example6DynamicCallingMutualInfo.r

##
##  Java Information Dynamics Toolkit (JIDT)
##  Copyright (C) 2012, Joseph T. Lizier
##  
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##  it under the terms of the GNU General Public License as published by
##  the Free Software Foundation, either version 3 of the License, or
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##  This program is distributed in the hope that it will be useful,
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##  GNU General Public License for more details.
##  
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##  along with this program.  If not, see <http://www.gnu.org/licenses/>.
##

# Example 6 - Mutual information calculation with dynamic specification of calculator

# This example shows how to write R code to take advantage of the
#  common interfaces defined for various information-theoretic calculators.
# Here, we use the common form of the infodynamics.measures.continuous.MutualInfoCalculatorMultiVariate
#  interface (which is never named here) to write common code into which we can plug
#  one of three concrete implementations (kernel estimator, Kraskov estimator or
#  linear-Gaussian estimator) by dynamically supplying the class name of
#  the concrete implementation.

# 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")

#---------------------
# 1. Properties for the calculation (these are dynamically changeable, you could
#    load them in from another properties file):
# The name of the data file (relative to this directory)
datafile <- "../data/4ColsPairedNoisyDependence-1.txt"
# List of column numbers for variables 1 and 2:
#  (you can select any columns you wish to be contained in each variable)
variable1Columns <- c(1,2) # array indices start from 1 in R
variable2Columns <- c(3,4)
# The name of the concrete implementation of the interface 
#  infodynamics.measures.continuous.MutualInfoCalculatorMultiVariate
#  which we wish to use for the calculation.
# Note that one could use any of the following calculators (try them all!):
#  implementingClass <- "infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov1" # MI([1,2], [3,4]) = 0.36353
#  implementingClass <- "infodynamics/measures/continuous/kernel/MutualInfoCalculatorMultiVariateKernel"
#  implementingClass <- "infodynamics/measures/continuous/gaussian/MutualInfoCalculatorMultiVariateGaussian"
implementingClass <- "infodynamics/measures/continuous/kraskov/MutualInfoCalculatorMultiVariateKraskov1"

#---------------------
# 2. Load in the data
data <- read.csv(datafile, header=FALSE, sep="")
# Pull out the columns from the data set which correspond to each of variable 1 and 2:
variable1 <- data[, variable1Columns]
variable2 <- data[, variable2Columns]
# Extra step to extract the raw values from these data.frame objects:
variable1 <- apply(variable1, 2, function(x) as.numeric(x))
variable2 <- apply(variable2, 2, function(x) as.numeric(x))

#---------------------
# 3. Dynamically instantiate an object of the given class:
# (in fact, all java object creation in octave/matlab is dynamic - it has to be,
#  since the languages are interpreted. This makes our life slightly easier at this
#  point than it is in demos/java/example6LateBindingMutualInfo where we have to handle this manually)
miCalc<-.jnew(implementingClass)

#---------------------
# 4. Start using the MI calculator, paying attention to only
#  call common methods defined in the interface type
#  infodynamics.measures.continuous.MutualInfoCalculatorMultiVariate
#  not methods only defined in a given implementation class.
# a. Initialise the calculator to use the required number of
#   dimensions for each variable:
.jcall(miCalc,"V","initialise", length(variable1Columns), length(variable2Columns))
# b. Supply the observations to compute the PDFs from:
.jcall(miCalc,"V","setObservations",
	.jarray(variable1, "[D", dispatch=TRUE),
	.jarray(variable2, "[D", dispatch=TRUE))
# c. Make the MI calculation:
miValue <- .jcall(miCalc,"D","computeAverageLocalOfObservations")

cat("MI calculator", implementingClass, "\n computed the joint MI as ",
	miValue, "\n")