Source

ITE / code / IPA / data_generation / models / generate_complex_ISA.m

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'
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