# ITE / code / H_I_D / utilities / compute_length_HRenyi_GSF.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``` ```function [L] = compute_length_HRenyi_GSF(Y,co) %Computes the length (L) associated to the 'Renyi_GSF' Renyi entropy estimator. % %INPUT: % Y: Y(:,t) is the t^th sample. % co: cost object. % %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 . [d,num_of_samples] = size(Y); %compute kNN graph (S={1,...,k}): [squared_distances,I] = kNN_squared_distances(Y,Y,co,1);%I:int32 %kNN relations -> weighted kNN graph (W): J = repmat(int32([1:num_of_samples]),co.k,1);%double->int32 D = squared_distances(:).^(d*(1-co.alpha)); W = spalloc(num_of_samples,num_of_samples,2*num_of_samples*co.k); W(I+(J-1)*num_of_samples) = D; W(J+(I-1)*num_of_samples) = D; %The result obtained by co.kNNmethod = 'knnFP1' (squared distances) may contain an '1e-15' rounding error which %can cause W to be not _perfectly_ sym. This '1e-15' difference must be/is corrected below: if strcmp(co.kNNmethod,'knnFP1') W = W + W.'; end %W->L (using MatlabBGL); minimal spanning forest, and its weight (L): L = compute_MST(W,co.GSFmethod); ```