From b751510d9d43f98c62185fc186faf27bcbea9a74 Mon Sep 17 00:00:00 2001 From: "joseph.lizier" Date: Wed, 10 Sep 2014 04:50:49 +0000 Subject: [PATCH] Adding Julia examples 1, 3, 4 and 6 (up to the point where multidimensional array is attempted to be sent to Java), and changing 4ColsPairedNoisyDependence-1.txt to remove leading spaces. --- demos/data/4ColsPairedNoisyDependence-1.txt | 6000 ++++++++--------- demos/julia/example1TeBinaryData.jl | 53 + demos/julia/example3TeContinuousDataKernel.jl | 62 + .../julia/example4TeContinuousDataKraskov.jl | 67 + .../julia/example6DynamicCallingMutualInfo.jl | 90 + 5 files changed, 3272 insertions(+), 3000 deletions(-) create mode 100755 demos/julia/example1TeBinaryData.jl create mode 100755 demos/julia/example3TeContinuousDataKernel.jl create mode 100755 demos/julia/example4TeContinuousDataKraskov.jl create mode 100755 demos/julia/example6DynamicCallingMutualInfo.jl diff --git a/demos/data/4ColsPairedNoisyDependence-1.txt b/demos/data/4ColsPairedNoisyDependence-1.txt index d7ea233..5cf1734 100755 --- a/demos/data/4ColsPairedNoisyDependence-1.txt +++ b/demos/data/4ColsPairedNoisyDependence-1.txt @@ -1,3000 +1,3000 @@ - 2.61679026e-01 9.83140551e-01 1.06061859e+00 8.66125082e-01 - 7.94322201e-01 4.13116893e-01 1.59291268e+00 2.00157865e-01 - 2.98114190e-01 5.04732669e-01 7.14715103e-01 1.36939568e-01 - 7.89069203e-01 8.24407116e-01 1.90657272e+00 8.35948234e-01 - 3.84944967e-01 2.53051509e-01 9.80789320e-01 7.50401093e-01 - 5.65179994e-01 6.30918776e-01 1.45700286e+00 8.65371098e-01 - 9.37373212e-01 6.75728671e-01 1.85422839e+00 9.05282722e-01 - 8.30566438e-01 4.31796803e-01 1.32048660e+00 6.72818354e-01 - 7.92375931e-01 1.76137261e-01 5.59927231e-01 9.10943208e-01 - 8.21957987e-01 7.42593513e-01 1.89322652e+00 9.39426844e-01 - 6.72378838e-02 4.38949492e-02 9.59204864e-01 7.83902781e-01 - 8.82555741e-01 7.40160679e-01 1.54652844e+00 7.79693304e-01 - 2.96425183e-01 1.57383928e-01 9.78938157e-01 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7.92991933e-01 +4.63156496e-01 3.79849328e-01 8.09928184e-01 3.48293231e-01 +2.27240181e-01 7.77105676e-01 1.03117262e+00 1.97042043e-01 +8.24655123e-01 4.10091412e-01 1.11386411e+00 3.60913862e-01 +6.37385206e-01 2.79424172e-01 9.15563602e-01 5.70103460e-01 +1.52357686e-01 7.13022202e-01 1.28307096e+00 3.81529303e-01 diff --git a/demos/julia/example1TeBinaryData.jl b/demos/julia/example1TeBinaryData.jl new file mode 100755 index 0000000..db44fc5 --- /dev/null +++ b/demos/julia/example1TeBinaryData.jl @@ -0,0 +1,53 @@ +## +## 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 . +## + +# = Example 1 - Transfer entropy on binary data = + +# Simple transfer entropy (TE) calculation on binary data using the discrete TE calculator: + +# Import the JavaCall package: +using JavaCall; + +# Change location of jar to match yours: +jarLocation = "../../infodynamics.jar"; +# Start the JVM supplying classpath and heap size +# (increase memory here if you get crashes due to not enough space) +JavaCall.init(["-Djava.class.path=$(jarLocation)", "-Xmx128M"]); + +# Generate some random binary data. +sourceArray=rand(0:1, 100); +destArray = [0; sourceArray[1:99]]; +sourceArray2=rand(0:1, 100); + +# Create a TE calculator and run it: +teClass = @jimport infodynamics.measures.discrete.TransferEntropyCalculatorDiscrete; +teCalc=teClass((jint, jint), 2, 1); +jcall(teCalc, "initialise", Void, ()); # This is how to indicate a void return type and arguments + +# We can pass simple arrays of ints directly in: +jcall(teCalc, "addObservations", Void, (Array{jint,1}, Array{jint,1}), + sourceArray, destArray); +result = jcall(teCalc, "computeAverageLocalOfObservations", jdouble, ()); +@printf("For copied source, result should be close to 1 bit : %.4f\n", result); + +jcall(teCalc, "initialise", Void, ()); +jcall(teCalc, "addObservations", Void, (Array{jint,1}, Array{jint,1}), + sourceArray2, destArray); +result2 = jcall(teCalc, "computeAverageLocalOfObservations", jdouble, ()); +@printf("For random source, result should be close to 0 bits: %.4f\n", result2); + diff --git a/demos/julia/example3TeContinuousDataKernel.jl b/demos/julia/example3TeContinuousDataKernel.jl new file mode 100755 index 0000000..2a9572a --- /dev/null +++ b/demos/julia/example3TeContinuousDataKernel.jl @@ -0,0 +1,62 @@ +## +## 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 . +## + +# = Example 3 - Transfer entropy on continuous data using kernel estimators = + +# Simple transfer entropy (TE) calculation on continuous-valued data using the (box) kernel-estimator TE calculator. + +# Import the JavaCall package: +using JavaCall; + +# Change location of jar to match yours: +jarLocation = "../../infodynamics.jar"; +# Start the JVM supplying classpath and heap size +# (increase memory here if you get crashes due to not enough space) +JavaCall.init(["-Djava.class.path=$(jarLocation)", "-Xmx128M"]); + +# Generate some random normalised data. +numObservations = 1000; +covariance=0.4; +sourceArray=randn(numObservations); +destArray = [0, covariance*sourceArray[1:numObservations-1] + (1-covariance)*randn(numObservations - 1)]; +sourceArray2=randn(numObservations); # Uncorrelated source +# Create a TE calculator and run it: +teClass = @jimport infodynamics.measures.continuous.kernel.TransferEntropyCalculatorKernel; +teCalc=teClass(()); +jcall(teCalc, "setProperty", Void, (JString, JString), "NORMALISE", "true"); # Normalise the individual variables +jcall(teCalc, "initialise", Void, (jint, jdouble), 1, 0.5); # Use history length 1 (Schreiber k=1), kernel width of 0.5 normalised units +jcall(teCalc, "setObservations", Void, (Array{jdouble,1}, Array{jdouble,1}), + sourceArray, destArray); +# For copied source, should give something close to expected value for correlated Gaussians: +result = jcall(teCalc, "computeAverageLocalOfObservations", jdouble, ()); +@printf("TE result %.4f bits; expected to be close to %.4f bits for these correlated Gaussians but biased upwards\n", result, log(1/(1-covariance^2))/log(2)); + +jcall(teCalc, "initialise", Void, ()); # Initialise leaving the parameters the same +jcall(teCalc, "setObservations", Void, (Array{jdouble,1}, Array{jdouble,1}), + sourceArray2, destArray); +# For random source, it should give something close to 0 bits +result2 = jcall(teCalc, "computeAverageLocalOfObservations", jdouble, ()); +@printf("TE result %.4f bits; expected to be close to 0 bits for uncorrelated Gaussians but will be biased upwards\n", result2); + +# We can get insight into the bias by examining the null distribution: +empiricalDistClass = @jimport infodynamics.utils.EmpiricalMeasurementDistribution; +nullDist = jcall(teCalc, "computeSignificance", empiricalDistClass, (jint,), 100); +@printf("Null distribution for unrelated source and destination (i.e. the bias) has mean %.4f and standard deviation %.4f\n", + jcall(nullDist, "getMeanOfDistribution", jdouble, ()), + jcall(nullDist, "getStdOfDistribution", jdouble, ())); + diff --git a/demos/julia/example4TeContinuousDataKraskov.jl b/demos/julia/example4TeContinuousDataKraskov.jl new file mode 100755 index 0000000..1a58930 --- /dev/null +++ b/demos/julia/example4TeContinuousDataKraskov.jl @@ -0,0 +1,67 @@ +## +## 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 . +## + +# = 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. + +# Import the JavaCall package: +using JavaCall; + +# Change location of jar to match yours: +jarLocation = "../../infodynamics.jar"; +# Start the JVM supplying classpath and heap size +# (increase memory here if you get crashes due to not enough space) +JavaCall.init(["-Djava.class.path=$(jarLocation)", "-Xmx128M"]); + +# Generate some random normalised data. +numObservations = 1000; +covariance=0.4; +sourceArray=randn(numObservations); +destArray = [0, covariance*sourceArray[1:numObservations-1] + (1-covariance)*randn(numObservations - 1)]; +sourceArray2=randn(numObservations); # Uncorrelated source + +# Create a TE calculator and run it: +teClass = @jimport infodynamics.measures.continuous.kraskov.TransferEntropyCalculatorKraskov; +teCalc=teClass(()); +jcall(teCalc, "setProperty", Void, (JString, JString), "k", "4"); # Use Kraskov parameter K=4 for 4 nearest points +jcall(teCalc, "initialise", Void, (jint, ), 1); # Use history length 1 (Schreiber k=1) +# Perform calculation with correlated source: +jcall(teCalc, "setObservations", Void, (Array{jdouble,1}, Array{jdouble,1}), + sourceArray, destArray); +result = jcall(teCalc, "computeAverageLocalOfObservations", jdouble, ()); +# 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. +@printf("TE result %.4f nats; expected to be close to %.4f nats for these correlated Gaussians\n", + result, log(1/(1-covariance^2))); + +# Perform calculation with uncorrelated source: +jcall(teCalc, "initialise", Void, ()); # Initialise leaving the parameters the same +jcall(teCalc, "setObservations", Void, (Array{jdouble,1}, Array{jdouble,1}), + sourceArray2, destArray); +result2 = jcall(teCalc, "computeAverageLocalOfObservations", jdouble, ()); +@printf("TE result %.4f nats; expected to be close to 0 nats for these uncorrelated Gaussians\n", result2); + +# We can also compute the local TE values for the time-series samples here: +# (See more about utility of local TE in the CA demos) +localTE = jcall(teCalc, "computeLocalOfPreviousObservations", Array{jdouble,1}, ()); +@printf("Notice that the mean of locals, %.4f nats, equals the previous result\n", + sum(localTE)/(numObservations-1)); + diff --git a/demos/julia/example6DynamicCallingMutualInfo.jl b/demos/julia/example6DynamicCallingMutualInfo.jl new file mode 100755 index 0000000..69ab0b7 --- /dev/null +++ b/demos/julia/example6DynamicCallingMutualInfo.jl @@ -0,0 +1,90 @@ +## +## 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 . +## + +# Example 6 - Mutual information calculation with dynamic specification of calculator + +# This example shows how to write Julia 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. + +# Import the JavaCall package: +using JavaCall; + +# Change location of jar to match yours: +jarLocation = "../../infodynamics.jar"; +# Start the JVM supplying classpath and heap size +# (increase memory here if you get crashes due to not enough space) +JavaCall.init(["-Djava.class.path=$(jarLocation)", "-Xmx128M"]); + +#--------------------- +# 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 = [1,2]; # array indices start from 1 in Julia +variable2Columns = [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 = readdlm(datafile, ' ', '\n') +# Pull out the columns from the data set which correspond to each of variable 1 and 2: +variable1 = data[:, variable1Columns]; +variable2 = data[:, variable2Columns]; + +#--------------------- +# 3. Dynamically instantiate an object of the given class: +# Since @jimport uses the provided text directly (rather than evaluating a variable) +# we need to do some Julia interpolation of $implementingClass to get our +# dynamic reference to the class. +miCalcClass = eval(:(@jimport $implementingClass)); +miCalc = miCalcClass(()); + +#--------------------- +# 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, "initialise", Void, (jint,jint), length(variable1Columns), length(variable2Columns)); +# b. Supply the observations to compute the PDFs from: + +# For the moment we have to exit this example here:: +@printf("We're stuck at this point until support for multidimensional arrays is included in JavaCall"); +exit(); + +jcall(miCalc, "setObservations", Void, (Array{jdouble,2}, Array{jdouble,2}), variable1, variable2); +# c. Make the MI calculation: +miValue = jcall(miCalc, "computeAverageLocalOfObservations", jdouble, ()); +@printf("MI calculator %s\ncomputed the joint MI as %.5f\n", + implementingClass, miValue); +