differential_entropy_kl_t.m

% DIFFERENTIAL_ENTROPY_KL_T
% A temporal differential entropy estimate from samples
% using Kozachenko-Leonenko nearest neighbor estimator.
%
% H = differential_entropy_kl_t(S, timeWindowRadius)
% H = differential_entropy_kl_t(S, timeWindowRadius, 'key', value, ...)
%
% where
%
% S is a signal set.
%
% TIMEWINDOWRADIUS is an integer which determines the temporal radius 
% around each point that will be used by the estimator.
%
% H is the estimated temporal differential entropy.
%
% Optional input arguments in 'key'-value pairs:
%
% K ('k') is an integer which denotes the number of nearest neighbors 
% to be used by the estimator. Default 1.
%
% FILTER ('filter') is a real array, which gives the temporal 
% weighting coefficients. Default 1.
%
% Type 'help tim' for more documentation.

% Description: Temporal differential entropy estimation
% Detail: Kozachenko-Leonenko nearest neighbor estimator
% Documentation: differential_entropy_kl.txt

function H = differential_entropy_kl_t(...
    S, timeWindowRadius, varargin)

import([tim_package, '.*']);

concept_check(nargin, 'inputs', 2);
concept_check(nargout, 'outputs', 0 : 1);

% Optional input arguments
k = 1;
filter = 1;
eval(process_options({'k', 'filter'}, varargin));

if isnumeric(S)
    S = {S};
end

pastelmatlab.concept_check(...
    S, tim_package('signal_set'), ...
    timeWindowRadius, 'integer', ...
    timeWindowRadius, 'non_negative', ...
    k, 'integer', ...
    k, 'positive', ...
    filter, tim_package('filter'));

H = tim_matlab('differential_entropy_kl_t', ...
    S, timeWindowRadius, k, filter);