1. Zoltán Szabó
  2. ITE


ITE / code / H_I_D / base_estimators / DHellinger_kNN_k_initialization.m

function [co] = DHellinger_kNN_k_initialization(mult)
%Initialization of the kNN (k-nearest neighbor, S={k}) based Hellinger distance estimator.
%   1)The estimator is treated as a cost object (co).
%   2)We make use of the naming convention 'D<name>_initialization', to ease embedding new divergence estimation methods.
%   mult: is a multiplicative constant relevant (needed) in the estimation; '=1' means yes, '=0' no.
%   co: cost object (structure).
%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/>.

%mandatory fields:
    co.name = 'Hellinger_kNN_k';
    co.mult = mult;
%other fields:
    %Possibilities for 'co.kNNmethod' (see 'kNN_squared_distances.m'): 
        %I: 'knnFP1': fast pairwise distance computation and C++ partial sort; parameter: co.k.        
        %II: 'knnFP2': fast pairwise distance computation; parameter: co.k.
        %III: 'knnsearch' (Matlab Statistics Toolbox): parameters: co.k, co.NSmethod ('kdtree' or 'exhaustive').
        %IV: 'ANN' (approximate nearest neighbor); parameters: co.k, co.epsi. 
            co.kNNmethod = 'knnFP1';
            co.k = 3;%k-nearest neighbors
            %co.kNNmethod = 'knnFP2';
            %co.k = 3;%k-nearest neighbors
            %co.kNNmethod = 'knnsearch';
            %co.k = 3;%k-nearest neighbors
            %co.NSmethod = 'kdtree';
            %co.kNNmethod = 'ANN';
            %co.k = 3;%k-nearest neighbors
            %co.epsi = 0; %=0: exact kNN; >0: approximate kNN, the true (not squared) distances can not exceed the real distance more than a factor of (1+epsi).
	%Possibilities for rewriting the Hellinger distance:
		%I [\int p^{1/2}(x)q^{1/2}(x)dx = \int p^{-1/2}(x)q^{1/2}(x) p(x)dx]:
			co.p = 1; %use p [p(x)dx]
		%II [\int p^{1/2}(x)q^{1/2}(x)dx = \int q^{-1/2}(x)p^{1/2}(x) q(x)dx]:
			%co.p = 0; %use q instead [q(x)dx]
	%Fixed, do not change it:
		co.a = -1/2; 
		co.b = 1/2;
%initialize the ann wrapper in Octave, if needed: