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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">PANDR</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>Visualizes solution of the Generalized Anderson's task.
</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> h = pandr(model)
</span><br><span class=help>
</span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> It vizualizes solution of the Generalized Anderson's task
</span><br><span class=help> for bivariate input Gaussians.
</span><br><span class=help>
</span><br><span class=help> The input of the task are two sets of Gaussians which
</span><br><span class=help> describe the first and second class. The Gaussians are denoted as
</span><br><span class=help> the ellipses (shape -> covariance, center -> mean).
</span><br><span class=help> The output of the task is the linear classifier denoted as a line
</span><br><span class=help> separating the 2D feature space.
</span><br><span class=help>
</span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> model [struct] Linear classifier:
</span><br><span class=help> .W [2 x 1] Normal vector of the separating hyperplane.
</span><br><span class=help> .b [real] Bias of the hyperplane.
</span><br><span class=help>
</span><br><span class=help> distrib [struct] Set of binary labeled Gaussians:
</span><br><span class=help> .Mean [2 x ncomp] Mean vectors.
</span><br><span class=help> .Cov [2 x 2 x ncomp] Covariance matrices.
</span><br><span class=help> .y [1 x ncomp] Labels 1 or 2.
</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>
</span><br></code></div> <hr> <b>Source:</b> <a href= "../visual/list/pandr.html">pandr.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> 4-may-2004, VF
<br> 24-feb-2003, VF
<br> 30-sep-2002, VF
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