Description:

Somoclu is a C++ tool for training self-organizing maps on large data sets using a massively parallel resources. It relies on OpenMP for multicore execution and it builds on MPI for distributing the workload across the nodes of the cluster. It is also able to boost training by using CUDA if graphics processing units are available. A sparse kernel is included, which is useful for high-dimensional but sparse data, such as the vector spaces common in text mining workflows. Python, Julia, R, and MATLAB interfaces facilitate use in data analysis. The code is released under GNU GPLv3 licence.

Key features:

• Fast execution by parallelization: OpenMP, MPI, and CUDA are supported.

• Python, Julia, R, and MATLAB interfaces for the dense multicore CPU kernel.

• Planar and toroid maps.

• Rectangular and hexagonal grids.

• Gaussian and bubble neighborhood functions.

• Both dense and sparse input data are supported.

• Large emergent maps of several hundred thousand neurons are feasible.

• Integration with Databionic ESOM Tools.

Changes to previous version:

• New: Julia interface is available (https://github.com/peterwittek/Somoclu.jl).
• New: Method get_surface_state of the Somoclu object in Python calculates the activation map for all data instances.
• New: Method view_activation_map of the Somoclu object in Python allows plotting the activation map for the training data instances or for a new data instance.
• New: Method view_similarity_matrix of the Somoclu object in Python visualizes the similarity matrix of data points according to their distance to the nodes in the map.
• Fixed: CRAN-friendliness improved.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Linux, Windows, Os X

Data Formats: Ascii, Libsvm, Esom

Tags: Cuda, Self Organizing Maps, Mpi, Esom, Openmp

Archive: download here

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