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ITE / README.md

ITE (Information Theoretical Estimators)

ITE is capable of estimating many different variants of entropy, mutual information, divergence, association measures and cross quantities. Thanks to its highly modular design, ITE supports additionally

  • the combinations of the estimation techniques,
  • the easy construction and embedding of novel information theoretical estimators, and
  • their immediate application in information theoretical optimization problems.

ITE is

  • written in Matlab/Octave,
  • multi-platform (tested extensively on Windows and Linux),
  • free and open source (released under the GNU GPLv3(>=) license).

ITE can estimate

  • entropy (H): Shannon entropy, Rényi entropy, Tsallis entropy (Havrda and Charvát entropy), complex entropy,
  • mutual information (I): generalized variance, kernel canonical correlation analysis, kernel generalized variance, Hilbert-Schmidt independence criterion, Shannon mutual information, L2 mutual information, Rényi mutual information, Tsallis mutual information, copula-based kernel dependency, multivariate version of Hoeffding's Phi, Schweizer-Wolff's sigma and kappa, complex mutual information, Cauchy-Schwartz quadratic mutual information, Euclidean distance based quadratic mutual information, distance covariance, distance correlation,
  • divergence (D): Kullback-Leibler divergence (relative entropy), L2 divergence, Rényi divergence, Tsallis divergence, Hellinger distance, Bhattacharyya distance, maximum mean discrepancy (kernel distance, an integral probability metric), J-distance (symmetrised Kullback-Leibler divergence), Cauchy-Schwartz divergence, Euclidean distance based divergence, energy distance (specially the Cramer-Von Mises distance),
  • association measures (A), including measures of concordance: multivariate extensions of Spearman's rho (Spearman's rank correlation coefficient, grade correlation coefficient),
  • cross quantities (C): cross-entropy.

ITE offers solution methods for

  • Independent Subspace Analysis (ISA) and
  • its extensions to different linear-, controlled-, post nonlinear-, complex valued-, partially observed models, as well as to systems with nonparametric source dynamics.

Note:

  • the evolution of the ITE code is briefly summarized in CHANGELOG.txt.
  • become a Follower to be always up-to-date with ITE.
  • if you have an H/I/D/A/C estimator/subtask solver with a GPLv3(>=)-compatible license that you would like to be embedded into ITE, feel free to contact me.

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