[640] | 1 | % load data created by the MpdfDS_example |
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| 2 | load mpdfds_results |
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| 3 | |
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| 4 | DS.class = 'MemDS'; |
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| 5 | DS.Data = Data; |
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| 6 | DS.drv = drv; |
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| 7 | |
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| 8 | %%%%%% ARX estimator conditioned on frg |
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| 9 | |
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| 10 | A1.class = 'ARXfrg'; |
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| 11 | A1.rv = y; |
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| 12 | A1.rgr = RVtimes([y,u],[-3,-1]) ; |
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| 13 | A1.options ='logbounds,logll'; |
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| 14 | A1.frg = 0.9; |
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| 15 | A1.name = 'A1'; |
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| 16 | |
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| 17 | %%%%%% Random walk on frg - Dirichlet |
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| 18 | phi_pdf.class = 'mDirich'; % random walk on coefficient phi |
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| 19 | phi_pdf.rv = RV('phi',2); % 2D random walk - frg is the first element |
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| 20 | phi_pdf.k = 0.01; % width of the random walk |
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| 21 | phi_pdf.betac = [0.01 0.01]; % stabilizing elememnt of random walk |
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| 22 | |
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| 23 | %%%%%% Combining estimators in Marginalized particle filter |
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| 24 | E.class = 'MPF'; |
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| 25 | E.BM = A1; % ARX is the analytical part |
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| 26 | E.parameter_pdf = phi_pdf; % Random walk is the parameter evolution model |
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| 27 | E.res_threshold = 1.0; % resampling parameter |
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| 28 | E.n = 20; % number of particles |
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| 29 | E.prior.class = 'eDirich'; % prior on non-linear part |
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[661] | 30 | E.prior.beta = [2 1]; % |
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[640] | 31 | E.options ='logbounds,logll'; |
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| 32 | E.name = 'MPF'; |
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| 33 | |
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| 34 | M=estimator(DS,{E}); |
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| 35 | |
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| 36 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
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| 37 | % plot results |
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| 38 | ndat = size(M.u,1); |
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| 39 | |
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| 40 | figure(1); |
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| 41 | subplot(2,2,1); |
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| 42 | plotestimates(true_theta, M.MPFmean_theta, M.MPFlb_theta, M.MPFub_theta); |
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| 43 | title(' Regression parameters \theta') |
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| 44 | set(gca,'YLim',[-1.5,1]); |
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| 45 | |
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| 46 | subplot(2,2,2); |
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| 47 | plotestimates(true_R, M.MPFmean_r,M.MPFlb_r,M.MPFub_r); |
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| 48 | title('Variance parameters r') |
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| 49 | |
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| 50 | subplot(2,2,3); |
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| 51 | plotestimates(1, M.MPFmean_phi(:,1),M.MPFlb_phi(:,1),M.MPFub_phi(:,1)); |
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| 52 | title('Forgetting factor') |
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| 53 | |
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