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

first hg version after svn

+function [mu, B] = clg_Mstep_simple(w, Y, YY, YTY, X, XX, XY)
+% CLG_MSTEP_SIMPLE Same as CLG_MSTEP, but doesn;t estimate Sigma,  so is slightly faster
+% function [mu, B] = clg_Mstep_simple(w, Y, YY, YTY, X, XX, XY)
+%
+% See clg_Mstep for details.
+% Unlike clg_Mstep, there are no optional arguments, which are slow to process
+% if this function is inside a tight loop.
+
+[Ysz Q] = size(Y);
+
+if isempty(X) % no regression
+  %B = [];
+  B2 = zeros(Ysz, 1, Q);
+  for i=1:Q
+    B(:,:,i) = B2(:,1:0,i); % make an empty array of size Ysz x 0 x Q
+  end
+  [mu, Sigma] = mixgauss_Mstep(w, Y, YY, YTY);
+  return;
+end
+
+N = sum(w);
+%YY = YY + cov_prior; % regularize the scatter matrix
+
+% Set any zero weights to one before dividing
+% This is valid because w(i)=0 => Y(:,i)=0, etc
+w = w + (w==0);
+
+Xsz = size(X,1);
+% Append 1 to X to get Z
+ZZ = zeros(Xsz+1, Xsz+1, Q);
+ZY = zeros(Xsz+1, Ysz, Q);
+for i=1:Q
+  ZZ(:,:,i) = [XX(:,:,i)  X(:,i);
+	       X(:,i)'    w(i)];
+  ZY(:,:,i) = [XY(:,:,i);
+	       Y(:,i)'];
+end
+
+mu = zeros(Ysz, Q);
+B = zeros(Ysz, Xsz, Q);
+for i=1:Q
+  % eqn 9
+  if rcond(ZZ(:,:,i)) < 1e-10
+    sprintf('clg_Mstep warning: ZZ(:,:,%d) is ill-conditioned', i);
+    %probably because there are too few cases for a high-dimensional input
+    ZZ(:,:,i) = ZZ(:,:,i) + 1e-5*eye(Xsz+1);
+  end
+  %A = ZY(:,:,i)' * inv(ZZ(:,:,i));
+  A = (ZZ(:,:,i) \ ZY(:,:,i))';
+  B(:,:,i) = A(:, 1:Xsz);
+  mu(:,i) = A(:, Xsz+1);
+end