ITE / code / H_I_D / base_estimators / DHellinger_kNN_k_initialization.m

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 function [co] = DHellinger_kNN_k_initialization(mult) %Initialization of the kNN (k-nearest neighbor, S={k}) based Hellinger distance estimator. % %Note: % 1)The estimator is treated as a cost object (co). % 2)We make use of the naming convention 'D_initialization', to ease embedding new divergence estimation methods. % %INPUT: % mult: is a multiplicative constant relevant (needed) in the estimation; '=1' means yes, '=0' no. %OUTPUT: % 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 . %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. %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 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: initialize_Octave_ann_wrapper_if_needed(co.kNNmethod); 
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