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 <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 sizes
if size(beta,1) ~= size(x,1),
error('Inputs beta,x not the same height.');
end
if nargin > 3 &amt; 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