comparison toolboxes/FullBNT-1.0.7/bnt/CPDs/@gaussian_CPD/maximize_params_debug.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function CPD = maximize_params(CPD, temp)
+% MAXIMIZE_PARAMS Set the params of a CPD to their ML values (Gaussian)
+% CPD = maximize_params(CPD, temperature)
+%
+% Temperature is currently ignored.
+
+if ~adjustable_CPD(CPD), return; end
+
+CPD1 = struct(new_maximize_params(CPD));
+CPD2 = struct(old_maximize_params(CPD));
+assert(approxeq(CPD1.mean, CPD2.mean))
+assert(approxeq(CPD1.cov, CPD2.cov))
+assert(approxeq(CPD1.weights, CPD2.weights))
+
+CPD = new_maximize_params(CPD);
+
+%%%%%%%
+function CPD = new_maximize_params(CPD)
+
+if CPD.clamped_mean
+  cl_mean = CPD.mean;
+else
+  cl_mean = [];
+end
+
+if CPD.clamped_cov
+  cl_cov = CPD.cov;
+else
+  cl_cov = [];
+end
+
+if CPD.clamped_weights
+  cl_weights = CPD.weights;
+else
+  cl_weights = [];
+end
+
+[ssz psz Q] = size(CPD.weights);
+
+prior =  repmat(CPD.cov_prior_weight*eye(ssz,ssz), [1 1 Q]);
+[CPD.mean, CPD.cov, CPD.weights] = ...
+    Mstep_clg('w', CPD.Wsum, 'YY', CPD.WYYsum, 'Y', CPD.WYsum, 'YTY', [], ...
+	      'XX', CPD.WXXsum, 'XY', CPD.WXYsum, 'X', CPD.WXsum, ...
+	      'cov_type', CPD.cov_type, 'clamped_mean', cl_mean, ...
+	      'clamped_cov', cl_cov, 'clamped_weights', cl_weights, ...
+	      'tied_cov', CPD.tied_cov, ...
+	      'cov_prior', prior);
+
+
+%%%%%%%%%%%
+
+function CPD = old_maximize_params(CPD)
+
+
+if ~adjustable_CPD(CPD), return; end
+
+%assert(approxeq(CPD.nsamples, sum(CPD.Wsum)));
+assert(~any(isnan(CPD.WXXsum)))
+assert(~any(isnan(CPD.WXYsum)))
+assert(~any(isnan(CPD.WYYsum)))
+
+[self_size cpsize dpsize] = size(CPD.weights);
+
+% Append 1s to the parents, and derive the corresponding cross products.
+% This is used when estimate the means and weights simultaneosuly,
+% and when estimatting Sigma.
+% Let x2 = [x 1]'
+XY = zeros(cpsize+1, self_size, dpsize); % XY(:,:,i) = sum_l w(l,i) x2(l) y(l)' 
+XX = zeros(cpsize+1, cpsize+1, dpsize); % XX(:,:,i) = sum_l w(l,i) x2(l) x2(l)' 
+YY = zeros(self_size, self_size, dpsize); % YY(:,:,i) = sum_l w(l,i) y(l) y(l)' 
+for i=1:dpsize
+  XY(:,:,i) = [CPD.WXYsum(:,:,i) % X*Y
+	       CPD.WYsum(:,i)']; % 1*Y
+  % [x  * [x' 1]  = [xx' x
+  %  1]              x'  1]
+  XX(:,:,i) = [CPD.WXXsum(:,:,i) CPD.WXsum(:,i);
+	       CPD.WXsum(:,i)'   CPD.Wsum(i)];
+  YY(:,:,i) = CPD.WYYsum(:,:,i);
+end
+
+w = CPD.Wsum(:);
+% Set any zeros to one before dividing
+% This is valid because w(i)=0 => WYsum(:,i)=0, etc
+w = w + (w==0);
+
+if CPD.clamped_mean
+  % Estimating B2 and then setting the last column (the mean) to the clamped mean is *not* equivalent
+  % to estimating B and then adding the clamped_mean to the last column.
+  if ~CPD.clamped_weights
+    B = zeros(self_size, cpsize, dpsize);
+    for i=1:dpsize
+      if det(CPD.WXXsum(:,:,i))==0
+	B(:,:,i) = 0;
+      else
+	% Eqn 9 in table 2 of TR
+	%B(:,:,i) = CPD.WXYsum(:,:,i)' * inv(CPD.WXXsum(:,:,i));
+	B(:,:,i) = (CPD.WXXsum(:,:,i) \ CPD.WXYsum(:,:,i))';
+      end
+    end
+    %CPD.weights = reshape(B, [self_size cpsize dpsize]);
+    CPD.weights = B;
+  end
+elseif CPD.clamped_weights % KPM 1/25/02
+  if ~CPD.clamped_mean % ML estimate is just sample mean of the residuals
+    for i=1:dpsize
+      CPD.mean(:,i) = (CPD.WYsum(:,i) - CPD.weights(:,:,i) * CPD.WXsum(:,i)) / w(i);
+    end
+  end
+else % nothing is clamped, so estimate mean and weights simultaneously
+  B2 = zeros(self_size, cpsize+1, dpsize);
+  for i=1:dpsize
+    if det(XX(:,:,i))==0  % fix by U. Sondhauss 6/27/99
+      B2(:,:,i)=0;          
+    else                    
+      % Eqn 9 in table 2 of TR
+      %B2(:,:,i) = XY(:,:,i)' * inv(XX(:,:,i));
+      B2(:,:,i) = (XX(:,:,i) \ XY(:,:,i))';
+    end                   
+    CPD.mean(:,i) = B2(:,cpsize+1,i);
+    CPD.weights(:,:,i) = B2(:,1:cpsize,i);
+  end
+end
+
+% Let B2 = [W mu]
+if cpsize>0
+  B2(:,1:cpsize,:) = reshape(CPD.weights, [self_size cpsize dpsize]);
+end
+B2(:,cpsize+1,:) = reshape(CPD.mean, [self_size dpsize]);
+
+% To avoid singular covariance matrices,
+% we use the regularization method suggested in "A Quasi-Bayesian approach to estimating
+% parameters for mixtures of normal distributions", Hamilton 91.
+% If the ML estimate is Sigma = M/N, the MAP estimate is (M+gamma*I) / (N+gamma),
+% where gamma >=0 is a smoothing parameter (equivalent sample size of I prior)
+
+gamma = CPD.cov_prior_weight;
+
+if ~CPD.clamped_cov
+  if CPD.cov_prior_entropic % eqn 12 of Brand AI/Stat 99
+    Z = 1-temp;
+    % When temp > 1, Z is negative, so we are dividing by a smaller
+    % number, ie. increasing the variance.
+  else
+    Z = 0;
+  end
+  if CPD.tied_cov
+    S = zeros(self_size, self_size);
+    % Eqn 2 from table 2 in TR
+    for i=1:dpsize
+      S = S + (YY(:,:,i) - B2(:,:,i)*XY(:,:,i));
+    end
+    %denom = CPD.nsamples + gamma + Z;
+    denom = CPD.nsamples +  Z;
+    S = (S + gamma*eye(self_size)) / denom;
+    if strcmp(CPD.cov_type, 'diag')
+      S = diag(diag(S));
+    end
+    CPD.cov = repmat(S, [1 1 dpsize]);
+  else 
+    for i=1:dpsize      
+      % Eqn 1 from table 2 in TR
+      S = YY(:,:,i) - B2(:,:,i)*XY(:,:,i);
+      %denom = w(i) + gamma + Z;
+      denom = w(i) + Z;
+      S = (S + gamma*eye(self_size)) / denom;
+      CPD.cov(:,:,i) = S;
+    end
+    if strcmp(CPD.cov_type, 'diag')
+      for i=1:dpsize      
+	CPD.cov(:,:,i) = diag(diag(CPD.cov(:,:,i)));
+      end
+    end
+  end
+end
+
+
+check_covars = 0;
+min_covar = 1e-5;
+if check_covars % prevent collapsing to a point
+  for i=1:dpsize
+    if min(svd(CPD.cov(:,:,i))) < min_covar
+      disp(['resetting singular covariance for node ' num2str(CPD.self)]);
+      CPD.cov(:,:,i) = CPD.init_cov(:,:,i);
+    end
+  end
+end
+
+
+