function [x,A,e,de,num_of_comps] = generate_complex_ISA(data_type,num_of_comps,num_of_samples) %Generates a complex ISA model. % %INPUT: % data_type: name(s) of the ISA source(s), see 'sample_subspaces.m'. Note: guarantee that the associated real subspaces are even dimensional, i.e. coordinates of de_real are even -- if you use the real -> complex subspace generation technique. % num_of_comps: number of ISA subspaces, see 'sample_subspaces.m'. % num_of_samples: number of samples. %OUTPUT: % x: x(:,t) is the observation at time t; size(x,2) = num_of_samples. % A: mixing matrix, random unitary (without loss of generality). % e: e(:,t) is the source at time t, size(s,2) = num_of_samples. % de: subspace dimensions. % num_of_comps: number of components; num_of_comps = length(de). %EXAMPLE: % [x,A,e,de,num_of_comps] = generate_complex_ISA('multi4-spherical',2,1000); % %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 <http://www.gnu.org/licenses/>. %source(e); below [2d_1,...2d_M]-dimensional real subspaces are generated, and then transformed to the complex domain: [e_real,de_real] = sample_subspaces(data_type,num_of_comps,num_of_samples); %verification: if ~all_even(de_real) error('The associated real valued subspaces must be even dimensional.'); end e = R2C_vector(e_real); de = de_real / 2; %mixing matrix(A): D = sum(de);%dimension of the hidden source A = random_unitary(D);%without loss of generality %observation(x): x = A * e; num_of_comps = sum(num_of_comps); %number of components/subspaces; until this point num_of_comps could be a vector; see 'demo_ISA.m'