mirror of https://github.com/jlizier/jidt
67 lines
2.9 KiB
Python
67 lines
2.9 KiB
Python
##
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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 1 - Transfer entropy on binary data =
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# Simple transfer entropy (TE) calculation on binary data using the discrete TE calculator:
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import jpype
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import random
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import numpy
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import os
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# Change location of jar to match yours (we assume script is called from demos/python):
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jarLocation = os.path.join(os.getcwd(), "..", "..", "infodynamics.jar");
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if (not(os.path.isfile(jarLocation))):
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exit("infodynamics.jar not found (expected at " + os.path.abspath(jarLocation) + ") - are you running from demos/python?")
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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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jpype.startJVM(jpype.getDefaultJVMPath(), "-ea", "-Djava.class.path=" + jarLocation)
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# Generate some random binary data.
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sourceArray = [random.randint(0,1) for r in range(100)]
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destArray = [0] + sourceArray[0:99]
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sourceArray2 = [random.randint(0,1) for r in range(100)]
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# Create a TE calculator and run it:
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teCalcClass = jpype.JPackage("infodynamics.measures.discrete").TransferEntropyCalculatorDiscrete
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teCalc = teCalcClass(2,1)
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teCalc.initialise()
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# First use simple arrays of ints, which we can directly pass in:
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teCalc.addObservations(sourceArray, destArray)
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print("For copied source, result should be close to 1 bit : %.4f" % teCalc.computeAverageLocalOfObservations())
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teCalc.initialise()
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teCalc.addObservations(sourceArray2, destArray)
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print("For random source, result should be close to 0 bits: %.4f" % teCalc.computeAverageLocalOfObservations())
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# Next, demonstrate how to do this with a numpy array
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teCalc.initialise()
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# Create the numpy arrays:
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sourceNumpy = numpy.array(sourceArray, dtype=int)
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destNumpy = numpy.array(destArray, dtype=int)
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# The above can be passed straight through to JIDT in python 2:
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# teCalc.addObservations(sourceNumpy, destNumpy)
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# But you need to do this in python 3:
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sourceNumpyJArray = jpype.JArray(jpype.JInt, 1)(sourceNumpy.tolist())
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destNumpyJArray = jpype.JArray(jpype.JInt, 1)(destNumpy.tolist())
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teCalc.addObservations(sourceNumpyJArray, destNumpyJArray)
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print("Using numpy array for copied source, result confirmed as: %.4f" % teCalc.computeAverageLocalOfObservations())
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jpype.shutdownJVM()
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