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Adding univariate MI calculation to example 6 for Python

This commit is contained in:
joseph.lizier 2015-01-23 03:39:42 +00:00
parent 2c4e8cca04
commit 413691bd4e
2 changed files with 34 additions and 15 deletions
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2
demos/octave/example6DynamicCallingMutualInfo.m
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@ -65,7 +65,7 @@ jointVariable2 = data(:, jointVariable2Columns);
% 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/lateBindingDemo where we have to handle this manually)
% point than it is in demos/java/example6 where we have to handle this manually)
miCalc = javaObject(implementingClass);
%---------------------

47
demos/python/example6DynamicCallingMutualInfo.py
View File

@ -41,15 +41,19 @@ startJVM(getDefaultJVMPath(), "-ea", "-Djava.class.path=" + jarLocation)
# 1. Properties for the calculation (these are dynamically changeable):
# The name of the data file (relative to this directory)
datafile = '../data/4ColsPairedNoisyDependence-1.txt'
# List of column numbers for variables 1 and 2:
# List of column numbers for univariate time seres 1 and 2:
# (you can select any columns you wish to be contained in each variable)
variable1Columns = [0,1] # array indices start from 0 in python
variable2Columns = [2,3]
univariateSeries1Column = 0; # array indices start from 0 in python
univariateSeries2Column = 2;
# List of column numbers for joint variables 1 and 2:
# (you can select any columns you wish to be contained in each variable)
jointVariable1Columns = [0,1] # array indices start from 0 in python
jointVariable2Columns = [2,3]
# 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([0,1], [2,3]) = 0.35507
# implementingClass = "infodynamics.measures.continuous.kraskov.MutualInfoCalculatorMultiVariateKraskov1" # MI(1;3) as 0.10044, MI([1,2], [3,4]) = 0.36353 (NATS not bits)
# implementingClass = "infodynamics.measures.continuous.kernel.MutualInfoCalculatorMultiVariateKernel"
# implementingClass = "infodynamics.measures.continuous.gaussian.MutualInfoCalculatorMultiVariateGaussian"
implementingClass = "infodynamics.measures.continuous.kraskov.MutualInfoCalculatorMultiVariateKraskov1"
@ -59,15 +63,18 @@ implementingClass = "infodynamics.measures.continuous.kraskov.MutualInfoCalculat
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]
# Pull out the columns from the data set for a univariate MI calculation:
univariateSeries1 = A[:,univariateSeries1Column];
univariateSeries2 = A[:,univariateSeries2Column];
# Pull out the columns from the data set for a multivariate MI calculation:
jointVariable1 = A[:,jointVariable1Columns]
jointVariable2 = A[:,jointVariable2Columns]
#--------------------
# 3. Dynamically instantiate an object of the given class:
# (in fact, all java object creation in python 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/lateBindingDemo where we have to handle this manually)
# point than it is in demos/java/example6 where we have to handle this manually)
indexOfLastDot = string.rfind(implementingClass, ".")
implementingPackage = implementingClass[:indexOfLastDot]
implementingBaseName = implementingClass[indexOfLastDot+1:]
@ -79,13 +86,25 @@ miCalc = miCalcClass()
# 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:
miCalc.initialise(len(variable1Columns), len(variable2Columns))
# a. Initialise the calculator for a univariate calculation:
miCalc.initialise(1, 1)
# b. Supply the observations to compute the PDFs from:
miCalc.setObservations(variable1, variable2)
miCalc.setObservations(univariateSeries1, univariateSeries2)
# c. Make the MI calculation:
miValue = miCalc.computeAverageLocalOfObservations()
miUnivariateValue = miCalc.computeAverageLocalOfObservations()
print("MI calculator %s computed the joint MI as %.5f\n" % (implementingClass, miValue))
#---------------------
# 5. Continue onto a multivariate calculation, still
# only calling common methods defined in the interface type.
# a. Initialise the calculator for a multivariate calculation
# to use the required number of dimensions for each variable:
miCalc.initialise(len(jointVariable1Columns), len(jointVariable2Columns))
# b. Supply the observations to compute the PDFs from:
miCalc.setObservations(jointVariable1, jointVariable2)
# c. Make the MI calculation:
miJointValue = miCalc.computeAverageLocalOfObservations()
print("MI calculator %s computed the univariate MI(%d;%d) as %.5f and joint MI([%s];[%s]) as %.5f\n" %
(implementingClass, univariateSeries1Column, univariateSeries2Column, miUnivariateValue,
str(jointVariable1Columns).strip('[]'), str(jointVariable2Columns).strip('[]'), miJointValue))