# ITE / code / IPA / demos / estimate_mAR_IPA.m

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44``` ```function [s_hat,e_hat,W_hat,de_hat,Fx_hat,Fs_hat] = estimate_mAR_IPA(x,mAR,ICA,ISA,unknown_dimensions,de) %Estimates the mAR-IPA model. Method: mAR identification + ISA on the estimated innovation. % %INPUT: % x: x(:,t) is the observation at time t. % mAR: mAR estimator, see 'estimate_mAR.m'. % ICA: solver for independent component analysis, see 'estimate_ICA.m'. % ISA: solver for independent subspace analysis (=clustering of the ICA elements). ISA.cost_type, ISA.cost_name, ISA.opt_type: cost type, cost name, optimization type. Example: ISA.cost_type = 'sumH', ISA.cost_name = 'Renyi_kNN_1tok', ISA.opt_type = 'greedy' means that we use an entropy sum ISA formulation ('sumH'), where the entropies are estimated Renyi entropies via kNN methods ('Renyi_kNN_1tok') and the optimization is greedy; see also 'demo_ISA.m' % unknown_dimensions: '0' means 'the subspace dimensions are known'; '1' means 'the number of the subspaces are known' (but the individual dimensions are unknown). % de: % 1)in case of 'unknown_dimensions = 0': 'de' contains the subspace dimensions. % 2)in case of 'unknown_dimensions = 1': the length of 'de' must be equal to the number of subspaces, but the coordinates of the vector can be arbitrary. %OUTPUT: % s_hat: estimated source, s_hat(:,t) corresponds to time t % e_hat: estimated driving noise, e_hat(:,t) corresponds to time t % W_hat: estimated demixing matrix. % de_hat: estimated subspace dimensions. % Fx_hat: estimated observation dynamics. % Fs_hat: estimated source dynamics. % %REFERENCE: % Zoltan Szabo. Autoregressive Independent Process Analysis with Missing Observations. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pp. 159-164, 2010. % %Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu") % %This file is part of the ITE (Information Theoretical Estimators) Matlab/Octave toolbox. % %ITE is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by %the Free Software Foundation, either version 3 of the License, or (at your option) any later version. % %This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of %MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. % %You should have received a copy of the GNU General Public License along with ITE. If not, see . %mAR fit to x: [x_innovation_hat,Fx_hat,SBCs] = estimate_mAR(x,mAR); %ISA on the estimated innovation of x: [e_hat,W_hat,de_hat] = estimate_ISA(x_innovation_hat,ICA,ISA,unknown_dimensions,de,size(x_innovation_hat,1)); %estimated source(s_hat) and its dynamics: s_hat = W_hat * x; Fs_hat = basis_transformation_AR(Fx_hat,W_hat,inv(W_hat)); ```
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