added testing script

tester.py

@@ -0,0 +1,81 @@
+##
+##  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 = 1e3*np.random.random(int(1e3))
+sourceArray.sort()
+destArray = 1e3*np.random.random(int(1e3))
+destArray.sort()
+
+# 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", "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)
+
+