mirror of https://github.com/jlizier/jidt
61 lines
3.0 KiB
Matlab
Executable File
61 lines
3.0 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 4 - Transfer entropy on continuous data using Kraskov estimators =
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% Simple transfer entropy (TE) calculation on 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 = 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.kraskov.TransferEntropyCalculatorKraskov');
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teCalc.setProperty('k', '4'); % Use Kraskov parameter K=4 for 4 nearest points
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teCalc.initialise(1); % Use history length 1 (Schreiber k=1)
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% Perform calculation with correlated source:
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teCalc.setObservations(sourceArray, destArray);
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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 a large variance around it.
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% Expected correlation is expected covariance / product of expected standard deviations:
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% (where square of destArray standard dev is sum of squares of std devs of
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% underlying distributions)
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corr_expected = covariance ./ (1 * sqrt(covariance^2 + (1-covariance)^2));
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fprintf('TE result %.4f nats; expected to be close to %.4f nats for these correlated Gaussians\n', ...
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result, - 0.5 * log(1-corr_expected^2));
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% Perform calculation with uncorrelated source:
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teCalc.initialise(); % Initialise leaving the parameters the same
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teCalc.setObservations(sourceArray2, destArray);
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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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% We can also compute the local TE values for the time-series samples here:
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% (See more about utility of local TE in the CA demos)
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localTE = teCalc.computeLocalOfPreviousObservations();
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fprintf('Notice that the mean of locals, %.4f nats, equals the previous result\n', ...
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sum(javaMatrixToOctave(localTE))/(numObservations-1));
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