mirror of https://github.com/jlizier/jidt
92 lines
4.3 KiB
Python
Executable File
92 lines
4.3 KiB
Python
Executable File
##
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## Java Information Dynamics Toolkit (JIDT)
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## Copyright (C) 2012, Joseph T. Lizier
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##
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## This program is free software: you can redistribute it and/or modify
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## it under the terms of the GNU General Public License as published by
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## the Free Software Foundation, either version 3 of the License, or
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## (at your option) any later version.
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##
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## This program is distributed in the hope that it will be useful,
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## but WITHOUT ANY WARRANTY; without even the implied warranty of
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## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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## GNU General Public License for more details.
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##
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## You should have received a copy of the GNU General Public License
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## along with this program. If not, see <http://www.gnu.org/licenses/>.
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##
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# = Example 6 - Mutual information calculation with dynamic specification of calculator =
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# This example shows how to write Python code to take advantage of the
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# common interfaces defined for various information-theoretic calculators.
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# Here, we use the common form of the infodynamics.measures.continuous.MutualInfoCalculatorMultiVariate
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# interface (which is never named here) to write common code into which we can plug
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# one of three concrete implementations (kernel estimator, Kraskov estimator or
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# linear-Gaussian estimator) by dynamically supplying the class name of
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# the concrete implementation.
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from jpype import *
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import random
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import string
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import numpy
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import readFloatsFile
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# Change location of jar to match yours:
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jarLocation = "../../infodynamics.jar"
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# Start the JVM (add the "-Xmx" option with say 1024M if you get crashes due to not enough memory space)
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startJVM(getDefaultJVMPath(), "-ea", "-Djava.class.path=" + jarLocation)
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#---------------------
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# 1. Properties for the calculation (these are dynamically changeable):
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# The name of the data file (relative to this directory)
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datafile = '../data/4ColsPairedNoisyDependence-1.txt'
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# List of column numbers for variables 1 and 2:
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# (you can select any columns you wish to be contained in each variable)
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variable1Columns = [0,1] # array indices start from 0 in python
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variable2Columns = [2,3]
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# The name of the concrete implementation of the interface
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# infodynamics.measures.continuous.MutualInfoCalculatorMultiVariate
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# which we wish to use for the calculation.
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# Note that one could use any of the following calculators (try them all!):
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# implementingClass = "infodynamics.measures.continuous.kraskov.MutualInfoCalculatorMultiVariateKraskov1" # MI([0,1], [2,3]) = 0.35507
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# implementingClass = "infodynamics.measures.continuous.kernel.MutualInfoCalculatorMultiVariateKernel"
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# implementingClass = "infodynamics.measures.continuous.gaussian.MutualInfoCalculatorMultiVariateGaussian"
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implementingClass = "infodynamics.measures.continuous.kraskov.MutualInfoCalculatorMultiVariateKraskov1"
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#---------------------
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# 2. Load in the data
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data = readFloatsFile.readFloatsFile(datafile)
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# As numpy array:
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A = numpy.array(data)
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# Pull out the columns from the data set which correspond to each of variable 1 and 2:
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variable1 = A[:,variable1Columns]
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variable2 = A[:,variable2Columns]
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#--------------------
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# 3. Dynamically instantiate an object of the given class:
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# (in fact, all java object creation in python is dynamic - it has to be,
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# since the languages are interpreted. This makes our life slightly easier at this
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# point than it is in demos/java/lateBindingDemo where we have to handle this manually)
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indexOfLastDot = string.rfind(implementingClass, ".")
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implementingPackage = implementingClass[:indexOfLastDot]
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implementingBaseName = implementingClass[indexOfLastDot+1:]
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miCalcClass = eval('JPackage(\'%s\').%s' % (implementingPackage, implementingBaseName))
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miCalc = miCalcClass()
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#--------------------
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# 4. Start using the MI calculator, paying attention to only
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# call common methods defined in the interface type
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# infodynamics.measures.continuous.MutualInfoCalculatorMultiVariate
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# not methods only defined in a given implementation class.
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# a. Initialise the calculator to use the required number of
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# dimensions for each variable:
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miCalc.initialise(len(variable1Columns), len(variable2Columns))
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# b. Supply the observations to compute the PDFs from:
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miCalc.setObservations(variable1, variable2)
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# c. Make the MI calculation:
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miValue = miCalc.computeAverageLocalOfObservations()
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print("MI calculator %s computed the joint MI as %.5f\n" % (implementingClass, miValue))
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