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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">ANDRORIG</b><td valign="baseline" align="right" class="function"><a href="../../linear/anderson/index.html" target="mdsdir"><img border = 0 src="../../up.gif"></a></table> <p><b>Original method to solve the Anderson-Bahadur'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> model = androrig(distrib)</span><br><span class=help> model = androrig(distrib,options)</span><br><span class=help> model = androrig(distrib,options,init_model)</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> It solves the original Anderson task [<a href="../../references.html#Anderson62" title = "" >Anderson62</a>]. The goal is to </span><br><span class=help> find binary linear classifier which minimizes probability of </span><br><span class=help> misclassification. The class conditional probability distributions </span><br><span class=help> are Gaussians. The a prior probabilities is unknown.</span><br><span class=help></span><br><span class=help> model = androrig( distrib ) solves the original Anderson's task </span><br><span class=help> for given two Gaussians distributions. The structure distrib </span><br><span class=help> <span class=help_field>contains:</span></span><br><span class=help> .Mean [dim x 2] Matrix containing mean vectors of the first and</span><br><span class=help> second class distributions.</span><br><span class=help> .Cov [dim x dim x 2]$ Matrix containing covariance matrices of the</span><br><span class=help> first and second distribution.</span><br><span class=help></span><br><span class=help> model = androrig( distrib, options ) allows to specify the maximal </span><br><span class=help> number of iterations options.tmax and the distance to the</span><br><span class=help> optimal solution options.eps defining the stopping condition.</span><br><span class=help></span><br><span class=help> model = androrig( distrib, options, init_model ) allows to specify </span><br><span class=help> the initial point init_model.gamma. The initial value of the</span><br><span class=help> counter of iterations can be specified in options.t.</span><br><span class=help></span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> distrib [struct] Two Gaussians:</span><br><span class=help> .Mean [ dim x 2] Mean veactors.</span><br><span class=help> .Cov [ dim x dim x 2] Covariance matrices.</span><br><span class=help></span><br><span class=help> options [struct] Defines stopping condition:</span><br><span class=help> .tmax [1x1] Maximal number of iteration.</span><br><span class=help> .eps [1x1] Closeness to the optimal solution. If eps=0 the</span><br><span class=help> algorithm converges to the optimal solution but it does not</span><br><span class=help> have to stop (default 0.001).</span><br><span class=help></span><br><span class=help> init_model [struct] Init model:</span><br><span class=help> .gamma [1x1] Auxciliary variable (default 1).</span><br><span class=help> .t [1x1] (optional) Counter of iterations.</span><br><span class=help> </span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> model [struct] Binary linear classifier:</span><br><span class=help> .W [dim x 1] Normal vector the found hyperplane W'*x+b=0.</span><br><span class=help> .b [1x1] Bias of the hyperplane.</span><br><span class=help> </span><br><span class=help> .err [1x1] Probability of misclassification.</span><br><span class=help> .t [1x1] Number of iterations.</span><br><span class=help> .r1 [1x1] Mahalanobis distance of the first Gaussian to the</span><br><span class=help> found hyperplane.</span><br><span class=help> .r2 [1x1] Mahalanobis distance of the second Gaussian to the</span><br><span class=help> found hyperplane. In the optimal solution r1 = r2.</span><br><span class=help> .exitflag [1x1] 0 ... maximal number of iterations tmax exceeded.</span><br><span class=help> 1 ... condition delta < eps satisfied.</span><br><span class=help> .delta [1x1] Indicates distance from the optimal solution.</span><br><span class=help> .gamma [1x1] Auxciliary variable.</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> distrib = mlcgmm(data);</span><br><span class=help> model = androrig(distrib);</span><br><span class=help> figure; pandr( model, distrib );</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 = "../../linear/anderson/ganders.html" target="mdsbody">GANDERS</a>, <a href = "../../linear/anderson/eanders.html" target="mdsbody">EANDERS</a>, <a href = "../../linear/anderson/ggradandr.html" target="mdsbody">GGRADANDR</a>, <a href = "../../linear/linclass.html" target="mdsbody">LINCLASS</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../../linear/anderson/list/androrig.html">androrig.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> 20-may-2004, VF<br> 24-Feb-2003, VF<br></body></html>⌨️ 快捷键说明
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