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comparison m-toolbox/classes/+utils/@math/loglikelihood_ssm_td.m @ 0:f0afece42f48

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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Wed, 23 Nov 2011 19:22:13 +0100
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1 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
2 %
3 % Compute log-likelihood in time domain for SSM objects
4 %
5 % INPUT
6 %
7 % - in, a vector of input signals aos
8 % - out, a vector of output data aos
9 % - parvals, a vector with parameters values
10 % - parnames, a cell array with parameters names
11 % - model, an ssm model
12 % - inNames, A cell-array of input port names corresponding to the
13 % different input AOs
14 % - outNames, A cell-array of output ports to return
15 % - Noise, a vector of noise aos
16 % - cutbefore, followed by the data samples to cut at the starting of the
17 % data series
18 % - cutafter, followed by the data samples to cut at the ending of the
19 % data series
20 %
21 % L Ferraioli 10-10-2010
22 %
23 % $Id: loglikelihood_ssm_td.m,v 1.1 2011/03/15 16:19:20 miquel Exp $
24 %
25 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
26 function loglk = loglikelihood_ssm_td(xp,in,out,parnames,model,inNames,outNames,Noise,varargin)
27 % xn,in,out,noise,model,params,inNames,outNames
28 cutbefore = [];
29 cutafter = [];
30 if ~isempty(varargin)
31 for j=1:length(varargin)
32 if strcmp(varargin{j},'cutbefore')
33 cutbefore = varargin{j+1};
34 end
35 if strcmp(varargin{j},'cutafter')
36 cutafter = varargin{j+1};
37 end
38 end
39 end
40
41 xp = double(xp);
42 fs = out(1).fs;
43
44 % set parameters in the model
45 evalm = model.setParameters(plist('names',parnames,'values',xp));
46 evalm.keepParameters();
47 evalm.modifyTimeStep(plist('newtimestep',1/fs));
48
49 %%% get expected outputs
50 plsym = plist('AOS VARIABLE NAMES',inNames,...
51 'RETURN OUTPUTS',outNames,...
52 'AOS',in);
53 eo = simulate(evalm,plsym);
54
55 % %%% get expected noise
56 % plsym = plist('AOS VARIABLE NAMES',inNoiseNames,...
57 % 'RETURN OUTPUTS',outNames,...
58 % 'AOS',inNoise);
59 % eon = simulate(evalm,plsym);
60
61 %%% get measurement noise
62 res = out-eo;
63
64 loglk = utils.math.loglikehood_td(res,Noise,'cutbefore',cutbefore,'cutafter',cutafter);
65
66
67
68 end