wolffd@0: function [post,lik,lli] = logistK_eval(beta,x,y)
wolffd@0: % [post,lik,lli] = logistK_eval(beta,x,y)
wolffd@0: %
wolffd@0: % Evaluate logistic regression model.
wolffd@0: % 
wolffd@0: % INPUT
wolffd@0: % 	beta 	dxk model coefficients (as returned by logistK)
wolffd@0: % 	x 	dxn matrix of n input column vectors
wolffd@0: % 	[y] 	kxn vector of class assignments
wolffd@0: %
wolffd@0: % OUTPUT
wolffd@0: % 	post 	kxn fitted class posteriors
wolffd@0: % 	lik 	1xn vector of sample likelihoods
wolffd@0: %	lli	log likelihood
wolffd@0: %
wolffd@0: % Let p(i,j) = exp(beta(:,j)'*x(:,i)),
wolffd@0: % Class j posterior for observation i is:
wolffd@0: %	post(j,i) = p(i,j) / (p(i,1) + ... p(i,k))
wolffd@0: % The likelihood of observation i given soft class assignments
wolffd@0: % y(:,i) is: 
wolffd@0: %	lik(i) = prod(post(:,i).^y(:,i))
wolffd@0: % The log-likelihood of the model given the labeled samples is:
wolffd@0: %	lli = sum(log(lik))
wolffd@0: % 
wolffd@0: % See also logistK.
wolffd@0: %
wolffd@0: % David Martin <dmartin@eecs.berkeley.edu> 
wolffd@0: % May 7, 2002
wolffd@0: 
wolffd@0: % Copyright (C) 2002 David R. Martin <dmartin@eecs.berkeley.edu>
wolffd@0: %
wolffd@0: % This program is free software; you can redistribute it and/or
wolffd@0: % modify it under the terms of the GNU General Public License as
wolffd@0: % published by the Free Software Foundation; either version 2 of the
wolffd@0: % License, or (at your option) any later version.
wolffd@0: % 
wolffd@0: % This program is distributed in the hope that it will be useful, but
wolffd@0: % WITHOUT ANY WARRANTY; without even the implied warranty of
wolffd@0: % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
wolffd@0: % General Public License for more details.
wolffd@0: % 
wolffd@0: % You should have received a copy of the GNU General Public License
wolffd@0: % along with this program; if not, write to the Free Software
wolffd@0: % Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
wolffd@0: % 02111-1307, USA, or see http://www.gnu.org/copyleft/gpl.html.
wolffd@0: 
wolffd@0: error(nargchk(2,3,nargin));
wolffd@0: 
wolffd@0: % check sizes
wolffd@0: if size(beta,1) ~= size(x,1),
wolffd@0:   error('Inputs beta,x not the same height.');
wolffd@0: end
wolffd@0: if nargin > 3 & size(y,2) ~= size(x,2), 
wolffd@0:   error('Inputs x,y not the same length.'); 
wolffd@0: end
wolffd@0: 
wolffd@0: % get sizes
wolffd@0: [d,k] = size(beta);
wolffd@0: [d,n] = size(x);
wolffd@0: 
wolffd@0: % class posteriors
wolffd@0: post = zeros(k,n);
wolffd@0: bx = zeros(k,n);
wolffd@0: for j = 1:k, 
wolffd@0:   bx(j,:) = beta(:,j)'*x; 
wolffd@0: end
wolffd@0: for j = 1:k, 
wolffd@0:   post(j,:) = 1 ./ sum(exp(bx - repmat(bx(j,:),k,1)),1);
wolffd@0: end
wolffd@0: clear bx;
wolffd@0: 
wolffd@0: % likelihood of each sample
wolffd@0: if nargout > 1,
wolffd@0:   y = y ./ repmat(sum(y,1),k,1); % L1-normalize class assignments
wolffd@0:   lik = prod(post.^y,1);
wolffd@0: end
wolffd@0: 
wolffd@0: % total log likelihood
wolffd@0: if nargout > 2,
wolffd@0:   lli = sum(log(lik+eps));
wolffd@0: end;
wolffd@0: 
wolffd@0: % eof