mirror of https://github.com/jlizier/jidt
84 lines
3.8 KiB
Python
84 lines
3.8 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 7 - Ensemble method with transfer entropy on continuous data using Kraskov estimators =
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# Calculation of transfer entropy (TE) by supplying an ensemble of samples from multiple time series.
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# We use continuous-valued data using the Kraskov-estimator TE calculator here.
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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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numTrials=10
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kHistoryLength=1
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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("k", "4") # Use Kraskov parameter K=4 for 4 nearest points
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teCalc.initialise(kHistoryLength) # Use target history length of kHistoryLength (Schreiber k)
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teCalc.startAddObservations()
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for trial in range(0,numTrials):
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# Create a new trial, with destArray correlated to
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# previous value of sourceArray:
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sourceArray = [random.normalvariate(0,1) for r in range(numObservations)]
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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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# Add observations for this trial:
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print("Adding samples from trial %d ..." % trial)
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teCalc.addObservations(JArray(JDouble, 1)(sourceArray), JArray(JDouble, 1)(destArray))
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# We've finished adding trials:
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print("Finished adding trials")
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teCalc.finaliseAddObservations()
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# Compute the result:
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print("Computing TE ...")
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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 some variance around it (smaller than example 4 since we have more samples).
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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, math.log(1.0/(1-math.pow(covariance,2)))))
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# And here's how to pull the local TEs out corresponding to each input time series.
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# Normally you would need to track how to split these up yourself -- here
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# it's easy because our input time series are all of the same length
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localTEs=teCalc.computeLocalOfPreviousObservations()
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localValuesPerTrial = int(len(localTEs)/numTrials) # Need to convert to int for indices later
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for trial in range(0,numTrials):
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startIndex = localValuesPerTrial*trial
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endIndex = localValuesPerTrial*(trial+1)-1
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print("Local TEs for trial %d go from array index %d to %d" % (trial, startIndex, endIndex))
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print(" corresponding to time points %d:%d (indexed from 0) of that trial" % (kHistoryLength, numObservations-1))
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# Access the local TEs for this trial as:
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localTEForThisTrial = localTEs[startIndex:endIndex]
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