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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">PGMM</b><td valign="baseline" align="right" class="function"><a href="../visual/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>Vizualizes 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> pgmm( model );
</span><br><span class=help> pgmm( model, options );
</span><br><span class=help> h = pgmm( ... );
</span><br><span class=help>
</span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> It vizualizes univariate (dim=1) or bivariate (dim=2) Gaussin mixture
</span><br><span class=help> model (GMM). In the univariate case it also displays mixture components.
</span><br><span class=help> It returns handles of used graphics objects.
</span><br><span class=help>
</span><br><span class=help> In the case of bivariate GMM trhere are two options of visualization:
</span><br><span class=help> countours of p.d.f. ... options.visual = 'contour' (default)
</span><br><span class=help> surface of p.d.f. ... options.visual = 'surf'
</span><br><span class=help>
</span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> model.Mean [dim x ncomp] Mean values.
</span><br><span class=help> model.Cov [dim x ncomp] Covariances.
</span><br><span class=help> model.Prior [dim x ncomp] Mixture weights.
</span><br><span class=help>
</span><br><span class=help> options.comp [1x1] If 1 (default) then it plots also mixture components.
</span><br><span class=help> options.visual [string] If equal to 'contour' then contour function is
</span><br><span class=help> used if 'surf' then surf functions is used (see above).
</span><br><span class=help> options.adj_axes [1x1] If 1 (default) then axes are set to display
</span><br><span class=help> whole mixture otherwise unchanged.
</span><br><span class=help>
</span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> h [1 x nobjects] Handles of used graphics object.
</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> model1 = c2s({'Mean',[-3 0 3],'Cov',[0.5 1 0.8],'Prior',[0.4 0.3 0.3]});
</span><br><span class=help> figure; pgmm(model1);
</span><br><span class=help>
</span><br><span class=help> Bivariate case:
</span><br><span class=help> model2.Mean(:,1) = [-1;-1]; model2.Cov(:,:,1) = [1,0.5;0.5,1];
</span><br><span class=help> model2.Mean(:,2) = [1;1]; model2.Cov(:,:,2) = [1,-0.5;-0.5,1];
</span><br><span class=help> model2.Prior = [0.4 0.6];
</span><br><span class=help> figure; pgmm(model2);
</span><br><span class=help> figure; pgmm(model2,{'visual','surf'});
</span><br><span class=help>
</span><br></code></div> <hr> <b>Source:</b> <a href= "../visual/list/pgmm.html">pgmm.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> 2-may-2004, VF
<br> 29-apr2004, VF
<br> 8-mar-2004, VF
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