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;