Reactive computation graph with distributed coordination via Redis
Spark Graph - Node-based computation graph system
DAG based computation graph
Spark Graph - Node-based computation graph system
Reactive computation graph for declarative activity wiring. Browser IIFE bundle.
Math.js is an extensive math library for JavaScript and Node.js. It features a flexible expression parser with support for symbolic computation, comes with a large set of built-in functions and constants, and offers an integrated solution to work with dif
Gas Town orchestrator integration for Claude Flow V3 with WASM-accelerated formula parsing and graph analysis
Proof-gated graph transformer with 8 verified modules — physics, biological, manifold, temporal, economic graph intelligence via NAPI-RS
Force directed graph drawing layout
Simple dependency graph.
Get the graph of dependents in a monorepo
Microsoft Graph Client Library
GradiatorJS is a lightweight, from-scratch autodiff engine and a neural network library written in typescript. Featuring a powerful automatic differentiation engine using a computation graph to enable backpropagation on dynamic network architectures. You
Example usage: ```javascript import { createClient, Graph } from 'redis';
Parse sass files and extract a graph of imports
2D force-directed graph rendered on HTML5 canvas
[](https://travis-ci.org/tmont/tarjan-graph) [](https://www.npmjs.com/package/tarjan-graph)
Graph layout for JavaScript
Types for Microsoft Graph objects
UI component for a 3D force-directed graph using ThreeJS and d3-force-3d layout engine
A polyfill for the TC39 Signal proposal.
A low-level utility for matching a string against a directed acyclic graph of regexes.
Library with base interfaces for LangGraph checkpoint savers.
A graph data structure with topological sort.
Computation graph library
Core types for Cloacina computation graph plugins
Structured computation graphs for ruby!
A gem that is used to compute distances between all nodes in graph
RubyVor provides efficient computation of Voronoi diagrams and Delaunay triangulation for a set of Ruby points. It is intended to function as a complemenet to GeoRuby. These structures can be used to compute a nearest-neighbor graph for a set of points. This graph can in turn be used for proximity-based clustering of the input points.
say-it-with-graphs let you actually write into a place where usually the computer tells you something.
RubyVor provides efficient computation of Voronoi diagrams and Delaunay triangulation for a set of Ruby points. It is intended to function as a complemenet to GeoRuby. These structures can be used to compute a nearest-neighbor graph for a set of points. This graph can in turn be used for proximity-based clustering of the input points.
JSON Driven, Stateless Rules Engine for JIT and efficient evaluation of complex rules and computation graphs.
RubyVor provides efficient computation of Voronoi diagrams and Delaunay triangulation for a set of Ruby points. It is intended to function as a complemenet to GeoRuby. These structures can be used to compute a nearest-neighbor graph for a set of points. This graph can in turn be used for proximity-based clustering of the input points.
GRATR is a framework for graph data structures and algorithms. This library is a fork of RGL. This version utilizes Ruby blocks and duck typing to greatly simplfy the code. It also supports export to DOT format for display as graphics. GRATR currently contains a core set of algorithm patterns: * Breadth First Search * Depth First Search * A* Search * Floyd-Warshall * Best First Search * Djikstra's Algorithm * Lexicographic Search The algorithm patterns by themselves do not compute any meaningful quantities over graphs, they are merely building blocks for constructing graph algorithms. The graph algorithms in GRATR currently include: * Topological Sort * Strongly Connected Components * Transitive Closure * Rural Chinese Postman * Biconnected
GRATR is a framework for graph data structures and algorithms. This library is a fork of RGL. This version utilizes Ruby blocks and duck typing to greatly simplfy the code. It also supports export to DOT format for display as graphics. GRATR currently contains a core set of algorithm patterns: * Breadth First Search * Depth First Search * A* Search * Floyd-Warshall * Best First Search * Djikstra's Algorithm * Lexicographic Search The algorithm patterns by themselves do not compute any meaningful quantities over graphs, they are merely building blocks for constructing graph algorithms. The graph algorithms in GRATR currently include: * Topological Sort * Strongly Connected Components * Transitive Closure * Rural Chinese Postman * Biconnected
This gem contains command-line utility for solving 0/1 knapsack problem using branch-and-bound method, dynamic programming, simple heuristic (weight/price) and fully polynomial time approximation scheme. It can measure CPU and wall-clock time spent by solving a problem, compute relative error of the result and generate graphs from those values.
ignis-autograd adds a differentiable tensor (Ignis::Tensor) and a reverse-mode autograd tape on top of the Ignis GPU foundation. Build computation graphs over GPU arrays and get exact gradients (verified against finite differences). The building block for neural-network training in pure Ruby on an NVIDIA GPU.
In computer science, a disjoint-set data structure, also called a union–find data structure or merge–find set, is a data structure that keeps track of a set of elements partitioned into a number of disjoint (non-overlapping) subsets. It provides near-constant-time operations (bounded by the inverse Ackermann function) to add new sets, to merge existing sets, and to determine whether elements are in the same set. In addition to many other uses (see the Applications section), disjoint-sets play a key role in Kruskal's algorithm for finding the minimum spanning tree of a graph. A disjoint-set forest consists of a number of elements each of which stores an id, a parent pointer, and, in efficient algorithms, a value called the "rank". The parent pointers of elements are arranged to form one or more trees, each representing a set. If an element's parent pointer points to no other element, then the element is the root of a tree and is the representative member of its set. A set may consist of only a single element. However, if the element has a parent, the element is part of whatever set is identified by following the chain of parents upwards until a representative element (one without a parent) is reached at the root of the tree. Forests can be represented compactly in memory as arrays in which parents are indicated by their array index. Disjoint-set data structures model the partitioning of a set, for example to keep track of the connected components of an undirected graph. This model can then be used to determine whether two vertices belong to the same component, or whether adding an edge between them would result in a cycle. The Union–Find algorithm is used in high-performance implementations of unification. This data structure is used by the Boost Graph Library to implement its Incremental Connected Components functionality. It is also a key component in implementing Kruskal's algorithm to find the minimum spanning tree of a graph. Note that the implementation as disjoint-set forests doesn't allow the deletion of edges, even without path compression or the rank heuristic. Sharir and Agarwal report connections between the worst-case behavior of disjoint-sets and the length of Davenport–Schinzel sequences, a combinatorial structure from computational geometry.
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