5 projects tagged "Scientific Computing"

Pop	158.44
Vit	14.48

FLENS is short for Flexible Library for Efficient Numerical Solutions. This C++ can be used as a builing block for the implementation of other (higher-level) numerical libraries or numerical applications. It is a C++ library (requires a C++11 conform compiler). Easy install, as FLENS is headers only. It gives you Matrix/vector types for dense linear algebra; a generic (i.e. templated) implementation of BLAS; and a generic reimplementation of LAPACK. If high performance BLAS libraries like ATLAS, GotoBLAS, etc. are available, you simply can link against them and boost performance.

Pop	116.91
Vit	4.63

PEDSIM is a microscopic pedestrian crowd simulation system. The PEDSIM library allows you to use pedestrian dynamics in your own software. Based on pure C++/STL without additional packages, it runs virtually on every operating system. The PEDSIM Demo Application gives you a quick overview of the capabilities, and is a starting point for your own experiments. PEDSIM is suitable for use in crowd simulations (e.g. indoor evacuation simulation, large scale outdoor simulations), where one is interested in output like pedestrian density or evacuation time. Since the quality of the individual agent's trajectory is high, PEDSIM can be used for creating massive pedestrian crowds in movies.

Pop	19.26
Vit	38.06

Tiny Calculator is a command line calculator for developers. It can resolve a complex expression mixing decimal, hexadecimal, and binary numbers. The result is automatically converted to decimal, hexadecimal, and binary formats.

Pop	122.84
Vit	6.14

FFTW++ is a C++ header class for the FFTW Fast Fourier Transform library that automates memory allocation, alignment, planning, and wisdom. In 2D and 3D, implicit dealiasing of convolutions substantially reduces memory usage and computation time. Wrappers for C, Python, and Fortran are included.

Pop	102.12
Vit	8.90

Sally is a tool for mapping a set of strings to a set of vectors. This mapping is referred to as embedding and allows techniques of machine learning and data mining to be applied for the analysis of string data. It can be used with data such as text documents, DNA sequences, or log files. The vector space model or bag-of-words model is used. Strings are characterized by a set of features, where each feature is associated with one dimension of the vector space. Occurrences of the features in each string are counted. Alternatively, binary or TF-IDF values can be computed. Vectors can be output in plain text, LibSVM, or Matlab format.