MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users.
* More info + downloads: https://mlpack.org
* Git repo: https://github.com/mlpack/mlpack
Automated accounting for splits, dividends, and corporate events like delistings and mergers. Avoid selection bias with dynamically generated assets. Create and select asset universes on proprietary data and indicators. Automatically track portfolio performance, profit and loss, and holdings across multiple asset classes and margin models in the same strategy. Trigger regular functions to occur at desired times, during market hours, on certain days of the week, or at specific times of day....
...The new implemented units are then automatically integrated with the rest of the library.
The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.
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Java Component created to persist and work with trees (it doesn't matter the type of persistence). Based on "Nested Set Model of Hierarchies" by Joe Celko. [Componente Java para persistir y trabajar con árboles (no importa el tipo de persistencia)]
Attempt to create a PHP-framework: component-oriented; with global DI-container; for micro-services and also for big module-builded projects; with its own ORM. Little demo: http://complex.php.gi.igor-gilman.de.