Mercurial > hg > ltpda
diff m-toolbox/classes/+utils/@math/fisher_1x1.m @ 0:f0afece42f48
Import.
author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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date | Wed, 23 Nov 2011 19:22:13 +0100 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/m-toolbox/classes/+utils/@math/fisher_1x1.m Wed Nov 23 19:22:13 2011 +0100 @@ -0,0 +1,117 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% Compute Fisher matrix +% +% Parameters are: +% i1 - input 1st channel (ao) +% n - noise both channels (matrix 1x1) +% mdl - model (matrix or ssm) +% params - parameters +% numparams - numerical value of parameters +% freqs - frequnecies being evaluated +% N - number of fft frequencies +% pl - plist +% +% M Nofrarias 20-09-11 +% +% $Id: fisher_1x1.m,v 1.1 2011/10/07 08:17:52 miquel Exp $ +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function FisMat = fisher_1x1(i1,n,mdl,params,numparams,freqs,N,pl,inNames,outNames) + +import utils.const.* + +% Compute psd +n1 = psd(n.getObjectAtIndex(1,1), pl); + +% interpolate to given frequencies +% noise +S11 = interp(n1,plist('vertices',freqs)); + +% get some parameters used below +fs = S11.fs; + +if ~isempty(mdl) && all(strcmp(class(mdl),'matrix')) + % compute built-in matrix + for i = 1:numel(mdl.objs) + % set Xvals + h(i) = mdl.getObjectAtIndex(i).setXvals(freqs); + % set alias + h(i).assignalias(mdl.objs(i),plist('xvals',freqs)); + % set paramaters + h(i).setParams(params,numparams); + end + % differentiate and eval + for i = 1:length(params) + utils.helper.msg(msg.IMPORTANT, sprintf('computing symbolic differentiation with respect %s',params{i}), mfilename('class'), mfilename); + % differentiate symbolically + dH11 = diff(h(1),params{i}); + % evaluate + d11(i) = eval(dH11); + end + +elseif ~isempty(mdl) && all(strcmp(class(mdl),'ssm')) + + meval = copy(mdl,1); + % set parameter values +% meval.doSetParameters(params, numparams); + meval.setParameters(params, numparams); + + % get the differentiation step + step = find(pl,'step'); + % case no diff. step introduced + if isempty(step) + utils.helper.msg(msg.IMPORTANT, ... + sprintf('computing optimal differentiation steps'), mfilename('class'), mfilename); + ranges = find(pl,'stepRanges'); + if isempty(ranges) + error('### Please input upper and lower ranges for the parameters: ''ranges''') + end + ngrid = find(pl,'ngrid'); + if isempty(ngrid) + error('### Please input a number of points for the grid to compute the diff. step : ''ngrid''') + end + % look for numerical differentiation step + step = utils.math.diffStepFish_1x1(i1,S11,N,meval,params,numparams,ngrid,ranges,freqs,inNames,outNames); + end + + % differentiate and eval + for i = 1:length(params) + utils.helper.msg(msg.IMPORTANT, ... + sprintf('computing numerical differentiation with respect %s, Step:%4.2d ',params{i},step(i)), mfilename('class'), mfilename); + % differentiate numerically + dH = meval.parameterDiff(plist('names', params(i),'values',step(i))); + % create plist with correct outNames (since parameterDiff change them) + out1 = strrep(outNames{1},'.', sprintf('_DIFF_%s.',params{i})); % 2x2 case + spl = plist('set', 'for bode', ... + 'outputs', out1, ... + 'inputs', inNames, ... + 'reorganize', true,... + 'f', freqs); + % do bode + d = bode(dH, spl); + % assign according matlab's matrix notation: H(1,1)->h(1) H(2,1)->h(2) H(1,2)->h(3) H(2,2)->h(4) + d11(i) = d.objs(1); + end + +else + error('### please introduce models for the transfer functions') +end + +% scaling of PSD +% PSD = 2/(N*fs) * FFT *conj(FFT) +C11 = N*fs/2.*S11.y; +% compute elements of inverse cross-spectrum matrix +InvS11 = 1./C11; + +% compute Fisher Matrix +for i =1:length(params) + for j =1:length(params) + + v1v1 = conj(d11(i).y.*i1.y).*(d11(j).y.*i1.y); + + FisMat(i,j) = sum(real(InvS11.*v1v1)); + end +end + +end \ No newline at end of file