JIDT: Java Information Dynamics Toolkit for studying information-theoretic measures of computation in complex systems
Full distribution release v1.6.1 (including source code, jar, demos, documentation, etc.)
Release notes:
(after 909 commits recorded by github) Minor release to capture latest before using JIDT in class. Minor updates to supporting use in Python, including virtual environments; Minor tweaks to fish schooling examples (mostly comments).
Full distribution release v1.5 (including source code, jar, demos, documentation, etc.)
Release notes:
(after 892 commits recorded by github) 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;
Full distribution release v1.5 (including source code, jar, demos, documentation, etc.)
Release notes:
(after 753 commits recorded by github) Added GPU (cuda) capability for KSG Conditional Mutual Information calculator (proper documentation to come), including unit tests and brief wiki page; 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.
Full distribution release v1.4 (including source code, jar, demos, documentation, etc.)
Release notes:
(after 638 commits recorded by github) 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.
Full distribution release v1.3.1 (including source code, jar, demos, documentation, etc.)
This was the first new distribution generated while the project was hosted on github.
Release notes:
(after 386 commits recorded by github) 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 auto-embedding method for AIS and TE which maximises bias corrected AIS; Added getSeparateNumObservations() 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;
Full distribution release v1.3 (including source code, jar, demos, documentation, etc.)
This was the last distribution generated while the project was hosted on google code; copied here.
Release notes:
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;