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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>andrerr.m</title><link rel="stylesheet" type="text/css" href="../../../m-syntax.css"></head><body><code><span class=defun_kw>function</span> <span class=defun_out>[err,r,inx] </span>= <span class=defun_name>andrerr</span>(<span class=defun_in> model, distrib </span>)<br><span class=h1>% ANDRERR Classification error of the Generalized Anderson's task.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Synopsis:</span></span><br><span class=help>% [err,r,inx] = andrerr( model, distrib )</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Description:</span></span><br><span class=help>% This function computes the classification error of</span><br><span class=help>% the given linear classifier and underlying set of Gaussian </span><br><span class=help>% distributions as defined in the Generalized Anderson's </span><br><span class=help>% task [SH10].</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 [dim x 1] Normal vector the separating hyperplane.</span><br><span class=help>% .b [real] Bias the hyperplane.</span><br><span class=help>% </span><br><span class=help>% distrib [struct] Set of Gaussians with assigned binary labels:</span><br><span class=help>% .Mean [dim x ncomp] Mean vectors.</span><br><span class=help>% .Cov [dim x dim x ncomp] Covariance matrices.</span><br><span class=help>% .y [1 x ncomp] Lables of Gaussians (1 or 2).</span><br><span class=help>% </span><br><span class=help>% <span class=help_field>Output:</span></span><br><span class=help>% err [real] Probability of misclassification.</span><br><span class=help>% r [real] Mahalanobis distance of the cloasest Gaussian.</span><br><span class=help>% inx [int] Index of the cloasest Gaussian.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Example:</span></span><br><span class=help>% distrib = load('mars');</span><br><span class=help>% model = eanders(distrib,{'err',0.06'});</span><br><span class=help>% figure; pandr( model, distrib );</span><br><span class=help>% error = andrerr( model, distrib )</span><br><span class=help>%</span><br><span class=help>% See also </span><br><span class=help>% ANDRORIG, GANDERS, EANDERS, GGRADANDR.</span><br><span class=help>%</span><br><hr><span class=help1>% <span class=help1_field>About:</span> Statistical Pattern Recognition Toolbox</span><br><span class=help1>% (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac</span><br><span class=help1>% <a href="http://www.cvut.cz">Czech Technical University Prague</a></span><br><span class=help1>% <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a></span><br><span class=help1>% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a></span><br><br><span class=help1>% <span class=help1_field>Modifications:</span></span><br><span class=help1>% 4-may-2004, VF</span><br><span class=help1>% 17-sep-2003, VF</span><br><br><hr><span class=keyword>if</span> ~isfield(distrib,<span class=quotes>'y'</span>), distrib.y = [1,2]; <span class=keyword>end</span><br>[dim,ncomp] = size(distrib.Mean);<br><br>Radius = zeros(ncomp,1);<br><br><span class=keyword>for</span> i=1:ncomp,<br> <br> <span class=keyword>if</span> distrib.y(i) == 1,<br> Radius(i) = (model.W'*distrib.Mean(:,i)+model.b)/...<br> sqrt(model.W'*distrib.Cov(:,:,i)*model.W);<br> <span class=keyword>else</span><br> Radius(i) = -(model.W'*distrib.Mean(:,i)+model.b)/...<br> sqrt(model.W'*distrib.Cov(:,:,i)*model.W);<br> <span class=keyword>end</span><br> <br><span class=keyword>end</span><br><br>[r,inx] = min( Radius );<br>err=1-cdf(<span class=quotes>'norm'</span>,r,0,1);<br><br><span class=jump>return</span>;<br></code>?? 快捷鍵說明
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