mirror of https://github.com/jlizier/jidt
86 lines
4.0 KiB
Python
Executable File
86 lines
4.0 KiB
Python
Executable File
##
|
|
## Java Information Dynamics Toolkit (JIDT)
|
|
## Copyright (C) 2012, Joseph T. Lizier
|
|
##
|
|
## This program is free software: you can redistribute it and/or modify
|
|
## it under the terms of the GNU General Public License as published by
|
|
## the Free Software Foundation, either version 3 of the License, or
|
|
## (at your option) any later version.
|
|
##
|
|
## This program is distributed in the hope that it will be useful,
|
|
## but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
## GNU General Public License for more details.
|
|
##
|
|
## You should have received a copy of the GNU General Public License
|
|
## along with this program. If not, see <http://www.gnu.org/licenses/>.
|
|
##
|
|
|
|
# = 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
|
|
import numpy as np
|
|
|
|
# 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)]] ) ]
|
|
|
|
sourceArray = 1e5*np.random.random(int(1e5))
|
|
sourceArray.sort()
|
|
#destArray = 1e5*np.random.random(int(1e5))
|
|
#destArray.sort()
|
|
destArray = sourceArray + 1
|
|
destArray += np.random.normal(scale = 0.01, size = destArray.shape)
|
|
|
|
# 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
|
|
teCalcClass = JPackage("infodynamics.measures.spiking.integration").TransferEntropyCalculatorSpikingIntegration
|
|
|
|
|
|
|
|
teCalc = teCalcClass()
|
|
teCalc.setProperty("NORMALISE", "true") # Normalise the individual variables
|
|
teCalc.initialise(1) # Use history length 1 (Schreiber k=1)
|
|
teCalc.setProperty("k_HISTORY", "2")
|
|
teCalc.setProperty("l_HISTORY", "2")
|
|
teCalc.setProperty("knns", "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" % \
|
|
(result,))
|
|
# # 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)
|
|
|
|
|