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Releases: pwollstadt/IDTxl

Updated development release

24 Aug 15:42
@pwollstadt pwollstadt
v0.3.0
4b1b4a4
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This is the second development release. Note that algorithms are still
in beta stage. Also, there may be changes to the API in future releases.

To get started with using IDTxl have a look at the wiki
pages describing the installation
process
and the example
script
for network inference. There are also examples in the docstrings of the
algorithm classes. Further documentation: http://pwollstadt.github.io/IDTxl/
and https://github.com/pwollstadt/IDTxl/wiki.

Stable algorithms (see the
demos for examples):

  • multivariate_transfer_entropy.py
  • bivariate_transfer_entropy.py
  • multivariate_mutual_information.py
  • bivariate_mutual_information.py
  • active_information_storage.py
  • partial_information_decomposition.py
  • network_comparison.py (group-level statistics)
  • visualise_graph.py
  • core-estimators, see the wiki page for examples

Added features:

  • (lagged) multivariate and bivariate MI estimation for network inference
  • bivariate TE estimation for network inference
  • Results() class: replaces results dictionary, adds functionality to generate
    adjacency matrices and access detailed results for individual targets
  • generation of synthetic test data in the Data() class (coupled logistic maps and autoregressive processes)
  • demo scripts for network inference algorithms and core estimators

Improvements:

  • add jar-file supporting JAVA v6 (fixes #9)
  • cleaned up console output
  • update of the Tartu estimator

Bug fixes:

  • OpenCL estimators now run on Nvidia and AMD cards (fixes #10)
  • Labeling of nodes in source graph
  • The minimum statistics did not use the correct conditioning set during the pruning step of the multivariate TE algorithm, causing a bias in the test
  • Non-uniform embedding was not built correctly for bivariate measures

Known issues and missing features:
- OpenCL estimators fail on AMD cards in some cases due to driver settings
that introduce limitations on maximum variable size
- spectral multivariate transfer entropy estimation will be added in a future release
- the Kraskov2 algorithm will be available for estimation in a future release (issue #15)

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First development release

20 Sep 09:26
@pwollstadt pwollstadt
v0.1.0
4ddb995
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First development release Pre-release
Pre-release

This is the first development release.

To get started with IDTxl have a look at the wiki pages describing the installation process and the example script for network inference. There are also examples in the docstrings of the algorithm classes. Further documentation: http://pwollstadt.github.io/IDTxl/ and https://github.com/pwollstadt/IDTxl/wiki (under development).

Stable algorithms:

  • multivariate_transfer_entropy.py
  • network_comparison.py (group-level statistics)
  • core-estimators of basic information-theoretic quantities, see the wiki page for examples

The following algorithms and modules are in alpha stage and may still contain bugs or may change until the official release:

  • active_information_storage.py
  • partial_information_decomposition.py
  • bivariate_transfer_entropy.py
  • visualise_graph.py

Known issues and missing features:

  • OpenCL estimators seem to run on Nvidia cards only (support for AMD cards will be added in the future)
  • load/save functionality in idtxl_io.py is not yet working, use pickle to load and save results and data files
  • the results format will change in a future release
  • spectral multivariate transfer entropy estimation will be added in a future release
  • (lagged) MI estimation for network inference will be added in a future release
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