1. Zoltán Szabó
  2. ITE


ITE / code / H_I_D / base_estimators / ISW1_estimation.m

function [I] = ISW1_estimation(Y,ds,co)
%Estimates mutual information (I) using Schweizer-Wolff's sigma. 
%   Y: Y(:,t) is the t^th sample.
%  ds: subspace dimensions.
%  co: initialized mutual information estimator object.
%  Sergey Kirshner and Barnabas Poczos. ICA and ISA Using Schweizer-Wolff Measure of Dependence. International Conference on Machine Learning (ICML), 464-471, 2008.
%  B. Schweizer and E. F. Wolff. On Nonparametric Measures of Dependence for Random Variables. The Annals of Statistics 9:879-885, 1981.
%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/>.


if one_dimensional_problem(ds) && length(ds)==2
        bound = 1;
        num_of_samples = size(Y,2);
    if (num_of_samples <= co.max_num_of_samples_without_appr) %directly
        r = rank_transformation(Y);
        I = SW_sigma(r,bound);
    else %approximation on a sparse grid
        bin_size = ceil(num_of_samples/co.max_num_of_bins);
        num_of_samples = floor(num_of_samples/bin_size)*bin_size;    
        r = rank_transformation(Y(:,1:num_of_samples));
        I = SW_sigma(r,bound,bin_size);
    disp('Error: there must be 2 pieces of one-dimensional subspaces (coordinates) for this estimator.');