# ITE / code / H_I_D / utilities / kNN_squared_distances.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``` ```function [squared_distances,indices] = kNN_squared_distances(Y,Q,co,Y_equals_to_Q) %Computes the k-nearest neighbor distances of each point (Q(:,t)) in %matrix Q from samples (Y(:,t)) in matrix Y. The number of samples in Q (size(Q,2)) and Y (size(Y,2)) can be different. Parameters in the cost object (co), can be used to fine-tune the kNN search (k,...). %Y_equals_to_Q is a flag indicating whether Y is equal to Q or not: true (=1), false (=0). % %OUTPUT: % squared_distances: squared distances of the nearest neighbors; max(co.k) x size(Q,2). % indices: indices(i,j) = i^th nearest neighbor from the samples {Y(:,t),t=1,2,...} to the j^th query point (Q(:,j)); max(co.k) x size(Q,2); int32. %EXAMPLE: % [squared_distances,indices] = kNN_squared_distances(Y,Y,co,1); % [squared_distances,indices] = kNN_squared_distances(Y,Q,co,0); % %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 . switch co.kNNmethod case 'knnFP1'%fast pairwise distance computation and C++ partial sort if Y_equals_to_Q %'Y==Q' [squared_distances,indices] = knn(Q, Y, max(co.k)+1);%assumption below:max(co.k)+1 <= size(Q,1)[=size(Y,1)] squared_distances = squared_distances(2:end,:); indices = int32(indices(2:end,:)); else [squared_distances,indices] = knn(Q, Y, max(co.k)); indices = int32(indices); end case 'knnFP2'%fast pairwise distance computation sq = sqdistance(Y,Q);%fast squared distance computation [S,I] = sort(sq); if Y_equals_to_Q %'Y==Q' => exclude the points themselves squared_distances = S([1:max(co.k)]+1,:); indices = int32(I([1:max(co.k)]+1,:)); else squared_distances = S(1:max(co.k),:); indices = int32(I(1:max(co.k),:)); end case 'knnsearch' %Statistics Toolbox:Matlab [indices,distances] = knnsearch(Y.',Q.','K',max(co.k),'NSMethod',co.NSmethod); %[double,... indices = int32(indices.');%.': to be compatible with 'ANN' squared_distances = (distances.').^2;%distances -> squared distances; .': to be compatible with 'ANN' case 'ANN'%ANN library/wrapper if working_environment_Matlab ann_object = ann(Y); [indices, squared_distances] = ksearch(ann_object, Q, max(co.k), co.epsi,0);%'0': nearest neighbors do not include the points themselves. max(co.k) is used in 'HRenyi_kNN_S_estimation.m' ann_object = close(ann_object); else [indices,squared_distances] = ann_octave(Y,Q,co,Y_equals_to_Q); end otherwise error('kNN method=?'); end ```
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