www.pudn.com > HMM1.zip > logistK_eval.m

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   
% May 7, 2002 
 
% Copyright (C) 2002 David R. Martin  
% 
% 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 sizes 
if size(beta,1) ~= size(x,1), 
  error('Inputs beta,x not the same height.'); 
end 
if 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 posteriors 
post = zeros(k,n); 
bx = zeros(k,n); 
for j = 1:k,  
  bx(j,:) = beta(:,j)'*x;  
end 
for j = 1:k,  
  post(j,:) = 1 ./ sum(exp(bx - repmat(bx(j,:),k,1)),1); 
end 
clear bx; 
 
% likelihood of each sample 
if nargout > 1, 
  y = y ./ repmat(sum(y,1),k,1); % L1-normalize class assignments 
  lik = prod(post.^y,1); 
end 
 
% total log likelihood 
if nargout > 2, 
  lli = sum(log(lik+eps)); 
end; 
 
% eof