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GURLS is the Grand Unified Regularized Least Squares.

Description

GURLS - (Grand Unified Regularized Least Squares) is a toolbox for supervised learning, based on the regularized least squares algorithm. GURLS key features are:

• Automatic parameter selection.
• Ability to handle massive datasets.
• Modularity, with possibility to use each method independently.
• Wide range of optimization routines

The GURLS toolbox includes the following libraries: GURLS, GURLS++ , bGURLS and bGURLS++. GURLS and bGURLS are developed in MATLAB. GURLS++ and bGURLS++ are their C++ counterparts, sharing the same design, with some improvements made possible by the power of a lower level language such as C++ that usually makes them significantly faster than the MATLAB versions. GURLS and GURLS++ are designed to deal with data sets that, once loaded, can fully reside in RAM without any memory mapping techniques (small data). bGURLS and bGURLS++ deal with all the other data sets (big data).

Design

GURLS (GURLS++) basically consists of a set of tasks, each one belonging to a predefined category, and of a method (a class in the C++ implementation) called GURLS Core that is responsible for processing an ordered sequence of tasks called pipeline. An additional ”options structure”, often refered to as OPT, is used to store all configuration parameters needed to customize the tasks behaviour. Tasks receive configuration parameters from the options structure in read-only mode and, after terminating, their results are appended to the structure by the GURLS Core in order to make them available to the subsequent tasks. This allows the user to easily skip the execution of some tasks in a pipeline, by simply inserting the desired results directly into the options structure. All tasks belonging to the same category can be interchanged with each other, so that the user can easily choose how each task shall be carried out.

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