# ITE / code / IPA / demos / estimate_fAR_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``` ```function [e_hat,W_hat,de_hat,s_hat] = estimate_fAR_IPA(x,L,ICA_method,opt_type,cost_type,cost_name,unknown_dimensions,de,fARmethod_parameters) %Estimates the fAR-IPA model. Method: fAR identification + ISA on the estimated innovation. % %INPUT: % x: x(:,t) is the t^th observation from the fAR model. % L: fAR order. % ICA_method: the name of the ICA method applied, see 'estimate_ICA.m'. % cost_type, cost_name, opt_type: cost type, cost name, optimization type. Example: cost_type = 'sumH', cost_name = 'Renyi_kNN_1tok', 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. % fARmethod_parameters: parameters of the fAR estimator, see 'estimate_fAR.m'. %OUTPUT: % e_hat: e_hat(:,t) is the estimated driving noise at time t. % W_hat: estimated demixing matrix. % de_hat: in case of known subspace dimensions ('unknown_dimensions = 0') de_hat = de; else it contains the estimated subspace dimensions; ordered increasingly. % s_hat: s_hat(:,t) is the estimated source at time t. %REFERENCE: % Zoltan Szabo and Barnabas Poczos. Nonparametric Independent Process Analysis. European Signal Processing Conference (EUSIPCO), pages 1718-1722, 2011. % %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 . %fAR identification, estimated innovation (x_innovation_hat): x_innovation_hat = estimate_fAR(x,L,fARmethod_parameters); %ISA on the estimated innovation: [e_hat,W_hat,de_hat] = estimate_ISA(x_innovation_hat,ICA_method,opt_type,cost_type,cost_name,unknown_dimensions,de,size(x_innovation_hat,1)); %estimated source (s_hat): s_hat = W_hat * x; ```