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Copy file name to clipboardExpand all lines: pyspi/lib/jidt/readme.txt
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Java Information Dynamics Toolkit (JIDT)
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Copyright (C) 2012-2014 Joseph T. Lizier
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Copyright (C) 2014-2016 Joseph T. Lizier and Ipek Özdemir
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Copyright (C) 2017- Joseph T. Lizier, Ipek Özdemir and Pedro Mediano
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Copyright (C) 2016-2019 Joseph T. Lizier, Ipek Özdemir and Pedro Mediano
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Copyright (C) 2019-2022 Joseph T. Lizier, Ipek Özdemir, Pedro Mediano, Emanuele Crosato, Sooraj Sekhar and Oscar Huaigu Xu
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Copyright (C) 2022- Joseph T. Lizier, Ipek Özdemir, Pedro Mediano, Emanuele Crosato, Sooraj Sekhar, Oscar Huaigu Xu and David Shorten
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Version 1.5 (see release notes below)
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Version 1.6.1 (see release notes below)
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JIDT provides a standalone, open source code Java implementation (usable in Matlab, Octave and Python) of information-theoretic statistics of distributed computation in complex systems: i.e. information storage, transfer and modification.
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JIDT provides a standalone, open source code Java implementation (usable in Matlab, Octave and Python) of information-theoretic measures of distributed computation in complex systems: i.e. information storage, transfer and modification.
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This includes implementations for:
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- both discrete and continuous-valued variables, principally for the statistics transfer entropy, mutual information and active information storage;
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- both discrete and continuous-valued variables, principally for the measures transfer entropy, mutual information and active information storage;
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- using various types of estimators (e.g. Kraskov-Stögbauer-Grassberger estimators, kernel estimation, linear-Gaussian).
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=============
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A research paper describing the toolkit is included in the top level directory -- "InfoDynamicsToolkit.pdf".
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A tutorial, providing background to the information-theoretic statistics, various estimators, and then to the JIDT toolkit itself is included in the tutorial folder (see "JIDT-TutorialSlides.pdf" for the tutorial slides, and "README-TutorialAndExercise.pdf" for further description of the tutorial exercises).
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A tutorial, providing background to the information-theoretic measures, various estimators, and then to the JIDT toolkit itself is included in the tutorial folder (see "JIDT-TutorialSlides.pdf" for the tutorial slides, and "README-TutorialAndExercise.pdf" for further description of the tutorial exercises).
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Javadocs for the toolkit are included in the full distribution at javadocs.
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They can also be generated using "ant javadocs" (useful if you are on a git clone).
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Several sets of demonstration code are distributed with the toolkit:
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a. demos/AutoAnalyser -- a GUI tool to compute the information-theoretic statistics on a chosen data set with the toolkit, and also automatically generate code in Java, Python and Matlab to show how to do this calculation with the toolkit. See description at https://github.com/jlizier/jidt/wiki/AutoAnalyser
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a. demos/AutoAnalyser -- a GUI tool to compute the information-theoretic measures on a chosen data set with the toolkit, and also automatically generate code in Java, Python and Matlab to show how to do this calculation with the toolkit. See description at https://github.com/jlizier/jidt/wiki/AutoAnalyser
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b. demos/java -- basic examples on easily using the Java toolkit -- run these from the shell scripts in this directory -- see description at https://github.com/jlizier/jidt/wiki/SimpleJavaExamples
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d. demos/octave/CellularAutomata -- using the Java toolkit to plot local information dynamics profiles in cellular automata; the toolkit is run under Octave or Matlab -- see description at https://github.com/jlizier/jidt/wiki/CellularAutomataDemos
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e. demos/octave/SchreiberTransferEntropyExamples -- recreates the transfer entropy examples in Schreiber's original paper presenting this statistic; shows the correct parameter settings to reproduce these results -- see description at https://github.com/jlizier/jidt/wiki/SchreiberTeDemos
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e. demos/octave/SchreiberTransferEntropyExamples -- recreates the transfer entropy examples in Schreiber's original paper presenting this measure; shows the correct parameter settings to reproduce these results -- see description at https://github.com/jlizier/jidt/wiki/SchreiberTeDemos
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f. demos/octave/DetectingInteractionLags -- demonstration of using the transfer entropy with source-destination lags; the demo is run under Octave or Matlab -- see description at https://github.com/jlizier/jidt/wiki/DetectingInteractionLags
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Release notes
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===============
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v1.6.1 22/8/2023
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-------------
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(after 909 commits recorded by github, repository as at https://github.com/jlizier/jidt/tree/90baf68ee7332e15030447b44d262a0fc54773f6 save for this file update)
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Minor updates to supporting use in Python, including virtual environments;
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Minor tweaks to fish schooling examples (mostly comments)
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v1.6 5/9/2022
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-------------
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(after 889 commits recorded by github, repository as at https://github.com/jlizier/jidt/tree/d750a737bea2a8b1f33b7cd0ad167ec999d907ef save for this file update)
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Adding Flocking/Schooling/Swarming demo;
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Included Pedro's code on IIT and O-/S-Information measures;
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Spiking TE estimator added from David;
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Fixed up AutoAnalyser to work well for Python3 and numpy;
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Links to lecture videos included in the beta wiki for the course;
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Added rudimentary effective network inference (simplified version of the IDTxl full algorithm) in demos/octave/EffectiveNetworkInference;
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v1.5 26/11/2018
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---------------
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(after 753 commits recorded by github, repository as at https://github.com/jlizier/jidt/tree/603445651cc0bf155a42c9ba336141bc7f29bccd save for this file update)
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Switched source->destination arguments for discrete TE calculators to be with source first in line with continuous calculators;
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Renamed all discrete calculators to have Discrete suffix -- TE and conditional TE calculators also renamed to remove "Apparent" prefix and change "Complete" to "Conditional";
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Kraskov estimators now using 4 nearest neighbours by default;
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Unit test for Gaussian TE against ChaLearn Granger causality statisticment;
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Unit test for Gaussian TE against ChaLearn Granger causality measurement;
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Added Schreiber TE demos; Interregional transfer demos; documentation for Interaction lag demos; added examples 7 and 8 to Simple Java demos;
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Added property to add noise to data for Kraskov MI;
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Added derivation of Apache Commons Math code for chi square distribution, and included relevant notices in our release;
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