mirror of https://github.com/jlizier/jidt
Adding sample tutorial solutions, updating image for the web, and updating build file to include tutorials
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joseph.lizier
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b07b6890ce
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@ -190,6 +190,7 @@
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<zipfileset dir="demos" includes="**/*.*,**/*" excludes="clojure/deploy,clojure/deploy/*.*,python/*.pyc" prefix="demos"/>
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<zipfileset dir="demos" includes="**/*.*,**/*" excludes="clojure/deploy,clojure/deploy/*.*,python/*.pyc" prefix="demos"/>
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<zipfileset dir="javadocs" includes="**/*.*,**/*" prefix="javadocs"/>
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<zipfileset dir="javadocs" includes="**/*.*,**/*" prefix="javadocs"/>
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<zipfileset dir="notices" includes="**/*.*,**/*" prefix="notices"/>
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<zipfileset dir="notices" includes="**/*.*,**/*" prefix="notices"/>
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<zipfileset dir="tutorial" prefix="tutorial"/>
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</zip>
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</zip>
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</target>
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</target>
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</project>
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</project>
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Before Width: | Height: | Size: 7.4 KiB After Width: | Height: | Size: 8.1 KiB |
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%%
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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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@ -0,0 +1,117 @@
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%%
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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] = runHeartBreathRateKraskovMIWithLags(lags, numSurrogates)
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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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% This script is used to address challenge 1, incorporating lags between heart and breath data.
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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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% Inputs
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% - lags - a scalar specifying a single, or vector specifying multiple, lag from heart to breath to evaluate MI with.
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% These lags may be negative, implying a (positive) lag from breath to heart.
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% - surrogates - number of surrogates to compute MI for, showing the null distribution if there were no
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% relationship at the given lag.
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% Outputs
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% - miHeartToBreath - MI (heart ; breath) for each value of lags
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function [miHeartToBreath] = runHeartBreathRateKraskovMIWithLags(lags, numSurrogates)
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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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if (nargin < 1)
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lags = [0];
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end
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if (nargin < 2)
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numSurrogates = 0;
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end
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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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for lagIndex = 1:length(lags)
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lag = lags(lagIndex);
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% Compute an MI value for this lag
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miCalc.initialise(1,1); % univariate calculation
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miCalc.setProperty('k', '4');
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if (lag < 0)
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source = chestVol;
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target = heart;
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else
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source = heart;
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target = chestVol;
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end
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miCalc.setProperty('TIME_DIFF', sprintf("%d", abs(lag)));
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miCalc.setObservations(octaveToJavaDoubleArray(source), ...
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octaveToJavaDoubleArray(target));
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miHeartToBreath(lagIndex) = miCalc.computeAverageLocalOfObservations();
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fprintf('MI(lag=%d): = %.3f nats', lag, miHeartToBreath(lagIndex));
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if (numSurrogates > 0)
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% My observations suggest that most results here for lags -15:15 are statistically significant
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% (threshold for multiple comparisons corrected 0.05 level would be about 0.08 nats.)
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miHeartToBreathNullDist = miCalc.computeSignificance(numSurrogates);
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miHeartToBreathNullMean = miHeartToBreathNullDist.getMeanOfDistribution();
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miHeartToBreathNullStd = miHeartToBreathNullDist.getStdOfDistribution();
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fprintf(" (null = %.3f +/- %.3f)", miHeartToBreathNullMean, miHeartToBreathNullStd),
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end
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fprintf("\n");
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end
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tElapsed = toc;
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fprintf('Total runtime was %.1f sec\n', tElapsed);
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plot(lags, miHeartToBreath, 'rx', 'markersize', 10);
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set (gca,'fontsize',26);
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xlabel('Lag', 'FontSize', 36, 'FontWeight', 'bold');
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ylabel('MI (nats)', 'FontSize', 36, 'FontWeight', 'bold');
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print('heartBreathResults-kraskovMI.eps', '-depsc');
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end
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