mirror of https://github.com/jlizier/jidt
49 lines
1.9 KiB
Python
Executable File
49 lines
1.9 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 1 - Transfer entropy on binary data =
|
|
|
|
# Simple transfer entropy (TE) calculation on binary data using the discrete TE calculator:
|
|
|
|
from jpype import *
|
|
import random
|
|
|
|
# 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 binary data.
|
|
sourceArray = [random.randint(0,1) for r in xrange(100)]
|
|
destArray = [0] + sourceArray[0:99];
|
|
sourceArray2 = [random.randint(0,1) for r in xrange(100)]
|
|
|
|
# Create a TE calculator and run it:
|
|
teCalcClass = JPackage("infodynamics.measures.discrete").TransferEntropyCalculator
|
|
teCalc = teCalcClass(2,1)
|
|
teCalc.initialise()
|
|
# Since we have simple arrays of ints, we can directly pass these in:
|
|
teCalc.addObservations(sourceArray, destArray)
|
|
print("For copied source, result should be close to 1 bit : %.4f" % teCalc.computeAverageLocalOfObservations())
|
|
teCalc.initialise()
|
|
teCalc.addObservations(sourceArray2, destArray)
|
|
print("For random source, result should be close to 0 bits: %.4f" % teCalc.computeAverageLocalOfObservations())
|
|
|
|
shutdownJVM()
|
|
|