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Adding sample tutorial solutions, updating image for the web, and updating build file to include tutorials

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joseph.lizier 2015-02-09 00:03:45 +00:00
parent b07b6890ce
commit af5f1264a0
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1
build.xml
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@ -190,6 +190,7 @@
<zipfileset dir="demos" includes="**/*.*,**/*" excludes="clojure/deploy,clojure/deploy/*.*,python/*.pyc" prefix="demos"/>
<zipfileset dir="javadocs" includes="**/*.*,**/*" prefix="javadocs"/>
<zipfileset dir="notices" includes="**/*.*,**/*" prefix="notices"/>
<zipfileset dir="tutorial" prefix="tutorial"/>
</zip>
</target>
</project>

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demos/octave/SchreiberTransferEntropyExamples/sampleResults/heartBreathResults-kraskovMI.png
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tutorial/sampleExerciseSolutions/matlabOctave/runHeartBreathRateKraskovMI.m Normal file
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%%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% This program is free software: you can redistribute it and/or modify
%% it under the terms of the GNU General Public License as published by
%% the Free Software Foundation, either version 3 of the License, or
%% (at your option) any later version.
%%
%% This program is distributed in the hope that it will be useful,
%% but WITHOUT ANY WARRANTY; without even the implied warranty of
%% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
%% GNU General Public License for more details.
%%
%% You should have received a copy of the GNU General Public License
%% along with this program. If not, see <http://www.gnu.org/licenses/>.
%%
% function [miHeartToBreath] = runHeartBreathRateKraskovMI()
%
% runHeartBreathRateKraskovMI
% Version 1.0
% Joseph Lizier
% 3/2/2015
%
% Used to explore mutual information in the heart rate / breath rate example of Schreiber --
% but estimates MI using Kraskov-Stoegbauer-Grassberger estimation.
%
% Note that the paths (to libraries, data, etc) are set assuming this code is run in
% the folder tutorial/sampleExercises/matlabOctave, relative to the main folder of the JIDT distribution.
%
%
% Outputs
% - miHeartToBreath - MI (heart ; breath)
function [miHeartToBreath] = runHeartBreathRateKraskovMI()
tic;
% Add Octave utilities to the path
addpath('../../../demos/octave/');
% Assumes the jar is three levels up - change this if this is not the case
% Octave is happy to have the path added multiple times; I'm unsure if this is true for matlab
javaaddpath('../../../infodynamics.jar');
data = load('../../../demos/data/SFI-heartRate_breathVol_bloodOx.txt');
% Restrict to the samples that Schreiber mentions:
data = data(2350:3550,:);
% Separate the data from each column:
heart = data(:,1);
chestVol = data(:,2);
bloodOx = data(:,3);
timeSteps = length(heart);
fprintf('MI for heart rate <-> breath rate for Kraskov estimation with %d samples:\n', timeSteps);
% Using a KSG estimator for MI is the least biased way to run this:
miCalc=javaObject('infodynamics.measures.continuous.kraskov.MutualInfoCalculatorMultiVariateKraskov2');
% Compute an MI value between heart and breath
miCalc.initialise(1,1); % univariate calculation
miCalc.setProperty('k', '4'); % 4 nearest neighbours for KSG estimator
miCalc.setObservations(octaveToJavaDoubleArray(heart), ...
octaveToJavaDoubleArray(chestVol));
miHeartToBreath = miCalc.computeAverageLocalOfObservations();
fprintf('MI: = %.3f nats\n', miHeartToBreath);
tElapsed = toc;
fprintf('Total runtime was %.1f sec\n', tElapsed);
end

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tutorial/sampleExerciseSolutions/matlabOctave/runHeartBreathRateKraskovMIWithLags.m Normal file
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%%
%% Java Information Dynamics Toolkit (JIDT)
%% Copyright (C) 2012, Joseph T. Lizier
%%
%% This program is free software: you can redistribute it and/or modify
%% it under the terms of the GNU General Public License as published by
%% the Free Software Foundation, either version 3 of the License, or
%% (at your option) any later version.
%%
%% This program is distributed in the hope that it will be useful,
%% but WITHOUT ANY WARRANTY; without even the implied warranty of
%% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
%% GNU General Public License for more details.
%%
%% You should have received a copy of the GNU General Public License
%% along with this program. If not, see <http://www.gnu.org/licenses/>.
%%
% function [miHeartToBreath] = runHeartBreathRateKraskovMIWithLags(lags, numSurrogates)
%
% runHeartBreathRateKraskovMI
% Version 1.0
% Joseph Lizier
% 3/2/2015
%
% Used to explore mutual information in the heart rate / breath rate example of Schreiber --
% but estimates MI using Kraskov-Stoegbauer-Grassberger estimation.
%
% This script is used to address challenge 1, incorporating lags between heart and breath data.
%
% Note that the paths (to libraries, data, etc) are set assuming this code is run in
% the folder tutorial/sampleExercises/matlabOctave, relative to the main folder of the JIDT distribution.
%
%
% Inputs
% - lags - a scalar specifying a single, or vector specifying multiple, lag from heart to breath to evaluate MI with.
% These lags may be negative, implying a (positive) lag from breath to heart.
% - surrogates - number of surrogates to compute MI for, showing the null distribution if there were no
% relationship at the given lag.
% Outputs
% - miHeartToBreath - MI (heart ; breath) for each value of lags
function [miHeartToBreath] = runHeartBreathRateKraskovMIWithLags(lags, numSurrogates)
tic;
% Add Octave utilities to the path
addpath('../../../demos/octave/');
% Assumes the jar is three levels up - change this if this is not the case
% Octave is happy to have the path added multiple times; I'm unsure if this is true for matlab
javaaddpath('../../../infodynamics.jar');
if (nargin < 1)
lags = [0];
end
if (nargin < 2)
numSurrogates = 0;
end
data = load('../../../demos/data/SFI-heartRate_breathVol_bloodOx.txt');
% Restrict to the samples that Schreiber mentions:
data = data(2350:3550,:);
% Separate the data from each column:
heart = data(:,1);
chestVol = data(:,2);
bloodOx = data(:,3);
timeSteps = length(heart);
fprintf('MI for heart rate <-> breath rate for Kraskov estimation with %d samples:\n', timeSteps);
% Using a KSG estimator for MI is the least biased way to run this:
miCalc=javaObject('infodynamics.measures.continuous.kraskov.MutualInfoCalculatorMultiVariateKraskov2');
for lagIndex = 1:length(lags)
lag = lags(lagIndex);
% Compute an MI value for this lag
miCalc.initialise(1,1); % univariate calculation
miCalc.setProperty('k', '4');
if (lag < 0)
source = chestVol;
target = heart;
else
source = heart;
target = chestVol;
end
miCalc.setProperty('TIME_DIFF', sprintf("%d", abs(lag)));
miCalc.setObservations(octaveToJavaDoubleArray(source), ...
octaveToJavaDoubleArray(target));
miHeartToBreath(lagIndex) = miCalc.computeAverageLocalOfObservations();
fprintf('MI(lag=%d): = %.3f nats', lag, miHeartToBreath(lagIndex));
if (numSurrogates > 0)
% My observations suggest that most results here for lags -15:15 are statistically significant
% (threshold for multiple comparisons corrected 0.05 level would be about 0.08 nats.)
miHeartToBreathNullDist = miCalc.computeSignificance(numSurrogates);
miHeartToBreathNullMean = miHeartToBreathNullDist.getMeanOfDistribution();
miHeartToBreathNullStd = miHeartToBreathNullDist.getStdOfDistribution();
fprintf(" (null = %.3f +/- %.3f)", miHeartToBreathNullMean, miHeartToBreathNullStd),
end
fprintf("\n");
end
tElapsed = toc;
fprintf('Total runtime was %.1f sec\n', tElapsed);
plot(lags, miHeartToBreath, 'rx', 'markersize', 10);
set (gca,'fontsize',26);
xlabel('Lag', 'FontSize', 36, 'FontWeight', 'bold');
ylabel('MI (nats)', 'FontSize', 36, 'FontWeight', 'bold');
print('heartBreathResults-kraskovMI.eps', '-depsc');
end