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Adding example 9 multivariate TE to octave/matlab demos

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joseph.lizier 2014-11-07 00:51:23 +00:00
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demos/octave/example9TeContinuousMultivariateDataKraskov.m Executable file
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%%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2014, Viola Priesemann, Joseph T. Lizier
%%
%% This program 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 program 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 this program. If not, see <http://www.gnu.org/licenses/>.
%%
% = Example 9 - Transfer entropy on continuous multivariate data using Kraskov estimators =
% Transfer entropy (TE) calculation on multivariate continuous-valued data using the Kraskov-estimator TE calculator.
% Change location of jar to match yours:
javaaddpath('../../infodynamics.jar')
% Generate some random normalised data.
numObservations = 10000;
covariance=0.4;
% Define the dimension of the states of the RVs
sourceDim = 2;
destDim = 3;
sourceMVArray = randn(numObservations, sourceDim);
% Set first two columns of dest to copy source values
destMVArray = [zeros(1,sourceDim); covariance*(sourceMVArray(1:numObservations-1,:)) + (1-covariance)*randn(numObservations-1, sourceDim)];
% Set a third colum to be randomised
destMVArray(:,3) = randn(numObservations, 1);
sourceMVArray2= randn(numObservations, sourceDim); % Uncorrelated source
% Create a TE calculator and run it:
teCalc=javaObject('infodynamics.measures.continuous.kraskov.TransferEntropyCalculatorMultiVariateKraskov');
teCalc.initialise(1,sourceDim,destDim); % Use history length 1 (Schreiber k=1)
teCalc.setProperty('k', '4'); % Use Kraskov parameter K=4 for 4 nearest points
teCalc.setObservations(octaveToJavaDoubleMatrix(sourceMVArray), octaveToJavaDoubleMatrix(destMVArray));
% Perform calculation with correlated source:
result = teCalc.computeAverageLocalOfObservations();
% Note that the calculation is a random variable (because the generated
% data is a set of random variables) - the result will be of the order
% of what we expect, but not exactly equal to it; in fact, there will
% be some variance around it. It will probably be biased down here
% due to small correlations between the supposedly uncorrelated variables.
fprintf('TE result %.4f nats; expected to be close to %.4f nats for the two correlated Gaussians\n', ...
result, 2*log(1/(1-covariance^2)));
% Perform calculation with uncorrelated source:
teCalc.initialise(1,sourceDim,destDim); % Initialise leaving the parameters the same
teCalc.setObservations(octaveToJavaDoubleMatrix(sourceMVArray2), octaveToJavaDoubleMatrix(destMVArray));
result2 = teCalc.computeAverageLocalOfObservations();
fprintf('TE result %.4f nats; expected to be close to 0 nats for these uncorrelated Gaussians\n', result2);
clear teCalc