function [Y, Y1, Y2] = ecocfwd(net, X)% ECOCFWD - Forward propagation for a set of learners with ECOC% %   Y = ECOCFWD(NET, X)%   Given an error correcting output code wrapper NET, the class%   predictions Y for a matrix of examples X (one example per row) are%   computed. Y(I) is the class label for X(I,:), Y(I) is a number in the%   range 1...NET.NCLASSES%   [Y, Y1, Y2] = ECOCFWD(NET, X)%   also returns a matrix Y1 of size [SIZE(X,1) NET.NCLASSES] that%   contains the Hamming distance of example X(I,:) to the codeword for%   class J in Y1(I,J). Y2 is a matrix of size [SIZE(X,1) NET.NBITS] with%   the outputs of all bit learners for each example.%%   See also ECOC, ECOCTRAIN, ECOCLOAD%% % Copyright (c) by Anton Schwaighofer (2001)% $Revision: 1.2 $ $Date: 2002/01/07 17:59:26 $% mailto:anton.schwaighofer@gmx.net% % This program is released unter the GNU General Public License.% error(nargchk(2, 2, nargin));error(consist(net, 'ecoc'));if ~(iscell(net.learner) & length(net.learner)==net.nbits),  error('NET.LEARNER must be a cell array with individual bit learners');end[N, d] = size(X);% Matrix of outputs for each learner and each test pointY2 = zeros([N net.nbits]);for i = 1:net.nbits,  if isa(net.learner{i}, 'numeric'),    % Handle trivial +1/-1 hypothesis    Y2(:,i) = net.learner{i};  else    % Compute output of each bit-learner for the test set    fwdFunc = [net.learner{i}.type 'fwd'];    Y2i = feval(fwdFunc, net.learner{i}, X);    % For learners that produce 0/1 outputs: rescale to -1/+1    if net.use01targets,      Y2i = Y2i*2-1;    end    Y2(:,i) = Y2i;  endend% Matrix of distance to each codeword for each test pointY1 = zeros([N net.nclasses]);for i = 1:net.nclasses,  % Compute L1 distance of each line Y2(j,:) to codeword i  Y1(:,i) = sum(abs(Y2-repmat(net.code(i,:), [N 1])), 2);end% Output: predict the class with minimum L1 distance[dummy, Y] = min(Y1, [], 2);