169779 } });
+<a class="jxr_linenumber" name="161" href="#161">161</a>
+<a class="jxr_linenumber" name="162" href="#162">162</a> correctCovMats[1] = <strong class="jxr_keyword">new</strong> Array2DRowRealMatrix( <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[][] {
+<a class="jxr_linenumber" name="163" href="#163">163</a> { 2.886778573963039, 1.5257474543463154 },
+<a class="jxr_linenumber" name="164" href="#164">164</a> { 1.5257474543463154, 3.3794567673616918 } });
+<a class="jxr_linenumber" name="165" href="#165">165</a>
+<a class="jxr_linenumber" name="166" href="#166">166</a> <strong class="jxr_keyword">final</strong> MultivariateNormalDistribution[] correctMVNs = <strong class="jxr_keyword">new</strong>
+<a class="jxr_linenumber" name="167" href="#167">167</a> MultivariateNormalDistribution[2];
+<a class="jxr_linenumber" name="168" href="#168">168</a>
+<a class="jxr_linenumber" name="169" href="#169">169</a> correctMVNs[0] = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution(correctMeans[0],
+<a class="jxr_linenumber" name="170" href="#170">170</a> correctCovMats[0].getData());
+<a class="jxr_linenumber" name="171" href="#171">171</a>
+<a class="jxr_linenumber" name="172" href="#172">172</a> correctMVNs[1] = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution(correctMeans[1],
+<a class="jxr_linenumber" name="173" href="#173">173</a> correctCovMats[1].getData());
+<a class="jxr_linenumber" name="174" href="#174">174</a>
+<a class="jxr_linenumber" name="175" href="#175">175</a> <strong class="jxr_keyword">final</strong> MixtureMultivariateNormalDistribution initialMix
+<a class="jxr_linenumber" name="176" href="#176">176</a> = MultivariateNormalMixtureExpectationMaximization.estimate(getTestSamples(), 2);
+<a class="jxr_linenumber" name="177" href="#177">177</a>
+<a class="jxr_linenumber" name="178" href="#178">178</a> <strong class="jxr_keyword">int</strong> i = 0;
+<a class="jxr_linenumber" name="179" href="#179">179</a> <strong class="jxr_keyword">for</strong> (Pair<Double, MultivariateNormalDistribution> component : initialMix
+<a class="jxr_linenumber" name="180" href="#180">180</a> .getComponents()) {
+<a class="jxr_linenumber" name="181" href="#181">181</a> Assert.assertEquals(correctWeights[i], component.getFirst(),
+<a class="jxr_linenumber" name="182" href="#182">182</a> Math.ulp(1d));
+<a class="jxr_linenumber" name="183" href="#183">183</a>
+<a class="jxr_linenumber" name="184" href="#184">184</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] means = component.getValue().getMeans();
+<a class="jxr_linenumber" name="185" href="#185">185</a> Assert.assertTrue(Arrays.equals(correctMeans[i], means));
+<a class="jxr_linenumber" name="186" href="#186">186</a>
+<a class="jxr_linenumber" name="187" href="#187">187</a> <strong class="jxr_keyword">final</strong> RealMatrix covMat = component.getValue().getCovariances();
+<a class="jxr_linenumber" name="188" href="#188">188</a> Assert.assertEquals(correctCovMats[i], covMat);
+<a class="jxr_linenumber" name="189" href="#189">189</a> i++;
+<a class="jxr_linenumber" name="190" href="#190">190</a> }
+<a class="jxr_linenumber" name="191" href="#191">191</a> }
+<a class="jxr_linenumber" name="192" href="#192">192</a>
+<a class="jxr_linenumber" name="193" href="#193">193</a> @Test
+<a class="jxr_linenumber" name="194" href="#194">194</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testFit() {
+<a class="jxr_linenumber" name="195" href="#195">195</a> <em class="jxr_comment">// Test that the loglikelihood, weights, and models are determined and</em>
+<a class="jxr_linenumber" name="196" href="#196">196</a> <em class="jxr_comment">// fitted correctly</em>
+<a class="jxr_linenumber" name="197" href="#197">197</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] data = getTestSamples();
+<a class="jxr_linenumber" name="198" href="#198">198</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> correctLogLikelihood = -4.292431006791994;
+<a class="jxr_linenumber" name="199" href="#199">199</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] correctWeights = <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[] { 0.2962324189652912, 0.7037675810347089 };
+<a class="jxr_linenumber" name="200" href="#200">200</a>
+<a class="jxr_linenumber" name="201" href="#201">201</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[][] correctMeans = <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[][]{
+<a class="jxr_linenumber" name="202" href="#202">202</a> {-1.4213112715121132, 1.6924690505757753},
+<a class="jxr_linenumber" name="203" href="#203">203</a> {4.213612224374709, 7.975621325853645}
+<a class="jxr_linenumber" name="204" href="#204">204</a> };
+<a class="jxr_linenumber" name="205" href="#205">205</a>
+<a class="jxr_linenumber" name="206" href="#206">206</a> <strong class="jxr_keyword">final</strong> RealMatrix[] correctCovMats = <strong class="jxr_keyword">new</strong> Array2DRowRealMatrix[2];
+<a class="jxr_linenumber" name="207" href="#207">207</a> correctCovMats[0] = <strong class="jxr_keyword">new</strong> Array2DRowRealMatrix(<strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[][] {
+<a class="jxr_linenumber" name="208" href="#208">208</a> { 1.739356907285747, -0.5867644251487614 },
+<a class="jxr_linenumber" name="209" href="#209">209</a> { -0.5867644251487614, 1.0232932029324642 } }
+<a class="jxr_linenumber" name="210" href="#210">210</a> );
+<a class="jxr_linenumber" name="211" href="#211">211</a> correctCovMats[1] = <strong class="jxr_keyword">new</strong> Array2DRowRealMatrix(<strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[][] {
+<a class="jxr_linenumber" name="212" href="#212">212</a> { 4.245384898007161, 2.5797798966382155 },
+<a class="jxr_linenumber" name="213" href="#213">213</a> { 2.5797798966382155, 3.9200272522448367 } });
+<a class="jxr_linenumber" name="214" href="#214">214</a>
+<a class="jxr_linenumber" name="215" href="#215">215</a> <strong class="jxr_keyword">final</strong> MultivariateNormalDistribution[] correctMVNs = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution[2];
+<a class="jxr_linenumber" name="216" href="#216">216</a> correctMVNs[0] = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution(correctMeans[0], correctCovMats[0].getData());
+<a class="jxr_linenumber" name="217" href="#217">217</a> correctMVNs[1] = <strong class="jxr_keyword">new</strong> MultivariateNormalDistribution(correctMeans[1], correctCovMats[1].getData());
+<a class="jxr_linenumber" name="218" href="#218">218</a>
+<a class="jxr_linenumber" name="219" href="#219">219</a> MultivariateNormalMixtureExpectationMaximization fitter
+<a class="jxr_linenumber" name="220" href="#220">220</a> = <strong class="jxr_keyword">new</strong> MultivariateNormalMixtureExpectationMaximization(data);
+<a class="jxr_linenumber" name="221" href="#221">221</a>
+<a class="jxr_linenumber" name="222" href="#222">222</a> MixtureMultivariateNormalDistribution initialMix
+<a class="jxr_linenumber" name="223" href="#223">223</a> = MultivariateNormalMixtureExpectationMaximization.estimate(data, 2);
+<a class="jxr_linenumber" name="224" href="#224">224</a> fitter.fit(initialMix);
+<a class="jxr_linenumber" name="225" href="#225">225</a> MixtureMultivariateNormalDistribution fittedMix = fitter.getFittedModel();
+<a class="jxr_linenumber" name="226" href="#226">226</a> List<Pair<Double, MultivariateNormalDistribution>> components = fittedMix.getComponents();
+<a class="jxr_linenumber" name="227" href="#227">227</a>
+<a class="jxr_linenumber" name="228" href="#228">228</a> Assert.assertEquals(correctLogLikelihood,
+<a class="jxr_linenumber" name="229" href="#229">229</a> fitter.getLogLikelihood(),
+<a class="jxr_linenumber" name="230" href="#230">230</a> Math.ulp(1d));
+<a class="jxr_linenumber" name="231" href="#231">231</a>
+<a class="jxr_linenumber" name="232" href="#232">232</a> <strong class="jxr_keyword">int</strong> i = 0;
+<a class="jxr_linenumber" name="233" href="#233">233</a> <strong class="jxr_keyword">for</strong> (Pair<Double, MultivariateNormalDistribution> component : components) {
+<a class="jxr_linenumber" name="234" href="#234">234</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> weight = component.getFirst();
+<a class="jxr_linenumber" name="235" href="#235">235</a> <strong class="jxr_keyword">final</strong> MultivariateNormalDistribution mvn = component.getSecond();
+<a class="jxr_linenumber" name="236" href="#236">236</a> <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] mean = mvn.getMeans();
+<a class="jxr_linenumber" name="237" href="#237">237</a> <strong class="jxr_keyword">final</strong> RealMatrix covMat = mvn.getCovariances();
+<a class="jxr_linenumber" name="238" href="#238">238</a> Assert.assertEquals(correctWeights[i], weight, Math.ulp(1d));
+<a class="jxr_linenumber" name="239" href="#239">239</a> Assert.assertTrue(Arrays.equals(correctMeans[i], mean));
+<a class="jxr_linenumber" name="240" href="#240">240</a> Assert.assertEquals(correctCovMats[i], covMat);
+<a class="jxr_linenumber" name="241" href="#241">241</a> i++;
+<a class="jxr_linenumber" name="242" href="#242">242</a> }
+<a class="jxr_linenumber" name="243" href="#243">243</a> }
+<a class="jxr_linenumber" name="244" href="#244">244</a>
+<a class="jxr_linenumber" name="245" href="#245">245</a> <strong class="jxr_keyword">private</strong> <strong class="jxr_keyword">double</strong>[][] getTestSamples() {
+<a class="jxr_linenumber" name="246" href="#246">246</a> <em class="jxr_comment">// generated using R Mixtools rmvnorm with mean vectors [-1.5, 2] and</em>
+<a class="jxr_linenumber" name="247" href="#247">247</a> <em class="jxr_comment">// [4, 8.2]</em>
+<a class="jxr_linenumber" name="248" href="#248">248</a> <strong class="jxr_keyword">return</strong> <strong class="jxr_keyword">new</strong> <strong class="jxr_keyword">double</strong>[][] { { 7.358553610469948, 11.31260831446758 },
+<a class="jxr_linenumber" name="249" href="#249">249</a> { 7.175770420124739, 8.988812210204454 },
+<a class="jxr_linenumber" name="250" href="#250">250</a> { 4.324151905768422, 6.837727899051482 },
+<a class="jxr_linenumber" name="251" href="#251">251</a> { 2.157832219173036, 6.317444585521968 },
+<a class="jxr_linenumber" name="252" href="#252">252</a> { -1.890157421896651, 1.74271202875498 },
+<a class="jxr_linenumber" name="253" href="#253">253</a> { 0.8922409354455803, 1.999119343923781 },
+<a class="jxr_linenumber" name="254" href="#254">254</a> { 3.396949764787055, 6.813170372579068 },
+<a class="jxr_linenumber" name="255" href="#255">255</a> { -2.057498232686068, -0.002522983830852255 },
+<a class="jxr_linenumber" name="256" href="#256">256</a> { 6.359932157365045, 8.343600029975851 },
+<a class="jxr_linenumber" name="257" href="#257">257</a> { 3.353102234276168, 7.087541882898689 },
+<a class="jxr_linenumber" name="258" href="#258">258</a> { -1.763877221595639, 0.9688890460330644 },
+<a class="jxr_linenumber" name="259" href="#259">259</a> { 6.151457185125111, 9.075011757431174 },
+<a class="jxr_linenumber" name="260" href="#260">260</a> { 4.281597398048899, 5.953270070976117 },
+<a class="jxr_linenumber" name="261" href="#261">261</a> { 3.549576703974894, 8.616038155992861 },
+<a class="jxr_linenumber" name="262" href="#262">262</a> { 6.004706732349854, 8.959423391087469 },
+<a class="jxr_linenumber" name="263" href="#263">263</a> { 2.802915014676262, 6.285676742173564 },
+<a class="jxr_linenumber" name="264" href="#264">264</a> { -0.6029879029880616, 1.083332958357485 },
+<a class="jxr_linenumber" name="265" href="#265">265</a> { 3.631827105398369, 6.743428504049444 },
+<a class="jxr_linenumber" name="266" href="#266">266</a> { 6.161125014007315, 9.60920569689001 },
+<a class="jxr_linenumber" name="267" href="#267">267</a> { -1.049582894255342, 0.2020017892080281 },
+<a class="jxr_linenumber" name="268" href="#268">268</a> { 3.910573022688315, 8.19609909534937 },
+<a class="jxr_linenumber" name="269" href="#269">269</a> { 8.180454017634863, 7.861055769719962 },
+<a class="jxr_linenumber" name="270" href="#270">270</a> { 1.488945440439716, 8.02699903761247 },
+<a class="jxr_linenumber" name="271" href="#271">271</a> { 4.813750847823778, 12.34416881332515 },
+<a class="jxr_linenumber" name="272" href="#272">272</a> { 0.0443208501259158, 5.901148093240691 },
+<a class="jxr_linenumber" name="273" href="#273">273</a> { 4.416417235068346, 4.465243084006094 },
+<a class="jxr_linenumber" name="274" href="#274">274</a> { 4.0002433603072, 6.721937850166174 },
+<a class="jxr_linenumber" name="275" href="#275">275</a> { 3.190113818788205, 10.51648348411058 },
+<a class="jxr_linenumber" name="276" href="#276">276</a> { 4.493600914967883, 7.938224231022314 },
+<a class="jxr_linenumber" name="277" href="#277">277</a> { -3.675669533266189, 4.472845076673303 },
+<a class="jxr_linenumber" name="278" href="#278">278</a> { 6.648645511703989, 12.03544085965724 },
+<a class="jxr_linenumber" name="279" href="#279">279</a> { -1.330031331404445, 1.33931042964811 },
+<a class="jxr_linenumber" name="280" href="#280">280</a> { -3.812111460708707, 2.50534195568356 },
+<a class="jxr_linenumber" name="281" href="#281">281</a> { 5.669339356648331, 6.214488981177026 },
+<a class="jxr_linenumber" name="282" href="#282">282</a> { 1.006596727153816, 1.51165463112716 },
+<a class="jxr_linenumber" name="283" href="#283">283</a> { 5.039466365033024, 7.476532610478689 },
+<a class="jxr_linenumber" name="284" href="#284">284</a> { 4.349091929968925, 7.446356406259756 },
+<a class="jxr_linenumber" name="285" href="#285">285</a> { -1.220289665119069, 3.403926955951437 },
+<a class="jxr_linenumber" name="286" href="#286">286</a> { 5.553003979122395, 6.886518211202239 },
+<a class="jxr_linenumber" name="287" href="#287">287</a> { 2.274487732222856, 7.009541508533196 },
+<a class="jxr_linenumber" name="288" href="#288">288</a> { 4.147567059965864, 7.34025244349202 },
+<a class="jxr_linenumber" name="289" href="#289">289</a> { 4.083882618965819, 6.362852861075623 },
+<a class="jxr_linenumber" name="290" href="#290">290</a> { 2.203122344647599, 7.260295257904624 },
+<a class="jxr_linenumber" name="291" href="#291">291</a> { -2.147497550770442, 1.262293431529498 },
+<a class="jxr_linenumber" name="292" href="#292">292</a> { 2.473700950426512, 6.558900135505638 },
+<a class="jxr_linenumber" name="293" href="#293">293</a> { 8.267081298847554, 12.10214104577748 },
+<a class="jxr_linenumber" name="294" href="#294">294</a> { 6.91977329776865, 9.91998488301285 },
+<a class="jxr_linenumber" name="295" href="#295">295</a> { 0.1680479852730894, 6.28286034168897 },
+<a class="jxr_linenumber" name="296" href="#296">296</a> { -1.268578659195158, 2.326711221485755 },
+<a class="jxr_linenumber" name="297" href="#297">297</a> { 1.829966451374701, 6.254187605304518 },
+<a class="jxr_linenumber" name="298" href="#298">298</a> { 5.648849025754848, 9.330002040750291 },
+<a class="jxr_linenumber" name="299" href="#299">299</a> { -2.302874793257666, 3.585545172776065 },
+<a class="jxr_linenumber" name="300" href="#300">300</a> { -2.629218791709046, 2.156215538500288 },
+<a class="jxr_linenumber" name="301" href="#301">301</a> { 4.036618140700114, 10.2962785719958 },
+<a class="jxr_linenumber" name="302" href="#302">302</a> { 0.4616386422783874, 0.6782756325806778 },
+<a class="jxr_linenumber" name="303" href="#303">303</a> { -0.3447896073408363, 0.4999834691645118 },
+<a class="jxr_linenumber" name="304" href="#304">304</a> { -0.475281453118318, 1.931470384180492 },
+<a class="jxr_linenumber" name="305" href="#305">305</a> { 2.382509690609731, 6.071782429815853 },
+<a class="jxr_linenumber" name="306" href="#306">306</a> { -3.203934441889096, 2.572079552602468 },
+<a class="jxr_linenumber" name="307" href="#307">307</a> { 8.465636032165087, 13.96462998683518 },
+<a class="jxr_linenumber" name="308" href="#308">308</a> { 2.36755660870416, 5.7844595007273 },
+<a class="jxr_linenumber" name="309" href="#309">309</a> { 0.5935496528993371, 1.374615871358943 },
+<a class="jxr_linenumber" name="310" href="#310">310</a> { -2.467481505748694, 2.097224634713005 },
+<a class="jxr_linenumber" name="311" href="#311">311</a> { 4.27867444328542, 10.24772361238549 },
+<a class="jxr_linenumber" name="312" href="#312">312</a> { -2.013791907543137, 2.013799426047639 },
+<a class="jxr_linenumber" name="313" href="#313">313</a> { 6.424588084404173, 9.185334939684516 },
+<a class="jxr_linenumber" name="314" href="#314">314</a> { -0.8448238876802175, 0.5447382022282812 },
+<a class="jxr_linenumber" name="315" href="#315">315</a> { 1.342955703473923, 8.645456317633556 },
+<a class="jxr_linenumber" name="316" href="#316">316</a> { 3.108712208751979, 8.512156853800064 },
+<a class="jxr_linenumber" name="317" href="#317">317</a> { 4.343205178315472, 8.056869549234374 },
+<a class="jxr_linenumber" name="318" href="#318">318</a> { -2.971767642212396, 3.201180146824761 },
+<a class="jxr_linenumber" name="319" href="#319">319</a> { 2.583820931523672, 5.459873414473854 },
+<a class="jxr_linenumber" name="320" href="#320">320</a> { 4.209139115268925, 8.171098193546225 },
+<a class="jxr_linenumber" name="321" href="#321">321</a> { 0.4064909057902746, 1.454390775518743 },
+<a class="jxr_linenumber" name="322" href="#322">322</a> { 3.068642411145223, 6.959485153620035 },
+<a class="jxr_linenumber" name="323" href="#323">323</a> { 6.085968972900461, 7.391429799500965 },
+<a class="jxr_linenumber" name="324" href="#324">324</a> { -1.342265795764202, 1.454550012997143 },
+<a class="jxr_linenumber" name="325" href="#325">325</a> { 6.249773274516883, 6.290269880772023 },
+<a class="jxr_linenumber" name="326" href="#326">326</a> { 4.986225847822566, 7.75266344868907 },
+<a class="jxr_linenumber" name="327" href="#327">327</a> { 7.642443254378944, 10.19914817500263 },
+<a class="jxr_linenumber" name="328" href="#328">328</a> { 6.438181159163673, 8.464396764810347 },
+<a class="jxr_linenumber" name="329" href="#329">329</a> { 2.520859761025108, 7.68222425260111 },
+<a class="jxr_linenumber" name="330" href="#330">330</a> { 2.883699944257541, 6.777960331348503 },
+<a class="jxr_linenumber" name="331" href="#331">331</a> { 2.788004550956599, 6.634735386652733 },
+<a class="jxr_linenumber" name="332" href="#332">332</a> { 3.331661231995638, 5.794191300046592 },
+<a class="jxr_linenumber" name="333" href="#333">333</a> { 3.526172276645504, 6.710802266815884 },
+<a class="jxr_linenumber" name="334" href="#334">334</a> { 3.188298528138741, 10.34495528210205 },
+<a class="jxr_linenumber" name="335" href="#335">335</a> { 0.7345539486114623, 5.807604004180681 },
+<a class="jxr_linenumber" name="336" href="#336">336</a> { 1.165044595880125, 7.830121829295257 },
+<a class="jxr_linenumber" name="337" href="#337">337</a> { 7.146962523500671, 11.62995162065415 },
+<a class="jxr_linenumber" name="338" href="#338">338</a> { 7.813872137162087, 10.62827008714735 },
+<a class="jxr_linenumber" name="339" href="#339">339</a> { 3.118099164870063, 8.286003148186371 },
+<a class="jxr_linenumber" name="340" href="#340">340</a> { -1.708739286262571, 1.561026755374264 },
+<a class="jxr_linenumber" name="341" href="#341">341</a> { 1.786163047580084, 4.172394388214604 },
+<a class="jxr_linenumber" name="342" href="#342">342</a> { 3.718506403232386, 7.807752990130349 },
+<a class="jxr_linenumber" name="343" href="#343">343</a> { 6.167414046828899, 10.01104941031293 },
+<a class="jxr_linenumber" name="344" href="#344">344</a> { -1.063477247689196, 1.61176085846339 },
+<a class="jxr_linenumber" name="345" href="#345">345</a> { -3.396739609433642, 0.7127911050002151 },
+<a class="jxr_linenumber" name="346" href="#346">346</a> { 2.438885945896797, 7.353011138689225 },
+<a class="jxr_linenumber" name="347" href="#347">347</a> { -0.2073204144780931, 0.850771146627012 }, };
+<a class="jxr_linenumber" name="348" href="#348">348</a> }
+<a class="jxr_linenumber" name="349" href="#349">349</a> }
+</pre>
+<hr/><div id="footer">This page was automatically generated by <a href="http://maven.apache.org/">Maven</a></div></body>
+</html>
+