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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">PGAUSS</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 set of bivariate Gaussians.</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> pgauss(model)</span><br><span class=help> pgauss(model,options)</span><br><span class=help> h = pgauss(...)</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> pgauss(model) visualizes a set of bivariate Gaussians as</span><br><span class=help> isolines (ellipse) with equal probability density functions.</span><br><span class=help> The Gaussians are given by mean vectors model.Mean [2xncomp]</span><br><span class=help> and covariance matrices model.Cov [2x2xncomp]. If labels</span><br><span class=help> model.y [1xncomp] are given then the Gaussians are distinguished</span><br><span class=help> by colors correspoding to labels.</span><br><span class=help> </span><br><span class=help> pgauss(model,options) structure options controls visualization;</span><br><span class=help> If options.fill equals 1 then Ellipses are filled otherwise only</span><br><span class=help> contours are plotted. The isolines to be drawn are given by </span><br><span class=help> values of probability distribution function in field </span><br><span class=help> options.p [1xncomp]. If length(option.p)==1 then isolines for</span><br><span class=help> all Gaussians are drawn for the same value.</span><br><span class=help> </span><br><span class=help> h = pgauss(...) returns handles of used graphics objects.</span><br><span class=help> </span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> model [struct] Parameters of Gaussian distributions:</span><br><span class=help> .Mean [2 x ncomp] Mean vectors of ncomp Gaussians.</span><br><span class=help> .Cov [2 x 2 x ncomp] Covariance matrices.</span><br><span class=help> .y [1 x ncomp] (optional) Labels of Gaussians used to distingush </span><br><span class=help> them by colors. If y is not given then y = 1:ncomp is used.</span><br><span class=help> </span><br><span class=help> options.p [1 x ncomp] Value of p.d.f on the draw isolines.</span><br><span class=help> If not given then p is computed to make non-overlapping isolines.</span><br><span class=help> options.fill [int] If 1 then ellipses are filled (default 0).</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 objects.</span><br><span class=help></span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> data = load('riply_trn');</span><br><span class=help> model = mlcgmm( data );</span><br><span class=help> figure; hold on;</span><br><span class=help> ppatterns(data);</span><br><span class=help> pgauss( model );</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../visual/list/pgauss.html">pgauss.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> 23-aug-2004, VF, uses model.y to color plots in 1D case<br> 30-apr-2004, VF<br></body></html>?? 快捷鍵說明
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