armadillo-code - Armadillo: fast C++ library for linear algebra & scientific computing - http://arma

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Armadillo: fast C++ library for linear algebra & scientific computing - http://arma.sourceforge.net

linear-algebra matrix matrix-functions linear-algebra-library statistics matlab blas lapack hpc scientific-computing mkl machine-learning armadillo openmp gaussian-mixture-models cpp11 vector sparse-matrix expression-template matrix-factorization

gosl - Go scientific library for machine learning, linear algebra, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, graph, plotting, visualisation, tensors, eigenvalues, differential equations, more

•    Go

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Gosl is a Go library to develop Artificial Intelligence and High-Performance Scientific Computations. The library tries to be as general and easy as possible. Gosl considers the use of both Go concurrency routines and parallel computing using the message passing interface (MPI). Gosl has several modules (sub-packages) for a variety of tasks in scientific computing, image analysis, and data post-processing.

scientific-computing visualization linear-algebra differential-equations sparse-systems plotting mkl parallel-computations computational-geometry graph-theory tensor-algebra fast-fourier-transform eigenvalues eigenvectors hacktoberfest machine-learning artificial-intelligence optimization optimization-algorithms linear-programming

Meta Numerics

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The Meta.Numerics math and statistics library supports scientific computing on the .NET platform. It offers an object-oriented API for matrix algebra, advanced functions of real and complex numbers, signal processing, and data analysis.

math numerics ironpython maths matrix numerical-algorithms science

Scirust - Scientific Computing Library in Rust

•    Rust

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SciRust is a Scientific computing library written in Rust programming language. The objective is to design a generic library which can be used as a backbone for scientific computing. Its current areas of focus includes Matrices, Linear algebra, Statistics, and Signal processing.

scientific computing algebra matrix

owl - Owl is an OCaml library for scientific and engineering computing.

•    OCaml

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Owl is an emerging numerical library for scientific computing and engineering. The library is developed in the OCaml language and inherits all its powerful features such as static type checking, powerful module system, and superior runtime efficiency. Owl allows you to write succinct type-safe numerical applications in functional language without sacrificing performance, significantly reduces the cost from prototype to production use. Owl's documentation contains a lot of learning materials to help you start. The full documentation consists of two parts: Tutorial Book and API Reference. Both are perfectly synchronised with the code in the repository by the automatic building system. You can access both parts with the following link.

matrix linear-algebra ndarray statistical-functions topic-modeling regression maths gsl plotting sparse-linear-systems scientific-computing numerical-calculations statistics mcmc optimization autograd algorithmic-differentation automatic-differentiation machine-learning neural-network

pycm - Multi-class confusion matrix library in Python

•    Python

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PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers. threshold is added in version 0.9 for real value prediction.

machine-learning confusion-matrix matrix statistics statistical-analysis accuracy ml ai mathematics data-mining data-analysis classification classifier data-science data neural-network multiclass-classification deep-learning artificial-intelligence deeplearning

data-science-with-ruby - Practical Data Science with Ruby based tools.

•    Ruby

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Data Science is a new "sexy" buzzword without specific meaning but often used to substitute Statistics, Scientific Computing, Text and Data Mining and Visualization, Machine Learning, Data Processing and Warehousing as well as Retrieval Algorithms of any kind. This curated list comprises awesome tutorials, libraries, information sources about various Data Science applications using the Ruby programming language.

data-science data-visualization data-analysis data-mining data-analytics visualization awesome awesome-list list rubydatascience

Colt - Scientific and Technical Computing in Java

•    Java

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Colt distribution consists of several free Java libraries bundled under one single uniform umbrella. Namely the Colt library, the Jet library, the CoreJava library, and the Concurrent library. It provides support for resizable arrays, dense, sparse matrices, histogramming functionality, Random Number Generators etc.

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bayadera - High-performance Bayesian Data Analysis on the GPU in Clojure

•    Clojure

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A Clojure Library for Bayesian Data Analysis and Machine Learning on the GPU. Distributed under the Eclipse Public License either version 1.0 or (at your option) any later version.

bayesian-inference bayesian-data-analysis gpu-computing gpu-acceleration statistics machine-learning clojure-library bayesian opencl cuda high-performance-computing gpu mcmc markov-chain-monte-carlo

plot - A repository for plotting and visualizing data

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gonum/plot is the new, official fork of code.google.com/p/plotinum. It provides an API for building and drawing plots in Go. Note that this new API is still in flux and may change. See the wiki for some example plots.For additional Plotters, see the Community Plotters Wiki page.

xarray - N-D labeled arrays and datasets in Python

•    Python

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xarray (formerly xray) is an open source project and Python package that aims to bring the labeled data power of pandas to the physical sciences, by providing N-dimensional variants of the core pandas data structures. Our goal is to provide a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels. Our approach adopts the Common Data Model for self- describing scientific data in widespread use in the Earth sciences: xarray.Dataset is an in-memory representation of a netCDF file.

scientific-computing netcdf numpy data-science pandas dataframes data-analysis pydata

reflow - A language and runtime for distributed, incremental data processing in the cloud

•    Go

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Reflow is a system for incremental data processing in the cloud. Reflow enables scientists and engineers to compose existing tools (packaged in Docker images) using ordinary programming constructs. Reflow then evaluates these programs in a cloud environment, transparently parallelizing work and memoizing results. Reflow was created at GRAIL to manage our NGS (next generation sequencing) bioinformatics workloads on AWS, but has also been used for many other applications, including model training and ad-hoc data analyses. Reflow thus allows scientists and engineers to write straightforward programs and then have them transparently executed in a cloud environment. Programs are automatically parallelized and distributed across multiple machines, and redundant computations (even across runs and users) are eliminated by its memoization cache. Reflow evaluates its programs incrementally: whenever the input data or program changes, only those outputs that depend on the changed data or code are recomputed.

bioinformatics-pipeline scientific-computing language runtime data-science cloud-computing aws analysis-pipeline

TileDB - TileDB array data management

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Array data management made fast and easy. TileDB allows you to manage the massive dense and sparse multi-dimensional array data that frequently arise in many important scientific applications.

tiledb arrays storage-engine scientific-computing data-analysis hdfs s3 s3-storage

learn-julia-the-hard-way - Learn Julia the hard way!

•    Makefile

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The Julia base package is pretty big, although at the same time, there are lots of other packages around to expand it with. The result is that on the whole, it is impossible to give a thorough overview of all that Julia can do in just a few brief exercises. Therefore, I had to adopt a little 'bias', or 'slant' if you please, in deciding what to focus on and what to ignore. Julia is a technical computing language, although it does have the capabilities of any general purpose language and you'd be hard-pressed to find tasks it's completely unsuitable for (although that does not mean it's the best or easiest choice for any of them). Julia was developed with the occasional reference to R, and with an avowed intent to improve upon R's clunkiness. R is a great language, but relatively slow, to the point that most people use it to rapid prototype, then implement the algorithm for production in Python or Java. Julia seeks to be as approachable as R but without the speed penalty.

julia learning-julia language learning learning-by-doing julia-language julialang data-science statistics technical-computing hpc scientific-computing

NumPy - fundamental package for scientific computing with Python

•    Python

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NumPy is the fundamental package needed for scientific computing with Python. Numerical Python adds a fast and sophisticated N-dimensional array facility to the Python language. NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

scientific scientific-computing mathematics n-array

Zipline - A Pythonic Algorithmic Trading Library

•    Python

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Zipline is a Pythonic algorithmic trading library. It is an event-driven system that supports both backtesting and live-trading. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies.Note: Installing Zipline via pip is slightly more involved than the average Python package. Simply running pip install zipline will likely fail if you've never installed any scientific Python packages before.

algorithmic-trading trading machine-learning stock-analysis

Stdlib - Standard library for JavaScript and Node.js.

•    Javascript

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Stdlib is a standard library for JavaScript and Node.js, with an emphasis on numeric computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

stdlib scientific-computing numerical-computing statistics maths

science.js - Scientific and statistical computing in JavaScript.

•    Javascript

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Scientific and statistical computing in JavaScript.

science statistics mathematics

Arraymancer - A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU, OpenCL and embedded devices

•    Nim

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Arraymancer is a tensor (N-dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing and in particular a deep learning ecosystem. The library is inspired by Numpy and PyTorch. The library provides ergonomics very similar to Numpy, Julia and Matlab but is fully parallel and significantly faster than those libraries. It is also faster than C-based Torch.

tensor nim multidimensional-arrays cuda deep-learning machine-learning cudnn high-performance-computing gpu-computing matrix-library neural-networks parallel-computing openmp linear-algebra ndarray opencl gpgpu iot automatic-differentiation autograd

GoldenOrb - Scalable Graph Analysis

•    Java

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GoldenOrb is a cloud-based project for massive-scale graph analysis, built upon Apache Hadoop and modeled after Google's Pregel architecture. It provides solutions to complex data problems, remove limits to innovation and contribute to the emerging ecosystem that spans all aspects of big data analysis. It enables users to run analytics on entire data sets instead of samples.

graph graph-analysis data-analysis scalable