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

function  [mu, Sigma, weights, mask] = cwr_predict(cwr, X, mask_data) 
% CWR_PREDICT cluster weighted regression: predict Y given X  
% function  [mu, Sigma] = cwr_predict(cwr, X) 
%  
% mu(:,t) = E[Y|X(:,t)] = sum_c P(c | X(:,t)) E[Y|c, X(:,t)] 
% Sigma(:,:,t) = Cov[Y|X(:,t)] 
% 
% [mu, Sigma, weights, mask] = cwr_predict(cwr, X, mask_data) 
% mask(i) = sum_t sum_c p(mask_data(:,i) | X(:,t), c) P(c|X(:,t)) 
% This evaluates the predictive density on a set of points 
% (This is only sensible if T=1, ie. X is a single vector) 
 
[nx T] = size(X); 
[ny nx nc] = size(cwr.weightsY); 
mu = zeros(ny, T); 
Sigma = zeros(ny, ny, T); 
 
if nargout == 4 
  comp_mask = 1; 
  N = size(mask_data,2); 
  mask = zeros(N,1); 
else 
  comp_mask = 0; 
end 
 
if nc==1 
  if isempty(cwr.weightsY) 
    mu = repmat(cwr.muY, 1, T); 
    Sigma = repmat(cwr.SigmaY, [1 1 T]); 
  else 
    mu = repmat(cwr.muY, 1, T) + cwr.weightsY * X; 
    Sigma = repmat(cwr.SigmaY, [1 1 T]); 
    %for t=1:T 
    %  mu(:,t) = cwr.muY + cwr.weightsY*X(:,t); 
    %  Sigma(:,:,t) = cwr.SigmaY; 
    %end 
  end 
  if comp_mask, mask = gaussian_prob(mask_data, mu, Sigma); end 
  weights = []; 
  return; 
end 
 
 
% likX(c,t) = p(x(:,t) | c) 
likX = mixgauss_prob(X, cwr.muX, cwr.SigmaX); 
weights = normalize(repmat(cwr.priorC, 1, T) .* likX, 1); 
for t=1:T 
  mut = zeros(ny, nc); 
  for c=1:nc 
    mut(:,c) = cwr.muY(:,c) + cwr.weightsY(:,:,c)*X(:,t); 
    if comp_mask 
      mask = mask + gaussian_prob(mask_data, mut(:,c), cwr.SigmaY(:,:,c)) * weights(c); 
    end 
  end 
  %w = normalise(cwr.priorC(:)  .* likX(:,t)); 
  [mu(:,t), Sigma(:,:,t)] = collapse_mog(mut, cwr.SigmaY, weights(:,t)); 
end