pyuds is a Python library for measuring uncertainty in the Dempster-Shafer theory of evidence. The functionals supported are the Generalized Hartley (GH) uncertainty functional, Generalized Shannon (GS) uncertainty functional, and Aggregate Uncertainty (AU) functional. The library can be utilized either through its API, or through a user-friendly Web interface.
SHOGUN is a machine learning toolbox whose focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, all making use of the same underlying, efficient kernel implementations. Apart from SVMs and regression, SHOGUN also features a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons, and algorithms to train hidden Markov models. SHOGUN can be used from within C++, Matlab, R, Octave, and Python.
libagf is a fast, innovative implementation of adaptive or variable-bandwidth kernel-based estimators for statistical classification, PDF estimation, and interpolation/non-linear regression. It is written in C++ and includes simple, command line executables as well as easy-to-use libraries.
Fuzzy machine learning framework is a library and a GUI front-end for machine learning using intuitionistic fuzzy data. The approach is based on the intuitionistic fuzzy sets and the possibility theory. Further characteristics are fuzzy features and classes; numeric, enumeration features and features based on linguistic variables; user-defined features; derived and evaluated features; classifiers as features for building hierarchical systems; automatic refinement in case of dependent features; incremental learning; fuzzy control language support; object-oriented software design with extensible objects and automatic garbage collection; generic data base support through ODBC; text I/O and HTML output; an advanced graphical user interface based on GTK+; and examples of use.
Fuzzy sets for Ada is a library providing implementations of confidence factors with the operations not, and, or, xor, +, and *, classical fuzzy sets with the set-theoretic operations and the operations of the possibility theory, intuitionistic fuzzy sets with the operations on them, fuzzy logic based on the intuitionistic fuzzy sets and the possibility theory; fuzzy numbers, both integer and floating-point with conventional arithmetical operations, and linguistic variables and sets of linguistic variables with operations on them. String-oriented I/O is supported. A rich set of GTK+ GUI widgets is provided.
ACL2 is a mathematical logic, programming language, and mechanical theorem prover based on the applicative subset of Common Lisp. It is an "industrial-strength" version of the NQTHM or Boyer/Moore theorem prover, and has been used for the formal verification of commercial microprocessors, the Java Virtual Machine, interesting algorithms, and so forth.
Daikon is an implementation of dynamic detection of likely invariants. An invariant is a property (such as "x=2*y+5" or "this.next.prev = this" or "myarray is sorted by <") that holds at a certain point or points in a program. Invariants are often seen in assert statements, documentation, and formal specifications. Invariants can be useful in program understanding and a host of other applications. Daikon runs a program, observes the values that the program computes, and then reports properties that were true over the observed executions. It can detect properties in Java, C, C++, Perl, and IOA programs, in spreadsheet files, and in other data sources.
The Orbital library is a Java class library providing object-oriented representations and algorithms for logic, mathematics, and computer science. It comprises theorem proving, computer algebra, search and planning, as well as machine learning algorithms. Generally speaking, the conceptual idea behind the Orbital library is to provide extensional services and components that surround the heart of many scientific applications, hence the name "Orbital library". In order to satisfy the requirements of high reusability, the design of this foundation class library favors flexibility, conceptual simplicity, and generalization. Many sophisticated problems can be solved easily with its adaptable components.