ITE / code / H_I_D / base_estimators / HRenyi_GSF_initialization.m

function [co] = HRenyi_GSF_initialization(mult)
%Initialization of the GSF (geodesic spanning forest) based Renyi entropy estimator.
%   2) We make use of the naming convention 'H<name>_initialization', to ease embedding new entropy estimation methods.
%   co: cost object (structure).

%mandatory fields: = 'Renyi_GSF';
    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. 
            co.kNNmethod = 'knnFP1';
            co.k = 3;%k-nearest neighbors
            %co.kNNmethod = 'knnFP2';
            %co.k = 3;%k-nearest neighbors
            %co.kNNmethod = 'knnsearch';
            %co.k = 3;%k-nearest neighbors
            %co.NSmethod = 'kdtree';
            %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).
    %Method to compute the geodesic spanning forest:
        co.GSFmethod = 'MatlabBGL_Kruskal';

    co.alpha = 0.99; %The Renyi entropy equals to the Shannon differential entropy, in limit, i.e., Renyi=H_{R,alpha} -> Shannon=H, provided that alpha ->1.
    co.additive_constant_is_relevant = 0; %1:additive constant is relevant (you can precompute it via 'estimate_HRenyi_constant.m'), 0:not relevant
%initialize the ann wrapper in Octave, if needed:
Tip: Use camelCasing e.g. ProjME to search for