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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">PDFGMM</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 gaussian mixture model.</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 = pdfgmm(X, model )</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> This function evaluates a probability density function </span><br><span class=help> determined by Gaussian mixture model (GMM) for given input column </span><br><span class=help> vectors in X. The GMM is defined as</span><br><span class=help> </span><br><span class=help> y(i) = sum model.Prior(j)*pdfgauss(X(:,i),model.Mean(:,j),model.Cov(:,:,j))</span><br><span class=help> j=1:ncomp</span><br><span class=help></span><br><span class=help> for all i=1:size(X,2).</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> model.Mean [dim x ncomp] Means of Gaussians.</span><br><span class=help> model.Cov [dim x dim x ncomp] Covarince matrices.</span><br><span class=help> model.Prior [ncomp x 1] Weights of components.</span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> y [1 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> distrib = struct('Mean',[-2 3],'Cov',[1 0.5],'Prior',[0.4 0.6]);</span><br><span class=help> y = pdfgmm(x,distrib);</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> model.Mean(:,1) = [-1;-1]; model.Cov(:,:,1) = [1,0.5;0.5,1]; </span><br><span class=help> model.Mean(:,2) = [1;1]; model.Cov(:,:,2) = [1,-0.5;-0.5,1]; </span><br><span class=help> model.Prior = [0.4 0.6];</span><br><span class=help> [Ax,Ay] = meshgrid(linspace(-5,5,100), linspace(-5,5,100));</span><br><span class=help> y = pdfgmm([Ax(:)';Ay(:)'],model);</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/gmmsamp.html" target="mdsbody">GMMSAMP</a>, <a href = "../probab/pdfgauss.html" target="mdsbody">PDFGAUSS</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../probab/list/pdfgmm.html">pdfgmm.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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