mirror of https://github.com/jlizier/jidt
68 lines
3.4 KiB
Python
Executable File
68 lines
3.4 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 4 - Transfer entropy on continuous data using Kraskov estimators =
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# Simple transfer entropy (TE) calculation on continuous-valued data using the Kraskov-estimator TE calculator.
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from jpype import *
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import random
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import math
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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 normalised data.
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numObservations = 1000
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covariance=0.4
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# Source array of random normals:
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sourceArray = [random.normalvariate(0,1) for r in range(numObservations)]
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# Destination array of random normals with partial correlation to previous value of sourceArray
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destArray = [0] + [sum(pair) for pair in zip([covariance*y for y in sourceArray[0:numObservations-1]], \
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[(1-covariance)*y for y in [random.normalvariate(0,1) for r in range(numObservations-1)]] ) ]
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# Uncorrelated source array:
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sourceArray2 = [random.normalvariate(0,1) for r in range(numObservations)]
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# Create a TE calculator and run it:
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teCalcClass = JPackage("infodynamics.measures.continuous.kraskov").TransferEntropyCalculatorKraskov
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teCalc = teCalcClass()
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teCalc.setProperty("NORMALISE", "true") # Normalise the individual variables
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teCalc.initialise(1) # Use history length 1 (Schreiber k=1)
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teCalc.setProperty("k", "4") # Use Kraskov parameter K=4 for 4 nearest points
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# Perform calculation with correlated source:
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teCalc.setObservations(JArray(JDouble, 1)(sourceArray), JArray(JDouble, 1)(destArray))
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result = teCalc.computeAverageLocalOfObservations()
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# Note that the calculation is a random variable (because the generated
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# data is a set of random variables) - the result will be of the order
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# of what we expect, but not exactly equal to it; in fact, there will
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# be a large variance around it.
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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 * math.sqrt(covariance**2 + (1-covariance)**2));
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print("TE result %.4f nats; expected to be close to %.4f nats for these correlated Gaussians" % \
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(result, -0.5 * math.log(1-corr_expected**2)))
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# Perform calculation with uncorrelated source:
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teCalc.initialise() # Initialise leaving the parameters the same
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teCalc.setObservations(JArray(JDouble, 1)(sourceArray2), JArray(JDouble, 1)(destArray))
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result2 = teCalc.computeAverageLocalOfObservations()
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print("TE result %.4f nats; expected to be close to 0 nats for these uncorrelated Gaussians" % result2)
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