mirror of https://github.com/jlizier/jidt
57 lines
1.8 KiB
Matlab
Executable File
57 lines
1.8 KiB
Matlab
Executable File
% Copyright (C) 2019, Joseph T. Lizier
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% Distributed under GNU General Public License v3
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%
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% Add JIDT jar library to the path, and disable warnings that it's already there:
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warning('off','MATLAB:Java:DuplicateClass');
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javaaddpath('/home/joseph/JIDT/infodynamics-dist-1.6.1/infodynamics.jar');
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% Add utilities to the path
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addpath('/home/joseph/JIDT/infodynamics-dist-1.6.1/demos/octave');
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% 0. Load/prepare the data:
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data = load('/home/joseph/TeachingPlayground/CSYS5030/Week10/ca54.txt');
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for k = 1:20
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% 1. Construct the calculator:
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calc = javaObject('infodynamics.measures.discrete.ActiveInformationCalculatorDiscrete', 2, k);
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% 2. No other properties to set for discrete calculators.
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% 3. Initialise the calculator for (re-)use:
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calc.initialise();
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% Compute for all variables:
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for v = 1:10000
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% For each variable:
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% Column indices start from 1 in Matlab:
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variable = octaveToJavaIntArray(data(:, v));
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% 4. Supply the sample data:
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calc.addObservations(variable);
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end
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% 5. Compute the estimate:
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result = calc.computeAverageLocalOfObservations();
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results(k) = result;
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% 6. Compute the (statistical significance via) null distribution analytically:
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measDist = calc.computeSignificance();
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bias(k) = measDist.getMeanOfDistribution();
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fprintf('AIS_Discrete(all cols, k=%d) = %.4f bits from %d samples (bias %.4f, bias corrected %.4f)\n', ...
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k, result, calc.getNumObservations(), bias(k), result - bias(k));
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end
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biasCorrectedAIS = results - bias;
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[maxAIS, optimalK] = max(biasCorrectedAIS);
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fprintf('Optimal k=%d, giving bias corrected AIS(k=%d)=%.4f bits\n', optimalK,optimalK,biasCorrectedAIS(optimalK));
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plot(results, 'rx');
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hold on;
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plot(bias, 'bo');
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plot(biasCorrectedAIS, 'g+');
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hold off;
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xlabel('k');
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ylabel('AIS (bits)');
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legend('AIS raw', 'bias', 'bias corrected AIS');
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