Import.
line source
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%
% Compute log-likelihood
%
% M Nofrarias 15-06-09
%
% $Id: loglikelihood_matrix.m,v 1.5 2011/11/16 08:52:50 nikos Exp $
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [loglk snr]= loglikelihood_matrix(xn,in,out,noise,model,params,inModel,outModel)
% parameters
fs = in(1).objs(1).fs;
% num. experiments
nexp = numel(in);
% num. transfer functions
nmod = numel(model(1).objs(:));
noutChannels = sqrt(numel(noise(1).objs));
% loop over experiments
loglk = 0;
snr = 0;
for i = 1:nexp
if ((numel(in(1).objs) == 1) && numel(out(1).objs) == 1)
freqs = in(i).objs(1).data.getX;
% evaluate models
if(isempty(outModel))
h11 = model(1).objs(1).setParams(params,xn).double;
elseif (~isempty(outModel))
h11 = outModel(1,1).y * model(1).getObjectAtIndex(1,1).setParams(params,xn).double';
end
% spectra to variance
% (N*fs/2)* this multiplication is done now in mcmc
C11 = noise(1).objs(1).data.getY;
% compute elements of inverse cross-spectrum matrix
InvS11 = 1./C11;
% compute log-likelihood terms first, all at once does not cancel the
% imag part when multiplying x.*conj(x)
in1 = in(i).objs(1).data.getY;
out1 = out(i).objs(1).data.getY;
tmplt1 = h11.*in1;
v1v1 = conj(out1 - tmplt1).*(out1 - tmplt1);
%computing SNR
snrexp = utils.math.stnr(tmplt1,0,out1,0,InvS11,0,0,0);
snr = snr + 20*log10(snrexp);
log1exp = sum(InvS11.*v1v1);
loglk = loglk + log1exp;
elseif ((numel(in(1).objs) == 2) && numel(out(1).objs) == 2)
freqs = in(i).objs(1).data.getX;
% loop over models
if(isempty(outModel))
for j = 1:nmod
% evaluate models
h(:,j) = model(i).objs(j).setParams(params,xn).double;
end
elseif (~isempty(outModel))
h(:,1) = outModel(1,1).y * model(i).getObjectAtIndex(1,1).setParams(params,xn).double';
h(:,2) = outModel(2,1).y * model(i).getObjectAtIndex(1,1).setParams(params,xn).double';
h(:,3) = outModel(1,2).y * model(i).getObjectAtIndex(2,2).setParams(params,xn).double';
h(:,4) = outModel(2,2).y * model(i).getObjectAtIndex(2,2).setParams(params,xn).double';
end
for j = 1:noutChannels^2
% spectra to variance
% (N*fs/2)* this multiplication is done now in mcmc
C(:,j) = noise(i).objs(j).data.getY;
end
% compute elements of inverse cross-spectrum matrix
detm = (C(:,1).*C(:,4) - C(:,2).*C(:,3));
InvS11 = C(:,4)./detm; %1 4
InvS22 = C(:,1)./detm; %4 1
InvS12 = C(:,2)./detm; %2 2
InvS21 = C(:,3)./detm; %3 3
% compute log-likelihood terms first, all at once does not cancel the
% imag part when multiplying x.*conj(x)
in1 = in(i).objs(1).data.getY;
in2 = in(i).objs(2).data.getY;
out1 = out(i).objs(1).data.getY;
out2 = out(i).objs(2).data.getY;
tmplt1 = h(:,1).*in1 + h(:,3).*in2;
tmplt2 = h(:,2).*in1 + h(:,4).*in2;
% matrix index convention: H(1,1)->h(1) H(2,1)->h(2) H(1,2)->h(3) H(2,2)->h(4)
v1v1 = conj(out1 - tmplt1).*(out1 - tmplt1);
v2v2 = conj(out2 - tmplt2).*(out2 - tmplt2);
v1v2 = conj(out1 - tmplt1).*(out2 - tmplt2);
v2v1 = conj(out2 - tmplt2).*(out1 - tmplt1);
%computing SNR
snrexp = utils.math.stnr(tmplt1,tmplt2,out1,out2,InvS11,InvS22,InvS12,InvS21);
snr = snr + 20*log10(snrexp);
log1exp = sum(InvS11.*v1v1 + InvS22.*v2v2 - InvS12.*v1v2 - InvS21.*v2v1);
loglk = loglk + log1exp;
elseif ((numel(in(1).objs) == 4) && numel(out(1).objs) == 3)
% here we are implementing only the magnetic case
% We have 4 inputs (the 4 conformator waveforms of the magnetic
% analysis and
% 3 outputs (that correspond to the IFO.x12 and IFO.ETA1 and
% IFO.PHI1
for j = 1:noutChannels^2
% spectra to variance
% (N*fs/2)* this factor multiplication is done now in mcmc,
% before splitting
C(:,j) = noise(i).objs(j).data.getY;
end
if( isempty(inModel) && ~isempty(outModel))
freqs = in(i).objs(1).data.getX;
% faster this way
h(:,1) = outModel(1,1).y * model(i).getObjectAtIndex(1,1).setParams(params,xn).double;
h(:,2) = outModel(2,1).y * model(i).getObjectAtIndex(1,1).setParams(params,xn).double;
h(:,3) = outModel(3,1).y * model(i).getObjectAtIndex(1,1).setParams(params,xn).double;
h(:,4) = outModel(1,1).y * model(i).getObjectAtIndex(1,2).setParams(params,xn).double;
h(:,5) = outModel(2,1).y * model(i).getObjectAtIndex(1,2).setParams(params,xn).double;
h(:,6) = outModel(3,1).y * model(i).getObjectAtIndex(1,2).setParams(params,xn).double;
h(:,7) = outModel(1,2).y * model(i).getObjectAtIndex(2,3).setParams(params,xn).double;
h(:,8) = outModel(2,2).y * model(i).getObjectAtIndex(2,3).setParams(params,xn).double;
h(:,9) = outModel(3,2).y * model(i).getObjectAtIndex(2,3).setParams(params,xn).double;
h(:,10) = outModel(1,3).y * model(i).getObjectAtIndex(3,4).setParams(params,xn).double;
h(:,11) = outModel(2,3).y * model(i).getObjectAtIndex(3,4).setParams(params,xn).double;
h(:,12) = outModel(3,3).y * model(i).getObjectAtIndex(3,4).setParams(params,xn).double;
% compute elements of inverse cross-spectrum matrix
detm = (C(:,1).*C(:,5).*C(:,9) + ...
C(:,2).*C(:,6).*C(:,7) + ...
C(:,3).*C(:,4).*C(:,8) -...
C(:,7).*C(:,5).*C(:,3) -...
C(:,8).*C(:,6).*C(:,1) -...
C(:,9).*C(:,4).*C(:,2));
InvS11 = (C(:,5).*C(:,9) - C(:,8).*C(:,6))./detm;
InvS12 = -(C(:,4).*C(:,9) - C(:,7).*C(:,6))./detm;
InvS13 = (C(:,4).*C(:,8) - C(:,7).*C(:,5))./detm;
InvS21 = -(C(:,2).*C(:,9) - C(:,8).*C(:,3))./detm;
InvS22 = (C(:,1).*C(:,9) - C(:,7).*C(:,3))./detm;
InvS23 = -(C(:,1).*C(:,8) - C(:,7).*C(:,2))./detm;
InvS31 = (C(:,2).*C(:,6) - C(:,5).*C(:,3))./detm;
InvS32 = -(C(:,1).*C(:,6) - C(:,4).*C(:,3))./detm;
InvS33 = (C(:,1).*C(:,5) - C(:,4).*C(:,2))./detm;
% compute log-likelihood terms first, all at once does not cancel the
% imag part when multiplying x.*conj(x)
for ll = 1:noutChannels
outV(:,ll) = out(i).objs(ll).data.getY;
end
for kk = 1:model(i).ncols
inV(:,kk) = in(i).objs(kk).data.getY;
end
% faster this way
v(:,1) = outV(:,1) - h(:,1).*inV(:,1) - h(:,4).*inV(:,2) - h(:,7).*inV(:,3) - h(:,10).*inV(:,4);
v(:,2) = outV(:,2) - h(:,2).*inV(:,1) - h(:,5).*inV(:,2) - h(:,8).*inV(:,3) - h(:,11).*inV(:,4);
v(:,3) = outV(:,3) - h(:,3).*inV(:,1) - h(:,6).*inV(:,2) - h(:,9).*inV(:,3) - h(:,12).*inV(:,4);
v1v1 = conj(v(:,1)).*v(:,1);
v1v2 = conj(v(:,1)).*v(:,2);
v1v3 = conj(v(:,1)).*v(:,3);
v2v1 = conj(v(:,2)).*v(:,1);
v2v2 = conj(v(:,2)).*v(:,2);
v2v3 = conj(v(:,2)).*v(:,3);
v3v1 = conj(v(:,3)).*v(:,1);
v3v2 = conj(v(:,3)).*v(:,2);
v3v3 = conj(v(:,3)).*v(:,3);
log1exp = sum(InvS11.*v1v1 +...
InvS12.*v1v2 +...
InvS13.*v1v3 +...
InvS21.*v2v1 +...
InvS22.*v2v2 +...
InvS23.*v2v3 +...
InvS31.*v3v1 +...
InvS32.*v3v2 +...
InvS33.*v3v3);
loglk = loglk + log1exp;
else
error('For the magnetic case, implement an outModel and leave your inModel blank')
end
else
error('Implemented cases: 1 input / 1output, 2 inputs / 2outputs (TN3045 analysis), and 4 inputs / 3 outpus (magnetic complete analysis model. Other cases have not been implemented yet. Sorry for the inconvenience)');
end
end
end