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annotate m-toolbox/classes/+utils/@math/diffStepFish.m @ 3:960fe1aa1c10 database-connection-manager

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Add LTPDADatabaseConnectionManager implementation. Java code
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 16:20:06 +0100
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1 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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2 %
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3 % Look for differentiation step for a given parameter and
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4 %
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5 % Parameters are:
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6
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7 %
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8 % $Id: diffStepFish.m,v 1.2 2011/09/19 06:17:45 miquel Exp $
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9 %
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10 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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11 function best = diffStepFish(i1,i2,S11,S12,S21,S22,N,meval,params,ngrid,ranges,freqs,inNames,outNames)
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12
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13 % remove aux file if existing
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14 if exist('diffStepFish.txt') == 2
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15 ! rm diffStepFish.txt
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16 end
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17
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18 step = ones(ngrid,numel(params));
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19 % build matrix of steps
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20 % for ii = 1:length(params)
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21 % step(:,ii) = [] logspace(ranges(1,ii),ranges(2,ii),ngrid);
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22 % end
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23 for ii = 1:ngrid
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24 step(ii,:) = ranges(1,:);
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25 end
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26
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27 % step(:,1) = logspace(ranges(1,1),ranges(2,1),ngrid);
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28
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29 for kk = 1:length(params)
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30 step(:,kk) = logspace(log10(ranges(1,kk)),log10(ranges(2,kk)),ngrid);
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31 Rmat = [];
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32 for jj = 1:ngrid
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33 for ii = 1:length(params)
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34 tic
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35 % differentiate numerically
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36 dH = meval.parameterDiff(plist('names', params(ii),'values',step(jj,ii)));
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37 % create plist with correct outNames (since parameterDiff change them)
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38 out1 = strrep(outNames{1},'.', sprintf('_DIFF_%s.',params{ii})); % 2x2 case
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39 out2 =strrep(outNames{2},'.', sprintf('_DIFF_%s.',params{ii}));
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40 spl = plist('set', 'for bode', ...
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41 'outputs', {out1,out2}, ...
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42 'inputs', inNames, ...
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43 'reorganize', true,...
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44 'f', freqs);
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45 % do bode
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46 d = bode(dH, spl);
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47 % assign according matlab's matrix notation:
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48 % H(1,1)->h(1) H(2,1)->h(2) H(1,2)->h(3) H(2,2)->h(4)
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49 d11(ii) = d.objs(1);
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50 d21(ii) = d.objs(2);
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51 d12(ii) = d.objs(3);
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52 d22(ii) = d.objs(4);
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53
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54 end
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55
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56 fs = S11.fs;
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57 % scaling of PSD
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58 % PSD = 2/(N*fs) * FFT *conj(FFT)
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59 C11 = N*fs/2.*S11.y;
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60 C22 = N*fs/2.*S22.y;
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61 C12 = N*fs/2.*S12.y;
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62 C21 = N*fs/2.*S21.y;
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63
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64 % compute elements of inverse cross-spectrum matrix
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65 InvS11 = (C22./(C11.*C22 - C12.*C21));
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66 InvS22 = (C11./(C11.*C22 - C12.*C21));
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67 InvS12 = (C21./(C11.*C22 - C12.*C21));
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68 InvS21 = (C12./(C11.*C22 - C12.*C21));
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69
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70
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71 % compute Fisher Matrix
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72 for i =1:length(params)
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73 for j =1:length(params)
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74
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75 v1v1 = conj(d11(i).y.*i1.y + d12(i).y.*i2.y).*(d11(j).y.*i1.y + d12(j).y.*i2.y);
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76 v2v2 = conj(d21(i).y.*i1.y + d22(i).y.*i2.y).*(d21(j).y.*i1.y + d22(j).y.*i2.y);
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77 v1v2 = conj(d11(i).y.*i1.y + d12(i).y.*i2.y).*(d21(j).y.*i1.y + d22(j).y.*i2.y);
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78 v2v1 = conj(d21(i).y.*i1.y + d22(i).y.*i2.y).*(d11(j).y.*i1.y + d12(j).y.*i2.y);
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79
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80 FisMat(i,j) = sum(real(InvS11.*v1v1 + InvS22.*v2v2 - InvS12.*v1v2 - InvS21.*v2v1));
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81 end
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82 end
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83
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84 detFisMat = det(FisMat);
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85 R = [step(jj,:) detFisMat];
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86 save('diffStepFish.txt','R','-ascii','-append');
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87 Rmat = [Rmat; R];
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88
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89 toc
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90 end
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91
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92 % look for the stable step: compute diff and
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93 % look for the smallest one in absolute value
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94 % The smallest slope marks the plateau
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95 diffDetFisMat = abs(diff(Rmat(:,end)));
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96 lowdet = diffDetFisMat(1);
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97 ind = 2;
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98 for k = 1:numel(diffDetFisMat)
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99 if diffDetFisMat(k) < lowdet
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100 lowdet = diffDetFisMat(k);
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101 ind = k+1; % index give by diff = x(2) - x(1). We take the step corresponding to x(2)
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102 end
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103 end
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104
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105 step(:,kk) = step(jj,kk)*ones(ngrid,1);
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106
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107 end
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108
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109 step(:,end) = logspace(log10(ranges(1,end)),log10(ranges(2,end)),ngrid);
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110 best = step(1,:);
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111
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112 end
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113