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

first hg version after svn

+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;