Find [nd-]array min/max values
TypeScript definitions for array-bounds
Move an array item to a different position within array bounds
Better binary searching
Normalize array (possibly n-dimensional) to zero mean and unit variance
Get bounding box of an svg path data
Better binary searching
Utilities to make your life with bounding boxes better
Primitives for tracking HTML element size and position on screen as it changes.
Fast nd point clustering.
Pure double-double precision functions *with strict error bounds*.
3D cone plot
Vega scenegraph and renderers.
Like an array, but rounder.
A React component for observing element's bounds
compute the bounding box of geojson features
Provides Beautiful Animated Marker Clustering functionality for Leaflet
3D stream tube plot
Array module.
Get signed distance field for a svg path
A tiny and standalone javascript library for classification and basic statistics
snap round 2d points
Utilities to make your life with bounding boxes better
convert between viewports and extents
Strict wrapper for Hashes and Arrays that doesn't return nil
A simple Ruby gem that handles the issue with Ruby’s Array class when inserting an element at an index greater than the current array size. It handles this by intelligently appending the result to the end of the array instead of creating a sparse array. It also includes some other useful Array methods for common uses.
Ignis is the foundation of a CUDA-backed deep-learning ecosystem for Ruby that actually targets native Windows. It provides a GPU n-dimensional array (Ignis::NDArray), CUDA memory/device management, a runtime kernel compiler (NVRTC) with a batteries-included kernel library, fp16/bf16 conversion, and cuBLAS GEMM. Kernels are compiled at runtime and libraries are bound via FFI — there are NO C extensions, so installation needs no compiler or devkit (the usual Windows native-gem killer). Requires an NVIDIA GPU + CUDA toolkit/runtime.
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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