ITE / code / H_I_D / utilities / ann_octave.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``` ```function [indices,squared_distances] = ann_octave(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). %The computations are based on the ann Octave bindings. % %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: % [indices,squared_distances] = ann_octave(Y,Y,co,1); % [indices,squared_distances] = ann_octave(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 . %initialization: kmax = max(co.k); [d,num_of_samplesY] = size(Y); num_of_samplesQ = size(Q,2); %preallocation: indices = zeros(kmax,num_of_samplesQ,'int32'); squared_distances = zeros(kmax,num_of_samplesQ); %create kd-tree (from Y): kd = new_ANNkd_tree(Y.');%t^th row = t^th sample %queries (from Q): nq = 0; for q = Q if Y_equals_to_Q [idx,dist] = kd.annkSearch(q,kmax+1,co.epsi); else [idx,dist] = kd.annkSearch(q,kmax,co.epsi); end nq = nq + 1; if Y_equals_to_Q indices(:,nq) = int32(idx(2:end)+1); squared_distances(:,nq) = dist(2:end); else indices(:,nq) = int32(idx(:)+1); squared_distances(:,nq) = dist(:); end end %delete kd-tree: delete_ANNkd_tree(kd); ```
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