mirror of https://github.com/jlizier/jidt
48 lines
2.3 KiB
Matlab
Executable File
48 lines
2.3 KiB
Matlab
Executable File
%%
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%% Java Information Dynamics Toolkit (JIDT)
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%% Copyright (C) 2012, Joseph T. Lizier
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%%
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%% This program is free software: you can redistribute it and/or modify
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%% it under the terms of the GNU General Public License as published by
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%% the Free Software Foundation, either version 3 of the License, or
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%% (at your option) any later version.
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%%
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%% This program is distributed in the hope that it will be useful,
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%% but WITHOUT ANY WARRANTY; without even the implied warranty of
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%% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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%% GNU General Public License for more details.
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%%
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%% You should have received a copy of the GNU General Public License
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%% along with this program. If not, see <http://www.gnu.org/licenses/>.
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%%
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% = Example 3 - Transfer entropy on continuous data using kernel estimators =
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% Simple transfer entropy (TE) calculation on continuous-valued data using the (box) kernel-estimator TE calculator.
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% Change location of jar to match yours:
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javaaddpath('../../infodynamics.jar');
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% Generate some random normalised data.
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numObservations = 1000;
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covariance=0.4;
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sourceArray=randn(numObservations, 1);
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destArray = [0; covariance*sourceArray(1:numObservations-1) + (1-covariance)*randn(numObservations - 1, 1)];
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sourceArray2=randn(numObservations, 1); % Uncorrelated source
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% Create a TE calculator and run it:
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teCalc=javaObject('infodynamics.measures.continuous.kernel.TransferEntropyCalculatorKernel');
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teCalc.setProperty('NORMALISE', 'true'); % Normalise the individual variables
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teCalc.initialise(1, 0.5); % Use history length 1 (Schreiber k=1), kernel width of 0.5 normalised units
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teCalc.setObservations(sourceArray, destArray);
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% For copied source, should give something close to expected value for correlated Gaussians:
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result = teCalc.computeAverageLocalOfObservations();
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fprintf('TE result %.4f bits; expected to be close to %.4f bits for these correlated Gaussians but biased upwards\n', ...
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result, log(1/(1-covariance^2))/log(2));
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teCalc.initialise(); % Initialise leaving the parameters the same
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teCalc.setObservations(sourceArray2, destArray);
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% For random source, it should give something close to 0 bits
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result2 = teCalc.computeAverageLocalOfObservations();
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fprintf('TE result %.4f bits; expected to be close to 0 bits for uncorrelated Gaussians but will be biased upwards\n', ...
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result2);
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