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

function  [likXandY, likYgivenX, post] = cwr_prob(cwr, X, Y); 
% CWR_EVAL_PDF cluster weighted regression: evaluate likelihood of Y given X  
% function  [likXandY, likYgivenX, post] = cwr_prob(cwr, X, Y); 
%  
% likXandY(t) = p(x(:,t), y(:,t)) 
% likXgivenY(t) = p(x(:,t)| y(:,t)) 
% post(c,t) = p(c | x(:,t), y(:,t)) 
 
[nx N] = size(X); 
nc = length(cwr.priorC); 
 
if nc == 1 
  [mu, Sigma] = cwr_predict(cwr, X); 
  likY = gaussian_prob(Y, mu, Sigma); 
  likXandY = likY; 
  likYgivenX = likY; 
  post = ones(1,N); 
  return; 
end 
 
 
% likY(c,t) = p(y(:,t) | c) 
likY = clg_prob(X, Y, cwr.muY, cwr.SigmaY, cwr.weightsY); 
 
% likX(c,t) = p(x(:,t) | c) 
[junk, likX] = mixgauss_prob(X, cwr.muX, cwr.SigmaX); 
likX = squeeze(likX); 
 
% prior(c,t) = p(c) 
prior = repmat(cwr.priorC(:), 1, N); 
 
post = likX .* likY .* prior; 
likXandY = sum(post, 1); 
post = post ./ repmat(likXandY, nc, 1); 
%loglik = sum(log(lik)); 
%loglik = log(lik); 
 
likX = sum(likX .* prior, 1); 
likYgivenX = likXandY ./ likX;