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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>Contents.m</title><link rel="stylesheet" type="text/css" href="../stpr.css"></head><body><table border=0 width="100%" cellpadding=0 cellspacing=0><tr valign="baseline"><td valign="baseline" class="function"><b class="function">PDFGAUSS</b><td valign="baseline" align="right" class="function"><a href="../probab/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>Evaluates multivariate Gaussian distribution.</b></p> <hr><div class='code'><code><span class=help></span><br><span class=help> <span class=help_field>Synopsis:</span></span><br><span class=help> y = pdfgauss(X, Mean, Cov)</span><br><span class=help> y = pdfgauss(X, model )</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> y = pdfgauss(X, Mean, Cov) evaluates a multi-variate Gaussian </span><br><span class=help> probability density function(s) for given input column vectors in X.</span><br><span class=help> Mean [dim x ncomp] and Cov [dim x dim x ncomp] describe a set of </span><br><span class=help> ncomp Gaussian distributions to be evaluted such that</span><br><span class=help></span><br><span class=help> y(i,j) = exp(-0.5(mahalan(X(:,j),Mean(:,i),Cov(:,:,i) )))/norm_const</span><br><span class=help></span><br><span class=help> where i=1:ncomp and j=1:size(X,2). If the Gaussians are</span><br><span class=help> uni-variate then the covariaves can be given as a vector</span><br><span class=help> Cov = [Cov_1, Cov_2, ..., Cov_comp].</span><br><span class=help></span><br><span class=help> y = pdfgauss( X, model ) takes Gaussian parameters from structure</span><br><span class=help> fields model.Mean and model.Cov.</span><br><span class=help></span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> X [dim x num_data] Input matrix of column vectors.</span><br><span class=help> Mean [dim x ncomp] Means of Gaussians.</span><br><span class=help> Cov [dim x dim x ncomp] Covarince matrices.</span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> y [ncomp x num_data] Values of probability density function.</span><br><span class=help></span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> </span><br><span class=help> Univariate case</span><br><span class=help> x = linspace(-5,5,100);</span><br><span class=help> y = pdfgauss(x,0,1);</span><br><span class=help> figure; plot(x,y)</span><br><span class=help></span><br><span class=help> Multivariate case</span><br><span class=help> [Ax,Ay] = meshgrid(linspace(-5,5,100), linspace(-5,5,100));</span><br><span class=help> y = pdfgauss([Ax(:)';Ay(:)'],[0;0],[1 0.5; 0.5 1]);</span><br><span class=help> figure; surf( Ax, Ay, reshape(y,100,100)); shading interp;</span><br><span class=help></span><br><span class=help> <span class=also_field>See also </span><span class=also></span><br><span class=help><span class=also> <a href = "../probab/gsamp.html" target="mdsbody">GSAMP</a>, <a href = "../probab/pdfgmm.html" target="mdsbody">PDFGMM</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../probab/list/pdfgauss.html">pdfgauss.m</a> <p><b class="info_field">About: </b> Statistical Pattern Recognition Toolbox<br> (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac<br> <a href="http://www.cvut.cz">Czech Technical University Prague</a><br> <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a><br> <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a><br> <p><b class="info_field">Modifications: </b> <br> 28-apr-2004, VF<br></body></html>⌨️ 快捷键说明
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