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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">GMMSAMP</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>Generates sample from 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> data = gmmsamp(model,num_data)
</span><br><span class=help>
</span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> This function generates num_data samples from a Gaussian
</span><br><span class=help> mixture given by structure model. It returnes samples X
</span><br><span class=help> and a vector y of Gaussian component responsible for
</span><br><span class=help> generating corresponding sample.
</span><br><span class=help>
</span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> model
</span><br><span class=help> .Mean [dim x ncomp] Mean vectors.
</span><br><span class=help> .Cov [dim x dim x ncomp] Covariance matrices. In the case of
</span><br><span class=help> univariate mixture (dim=0) the variances can enter
</span><br><span class=help> as a vector Cov=[var1 var2 ... var_ncomp].
</span><br><span class=help> .Prior [ncomp x 1] Weighting coefficients of Gaussians.
</span><br><span class=help> num_data [int] Number of samples.
</span><br><span class=help>
</span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> data.X [dim x num_data] Generated sample data.
</span><br><span class=help> data.y [1 x num_data] Identifier of Gaussian which generated
</span><br><span class=help> given vector.
</span><br><span class=help>
</span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> model = struct('Mean',[-2 3],'Cov',[1 0.5],'Prior',[0.4 0.6]);
</span><br><span class=help> figure; hold on;
</span><br><span class=help> plot([-4:0.1:5], pdfgmm([-4:0.1:5],model),'r');
</span><br><span class=help> sample = gmmsamp(model,500);
</span><br><span class=help> [Y,X] = hist(sample.X,10);
</span><br><span class=help> bar(X,Y/500);
</span><br><span class=help>
</span><br><span class=help> See also
</span><br><span class=help> PDFGMM, GSAMP.
</span><br><span class=help>
</span><br></code></div> <hr> <b>Source:</b> <a href= "../probab/list/gmmsamp.html">gmmsamp.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
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