1. Zoltán Szabó
  2. ITE


ITE / code / estimators / base_estimators / DChiSquare_kNN_k_estimation.m

function [D] = DChiSquare_kNN_k_estimation(Y1,Y2,co)
%function [D] = DChiSquare_kNN_k_estimation(Y1,Y2,co)
%Estimates the Pearson chi square divergence (D) of Y1 and Y2 using the kNN method (S={k}). 
%We use the naming convention 'D<name>_estimation' to ease embedding new divergence estimation methods.
%  Y1: Y1(:,t) is the t^th sample from the first distribution.
%  Y2: Y2(:,t) is the t^th sample from the second distribution. Note: the number of samples in Y1 [=size(Y1,2)] and Y2 [=size(Y2,2)] can be different.
%  co: divergence estimator object.
%   Barnabas Poczos, Liang Xiong, Dougal Sutherland, and Jeff Schneider. Support distribution machines. Technical Report, Carnegie Mellon University, 2012. http://arxiv.org/abs/1202.0302. (estimation: Dtemp2 below)
%   Karl Pearson. On the criterion that a given system of deviations from the probable in the case of correlated system of variables is such that it can be reasonable supposed to have arisen from random sampling. Philosophical Magazine Series, 50:157-172, 1900.

%Copyright (C) 2013 Zoltan Szabo ("http://www.gatsby.ucl.ac.uk/~szabo/", "zoltan (dot) szabo (at) gatsby (dot) ucl (dot) ac (dot) uk")
%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/>.

%co.mult:OK. The information theoretical quantity of interest can be (and is!) estimated exactly [co.mult=1]; the computational complexity of the estimation is essentially the same as that of the 'up to multiplicative constant' case [co.mult=0].

[dY1,num_of_samplesY1] = size(Y1);
[dY2,num_of_samplesY2] = size(Y2);

    if dY1~=dY2
        error('The dimension of the samples in Y1 and Y2 must be equal.');
if co.p %[p(x)dx]
    D = estimate_Dtemp2(Y1,Y2,co) - 1;
else %[q(x)dx]
    D = estimate_Dtemp2(Y2,Y1,co) - 1;