http://git-wip-us.apache.org/repos/asf/commons-complex/blob/b3576eeb/site-content/.svn/pristine/03/03601cd8f94efbdcd90b2b550fa156ead9192d2b.svn-base ---------------------------------------------------------------------- diff --git a/site-content/.svn/pristine/03/03601cd8f94efbdcd90b2b550fa156ead9192d2b.svn-base b/site-content/.svn/pristine/03/03601cd8f94efbdcd90b2b550fa156ead9192d2b.svn-base deleted file mode 100644 index 2785ebc..0000000 --- a/site-content/.svn/pristine/03/03601cd8f94efbdcd90b2b550fa156ead9192d2b.svn-base +++ /dev/null @@ -1,56 +0,0 @@ -<!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>NormalApproximationIntervalTest xref</title> -<link type="text/css" rel="stylesheet" href="../../../../../../stylesheet.css" /> -</head> -<body> -<div id="overview"><a href="../../../../../../../testapidocs/org/apache/commons/math3/stat/interval/NormalApproximationIntervalTest.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."; target="alexandria_uri">http://www.apache.org/licenses/LICENSE-2.</a>0</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.math3.stat.interval; -<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> org.apache.commons.math3.stat.interval.BinomialConfidenceInterval; -<a class="jxr_linenumber" name="L20" href="#L20">20</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.stat.interval.ConfidenceInterval; -<a class="jxr_linenumber" name="L21" href="#L21">21</a> <strong class="jxr_keyword">import</strong> org.apache.commons.math3.stat.interval.NormalApproximationInterval; -<a class="jxr_linenumber" name="L22" href="#L22">22</a> <strong class="jxr_keyword">import</strong> org.junit.Assert; -<a class="jxr_linenumber" name="L23" href="#L23">23</a> <strong class="jxr_keyword">import</strong> org.junit.Test; -<a class="jxr_linenumber" name="L24" href="#L24">24</a> -<a class="jxr_linenumber" name="L25" href="#L25">25</a> <em class="jxr_javadoccomment">/**</em> -<a class="jxr_linenumber" name="L26" href="#L26">26</a> <em class="jxr_javadoccomment"> * Test cases for the NormalApproximationInterval class.</em> -<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"> */</em> -<a class="jxr_linenumber" name="L29" href="#L29">29</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">class</strong> <a href="../../../../../../org/apache/commons/math3/stat/interval/NormalApproximationIntervalTest.html">NormalApproximationIntervalTest</a> <strong class="jxr_keyword">extends</strong> <a href="../../../../../../org/apache/commons/math3/stat/interval/BinomialConfidenceIntervalAbstractTest.html">BinomialConfidenceIntervalAbstractTest</a> { -<a class="jxr_linenumber" name="L30" href="#L30">30</a> -<a class="jxr_linenumber" name="L31" href="#L31">31</a> @Override -<a class="jxr_linenumber" name="L32" href="#L32">32</a> <strong class="jxr_keyword">protected</strong> BinomialConfidenceInterval createBinomialConfidenceInterval() { -<a class="jxr_linenumber" name="L33" href="#L33">33</a> <strong class="jxr_keyword">return</strong> <strong class="jxr_keyword">new</strong> NormalApproximationInterval(); -<a class="jxr_linenumber" name="L34" href="#L34">34</a> } -<a class="jxr_linenumber" name="L35" href="#L35">35</a> -<a class="jxr_linenumber" name="L36" href="#L36">36</a> @Test -<a class="jxr_linenumber" name="L37" href="#L37">37</a> <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> testStandardInterval() { -<a class="jxr_linenumber" name="L38" href="#L38">38</a> ConfidenceInterval confidenceInterval = createStandardTestInterval(); -<a class="jxr_linenumber" name="L39" href="#L39">39</a> Assert.assertEquals(0.07793197, confidenceInterval.getLowerBound(), 1E-5); -<a class="jxr_linenumber" name="L40" href="#L40">40</a> Assert.assertEquals(0.1220680, confidenceInterval.getUpperBound(), 1E-5); -<a class="jxr_linenumber" name="L41" href="#L41">41</a> } -<a class="jxr_linenumber" name="L42" href="#L42">42</a> -<a class="jxr_linenumber" name="L43" href="#L43">43</a> } -</pre> -<hr/> -<div id="footer">Copyright © 2003–2016 <a href="http://www.apache.org/";>The Apache Software Foundation</a>. All rights reserved.</div> -</body> -</html>
http://git-wip-us.apache.org/repos/asf/commons-complex/blob/b3576eeb/site-content/.svn/pristine/03/0363ca024da7cae3bb2e5a49cdc13667f6fd233e.svn-base ---------------------------------------------------------------------- diff --git a/site-content/.svn/pristine/03/0363ca024da7cae3bb2e5a49cdc13667f6fd233e.svn-base b/site-content/.svn/pristine/03/0363ca024da7cae3bb2e5a49cdc13667f6fd233e.svn-base deleted file mode 100644 index eef691c..0000000 --- a/site-content/.svn/pristine/03/0363ca024da7cae3bb2e5a49cdc13667f6fd233e.svn-base +++ /dev/null @@ -1,170 +0,0 @@ -<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd";><html xmlns="http://www.w3.org/1999/xhtml"; lang="en"><head><meta http-equiv="Content-Type" content="text/html;charset=UTF-8"/><link rel="stylesheet" href="../.resources/report.css" type="text/css"/><link rel="shortcut icon" href="../.resources/report.gif" type="image/gif"/><title>EdgesBuilder.java</title><link rel="stylesheet" href="../.resources/prettify.css" type="text/css"/><script type="text/javascript" src="../.resources/prettify.js"></script></head><body onload="window['PR_TAB_WIDTH']=4;prettyPrint()"><div class="breadcrumb" id="breadcrumb"><span class="info"><a href="../.sessions.html" class="el_session">Sessions</a></span><a href="../index.html" class="el_report">Apache Commons Math</a> > <a href="index.source.html" class="el_package">org.apache.commons.math3.geometry.spherical.twod</a> > <span class="el_source">EdgesBuilder .java</span></div><h1>EdgesBuilder.java</h1><pre class="source lang-java linenums">/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.commons.math3.geometry.spherical.twod; - -import java.util.ArrayList; -import java.util.IdentityHashMap; -import java.util.List; -import java.util.Map; - -import org.apache.commons.math3.exception.MathIllegalStateException; -import org.apache.commons.math3.exception.util.LocalizedFormats; -import org.apache.commons.math3.geometry.euclidean.threed.Vector3D; -import org.apache.commons.math3.geometry.partitioning.BSPTree; -import org.apache.commons.math3.geometry.partitioning.BSPTreeVisitor; -import org.apache.commons.math3.geometry.partitioning.BoundaryAttribute; -import org.apache.commons.math3.geometry.spherical.oned.Arc; -import org.apache.commons.math3.geometry.spherical.oned.ArcsSet; -import org.apache.commons.math3.geometry.spherical.oned.S1Point; - -/** Visitor building edges. - * @since 3.3 - */ -class EdgesBuilder implements BSPTreeVisitor<Sphere2D> { - - /** Root of the tree. */ - private final BSPTree<Sphere2D> root; - - /** Tolerance below which points are consider to be identical. */ - private final double tolerance; - - /** Built edges and their associated nodes. */ - private final Map<Edge, BSPTree<Sphere2D>> edgeToNode; - - /** Reversed map. */ - private final Map<BSPTree<Sphere2D>, List<Edge>> nodeToEdgesList; - - /** Simple constructor. - * @param root tree root - * @param tolerance below which points are consider to be identical - */ -<span class="fc" id="L55"> EdgesBuilder(final BSPTree<Sphere2D> root, final double tolerance) {</span> -<span class="fc" id="L56"> this.root = root;</span> -<span class="fc" id="L57"> this.tolerance = tolerance;</span> -<span class="fc" id="L58"> this.edgeToNode = new IdentityHashMap<Edge, BSPTree<Sphere2D>>();</span> -<span class="fc" id="L59"> this.nodeToEdgesList = new IdentityHashMap<BSPTree<Sphere2D>, List<Edge>>();</span> -<span class="fc" id="L60"> }</span> - - /** {@inheritDoc} */ - public Order visitOrder(final BSPTree<Sphere2D> node) { -<span class="fc" id="L64"> return Order.MINUS_SUB_PLUS;</span> - } - - /** {@inheritDoc} */ - public void visitInternalNode(final BSPTree<Sphere2D> node) { -<span class="fc" id="L69"> nodeToEdgesList.put(node, new ArrayList<Edge>());</span> - @SuppressWarnings("unchecked") -<span class="fc" id="L71"> final BoundaryAttribute<Sphere2D> attribute = (BoundaryAttribute<Sphere2D>) node.getAttribute();</span> -<span class="fc bfc" id="L72" title="All 2 branches covered."> if (attribute.getPlusOutside() != null) {</span> -<span class="fc" id="L73"> addContribution((SubCircle) attribute.getPlusOutside(), false, node);</span> - } -<span class="fc bfc" id="L75" title="All 2 branches covered."> if (attribute.getPlusInside() != null) {</span> -<span class="fc" id="L76"> addContribution((SubCircle) attribute.getPlusInside(), true, node);</span> - } -<span class="fc" id="L78"> }</span> - - /** {@inheritDoc} */ - public void visitLeafNode(final BSPTree<Sphere2D> node) { -<span class="fc" id="L82"> }</span> - - /** Add the contribution of a boundary edge. - * @param sub boundary facet - * @param reversed if true, the facet has the inside on its plus side - * @param node node to which the edge belongs - */ - private void addContribution(final SubCircle sub, final boolean reversed, - final BSPTree<Sphere2D> node) { -<span class="fc" id="L91"> final Circle circle = (Circle) sub.getHyperplane();</span> -<span class="fc" id="L92"> final List<Arc> arcs = ((ArcsSet) sub.getRemainingRegion()).asList();</span> -<span class="fc bfc" id="L93" title="All 2 branches covered."> for (final Arc a : arcs) {</span> -<span class="fc" id="L94"> final Vertex start = new Vertex((S2Point) circle.toSpace(new S1Point(a.getInf())));</span> -<span class="fc" id="L95"> final Vertex end = new Vertex((S2Point) circle.toSpace(new S1Point(a.getSup())));</span> -<span class="fc" id="L96"> start.bindWith(circle);</span> -<span class="fc" id="L97"> end.bindWith(circle);</span> - final Edge edge; -<span class="fc bfc" id="L99" title="All 2 branches covered."> if (reversed) {</span> -<span class="fc" id="L100"> edge = new Edge(end, start, a.getSize(), circle.getReverse());</span> - } else { -<span class="fc" id="L102"> edge = new Edge(start, end, a.getSize(), circle);</span> - } -<span class="fc" id="L104"> edgeToNode.put(edge, node);</span> -<span class="fc" id="L105"> nodeToEdgesList.get(node).add(edge);</span> -<span class="fc" id="L106"> }</span> -<span class="fc" id="L107"> }</span> - - /** Get the edge that should naturally follow another one. - * @param previous edge to be continued - * @return other edge, starting where the previous one ends (they - * have not been connected yet) - * @exception MathIllegalStateException if there is not a single other edge - */ - private Edge getFollowingEdge(final Edge previous) - throws MathIllegalStateException { - - // get the candidate nodes -<span class="fc" id="L119"> final S2Point point = previous.getEnd().getLocation();</span> -<span class="fc" id="L120"> final List<BSPTree<Sphere2D>> candidates = root.getCloseCuts(point, tolerance);</span> - - // the following edge we are looking for must start from one of the candidates nodes -<span class="fc" id="L123"> double closest = tolerance;</span> -<span class="fc" id="L124"> Edge following = null;</span> -<span class="fc bfc" id="L125" title="All 2 branches covered."> for (final BSPTree<Sphere2D> node : candidates) {</span> -<span class="fc bfc" id="L126" title="All 2 branches covered."> for (final Edge edge : nodeToEdgesList.get(node)) {</span> -<span class="fc bfc" id="L127" title="All 4 branches covered."> if (edge != previous && edge.getStart().getIncoming() == null) {</span> -<span class="fc" id="L128"> final Vector3D edgeStart = edge.getStart().getLocation().getVector();</span> -<span class="fc" id="L129"> final double gap = Vector3D.angle(point.getVector(), edgeStart);</span> -<span class="fc bfc" id="L130" title="All 2 branches covered."> if (gap <= closest) {</span> -<span class="fc" id="L131"> closest = gap;</span> -<span class="fc" id="L132"> following = edge;</span> - } - } -<span class="fc" id="L135"> }</span> -<span class="fc" id="L136"> }</span> - -<span class="fc bfc" id="L138" title="All 2 branches covered."> if (following == null) {</span> -<span class="fc" id="L139"> final Vector3D previousStart = previous.getStart().getLocation().getVector();</span> -<span class="pc bpc" id="L140" title="1 of 2 branches missed."> if (Vector3D.angle(point.getVector(), previousStart) <= tolerance) {</span> - // the edge connects back to itself -<span class="fc" id="L142"> return previous;</span> - } - - // this should never happen -<span class="nc" id="L146"> throw new MathIllegalStateException(LocalizedFormats.OUTLINE_BOUNDARY_LOOP_OPEN);</span> - - } - -<span class="fc" id="L150"> return following;</span> - - } - - /** Get the boundary edges. - * @return boundary edges - * @exception MathIllegalStateException if there is not a single other edge - */ - public List<Edge> getEdges() throws MathIllegalStateException { - - // connect the edges -<span class="fc bfc" id="L161" title="All 2 branches covered."> for (final Edge previous : edgeToNode.keySet()) {</span> -<span class="fc" id="L162"> previous.setNextEdge(getFollowingEdge(previous));</span> -<span class="fc" id="L163"> }</span> - -<span class="fc" id="L165"> return new ArrayList<Edge>(edgeToNode.keySet());</span> - - } - -} -</pre><div class="footer"><span class="right">Created with <a href="http://www.eclemma.org/jacoco";>JaCoCo</a> 0.7.5.201505241946</span></div></body></html> \ No newline at end of file http://git-wip-us.apache.org/repos/asf/commons-complex/blob/b3576eeb/site-content/.svn/pristine/03/0364fe56afa958763e6b75322339537d1101e1fd.svn-base ---------------------------------------------------------------------- diff --git a/site-content/.svn/pristine/03/0364fe56afa958763e6b75322339537d1101e1fd.svn-base b/site-content/.svn/pristine/03/0364fe56afa958763e6b75322339537d1101e1fd.svn-base deleted file mode 100644 index b1efc1c..0000000 --- a/site-content/.svn/pristine/03/0364fe56afa958763e6b75322339537d1101e1fd.svn-base +++ /dev/null @@ -1,54 +0,0 @@ - -<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd";> -<html xml:lang="en" lang="en"> - <head> - <meta http-equiv="content-type" content="text/html; 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See the NOTICE file distributed with<a name="line.3"></a> -<span class="sourceLineNo">004</span> * this work for additional information regarding copyright ownership.<a name="line.4"></a> -<span class="sourceLineNo">005</span> * The ASF licenses this file to You under the Apache License, Version 2.0<a name="line.5"></a> -<span class="sourceLineNo">006</span> * (the "License"); you may not use this file except in compliance with<a name="line.6"></a> -<span class="sourceLineNo">007</span> * the License. You may obtain a copy of the License at<a name="line.7"></a> -<span class="sourceLineNo">008</span> *<a name="line.8"></a> -<span class="sourceLineNo">009</span> * http://www.apache.org/licenses/LICENSE-2.0<a name="line.9"></a> -<span class="sourceLineNo">010</span> *<a name="line.10"></a> -<span class="sourceLineNo">011</span> * Unless required by applicable law or agreed to in writing, software<a name="line.11"></a> -<span class="sourceLineNo">012</span> * distributed under the License is distributed on an "AS IS" BASIS,<a name="line.12"></a> -<span class="sourceLineNo">013</span> * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.<a name="line.13"></a> -<span class="sourceLineNo">014</span> * See the License for the specific language governing permissions and<a name="line.14"></a> -<span class="sourceLineNo">015</span> * limitations under the License.<a name="line.15"></a> -<span class="sourceLineNo">016</span> */<a name="line.16"></a> -<span class="sourceLineNo">017</span>package org.apache.commons.math3.random;<a name="line.17"></a> -<span class="sourceLineNo">018</span><a name="line.18"></a> -<span class="sourceLineNo">019</span>import java.io.BufferedReader;<a name="line.19"></a> -<span class="sourceLineNo">020</span>import java.io.File;<a name="line.20"></a> -<span class="sourceLineNo">021</span>import java.io.IOException;<a name="line.21"></a> -<span class="sourceLineNo">022</span>import java.io.InputStreamReader;<a name="line.22"></a> -<span class="sourceLineNo">023</span>import java.net.URL;<a name="line.23"></a> -<span class="sourceLineNo">024</span>import java.util.ArrayList;<a name="line.24"></a> -<span class="sourceLineNo">025</span>import java.util.Arrays;<a name="line.25"></a> -<span class="sourceLineNo">026</span><a name="line.26"></a> -<span class="sourceLineNo">027</span>import org.apache.commons.math3.TestUtils;<a name="line.27"></a> -<span class="sourceLineNo">028</span>import org.apache.commons.math3.analysis.UnivariateFunction;<a name="line.28"></a> -<span class="sourceLineNo">029</span>import org.apache.commons.math3.analysis.integration.BaseAbstractUnivariateIntegrator;<a name="line.29"></a> -<span class="sourceLineNo">030</span>import org.apache.commons.math3.analysis.integration.IterativeLegendreGaussIntegrator;<a name="line.30"></a> -<span class="sourceLineNo">031</span>import org.apache.commons.math3.distribution.ConstantRealDistribution;<a name="line.31"></a> -<span class="sourceLineNo">032</span>import org.apache.commons.math3.distribution.NormalDistribution;<a name="line.32"></a> -<span class="sourceLineNo">033</span>import org.apache.commons.math3.distribution.RealDistribution;<a name="line.33"></a> -<span class="sourceLineNo">034</span>import org.apache.commons.math3.distribution.RealDistributionAbstractTest;<a name="line.34"></a> -<span class="sourceLineNo">035</span>import org.apache.commons.math3.distribution.UniformRealDistribution;<a name="line.35"></a> -<span class="sourceLineNo">036</span>import org.apache.commons.math3.exception.NullArgumentException;<a name="line.36"></a> -<span class="sourceLineNo">037</span>import org.apache.commons.math3.stat.descriptive.SummaryStatistics;<a name="line.37"></a> -<span class="sourceLineNo">038</span>import org.apache.commons.math3.util.FastMath;<a name="line.38"></a> -<span class="sourceLineNo">039</span>import org.junit.Assert;<a name="line.39"></a> -<span class="sourceLineNo">040</span>import org.junit.Before;<a name="line.40"></a> -<span class="sourceLineNo">041</span>import org.junit.Test;<a name="line.41"></a> -<span class="sourceLineNo">042</span><a name="line.42"></a> -<span class="sourceLineNo">043</span>/**<a name="line.43"></a> -<span class="sourceLineNo">044</span> * Test cases for the EmpiricalDistribution class<a name="line.44"></a> -<span class="sourceLineNo">045</span> *<a name="line.45"></a> -<span class="sourceLineNo">046</span> */<a name="line.46"></a> -<span class="sourceLineNo">047</span><a name="line.47"></a> -<span class="sourceLineNo">048</span>public final class EmpiricalDistributionTest extends RealDistributionAbstractTest {<a name="line.48"></a> -<span class="sourceLineNo">049</span><a name="line.49"></a> -<span class="sourceLineNo">050</span> protected EmpiricalDistribution empiricalDistribution = null;<a name="line.50"></a> -<span class="sourceLineNo">051</span> protected EmpiricalDistribution empiricalDistribution2 = null;<a name="line.51"></a> -<span class="sourceLineNo">052</span> protected File file = null;<a name="line.52"></a> -<span class="sourceLineNo">053</span> protected URL url = null;<a name="line.53"></a> -<span class="sourceLineNo">054</span> protected double[] dataArray = null;<a name="line.54"></a> -<span class="sourceLineNo">055</span> protected final int n = 10000;<a name="line.55"></a> -<span class="sourceLineNo">056</span><a name="line.56"></a> -<span class="sourceLineNo">057</span> @Override<a name="line.57"></a> -<span class="sourceLineNo">058</span> @Before<a name="line.58"></a> -<span class="sourceLineNo">059</span> public void setUp() {<a name="line.59"></a> -<span class="sourceLineNo">060</span> super.setUp();<a name="line.60"></a> -<span class="sourceLineNo">061</span> empiricalDistribution = new EmpiricalDistribution(100);<a name="line.61"></a> -<span class="sourceLineNo">062</span>// empiricalDistribution = new EmpiricalDistribution(100, new RandomDataImpl()); // XXX Deprecated API<a name="line.62"></a> -<span class="sourceLineNo">063</span> url = getClass().getResource("testData.txt");<a name="line.63"></a> -<span class="sourceLineNo">064</span> final ArrayList<Double> list = new ArrayList<Double>();<a name="line.64"></a> -<span class="sourceLineNo">065</span> try {<a name="line.65"></a> -<span class="sourceLineNo">066</span> empiricalDistribution2 = new EmpiricalDistribution(100);<a name="line.66"></a> -<span class="sourceLineNo">067</span>// empiricalDistribution2 = new EmpiricalDistribution(100, new RandomDataImpl()); // XXX Deprecated API<a name="line.67"></a> -<span class="sourceLineNo">068</span> BufferedReader in =<a name="line.68"></a> -<span class="sourceLineNo">069</span> new BufferedReader(new InputStreamReader(<a name="line.69"></a> -<span class="sourceLineNo">070</span> url.openStream()));<a name="line.70"></a> -<span class="sourceLineNo">071</span> String str = null;<a name="line.71"></a> -<span class="sourceLineNo">072</span> while ((str = in.readLine()) != null) {<a name="line.72"></a> -<span class="sourceLineNo">073</span> list.add(Double.valueOf(str));<a name="line.73"></a> -<span class="sourceLineNo">074</span> }<a name="line.74"></a> -<span class="sourceLineNo">075</span> in.close();<a name="line.75"></a> -<span class="sourceLineNo">076</span> in = null;<a name="line.76"></a> -<span class="sourceLineNo">077</span> } catch (IOException ex) {<a name="line.77"></a> -<span class="sourceLineNo">078</span> Assert.fail("IOException " + ex);<a name="line.78"></a> -<span class="sourceLineNo">079</span> }<a name="line.79"></a> -<span class="sourceLineNo">080</span><a name="line.80"></a> -<span class="sourceLineNo">081</span> dataArray = new double[list.size()];<a name="line.81"></a> -<span class="sourceLineNo">082</span> int i = 0;<a name="line.82"></a> -<span class="sourceLineNo">083</span> for (Double data : list) {<a name="line.83"></a> -<span class="sourceLineNo">084</span> dataArray[i] = data.doubleValue();<a name="line.84"></a> -<span class="sourceLineNo">085</span> i++;<a name="line.85"></a> -<span class="sourceLineNo">086</span> }<a name="line.86"></a> -<span class="sourceLineNo">087</span> }<a name="line.87"></a> -<span class="sourceLineNo">088</span><a name="line.88"></a> -<span class="sourceLineNo">089</span> /**<a name="line.89"></a> -<span class="sourceLineNo">090</span> * Test EmpiricalDistrbution.load() using sample data file.<br><a name="line.90"></a> -<span class="sourceLineNo">091</span> * Check that the sampleCount, mu and sigma match data in<a name="line.91"></a> -<span class="sourceLineNo">092</span> * the sample data file. Also verify that load is idempotent.<a name="line.92"></a> -<span class="sourceLineNo">093</span> */<a name="line.93"></a> -<span class="sourceLineNo">094</span> @Test<a name="line.94"></a> -<span class="sourceLineNo">095</span> public void testLoad() throws Exception {<a name="line.95"></a> -<span class="sourceLineNo">096</span> // Load from a URL<a name="line.96"></a> -<span class="sourceLineNo">097</span> empiricalDistribution.load(url);<a name="line.97"></a> -<span class="sourceLineNo">098</span> checkDistribution();<a name="line.98"></a> -<span class="sourceLineNo">099</span> <a name="line.99"></a> -<span class="sourceLineNo">100</span> // Load again from a file (also verifies idempotency of load)<a name="line.100"></a> -<span class="sourceLineNo">101</span> File file = new File(url.toURI());<a name="line.101"></a> -<span class="sourceLineNo">102</span> empiricalDistribution.load(file);<a name="line.102"></a> -<span class="sourceLineNo">103</span> checkDistribution();<a name="line.103"></a> -<span class="sourceLineNo">104</span> }<a name="line.104"></a> -<span class="sourceLineNo">105</span> <a name="line.105"></a> -<span class="sourceLineNo">106</span> private void checkDistribution() {<a name="line.106"></a> -<span class="sourceLineNo">107</span> // testData File has 10000 values, with mean ~ 5.0, std dev ~ 1<a name="line.107"></a> -<span class="sourceLineNo">108</span> // Make sure that loaded distribution matches this<a name="line.108"></a> -<span class="sourceLineNo">109</span> Assert.assertEquals(empiricalDistribution.getSampleStats().getN(),1000,10E-7);<a name="line.109"></a> -<span class="sourceLineNo">110</span> //TODO: replace with statistical tests<a name="line.110"></a> -<span class="sourceLineNo">111</span> Assert.assertEquals(empiricalDistribution.getSampleStats().getMean(),<a name="line.111"></a> -<span class="sourceLineNo">112</span> 5.069831575018909,10E-7);<a name="line.112"></a> -<span class="sourceLineNo">113</span> Assert.assertEquals(empiricalDistribution.getSampleStats().getStandardDeviation(),<a name="line.113"></a> -<span class="sourceLineNo">114</span> 1.0173699343977738,10E-7);<a name="line.114"></a> -<span class="sourceLineNo">115</span> }<a name="line.115"></a> -<span class="sourceLineNo">116</span><a name="line.116"></a> -<span class="sourceLineNo">117</span> /**<a name="line.117"></a> -<span class="sourceLineNo">118</span> * Test EmpiricalDistrbution.load(double[]) using data taken from<a name="line.118"></a> -<span class="sourceLineNo">119</span> * sample data file.<br><a name="line.119"></a> -<span class="sourceLineNo">120</span> * Check that the sampleCount, mu and sigma match data in<a name="line.120"></a> -<span class="sourceLineNo">121</span> * the sample data file.<a name="line.121"></a> -<span class="sourceLineNo">122</span> */<a name="line.122"></a> -<span class="sourceLineNo">123</span> @Test<a name="line.123"></a> -<span class="sourceLineNo">124</span> public void testDoubleLoad() throws Exception {<a name="line.124"></a> -<span class="sourceLineNo">125</span> empiricalDistribution2.load(dataArray);<a name="line.125"></a> -<span class="sourceLineNo">126</span> // testData File has 10000 values, with mean ~ 5.0, std dev ~ 1<a name="line.126"></a> -<span class="sourceLineNo">127</span> // Make sure that loaded distribution matches this<a name="line.127"></a> -<span class="sourceLineNo">128</span> Assert.assertEquals(empiricalDistribution2.getSampleStats().getN(),1000,10E-7);<a name="line.128"></a> -<span class="sourceLineNo">129</span> //TODO: replace with statistical tests<a name="line.129"></a> -<span class="sourceLineNo">130</span> Assert.assertEquals(empiricalDistribution2.getSampleStats().getMean(),<a name="line.130"></a> -<span class="sourceLineNo">131</span> 5.069831575018909,10E-7);<a name="line.131"></a> -<span class="sourceLineNo">132</span> Assert.assertEquals(empiricalDistribution2.getSampleStats().getStandardDeviation(),<a name="line.132"></a> -<span class="sourceLineNo">133</span> 1.0173699343977738,10E-7);<a name="line.133"></a> -<span class="sourceLineNo">134</span><a name="line.134"></a> -<span class="sourceLineNo">135</span> double[] bounds = empiricalDistribution2.getGeneratorUpperBounds();<a name="line.135"></a> -<span class="sourceLineNo">136</span> Assert.assertEquals(bounds.length, 100);<a name="line.136"></a> -<span class="sourceLineNo">137</span> Assert.assertEquals(bounds[99], 1.0, 10e-12);<a name="line.137"></a> -<span class="sourceLineNo">138</span><a name="line.138"></a> -<span class="sourceLineNo">139</span> }<a name="line.139"></a> -<span class="sourceLineNo">140</span><a name="line.140"></a> -<span class="sourceLineNo">141</span> /**<a name="line.141"></a> -<span class="sourceLineNo">142</span> * Generate 1000 random values and make sure they look OK.<br><a name="line.142"></a> -<span class="sourceLineNo">143</span> * Note that there is a non-zero (but very small) probability that<a name="line.143"></a> -<span class="sourceLineNo">144</span> * these tests will fail even if the code is working as designed.<a name="line.144"></a> -<span class="sourceLineNo">145</span> */<a name="line.145"></a> -<span class="sourceLineNo">146</span> @Test<a name="line.146"></a> -<span class="sourceLineNo">147</span> public void testNext() throws Exception {<a name="line.147"></a> -<span class="sourceLineNo">148</span> tstGen(0.1);<a name="line.148"></a> -<span class="sourceLineNo">149</span> tstDoubleGen(0.1);<a name="line.149"></a> -<span class="sourceLineNo">150</span> }<a name="line.150"></a> -<span class="sourceLineNo">151</span><a name="line.151"></a> -<span class="sourceLineNo">152</span> /**<a name="line.152"></a> -<span class="sourceLineNo">153</span> * Make sure exception thrown if digest getNext is attempted<a name="line.153"></a> -<span class="sourceLineNo">154</span> * before loading empiricalDistribution.<a name="line.154"></a> -<span class="sourceLineNo">155</span> */<a name="line.155"></a> -<span class="sourceLineNo">156</span> @Test<a name="line.156"></a> -<span class="sourceLineNo">157</span> public void testNexFail() {<a name="line.157"></a> -<span class="sourceLineNo">158</span> try {<a name="line.158"></a> -<span class="sourceLineNo">159</span> empiricalDistribution.getNextValue();<a name="line.159"></a> -<span class="sourceLineNo">160</span> empiricalDistribution2.getNextValue();<a name="line.160"></a> -<span class="sourceLineNo">161</span> Assert.fail("Expecting IllegalStateException");<a name="line.161"></a> -<span class="sourceLineNo">162</span> } catch (IllegalStateException ex) {<a name="line.162"></a> -<span class="sourceLineNo">163</span> // expected<a name="line.163"></a> -<span class="sourceLineNo">164</span> }<a name="line.164"></a> -<span class="sourceLineNo">165</span> }<a name="line.165"></a> -<span class="sourceLineNo">166</span><a name="line.166"></a> -<span class="sourceLineNo">167</span> /**<a name="line.167"></a> -<span class="sourceLineNo">168</span> * Make sure we can handle a grid size that is too fine<a name="line.168"></a> -<span class="sourceLineNo">169</span> */<a name="line.169"></a> -<span class="sourceLineNo">170</span> @Test<a name="line.170"></a> -<span class="sourceLineNo">171</span> public void testGridTooFine() throws Exception {<a name="line.171"></a> -<span class="sourceLineNo">172</span> empiricalDistribution = new EmpiricalDistribution(1001);<a name="line.172"></a> -<span class="sourceLineNo">173</span> tstGen(0.1);<a name="line.173"></a> -<span class="sourceLineNo">174</span> empiricalDistribution2 = new EmpiricalDistribution(1001);<a name="line.174"></a> -<span class="sourceLineNo">175</span> tstDoubleGen(0.1);<a name="line.175"></a> -<span class="sourceLineNo">176</span> }<a name="line.176"></a> -<span class="sourceLineNo">177</span><a name="line.177"></a> -<span class="sourceLineNo">178</span> /**<a name="line.178"></a> -<span class="sourceLineNo">179</span> * How about too fat?<a name="line.179"></a> -<span class="sourceLineNo">180</span> */<a name="line.180"></a> -<span class="sourceLineNo">181</span> @Test<a name="line.181"></a> -<span class="sourceLineNo">182</span> public void testGridTooFat() throws Exception {<a name="line.182"></a> -<span class="sourceLineNo">183</span> empiricalDistribution = new EmpiricalDistribution(1);<a name="line.183"></a> -<span class="sourceLineNo">184</span> tstGen(5); // ridiculous tolerance; but ridiculous grid size<a name="line.184"></a> -<span class="sourceLineNo">185</span> // really just checking to make sure we do not bomb<a name="line.185"></a> -<span class="sourceLineNo">186</span> empiricalDistribution2 = new EmpiricalDistribution(1);<a name="line.186"></a> -<span class="sourceLineNo">187</span> tstDoubleGen(5);<a name="line.187"></a> -<span class="sourceLineNo">188</span> }<a name="line.188"></a> -<span class="sourceLineNo">189</span><a name="line.189"></a> -<span class="sourceLineNo">190</span> /**<a name="line.190"></a> -<span class="sourceLineNo">191</span> * Test bin index overflow problem (BZ 36450)<a name="line.191"></a> -<span class="sourceLineNo">192</span> */<a name="line.192"></a> -<span class="sourceLineNo">193</span> @Test<a name="line.193"></a> -<span class="sourceLineNo">194</span> public void testBinIndexOverflow() throws Exception {<a name="line.194"></a> -<span class="sourceLineNo">195</span> double[] x = new double[] {9474.94326071674, 2080107.8865462579};<a name="line.195"></a> -<span class="sourceLineNo">196</span> new EmpiricalDistribution().load(x);<a name="line.196"></a> -<span class="sourceLineNo">197</span> }<a name="line.197"></a> -<span class="sourceLineNo">198</span><a name="line.198"></a> -<span class="sourceLineNo">199</span> @Test<a name="line.199"></a> -<span class="sourceLineNo">200</span> public void testSerialization() {<a name="line.200"></a> -<span class="sourceLineNo">201</span> // Empty<a name="line.201"></a> -<span class="sourceLineNo">202</span> EmpiricalDistribution dist = new EmpiricalDistribution();<a name="line.202"></a> -<span class="sourceLineNo">203</span> EmpiricalDistribution dist2 = (EmpiricalDistribution) TestUtils.serializeAndRecover(dist);<a name="line.203"></a> -<span class="sourceLineNo">204</span> verifySame(dist, dist2);<a name="line.204"></a> -<span class="sourceLineNo">205</span><a name="line.205"></a> -<span class="sourceLineNo">206</span> // Loaded<a name="line.206"></a> -<span class="sourceLineNo">207</span> empiricalDistribution2.load(dataArray);<a name="line.207"></a> -<span class="sourceLineNo">208</span> dist2 = (EmpiricalDistribution) TestUtils.serializeAndRecover(empiricalDistribution2);<a name="line.208"></a> -<span class="sourceLineNo">209</span> verifySame(empiricalDistribution2, dist2);<a name="line.209"></a> -<span class="sourceLineNo">210</span> }<a name="line.210"></a> -<span class="sourceLineNo">211</span><a name="line.211"></a> -<span class="sourceLineNo">212</span> @Test(expected=NullArgumentException.class)<a name="line.212"></a> -<span class="sourceLineNo">213</span> public void testLoadNullDoubleArray() {<a name="line.213"></a> -<span class="sourceLineNo">214</span> new EmpiricalDistribution().load((double[]) null);<a name="line.214"></a> -<span class="sourceLineNo">215</span> }<a name="line.215"></a> -<span class="sourceLineNo">216</span><a name="line.216"></a> -<span class="sourceLineNo">217</span> @Test(expected=NullArgumentException.class)<a name="line.217"></a> -<span class="sourceLineNo">218</span> public void testLoadNullURL() throws Exception {<a name="line.218"></a> -<span class="sourceLineNo">219</span> new EmpiricalDistribution().load((URL) null);<a name="line.219"></a> -<span class="sourceLineNo">220</span> }<a name="line.220"></a> -<span class="sourceLineNo">221</span><a name="line.221"></a> -<span class="sourceLineNo">222</span> @Test(expected=NullArgumentException.class)<a name="line.222"></a> -<span class="sourceLineNo">223</span> public void testLoadNullFile() throws Exception {<a name="line.223"></a> -<span class="sourceLineNo">224</span> new EmpiricalDistribution().load((File) null);<a name="line.224"></a> -<span class="sourceLineNo">225</span> }<a name="line.225"></a> -<span class="sourceLineNo">226</span><a name="line.226"></a> -<span class="sourceLineNo">227</span> /**<a name="line.227"></a> -<span class="sourceLineNo">228</span> * MATH-298<a name="line.228"></a> -<span class="sourceLineNo">229</span> */<a name="line.229"></a> -<span class="sourceLineNo">230</span> @Test<a name="line.230"></a> -<span class="sourceLineNo">231</span> public void testGetBinUpperBounds() {<a name="line.231"></a> -<span class="sourceLineNo">232</span> double[] testData = {0, 1, 1, 2, 3, 4, 4, 5, 6, 7, 8, 9, 10};<a name="line.232"></a> -<span class="sourceLineNo">233</span> EmpiricalDistribution dist = new EmpiricalDistribution(5);<a name="line.233"></a> -<span class="sourceLineNo">234</span> dist.load(testData);<a name="line.234"></a> -<span class="sourceLineNo">235</span> double[] expectedBinUpperBounds = {2, 4, 6, 8, 10};<a name="line.235"></a> -<span class="sourceLineNo">236</span> double[] expectedGeneratorUpperBounds = {4d/13d, 7d/13d, 9d/13d, 11d/13d, 1};<a name="line.236"></a> -<span class="sourceLineNo">237</span> double tol = 10E-12;<a name="line.237"></a> -<span class="sourceLineNo">238</span> TestUtils.assertEquals(expectedBinUpperBounds, dist.getUpperBounds(), tol);<a name="line.238"></a> -<span class="sourceLineNo">239</span> TestUtils.assertEquals(expectedGeneratorUpperBounds, dist.getGeneratorUpperBounds(), tol);<a name="line.239"></a> -<span class="sourceLineNo">240</span> }<a name="line.240"></a> -<span class="sourceLineNo">241</span> <a name="line.241"></a> -<span class="sourceLineNo">242</span> @Test<a name="line.242"></a> -<span class="sourceLineNo">243</span> public void testGeneratorConfig() {<a name="line.243"></a> -<span class="sourceLineNo">244</span> double[] testData = {0, 1, 2, 3, 4};<a name="line.244"></a> -<span class="sourceLineNo">245</span> RandomGenerator generator = new RandomAdaptorTest.ConstantGenerator(0.5);<a name="line.245"></a> -<span class="sourceLineNo">246</span> <a name="line.246"></a> -<span class="sourceLineNo">247</span> EmpiricalDistribution dist = new EmpiricalDistribution(5, generator);<a name="line.247"></a> -<span class="sourceLineNo">248</span> dist.load(testData);<a name="line.248"></a> -<span class="sourceLineNo">249</span> for (int i = 0; i < 5; i++) {<a name="line.249"></a> -<span class="sourceLineNo">250</span> Assert.assertEquals(2.0, dist.getNextValue(), 0d);<a name="line.250"></a> -<span class="sourceLineNo">251</span> }<a name="line.251"></a> -<span class="sourceLineNo">252</span> <a name="line.252"></a> -<span class="sourceLineNo">253</span> // Verify no NPE with null generator argument<a name="line.253"></a> -<span class="sourceLineNo">254</span> dist = new EmpiricalDistribution(5, (RandomGenerator) null);<a name="line.254"></a> -<span class="sourceLineNo">255</span> dist.load(testData);<a name="line.255"></a> -<span class="sourceLineNo">256</span> dist.getNextValue();<a name="line.256"></a> -<span class="sourceLineNo">257</span> }<a name="line.257"></a> -<span class="sourceLineNo">258</span> <a name="line.258"></a> -<span class="sourceLineNo">259</span> @Test<a name="line.259"></a> -<span class="sourceLineNo">260</span> public void testReSeed() throws Exception {<a name="line.260"></a> -<span class="sourceLineNo">261</span> empiricalDistribution.load(url);<a name="line.261"></a> -<span class="sourceLineNo">262</span> empiricalDistribution.reSeed(100);<a name="line.262"></a> -<span class="sourceLineNo">263</span> final double [] values = new double[10];<a name="line.263"></a> -<span class="sourceLineNo">264</span> for (int i = 0; i < 10; i++) {<a name="line.264"></a> -<span class="sourceLineNo">265</span> values[i] = empiricalDistribution.getNextValue();<a name="line.265"></a> -<span class="sourceLineNo">266</span> }<a name="line.266"></a> -<span class="sourceLineNo">267</span> empiricalDistribution.reSeed(100);<a name="line.267"></a> -<span class="sourceLineNo">268</span> for (int i = 0; i < 10; i++) {<a name="line.268"></a> -<span class="sourceLineNo">269</span> Assert.assertEquals(values[i],empiricalDistribution.getNextValue(), 0d);<a name="line.269"></a> -<span class="sourceLineNo">270</span> }<a name="line.270"></a> -<span class="sourceLineNo">271</span> }<a name="line.271"></a> -<span class="sourceLineNo">272</span><a name="line.272"></a> -<span class="sourceLineNo">273</span> private void verifySame(EmpiricalDistribution d1, EmpiricalDistribution d2) {<a name="line.273"></a> -<span class="sourceLineNo">274</span> Assert.assertEquals(d1.isLoaded(), d2.isLoaded());<a name="line.274"></a> -<span class="sourceLineNo">275</span> Assert.assertEquals(d1.getBinCount(), d2.getBinCount());<a name="line.275"></a> -<span class="sourceLineNo">276</span> Assert.assertEquals(d1.getSampleStats(), d2.getSampleStats());<a name="line.276"></a> -<span class="sourceLineNo">277</span> if (d1.isLoaded()) {<a name="line.277"></a> -<span class="sourceLineNo">278</span> for (int i = 0; i < d1.getUpperBounds().length; i++) {<a name="line.278"></a> -<span class="sourceLineNo">279</span> Assert.assertEquals(d1.getUpperBounds()[i], d2.getUpperBounds()[i], 0);<a name="line.279"></a> -<span class="sourceLineNo">280</span> }<a name="line.280"></a> -<span class="sourceLineNo">281</span> Assert.assertEquals(d1.getBinStats(), d2.getBinStats());<a name="line.281"></a> -<span class="sourceLineNo">282</span> }<a name="line.282"></a> -<span class="sourceLineNo">283</span> }<a name="line.283"></a> -<span class="sourceLineNo">284</span><a name="line.284"></a> -<span class="sourceLineNo">285</span> private void tstGen(double tolerance)throws Exception {<a name="line.285"></a> -<span class="sourceLineNo">286</span> empiricalDistribution.load(url);<a name="line.286"></a> -<span class="sourceLineNo">287</span> empiricalDistribution.reSeed(1000);<a name="line.287"></a> -<span class="sourceLineNo">288</span> SummaryStatistics stats = new SummaryStatistics();<a name="line.288"></a> -<span class="sourceLineNo">289</span> for (int i = 1; i < 1000; i++) {<a name="line.289"></a> -<span class="sourceLineNo">290</span> stats.addValue(empiricalDistribution.getNextValue());<a name="line.290"></a> -<span class="sourceLineNo">291</span> }<a name="line.291"></a> -<span class="sourceLineNo">292</span> Assert.assertEquals("mean", 5.069831575018909, stats.getMean(),tolerance);<a name="line.292"></a> -<span class="sourceLineNo">293</span> Assert.assertEquals("std dev", 1.0173699343977738, stats.getStandardDeviation(),tolerance);<a name="line.293"></a> -<span class="sourceLineNo">294</span> }<a name="line.294"></a> -<span class="sourceLineNo">295</span><a name="line.295"></a> -<span class="sourceLineNo">296</span> private void tstDoubleGen(double tolerance)throws Exception {<a name="line.296"></a> -<span class="sourceLineNo">297</span> empiricalDistribution2.load(dataArray);<a name="line.297"></a> -<span class="sourceLineNo">298</span> empiricalDistribution2.reSeed(1000);<a name="line.298"></a> -<span class="sourceLineNo">299</span> SummaryStatistics stats = new SummaryStatistics();<a name="line.299"></a> -<span class="sourceLineNo">300</span> for (int i = 1; i < 1000; i++) {<a name="line.300"></a> -<span class="sourceLineNo">301</span> stats.addValue(empiricalDistribution2.getNextValue());<a name="line.301"></a> -<span class="sourceLineNo">302</span> }<a name="line.302"></a> -<span class="sourceLineNo">303</span> Assert.assertEquals("mean", 5.069831575018909, stats.getMean(), tolerance);<a name="line.303"></a> -<span class="sourceLineNo">304</span> Assert.assertEquals("std dev", 1.0173699343977738, stats.getStandardDeviation(), tolerance);<a name="line.304"></a> -<span class="sourceLineNo">305</span> }<a name="line.305"></a> -<span class="sourceLineNo">306</span> <a name="line.306"></a> -<span class="sourceLineNo">307</span> // Setup for distribution tests<a name="line.307"></a> -<span class="sourceLineNo">308</span> <a name="line.308"></a> -<span class="sourceLineNo">309</span> @Override<a name="line.309"></a> -<span class="sourceLineNo">310</span> public RealDistribution makeDistribution() {<a name="line.310"></a> -<span class="sourceLineNo">311</span> // Create a uniform distribution on [0, 10,000]<a name="line.311"></a> -<span class="sourceLineNo">312</span> final double[] sourceData = new double[n + 1];<a name="line.312"></a> -<span class="sourceLineNo">313</span> for (int i = 0; i < n + 1; i++) {<a name="line.313"></a> -<span class="sourceLineNo">314</span> sourceData[i] = i;<a name="line.314"></a> -<span class="sourceLineNo">315</span> }<a name="line.315"></a> -<span class="sourceLineNo">316</span> EmpiricalDistribution dist = new EmpiricalDistribution();<a name="line.316"></a> -<span class="sourceLineNo">317</span> dist.load(sourceData);<a name="line.317"></a> -<span class="sourceLineNo">318</span> return dist;<a name="line.318"></a> -<span class="sourceLineNo">319</span> }<a name="line.319"></a> -<span class="sourceLineNo">320</span> <a name="line.320"></a> -<span class="sourceLineNo">321</span> /** Uniform bin mass = 10/10001 == mass of all but the first bin */<a name="line.321"></a> -<span class="sourceLineNo">322</span> private final double binMass = 10d / (n + 1);<a name="line.322"></a> -<span class="sourceLineNo">323</span> <a name="line.323"></a> -<span class="sourceLineNo">324</span> /** Mass of first bin = 11/10001 */<a name="line.324"></a> -<span class="sourceLineNo">325</span> private final double firstBinMass = 11d / (n + 1);<a name="line.325"></a> -<span class="sourceLineNo">326</span><a name="line.326"></a> -<span class="sourceLineNo">327</span> @Override<a name="line.327"></a> -<span class="sourceLineNo">328</span> public double[] makeCumulativeTestPoints() {<a name="line.328"></a> -<span class="sourceLineNo">329</span> final double[] testPoints = new double[] {9, 10, 15, 1000, 5004, 9999};<a name="line.329"></a> -<span class="sourceLineNo">330</span> return testPoints;<a name="line.330"></a> -<span class="sourceLineNo">331</span> }<a name="line.331"></a> -<span class="sourceLineNo">332</span> <a name="line.332"></a> -<span class="sourceLineNo">333</span><a name="line.333"></a> -<span class="sourceLineNo">334</span> @Override<a name="line.334"></a> -<span class="sourceLineNo">335</span> public double[] makeCumulativeTestValues() {<a name="line.335"></a> -<span class="sourceLineNo">336</span> /* <a name="line.336"></a> -<span class="sourceLineNo">337</span> * Bins should be [0, 10], (10, 20], ..., (9990, 10000]<a name="line.337"></a> -<span class="sourceLineNo">338</span> * Kernels should be N(4.5, 3.02765), N(14.5, 3.02765)...<a name="line.338"></a> -<span class="sourceLineNo">339</span> * Each bin should have mass 10/10000 = .001<a name="line.339"></a> -<span class="sourceLineNo">340</span> */<a name="line.340"></a> -<span class="sourceLineNo">341</span> final double[] testPoints = getCumulativeTestPoints();<a name="line.341"></a> -<span class="sourceLineNo">342</span> final double[] cumValues = new double[testPoints.length];<a name="line.342"></a> -<span class="sourceLineNo">343</span> final EmpiricalDistribution empiricalDistribution = (EmpiricalDistribution) makeDistribution();<a name="line.343"></a> -<span class="sourceLineNo">344</span> final double[] binBounds = empiricalDistribution.getUpperBounds();<a name="line.344"></a> -<span class="sourceLineNo">345</span> for (int i = 0; i < testPoints.length; i++) {<a name="line.345"></a> -<span class="sourceLineNo">346</span> final int bin = findBin(testPoints[i]);<a name="line.346"></a> -<span class="sourceLineNo">347</span> final double lower = bin == 0 ? empiricalDistribution.getSupportLowerBound() :<a name="line.347"></a> -<span class="sourceLineNo">348</span> binBounds[bin - 1];<a name="line.348"></a> -<span class="sourceLineNo">349</span> final double upper = binBounds[bin];<a name="line.349"></a> -<span class="sourceLineNo">350</span> // Compute bMinus = sum or mass of bins below the bin containing the point<a name="line.350"></a> -<span class="sourceLineNo">351</span> // First bin has mass 11 / 10000, the rest have mass 10 / 10000.<a name="line.351"></a> -<span class="sourceLineNo">352</span> final double bMinus = bin == 0 ? 0 : (bin - 1) * binMass + firstBinMass;<a name="line.352"></a> -<span class="sourceLineNo">353</span> final RealDistribution kernel = findKernel(lower, upper);<a name="line.353"></a> -<span class="sourceLineNo">354</span> final double withinBinKernelMass = kernel.cumulativeProbability(lower, upper);<a name="line.354"></a> -<span class="sourceLineNo">355</span> final double kernelCum = kernel.cumulativeProbability(lower, testPoints[i]);<a name="line.355"></a> -<span class="sourceLineNo">356</span> cumValues[i] = bMinus + (bin == 0 ? firstBinMass : binMass) * kernelCum/withinBinKernelMass;<a name="line.356"></a> -<span class="sourceLineNo">357</span> }<a name="line.357"></a> -<span class="sourceLineNo">358</span> return cumValues;<a name="line.358"></a> -<span class="sourceLineNo">359</span> }<a name="line.359"></a> -<span class="sourceLineNo">360</span><a name="line.360"></a> -<span class="sourceLineNo">361</span> @Override<a name="line.361"></a> -<span class="sourceLineNo">362</span> public double[] makeDensityTestValues() {<a name="line.362"></a> -<span class="sourceLineNo">363</span> final double[] testPoints = getCumulativeTestPoints();<a name="line.363"></a> -<span class="sourceLineNo">364</span> final double[] densityValues = new double[testPoints.length];<a name="line.364"></a> -<span class="sourceLineNo">365</span> final EmpiricalDistribution empiricalDistribution = (EmpiricalDistribution) makeDistribution();<a name="line.365"></a> -<span class="sourceLineNo">366</span> final double[] binBounds = empiricalDistribution.getUpperBounds();<a name="line.366"></a> -<span class="sourceLineNo">367</span> for (int i = 0; i < testPoints.length; i++) {<a name="line.367"></a> -<span class="sourceLineNo">368</span> final int bin = findBin(testPoints[i]);<a name="line.368"></a> -<span class="sourceLineNo">369</span> final double lower = bin == 0 ? empiricalDistribution.getSupportLowerBound() :<a name="line.369"></a> -<span class="sourceLineNo">370</span> binBounds[bin - 1];<a name="line.370"></a> -<span class="sourceLineNo">371</span> final double upper = binBounds[bin];<a name="line.371"></a> -<span class="sourceLineNo">372</span> final RealDistribution kernel = findKernel(lower, upper);<a name="line.372"></a> -<span class="sourceLineNo">373</span> final double withinBinKernelMass = kernel.cumulativeProbability(lower, upper);<a name="line.373"></a> -<span class="sourceLineNo">374</span> final double density = kernel.density(testPoints[i]);<a name="line.374"></a> -<span class="sourceLineNo">375</span> densityValues[i] = density * (bin == 0 ? firstBinMass : binMass) / withinBinKernelMass; <a name="line.375"></a> -<span class="sourceLineNo">376</span> }<a name="line.376"></a> -<span class="sourceLineNo">377</span> return densityValues;<a name="line.377"></a> -<span class="sourceLineNo">378</span> }<a name="line.378"></a> -<span class="sourceLineNo">379</span> <a name="line.379"></a> -<span class="sourceLineNo">380</span> /** <a name="line.380"></a> -<span class="sourceLineNo">381</span> * Modify test integration bounds from the default. Because the distribution<a name="line.381"></a> -<span class="sourceLineNo">382</span> * has discontinuities at bin boundaries, integrals spanning multiple bins<a name="line.382"></a> -<span class="sourceLineNo">383</span> * will face convergence problems. Only test within-bin integrals and spans<a name="line.383"></a> -<span class="sourceLineNo">384</span> * across no more than 3 bin boundaries.<a name="line.384"></a> -<span class="sourceLineNo">385</span> */<a name="line.385"></a> -<span class="sourceLineNo">386</span> @Override<a name="line.386"></a> -<span class="sourceLineNo">387</span> @Test<a name="line.387"></a> -<span class="sourceLineNo">388</span> public void testDensityIntegrals() {<a name="line.388"></a> -<span class="sourceLineNo">389</span> final RealDistribution distribution = makeDistribution();<a name="line.389"></a> -<span class="sourceLineNo">390</span> final double tol = 1.0e-9;<a name="line.390"></a> -<span class="sourceLineNo">391</span> final BaseAbstractUnivariateIntegrator integrator =<a name="line.391"></a> -<span class="sourceLineNo">392</span> new IterativeLegendreGaussIntegrator(5, 1.0e-12, 1.0e-10);<a name="line.392"></a> -<span class="sourceLineNo">393</span> final UnivariateFunction d = new UnivariateFunction() {<a name="line.393"></a> -<span class="sourceLineNo">394</span> public double value(double x) {<a name="line.394"></a> -<span class="sourceLineNo">395</span> return distribution.density(x);<a name="line.395"></a> -<span class="sourceLineNo">396</span> }<a name="line.396"></a> -<span class="sourceLineNo">397</span> };<a name="line.397"></a> -<span class="sourceLineNo">398</span> final double[] lower = {0, 5, 1000, 5001, 9995};<a name="line.398"></a> -<span class="sourceLineNo">399</span> final double[] upper = {5, 12, 1030, 5010, 10000};<a name="line.399"></a> -<span class="sourceLineNo">400</span> for (int i = 1; i < 5; i++) {<a name="line.400"></a> -<span class="sourceLineNo">401</span> Assert.assertEquals(<a name="line.401"></a> -<span class="sourceLineNo">402</span> distribution.cumulativeProbability( <a name="line.402"></a> -<span class="sourceLineNo">403</span> lower[i], upper[i]),<a name="line.403"></a> -<span class="sourceLineNo">404</span> integrator.integrate(<a name="line.404"></a> -<span class="sourceLineNo">405</span> 1000000, // Triangle integrals are very slow to converge<a name="line.405"></a> -<span class="sourceLineNo">406</span> d, lower[i], upper[i]), tol);<a name="line.406"></a> -<span class="sourceLineNo">407</span> }<a name="line.407"></a> -<span class="sourceLineNo">408</span> }<a name="line.408"></a> -<span class="sourceLineNo">409</span> <a name="line.409"></a> -<span class="sourceLineNo">410</span> /** <a name="line.410"></a> -<span class="sourceLineNo">411</span> * MATH-984<a name="line.411"></a> -<span class="sourceLineNo">412</span> * Verify that sampled values do not go outside of the range of the data.<a name="line.412"></a> -<span class="sourceLineNo">413</span> */<a name="line.413"></a> -<span class="sourceLineNo">414</span> @Test<a name="line.414"></a> -<span class="sourceLineNo">415</span> public void testSampleValuesRange() {<a name="line.415"></a> -<span class="sourceLineNo">416</span> // Concentrate values near the endpoints of (0, 1).<a name="line.416"></a> -<span class="sourceLineNo">417</span> // Unconstrained Gaussian kernel would generate values outside the interval.<a name="line.417"></a> -<span class="sourceLineNo">418</span> final double[] data = new double[100];<a name="line.418"></a> -<span class="sourceLineNo">419</span> for (int i = 0; i < 50; i++) {<a name="line.419"></a> -<span class="sourceLineNo">420</span> data[i] = 1 / ((double) i + 1);<a name="line.420"></a> -<span class="sourceLineNo">421</span> }<a name="line.421"></a> -<span class="sourceLineNo">422</span> for (int i = 51; i < 100; i++) {<a name="line.422"></a> -<span class="sourceLineNo">423</span> data[i] = 1 - 1 / (100 - (double) i + 2);<a name="line.423"></a> -<span class="sourceLineNo">424</span> }<a name="line.424"></a> -<span class="sourceLineNo">425</span> EmpiricalDistribution dist = new EmpiricalDistribution(10);<a name="line.425"></a> -<span class="sourceLineNo">426</span> dist.load(data);<a name="line.426"></a> -<span class="sourceLineNo">427</span> dist.reseedRandomGenerator(1000);<a name="line.427"></a> -<span class="sourceLineNo">428</span> for (int i = 0; i < 1000; i++) {<a name="line.428"></a> -<span class="sourceLineNo">429</span> final double dev = dist.sample();<a name="line.429"></a> -<span class="sourceLineNo">430</span> Assert.assertTrue(dev < 1);<a name="line.430"></a> -<span class="sourceLineNo">431</span> Assert.assertTrue(dev > 0);<a name="line.431"></a> -<span class="sourceLineNo">432</span> }<a name="line.432"></a> -<span class="sourceLineNo">433</span> }<a name="line.433"></a> -<span class="sourceLineNo">434</span> <a name="line.434"></a> -<span class="sourceLineNo">435</span> /**<a name="line.435"></a> -<span class="sourceLineNo">436</span> * MATH-1203, MATH-1208<a name="line.436"></a> -<span class="sourceLineNo">437</span> */<a name="line.437"></a> -<span class="sourceLineNo">438</span> @Test<a name="line.438"></a> -<span class="sourceLineNo">439</span> public void testNoBinVariance() {<a name="line.439"></a> -<span class="sourceLineNo">440</span> final double[] data = {0, 0, 1, 1};<a name="line.440"></a> -<span class="sourceLineNo">441</span> EmpiricalDistribution dist = new EmpiricalDistribution(2);<a name="line.441"></a> -<span class="sourceLineNo">442</span> dist.load(data);<a name="line.442"></a> -<span class="sourceLineNo">443</span> dist.reseedRandomGenerator(1000);<a name="line.443"></a> -<span class="sourceLineNo">444</span> for (int i = 0; i < 1000; i++) {<a name="line.444"></a> -<span class="sourceLineNo">445</span> final double dev = dist.sample();<a name="line.445"></a> -<span class="sourceLineNo">446</span> Assert.assertTrue(dev == 0 || dev == 1);<a name="line.446"></a> -<span class="sourceLineNo">447</span> }<a name="line.447"></a> -<span class="sourceLineNo">448</span> Assert.assertEquals(0.5, dist.cumulativeProbability(0), Double.MIN_VALUE);<a name="line.448"></a> -<span class="sourceLineNo">449</span> Assert.assertEquals(1.0, dist.cumulativeProbability(1), Double.MIN_VALUE);<a name="line.449"></a> -<span class="sourceLineNo">450</span> Assert.assertEquals(0.5, dist.cumulativeProbability(0.5), Double.MIN_VALUE);<a name="line.450"></a> -<span class="sourceLineNo">451</span> Assert.assertEquals(0.5, dist.cumulativeProbability(0.7), Double.MIN_VALUE);<a name="line.451"></a> -<span class="sourceLineNo">452</span> } <a name="line.452"></a> -<span class="sourceLineNo">453</span> <a name="line.453"></a> -<span class="sourceLineNo">454</span> /**<a name="line.454"></a> -<span class="sourceLineNo">455</span> * Find the bin that x belongs (relative to {@link #makeDistribution()}).<a name="line.455"></a> -<span class="sourceLineNo">456</span> */<a name="line.456"></a> -<span class="sourceLineNo">457</span> private int findBin(double x) {<a name="line.457"></a> -<span class="sourceLineNo">458</span> // Number of bins below x should be trunc(x/10)<a name="line.458"></a> -<span class="sourceLineNo">459</span> final double nMinus = FastMath.floor(x / 10);<a name="line.459"></a> -<span class="sourceLineNo">460</span> final int bin = (int) FastMath.round(nMinus);<a name="line.460"></a> -<span class="sourceLineNo">461</span> // If x falls on a bin boundary, it is in the lower bin<a name="line.461"></a> -<span class="sourceLineNo">462</span> return FastMath.floor(x / 10) == x / 10 ? bin - 1 : bin;<a name="line.462"></a> -<span class="sourceLineNo">463</span> }<a name="line.463"></a> -<span class="sourceLineNo">464</span> <a name="line.464"></a> -<span class="sourceLineNo">465</span> /**<a name="line.465"></a> -<span class="sourceLineNo">466</span> * Find the within-bin kernel for the bin with lower bound lower<a name="line.466"></a> -<span class="sourceLineNo">467</span> * and upper bound upper. All bins other than the first contain 10 points<a name="line.467"></a> -<span class="sourceLineNo">468</span> * exclusive of the lower bound and are centered at (lower + upper + 1) / 2.<a name="line.468"></a> -<span class="sourceLineNo">469</span> * The first bin includes its lower bound, 0, so has different mean and<a name="line.469"></a> -<span class="sourceLineNo">470</span> * standard deviation.<a name="line.470"></a> -<span class="sourceLineNo">471</span> */<a name="line.471"></a> -<span class="sourceLineNo">472</span> private RealDistribution findKernel(double lower, double upper) {<a name="line.472"></a> -<span class="sourceLineNo">473</span> if (lower < 1) {<a name="line.473"></a> -<span class="sourceLineNo">474</span> return new NormalDistribution(5d, 3.3166247903554);<a name="line.474"></a> -<span class="sourceLineNo">475</span> } else {<a name="line.475"></a> -<span class="sourceLineNo">476</span> return new NormalDistribution((upper + lower + 1) / 2d, 3.0276503540974917); <a name="line.476"></a> -<span class="sourceLineNo">477</span> }<a name="line.477"></a> -<span class="sourceLineNo">478</span> }<a name="line.478"></a> -<span class="sourceLineNo">479</span> <a name="line.479"></a> -<span class="sourceLineNo">480</span> @Test<a name="line.480"></a> -<span class="sourceLineNo">481</span> public void testKernelOverrideConstant() {<a name="line.481"></a> -<span class="sourceLineNo">482</span> final EmpiricalDistribution dist = new ConstantKernelEmpiricalDistribution(5);<a name="line.482"></a> -<span class="sourceLineNo">483</span> final double[] data = {1d,2d,3d, 4d,5d,6d, 7d,8d,9d, 10d,11d,12d, 13d,14d,15d};<a name="line.483"></a> -<span class="sourceLineNo">484</span> dist.load(data);<a name="line.484"></a> -<span class="sourceLineNo">485</span> // Bin masses concentrated on 2, 5, 8, 11, 14 <- effectively discrete uniform distribution over these<a name="line.485"></a> -<span class="sourceLineNo">486</span> double[] values = {2d, 5d, 8d, 11d, 14d};<a name="line.486"></a> -<span class="sourceLineNo">487</span> for (int i = 0; i < 20; i++) {<a name="line.487"></a> -<span class="sourceLineNo">488</span> Assert.assertTrue(Arrays.binarySearch(values, dist.sample()) >= 0);<a name="line.488"></a> -<span class="sourceLineNo">489</span> }<a name="line.489"></a> -<span class="sourceLineNo">490</span> final double tol = 10E-12;<a name="line.490"></a> -<span class="sourceLineNo">491</span> Assert.assertEquals(0.0, dist.cumulativeProbability(1), tol);<a name="line.491"></a> -<span class="sourceLineNo">492</span> Assert.assertEquals(0.2, dist.cumulativeProbability(2), tol);<a name="line.492"></a> -<span class="sourceLineNo">493</span> Assert.assertEquals(0.6, dist.cumulativeProbability(10), tol);<a name="line.493"></a> -<span class="sourceLineNo">494</span> Assert.assertEquals(0.8, dist.cumulativeProbability(12), tol);<a name="line.494"></a> -<span class="sourceLineNo">495</span> Assert.assertEquals(0.8, dist.cumulativeProbability(13), tol);<a name="line.495"></a> -<span class="sourceLineNo">496</span> Assert.assertEquals(1.0, dist.cumulativeProbability(15), tol);<a name="line.496"></a> -<span class="sourceLineNo">497</span><a name="line.497"></a> -<span class="sourceLineNo">498</span> Assert.assertEquals(2.0, dist.inverseCumulativeProbability(0.1), tol);<a name="line.498"></a> -<span class="sourceLineNo">499</span> Assert.assertEquals(2.0, dist.inverseCumulativeProbability(0.2), tol);<a name="line.499"></a> -<span class="sourceLineNo">500</span> Assert.assertEquals(5.0, dist.inverseCumulativeProbability(0.3), tol);<a name="line.500"></a> -<span class="sourceLineNo">501</span> Assert.assertEquals(5.0, dist.inverseCumulativeProbability(0.4), tol);<a name="line.501"></a> -<span class="sourceLineNo">502</span> Assert.assertEquals(8.0, dist.inverseCumulativeProbability(0.5), tol);<a name="line.502"></a> -<span class="sourceLineNo">503</span> Assert.assertEquals(8.0, dist.inverseCumulativeProbability(0.6), tol);<a name="line.503"></a> -<span class="sourceLineNo">504</span> }<a name="line.504"></a> -<span class="sourceLineNo">505</span> <a name="line.505"></a> -<span class="sourceLineNo">506</span> @Test<a name="line.506"></a> -<span class="sourceLineNo">507</span> public void testKernelOverrideUniform() {<a name="line.507"></a> -<span class="sourceLineNo">508</span> final EmpiricalDistribution dist = new UniformKernelEmpiricalDistribution(5);<a name="line.508"></a> -<span class="sourceLineNo">509</span> final double[] data = {1d,2d,3d, 4d,5d,6d, 7d,8d,9d, 10d,11d,12d, 13d,14d,15d};<a name="line.509"></a> -<span class="sourceLineNo">510</span> dist.load(data);<a name="line.510"></a> -<span class="sourceLineNo">511</span> // Kernels are uniform distributions on [1,3], [4,6], [7,9], [10,12], [13,15]<a name="line.511"></a> -<span class="sourceLineNo">512</span> final double bounds[] = {3d, 6d, 9d, 12d};<a name="line.512"></a> -<span class="sourceLineNo">513</span> final double tol = 10E-12; <a name="line.513"></a> -<span class="sourceLineNo">514</span> for (int i = 0; i < 20; i++) {<a name="line.514"></a> -<span class="sourceLineNo">515</span> final double v = dist.sample();<a name="line.515"></a> -<span class="sourceLineNo">516</span> // Make sure v is not in the excluded range between bins - that is (bounds[i], bounds[i] + 1)<a name="line.516"></a> -<span class="sourceLineNo">517</span> for (int j = 0; j < bounds.length; j++) {<a name="line.517"></a> -<span class="sourceLineNo">518</span> Assert.assertFalse(v > bounds[j] + tol && v < bounds[j] + 1 - tol);<a name="line.518"></a> -<span class="sourceLineNo">519</span> }<a name="line.519"></a> -<span class="sourceLineNo">520</span> } <a name="line.520"></a> -<span class="sourceLineNo">521</span> Assert.assertEquals(0.0, dist.cumulativeProbability(1), tol);<a name="line.521"></a> -<span class="sourceLineNo">522</span> Assert.assertEquals(0.1, dist.cumulativeProbability(2), tol);<a name="line.522"></a> -<span class="sourceLineNo">523</span> Assert.assertEquals(0.6, dist.cumulativeProbability(10), tol);<a name="line.523"></a> -<span class="sourceLineNo">524</span> Assert.assertEquals(0.8, dist.cumulativeProbability(12), tol);<a name="line.524"></a> -<span class="sourceLineNo">525</span> Assert.assertEquals(0.8, dist.cumulativeProbability(13), tol);<a name="line.525"></a> -<span class="sourceLineNo">526</span> Assert.assertEquals(1.0, dist.cumulativeProbability(15), tol);<a name="line.526"></a> -<span class="sourceLineNo">527</span><a name="line.527"></a> -<span class="sourceLineNo">528</span> Assert.assertEquals(2.0, dist.inverseCumulativeProbability(0.1), tol);<a name="line.528"></a> -<span class="sourceLineNo">529</span> Assert.assertEquals(3.0, dist.inverseCumulativeProbability(0.2), tol);<a name="line.529"></a> -<span class="sourceLineNo">530</span> Assert.assertEquals(5.0, dist.inverseCumulativeProbability(0.3), tol);<a name="line.530"></a> -<span class="sourceLineNo">531</span> Assert.assertEquals(6.0, dist.inverseCumulativeProbability(0.4), tol);<a name="line.531"></a> -<span class="sourceLineNo">532</span> Assert.assertEquals(8.0, dist.inverseCumulativeProbability(0.5), tol);<a name="line.532"></a> -<span class="sourceLineNo">533</span> Assert.assertEquals(9.0, dist.inverseCumulativeProbability(0.6), tol);<a name="line.533"></a> -<span class="sourceLineNo">534</span> }<a name="line.534"></a> -<span class="sourceLineNo">535</span> <a name="line.535"></a> -<span class="sourceLineNo">536</span> <a name="line.536"></a> -<span class="sourceLineNo">537</span> /**<a name="line.537"></a> -<span class="sourceLineNo">538</span> * Empirical distribution using a constant smoothing kernel.<a name="line.538"></a> -<span class="sourceLineNo">539</span> */<a name="line.539"></a> -<span class="sourceLineNo">540</span> private class ConstantKernelEmpiricalDistribution extends EmpiricalDistribution {<a name="line.540"></a> -<span class="sourceLineNo">541</span> private static final long serialVersionUID = 1L;<a name="line.541"></a> -<span class="sourceLineNo">542</span> public ConstantKernelEmpiricalDistribution(int i) {<a name="line.542"></a> -<span class="sourceLineNo">543</span> super(i);<a name="line.543"></a> -<span class="sourceLineNo">544</span> }<a name="line.544"></a> -<span class="sourceLineNo">545</span> // Use constant distribution equal to bin mean within bin<a name="line.545"></a> -<span class="sourceLineNo">546</span> @Override<a name="line.546"></a> -<span class="sourceLineNo">547</span> protected RealDistribution getKernel(SummaryStatistics bStats) {<a name="line.547"></a> -<span class="sourceLineNo">548</span> return new ConstantRealDistribution(bStats.getMean());<a name="line.548"></a> -<span class="sourceLineNo">549</span> }<a name="line.549"></a> -<span class="sourceLineNo">550</span> }<a name="line.550"></a> -<span class="sourceLineNo">551</span> <a name="line.551"></a> -<span class="sourceLineNo">552</span> /**<a name="line.552"></a> -<span class="sourceLineNo">553</span> * Empirical distribution using a uniform smoothing kernel.<a name="line.553"></a> -<span class="sourceLineNo">554</span> */<a name="line.554"></a> -<span class="sourceLineNo">555</span> private class UniformKernelEmpiricalDistribution extends EmpiricalDistribution {<a name="line.555"></a> -<span class="sourceLineNo">556</span> private static final long serialVersionUID = 2963149194515159653L;<a name="line.556"></a> -<span class="sourceLineNo">557</span> public UniformKernelEmpiricalDistribution(int i) {<a name="line.557"></a> -<span class="sourceLineNo">558</span> super(i);<a name="line.558"></a> -<span class="sourceLineNo">559</span> }<a name="line.559"></a> -<span class="sourceLineNo">560</span> @Override<a name="line.560"></a> -<span class="sourceLineNo">561</span> protected RealDistribution getKernel(SummaryStatistics bStats) {<a name="line.561"></a> -<span class="sourceLineNo">562</span> return new UniformRealDistribution(randomData.getRandomGenerator(), bStats.getMin(), bStats.getMax(),<a name="line.562"></a> -<span class="sourceLineNo">563</span> UniformRealDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);<a name="line.563"></a> -<span class="sourceLineNo">564</span> }<a name="line.564"></a> -<span class="sourceLineNo">565</span> }<a name="line.565"></a> -<span class="sourceLineNo">566</span>}<a name="line.566"></a> - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -</pre> -</div> -</body> -</html>
