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annotate m-toolbox/classes/+utils/@math/loglikelihood_ssm_td.m @ 39:11e3ed9d2115 database-connection-manager

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Implement databases listing in database connection dialog
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 16:20:06 +0100
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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2 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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3 % Compute log-likelihood in time domain for SSM objects
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Daniele Nicolodi <nicolodi@science.unitn.it>
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4 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 % INPUT
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6 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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7 % - in, a vector of input signals aos
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Daniele Nicolodi <nicolodi@science.unitn.it>
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8 % - out, a vector of output data aos
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9 % - parvals, a vector with parameters values
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 % - parnames, a cell array with parameters names
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Daniele Nicolodi <nicolodi@science.unitn.it>
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11 % - model, an ssm model
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12 % - inNames, A cell-array of input port names corresponding to the
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Daniele Nicolodi <nicolodi@science.unitn.it>
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13 % different input AOs
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14 % - outNames, A cell-array of output ports to return
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15 % - Noise, a vector of noise aos
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16 % - cutbefore, followed by the data samples to cut at the starting of the
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17 % data series
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18 % - cutafter, followed by the data samples to cut at the ending of the
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19 % data series
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20 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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21 % L Ferraioli 10-10-2010
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22 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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23 % $Id: loglikelihood_ssm_td.m,v 1.1 2011/03/15 16:19:20 miquel Exp $
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24 %
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25 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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Daniele Nicolodi <nicolodi@science.unitn.it>
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26 function loglk = loglikelihood_ssm_td(xp,in,out,parnames,model,inNames,outNames,Noise,varargin)
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27 % xn,in,out,noise,model,params,inNames,outNames
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28 cutbefore = [];
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29 cutafter = [];
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30 if ~isempty(varargin)
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31 for j=1:length(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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32 if strcmp(varargin{j},'cutbefore')
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33 cutbefore = varargin{j+1};
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34 end
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35 if strcmp(varargin{j},'cutafter')
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36 cutafter = varargin{j+1};
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37 end
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38 end
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39 end
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40
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41 xp = double(xp);
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42 fs = out(1).fs;
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43
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44 % set parameters in the model
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45 evalm = model.setParameters(plist('names',parnames,'values',xp));
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46 evalm.keepParameters();
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47 evalm.modifyTimeStep(plist('newtimestep',1/fs));
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48
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49 %%% get expected outputs
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50 plsym = plist('AOS VARIABLE NAMES',inNames,...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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51 'RETURN OUTPUTS',outNames,...
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52 'AOS',in);
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53 eo = simulate(evalm,plsym);
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54
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55 % %%% get expected noise
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56 % plsym = plist('AOS VARIABLE NAMES',inNoiseNames,...
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57 % 'RETURN OUTPUTS',outNames,...
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58 % 'AOS',inNoise);
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59 % eon = simulate(evalm,plsym);
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60
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61 %%% get measurement noise
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62 res = out-eo;
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63
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64 loglk = utils.math.loglikehood_td(res,Noise,'cutbefore',cutbefore,'cutafter',cutafter);
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65
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66
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67
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68 end