Search Results for "numerical" - Page 2
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Showing 528 open source projects for "numerical"
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CatBoost
High-performance library for gradient boosting on decision trees
...CatBoost offers superior performance over other GBDT libraries on many datasets, and has several superb features. It has best in class prediction speed, supports both numerical and categorical features, has a fast and scalable GPU version, and readily comes with visualization tools. CatBoost was developed by Yandex and is used in various areas including search, self-driving cars, personal assistance, weather prediction and more. -
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CountUp.js
Animates a numerical value by counting to it
CountUp.js is a dependency-free, lightweight JavaScript class that can be used to quickly create animations that display numerical data in a more interesting way. Install via npm/yarn using the package name countup.js. Despite its name, CountUp can count in either direction, depending on the start and end values that you pass. CountUp.js supports all browsers. MIT license. CountUp v2 is distributed as an ES6 module because it is the most standardized and most widely compatible module for browsers, though a UMD module is also included. ... -
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Surrogates.jl
Surrogate modeling and optimization for scientific machine learning
...In more mathematical terms: suppose we are attempting to optimize a function f(p), but each calculation of f is very expensive. It may be the case we need to solve a PDE for each point or use advanced numerical linear algebra machinery, which is usually costly. The idea is then to develop a surrogate model g which approximates f by training on previous data collected from evaluations of f. -
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go-algorithms
Algorithms and data structures for golang
...The project is designed primarily as a learning resource to help developers understand how fundamental computer science concepts can be implemented idiomatically in Go. It includes a wide range of classic sorting, searching, and numerical algorithms alongside basic collection structures such as stacks, queues, linked lists, and binary trees. The repository is organized into clear directories by topic, making it easy for learners to explore specific categories of algorithms. Because the code focuses on clarity over heavy abstraction, it is especially useful for students and developers studying algorithmic fundamentals. ... -
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kube-ps1
Kubernetes prompt info for bash and zsh
...Blue was used for the default symbol to match the Kubernetes color as closely as possible. Red was chosen as the context name to stand out, and cyan for the namespace. 256 colors are available by specifying the numerical value as the variable argument. Due to the vast ways of customizing the shell, please try the prompt with a minimal configuration before submitting a bug report. -
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Elastiknn
Elasticsearch plugin for nearest neighbor search
...Store vectors and run similarity searches using exact and approximate algorithms. Methods like word2vec and convolutional neural nets can convert many data modalities (text, images, users, items, etc.) into numerical vectors, such that pairwise distance computations on the vectors correspond to semantic similarity of the original data. Elasticsearch is a ubiquitous search solution, but its support for vectors is limited. This plugin fills the gap by bringing efficient exact and approximate vector search to Elasticsearch. This enables users to combine traditional queries (e.g., “some product”) with vector search queries (e.g., an image (vector) of a product) for an enhanced search experience. -
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SciMLBase.jl
The Base interface of the SciML ecosystem
...It is a low-dependency library made to be depended on by the downstream libraries to supply the common interface and allow for the interexchange of mathematical problems. The SciML common interface ties together the numerical solvers of the Julia package ecosystem into a single unified interface. It is designed for maximal efficiency and parallelism, while incorporating essential features for large-scale scientific machine learning such as differentiability, composability, and sparsity. -
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Parameters.jl
Types w/ default field values, keyword constructors, (un-)pack macros
This is a package I use to handle numerical-model parameters, thus the name. However, it should be useful otherwise too. -
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ReachabilityAnalysis.jl
Compute reachable states of dynamical systems
Reachability analysis is concerned with computing rigorous approximations of the set of states reachable by a dynamical system. In the scope of this package are systems modeled by continuous or hybrid dynamical systems, where the dynamics change with discrete events. Systems are modeled by ordinary differential equations (ODEs) or semi-discrete partial differential equations (PDEs), with uncertain initial states, uncertain parameters or non-deterministic inputs. -
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CUDA Python
Performance meets Productivity
...It integrates tightly with the broader Python GPU ecosystem, including Numba for kernel compilation and CCCL for parallel primitives, allowing developers to write performant code without leaving Python. The toolkit also includes utilities for profiling, memory management, distributed computing, and numerical operations, making it suitable for scientific computing, AI, and data processing workloads. -
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StaticArrays.jl
Statically sized arrays for Julia
StaticArrays.jl is a Julia package that provides statically sized arrays with fast, stack-allocated memory storage and optimized performance for small array computations. It is particularly useful in numerical computing where small fixed-size matrices or vectors are used frequently, such as in robotics, physics simulations, or linear algebra. StaticArrays eliminate dynamic memory allocation overhead and enable compile-time optimizations for performance close to hand-written loops. -
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Diffrax
Numerical differential equation solvers in JAX
Diffrax is a numerical differential equation solving library built for the JAX ecosystem, with a strong focus on composability, differentiability, and high-performance scientific computing. The project provides tools for solving ordinary differential equations, stochastic differential equations, controlled differential equations, and related systems in a way that fits naturally into modern machine learning and differentiable programming workflows. -
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CasADi
CasADi is a symbolic framework for numeric optimization
CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT, etc. It can be used in C++, Python, or Matlab/Octave. CasADi's backbone is a symbolic framework implementing forward and reverse modes of AD on expression graphs to construct gradients, large-and-sparse Jacobians, and... -
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FiniteDifferences.jl
High accuracy derivatives, estimated via numerical finite differences
FiniteDifferences.jl estimates derivatives with finite differences. See also the Python package FDM. FiniteDiff.jl and FiniteDifferences.jl are similar libraries: both calculate approximate derivatives numerically. You should definitely use one or the other, rather than the legacy Calculus.jl finite differencing, or reimplementing it yourself. At some point in the future, they might merge, or one might depend on the other. -
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HomotopyContinuation.jl
A Julia package for solving systems of polynomials
HomotopyContinuation.jl is a Julia package for solving systems of polynomial equations by numerical homotopy continuation. Many models in the sciences and engineering are expressed as sets of real solutions to systems of polynomial equations. We can optimize any objective whose gradient is an algebraic function using homotopy methods by computing all critical points of the objective function. An important special case is when the objective function is the euclidean distance to a given point. ... -
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mathjs
An extensive math library for JavaScript and Node.js
mathjs is a comprehensive mathematics library for JavaScript and Node.js that provides advanced numerical, symbolic, and matrix computation capabilities through a flexible expression-based interface. The library supports a wide range of mathematical operations including algebra, statistics, complex numbers, fractions, units, matrices, and symbolic manipulation while maintaining compatibility across browsers and server environments. -
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Taichi
Productive, portable, and performant GPU programming in Python
Taichi is an open-source, embedded DSL within Python designed for high-performance numerical and physical simulations. It uses JIT compilation (via LLVM and its runtime TiRT) to offload compute-heavy code to CPUs, GPUs, mobile devices, and embedded systems. With built-in support for sparse data structures (SNode), automatic differentiation, AOT deployment, and compatibility with CUDA, Vulkan, Metal, and OpenGL ES, it empowers disciplines like simulation, graphics, AI, and robotics -
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ArrayFire
ArrayFire, a general purpose GPU library
...Together we can fulfill The ArrayFire Mission under an excellent Code of Conduct that promotes a respectful and friendly building experience. Rigorous benchmarks and tests ensuring top performance and numerical accuracy. Cross-platform compatibility with support for CUDA, OpenCL, and native CPU on Windows, Mac, and Linux. Built-in visualization functions through Forge. -
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DataDreamer
DataDreamer: Prompt. Generate Synthetic Data. Train & Align Models
DataDreamer is a tool designed to assist in the generation and manipulation of synthetic data for various applications, including testing and machine learning. -
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ModelingToolkit.jl
Modeling framework for automatically parallelized scientific ML
ModelingToolkit.jl is a modeling language for high-performance symbolic-numeric computation in scientific computing and scientific machine learning. It then mixes ideas from symbolic computational algebra systems with causal and acausal equation-based modeling frameworks to give an extendable and parallel modeling system. It allows for users to give a high-level description of a model for symbolic preprocessing to analyze and enhance the model. Automatic symbolic transformations, such as... -
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Copulas
A library to model multivariate data using copulas
Copulas is a Python library for modeling multivariate distributions and sampling from them using copula functions. Given a table of numerical data, use Copulas to learn the distribution and generate new synthetic data following the same statistical properties. Choose from a variety of univariate distributions and copulas – including Archimedian Copulas, Gaussian Copulas and Vine Copulas. Compare real and synthetic data visually after building your model. Visualizations are available as 1D histograms, 2D scatterplots and 3D scatterplots. ... -
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FLUX.2-klein-4B
Flux 2 image generation model pure C inference
FLUX.2-klein-4B is a compact, high-performance C library implementation of the Flux optimization algorithm — an iterative approach for solving large-scale optimization problems common in scientific computing, machine learning, and numerical simulation. Written with a strong emphasis on simplicity, correctness, and performance, it abstracts the core logic of flux-based optimization into a minimal C API that can be embedded in broader applications without pulling in heavy dependencies. Because the implementation is in plain C and focuses on data locality and vectorized operations, flux2.c can be integrated into performance-critical code paths where control over memory layout and execution behavior matters, such as GPU kernels, embedded systems, or custom ML runtime engines. -
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HCubature.jl
Pure-Julia multidimensional h-adaptive integration
...You can integrate real, complex, and matrix-valued integrands, for example. Note that HCubature assumes that your function f(x) can be computed at arbitrary points in the integration domain. (This is the ideal way to do numerical integration.) If you instead have f(x) precomputed at a fixed set of points, such as a Cartesian grid, you will need to use some other method (e.g. Trapz.jl for a multidimensional trapezoidal rule). -
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ydata-profiling
Create HTML profiling reports from pandas DataFrame objects
ydata-profiling primary goal is to provide a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution. Like pandas df.describe() function, that is so handy, ydata-profiling delivers an extended analysis of a DataFrame while allowing the data analysis to be exported in different formats such as html and json. -
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TensorZero
TensorZero is an open-source stack for industrial-grade LLM apps
tensorzero is a lightweight C++ library designed for tensor operations and numerical computing. It offers a minimal and readable implementation of core tensor functionality, making it ideal for educational purposes, lightweight applications, or those wanting to understand how tensor libraries work under the hood. With no external dependencies, tensorzero is easy to integrate into C++ projects needing basic multi-dimensional array support.
