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