mirror of https://github.com/jlizier/jidt
52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
from jpype import *
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import numpy
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import sys
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# Our python data file readers are a bit of a hack, python users will do better on this:
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sys.path.append("/home/joseph/JIDT/infodynamics-dist-1.6/demos/python")
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import readFloatsFile
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# Add JIDT jar library to the path
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jarLocation = "/home/joseph/JIDT/infodynamics-dist-1.6/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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# 0. Load/prepare the data:
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dataRaw = readFloatsFile.readFloatsFile("/home/joseph/JIDT/infodynamics-dist-1.6/demos/data/2coupledRandomCols-1.txt")
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# As numpy array:
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data = numpy.array(dataRaw)
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source = JArray(JDouble, 1)(data[:,0].tolist())
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destination = JArray(JDouble, 1)(data[:,1].tolist())
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# 1. Construct the calculator:
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calcClass = JPackage("infodynamics.measures.continuous.kernel").MutualInfoCalculatorMultiVariateKernel
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calc = calcClass()
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results = []
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kernels = numpy.arange(0.1, 1.05, 0.05);
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for kernelWidth in kernels:
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# 2. Set any properties to non-default values:
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calc.setProperty("TIME_DIFF", "1")
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calc.setProperty("KERNEL_WIDTH", str(kernelWidth))
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# 3. Initialise the calculator for (re-)use:
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calc.initialise()
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# 4. Supply the sample data:
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calc.setObservations(source, destination)
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# 5. Compute the estimate:
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result = calc.computeAverageLocalOfObservations()
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results.append(result);
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print("MI_Kernel(col_0 -> col_1, kernel=%.3f) = %.4f bits" %\
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(kernelWidth, result))
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# Now plot the results:
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import matplotlib.pyplot as plt
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plt.figure();
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plt.scatter(kernels, results, c='red', marker='x');
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plt.title('MI versus kernel width')
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plt.xlabel('Kernel width')
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plt.ylabel('MI (bits)')
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# If we're running this in a script you will need the following
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plt.show()
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