function [y,dfce] = adaclass(X,model)% ADACLASS AdaBoost classifier.%% Synopsis:%  [y,dfce] = adaclass(X,model)%% Description:%  This function implements the AdaBoost classifier which%  its discriminant function is composed of a weighted sum%  of binary rules. It is assumed here that the binary rules%  respond with label 1 or 2 (not 1 and -1 as used in %  AdaBoost literature).%% Input:%  X [dim x num_data] Vectors to be classified.%  model [struct] AdaBoost classifier:%   .rule [cell 1 x T] Binary weak rules.%   .Alpha [1 x T] Weights of the weak rules.%   .fun = 'adaclass' (optinal).%% Output:%  y [1 x num_data] Predicted labels.%  dfce [1 x num_data] Values of weighted sum of %   weak rules; y(i) = 1 if dfce(i) >= 0 and%   y(i) = 2 if dfce(i) < 0.%% Example:%  trn_data = load('riply_trn');%  tst_data = load('riply_tst');%  options.learner = 'weaklearner';%  options.max_rules = 50;%  options.verb = 1;%  model = adaboost(trn_data, options);%  ypred1 = adaclass(trn_data.X,model);%  ypred2 = adaclass(tst_data.X,model);%  trn_err = cerror(ypred1,trn_data.y)%  tst_err = cerror(ypred2,tst_data.y)%% See also: %  ADABOOST, WEAKLEARNER.%% About: Statistical Pattern Recognition Toolbox% (C) 1999-2004, Written by Vojtech Franc and Vaclav Hlavac% <a href="http://www.cvut.cz">Czech Technical University Prague</a>% <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a>% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a>% Modifications:% 25-aug-2004, VF% 11-aug-2004, VFdfce = [];for i=1:length(model.rule),  curr_y = feval(model.rule{i}.fun,X,model.rule{i});  curr_y = 3-2*curr_y;    if isempty(dfce),    dfce = curr_y*model.Alpha(i);  else    dfce = dfce + curr_y*model.Alpha(i);  endendy = zeros(size(dfce));y(find(dfce>=0)) = 1;y(find(dfce<0)) = 2;return;% EOF