mirror of https://github.com/jlizier/jidt
63 lines
3.0 KiB
Matlab
Executable File
63 lines
3.0 KiB
Matlab
Executable File
%%
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%% Java Information Dynamics Toolkit (JIDT)
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%% Copyright (C) 2014, Viola Priesemann, 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 9 - Transfer entropy on continuous multivariate data using Kraskov estimators =
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% Transfer entropy (TE) calculation on multivariate continuous-valued data using the Kraskov-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 = 10000;
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covariance=0.4;
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% Define the dimension of the states of the RVs
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sourceDim = 2;
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destDim = 3;
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sourceMVArray = randn(numObservations, sourceDim);
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% Set first two columns of dest to copy source values
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destMVArray = [zeros(1,sourceDim); covariance*(sourceMVArray(1:numObservations-1,:)) + (1-covariance)*randn(numObservations-1, sourceDim)];
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% Set a third colum to be randomised
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destMVArray(:,3) = randn(numObservations, 1);
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sourceMVArray2= randn(numObservations, sourceDim); % Uncorrelated source
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% Create a TE calculator and run it:
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teCalc=javaObject('infodynamics.measures.continuous.kraskov.TransferEntropyCalculatorMultiVariateKraskov');
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teCalc.initialise(1,sourceDim,destDim); % Use history length 1 (Schreiber k=1)
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teCalc.setProperty('k', '4'); % Use Kraskov parameter K=4 for 4 nearest points
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teCalc.setObservations(octaveToJavaDoubleMatrix(sourceMVArray), octaveToJavaDoubleMatrix(destMVArray));
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% Perform calculation with correlated source:
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result = teCalc.computeAverageLocalOfObservations();
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% Note that the calculation is a random variable (because the generated
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% data is a set of random variables) - the result will be of the order
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% of what we expect, but not exactly equal to it; in fact, there will
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% be some variance around it. It will probably be biased down here
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% due to small correlations between the supposedly uncorrelated variables.
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fprintf('TE result %.4f nats; expected to be close to %.4f nats for the two correlated Gaussians\n', ...
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result, 2*log(1/(1-covariance^2)));
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% Perform calculation with uncorrelated source:
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teCalc.initialise(1,sourceDim,destDim); % Initialise leaving the parameters the same
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teCalc.setObservations(octaveToJavaDoubleMatrix(sourceMVArray2), octaveToJavaDoubleMatrix(destMVArray));
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result2 = teCalc.computeAverageLocalOfObservations();
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fprintf('TE result %.4f nats; expected to be close to 0 nats for these uncorrelated Gaussians\n', result2);
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clear teCalc
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