Source

ITE / code / estimators / base_estimators / DChiSquare_kNN_k_initialization.m

function [co] = DChiSquare_kNN_k_initialization(mult,post_init)
%function [co] = DChiSquare_kNN_k_initialization(mult)
%function [co] = DChiSquare_kNN_k_initialization(mult,post_init)
%Initialization of the kNN (k-nearest neighbor, S={k}) based Pearson chi square divergence estimator.
%
%Note:
%   1)The estimator is treated as a cost object (co).
%   2)We use the naming convention 'D<name>_initialization' to ease embedding new divergence estimation methods.
%
%INPUT:
%   mult: is a multiplicative constant relevant (needed) in the estimation; '=1' means yes, '=0' no.
%   post_init: {field_name1,field_value1,field_name2,field_value2,...}; cell array containing the names and the values of the cost object fields that are to be used
%   (instead of their default values). For further details, see 'post_initialization.m'.
%OUTPUT:
%   co: cost object (structure).

%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/>.

%mandatory fields (following the template structure of the estimators to make uniform usage of the estimators possible):
    co.name = 'ChiSquare_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. 
		%I:
            co.kNNmethod = 'knnFP1';
            co.k = 3;%k-nearest neighbors				
		%II:
            %co.kNNmethod = 'knnFP2';
            %co.k = 3;%k-nearest neighbors				
        %III:
            %co.kNNmethod = 'knnsearch';
            %co.k = 3;%k-nearest neighbors
            %co.NSmethod = 'kdtree';
        %IV:
            %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 Chi^2 distance:
		%I [\int p^2(x)q^{-1}(x)dx - 1 = \int p^1(x)q^{-1}(x) p(x)dx - 1]:
			co.p = 1; %use p [p(x)dx]
		%II [\int p^2(x)q^{-1}(x)dx - 1= \int q^{-2}(x)p^2(x) q(x)dx -1]:
			%co.p = 0; %use q instead [q(x)dx]
        %fixed, do not change it (co.p => co.a, co.b):
            if co.p
                co.a = 1; 
                co.b = -1;
            else
                co.a = -2;
                co.b = 2;
            end

%initialize the ann wrapper in Octave, if needed:
    initialize_Octave_ann_wrapper_if_needed(co.kNNmethod);

%post initialization (put it _before_ initialization of the members in case of a meta estimator):    
    if nargin==2 %there are given (name,value) cost object fields
        co = post_initialization(co,post_init);
    end     
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