<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
<html><head><meta http-equiv="Content-Type" content="text/html;charset=iso-8859-1">
<title>Gaussian PDF transformation functions</title>
<link href="doxygen.css" rel="stylesheet" type="text/css">
<link href="tabs.css" rel="stylesheet" type="text/css">
</head><body>
<div align="left"><a href="http://www.mrpt.org/">Main MRPT website</a> > <b>C++ reference</b> </div>
<div align="right">
<a href="index.html"><img border="0" src="mrpt_logo.png" alt="MRPT logo"></a>
</div>
<!-- Generated by Doxygen 1.7.5 -->
<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 Page</span></a></li>
<li><a href="pages.html"><span>Related Pages</span></a></li>
<li><a href="modules.html"><span>Modules</span></a></li>
<li><a href="namespaces.html"><span>Namespaces</span></a></li>
<li><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">
<div class="left">
<form id="FSearchBox" action="search.php" method="get">
<img id="MSearchSelect" src="search/mag.png" alt=""/>
<input type="text" id="MSearchField" name="query" value="Search" size="20" accesskey="S"
onfocus="searchBox.OnSearchFieldFocus(true)"
onblur="searchBox.OnSearchFieldFocus(false)"/>
</form>
</div><div class="right"></div>
</div>
</li>
</ul>
</div>
</div>
<div class="header">
<div class="summary">
<a href="#func-members">Functions</a> </div>
<div class="headertitle">
<div class="title">Gaussian PDF transformation functions</div> </div>
<div class="ingroups"><a class="el" href="group__mrpt__base__grp.html">[mrpt-base]</a></div></div>
<div class="contents">
<div class="dynheader">
Collaboration diagram for Gaussian PDF transformation functions:</div>
<div class="dyncontent">
<center><table><tr><td><img src="group__gausspdf__transform__grp.png" border="0" alt="" usemap="#group____gausspdf____transform____grp"/>
<map name="group____gausspdf____transform____grp" id="group____gausspdf____transform____grp">
<area shape="rect" id="node2" href="group__mrpt__base__grp.html" title=" Back to list of all libraries | See all modules   " alt="" coords="5,5,93,32"/></map>
</td></tr></table></center>
</div>
<table class="memberdecls">
<tr><td colspan="2"><h2><a name="func-members"></a>
Functions</h2></td></tr>
<tr><td class="memTemplParams" colspan="2">template<class VECTORLIKE1 , class MATLIKE1 , class USERPARAM , class VECTORLIKE2 , class VECTORLIKE3 , class MATLIKE2 > </td></tr>
<tr><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="group__gausspdf__transform__grp.html#gaabbaca6808e3d9be3fd37f82f98049f0">mrpt::math::transform_gaussian_unscented</a> (const VECTORLIKE1 &x_mean, const MATLIKE1 &x_cov, void(*functor)(const VECTORLIKE1 &x, const USERPARAM &fixed_param, VECTORLIKE3 &y), const USERPARAM &fixed_param, VECTORLIKE2 &y_mean, MATLIKE2 &y_cov, const bool *elem_do_wrap2pi=NULL, const double alpha=1e-3, const double K=0, const double beta=2.0)</td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Scaled unscented transformation (SUT) for estimating the Gaussian distribution of a variable Y=f(X) for an arbitrary function f() provided by the user. <a href="#gaabbaca6808e3d9be3fd37f82f98049f0"></a><br/></td></tr>
<tr><td class="memTemplParams" colspan="2">template<class VECTORLIKE1 , class MATLIKE1 , class USERPARAM , class VECTORLIKE2 , class VECTORLIKE3 , class MATLIKE2 > </td></tr>
<tr><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="group__gausspdf__transform__grp.html#ga690d0b41f293578fcfd067e9177fc863">mrpt::math::transform_gaussian_montecarlo</a> (const VECTORLIKE1 &x_mean, const MATLIKE1 &x_cov, void(*functor)(const VECTORLIKE1 &x, const USERPARAM &fixed_param, VECTORLIKE3 &y), const USERPARAM &fixed_param, VECTORLIKE2 &y_mean, MATLIKE2 &y_cov, const size_t num_samples=1000, typename <a class="el" href="structmrpt_1_1aligned__containers.html">mrpt::aligned_containers</a>< VECTORLIKE3 >::vector_t *out_samples_y=NULL)</td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Simple Montecarlo-base estimation of the Gaussian distribution of a variable Y=f(X) for an arbitrary function f() provided by the user. <a href="#ga690d0b41f293578fcfd067e9177fc863"></a><br/></td></tr>
<tr><td class="memTemplParams" colspan="2">template<class VECTORLIKE1 , class MATLIKE1 , class USERPARAM , class VECTORLIKE2 , class VECTORLIKE3 , class MATLIKE2 > </td></tr>
<tr><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="group__gausspdf__transform__grp.html#gac9caace672d90279c312d43877d8480b">mrpt::math::transform_gaussian_linear</a> (const VECTORLIKE1 &x_mean, const MATLIKE1 &x_cov, void(*functor)(const VECTORLIKE1 &x, const USERPARAM &fixed_param, VECTORLIKE3 &y), const USERPARAM &fixed_param, VECTORLIKE2 &y_mean, MATLIKE2 &y_cov, const VECTORLIKE1 &x_increments)</td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">First order uncertainty propagation estimator of the Gaussian distribution of a variable Y=f(X) for an arbitrary function f() provided by the user. <a href="#gac9caace672d90279c312d43877d8480b"></a><br/></td></tr>
</table>
<hr/><h2>Function Documentation</h2>
<a class="anchor" id="gac9caace672d90279c312d43877d8480b"></a><!-- doxytag: member="mrpt::math::transform_gaussian_linear" ref="gac9caace672d90279c312d43877d8480b" args="(const VECTORLIKE1 &x_mean, const MATLIKE1 &x_cov, void(*functor)(const VECTORLIKE1 &x, const USERPARAM &fixed_param, VECTORLIKE3 &y), const USERPARAM &fixed_param, VECTORLIKE2 &y_mean, MATLIKE2 &y_cov, const VECTORLIKE1 &x_increments)" -->
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template<class VECTORLIKE1 , class MATLIKE1 , class USERPARAM , class VECTORLIKE2 , class VECTORLIKE3 , class MATLIKE2 > </div>
<table class="memname">
<tr>
<td class="memname">void mrpt::math::transform_gaussian_linear </td>
<td>(</td>
<td class="paramtype">const VECTORLIKE1 & </td>
<td class="paramname"><em>x_mean</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const MATLIKE1 & </td>
<td class="paramname"><em>x_cov</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">void(*)(const VECTORLIKE1 &x, const USERPARAM &fixed_param, VECTORLIKE3 &y) </td>
<td class="paramname"><em>functor</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const USERPARAM & </td>
<td class="paramname"><em>fixed_param</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">VECTORLIKE2 & </td>
<td class="paramname"><em>y_mean</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">MATLIKE2 & </td>
<td class="paramname"><em>y_cov</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const VECTORLIKE1 & </td>
<td class="paramname"><em>x_increments</em> </td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div>
<div class="memdoc">
<p>First order uncertainty propagation estimator of the Gaussian distribution of a variable Y=f(X) for an arbitrary function f() provided by the user. </p>
<p>The user must supply the function in "functor" which takes points in the X space and returns the mapped point in Y, optionally using an extra, constant parameter ("fixed_param") which remains constant. The Jacobians are estimated numerically using the vector of small increments "x_increments". </p>
<dl class="see"><dt><b>See also:</b></dt><dd>The example in MRPT/samples/unscented_transform_test </dd>
<dd>
<a class="el" href="group__gausspdf__transform__grp.html#gaabbaca6808e3d9be3fd37f82f98049f0" title="Scaled unscented transformation (SUT) for estimating the Gaussian distribution of a variable Y=f(X) f...">transform_gaussian_unscented</a>, <a class="el" href="group__gausspdf__transform__grp.html#ga690d0b41f293578fcfd067e9177fc863" title="Simple Montecarlo-base estimation of the Gaussian distribution of a variable Y=f(X) for an arbitrary ...">transform_gaussian_montecarlo</a> </dd></dl>
<p>Definition at line <a class="el" href="transform__gaussian_8h_source.html#l00145">145</a> of file <a class="el" href="transform__gaussian_8h_source.html">transform_gaussian.h</a>.</p>
<p>References <a class="el" href="mrpt__macros_8h_source.html#l00370">MRPT_START</a>, <a class="el" href="jacobians_8h_source.html#l00128">mrpt::math::jacobians::jacob_numeric_estimate()</a>, and <a class="el" href="mrpt__macros_8h_source.html#l00374">MRPT_END</a>.</p>
</div>
</div>
<a class="anchor" id="ga690d0b41f293578fcfd067e9177fc863"></a><!-- doxytag: member="mrpt::math::transform_gaussian_montecarlo" ref="ga690d0b41f293578fcfd067e9177fc863" args="(const VECTORLIKE1 &x_mean, const MATLIKE1 &x_cov, void(*functor)(const VECTORLIKE1 &x, const USERPARAM &fixed_param, VECTORLIKE3 &y), const USERPARAM &fixed_param, VECTORLIKE2 &y_mean, MATLIKE2 &y_cov, const size_t num_samples=1000, typename mrpt::aligned_containers< VECTORLIKE3 >::vector_t *out_samples_y=NULL)" -->
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template<class VECTORLIKE1 , class MATLIKE1 , class USERPARAM , class VECTORLIKE2 , class VECTORLIKE3 , class MATLIKE2 > </div>
<table class="memname">
<tr>
<td class="memname">void mrpt::math::transform_gaussian_montecarlo </td>
<td>(</td>
<td class="paramtype">const VECTORLIKE1 & </td>
<td class="paramname"><em>x_mean</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const MATLIKE1 & </td>
<td class="paramname"><em>x_cov</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">void(*)(const VECTORLIKE1 &x, const USERPARAM &fixed_param, VECTORLIKE3 &y) </td>
<td class="paramname"><em>functor</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const USERPARAM & </td>
<td class="paramname"><em>fixed_param</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">VECTORLIKE2 & </td>
<td class="paramname"><em>y_mean</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">MATLIKE2 & </td>
<td class="paramname"><em>y_cov</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const size_t </td>
<td class="paramname"><em>num_samples</em> = <code>1000</code>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">typename <a class="el" href="structmrpt_1_1aligned__containers.html">mrpt::aligned_containers</a>< VECTORLIKE3 >::vector_t * </td>
<td class="paramname"><em>out_samples_y</em> = <code>NULL</code> </td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div>
<div class="memdoc">
<p>Simple Montecarlo-base estimation of the Gaussian distribution of a variable Y=f(X) for an arbitrary function f() provided by the user. </p>
<p>The user must supply the function in "functor" which takes points in the X space and returns the mapped point in Y, optionally using an extra, constant parameter ("fixed_param") which remains constant. </p>
<dl><dt><b>Parameters:</b></dt><dd>
<table class="params">
<tr><td class="paramname">out_samples_y</td><td>If !=NULL, this vector will contain, upon return, the sequence of random samples generated and propagated through the functor(). </td></tr>
</table>
</dd>
</dl>
<dl class="see"><dt><b>See also:</b></dt><dd>The example in MRPT/samples/unscented_transform_test </dd>
<dd>
<a class="el" href="group__gausspdf__transform__grp.html#gaabbaca6808e3d9be3fd37f82f98049f0" title="Scaled unscented transformation (SUT) for estimating the Gaussian distribution of a variable Y=f(X) f...">transform_gaussian_unscented</a>, <a class="el" href="group__gausspdf__transform__grp.html#gac9caace672d90279c312d43877d8480b" title="First order uncertainty propagation estimator of the Gaussian distribution of a variable Y=f(X) for a...">transform_gaussian_linear</a> </dd></dl>
<p>Definition at line <a class="el" href="transform__gaussian_8h_source.html#l00116">116</a> of file <a class="el" href="transform__gaussian_8h_source.html">transform_gaussian.h</a>.</p>
<p>References <a class="el" href="mrpt__macros_8h_source.html#l00370">MRPT_START</a>, <a class="el" href="namespacemrpt_1_1random.html#a4743bfa8fcb282b6f5d66395ccabaa73">mrpt::random::randomGenerator</a>, <a class="el" href="_random_generators_8h_source.html#l00239">mrpt::random::CRandomGenerator::drawGaussianMultivariateMany()</a>, <a class="el" href="base_2include_2mrpt_2math_2utils_8h_source.html#l00336">mrpt::math::covariancesAndMean()</a>, and <a class="el" href="mrpt__macros_8h_source.html#l00374">MRPT_END</a>.</p>
</div>
</div>
<a class="anchor" id="gaabbaca6808e3d9be3fd37f82f98049f0"></a><!-- doxytag: member="mrpt::math::transform_gaussian_unscented" ref="gaabbaca6808e3d9be3fd37f82f98049f0" args="(const VECTORLIKE1 &x_mean, const MATLIKE1 &x_cov, void(*functor)(const VECTORLIKE1 &x, const USERPARAM &fixed_param, VECTORLIKE3 &y), const USERPARAM &fixed_param, VECTORLIKE2 &y_mean, MATLIKE2 &y_cov, const bool *elem_do_wrap2pi=NULL, const double alpha=1e-3, const double K=0, const double beta=2.0)" -->
<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template<class VECTORLIKE1 , class MATLIKE1 , class USERPARAM , class VECTORLIKE2 , class VECTORLIKE3 , class MATLIKE2 > </div>
<table class="memname">
<tr>
<td class="memname">void mrpt::math::transform_gaussian_unscented </td>
<td>(</td>
<td class="paramtype">const VECTORLIKE1 & </td>
<td class="paramname"><em>x_mean</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const MATLIKE1 & </td>
<td class="paramname"><em>x_cov</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">void(*)(const VECTORLIKE1 &x, const USERPARAM &fixed_param, VECTORLIKE3 &y) </td>
<td class="paramname"><em>functor</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const USERPARAM & </td>
<td class="paramname"><em>fixed_param</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">VECTORLIKE2 & </td>
<td class="paramname"><em>y_mean</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">MATLIKE2 & </td>
<td class="paramname"><em>y_cov</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const bool * </td>
<td class="paramname"><em>elem_do_wrap2pi</em> = <code>NULL</code>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const double </td>
<td class="paramname"><em>alpha</em> = <code>1e-3</code>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const double </td>
<td class="paramname"><em>K</em> = <code>0</code>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">const double </td>
<td class="paramname"><em>beta</em> = <code>2.0</code> </td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td>
</tr>
</table>
</div>
<div class="memdoc">
<p>Scaled unscented transformation (SUT) for estimating the Gaussian distribution of a variable Y=f(X) for an arbitrary function f() provided by the user. </p>
<p>The user must supply the function in "functor" which takes points in the X space and returns the mapped point in Y, optionally using an extra, constant parameter ("fixed_param") which remains constant.</p>
<p>The parameters alpha, K and beta are the common names of the SUT method, and the default values are those recommended in most papers.</p>
<dl><dt><b>Parameters:</b></dt><dd>
<table class="params">
<tr><td class="paramname">elem_do_wrap2pi</td><td>If !=NULL; it must point to an array of "bool" of <a class="el" href="namespacemrpt_1_1math.html#a632ae0aecf78103f87f18f9ac33f7170">size()</a>==dimension of each element, stating if it's needed to do a wrap to [-pi,pi] to each dimension. </td></tr>
</table>
</dd>
</dl>
<dl class="see"><dt><b>See also:</b></dt><dd>The example in MRPT/samples/unscented_transform_test </dd>
<dd>
<a class="el" href="group__gausspdf__transform__grp.html#ga690d0b41f293578fcfd067e9177fc863" title="Simple Montecarlo-base estimation of the Gaussian distribution of a variable Y=f(X) for an arbitrary ...">transform_gaussian_montecarlo</a>, <a class="el" href="group__gausspdf__transform__grp.html#gac9caace672d90279c312d43877d8480b" title="First order uncertainty propagation estimator of the Gaussian distribution of a variable Y=f(X) for a...">transform_gaussian_linear</a> </dd></dl>
<p>Definition at line <a class="el" href="transform__gaussian_8h_source.html#l00057">57</a> of file <a class="el" href="transform__gaussian_8h_source.html">transform_gaussian.h</a>.</p>
<p>References <a class="el" href="mrpt__macros_8h_source.html#l00370">MRPT_START</a>, <a class="el" href="base_2include_2mrpt_2math_2utils_8h_source.html#l00233">mrpt::math::covariancesAndMeanWeighted()</a>, and <a class="el" href="mrpt__macros_8h_source.html#l00374">MRPT_END</a>.</p>
</div>
</div>
</div>
<br><hr><br> <table border="0" width="100%"> <tr> <td> Page generated by <a href="http://www.doxygen.org" target="_blank">Doxygen 1.7.5</a> for MRPT 0.9.5 SVN: at Sun Sep 25 17:20:18 UTC 2011</td><td></td> <td width="100"> </td> <td width="150"> </td></tr> </table> </body></html>
