mirror of https://github.com/jlizier/jidt
60 lines
3.0 KiB
Python
Executable File
60 lines
3.0 KiB
Python
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 5 - Multivariate transfer entropy on binary data =
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# Multivariate transfer entropy (TE) calculation on binary data using the discrete TE calculator:
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from jpype import *
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import random
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from operator import xor
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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 (add the "-Xmx" option with say 1024M if you get crashes due to not enough memory space)
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startJVM(getDefaultJVMPath(), "-ea", "-Djava.class.path=" + jarLocation)
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# Generate some random binary data.
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numObservations = 100
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sourceArray = [[random.randint(0,1) for y in xrange(2)] for x in xrange(numObservations)] # for 10 rows (time-steps) for 2 variables
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sourceArray2= [[random.randint(0,1) for y in xrange(2)] for x in xrange(numObservations)] # for 10 rows (time-steps) for 2 variables
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# Destination variable takes a copy of the first bit of the source in bit 1,
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# and an XOR of the two bits of the source in bit 2:
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destArray = [[0, 0]]
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for j in range(1,numObservations):
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destArray.append([sourceArray[j-1][0], xor(sourceArray[j-1][0], sourceArray[j-1][1])])
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# Create a TE calculator and run it:
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teCalcClass = JPackage("infodynamics.measures.discrete").TransferEntropyCalculator
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teCalc = teCalcClass(4,1)
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teCalc.initialise()
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# We need to construct the joint values of the dest and source before we pass them in,
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# and need to use the matrix conversion routine when calling from Matlab/Octave:
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mUtils= JPackage('infodynamics.utils').MatrixUtils
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teCalc.addObservations(mUtils.computeCombinedValues(sourceArray, 2), \
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mUtils.computeCombinedValues(destArray, 2))
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result = teCalc.computeAverageLocalOfObservations()
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print('For source which the 2 bits are determined from, result should be close to 2 bits : %.3f' % result)
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teCalc.initialise()
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teCalc.addObservations(mUtils.computeCombinedValues(sourceArray2, 2), \
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mUtils.computeCombinedValues(destArray, 2))
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result2 = teCalc.computeAverageLocalOfObservations()
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print('For random source, result should be close to 0 bits in theory: %.3f' % result2)
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print('The result for random source is inflated towards 0.3 due to finite observation length (%d). One can verify that the answer is consistent with that from a random source by checking: teCalc.computeSignificance(1000); ans.pValue\n' % teCalc.getNumObservations())
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