mirror of https://github.com/jlizier/jidt
77 lines
2.7 KiB
Matlab
77 lines
2.7 KiB
Matlab
%%
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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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% function [miHeartToBreath] = runHeartBreathRateKraskovMI()
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%
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% runHeartBreathRateKraskovMI
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% Version 1.0
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% Joseph Lizier
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% 3/2/2015
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%
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% Used to explore mutual information in the heart rate / breath rate example of Schreiber --
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% but estimates MI using Kraskov-Stoegbauer-Grassberger estimation.
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%
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% Note that the paths (to libraries, data, etc) are set assuming this code is run in
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% the folder tutorial/sampleExercises/matlabOctave, relative to the main folder of the JIDT distribution.
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%
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%
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% Outputs
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% - miHeartToBreath - MI (heart ; breath)
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function [miHeartToBreath] = runHeartBreathRateKraskovMI()
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tic;
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% Add Octave utilities to the path
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addpath('../../../demos/octave/');
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% Assumes the jar is three levels up - change this if this is not the case
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% Octave is happy to have the path added multiple times; I'm unsure if this is true for matlab
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javaaddpath('../../../infodynamics.jar');
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data = load('../../../demos/data/SFI-heartRate_breathVol_bloodOx.txt');
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% Restrict to the samples that Schreiber mentions:
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data = data(2350:3550,:);
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% Separate the data from each column:
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heart = data(:,1);
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chestVol = data(:,2);
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bloodOx = data(:,3);
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timeSteps = length(heart);
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fprintf('MI for heart rate <-> breath rate for Kraskov estimation with %d samples:\n', timeSteps);
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% Using a KSG estimator for MI is the least biased way to run this:
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miCalc=javaObject('infodynamics.measures.continuous.kraskov.MutualInfoCalculatorMultiVariateKraskov2');
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% Compute an MI value between heart and breath
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miCalc.initialise(1,1); % univariate calculation
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miCalc.setProperty('k', '4'); % 4 nearest neighbours for KSG estimator
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miCalc.setObservations(octaveToJavaDoubleArray(heart), ...
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octaveToJavaDoubleArray(chestVol));
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miHeartToBreath = miCalc.computeAverageLocalOfObservations();
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fprintf('MI: = %.3f nats\n', miHeartToBreath);
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tElapsed = toc;
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fprintf('Total runtime was %.1f sec\n', tElapsed);
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end
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