# = Example 3 - Transfer entropy on continuous data using kernel estimators =

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

from jpype import *
import random
import math

# Change location of jar to match yours:
jarLocation = "../../infodynamics.jar"
# 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 xrange(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 xrange(numObservations-1)]] ) ];
# Uncorrelated source array:
sourceArray2 = [random.normalvariate(0,1) for r in xrange(numObservations)];
# Create a TE calculator and run it:
teCalcClass = JPackage("infodynamics.measures.continuous.kernel").TransferEntropyCalculatorKernel
teCalc = teCalcClass();
teCalc.setProperty("NORMALISE", "true"); # Normalise the individual variables
teCalc.initialise(1, 0.5); # Use history length 1 (Schreiber k=1), kernel width of 0.5 normalised units
teCalc.setObservations(JArray(JDouble, 1)(sourceArray), JArray(JDouble, 1)(destArray));
# For copied source, should give something close to 1 bit:
result = teCalc.computeAverageLocalOfObservations();
print("TE result %.4f bits; expected to be close to %.4f bits for these correlated Gaussians but biased upwards" % \
    (result, math.log(1/(1-math.pow(covariance,2)))/math.log(2)));
teCalc.initialise(); # Initialise leaving the parameters the same
teCalc.setObservations(JArray(JDouble, 1)(sourceArray2), JArray(JDouble, 1)(destArray));
# For random source, it should give something close to 0 bits
result2 = teCalc.computeAverageLocalOfObservations();
print("TE result %.4f bits; expected to be close to 0 bits for uncorrelated Gaussians but will be biased upwards" % \
    result2);

