Showing 2 open source projects for "automatic"

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    Fuzzy machine learning framework

    Fuzzy machine learning framework

    A library and a GUI front-end for fuzzy machine learning

    ...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 or SQLite; text I/O and HTML output; an advanced graphical user interface based on GTK+; and examples of use.
    Downloads: 1 This Week
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    AdaAutoDiff

    AdaAutoDiff

    C++ Templates and Ada Package for Automatic Differentiation

    Operators are overloaded so that a normal looking function definition provides access to not only evaluations of itself, but to evaluations of any of its analytic derivatives. Automatic differentiation means the user does not need to define the analytic expressions for all the various partial derivatives. It also means that those complex expressions are essentially calculated at compile time, and merely evaluated at runtime. First order derivatives only, forward accumulation. Choose "files" and either the "initial_submission" directory for the Ada version, or the other directory "cpp_AD" for the C++ version.
    Downloads: 0 This Week
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