Search Results for "wsl2-linux-kernel" - Page 5
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Showing 2663 open source projects for "wsl2-linux-kernel"
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Primes.jl
Prime numbers in Julia
Julia functions for computing prime numbers. -
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ConformalPrediction.jl
Predictive Uncertainty Quantification through Conformal Prediction
ConformalPrediction.jl is a package for Predictive Uncertainty Quantification (UQ) through Conformal Prediction (CP) in Julia. It is designed to work with supervised models trained in MLJ (Blaom et al. 2020). Conformal Prediction is easy-to-understand, easy-to-use and model-agnostic and it works under minimal distributional assumptions. Intuitively, CP works under the premise of turning heuristic notions of uncertainty into rigorous uncertainty estimates through repeated sampling or the use... -
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Vulkan.jl
Using Vulkan from Julia
Vulkan.jl is a lightweight wrapper around the Vulkan graphics and compute library. It exposes abstractions over the underlying C interface, primarily geared toward developers looking for a more natural way to work with Vulkan with minimal overhead. It builds upon the core API provided by VulkanCore.jl. Because Vulkan is originally a C specification, interfacing with it requires some knowledge before correctly being used from Julia. This package acts as an abstraction layer, so that you don't... -
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POMDPs
Interface for defining, solving, simulating Markov decision processes
A Julia interface for defining, solving and simulating partially observable Markov decision processes and their fully observable counterparts. The POMDPs.jl package contains only the interface used for expressing and solving Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). The POMDPTools package acts as a "standard library" for the POMDPs.jl interface, providing implementations of commonly-used components such as policies, belief updaters,... -
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esquisse
RStudio add-in to make plots interactively with ggplot2
The purpose of this add-in is to let you explore your data quickly to extract the information they hold. You can create visualization with {ggplot2}, filter data with {dplyr} and retrieve generated code. This addin allows you to interactively explore your data by visualizing it with the ggplot2 package. It allows you to draw bar plots, curves, scatter plots, histograms, boxplot and sf objects, then export the graph or retrieve the code to reproduce the graph. This addin allows you to... -
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CondaPkg.jl
Add Conda dependencies to your Julia project
Add Conda dependencies to your Julia project. This package is a lot like Pkg from the Julia standard library, except that it is for managing Conda packages. Conda dependencies are defined in CondaPkg.toml, which is analogous to Project.toml. CondaPkg will install these dependencies into a Conda environment specific to the current Julia project. Hence dependencies are isolated from other projects or environments. Functions like add, rm, status exist to edit the dependencies programmatically.... -
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NBInclude.jl
import code from IJulia Jupyter notebooks into Julia programs
NBInclude is a package for the Julia language that allows you to include and execute IJulia (Julia-language Jupyter) notebook files just as you would include an ordinary Julia file. The goal of this package is to make notebook files just as easy to incorporate into Julia programs as ordinary Julia (.jl) files, giving you the advantages of a notebook (integrated code, formatted text, equations, graphics, and other results) while retaining the modularity and re-usability of .jl files. -
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DynamicHMC
Implementation of robust dynamic Hamiltonian Monte Carlo methods
Implementation of robust dynamic Hamiltonian Monte Carlo methods in Julia. In contrast to frameworks that utilize a directed acyclic graph to build a posterior for a Bayesian model from small components, this package requires that you code a log-density function of the posterior in Julia. Derivatives can be provided manually, or using automatic differentiation. Consequently, this package requires that the user is comfortable with the basics of the theory of Bayesian inference, to the extent... -
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Clustering.jl
A Julia package for data clustering
Methods for data clustering and evaluation of clustering quality. -
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beautiful-mermaid
Render Mermaid diagrams as beautiful SVGs or ASCII art
beautiful-mermaid is a styling and rendering toolkit built to produce visually enhanced diagrams from Mermaid syntax, aiming to bridge the gap between simple technical diagrams and rich, presentation-ready visualizations, all while preserving the lightweight text-to-diagram workflow that Mermaid offers. Instead of plain, utilitarian shapes and lines, Beautiful Mermaid applies themes, typography enhancements, color palettes, and layout optimizations so diagrams look polished and professional... -
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SymbolicNumericIntegration.jl
SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals
SymbolicNumericIntegration.jl is a hybrid symbolic/numerical integration package that works on the Julia Symbolics expressions. -
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HCubature.jl
Pure-Julia multidimensional h-adaptive integration
The HCubature module is a pure-Julia implementation of multidimensional "h-adaptive" integration. then hcubature(f, a, b) computes the integral, adaptively subdividing the integration volume into smaller and smaller pieces until convergence is achieved to the desired tolerance (specified by optional rtol and atol keyword arguments. Because hcubature is written purely in Julia, the integrand f(x) can return any vector-like object (technically, any type supporting +, -, * real, and norm: a... -
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SciML Style Guide for Julia
A style guide for stylish Julia developers
The SciML Style Guide is a style guide for the Julia programming language. It is used by the SciML Open Source Scientific Machine Learning Organization. As such, it is open to discussion with the community. If the standard for code contributions is that every PR needs to support every possible input type that anyone can think of, the barrier would be too high for newcomers. Instead, the principle is to be as correct as possible to begin with, and grow the generic support over time. All... -
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GMT.jl
Generic Mapping Tools Library Wrapper for Julia
The Generic Mapping Tools, GMT, is an open source collection of tools for manipulating geographic and Cartesian data sets (including filtering, trend fitting, gridding, projecting, etc.) and producing PostScript illustrations ranging from simple x–y plots via contour maps to artificially illuminated surfaces and 3D perspective views. This link will take you to an impressive collection of figures made with GMT. The GMT Julia wrapper was designed to work in a way the close as possible to the... -
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Wavelets.jl
A Julia package for fast discrete wavelet transforms and utilities
A Julia package for fast wavelet transforms (1-D, 2-D, 3-D, by filtering or lifting). The package includes discrete wavelet transforms, column-wise discrete wavelet transforms, and wavelet packet transforms. 1st generation wavelets using filter banks (periodic and orthogonal). Filters are included for the following types: Haar, Daubechies, Coiflet, Symmlet, Battle-Lemarie, Beylkin, Vaidyanathan. 2nd generation wavelets by lifting (periodic and general type including orthogonal and... -
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Vizzu
Library for animated data visualizations and data stories
Vizzu is a free, open-source Javascript/C++ library utilizing a generic dataviz engine that generates many types of charts and seamlessly animates between them. It can be used to create static charts but more importantly, it is designed for building animated data stories and interactive explorers as Vizzu enables showing different perspectives of the data that the viewers can easily follow due to the animation. Create a placeholder element that will contain the rendered chart. The Vizzu... -
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DataStructures.jl
Julia implementation of Data structures
Julia implementation of Data structures. -
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DiffEqBayes.jl
Extension functionality which uses Stan.jl, DynamicHMC.jl
This repository is a set of extension functionality for estimating the parameters of differential equations using Bayesian methods. It allows the choice of using CmdStan.jl, Turing.jl, DynamicHMC.jl and ApproxBayes.jl to perform a Bayesian estimation of a differential equation problem specified via the DifferentialEquations.jl interface. -
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InvertibleNetworks.jl
A Julia framework for invertible neural networks
Building blocks for invertible neural networks in the Julia programming language. -
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Setfield.jl
Update deeply nested immutable structs
Update deeply nested immutable structs. We plan to maintain Setfield.jl for a long time. We will however not add new features. -
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NNlib.jl
Neural Network primitives with multiple backends
This package provides a library of functions useful for neural networks, such as softmax, sigmoid, batched multiplication, convolutions and pooling. Many of these are used by Flux.jl, which loads this package, but they may be used independently. -
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ProtoBuf.jl
Julia protobuf implementation
This is a Julia package that provides a compiler and a codec for Protocol Buffers. Protocol Buffers are a language-neutral, platform-neutral extensible mechanism for serializing structured data. -
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SatelliteToolbox.jl
A toolbox for satellite analysis written in julia language
The SatelliteToolbox.jl contains a set of packages with functions to perform analysis and build simulations related to satellites. It is used on a daily basis on projects at the Brazilian National Institute for Space Research (INPE). -
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Measurements.jl
Error propagation calculator and library for physical measurements
Error propagation calculator and library for physical measurements. It supports real and complex numbers with uncertainty, arbitrary precision calculations, operations with arrays, and numerical integration. Physical measures are typically reported with an error, a quantification of the uncertainty of the accuracy of the measurement. Whenever you perform mathematical operations involving these quantities you have also to propagate the uncertainty, so that the resulting number will also have... -
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JavaCall.jl
Call Java from Julia
Call Java programs from Julia. Julia 1.3.0 through Julia 1.6.2 are tested and guaranteed to work on Linux, macOS, and Windows via continuous integration. Julia 1.6.2 and newer should work on Linux and Windows. The JULIA_COPY_STACKS environment variable should be set to 1 on macOS and Linux, but not Windows.
