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.
The fstrcmp library provides an fstrcmp function that returns a number between 0.0 (nothing alike) and 1.0 (identical); this can be used to suggest likely alternatives in error messages. Fuzzy comparisons for byte arrays, wide character strings, and multi-byte character strings are also available. In addition, there are integer alternatives for systems with slow floating point emulation.