Java Information Dynamics Toolkit (JIDT)

Copyright (C) 2012- Joseph T. Lizier; 2014- Ipek Özdemir; 2017- Pedro Mediano; 2019- Emanuele Crosato, Sooraj Sekhar, Oscar Huaigu Xu; 2022- David Shorten

JIDT provides a stand-alone, open-source code Java implementation (also usable in Matlab, Octave, Python, R, Julia and Clojure) of information-theoretic measures of distributed computation in complex systems: i.e. information storage, transfer and modification.

JIDT includes implementations:

• principally for the measures transfer entropy, mutual information, and their conditional variants, as well as active information storage, entropy, etc;
• for both discrete and continuous-valued data;
• using various types of estimators (e.g. Kraskov-Stögbauer-Grassberger estimators, box-kernel estimation, linear-Gaussian), as described in full at ImplementedMeasures.

JIDT is easy to use:

• It ships with a GUI application -- the AutoAnalyser, see picture below -- to facilitate point-and-click analysis, as well as code template generation for more complex analysis.
• We provide short video lectures and corresponding slides in a (beta) Course on how to understand using information-theoretic tools to analyse complex systems, and to implement such analysis with JIDT.

JIDT is distributed under the GNU GPL v3 license (or later).

Getting started

1. Download and Installation is very easy!
   1. Quick start: take a git clone (then build via AntScripts) OR download the latest v1.6.1 full distribution (suitable for all platforms) and see the readme.txt file therein.
2. Documentation including: the paper describing JIDT at arXiv:1408.3270 (distributed with the toolkit), a (beta) Course including short video lectures and a shorter Tutorial, and Javadocs (v1.6.1 here);
3. Demos are included with the full distribution, including a GUI app for automatic analysis and code generation (see picture below), simple java demos and cellular automata (CA) demos.
   1. These Java tools can easily be used in Matlab/Octave, Python, R, Julia and Clojure! (click on each language here for examples)

For further information or announcements:

• Join our discussion group: http://groups.google.com/d/forum/jidt-discuss
• See also the FAQs
• Follow @infodynamicstkt on twitter

Citation

Please cite your use of this toolkit as:

Joseph T. Lizier, "JIDT: An information-theoretic toolkit for studying the dynamics of complex systems", Frontiers in Robotics and AI 1:11, 2014; doi:10.3389/frobt.2014.00011 (pre-print: arXiv:1408.3270)

And please let me know about any publications resulting from its use!

See other PublicationsUsingThisToolkit.

News

22/08/2023 - New full distribution files available for release v1.6.1; Changes for v1.6.1 include: Minor updates to supporting use in Python, including virtual environments; Minor tweaks to fish schooling examples (mostly comments).

5/09/2022 - New full distribution files available for release v1.6; Changes for v1.6 include: Adding Flocking/Schooling/Swarming demo; Included Pedro's code on IIT and O-/S-Information measures; Spiking TE estimator added from David; Fixed up AutoAnalyser to work well for Python3 and numpy; Links to lecture videos included in the beta wiki for the course; Added rudimentary effective network inference (simplified version of the IDTxl full algorithm) in demos/octave/EffectiveNetworkInference;

26/11/2018 - New jar and full distribution files available for release v1.5; Changes for v1.5 include: Added GPU (cuda) capability for KSG Conditional Mutual Information calculator (proper documentation to come), brief wiki page and unit tests included; Added auto-embedding for TE/AIS with multivariate KSG, and univariate and multivariate Gaussian estimator (plus unit tests), for Ragwitz criteria and Maximum bias-corrected AIS, and also added Maximum bias corrected AIS and TE to handle source embedding as well; Kozachenko entropy estimator adds noise to data by default; Added bias-correction property to Gaussian and Kernel estimators for MI and conditional MI, including with surrogates (only option for kernel); Enabled use of different bases for different variables in MI discrete estimator; All new above features enabled in AutoAnalyser; Added drop-down menus for parameters in AutoAnalyser; Included long-form lecture slides in course folder;

26/11/2017 - New jar and full distribution files available for release v1.4; Changes for v1.4 include: Major expansion of functionality for AutoAnalysers: adding Launcher applet and capability to double click jar to launch, added Entropy, CMI, CTE and AIS AutoAnalysers, also added binned estimator type, added all variables/pairs analysis, added statistical significance analysis, and ensured functionality of generated Python code with Python3; Added GPU (cuda) capability for KSG Mutual Information calculator (proper documentation and wiki page to come), including unit tests; Added fast neighbour search implementations for mixed discrete-continuous KSG MI estimators; Expanded Gaussian estimator for multi-information (integration); Made all demo/data files readable by Matlab.

17/12/2016 - New book out from J. Lizier et al., "An Introduction to Transfer Entropy: Information Flow in Complex Systems" published by Springer, which contains various examples using JIDT (distributed in our releases)

21/10/2016 - New jar and full distribution files available for release v1.3.1; Changes for v1.3.1 include: Major update to TransferEntropyCalculatorDiscrete so as to implement arbitrary source and dest embeddings and source-dest delay; Conditional TE calculators (continuous) handle empty conditional variables; Added new auto-embedding method for AIS and TE which maximises bias corrected AIS; Added getNumSeparateObservations() method to TE calculators to make reconstructing/separating local values easier after multiple addObservations() calls; Fixed kernel estimator classes to return proper densities, not probabilities; Bug fix in mixed discrete-continuous MI (Kraskov) implementation; Added simple interface for adding joint observations for MultiInfoCalculatorDiscrete Including compiled class files for the AutoAnalyser demo in distribution; Updated Python demo 1 to show use of numpy arrays with ints; Added Python demo 7 and 9 for TE Kraskov with ensemble method and auto-embedding respectively; Added Matlab/Octave example 10 for conditional TE via Kraskov (KSG) algorithm; Added utilities to prepare for enhancing surrogate calculations with fast nearest neighbour search; Minor bug patch to Python readFloatsFile utility.

19/7/2015 - New jar and full distribution files available for release v1.3; Changes for v1.3 include: Added AutoAnalyser (Code Generator) GUI demo for MI and TE; Added auto-embedding capability via Ragwitz criteria for AIS and TE calculators (KSG estimators); Added Java demo 9 for showcasing use of Ragwitz auto-embedding; Adding small amount of noise to data in all KSG estimators now by default (may be disabled via setProperty()); Added getProperty() methods for all conditional MI and TE calculators; Upgraded Python demos for Python 3 compatibility; Fixed bias correction on mixed discrete-continuous KSG calculators; Updated the tutorial slides to those in use for ECAL 2015 JIDT tutorial.

12/2/2015 - New jar and full distribution files available for release v1.2.1; Changes for v1.2.1 include: Added tutorial slides, description of exercises and sample exercise solutions; Made jar target Java 1.6; Added Schreiber TE heart-breath rate with KSG estimator demo code for Python.

28/1/2015 - New jar and full distribution files available for release v1.2; Changes for v1.2 include: Dynamic correlation exclusion, or Theiler window, added to all Kraskov estimators; Added univariate MI calculation to simple demo 6; Added Java code for Schreiber TE heart-breath rate with KSG estimator, ready for use as a template in Tutorial; Patch for crashes in KSG conditional MI algorithm 2.

20/11/2014 - New jar and full distribution files available for release v1.1; Changes for v1.1 include: Implemented Fast Nearest Neighbour Search for Kraskov-Stögbauer-Grassberger (KSG) estimators for MI, conditional MI, TE, conditional TE, AIS, Predictive info, and multi-information. This includes a general (multivariate) k-d tree implementation; Added multi-threading (using all available processors by default) for the KSG estimators -- code contributed by Ipek Özdemir; Added Predictive information / Excess entropy implementations for KSG, kernel and Gaussian estimators; Added R, Julia, and Clojure demos; Added Windows batch files for the Simple Java Demos; Added property for adding a small amount of noise to data in all KSG estimators;

15/8/2014 JIDT paper finalised and uploaded to the website and arXiv:1408.3270

14/8/2014 - New jar and full distribution files available for our first official release, v1.0; Changes for v1.0 include: Added the draft of the paper on the toolkit to the release; Javadocs made ready for release; Switched source->destination arguments for discrete TE calculators to be with source first in line with continuous calculators; Renamed all discrete calculators to have Discrete suffix -- TE and conditional TE calculators also renamed to remove "Apparent" prefix and change "Complete" to "Conditional"; Kraskov estimators now using 4 nearest neighbours by default; Unit test for Gaussian TE against ChaLearn Granger causality measurement; Added Schreiber TE demos; Interregional transfer demos; documentation for Interaction lag demos; added examples 7 and 8 to Simple Java demos; Added property to add noise to data for Kraskov MI; Added derivation of Apache Commons Math code for chi square distribution, and included relevant notices in our release; Inserted translation class for arrays between Octave and Java; Added analytic statistical significance calculation to Gaussian calculators and discrete TE; Corrected Kraskov algorithm 2 for conditional MI to follow equation in Wibral et al. 2014.

20/4/2014 - New jar and full distribution files available for v0.2.0; Moved downloads to http://lizier.me/joseph/ since google code has stopped the download facility here :(. Changes for v0.2.0 include: Rearchitected (most) Transfer Entropy and Multivariate TE calculators to use an underlying conditional mutual information calculator, and have arbitrary embedding delay, source-dest delay; this includes moving Kraskov-Grassberger Transfer Entropy calculator to use a single conditional mutual information estimator instead of two mutual information estimators; Rearchitected (most) Active Information Storage calculators to use an underlying mutual information calculator; Added Conditional Transfer Entropy calculators using underlying conditional mutual information calculators; Moved mixed discrete-continuous calculators to a new "mixed" package; bug fixes.

11/9/2013 - New jar and full distribution files available for v0.1.4; added scripts to generate CA figures for 2013 book chapters; added general Java demo code; added Python demo code; made Octave/Matlab demos and CA demos properly compatible for Matlab; added extra Octave/Matlab general demos; added more unit tests for MI and conditional MI calculators, including against results from Wibral's TRENTOOL; bug fixes.

11/9/2013 - New CA demo scripts for several review book chapters we're preparing in 2013 have been uploaded - see CellularAutomataDemos.

4/6/2013 - Added instructions on how to use in python and several PythonExamples.

13/01/2013 - New jar and full distribution files available for v0.1.3; existing Octave/Matlab demo code made compatible with Matlab; several bug fixes, including using max norm by default in Kraskov calculator (instead of requiring this to be set explicitly); more unit tests (including against results from Kraskov's own MI implementation)

19/11/2012 - New jar and full distribution files available for v0.1.2, including demo code for two newly submitted papers

31/10/2012 - Jar and full distribution files available for v0.1.1 (first distribution)

7/5/2012 - JIDT project created and code uploaded

Acknowledgements

This project has been supported by funding through:

• Australian Research Council Discovery Early Career Researcher Award (DECRA) "Relating function of complex networks to structure using information theory", J.T. Lizier, 2016-19 DE160100630
• Universities Australia - Deutscher Akademischer Austauschdienst (German Academic Exchange Service) UA-DAAD Australia-Germany Joint Research Co-operation grant "Measuring neural information synthesis and its impairment", Wibral, Lizier, Priesemann, Wollstadt, Finn, 2016-17
• University of Sydney Research Accelerator (SOAR) Fellowship 2019 Scheme, J.T. Lizier (CI), 2019-2020
• Australian Research Council Discovery Project "Large-scale computational modelling of epidemics in Australia: analysis, prediction and mitigation", M. Prokopenko, P. Pattison, M. Gambhir, J.T. Lizier, M. Piraveenan, 2016-19 DP160102742