Search Results for "sparse"
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Showing 8 open source projects for "sparse"
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Armadillo
fast C++ library for linear algebra & scientific computing
* Fast C++ library for linear algebra (matrix maths) and scientific computing * Easy to use functions and syntax, deliberately similar to Matlab / Octave * Uses template meta-programming techniques to increase efficiency * Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries * Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. * Downloads:... -
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MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
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ViennaCL
Linear algebra and solver library using CUDA, OpenCL, and OpenMP
ViennaCL provides high level C++ interfaces for linear algebra routines on CPUs and GPUs using CUDA, OpenCL, and OpenMP. The focus is on generic implementations of iterative solvers often used for large linear systems and simple integration into existing projects. -
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The library for utilization of Minimal Quadtree format for storing very large sparse matrices. Please see the paper referenced below for the description of a problem. Corresponding papers: Tree-based Space Efficient Formats for Storing the Structure of Sparse Matrices (I. Šimeček, D. Langr, P. Tvrdík), In Scalable Computing: Practice and Experience, volume 15, 2014. Space Efficient Formats for Structure of Sparse Matrices Based on Tree Structures (I.
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C++, Matlab and Python library for Hidden-state Conditional Random Fields. Implements 3 algorithms: LDCRF, HCRF and CRF. For Windows and Linux, 32- and 64-bits. Optimized for multi-threading. Works with sparse or dense input features.
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This project provides a set of Python tools for creating various kinds of neural networks, which can also be powered by genetic algorithms using grammatical evolution. MLP, backpropagation, recurrent, sparse, and skip-layer networks are supported.
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The JSparse Matrix Package, developed by Philipp Geigenmüller during an internship at the prudsys AG in Chemnitz, Germany, is an extension of the well-known Java Matrix Package (JAMA) and allows the use of sparse matrices and related algorithms.
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LSGTL means LLX’s Static Graph Template Library which is a light-weighted header-only template library developed mainly for static graph analysis. LSGTL is expected to be used in laboratories for research purposes mostly.
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