mirror of https://github.com/jlizier/jidt
37 lines
1.9 KiB
Python
Executable File
37 lines
1.9 KiB
Python
Executable File
# = Example 2 - Transfer entropy on multidimensional binary data =
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# Simple transfer entropy (TE) calculation on multidimensional binary data using the discrete TE calculator.
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# This example is important for Python users using JPype, because it shows how to handle multidimensional arrays from Python to Java.
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from jpype import *
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import random
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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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# Create many columns in a multidimensional array, e.g. for fully random values:
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# twoDTimeSeriesOctave = [[random.randint(0,1)]*2 for x in xrange(10)] # for 10 rows (time-steps) for 2 variables
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# However here we want 2 rows by 100 columns where the next time step (row 2) is to copy the
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# value of the column on the left from the previous time step (row 1):
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numObservations = 100
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row1 = [random.randint(0,1) for r in xrange(numObservations)]
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row2 = [row1[numObservations-1]] + row1[0:numObservations-1] # Copy the previous row, offset one column to the right
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twoDTimeSeriesPython = []
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twoDTimeSeriesPython.append(row1)
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twoDTimeSeriesPython.append(row2)
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twoDTimeSeriesJavaInt = JArray(JInt, 2)(twoDTimeSeriesPython);
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# Create a TE calculator and run it:
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teCalcClass = JPackage("infodynamics.measures.discrete").ApparentTransferEntropyCalculator
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teCalc = teCalcClass(2,1)
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teCalc.initialise()
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# Add observations of transfer across one cell to the right per time step:
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teCalc.addObservations(twoDTimeSeriesJavaInt, 1)
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result2D = teCalc.computeAverageLocalOfObservations()
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print('The result should be close to 1 bit here, since we are executing copy operations of what is effectively a random bit to each cell here: %.3f bits from %d observations' % (result2D, teCalc.getNumObservations()))
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