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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/REC-html40/loose.dtd"><html><head> <title>Description of gaussian</title> <meta name="keywords" content="gaussian"> <meta name="description" content="gaussian: Multi-dimensional Gaussian propability density function"> <meta http-equiv="Content-Type" content="text/html; charset=big5"> <meta name="generator" content="m2html © 2003 Guillaume Flandin"> <meta name="robots" content="index, follow"> <link type="text/css" rel="stylesheet" href="../m2html.css"></head><body><a name="_top"></a><div><a href="../index.html">Home</a> > <a href="index.html">dcpr</a> > gaussian.m</div><!--<table width="100%"><tr><td align="left"><a href="../index.html"><img alt="<" border="0" src="../left.png"> Master index</a></td><td align="right"><a href="index.html">Index for dcpr <img alt=">" border="0" src="../right.png"></a></td></tr></table>--><h1>gaussian</h1><h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>gaussian: Multi-dimensional Gaussian propability density function</strong></div><h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="box"><strong>function out = gaussian(data, gParam); </strong></div><h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2><div class="fragment"><pre class="comment"> gaussian: Multi-dimensional Gaussian propability density function
Usage: out = gaussian(data, gParam)
data: d x n data matrix, representing n data vector of dimension d
gParam.mu: d x 1 vector
gParam.sigma: covariance matrix of 3 possible sizes
1 x 1: scalar times an identity matrix
d x 1: diagonal
d x d: full
out: 1 x n vector
Type "gaussian" for a self demo.
For example:
gParam.mu = [0; 0];
gParam.sigma = [9 3; 3, 4];
bound = 8;
pointNum = 31;
x = linspace(-bound, bound, pointNum);
y = linspace(-bound, bound, pointNum);
[xx, yy] = meshgrid(x, y);
data = [xx(:), yy(:)]';
out = gaussian(data, gParam);
zz = reshape(out, pointNum, pointNum);
subplot(2,2,1);
mesh(xx, yy, zz);
axis([-inf inf -inf inf -inf inf]);
set(gca, 'box', 'on');
subplot(2,2,2);
contour(xx, yy, zz, 15);
axis image;</pre></div><!-- crossreference --><h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>This function calls:<ul style="list-style-image:url(../matlabicon.gif)"></ul>This function is called by:<ul style="list-style-image:url(../matlabicon.gif)"><li><a href="gaussianLog.html" class="code" title="function out = gaussianLog(data, gaussianParam, gConst);">gaussianLog</a> gaussianLog: Multi-dimensional log Gaussian propability density function</li><li><a href="gaussianMle.html" class="code" title="function gaussianParam = gaussianMle(feature, plotOpt)">gaussianMle</a> mleGaussian: Maximum likelihood estimator for Gaussian distribution</li><li><a href="gaussianSimilarity.html" class="code" title="function similarity = gaussianSimilarity(x, binNum, plotOpt)">gaussianSimilarity</a> Evaluation of a PDF to see if it is close to Gaussian distribution</li><li><a href="gmmTrainDemo1d.html" class="code" title="">gmmTrainDemo1d</a> Example of using GMM (gaussian mixture model) for 1-D data</li><li><a href="interpGauss.html" class="code" title="function finalOutput=interpGauss(x, sampleData)">interpGauss</a> INTERPGAUSS ?H normalized gaussian basis function ???i??⌨️ 快捷键说明
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