function [accuracy,labelsEstim,probs,probsAll] = predict_LRoma(w,X,y)
  
% logreg prediction results
%
% Inputs:
% 
% w - esimated model parameters (see "objFuncLR.m" for details)
% X - data matrix (see "objFuncLR.m" for details)
% y - category labels. NOTE! These must be given in conventional
% format as a vector of numeric values {1,2,...,M}, where M is 
% the number of categories. "objFuncLR.m" requires binary format.
% 
% accuracy - prediction accuracy
% labelsEstim - estimated category labels
% probs - probability estimates of the predictions
% probsAll - probability estimates of all categories
%
% Jukka-Pekka Kauppi
% jukka-pekka.kauppi@helsinki.fi
% University of Helsinki, Department of Computer Science
% Last modified: 23.8.2012

n = size(X,1);
d = size(X,2);
M = max(y);
  
X = [X ones(size(X,1),1)];
  
if M > 2
  w = reshape(w,(d+1),M);
  
  q_tm = zeros(n,M); %n*M
  for m = 1:M
    q_tm(:,m) = X*w(:,m);
  end    
  R = sum(exp(q_tm),2); % n*1;
  Ptm = (exp(q_tm)./repmat(R,1,M))'; % M*n
  [probs labelsEstim] = max(Ptm);
  probsAll = Ptm;
else

  q_t = X*w;  
  exp_q_t = exp(q_t);
  R = 1 + exp_q_t;
  P_t = exp_q_t ./ R;
  probsAll = [P_t' ;1-P_t'];
  [probs labelsEstim] = max(probsAll);
end

labelsEstim = labelsEstim(:)';
probs = probs(:)';
% compute total classification accuracy:
y = y(:)';
accuracy = sum(labelsEstim == y)/length(y);