# ITE / code / IPA / demos / estimate_uMA_IPA_LPA.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``` ```function [e_hat,W_hat,de_hat] = estimate_uMA_IPA_LPA(x,ARmethod_parameters,ICA_method,opt_type,cost_type,cost_name,unknown_dimensions,de,De) %Estimates the uMA-IPA model. Method: LPA (Linear Predictive Approximation, =AR fit) + ISA on the estimated innovation. % %INPUT: % x: x(:,t) is the observation at time t. % ARmethod_parameters: % ARmethod_parameters.L: AR order; can be vector, too; in that case the 'best' AR order is chosen according to SBC, see 'estimate_AR.m'. % ARmethod_parameters.method: AR estimation method. Possibilities: 'NIW', 'subspace', 'subspace-LL', 'LL'. % 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. % De: dimension of the source (e). %OUTPUT: % e_hat: e_hat(:,t) is the estimated source 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. %REFERENCE: % Zoltan Szabo, Barnabas Poczos, and Andras Lorincz. Undercomplete Blind Subspace Deconvolution via Linear Prediction. European Conference on Machine Learning (ECML), pages 740-747, 2007. % %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 . %AR fit to x: [x_innovation_hat,Fx_hat,SBCs] = estimate_AR(x,ARmethod_parameters); %ISA on the estimated innovation of x: [e_hat,W_hat,de_hat] = estimate_ISA(x_innovation_hat,ICA_method,opt_type,cost_type,cost_name,unknown_dimensions,de,De); ```