ITE / code / IPA / data_generation / datasets / sample_subspaces_IFS_Sierpinski.m

function [e] = sample_subspaces_IFS_Sierpinski(num_of_samples,n_v)
%Sampling from the Sierpinski n-gon. Number of samples: num_of_samples. Multiple n values can be given in vector n_v.
%   e: e(:,t) is the t^th sample. size(e,2) = num_of_samples.
%   e = sample_subspaces_IFS_sierpinski(10000,[5:6]);
%Copyright (C) 2012 Zoltan Szabo ("", "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 <>.

    e = zeros(2*length(n_v),num_of_samples);
    k = 0;
%data generation:    
    for n = n_v
        k = k + 1;
        [f,p,x1] =  f_Sierpinski_n_gon(n);    
        e(2*k-1:2*k,:) = sample_subspaces_IFS_iteration(f,p,num_of_samples,x1);
function [f,p,x1] = f_Sierpinski_n_gon(n)
%Returns IFS (f), probability distribution (p) and initial point (x1) of a Sierpinski n-gon.

    p = ones(n,1)/n;
        K = floor(n/4);
        r = 1/(2*(1+sum(cos(2*pi/n*[1:K]))));

    %A,b -> f:    
        w = 10;
        for k = 1 : n
            f{k}.A = r * eye(2);
            f{k}.b = (1-r) * w * [cos((2*pi*k)/n);sin((2*pi*k)/n)];
    x1 = [.5;.5];