mirror of https://github.com/jlizier/jidt
67 lines
3.5 KiB
Julia
Executable File
67 lines
3.5 KiB
Julia
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 3 - Transfer entropy on continuous data using kernel estimators =
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# Simple transfer entropy (TE) calculation on continuous-valued data using the (box) kernel-estimator TE calculator.
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# Import the JavaCall package:
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using JavaCall;
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# Change location of jar to match yours:
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jarLocation = "../../infodynamics.jar";
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# Start the JVM supplying classpath and heap size
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# (increase memory here if you get crashes due to not enough space)
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JavaCall.init(["-Djava.class.path=$(jarLocation)", "-Xmx128M"]);
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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);
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destArray = [0, covariance*sourceArray[1:numObservations-1] + (1-covariance)*randn(numObservations - 1)];
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sourceArray2=randn(numObservations); # Uncorrelated source
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# Create a TE calculator and run it:
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teClass = @jimport infodynamics.measures.continuous.kernel.TransferEntropyCalculatorKernel;
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teCalc=teClass(());
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jcall(teCalc, "setProperty", Void, (JString, JString), "NORMALISE", "true"); # Normalise the individual variables
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jcall(teCalc, "initialise", Void, (jint, jdouble), 1, 0.5); # Use history length 1 (Schreiber k=1), kernel width of 0.5 normalised units
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jcall(teCalc, "setObservations", Void, (Array{jdouble,1}, Array{jdouble,1}),
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sourceArray, destArray);
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result = jcall(teCalc, "computeAverageLocalOfObservations", jdouble, ());
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# For copied source, should give something close to expected value for correlated Gaussians:
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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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@printf("TE result %.4f bits; expected to be close to %.4f bits for these correlated Gaussians but biased upwards\n", result, -0.5*log(1-corr_expected^2)/log(2));
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jcall(teCalc, "initialise", Void, ()); # Initialise leaving the parameters the same
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jcall(teCalc, "setObservations", Void, (Array{jdouble,1}, Array{jdouble,1}),
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sourceArray2, destArray);
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# For random source, it should give something close to 0 bits
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result2 = jcall(teCalc, "computeAverageLocalOfObservations", jdouble, ());
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@printf("TE result %.4f bits; expected to be close to 0 bits for uncorrelated Gaussians but will be biased upwards\n", result2);
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# We can get insight into the bias by examining the null distribution:
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empiricalDistClass = @jimport infodynamics.utils.EmpiricalMeasurementDistribution;
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nullDist = jcall(teCalc, "computeSignificance", empiricalDistClass, (jint,), 100);
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@printf("Null distribution for unrelated source and destination (i.e. the bias) has mean %.4f and standard deviation %.4f\n",
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jcall(nullDist, "getMeanOfDistribution", jdouble, ()),
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jcall(nullDist, "getStdOfDistribution", jdouble, ()));
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