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annotate m-toolbox/classes/+utils/@math/fisher_1x1.m @ 2:18e956c96a1b database-connection-manager

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Add LTPDADatabaseConnectionManager implementation. Matlab code
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Sun, 04 Dec 2011 21:23:09 +0100
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1 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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2 %
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3 % Compute Fisher matrix
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4 %
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5 % Parameters are:
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6 % i1 - input 1st channel (ao)
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7 % n - noise both channels (matrix 1x1)
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8 % mdl - model (matrix or ssm)
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9 % params - parameters
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10 % numparams - numerical value of parameters
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11 % freqs - frequnecies being evaluated
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12 % N - number of fft frequencies
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13 % pl - plist
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14 %
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15 % M Nofrarias 20-09-11
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16 %
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17 % $Id: fisher_1x1.m,v 1.1 2011/10/07 08:17:52 miquel Exp $
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18 %
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19 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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20 function FisMat = fisher_1x1(i1,n,mdl,params,numparams,freqs,N,pl,inNames,outNames)
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21
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22 import utils.const.*
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23
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24 % Compute psd
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25 n1 = psd(n.getObjectAtIndex(1,1), pl);
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26
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27 % interpolate to given frequencies
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28 % noise
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29 S11 = interp(n1,plist('vertices',freqs));
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30
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31 % get some parameters used below
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32 fs = S11.fs;
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33
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34 if ~isempty(mdl) && all(strcmp(class(mdl),'matrix'))
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35 % compute built-in matrix
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36 for i = 1:numel(mdl.objs)
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37 % set Xvals
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38 h(i) = mdl.getObjectAtIndex(i).setXvals(freqs);
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39 % set alias
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40 h(i).assignalias(mdl.objs(i),plist('xvals',freqs));
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41 % set paramaters
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42 h(i).setParams(params,numparams);
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43 end
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44 % differentiate and eval
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45 for i = 1:length(params)
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46 utils.helper.msg(msg.IMPORTANT, sprintf('computing symbolic differentiation with respect %s',params{i}), mfilename('class'), mfilename);
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47 % differentiate symbolically
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48 dH11 = diff(h(1),params{i});
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49 % evaluate
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50 d11(i) = eval(dH11);
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51 end
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52
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53 elseif ~isempty(mdl) && all(strcmp(class(mdl),'ssm'))
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54
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55 meval = copy(mdl,1);
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56 % set parameter values
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57 % meval.doSetParameters(params, numparams);
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58 meval.setParameters(params, numparams);
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59
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60 % get the differentiation step
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61 step = find(pl,'step');
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62 % case no diff. step introduced
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63 if isempty(step)
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64 utils.helper.msg(msg.IMPORTANT, ...
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65 sprintf('computing optimal differentiation steps'), mfilename('class'), mfilename);
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66 ranges = find(pl,'stepRanges');
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67 if isempty(ranges)
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68 error('### Please input upper and lower ranges for the parameters: ''ranges''')
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69 end
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70 ngrid = find(pl,'ngrid');
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71 if isempty(ngrid)
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72 error('### Please input a number of points for the grid to compute the diff. step : ''ngrid''')
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73 end
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74 % look for numerical differentiation step
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75 step = utils.math.diffStepFish_1x1(i1,S11,N,meval,params,numparams,ngrid,ranges,freqs,inNames,outNames);
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76 end
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77
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78 % differentiate and eval
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79 for i = 1:length(params)
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80 utils.helper.msg(msg.IMPORTANT, ...
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81 sprintf('computing numerical differentiation with respect %s, Step:%4.2d ',params{i},step(i)), mfilename('class'), mfilename);
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82 % differentiate numerically
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83 dH = meval.parameterDiff(plist('names', params(i),'values',step(i)));
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84 % create plist with correct outNames (since parameterDiff change them)
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85 out1 = strrep(outNames{1},'.', sprintf('_DIFF_%s.',params{i})); % 2x2 case
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86 spl = plist('set', 'for bode', ...
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87 'outputs', out1, ...
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88 'inputs', inNames, ...
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89 'reorganize', true,...
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90 'f', freqs);
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91 % do bode
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92 d = bode(dH, spl);
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93 % 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)
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94 d11(i) = d.objs(1);
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95 end
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96
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97 else
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98 error('### please introduce models for the transfer functions')
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99 end
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100
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101 % scaling of PSD
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102 % PSD = 2/(N*fs) * FFT *conj(FFT)
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103 C11 = N*fs/2.*S11.y;
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104 % compute elements of inverse cross-spectrum matrix
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105 InvS11 = 1./C11;
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106
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107 % compute Fisher Matrix
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108 for i =1:length(params)
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109 for j =1:length(params)
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110
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111 v1v1 = conj(d11(i).y.*i1.y).*(d11(j).y.*i1.y);
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112
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113 FisMat(i,j) = sum(real(InvS11.*v1v1));
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114 end
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115 end
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116
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117 end