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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>adaclass.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>adaclass</span>(<span class=defun_in>X,model</span>)<br><span class=h1>% ADACLASS AdaBoost classifier.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Synopsis:</span></span><br><span class=help>% [y,dfce] = adaclass(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 implements the AdaBoost classifier which</span><br><span class=help>% its discriminant function is composed of a weighted sum</span><br><span class=help>% of binary rules. It is assumed here that the binary rules</span><br><span class=help>% respond with label 1 or 2 (not 1 and -1 as used in </span><br><span class=help>% AdaBoost literature).</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] Vectors to be classified.</span><br><span class=help>% model [struct] AdaBoost classifier:</span><br><span class=help>% .rule [cell 1 x T] Binary weak rules.</span><br><span class=help>% .Alpha [1 x T] Weights of the weak rules.</span><br><span class=help>% .fun = 'adaclass' (optinal).</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 [1 x num_data] Values of weighted sum of </span><br><span class=help>% weak rules; y(i) = 1 if dfce(i) >= 0 and</span><br><span class=help>% y(i) = 2 if dfce(i) < 0.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Example:</span></span><br><span class=help>% trn_data = load('riply_trn');</span><br><span class=help>% tst_data = load('riply_tst');</span><br><span class=help>% options.learner = 'weaklearner';</span><br><span class=help>% options.max_rules = 50;</span><br><span class=help>% options.verb = 1;</span><br><span class=help>% model = adaboost(trn_data, options);</span><br><span class=help>% ypred1 = adaclass(trn_data.X,model);</span><br><span class=help>% ypred2 = adaclass(tst_data.X,model);</span><br><span class=help>% trn_err = cerror(ypred1,trn_data.y)</span><br><span class=help>% tst_err = cerror(ypred2,tst_data.y)</span><br><span class=help>%</span><br><span class=help>% See also: </span><br><span class=help>% ADABOOST, WEAKLEARNER.</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) 1999-2004, 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>% 25-aug-2004, VF</span><br><span class=help1>% 11-aug-2004, VF</span><br><br><hr>dfce = [];<br><span class=keyword>for</span> i=1:length(model.rule),<br><br> curr_y = <span class=eval>feval</span>(model.rule{i}.fun,X,model.rule{i});<br> curr_y = 3-2*curr_y;<br> <br> <span class=keyword>if</span> isempty(dfce),<br> dfce = curr_y*model.Alpha(i);<br> <span class=keyword>else</span><br> dfce = dfce + curr_y*model.Alpha(i);<br> <span class=keyword>end</span><br><span class=keyword>end</span><br><br>y = zeros(size(dfce));<br>y(find(dfce>=0)) = 1;<br>y(find(dfce<0)) = 2;<br><br><span class=jump>return</span>;<br><span class=comment>% EOF</span><br></code>
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