example4TeContinuousDataKraskov.py

##
##  Java Information Dynamics Toolkit (JIDT)
##  Copyright (C) 2012, Joseph T. Lizier
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# = Example 4 - Transfer entropy on continuous data using Kraskov estimators =

# Simple transfer entropy (TE) calculation on continuous-valued data using the Kraskov-estimator TE calculator.

from jpype import *
import random
import math
import os

# Change location of jar to match yours (we assume script is called from demos/python):
jarLocation = os.path.join(os.getcwd(), "..", "..", "infodynamics.jar");
if (not(os.path.isfile(jarLocation))):
	exit("infodynamics.jar not found (expected at " + os.path.abspath(jarLocation) + ") - are you running from demos/python?")
# Start the JVM (add the "-Xmx" option with say 1024M if you get crashes due to not enough memory space)
startJVM(getDefaultJVMPath(), "-ea", "-Djava.class.path=" + jarLocation)

# Generate some random normalised data.
numObservations = 1000
covariance=0.4
# Source array of random normals:
sourceArray = [random.normalvariate(0,1) for r in range(numObservations)]
# Destination array of random normals with partial correlation to previous value of sourceArray
destArray = [0] + [sum(pair) for pair in zip([covariance*y for y in sourceArray[0:numObservations-1]], \
                                             [(1-covariance)*y for y in [random.normalvariate(0,1) for r in range(numObservations-1)]] ) ]
# Uncorrelated source array:
sourceArray2 = [random.normalvariate(0,1) for r in range(numObservations)]
# Create a TE calculator and run it:
teCalcClass = JPackage("infodynamics.measures.continuous.kraskov").TransferEntropyCalculatorKraskov
teCalc = teCalcClass()
teCalc.setProperty("NORMALISE", "true") # Normalise the individual variables
teCalc.initialise(1) # Use history length 1 (Schreiber k=1)
teCalc.setProperty("k", "4") # Use Kraskov parameter K=4 for 4 nearest points
# Perform calculation with correlated source:
teCalc.setObservations(JArray(JDouble, 1)(sourceArray), JArray(JDouble, 1)(destArray))
result = teCalc.computeAverageLocalOfObservations()
# Note that the calculation is a random variable (because the generated
#  data is a set of random variables) - the result will be of the order
#  of what we expect, but not exactly equal to it; in fact, there will
#  be a large variance around it.
# Expected correlation is expected covariance / product of expected standard deviations:
#  (where square of destArray standard dev is sum of squares of std devs of
#  underlying distributions)
corr_expected = covariance / (1 * math.sqrt(covariance**2 + (1-covariance)**2));
print("TE result %.4f nats; expected to be close to %.4f nats for these correlated Gaussians" % \
    (result, -0.5 * math.log(1-corr_expected**2)))
# Perform calculation with uncorrelated source:
teCalc.initialise() # Initialise leaving the parameters the same
teCalc.setObservations(JArray(JDouble, 1)(sourceArray2), JArray(JDouble, 1)(destArray))
result2 = teCalc.computeAverageLocalOfObservations()
print("TE result %.4f nats; expected to be close to 0 nats for these uncorrelated Gaussians" % result2)