Search Results for "math function graph"
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Showing 180 open source projects for "math function graph"
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DeepCopy
Create deep copies (clones) of your objects
...Alternatively, you can create your own DeepCopy instance to configure it differently. You may want to roll your own deep copy function. You can add filters to customize the copy process. DeepCopy\Matcher applies on a object attribute. DeepCopy\TypeMatcher applies on any element found in graph, including array elements. The PropertyNameMatcher will match a property by its name. -
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PyKEEN
A Python library for learning and evaluating knowledge graph embedding
PyKEEN (Python KnowlEdge EmbeddiNgs) is a Python package designed to train and evaluate knowledge graph embedding models (incorporating multi-modal information). PyKEEN is a Python package for reproducible, facile knowledge graph embeddings. PyKEEN has a function pykeen.env() that magically prints relevant version information about PyTorch, CUDA, and your operating system that can be used for debugging. If you’re in a Jupyter Notebook, it will be pretty-printed as an HTML table. -
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NetworkX
Network analysis in Python
...Find the shortest path between two nodes in an undirected graph. Python’s None object is not allowed to be used as a node. It determines whether optional function arguments have been assigned in many functions. And it can be used as a sentinel object meaning “not a node”. -
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openbench
Provider-agnostic, open-source evaluation infrastructure
openbench is an open-source, provider-agnostic evaluation infrastructure designed to run standardized, reproducible benchmarks on large language models (LLMs), enabling fair comparison across different model providers. It bundles dozens of evaluation suites — covering knowledge, reasoning, math, code, science, reading comprehension, long-context recall, graph reasoning, and more — so users don’t need to assemble disparate datasets themselves. With a simple CLI interface (e.g. bench eval <benchmark> --model <model-id>), you can quickly evaluate any model supported by Groq or other providers (OpenAI, Anthropic, HuggingFace, local models, etc.). openbench also supports private/local evaluations: you can integrate your own custom benchmarks or data (e.g. internal test suites, domain-specific tasks) to evaluate models in a privacy-preserving way. -
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micrograd
A tiny scalar-valued autograd engine and a neural net library
micrograd is a tiny, educational automatic differentiation engine focused on scalar values, built to show how backpropagation works end to end with minimal code. It constructs a dynamic computation graph as you perform math operations and then computes gradients by walking that graph backward, making it an approachable “from scratch” autograd reference. On top of the core autograd “Value” concept, the project includes a small neural network library that lets you define and train simple models with a PyTorch-like feel, including multilayer perceptrons. ... -
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Webgrind
Xdebug Profiling Web Frontend in PHP
Webgrind is an Xdebug profiling web frontend for PHP, offering a lightweight and easy-to-use interface to visualize profiling data. It helps developers identify performance bottlenecks in their applications by displaying function call statistics, memory usage, and execution time. Webgrind is ideal for performance analysis and optimization of PHP code, providing insights into code efficiency. -
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latexify
A library to generate LaTeX expression from Python code
latexify_py converts small, math-heavy pieces of Python code into human-readable LaTeX that mirrors the intent of the computation, not just its surface syntax. It parses Python functions and expressions into an abstract syntax tree (AST), applies symbolic rewrites for common mathematical constructs, and then emits LaTeX that compiles cleanly in standard environments. -
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Periphery
A tool to identify unused code in Swift projects
A tool to identify unused code in Swift projects. The scan command is Periphery's primary function. To begin a guided setup, simply change to your project directory. To get coherent results from Periphery, it's crucial to understand the implications of the build targets you choose to analyze. For example, imagine a project consisting of three targets: App, Lib and Tests. The App target imports Lib, and the Tests targets imports both App and Lib. If you were to provide all three to the... -
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pylatexenc
Simple LaTeX parser providing latex-to-unicode and unicode-to-latex
...The library is designed to be as backward-compatible as reasonably possible and is able to run on old Python versions should it be necessary. (Use the setup.py script directly if you have Python 3.7, poetry doesn't seem to work with old Python versions.) The pylatexenc.latexencode module provides a function unicode_to_latex() which converts a Unicode string into LaTeX text and escape sequences. It should recognize accented characters and most math symbols. A couple of switches allow you to alter how this function behaves. -
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Roots.jl
Root finding functions for Julia
This package contains simple routines for finding roots, or zeros, of scalar functions of a single real variable using floating-point math. The find_zero function provides the primary interface. The basic call is find_zero(f, x0, [M], [p]; kws...) where, typically, f is a function, x0 a starting point or bracketing interval, M is used to adjust the default algorithms used, and p can be used to pass in parameters. Bisection-like algorithms. For functions where a bracketing interval is known (one where f(a) and f(b) have alternate signs), a bracketing method, like Bisection, can be specified. ... -
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NeuralOperators.jl
DeepONets, Neural Operators, Physics-Informed Neural Ops in Julia
...The kernel can be trained on different geometry, which is learned from a graph. Fourier neural operator learns a neural operator with Dirichlet kernel to form a Fourier transformation. It performs Fourier transformation across infinite-dimensional function spaces and learns better than neural operators. Markov neural operator learns a neural operator with Fourier operators. -
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OpenTK
The Open Toolkit library is a fast, low-level C# wrapper for OpenGL
The Open Toolkit is set of fast, portable, low-level C# bindings for OpenGL, OpenGL ES, OpenAL, and OpenCL. It runs on all major platforms and powers hundreds of apps, games, and scientific research programs. OpenTK provides several utility libraries, including a math/linear algebra package, a windowing system, and input handling. OpenTK is not a game engine, nor does it try to be one: It offers a useful set of tools and lets you decide how to use them. It is the ideal starting point if you want to write a game or scientific application from the ground up. The OpenGL specification specifies exactly what the result/output of each function should be and how it should perform. ... -
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plotly
An interactive graphing library for R
...Moreover, both approaches are an implementation of the Grammar of Graphics and both are powered by the JavaScript graphing library plotly.js, so many of the same concepts and tools that you learn for one interface can be reused in the other. Any graph made with the plotly R package is powered by the JavaScript library plotly.js. The plot_ly() function provides a ‘direct’ interface to plotly.js with some additional abstractions to help reduce typing. -
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Apache Hamilton
Helps data scientists define testable self-documenting dataflows
Apache Hamilton is an open-source Python framework designed to simplify the creation and management of dataflows used in analytics, machine learning pipelines, and data engineering workflows. The framework enables developers to define data transformations as simple Python functions, where each function represents a node in a dataflow graph and its parameters define dependencies on other nodes. Hamilton automatically analyzes these functions and constructs a directed acyclic graph representing the pipeline, allowing the system to execute transformations in the correct order. This approach encourages modular, testable, and maintainable data pipelines because each transformation is isolated and easily unit tested. ... -
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Wire
Compile-time dependency injection for Go
...Dependency injection tools like Wire aim to simplify the management of initialization code. You describe your services and their dependencies, either as code or as configuration, then Wire processes the resulting graph to figure out ordering and how to pass each service what it needs. -
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Go Recipes
Collection of handy tools for Go projects
Visualize the distribution of code coverage in your project. This helps to identify code areas with high and low coverage. Useful when you have a large project with lots of files and packages. This 2D image-hash of your project should be more representative than a single number. For each module, the node representing the greatest version (i.e., the version chosen by Go's minimal version selection algorithm) is colored green. Other nodes, which aren't in the final build list, are colored grey... -
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glMatrix
Javascript Matrix and Vector library for High Performance WebGL apps
Javascript has evolved into a language capable of handling realtime 3D graphics, via WebGL, and computationally intensive tasks such as physics simulations. These types of applications demand high performance vector and matrix math, which is something that Javascript doesn't provide by default. glMatrix to the rescue! glMatrix is designed to perform vector and matrix operations stupidly fast! By hand-tuning each function for maximum performance and encouraging efficient usage patterns through API conventions, glMatrix will help you get the most out of your browsers Javascript engine. -
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Jazzy
Soulful docs for Swift & Objective-C
jazzy is a command-line utility that generates documentation for Swift or Objective-C. Instead of parsing your source files, jazzy hooks into Clang and SourceKit to use the AST representation of your code and its comments for more accurate results. The output matches the look and feel of Apple’s official reference documentation, post WWDC 2014. Jazzy can also generate documentation from compiled Swift modules using their symbol graph instead of source code. You need development tools to... -
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REDasm
The Open Source Disassembler
REDasm is a cross platform disassembler and binary analysis tool, built for both hobbyists and professional reverse engineers. It supports various CPU architectures and executable formats and also offers an interactive listing, control flow graph view, cross-references, string detection and automatic function recovery. [SUPPORTED FILE FORMATS] PE, ELF, MZ, NE, LE, PSX-EXE, PSX-BIOS, XBE (Xbox), ZX Spectrum (SNA, Z80, TAP), iNES (mapper 0 only) [SUPPORTED ARCHITECTURES] x86/x86_64, ARM/THUMB, AArch64, Z80, MOS6502 [EXTRA FEATURES] VB5/6 Decompiler, MSVC RTTI and EH (EXPERIMENTAL) For detailed tier list of every feature see https://redasm.dev -
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Super comprehensive deep learning notes
Super Comprehensive Deep Learning Notes
Super comprehensive deep learning notes is a massive and well-structured collection of deep learning notebooks that serve as a comprehensive study resource for anyone wanting to learn or reinforce concepts in computer vision, natural language processing, deep learning architectures, and even large-model agents. The repository contains hundreds of Jupyter notebooks that are richly annotated and organized by topic, progressing from basic Python and PyTorch fundamentals to advanced neural... -
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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 of coding a (log) posterior density in Julia. ... -
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Ray Tracing in One Weekend Book Series
The Ray Tracing in One Weekend series of books
The Ray Tracing in One Weekend series of books are now available to the public for free online. They are now released under the CC0 license. This means that they are as close to public domain as we can get. (While that also frees you from the requirement of providing attribution, it would help the overall project if you could point back to this web site as a service to other users.) These books are formatted for printing directly from your browser, where you can also (on most browsers) save... -
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gojq
Pure Go implementation of jq
...Due to this limitation, gojq does not have the keys_unsorted function and --sort-keys (-S) option. gojq supports arbitrary-precision integer calculation while jq does not; jq loses the precision of large integers when calculation is involved. -
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targets
Function-oriented Make-like declarative workflows for R
The targets package is a pipeline / workflow management tool in R, designed to coordinate multi‐step computational workflows in data science / statistics. It tracks dependencies between “targets” (computational steps), skips steps whose upstream data or code hasn’t changed, supports parallel computation, branching (dynamic generation of sub‐targets), file format abstractions, and encourages reproducible and efficient analyses. It’s something like GNU Make for R, but more integrated. Skipping... -
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GLM-4
GLM-4 series: Open Multilingual Multimodal Chat LMs
GLM-4 is a family of open models from ZhipuAI that spans base, chat, and reasoning variants at both 32B and 9B scales, with long-context support and practical local-deployment options. The GLM-4-32B-0414 models are trained on ~15T high-quality data (including substantial synthetic reasoning data), then post-trained with preference alignment, rejection sampling, and reinforcement learning to improve instruction following, coding, function calling, and agent-style behaviors. The...
