comparison toolboxes/FullBNT-1.0.7/KPMstats/logistK_eval.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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 <[e-mail]> 
+% May 7, 2002
+
+% Copyright (C) 2002 David R. Martin <[e-mail]>
+%
+% 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