mirror of https://github.com/jlizier/jidt
Adding R example 5 and 6, adding notices to jar dist, finalising python example 6 using file reading utilities; adding extra printout to Example 4 in Java
This commit is contained in:
joseph.lizier
parent
720d3f1e1a
commit
643d76fe19
|
|
@ -151,6 +151,7 @@
|
|||
<fileset file="license-gplv3.txt"/>
|
||||
<fileset file="readme.txt"/>
|
||||
<fileset file="${versionfile}"/>
|
||||
<zipfileset dir="notices" includes="**/*.*,**/*" prefix="notices"/>
|
||||
</zip>
|
||||
</target>
|
||||
|
||||
|
|
|
|||
|
|
@ -18,6 +18,7 @@
|
|||
|
||||
package infodynamics.demos;
|
||||
|
||||
import infodynamics.utils.MatrixUtils;
|
||||
import infodynamics.utils.RandomGenerator;
|
||||
import infodynamics.measures.continuous.kraskov.TransferEntropyCalculatorKraskov;
|
||||
|
||||
|
|
@ -78,7 +79,9 @@ public class Example4TeContinuousDataKraskov {
|
|||
|
||||
// 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)
|
||||
@SuppressWarnings("unused")
|
||||
double[] localTE = teCalc.computeLocalOfPreviousObservations();
|
||||
System.out.printf("Notice that the mean of locals, %.4f nats," +
|
||||
" equals the previous result\n",
|
||||
MatrixUtils.sum(localTE)/(double)(numObservations-1));
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -25,14 +25,13 @@
|
|||
% one of three concrete implementations (kernel estimator, Kraskov estimator or
|
||||
% linear-Gaussian estimator) by dynamically supplying the class name of
|
||||
% the concrete implementation.
|
||||
%
|
||||
% This is the Octave/Matlab equivalent to the demos/java/lateBindingDemo
|
||||
|
||||
% Change location of jar to match yours:
|
||||
javaaddpath('../../infodynamics.jar');
|
||||
|
||||
%---------------------
|
||||
% 1. Properties for the calculation (these are dynamically changeable):
|
||||
% 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:
|
||||
|
|
@ -43,7 +42,7 @@ variable2Columns = [3,4];
|
|||
% 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]) = 0.35507
|
||||
% 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';
|
||||
|
|
|
|||
|
|
@ -25,13 +25,12 @@
|
|||
# one of three concrete implementations (kernel estimator, Kraskov estimator or
|
||||
# linear-Gaussian estimator) by dynamically supplying the class name of
|
||||
# the concrete implementation.
|
||||
#
|
||||
# This is the Python equivalent to the demos/java/lateBindingDemo
|
||||
|
||||
from jpype import *
|
||||
import random
|
||||
import string
|
||||
import numpy
|
||||
import readFloatsFile
|
||||
|
||||
# Change location of jar to match yours:
|
||||
jarLocation = "../../infodynamics.jar"
|
||||
|
|
@ -56,15 +55,10 @@ variable2Columns = [2,3]
|
|||
implementingClass = "infodynamics.measures.continuous.kraskov.MutualInfoCalculatorMultiVariateKraskov1"
|
||||
|
||||
#---------------------
|
||||
# 2. Load in the data (space separate numbers, one time step per line, each column is a variable)
|
||||
f = open(datafile)
|
||||
data = []
|
||||
for line in f:
|
||||
data.append([float(x) for x in line.split()])
|
||||
|
||||
# 2. Load in the data
|
||||
data = readFloatsFile.readFloatsFile(datafile)
|
||||
# As numpy array:
|
||||
A = numpy.array(data)
|
||||
|
||||
# Pull out the columns from the data set which correspond to each of variable 1 and 2:
|
||||
variable1 = A[:,variable1Columns]
|
||||
variable2 = A[:,variable2Columns]
|
||||
|
|
|
|||
|
|
@ -0,0 +1,27 @@
|
|||
##
|
||||
## 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/>.
|
||||
##
|
||||
|
||||
def readFloatsFile(filename):
|
||||
"Read a 2D array of floats from a given file"
|
||||
with open(filename) as f:
|
||||
# Space separate numbers, one time step per line, each column is a variable
|
||||
array = []
|
||||
for line in f: # read all lines
|
||||
array.append([float(x) for x in line.split()])
|
||||
return array
|
||||
|
||||
|
|
@ -0,0 +1,27 @@
|
|||
##
|
||||
## 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/>.
|
||||
##
|
||||
|
||||
def readIntsFile(filename):
|
||||
"Read a 2D array of int from a given file"
|
||||
with open(filename) as f:
|
||||
# Space separate numbers, one time step per line, each column is a variable
|
||||
array = []
|
||||
for line in f: # read all lines
|
||||
array.append([int(x) for x in line.split()])
|
||||
return array
|
||||
|
||||
|
|
@ -51,3 +51,10 @@ cat("TE result ", result, "bits; expected to be close to ", log(1/(1-covariance
|
|||
result2 <- .jcall(teCalc,"D","computeAverageLocalOfObservations")
|
||||
cat("TE result ", result2, "bits; expected to be close to 0 bits for uncorrelated Gaussians but will be biased upwards\n")
|
||||
|
||||
# We can get insight into the bias by examining the null distribution:
|
||||
nullDist <- .jcall(teCalc,"Linfodynamics/utils/EmpiricalMeasurementDistribution;",
|
||||
"computeSignificance", 100L)
|
||||
cat("Null distribution for unrelated source and destination",
|
||||
"(i.e. the bias) has mean", .jcall(nullDist, "D", "getMeanOfDistribution"),
|
||||
"bits and standard deviation", .jcall(nullDist, "D", "getStdOfDistribution"), "\n")
|
||||
|
||||
|
|
|
|||
|
|
@ -56,3 +56,9 @@ cat("TE result ", result, "nats; expected to be close to ", log(1/(1-covariance
|
|||
result2 <- .jcall(teCalc,"D","computeAverageLocalOfObservations")
|
||||
cat("TE result ", result2, "nats; expected to be close to 0 nats for uncorrelated Gaussians\n")
|
||||
|
||||
# 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,"[D","computeLocalOfPreviousObservations")
|
||||
cat("Notice that the mean of locals", sum(localTE)/(numObservations-1),
|
||||
"nats equals the above result\n")
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,69 @@
|
|||
##
|
||||
## 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 5 - Multivariate transfer entropy on binary data =
|
||||
|
||||
# Multivariate transfer entropy (TE) calculation on binary data using the discrete 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 binary data.
|
||||
numObservations <- 100
|
||||
sourceArray<-matrix(sample(0:1,numObservations*2, replace=TRUE),numObservations,2)
|
||||
sourceArray2<-matrix(sample(0:1,numObservations*2, replace=TRUE),numObservations,2)
|
||||
# Destination variable takes a copy of the first bit of the source in bit 1,
|
||||
# and an XOR of the two bits of the source in bit 2:
|
||||
destArray <- cbind( c(0L, sourceArray[1:numObservations-1,1]), # column 1
|
||||
c(0L, 1L*xor(sourceArray[1:numObservations-1,1],
|
||||
sourceArray[1:numObservations-1,2]))) # column 2
|
||||
|
||||
# Convert the 2D arrays to Java format:
|
||||
sourceArrayJava <- .jarray(sourceArray, "[I", dispatch=TRUE)
|
||||
sourceArray2Java <- .jarray(sourceArray2, "[I", dispatch=TRUE)
|
||||
destArrayJava <- .jarray(destArray, "[I", dispatch=TRUE)
|
||||
|
||||
# Create a TE calculator and run it:
|
||||
teCalc<-.jnew("infodynamics/measures/discrete/TransferEntropyCalculatorDiscrete", 4L, 1L)
|
||||
.jcall(teCalc,"V","initialise") # V for void return value
|
||||
# We need to construct the joint values for the dest and source before we pass them in,
|
||||
# and need to use the matrix conversion routine when calling from Matlab/Octave:
|
||||
mUtils<-.jnew("infodynamics/utils/MatrixUtils")
|
||||
.jcall(teCalc,"V","addObservations",
|
||||
.jcall(mUtils,"[I","computeCombinedValues", sourceArrayJava, 2L),
|
||||
.jcall(mUtils,"[I","computeCombinedValues", destArrayJava, 2L))
|
||||
result<-.jcall(teCalc,"D","computeAverageLocalOfObservations")
|
||||
cat("For source which the 2 bits are determined from, result should be close to 2 bits : ", result, "\n")
|
||||
|
||||
.jcall(teCalc,"V","initialise")
|
||||
.jcall(teCalc,"V","addObservations",
|
||||
.jcall(mUtils,"[I","computeCombinedValues", sourceArray2Java, 2L),
|
||||
.jcall(mUtils,"[I","computeCombinedValues", destArrayJava, 2L))
|
||||
result2<-.jcall(teCalc,"D","computeAverageLocalOfObservations")
|
||||
cat("For random source, result should be close to 0 bits in theory: ", result2, "\n");
|
||||
cat("Result for random source is inflated towards 0.3 due to finite observation length ",
|
||||
.jcall(teCalc,"I","getNumObservations"), "\n",
|
||||
"One can verify that the answer is consistent with that from a\n",
|
||||
"random source by checking: teCalc.computeSignificance(1000); ans.pValue\n");
|
||||
|
||||
|
|
@ -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 <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")
|
||||
|
||||
|
|
@ -61,6 +61,8 @@ Javadocs for the toolkit are included in the full distribution at javadocs.
|
|||
They can also be generated using "ant javadocs" (useful if you are on an SVN view).
|
||||
Further, they will soon be posted on the web.
|
||||
|
||||
The project wiki also contains further information on various aspects; see http://code.google.com/p/information-dynamics-toolkit/ to start.
|
||||
|
||||
Further documentation is provided by the Usage examples below.
|
||||
|
||||
You can also join our email discussion group jidt-discuss at http://groups.google.com/d/forum/jidt-discuss
|
||||
|
|
@ -73,21 +75,25 @@ Several sets of demonstration code are distributed with the toolkit:
|
|||
|
||||
a. demos/java -- basic examples on easily using the Java toolkit -- run these from the shell scripts in this directory -- see description at http://code.google.com/p/information-dynamics-toolkit/wiki/SimpleJavaExamples
|
||||
|
||||
b. demos/octave -- basic examples on easily using the Java toolkit from Octave or Matlab environments -- see description at http://code.google.com/p/information-dynamics-toolkit/wiki/OctaveMatlabExamples
|
||||
b. Several demo sets mirror the SimpleJavaExamples to demonstrate the use of the toolkit in non-Java environments:
|
||||
|
||||
c. demos/python -- basic examples on easily using the Java toolkit from Python -- see description at http://code.google.com/p/information-dynamics-toolkit/wiki/PythonExamples
|
||||
|
||||
d. demos/octave/CellularAutomata -- using the Java toolkit to plot local information dynamics profiles in cellular automata; the toolkit is run under Octave or Matlab -- see description at http://code.google.com/p/information-dynamics-toolkit/wiki/CellularAutomataDemos
|
||||
i. demos/octave -- basic examples on easily using the Java toolkit from Octave or Matlab environments -- see description at http://code.google.com/p/information-dynamics-toolkit/wiki/OctaveMatlabExamples
|
||||
|
||||
e. demos/octave/SchreiberTransferEntropyExamples -- recreates the transfer entropy examples in Schreiber's original paper presenting this measure; shows the correct parameter settings to reproduce these results -- see description at http://code.google.com/p/information-dynamics-toolkit/wiki/SchreiberTeDemos
|
||||
ii. demos/python -- basic examples on easily using the Java toolkit from Python -- see description at http://code.google.com/p/information-dynamics-toolkit/wiki/PythonExamples
|
||||
|
||||
iii. demos/r -- basic examples on easily using the Java toolkit from R -- see description at http://code.google.com/p/information-dynamics-toolkit/wiki/R_Examples
|
||||
|
||||
c. demos/octave/CellularAutomata -- using the Java toolkit to plot local information dynamics profiles in cellular automata; the toolkit is run under Octave or Matlab -- see description at http://code.google.com/p/information-dynamics-toolkit/wiki/CellularAutomataDemos
|
||||
|
||||
f. demos/octave/DetectingInteractionLags -- demonstration of using the transfer entropy with source-destination lags; the demo is run under Octave or Matlab -- see description at http://code.google.com/p/information-dynamics-toolkit/wiki/DetectingInteractionLags
|
||||
d. demos/octave/SchreiberTransferEntropyExamples -- recreates the transfer entropy examples in Schreiber's original paper presenting this measure; shows the correct parameter settings to reproduce these results -- see description at http://code.google.com/p/information-dynamics-toolkit/wiki/SchreiberTeDemos
|
||||
|
||||
e. demos/octave/DetectingInteractionLags -- demonstration of using the transfer entropy with source-destination lags; the demo is run under Octave or Matlab -- see description at http://code.google.com/p/information-dynamics-toolkit/wiki/DetectingInteractionLags
|
||||
|
||||
g. demos/java/InterregionalTransfer -- higher level example using collective transfer entropy to infer effective connections between "regions" of data -- see description at http://code.google.com/p/information-dynamics-toolkit/wiki/InterregionalTransfer
|
||||
f. demos/java/InterregionalTransfer -- higher level example using collective transfer entropy to infer effective connections between "regions" of data -- see description at http://code.google.com/p/information-dynamics-toolkit/wiki/InterregionalTransfer
|
||||
|
||||
h. demos/octave/NullDistributions -- investigating the correspondence between analytic and bootstrapped distributions for TE and MI under null hypotheses of no relationship; the demo is run under Octave or Matlab -- see description at https://code.google.com/p/information-dynamics-toolkit/wiki/NullDistributions
|
||||
g. demos/octave/NullDistributions -- investigating the correspondence between analytic and bootstrapped distributions for TE and MI under null hypotheses of no relationship; the demo is run under Octave or Matlab -- see description at https://code.google.com/p/information-dynamics-toolkit/wiki/NullDistributions
|
||||
|
||||
i. java/unittests -- the JUnit test cases for the Java toolkit are included in the distribution -- these case also be browsed to see simple use cases for the various calculators in the toolkit -- see description at http://code.google.com/p/information-dynamics-toolkit/wiki/JUnitTestCases
|
||||
h. java/unittests -- the JUnit test cases for the Java toolkit are included in the distribution -- these case also be browsed to see simple use cases for the various calculators in the toolkit -- see description at http://code.google.com/p/information-dynamics-toolkit/wiki/JUnitTestCases
|
||||
|
||||
=============
|
||||
Citation
|
||||
|
|
|
|||
Loading…
Reference in New Issue