Files
mlpack/doc/html/classmlpack_1_1kernel_1_1GaussianKernel.html
T

691 lines
34 KiB
HTML

<!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">
<head>
<meta http-equiv="Content-Type" content="text/xhtml;charset=UTF-8"/>
<meta http-equiv="X-UA-Compatible" content="IE=9"/>
<meta name="generator" content="Doxygen 1.8.11"/>
<title>mlpack: mlpack::kernel::GaussianKernel Class Reference</title>
<link href="tabs.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="jquery.js"></script>
<script type="text/javascript" src="dynsections.js"></script>
<link href="search/search.css" rel="stylesheet" type="text/css"/>
<script type="text/javascript" src="search/searchdata.js"></script>
<script type="text/javascript" src="search/search.js"></script>
<script type="text/javascript">
$(document).ready(function() { init_search(); });
</script>
<link href="doxygen.css" rel="stylesheet" type="text/css" />
<link href="extra-stylesheet.css" rel="stylesheet" type="text/css"/>
</head>
<body>
<div id="top"><!-- do not remove this div, it is closed by doxygen! -->
<div id="titlearea">
<table cellspacing="0" cellpadding="0">
<tbody>
<tr style="height: 56px;">
<td id="projectalign" style="padding-left: 0.5em;">
<div id="projectname">mlpack
&#160;<span id="projectnumber">master</span>
</div>
</td>
</tr>
</tbody>
</table>
</div>
<!-- end header part -->
<!-- Generated by Doxygen 1.8.11 -->
<script type="text/javascript">
var searchBox = new SearchBox("searchBox", "search",false,'Search');
</script>
<div id="navrow1" class="tabs">
<ul class="tablist">
<li><a href="index.html"><span>Main&#160;Page</span></a></li>
<li><a href="pages.html"><span>Related&#160;Pages</span></a></li>
<li><a href="namespaces.html"><span>Namespaces</span></a></li>
<li class="current"><a href="annotated.html"><span>Classes</span></a></li>
<li><a href="files.html"><span>Files</span></a></li>
<li>
<div id="MSearchBox" class="MSearchBoxInactive">
<span class="left">
<img id="MSearchSelect" src="search/mag_sel.png"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
alt=""/>
<input type="text" id="MSearchField" value="Search" accesskey="S"
onfocus="searchBox.OnSearchFieldFocus(true)"
onblur="searchBox.OnSearchFieldFocus(false)"
onkeyup="searchBox.OnSearchFieldChange(event)"/>
</span><span class="right">
<a id="MSearchClose" href="javascript:searchBox.CloseResultsWindow()"><img id="MSearchCloseImg" border="0" src="search/close.png" alt=""/></a>
</span>
</div>
</li>
</ul>
</div>
<div id="navrow2" class="tabs2">
<ul class="tablist">
<li><a href="annotated.html"><span>Class&#160;List</span></a></li>
<li><a href="classes.html"><span>Class&#160;Index</span></a></li>
<li><a href="inherits.html"><span>Class&#160;Hierarchy</span></a></li>
<li><a href="functions.html"><span>Class&#160;Members</span></a></li>
</ul>
</div>
<!-- window showing the filter options -->
<div id="MSearchSelectWindow"
onmouseover="return searchBox.OnSearchSelectShow()"
onmouseout="return searchBox.OnSearchSelectHide()"
onkeydown="return searchBox.OnSearchSelectKey(event)">
</div>
<!-- iframe showing the search results (closed by default) -->
<div id="MSearchResultsWindow">
<iframe src="javascript:void(0)" frameborder="0"
name="MSearchResults" id="MSearchResults">
</iframe>
</div>
<div id="nav-path" class="navpath">
<ul>
<li class="navelem"><a class="el" href="namespacemlpack.html">mlpack</a></li><li class="navelem"><a class="el" href="namespacemlpack_1_1kernel.html">kernel</a></li><li class="navelem"><a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html">GaussianKernel</a></li> </ul>
</div>
</div><!-- top -->
<div class="header">
<div class="summary">
<a href="#pub-methods">Public Member Functions</a> &#124;
<a href="#pri-attribs">Private Attributes</a> &#124;
<a href="classmlpack_1_1kernel_1_1GaussianKernel-members.html">List of all members</a> </div>
<div class="headertitle">
<div class="title">mlpack::kernel::GaussianKernel Class Reference</div> </div>
</div><!--header-->
<div class="contents">
<p>The standard Gaussian kernel.
<a href="classmlpack_1_1kernel_1_1GaussianKernel.html#details">More...</a></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a1982249e5fbb7e4b94ae88f1e0218303"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a1982249e5fbb7e4b94ae88f1e0218303">GaussianKernel</a> ()</td></tr>
<tr class="memdesc:a1982249e5fbb7e4b94ae88f1e0218303"><td class="mdescLeft">&#160;</td><td class="mdescRight">Default constructor; sets bandwidth to 1.0. <a href="#a1982249e5fbb7e4b94ae88f1e0218303">More...</a><br /></td></tr>
<tr class="separator:a1982249e5fbb7e4b94ae88f1e0218303"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab508d841b64919cd395fe5ad959c2fe5"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html#ab508d841b64919cd395fe5ad959c2fe5">GaussianKernel</a> (const double <a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html#abda0129ea431b857678f9c4ec9fab151">bandwidth</a>)</td></tr>
<tr class="memdesc:ab508d841b64919cd395fe5ad959c2fe5"><td class="mdescLeft">&#160;</td><td class="mdescRight">Construct the Gaussian kernel with a custom bandwidth. <a href="#ab508d841b64919cd395fe5ad959c2fe5">More...</a><br /></td></tr>
<tr class="separator:ab508d841b64919cd395fe5ad959c2fe5"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a80caca0e054169dbc73aeaed0c9ce2da"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a80caca0e054169dbc73aeaed0c9ce2da">Bandwidth</a> () const </td></tr>
<tr class="memdesc:a80caca0e054169dbc73aeaed0c9ce2da"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the bandwidth. <a href="#a80caca0e054169dbc73aeaed0c9ce2da">More...</a><br /></td></tr>
<tr class="separator:a80caca0e054169dbc73aeaed0c9ce2da"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ac7723e82d94352245c203b9100485ae0"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html#ac7723e82d94352245c203b9100485ae0">Bandwidth</a> (const double <a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html#abda0129ea431b857678f9c4ec9fab151">bandwidth</a>)</td></tr>
<tr class="memdesc:ac7723e82d94352245c203b9100485ae0"><td class="mdescLeft">&#160;</td><td class="mdescRight">Modify the bandwidth. <a href="#ac7723e82d94352245c203b9100485ae0">More...</a><br /></td></tr>
<tr class="separator:ac7723e82d94352245c203b9100485ae0"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a02b3cab5d575452053f69e9f69dfc47a"><td class="memTemplParams" colspan="2">template&lt;typename VecTypeA , typename VecTypeB &gt; </td></tr>
<tr class="memitem:a02b3cab5d575452053f69e9f69dfc47a"><td class="memTemplItemLeft" align="right" valign="top">double&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a02b3cab5d575452053f69e9f69dfc47a">ConvolutionIntegral</a> (const VecTypeA &amp;a, const VecTypeB &amp;b)</td></tr>
<tr class="memdesc:a02b3cab5d575452053f69e9f69dfc47a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Obtain a convolution integral of the Gaussian kernel. <a href="#a02b3cab5d575452053f69e9f69dfc47a">More...</a><br /></td></tr>
<tr class="separator:a02b3cab5d575452053f69e9f69dfc47a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a574ef2113ec8ac965df031eee219dd73"><td class="memTemplParams" colspan="2">template&lt;typename VecTypeA , typename VecTypeB &gt; </td></tr>
<tr class="memitem:a574ef2113ec8ac965df031eee219dd73"><td class="memTemplItemLeft" align="right" valign="top">double&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a574ef2113ec8ac965df031eee219dd73">Evaluate</a> (const VecTypeA &amp;a, const VecTypeB &amp;b) const </td></tr>
<tr class="memdesc:a574ef2113ec8ac965df031eee219dd73"><td class="mdescLeft">&#160;</td><td class="mdescRight">Evaluation of the Gaussian kernel. <a href="#a574ef2113ec8ac965df031eee219dd73">More...</a><br /></td></tr>
<tr class="separator:a574ef2113ec8ac965df031eee219dd73"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a5336c3dbf963c079757ed35fe685c39b"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a5336c3dbf963c079757ed35fe685c39b">Evaluate</a> (const double t) const </td></tr>
<tr class="memdesc:a5336c3dbf963c079757ed35fe685c39b"><td class="mdescLeft">&#160;</td><td class="mdescRight">Evaluation of the Gaussian kernel given the distance between two points. <a href="#a5336c3dbf963c079757ed35fe685c39b">More...</a><br /></td></tr>
<tr class="separator:a5336c3dbf963c079757ed35fe685c39b"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7dd6194dab1f6d6562ac0be4fa00fcae"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7dd6194dab1f6d6562ac0be4fa00fcae">Gamma</a> () const </td></tr>
<tr class="memdesc:a7dd6194dab1f6d6562ac0be4fa00fcae"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the precalculated constant. <a href="#a7dd6194dab1f6d6562ac0be4fa00fcae">More...</a><br /></td></tr>
<tr class="separator:a7dd6194dab1f6d6562ac0be4fa00fcae"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a5faea1b64eff5c1e0705d560e742e2f7"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a5faea1b64eff5c1e0705d560e742e2f7">Gradient</a> (const double t) const </td></tr>
<tr class="memdesc:a5faea1b64eff5c1e0705d560e742e2f7"><td class="mdescLeft">&#160;</td><td class="mdescRight">Evaluation of the gradient of Gaussian kernel given the distance between two points. <a href="#a5faea1b64eff5c1e0705d560e742e2f7">More...</a><br /></td></tr>
<tr class="separator:a5faea1b64eff5c1e0705d560e742e2f7"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab2bd0f1eb24fe8a7508d49fba1659942"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html#ab2bd0f1eb24fe8a7508d49fba1659942">GradientForSquaredDistance</a> (const double t) const </td></tr>
<tr class="memdesc:ab2bd0f1eb24fe8a7508d49fba1659942"><td class="mdescLeft">&#160;</td><td class="mdescRight">Evaluation of the gradient of Gaussian kernel given the squared distance between two points. <a href="#ab2bd0f1eb24fe8a7508d49fba1659942">More...</a><br /></td></tr>
<tr class="separator:ab2bd0f1eb24fe8a7508d49fba1659942"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a17bc9e87e69d5187a11fa12e2f9a3ba2"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a17bc9e87e69d5187a11fa12e2f9a3ba2">Normalizer</a> (const size_t dimension)</td></tr>
<tr class="memdesc:a17bc9e87e69d5187a11fa12e2f9a3ba2"><td class="mdescLeft">&#160;</td><td class="mdescRight">Obtain the normalization constant of the Gaussian kernel. <a href="#a17bc9e87e69d5187a11fa12e2f9a3ba2">More...</a><br /></td></tr>
<tr class="separator:a17bc9e87e69d5187a11fa12e2f9a3ba2"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae459d2087dbad74c8aa57515db45123b"><td class="memTemplParams" colspan="2">template&lt;typename Archive &gt; </td></tr>
<tr class="memitem:ae459d2087dbad74c8aa57515db45123b"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html#ae459d2087dbad74c8aa57515db45123b">Serialize</a> (Archive &amp;ar, const unsigned int)</td></tr>
<tr class="memdesc:ae459d2087dbad74c8aa57515db45123b"><td class="mdescLeft">&#160;</td><td class="mdescRight">Serialize the kernel. <a href="#ae459d2087dbad74c8aa57515db45123b">More...</a><br /></td></tr>
<tr class="separator:ae459d2087dbad74c8aa57515db45123b"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-attribs"></a>
Private Attributes</h2></td></tr>
<tr class="memitem:abda0129ea431b857678f9c4ec9fab151"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html#abda0129ea431b857678f9c4ec9fab151">bandwidth</a></td></tr>
<tr class="memdesc:abda0129ea431b857678f9c4ec9fab151"><td class="mdescLeft">&#160;</td><td class="mdescRight">Kernel bandwidth. <a href="#abda0129ea431b857678f9c4ec9fab151">More...</a><br /></td></tr>
<tr class="separator:abda0129ea431b857678f9c4ec9fab151"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7f019a726e3dca20edb7721ad994fe6a"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classmlpack_1_1kernel_1_1GaussianKernel.html#a7f019a726e3dca20edb7721ad994fe6a">gamma</a></td></tr>
<tr class="memdesc:a7f019a726e3dca20edb7721ad994fe6a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Precalculated constant depending on the bandwidth; <img class="formulaInl" alt="$ \gamma = -\frac{1}{2 \mu^2} $" src="form_13.png"/>. <a href="#a7f019a726e3dca20edb7721ad994fe6a">More...</a><br /></td></tr>
<tr class="separator:a7f019a726e3dca20edb7721ad994fe6a"><td class="memSeparator" colspan="2">&#160;</td></tr>
</table>
<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>The standard Gaussian kernel. </p>
<p>Given two vectors <img class="formulaInl" alt="$ x $" src="form_7.png"/>, <img class="formulaInl" alt="$ y $" src="form_8.png"/>, and a bandwidth <img class="formulaInl" alt="$ \mu $" src="form_11.png"/> (set in the constructor),</p>
<p class="formulaDsp">
<img class="formulaDsp" alt="\[ K(x, y) = \exp(-\frac{|| x - y ||^2}{2 \mu^2}). \]" src="form_12.png"/>
</p>
<p>The implementation is all in the header file because it is so simple. </p>
<p>Definition at line <a class="el" href="gaussian__kernel_8hpp_source.html#l00034">34</a> of file <a class="el" href="gaussian__kernel_8hpp_source.html">gaussian_kernel.hpp</a>.</p>
</div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
<a class="anchor" id="a1982249e5fbb7e4b94ae88f1e0218303"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
<tr>
<td class="mlabels-left">
<table class="memname">
<tr>
<td class="memname">mlpack::kernel::GaussianKernel::GaussianKernel </td>
<td>(</td>
<td class="paramname"></td><td>)</td>
<td></td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>Default constructor; sets bandwidth to 1.0. </p>
<p>Definition at line <a class="el" href="gaussian__kernel_8hpp_source.html#l00040">40</a> of file <a class="el" href="gaussian__kernel_8hpp_source.html">gaussian_kernel.hpp</a>.</p>
</div>
</div>
<a class="anchor" id="ab508d841b64919cd395fe5ad959c2fe5"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
<tr>
<td class="mlabels-left">
<table class="memname">
<tr>
<td class="memname">mlpack::kernel::GaussianKernel::GaussianKernel </td>
<td>(</td>
<td class="paramtype">const double&#160;</td>
<td class="paramname"><em>bandwidth</em></td><td>)</td>
<td></td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>Construct the Gaussian kernel with a custom bandwidth. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">bandwidth</td><td>The bandwidth of the kernel ( <img class="formulaInl" alt="$\mu$" src="form_2.png"/>). </td></tr>
</table>
</dd>
</dl>
<p>Definition at line <a class="el" href="gaussian__kernel_8hpp_source.html#l00048">48</a> of file <a class="el" href="gaussian__kernel_8hpp_source.html">gaussian_kernel.hpp</a>.</p>
</div>
</div>
<h2 class="groupheader">Member Function Documentation</h2>
<a class="anchor" id="a80caca0e054169dbc73aeaed0c9ce2da"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
<tr>
<td class="mlabels-left">
<table class="memname">
<tr>
<td class="memname">double mlpack::kernel::GaussianKernel::Bandwidth </td>
<td>(</td>
<td class="paramname"></td><td>)</td>
<td> const</td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>Get the bandwidth. </p>
<p>Definition at line <a class="el" href="gaussian__kernel_8hpp_source.html#l00135">135</a> of file <a class="el" href="gaussian__kernel_8hpp_source.html">gaussian_kernel.hpp</a>.</p>
<p>References <a class="el" href="gaussian__kernel_8hpp_source.html#l00158">bandwidth</a>.</p>
</div>
</div>
<a class="anchor" id="ac7723e82d94352245c203b9100485ae0"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
<tr>
<td class="mlabels-left">
<table class="memname">
<tr>
<td class="memname">void mlpack::kernel::GaussianKernel::Bandwidth </td>
<td>(</td>
<td class="paramtype">const double&#160;</td>
<td class="paramname"><em>bandwidth</em></td><td>)</td>
<td></td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>Modify the bandwidth. </p>
<p>This takes an argument because we must update the precalculated constant (gamma). </p>
<p>Definition at line <a class="el" href="gaussian__kernel_8hpp_source.html#l00139">139</a> of file <a class="el" href="gaussian__kernel_8hpp_source.html">gaussian_kernel.hpp</a>.</p>
<p>References <a class="el" href="gaussian__kernel_8hpp_source.html#l00158">bandwidth</a>, and <a class="el" href="gaussian__kernel_8hpp_source.html#l00162">gamma</a>.</p>
</div>
</div>
<a class="anchor" id="a02b3cab5d575452053f69e9f69dfc47a"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename VecTypeA , typename VecTypeB &gt; </div>
<table class="mlabels">
<tr>
<td class="mlabels-left">
<table class="memname">
<tr>
<td class="memname">double mlpack::kernel::GaussianKernel::ConvolutionIntegral </td>
<td>(</td>
<td class="paramtype">const VecTypeA &amp;&#160;</td>
<td class="paramname"><em>a</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const VecTypeB &amp;&#160;</td>
<td class="paramname"><em>b</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>Obtain a convolution integral of the Gaussian kernel. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">a</td><td>First vector. </td></tr>
<tr><td class="paramname">b</td><td>Second vector. </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>The convolution integral. </dd></dl>
<p>Definition at line <a class="el" href="gaussian__kernel_8hpp_source.html#l00127">127</a> of file <a class="el" href="gaussian__kernel_8hpp_source.html">gaussian_kernel.hpp</a>.</p>
<p>References <a class="el" href="gaussian__kernel_8hpp_source.html#l00065">Evaluate()</a>, <a class="el" href="classmlpack_1_1metric_1_1LMetric.html#ac55458730b8e36f51d2f2a3741b77181">mlpack::metric::LMetric&lt; TPower, TTakeRoot &gt;::Evaluate()</a>, and <a class="el" href="gaussian__kernel_8hpp_source.html#l00114">Normalizer()</a>.</p>
</div>
</div>
<a class="anchor" id="a574ef2113ec8ac965df031eee219dd73"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename VecTypeA , typename VecTypeB &gt; </div>
<table class="mlabels">
<tr>
<td class="mlabels-left">
<table class="memname">
<tr>
<td class="memname">double mlpack::kernel::GaussianKernel::Evaluate </td>
<td>(</td>
<td class="paramtype">const VecTypeA &amp;&#160;</td>
<td class="paramname"><em>a</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const VecTypeB &amp;&#160;</td>
<td class="paramname"><em>b</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td> const</td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>Evaluation of the Gaussian kernel. </p>
<p>This could be generalized to use any distance metric, not the Euclidean distance, but for now, the Euclidean distance is used.</p>
<dl class="tparams"><dt>Template Parameters</dt><dd>
<table class="tparams">
<tr><td class="paramname">VecType</td><td>Type of vector (likely arma::vec or arma::spvec). </td></tr>
</table>
</dd>
</dl>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">a</td><td>First vector. </td></tr>
<tr><td class="paramname">b</td><td>Second vector. </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>K(a, b) using the bandwidth ( <img class="formulaInl" alt="$\mu$" src="form_2.png"/>) specified in the constructor. </dd></dl>
<p>Definition at line <a class="el" href="gaussian__kernel_8hpp_source.html#l00065">65</a> of file <a class="el" href="gaussian__kernel_8hpp_source.html">gaussian_kernel.hpp</a>.</p>
<p>References <a class="el" href="classmlpack_1_1metric_1_1LMetric.html#ac55458730b8e36f51d2f2a3741b77181">mlpack::metric::LMetric&lt; TPower, TTakeRoot &gt;::Evaluate()</a>, and <a class="el" href="gaussian__kernel_8hpp_source.html#l00162">gamma</a>.</p>
<p>Referenced by <a class="el" href="gaussian__kernel_8hpp_source.html#l00127">ConvolutionIntegral()</a>.</p>
</div>
</div>
<a class="anchor" id="a5336c3dbf963c079757ed35fe685c39b"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
<tr>
<td class="mlabels-left">
<table class="memname">
<tr>
<td class="memname">double mlpack::kernel::GaussianKernel::Evaluate </td>
<td>(</td>
<td class="paramtype">const double&#160;</td>
<td class="paramname"><em>t</em></td><td>)</td>
<td> const</td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>Evaluation of the Gaussian kernel given the distance between two points. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">t</td><td>The distance between the two points the kernel is evaluated on. </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>K(t) using the bandwidth ( <img class="formulaInl" alt="$\mu$" src="form_2.png"/>) specified in the constructor. </dd></dl>
<p>Definition at line <a class="el" href="gaussian__kernel_8hpp_source.html#l00078">78</a> of file <a class="el" href="gaussian__kernel_8hpp_source.html">gaussian_kernel.hpp</a>.</p>
<p>References <a class="el" href="gaussian__kernel_8hpp_source.html#l00162">gamma</a>.</p>
</div>
</div>
<a class="anchor" id="a7dd6194dab1f6d6562ac0be4fa00fcae"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
<tr>
<td class="mlabels-left">
<table class="memname">
<tr>
<td class="memname">double mlpack::kernel::GaussianKernel::Gamma </td>
<td>(</td>
<td class="paramname"></td><td>)</td>
<td> const</td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>Get the precalculated constant. </p>
<p>Definition at line <a class="el" href="gaussian__kernel_8hpp_source.html#l00146">146</a> of file <a class="el" href="gaussian__kernel_8hpp_source.html">gaussian_kernel.hpp</a>.</p>
<p>References <a class="el" href="gaussian__kernel_8hpp_source.html#l00162">gamma</a>.</p>
</div>
</div>
<a class="anchor" id="a5faea1b64eff5c1e0705d560e742e2f7"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
<tr>
<td class="mlabels-left">
<table class="memname">
<tr>
<td class="memname">double mlpack::kernel::GaussianKernel::Gradient </td>
<td>(</td>
<td class="paramtype">const double&#160;</td>
<td class="paramname"><em>t</em></td><td>)</td>
<td> const</td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>Evaluation of the gradient of Gaussian kernel given the distance between two points. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">t</td><td>The distance between the two points the kernel is evaluated on. </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>K(t) using the bandwidth ( <img class="formulaInl" alt="$\mu$" src="form_2.png"/>) specified in the constructor. </dd></dl>
<p>Definition at line <a class="el" href="gaussian__kernel_8hpp_source.html#l00092">92</a> of file <a class="el" href="gaussian__kernel_8hpp_source.html">gaussian_kernel.hpp</a>.</p>
<p>References <a class="el" href="gaussian__kernel_8hpp_source.html#l00162">gamma</a>.</p>
</div>
</div>
<a class="anchor" id="ab2bd0f1eb24fe8a7508d49fba1659942"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
<tr>
<td class="mlabels-left">
<table class="memname">
<tr>
<td class="memname">double mlpack::kernel::GaussianKernel::GradientForSquaredDistance </td>
<td>(</td>
<td class="paramtype">const double&#160;</td>
<td class="paramname"><em>t</em></td><td>)</td>
<td> const</td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>Evaluation of the gradient of Gaussian kernel given the squared distance between two points. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">t</td><td>The squared distance between the two points </td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>K(t) using the bandwidth ( <img class="formulaInl" alt="$\mu$" src="form_2.png"/>) specified in the constructor. </dd></dl>
<p>Definition at line <a class="el" href="gaussian__kernel_8hpp_source.html#l00104">104</a> of file <a class="el" href="gaussian__kernel_8hpp_source.html">gaussian_kernel.hpp</a>.</p>
<p>References <a class="el" href="gaussian__kernel_8hpp_source.html#l00162">gamma</a>.</p>
</div>
</div>
<a class="anchor" id="a17bc9e87e69d5187a11fa12e2f9a3ba2"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
<tr>
<td class="mlabels-left">
<table class="memname">
<tr>
<td class="memname">double mlpack::kernel::GaussianKernel::Normalizer </td>
<td>(</td>
<td class="paramtype">const size_t&#160;</td>
<td class="paramname"><em>dimension</em></td><td>)</td>
<td></td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>Obtain the normalization constant of the Gaussian kernel. </p>
<dl class="params"><dt>Parameters</dt><dd>
<table class="params">
<tr><td class="paramname">dimension</td><td></td></tr>
</table>
</dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>the normalization constant </dd></dl>
<p>Definition at line <a class="el" href="gaussian__kernel_8hpp_source.html#l00114">114</a> of file <a class="el" href="gaussian__kernel_8hpp_source.html">gaussian_kernel.hpp</a>.</p>
<p>References <a class="el" href="gaussian__kernel_8hpp_source.html#l00158">bandwidth</a>, and <a class="el" href="prereqs_8hpp_source.html#l00040">M_PI</a>.</p>
<p>Referenced by <a class="el" href="gaussian__kernel_8hpp_source.html#l00127">ConvolutionIntegral()</a>.</p>
</div>
</div>
<a class="anchor" id="ae459d2087dbad74c8aa57515db45123b"></a>
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename Archive &gt; </div>
<table class="mlabels">
<tr>
<td class="mlabels-left">
<table class="memname">
<tr>
<td class="memname">void mlpack::kernel::GaussianKernel::Serialize </td>
<td>(</td>
<td class="paramtype">Archive &amp;&#160;</td>
<td class="paramname"><em>ar</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const unsigned&#160;</td>
<td class="paramname"><em>int</em>&#160;</td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">inline</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>Serialize the kernel. </p>
<p>Definition at line <a class="el" href="gaussian__kernel_8hpp_source.html#l00150">150</a> of file <a class="el" href="gaussian__kernel_8hpp_source.html">gaussian_kernel.hpp</a>.</p>
<p>References <a class="el" href="gaussian__kernel_8hpp_source.html#l00158">bandwidth</a>, <a class="el" href="serialization__shim_8hpp_source.html#l00094">mlpack::data::CreateNVP()</a>, and <a class="el" href="gaussian__kernel_8hpp_source.html#l00162">gamma</a>.</p>
</div>
</div>
<h2 class="groupheader">Member Data Documentation</h2>
<a class="anchor" id="abda0129ea431b857678f9c4ec9fab151"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
<tr>
<td class="mlabels-left">
<table class="memname">
<tr>
<td class="memname">double mlpack::kernel::GaussianKernel::bandwidth</td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">private</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>Kernel bandwidth. </p>
<p>Definition at line <a class="el" href="gaussian__kernel_8hpp_source.html#l00158">158</a> of file <a class="el" href="gaussian__kernel_8hpp_source.html">gaussian_kernel.hpp</a>.</p>
<p>Referenced by <a class="el" href="gaussian__kernel_8hpp_source.html#l00135">Bandwidth()</a>, <a class="el" href="gaussian__kernel_8hpp_source.html#l00114">Normalizer()</a>, and <a class="el" href="gaussian__kernel_8hpp_source.html#l00150">Serialize()</a>.</p>
</div>
</div>
<a class="anchor" id="a7f019a726e3dca20edb7721ad994fe6a"></a>
<div class="memitem">
<div class="memproto">
<table class="mlabels">
<tr>
<td class="mlabels-left">
<table class="memname">
<tr>
<td class="memname">double mlpack::kernel::GaussianKernel::gamma</td>
</tr>
</table>
</td>
<td class="mlabels-right">
<span class="mlabels"><span class="mlabel">private</span></span> </td>
</tr>
</table>
</div><div class="memdoc">
<p>Precalculated constant depending on the bandwidth; <img class="formulaInl" alt="$ \gamma = -\frac{1}{2 \mu^2} $" src="form_13.png"/>. </p>
<p>Definition at line <a class="el" href="gaussian__kernel_8hpp_source.html#l00162">162</a> of file <a class="el" href="gaussian__kernel_8hpp_source.html">gaussian_kernel.hpp</a>.</p>
<p>Referenced by <a class="el" href="gaussian__kernel_8hpp_source.html#l00139">Bandwidth()</a>, <a class="el" href="gaussian__kernel_8hpp_source.html#l00065">Evaluate()</a>, <a class="el" href="gaussian__kernel_8hpp_source.html#l00146">Gamma()</a>, <a class="el" href="gaussian__kernel_8hpp_source.html#l00092">Gradient()</a>, <a class="el" href="gaussian__kernel_8hpp_source.html#l00104">GradientForSquaredDistance()</a>, and <a class="el" href="gaussian__kernel_8hpp_source.html#l00150">Serialize()</a>.</p>
</div>
</div>
<hr/>The documentation for this class was generated from the following file:<ul>
<li>src/mlpack/core/kernels/<a class="el" href="gaussian__kernel_8hpp_source.html">gaussian_kernel.hpp</a></li>
</ul>
</div><!-- contents -->
<!-- start footer part -->
<hr class="footer"/><address class="footer"><small>
Generated by &#160;<a href="http://www.doxygen.org/index.html">
<img class="footer" src="doxygen.png" alt="doxygen"/>
</a> 1.8.11
</small></address>
</body>
<script type="text/javascript">
var x = document.getElementsByClassName("formulaDsp");
var i;
for (i = 0; i < x.length; i++)
{
x[i].width /= 4;
}
</script>
</html>