Adding R example 4, and correcting random normal generation in example 3

demos/octave/example4TeContinuousDataKraskov.m

@@ -31,8 +31,8 @@ destArray = [0; covariance*sourceArray(1:numObservations-1) + (1-covariance)*ran
 sourceArray2=randn(numObservations, 1); % Uncorrelated source
 % Create a TE calculator and run it:
 teCalc=javaObject('infodynamics.measures.continuous.kraskov.TransferEntropyCalculatorKraskov');
-teCalc.initialise(1); % Use history length 1 (Schreiber k=1)
 teCalc.setProperty('k', '4'); % Use Kraskov parameter K=4 for 4 nearest points
+teCalc.initialise(1); % Use history length 1 (Schreiber k=1)
 % Perform calculation with correlated source:
 teCalc.setObservations(sourceArray, destArray);
 result = teCalc.computeAverageLocalOfObservations();

demos/r/example3TeContinuousDataKernel.r

@@ -32,9 +32,10 @@ library("rJava")
 # Generate some random normalised data.
 numObservations<-1000
 covariance<-0.4
-sourceArray<-runif(numObservations, 0, 1)
-destArray = c(0, covariance*sourceArray[1:numObservations-1] + (1-covariance)*runif(numObservations-1, 0, 1))
-sourceArray2<-runif(numObservations, 0, 1) # Uncorrelated source
+sourceArray<-rnorm(numObservations)
+destArray = c(0, covariance*sourceArray[1:numObservations-1] + (1-covariance)*rnorm(numObservations-1, 0, 1))
+sourceArray2<-rnorm(numObservations) # Uncorrelated source
+
 # Create a TE calculator and run it:
 teCalc<-.jnew("infodynamics/measures/continuous/kernel/TransferEntropyCalculatorKernel")
 .jcall(teCalc,"V","setProperty", "NORMALISE", "true") # Normalise the individual variables

demos/r/example4TeContinuousDataKraskov.r

@@ -0,0 +1,58 @@
+##
+##  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.
+
+# Load the rJava library and start the JVM
+library("rJava")
+.jinit()
+
+# Change location of jar to match yours:
+#  IMPORTANT -- If using the default below, make sure you have set the working directory
+#   in R (e.g. with setwd()) to the location of this file (i.e. demos/r) !!
+.jaddClassPath("../../infodynamics.jar")
+
+# Generate some random normalised data.
+numObservations<-1000
+covariance<-0.4
+sourceArray<-rnorm(numObservations)
+destArray = c(0, covariance*sourceArray[1:numObservations-1] + (1-covariance)*rnorm(numObservations-1, 0, 1))
+sourceArray2<-rnorm(numObservations) # Uncorrelated source
+
+# Create a TE calculator:
+teCalc<-.jnew("infodynamics/measures/continuous/kraskov/TransferEntropyCalculatorKraskov")
+.jcall(teCalc,"V","setProperty", "k", "4") # Use Kraskov parameter K=4 for 4 nearest points
+
+# Perform calculation with correlated source:
+.jcall(teCalc,"V","initialise", 1L) # Use history length 1 (Schreiber k=1)
+.jcall(teCalc,"V","setObservations", sourceArray, destArray)
+result <- .jcall(teCalc,"D","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.
+cat("TE result ",  result, "nats; expected to be close to ", log(1/(1-covariance^2)), " nats for these correlated Gaussians\n")
+
+# Perform calculation with uncorrelated source:
+.jcall(teCalc,"V","initialise") # Initialise leaving the parameters the same
+.jcall(teCalc,"V","setObservations", sourceArray2, destArray)
+result2 <- .jcall(teCalc,"D","computeAverageLocalOfObservations")
+cat("TE result ",  result2, "nats; expected to be close to 0 nats for uncorrelated Gaussians\n")
+