EO is a template-based, ANSI-C++ evolutionary computation library which helps you to write your own stochastic optimization algorithms insanely fast.
With the help of EO, you can easily design evolutionary algorithms that will find solutions to virtually all kind of hard optimization problems
... [More], from continuous to combinatorial ones.
Designing an algorithm with EO consists in choosing what components you want to use for your specific needs, just as building a structure with Lego blocks. [Less]
graph-tool is a python module to help with statistical analysis of graphs.
Its feature set includes support for both directed and undirected graphs with arbitrary vertex and edge properties, edge/vertex filtering, correlated random graph generation and community detection.
It supports also
... [More] several statistical measurements, such as: degree histogram, combined degree histogram, vertex-vertex degree correlation, average nearest neighbours degree, vertex-edge-vertex correlation, clustering coefficients, extended clustering coefficient, assortativity coefficient, betweenness centrality, average distance, component statistics and reciprocity.
The core algorithms are written in C++, making use of the Boost Graph Library, and template metaprogramming techniques, with performance in mind. [Less]
Building and Testing, with CMake, a the monolithic Boost (as opposed to the current Ryppl initiative).
That project will migrate to Ryppl as soon as possible, i.e., when "modularised" will have become packaging-friendly enough.
The goal of this library is to have a extendable headers only C++ library for converting strings that represent the python types (primitive types, lists, dictionaries, sets) to the corresponding C++ types.
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