wolffd@0: function CPD = maximize_params(CPD, temp)
wolffd@0: % MAXIMIZE_PARAMS Set the params of a CPD to their ML values (Gaussian)
wolffd@0: % CPD = maximize_params(CPD, temperature)
wolffd@0: %
wolffd@0: % Temperature is currently ignored.
wolffd@0: 
wolffd@0: if ~adjustable_CPD(CPD), return; end
wolffd@0: 
wolffd@0: 
wolffd@0: if CPD.clamped_mean
wolffd@0:   cl_mean = CPD.mean;
wolffd@0: else
wolffd@0:   cl_mean = [];
wolffd@0: end
wolffd@0: 
wolffd@0: if CPD.clamped_cov
wolffd@0:   cl_cov = CPD.cov;
wolffd@0: else
wolffd@0:   cl_cov = [];
wolffd@0: end
wolffd@0: 
wolffd@0: if CPD.clamped_weights
wolffd@0:   cl_weights = CPD.weights;
wolffd@0: else
wolffd@0:   cl_weights = [];
wolffd@0: end
wolffd@0: 
wolffd@0: [ssz psz Q] = size(CPD.weights);
wolffd@0: 
wolffd@0: [ss cpsz dpsz] = size(CPD.weights); % ss = self size = ssz
wolffd@0: if cpsz > CPD.nsamples
wolffd@0:   fprintf('gaussian_CPD/maximize_params: warning: input dimension (%d) > nsamples (%d)\n', ...
wolffd@0: 	  cpsz, CPD.nsamples);
wolffd@0: end
wolffd@0: 
wolffd@0: prior =  repmat(CPD.cov_prior_weight*eye(ssz,ssz), [1 1 Q]);
wolffd@0: 
wolffd@0: 
wolffd@0: [CPD.mean, CPD.cov, CPD.weights] = ...
wolffd@0:     clg_Mstep(CPD.Wsum, CPD.WYsum, CPD.WYYsum, [], CPD.WXsum, CPD.WXXsum, CPD.WXYsum, ...
wolffd@0: 	      'cov_type', CPD.cov_type, 'clamped_mean', cl_mean, ...
wolffd@0: 	      'clamped_cov', cl_cov, 'clamped_weights', cl_weights, ...
wolffd@0: 	      'tied_cov', CPD.tied_cov, ...
wolffd@0: 	      'cov_prior', prior);
wolffd@0: 
wolffd@0: if 0
wolffd@0: CPD.mean = reshape(CPD.mean, [ss dpsz]);
wolffd@0: CPD.cov = reshape(CPD.cov, [ss ss dpsz]);
wolffd@0: CPD.weights = reshape(CPD.weights, [ss cpsz dpsz]);
wolffd@0: end
wolffd@0: 
wolffd@0: % Bug fix 11 May 2003 KPM
wolffd@0: % clg_Mstep collapses all discrete parents into one mega-node
wolffd@0: % but convert_to_CPT needs access to each parent separately
wolffd@0: sz = CPD.sizes;
wolffd@0: ss = sz(end);
wolffd@0: 
wolffd@0: % Bug fix KPM 20 May 2003: 
wolffd@0: cpsz = sum(sz(CPD.cps));
wolffd@0: %if isempty(CPD.cps)
wolffd@0: %  cpsz = 0;
wolffd@0: %else
wolffd@0: %  cpsz = sz(CPD.cps);
wolffd@0: %end
wolffd@0: dpsz = sz(CPD.dps);
wolffd@0: CPD.mean = myreshape(CPD.mean, [ss dpsz]);
wolffd@0: CPD.cov = myreshape(CPD.cov, [ss ss dpsz]);
wolffd@0: CPD.weights = myreshape(CPD.weights, [ss cpsz dpsz]);