function model = weaklearner(data)% WEAKLEARNER Produce classifier thresholding single feature.%% Synopsis:%  model = weaklearner(data)%% Description:%  This function produce a weak binary classifier which assigns%  input vector x to classes [1,2] based on thresholding a single %  feature. The output is a model which defines the threshold %  and feature index such that the weighted error is minimized.%  This weak learner can be used with the AdaBoost classifier%  (see 'help adaboost') as a feature selection method.%  % Input:%  data [struct] Training data:%   .X [dim x num_data] Training vectors.%   .y [1 x num_data] Binary labels (1 or 2).%   .D [1 x num_data] Weights of training vectors (optional).%    If not given then D is set to be uniform distribution.% % Output:%  model [struct] Binary linear classifier:%   .W [dim x 1] Normal vector of hyperplane.%   .b [1x1] Bias of the hyperplane.%   .fun = 'linclass'.%% Example:%  help adaboost%% See also: %  ADABOOST, ADACLASS.% % 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, VF[dim,num_data] = size(data.X);W = zeros(dim,1);Errors = zeros(dim,1);for i=1:dim,  x = [-inf,sort(unique(data.X(i,:)))];   err = []; f = [];  for j=1:length(x)-1,        f(j) = 0.5*(x(j)+x(j+1));    y = ones(1,num_data);    y(find(data.X(i,:)< f(j))) = 2;    err(j) = sum((y(:)~=data.y(:)).*data.D(:));      end  [minerr1,inx1] = min(err);  [minerr2,inx2] = min(1-err);  if minerr1 < minerr2,    W(i) = 1;    Errors(i) = minerr1;    b(i) = -f(inx1);  else    W(i) = - 1;    Errors(i) = minerr2;    b(i) = f(inx2);  end  end[dummy,inx] = min(Errors);model.W = zeros(dim,1);model.W(inx) = W(inx);model.b = b(inx);model.fun = 'linclass';y = linclass(data.X,model);err = sum((y(:)~=data.y(:)).*data.D(:));return;%EOF