1. Zoltán Szabó
  2. ITE


ITE / code / H_I_D_A_C / base_estimators / HShannon_KDP_estimation.m

function [H] = HShannon_KDP_estimation(Y,co)
%Estimates the Shannon differential entropy (H) of Y using k-d partitioning.
%We use the naming convention 'H<name>_estimation' to ease embedding new entropy estimation methods.
%   Y: Y(:,t) is the t^th sample.
%  co: entropy estimator object.
%   Dan Stowell and Mark D. Plumbley. Fast multidimensional entropy estimation by k-d partitioning. IEEE Signal Processing Letters 16 (6), 537-540, 2009.
%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/>.


%H estimation:
     H =  kdpee(Y.');