mirror of https://github.com/jlizier/jidt
67 lines
2.9 KiB
Clojure
Executable File
67 lines
2.9 KiB
Clojure
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 3 - Transfer entropy on continuous data using kernel estimators =
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; Simple transfer entropy (TE) calculation on continuous-valued data using the (box) kernel-estimator TE calculator.
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; Import relevant classes:
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(import infodynamics.measures.continuous.kernel.TransferEntropyCalculatorKernel)
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(import java.util.Random)
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(def rg (Random.))
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(let
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[numObservations 1000
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covariance 0.4
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; Generate some random normalised data.
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sourceArray (double-array (take numObservations (repeatedly #(.nextGaussian rg))))
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destArray (double-array
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(cons 0
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(map +
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(map (partial * covariance) (butlast sourceArray))
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(map (partial * (- covariance 1)) (double-array (take (- numObservations 1) (repeatedly #(.nextGaussian rg))))) )))
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sourceArray2 (double-array (take numObservations (repeatedly #(.nextGaussian rg))))
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teCalc (TransferEntropyCalculatorKernel. )
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]
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; Set up the calculator
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(.setProperty teCalc "NORMALISE" "true")
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(.initialise teCalc 1 0.5) ; Use history length 1 (Schreiber k=1), kernel width of 0.5 normalised units
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(.setObservations teCalc sourceArray destArray)
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; For copied source, should give something close to expected value for correlated Gaussians:
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; TODO The analytic result quoted here isn't quite right, see e.g. octave demos (can't be bothered fixing here...)
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(println "TE result " (.computeAverageLocalOfObservations teCalc)
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" expected to be close to " (/ (Math/log (/ 1 (- 1 (* covariance covariance)))) (Math/log 2))
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" for these correlated Gaussians but biased upward")
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(.initialise teCalc ) ; Initialise leaving the parameters the same
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(.setObservations teCalc sourceArray2 destArray)
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; For random source, it should give something close to 0 bits
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(println "TE result " (.computeAverageLocalOfObservations teCalc)
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" expected to be close to 0 bits for these uncorrelated Gaussians but will be biased upward")
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; We can get insight into the bias by examining the null distribution:
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(def nullDist (.computeSignificance teCalc 100))
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(println "Null distribution for unrelated source and destination "
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"(i.e. the bias) has mean " (.getMeanOfDistribution nullDist)
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" and standard deviation " (.getStdOfDistribution nullDist))
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)
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