Tools

In Work Package 4 - "Capturing content semantics and environment" tools were developed that were then used in WP7 - "Training". The tools are software used for analysis of data in general but in this project, they have been used for analysis of semantic drift. The tools are available to anyone through open access.

SOMOCLU

Somoclu is a massively parallel implementation of self-organizing maps. It exploits multicore CPUs, it is able to rely on MPI for distributing the workload in a cluster, and it can be accelerated by CUDA. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes.
Key features:
• Fast execution by parallelization: OpenMP, MPI, and CUDA are supported.
• Multi-platform: Linux, OS X, and Windows are supported.
• Planar and toroid maps.
• Rectangular and hexagonal grids.
• Gaussian and bubble neighbourhood functions.
• Both dense and sparse input data are supported.
• Large maps of several hundred thousand neurons are feasible.
• Integration with Databionic ESOM Tools.
• Python, R, Julia, and MATLAB interfaces for the dense CPU and GPU kernels.

Download: https://github.com/peterwittek/somoclu

Ncpol2sdpa

Algorithm 950: Ncpol2sdpa---Sparse Semidefinite Programming Relaxations for Polynomial Optimization Problems of Noncommuting Variables (PDF Download Available). Available from: https://www.researchgate.net/publication/256187089_Algorithm_950_Ncpol2sdpa---Sparse_Semidefinite_Programming_Relaxations_for_Polynomial_Optimization_Problems_of_Noncommuting_Variables [accessed Mar 29, 2017]. 

A hierarchy of semidefinite programming (SDP) relaxations approximates the global optimum of polynomial optimization problems of noncommuting variables. Generating the relaxation, however, is a computationally demanding task, and only problems of commuting variables have efficient generators. We develop an implementation for problems of noncommuting problems that creates the relaxation to be solved by SDPA -- a high-performance solver that runs in a distributed environment. We further exploit the inherent sparsity of optimization problems in quantum physics to reduce the complexity of resulting relaxation. Constrained problems with a relaxation of order two may contain up to a hundred variables. The implementation is available in C++ and Python. The tool helps solve problems such as finding the ground state energy or testing quantum correlations.

Download: https://pypi.python.org/pypi/ncpol2sdpa/