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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>linclass.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>[y,dfce]</span>=<span class=defun_name>linclass</span>(<span class=defun_in> X, model</span>)<br><span class=h1>% LINCLASS Linear classifier.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Synopsis:</span></span><br><span class=help>% [y,dfce] = linclass( X, model)</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Description:</span></span><br><span class=help>% This function classifies input data X using linear</span><br><span class=help>% discriminant function:</span><br><span class=help>%</span><br><span class=help>% y(i) = argmax W(:,y)'*X(:,i) + b(y)</span><br><span class=help>% y</span><br><span class=help>%</span><br><span class=help>% where parameters W [dim x nfun] and b [1 x nfun] are given </span><br><span class=help>% in model and nfun is number of discriminant functions.</span><br><span class=help>%</span><br><span class=help>% In the binary case (nfun=1) the classification rule is following</span><br><span class=help>% y(i) = 1 if W'*X(:,i) + b >= 0</span><br><span class=help>% 2 if W'*X(:,i) + b < 0</span><br><span class=help>% </span><br><span class=help>% where W [dim x 1], b [1x1] are parameters given in model.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Input:</span></span><br><span class=help>% X [dim x num_data] Data to be classified.</span><br><span class=help>%</span><br><span class=help>% model [struct] Parameters of linear classifier:</span><br><span class=help>% .W [dim x nfun] Linear term.</span><br><span class=help>% .b [nfun x 1] Bias.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Output:</span></span><br><span class=help>% y [1 x num_data] Predicted labels.</span><br><span class=help>% dfce [nfun x num_data] Values of discriminat function.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Examples:</span></span><br><span class=help>% trn = load('riply_trn');</span><br><span class=help>% tst = load('riply_tst');</span><br><span class=help>% model = fld( trn );</span><br><span class=help>% ypred = linclass( tst.X, model );</span><br><span class=help>% cerror( ypred, tst.y )</span><br><span class=help>% figure; ppatterns( trn ); pline( model );</span><br><span class=help>%</span><br><span class=help>% See also </span><br><span class=help>% PERCEPTRON, MPERCEPTRON, FLD, ANDERSON.</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>% 2-may-2004, VF</span><br><br><hr><span class=comment>% allow model to be gievn as a cell</span><br>model = c2s(model);<br><br>[dim, num_data] = size(X);<br><br>nfun = size(model.W,2);<br><br><span class=keyword>if</span> nfun == 1,<br> <span class=comment>% binary case</span><br> dfce = model.W'*X + model.b;<br> y = ones(1,num_data);<br> y(find(dfce < 0)) = 2;<br><span class=keyword>else</span><br> <span class=comment>% multi-class case </span><br> dfce = model.W'*X + model.b(:)*ones(1,num_data);<br> [dummy,y] = max( dfce );<br><span class=keyword>end</span><br><br><span class=jump>return</span>;<br></code>?? 快捷鍵說明
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