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Schmidt","url":"https://thi.ng"},"license":"Apache-2.0","homepage":"https://thi.ng/distance","keywords":["2d","3d","4d","distance","eucledian","haversine","jaccard","k-nearest","manhattan","metric","nd","neighborhood","points","query","region","spatial","typescript","vector"],"repository":{"type":"git","url":"git+https://codeberg.org/thi.ng/umbrella.git","directory":"packages/distance"},"description":"N-dimensional distance metrics & K-nearest neighborhoods for point queries","maintainers":[{"name":"thi.ng","email":"k+npm@thi.ng"}],"readme":"<!-- This file is generated - DO NOT EDIT! -->\n<!-- Please see: https://codeberg.org/thi.ng/umbrella/src/branch/develop/CONTRIBUTING.md#changes-to-readme-files -->\n# ![@thi.ng/distance](https://codeberg.org/thi.ng/umbrella/media/branch/develop/assets/banners/thing-distance.svg?7bd4f62f)\n\n[![npm version](https://img.shields.io/npm/v/@thi.ng/distance.svg)](https://www.npmjs.com/package/@thi.ng/distance)\n![npm downloads](https://img.shields.io/npm/dm/@thi.ng/distance.svg)\n[![Mastodon Follow](https://img.shields.io/mastodon/follow/109331703950160316?domain=https%3A%2F%2Fmastodon.thi.ng&style=social)](https://mastodon.thi.ng/@toxi)\n\n> [!NOTE]\n\n> This is one of 217 standalone projects. LLM-free, human-made and\n> cared for software, maintained as part of the\n> [@thi.ng/umbrella](https://codeberg.org/thi.ng/umbrella/) ecosystem and\n> anti-framework.\n>\n> 🚀 Please help me to work full-time on these projects by [sponsoring\n> me](https://codeberg.org/thi.ng/umbrella/src/branch/develop/CONTRIBUTING.md#donations).\n> Thank you! ❤️\n\n- [About](#about)\n  - [Distance metrics](#distance-metrics)\n  - [Neighborhoods](#neighborhoods)\n    - [Nearest](#nearest)\n    - [KNearest](#knearest)\n    - [Radial](#radial)\n- [Status](#status)\n- [Support packages](#support-packages)\n- [Related packages](#related-packages)\n- [Installation](#installation)\n- [Dependencies](#dependencies)\n- [Usage examples](#usage-examples)\n- [API](#api)\n- [Authors](#authors)\n- [License](#license)\n\n## About\n\nN-dimensional distance metrics & K-nearest neighborhoods for point queries.\n\n### Distance metrics\n\nThe package provides the\n[`IDistance`](https://docs.thi.ng/umbrella/distance/interfaces/IDistance.html)\ninterface for custom distance metric implementations & conversions from/to raw\ndistance values. The following preset metrics are provided too:\n\n| **Preset**         | **Number** | **nD** | **2D** | **3D** | **Comments**                                                         |\n|--------------------|------------|--------|--------|--------|----------------------------------------------------------------------|\n| `EUCLEDIAN`        |            | ✅      |        |        | Eucledian distance                                                   |\n| `EUCLEDIAN1`       | ✅          |        |        |        |                                                                      |\n| `EUCLEDIAN2`       |            |        | ✅      |        |                                                                      |\n| `EUCLEDIAN3`       |            |        |        | ✅      |                                                                      |\n| `HAVERSINE_LATLON` |            |        | ✅      |        | Great-circle distance for lat/lon geo locations                      |\n| `HAVERSINE_LONLAT` |            |        | ✅      |        | Great-circle distance for lon/lat geo locations                      |\n| `DIST_SQ`          |            | ✅      |        |        | Squared dist (avoids `Math.sqrt`)                                    |\n| `DIST_SQ1`         | ✅          |        |        |        |                                                                      |\n| `DIST_SQ2`         |            |        | ✅      |        |                                                                      |\n| `DIST_SQ3`         |            |        |        | ✅      |                                                                      |\n| `defManhattan(n)`  |            | ✅      |        |        | [Manhattan distance](https://en.wikipedia.org/wiki/Taxicab_geometry) |\n| `MANHATTAN2`       |            |        | ✅      |        |                                                                      |\n| `MANHATTAN3`       |            |        |        | ✅      |                                                                      |\n\n### Neighborhoods\n\nNeighborhoods can be used to select n-D spatial items around a given target\nlocation and an optional catchment radius (infinite by default). Neighborhoods\nalso use one of the given distance metrics and implement the widely used\n[`IDeref`](https://codeberg.org/thi.ng/umbrella/src/branch/develop/packages/api/src/deref.ts)\ninterface to obtain the final query results.\n\nCustom neighborhood selections can be defined via the\n[`INeighborhood`](https://docs.thi.ng/umbrella/distance/interfaces/INeighborhood.html)\ninterface. Currently, there are two different implementations available, each\nproviding several factory functions to instantiate and provide defaults for\ndifferent dimensions. See documentation and examples below.\n\n#### Nearest\n\nAn `INeighborhood` implementation for nearest neighbor queries around a given\ntarget location, initial query radius and `IDistance` metric to determine\nproximity.\n\n#### KNearest\n\nAn `INeighborhood` implementation for K-nearest neighbor queries around a given\ntarget location, initial query radius and `IDistance` metric to determine\nproximity. The K-nearest neighbors will be accumulated via an internal\n[heap](https://codeberg.org/thi.ng/umbrella/src/branch/develop/packages/heaps) and\nresults can be optionally returned in order of proximity (via `.deref()` or\n`.values()`). For K=1 it will be more efficient to use `Nearest` to avoid the\nadditional overhead.\n\n#### Radial\n\nAn unbounded and unsorted version of [`KNearest`](#knearest), selecting _all_\nitems around the target location and given search radius. Qualifying neighbors\nwill be accumulated in order of processing via an internal array.\n\n## Status\n\n**STABLE** - used in production\n\n[Search or submit any issues for this package](https://codeberg.org/thi.ng/umbrella/issues?q=%5Bdistance%5D)\n\nWork is underway integrating this approach into the spatial indexing data\nstructures provided by the\n[@thi.ng/geom-accel](https://codeberg.org/thi.ng/umbrella/src/branch/develop/packages/geom-accel)\npackage.\n\n## Support packages\n\n- [@thi.ng/distance-transform](https://codeberg.org/thi.ng/umbrella/src/branch/develop/packages/distance-transform) - Binary image to Distance Field transformation\n\n## Related packages\n\n- [@thi.ng/geom-accel](https://codeberg.org/thi.ng/umbrella/src/branch/develop/packages/geom-accel) - n-D spatial indexing data structures with a shared ES6 Map/Set-like API\n- [@thi.ng/k-means](https://codeberg.org/thi.ng/umbrella/src/branch/develop/packages/k-means) - k-means & k-medians with customizable distance functions and centroid initializations for n-D vectors\n- [@thi.ng/vectors](https://codeberg.org/thi.ng/umbrella/src/branch/develop/packages/vectors) - Optimized 2d/3d/4d and arbitrary length vector operations, support for memory mapping/layouts\n\n## Installation\n\n```bash\nyarn add @thi.ng/distance\n```\n\nESM import:\n\n```ts\nimport * as dist from \"@thi.ng/distance\";\n```\n\nBrowser ESM import:\n\n```html\n<script type=\"module\" src=\"https://esm.run/@thi.ng/distance\"></script>\n```\n\n[JSDelivr documentation](https://www.jsdelivr.com/)\n\nFor Node.js REPL:\n\n```js\nconst dist = await import(\"@thi.ng/distance\");\n```\n\nPackage sizes (brotli'd, pre-treeshake): ESM: 1.44 KB\n\n## Dependencies\n\n- [@thi.ng/api](https://codeberg.org/thi.ng/umbrella/src/branch/develop/packages/api)\n- [@thi.ng/checks](https://codeberg.org/thi.ng/umbrella/src/branch/develop/packages/checks)\n- [@thi.ng/errors](https://codeberg.org/thi.ng/umbrella/src/branch/develop/packages/errors)\n- [@thi.ng/heaps](https://codeberg.org/thi.ng/umbrella/src/branch/develop/packages/heaps)\n- [@thi.ng/math](https://codeberg.org/thi.ng/umbrella/src/branch/develop/packages/math)\n- [@thi.ng/vectors](https://codeberg.org/thi.ng/umbrella/src/branch/develop/packages/vectors)\n\nNote: @thi.ng/api is in _most_ cases a type-only import (not used at runtime)\n\n## Usage examples\n\nOne project in this repo's\n[/examples](https://codeberg.org/thi.ng/umbrella/src/branch/develop/examples)\ndirectory is using this package:\n\n| Screenshot                                                                                                           | Description                               | Live demo                                           | Source                                                                                   |\n|:---------------------------------------------------------------------------------------------------------------------|:------------------------------------------|:----------------------------------------------------|:-----------------------------------------------------------------------------------------|\n| <img src=\"https://codeberg.org/thi.ng/umbrella/media/branch/develop/assets/examples/geom-knn-hash.jpg\" width=\"240\"/> | K-nearest neighbor search in an hash grid | [Demo](https://demo.thi.ng/umbrella/geom-knn-hash/) | [Source](https://codeberg.org/thi.ng/umbrella/src/branch/develop/examples/geom-knn-hash) |\n\n## API\n\n[Generated API docs](https://docs.thi.ng/umbrella/distance/)\n\n```ts\nimport * as d from \"@thi.ng/distance\";\n\nconst items = { a: 5, b: 16, c: 9.5, d: 2, e: 12 };\n\n// collect the 3 nearest numbers for target=10 and using\n// infinite selection radius and squared distance metric (defaults)\nconst k = d.knearestN(10, 3);\n// consider each item for inclusion\nObject.entries(items).forEach(([id, x]) => k.consider(x, id));\n\n// retrieve result tuples of [distance, value]\nk.deref()\n// [ [ 25, 'a' ], [ 4, 'e' ], [ 0.25, 'c' ] ]\n\n// result values only\nk.values()\n// [ 'a', 'e', 'c' ]\n\n// neighborhood around 10, K=3 w/ max radius 5\n// also use Eucledian distance and sort results by proximity\nconst k2 = d.knearestN(10, 3, 5, d.EUCLEDIAN1, true);\nObject.entries(items).forEach(([id, x]) => k2.consider(x, id));\n\nk2.deref()\n// [ [ 0.5, 'c' ], [ 2, 'e' ], [ 5, 'a' ] ]\n```\n\n## Authors\n\n- [Karsten Schmidt](https://thi.ng)\n\nIf this project contributes to an academic publication, please cite it as:\n\n```bibtex\n@misc{thing-distance,\n  title = \"@thi.ng/distance\",\n  author = \"Karsten Schmidt\",\n  note = \"https://thi.ng/distance\",\n  year = 2021\n}\n```\n\n## License\n\n&copy; 2021 - 2026 Karsten Schmidt // Apache License 2.0\n","readmeFilename":"README.md"}