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


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