xtensor-python - Python bindings for xtensor

•    C++

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Python bindings for the xtensor C++ multi-dimensional array library. xtensor is a C++ library for multi-dimensional arrays enabling numpy-style broadcasting and lazy computing.

c-plus-plus python-bindings tensor numpy-arrays

xtensor - C++ tensors with broadcasting and lazy computing

•    C++

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Multi-dimensional arrays with broadcasting and lazy computing. xtensor is a C++ library meant for numerical analysis with multi-dimensional array expressions.

c-plus-plus-14 numpy multidimensional-arrays tensors

xtensor - C++ tensors with broadcasting and lazy computing

•    C++

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Multi-dimensional arrays with broadcasting and lazy computing. xtensor is a C++ library meant for numerical analysis with multi-dimensional array expressions.

numpy c-plus-plus-14 tensors multidimensional-arrays

NumCpp - C++ implementation of the Python Numpy library

•    C++

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This quick start guide is meant as a very brief overview of some of the things that can be done with NumCpp. For a full breakdown of everything available in the NumCpp library please visit the Full Documentation. The main data structure in NumCpp is the NdArray. It is inherently a 2D array class, with 1D arrays being implemented as 1xN arrays. There is also a DataCube class that is provided as a convenience container for storing an array of 2D NdArrays, but it has limited usefulness past a simple container.

c-plus-plus algorithms cpp numpy data-structures scientific-computing mathematical-functions numerical-analysis

tensorly - TensorLy: Tensor Learning in Python.

•    Python

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TensorLy is a Python library that aims at making tensor learning simple and accessible. It allows to easily perform tensor decomposition, tensor learning and tensor algebra. Its backend system allows to seamlessly perform computation with NumPy, PyTorch, JAX, MXNet, TensorFlow or CuPy, and run methods at scale on CPU or GPU. The only pre-requisite is to have Python 3 installed. The easiest way is via the Anaconda distribution.

machine-learning mxnet tensorflow numpy pytorch decomposition tensor-factorization tensor tensor-algebra tensorly tensor-learning tensor-decomposition cupy tensor-regressions tensor-methods jax

glbinding - A C++ binding for the OpenGL API, generated using the gl.xml specification.

•    C++

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glbinding is a cross-platform C++ binding for the OpenGL API. glbinding leverages modern C++11 features like enum classes, lambdas, and variadic templates, instead of relying on macros; all OpenGL symbols are real functions and variables. It provides type-safe parameters, per feature API header, lazy function resolution, multi-context and multi-thread support, global and local function callbacks, meta information about the generated OpenGL binding and the OpenGL runtime, as well as tools and examples for quick-starting your projects. Based on the OpenGL API specification (gl.xml) glbinding is generated using python scripts and templates that can be easily adapted to fit custom needs.

c-plus-plus c-plus-plus-11 library opengl-bindings opengl-library

ultramemcache - Ultra fast memcache client written in highly optimized C++ with Python bindings.

•    C++

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ultramemcache is an ultra fast Memcache client written in highly optimized C++ with Python bindings. By design, ultramemcache limits the size of Memcache items to 1000*1000 bytes, but you can change this limitation by using the max_item_size argument when creating a Client class.

c-plus-plus memcached

PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

•    Python

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PyTorch is a deep learning framework that puts Python first. It is a python package that provides Tensor computation (like numpy) with strong GPU acceleration, Deep Neural Networks built on a tape-based autograd system. You can reuse your favorite python packages such as numpy, scipy and Cython to extend PyTorch when needed.

neural-network autograd gpu numpy deep-learning tensor

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

mlpack - A scalable C++ machine learning library

•    C++

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mlpack is an intuitive, fast, and flexible C++ machine learning library with bindings to other languages. It is meant to be a machine learning analog to LAPACK, and aims to implement a wide array of machine learning methods and functions as a "swiss army knife" for machine learning researchers. In addition to its powerful C++ interface, mlpack also provides command-line programs and Python bindings.

machine-learning-library c-plus-plus deep-learning nearest-neighbor-search regression machine-learning

numba - NumPy aware dynamic Python compiler using LLVM

•    Python

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Numba is an Open Source NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the remarkable LLVM compiler infrastructure to compile Python syntax to machine code. It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls effectively removing the "interpreter" but not removing the dynamic indirection.

mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions

•    Python

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Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and many other libraries. More details about installing Mars can be found at installation section in Mars document.

machine-learning tensorflow numpy scikit-learn pandas pytorch xgboost lightgbm tensor dask ray dataframe statsmodels joblib

annoy - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

•    C++

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Annoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.To install, simply do sudo pip install annoy to pull down the latest version from PyPI.

c-plus-plus nearest-neighbor-search locality-sensitive-hashing approximate-nearest-neighbor-search

essentia - C++ library for audio and music analysis, description and synthesis, including Python bindings

•    Jupyter

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Essentia is an open-source C++ library for audio analysis and audio-based music information retrieval released under the Affero GPL license. It contains an extensive collection of reusable algorithms which implement audio input/output functionality, standard digital signal processing blocks, statistical characterization of data, and a large set of spectral, temporal, tonal and high-level music descriptors. The library is also wrapped in Python and includes a number of predefined executable extractors for the available music descriptors, which facilitates its use for fast prototyping and allows setting up research experiments very rapidly. Furthermore, it includes a Vamp plugin to be used with Sonic Visualiser for visualization purposes. Essentia is designed with a focus on the robustness of the provided music descriptors and is optimized in terms of the computational cost of the algorithms. The provided functionality, specifically the music descriptors included in-the-box and signal processing algorithms, is easily expandable and allows for both research experiments and development of large-scale industrial applications. If you use example extractors (located in src/examples), or your own code employing Essentia algorithms to compute descriptors, you should be aware of possible incompatibilities when using different versions of Essentia.

audio music dsp essentia c-plus-plus music-information-retrieval audio-analysis sound-processing

assimp - Official Open Asset Import Library Repository

•    C++

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A library to import and export various 3d-model-formats including scene-post-processing to generate missing render data. APIs are provided for C and C++. There are various bindings to other languages (C#, Java, Python, Delphi, D). Assimp also runs on Android and iOS.

asset-pipeline assets game-development c-plus-plus assimp patreon stl dae collada cmake ifc fbx android 3mf

MITIE - MITIE: library and tools for information extraction

•    C++

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This project provides free (even for commercial use) state-of-the-art information extraction tools. The current release includes tools for performing named entity extraction and binary relation detection as well as tools for training custom extractors and relation detectors. MITIE is built on top of dlib, a high-performance machine-learning library[1], MITIE makes use of several state-of-the-art techniques including the use of distributional word embeddings[2] and Structural Support Vector Machines[3]. MITIE offers several pre-trained models providing varying levels of support for both English, Spanish, and German trained using a variety of linguistic resources (e.g., CoNLL 2003, ACE, Wikipedia, Freebase, and Gigaword). The core MITIE software is written in C++, but bindings for several other software languages including Python, R, Java, C, and MATLAB allow a user to quickly integrate MITIE into his/her own applications.

machine-learning natural-language-processing information-extraction c-plus-plus

SFML - Simple and Fast Multimedia Library

•    C++

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SFML is a simple, fast, cross-platform and object-oriented multimedia API. It provides access to windowing, graphics, audio and network. It is written in C++, and has bindings for various languages such as C, .Net, Ruby, Python. You can get the latest official release on SFML's website. You can also get the current development version from the Git repository.

sfml c-plus-plus multimedia games opengl sdk graphics audio cross-platform

sms-tools - Sound analysis/synthesis tools for music applications

•    Python

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Sound analysis/synthesis tools for music applications written in python (with a bit of C) plus complementary lecture materials. In order to use these tools you have to install python (recommended 3.6) and the following modules: ipython, numpy, matplotlib, scipy, and cython.

frugally-deep - Header-only library for using Keras models in C++.

•    C++

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Would you like to build/train a model using Keras/Python? And would you like run the prediction (forward pass) on your model in C++ without linking your application against TensorFlow? Then frugally-deep is exactly for you. Layer types typically used in image recognition/generation are supported, making many popular model architectures possible (see Performance section).

tensorflow deep-learning keras cpp cpp14 header-only library c-plus-plus c-plus-plus-14 convolutional-neural-networks prediction machine-learning

pandapy - PandaPy has the speed of NumPy and the usability of Pandas 10x to 50x faster (by @firmai)

•    Python

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Animated investment research at Sov.ai, sponsoring open source initiatives. PandaPy software, similar to the original Pandas project, is developed to improve the usability of python for finance. Structured datatypes are designed to be able to mimic ‘structs’ in the C language, and share a similar memory layout. PandaPy currently houses more than 30 functions. Structured NumPy are meant for interfacing with C code and for low-level manipulation of structured buffers, for example for interpreting binary blobs. For these purposes they support specialized features such as subarrays, nested datatypes, and unions, and allow control over the memory layout of the structure.

finance data-science machine-learning numpy pandas data-structures arrays structured-data algorithmic-trading