talisman - A straightforward & modular NLP, machine learning & fuzzy matching library for JavaScript

•    Javascript

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The library's full documentation can be found here. Be sure to lint & pass the unit tests before submitting your pull request.

natural-language-processing machine-learning fuzzy-matching clustering record-linkage bayes bloom-filter canberra caverphone chebyshev cologne cosine classifier daitch-mokotoff dice fingerprint fuzzy hamming k-means jaccard jaro lancaster levenshtein lig metaphone mra ngrams nlp nysiis perceptron phonetic porter punkt schinke sorensen soundex stats tfidf tokenizer tversky vectorizer winkler

distance-transform - Distance transforms for ndarrays

•    Javascript

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Performs a distance transform of array in place using Meijster's algorithm.array is updated in place and gets the distance values.

scijs distance transform euclidean manhattan taxicab morphology chebyshev chessboard lp lebesgue metric

FastTransforms.jl - Julia package for fast orthogonal polynomial transforms

•    Julia

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FastTransforms.jl allows the user to conveniently work with orthogonal polynomials with degrees well into the millions. Transforms include conversion between Jacobi polynomial expansions, with Chebyshev, Legendre, and ultraspherical polynomial transforms as special cases. For the signal processor, all three types of nonuniform fast Fourier transforms available. As well, spherical harmonic transforms and transforms between orthogonal polynomials on the triangle allow for the efficient simulation of partial differential equations of evolution.

jacobi chebyshev padua gaunt legendre julia spherical-harmonics structured-linear-algebra

shenfun - High performance computational platform in Python for the spectral Galerkin method

•    Python

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Shenfun is a high performance computing platform for solving partial differential equations (PDEs) by the spectral Galerkin method. The user interface to shenfun is very similar to FEniCS, but applications are limited to multidimensional tensor product grids, using either Cartesian or curvilinear grids (e.g., but not limited to, polar, cylindrical, spherical or parabolic). The code is parallelized with MPI through the mpi4py-fft package. Shenfun enables fast development of efficient and accurate PDE solvers (spectral order and accuracy), in the comfortable high-level Python language. The spectral accuracy is ensured by using high-order global orthogonal basis functions (Fourier, Legendre, Chebyshev, Laguerre, Hermite and Jacobi), as opposed to finite element codes that are using low-order local basis functions. Efficiency is ensured through vectorization (Numpy), parallelization (mpi4py) and by moving critical routines to Cython or Numba. Shenfun has been used to run turbulence simulations (Direct Numerical Simulations) on thousands of processors on high-performance supercomputers, see the spectralDNS repository.

hpc spectral fourier turbulence fenics mobius galerkin laguerre chebyshev legendre spectral-methods spherical-coordinates cylindrical-coordinates hermite curvilinear-coordinates polar-coordinates