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;