macOS
brew install flannlocal Homebrew formula metadata
sudo port install flannMacPorts ports tree · science/flann/Portfile · ソース: api.github.com
brew
flann のインストール経路、実行ファイル、メタデータ、AI エージェント向けセキュリティノートを確認します。
インストール
brew install flannlocal Homebrew formula metadata
sudo port install flannMacPorts ports tree · science/flann/Portfile · ソース: api.github.com
sudo apk add flannAlpine Linux edge package indexes · flann · ソース: dl-cdn.alpinelinux.org
sudo dnf install flannFedora Rawhide package metadata · flann · ソース: dl.fedoraproject.org
nix profile install nixpkgs#flannnixpkgs package indexes · pkgs/by-name/fl/flann/package.nix · ソース: api.github.com
sudo pacman -S flannArch Linux sync databases · flann · ソース: geo.mirror.pkgbuild.com
sudo apt install flann-docDebian stable package indexes · flann-doc · ソース: deb.debian.org
sudo zypper install flann-developenSUSE Tumbleweed package metadata · flann-devel · ソース: download.opensuse.org
概要
Fast Library for Approximate Nearest Neighbors
履歴
FLANN, the Fast Library for Approximate Nearest Neighbors, is a C++ library for fast approximate nearest-neighbor search in high-dimensional spaces. Its README describes both a collection of nearest-neighbor algorithms and automatic selection of an algorithm and parameters for a given dataset.
The public repository history begins in 2007, and the project was documented academically in the 2009 Muja and Lowe VISAPP paper on automatic algorithm configuration for approximate nearest neighbors. The project later exposed C, MATLAB, Python, and Ruby bindings around the C++ implementation, making the research code useful as a packaged native library rather than only as an experiment.
FLANN became a common dependency in scientific, machine-learning, and computer-vision packaging because it provides a reusable native nearest-neighbor engine with bindings. Homebrew packages it as a formula, while the official README points users to release archives and source checkout for development.
Users bring FLANN into applications that need approximate nearest-neighbor lookup over feature vectors or other high-dimensional data. The bundled package executables are examples; the main package value is the library and its language bindings.
FLANN is package-nerd interesting because it is classic research software that became infrastructure: a C++ core, language bindings, release archives, example binaries, and packaging concerns around native builds rather than a single end-user CLI.
セキュリティ状態
library-like package without higher-risk signals.
リスク グリーン · 信頼度 低 · appliance
エージェントに無人実行させる前に、このツールが平文の認証情報を読むか、リモート状態を書き込むか、成果物を公開するか、プラグインを起動するかを確認してください。
実行可能ファイル
| コマンド | 種類 | 公開範囲 | メモ |
|---|---|---|---|
flann_example_c | cli | グローバル実行可能ファイル | |
flann_example_cpp | cli | グローバル実行可能ファイル |
鮮度
これらの信号は、ページ生成時期、パッケージマネージャの活動、上流リリース比較を分けて示します。バージョン遅れは、証拠 URL と比較可能なバージョンがある場合だけ警告されます。
https://github.com/flann-lib/flann
インストールメタデータ
| パッケージキー | brew:flann |
|---|---|
| バージョン | 1.9.2 |
| パッケージマネージャ | Homebrew |
| パッケージマネージャページ | https://formulae.brew.sh/formula/flann |
| ホームページ | https://github.com/flann-lib/flann |
| リポジトリ | https://github.com/flann-lib/flann |
| 上流ドキュメント | https://github.com/flann-lib/flann#readme |
| ライセンス | BSD-3-Clause |
| ソースアーカイブ | https://github.com/flann-lib/flann/archive/refs/tags/1.9.2.tar.gz |
| 依存関係 | hdf5, lz4 |
| ビルド依存関係 | cmake, pkgconf |
| Bottle | 利用可能 (対象 arm64_linux, arm64_sequoia, arm64_sonoma, arm64_tahoe, sonoma, x86_64_linux) |
| Homebrew post-install | 未定義 |
| サービス | 宣言なし |
レジストリ情報
| Source Database | Homebrew formula API |
|---|---|
| Tap | homebrew/core |
| Full Name | flann |
| Version Scheme | 0 |
| Revision | 4 |
| Bottle Stable Root URL | https://ghcr.io/v2/homebrew/core |
| Deprecated | no |
| Disabled | no |
| Keg Only | no |
| URL Keys |
|
ソースデータベース一致
一致は外部パッケージマネージャインデックスから取得され、ローカルの Automic Vault パッケージリンクとは分けて表示されます。
flann-doc 1.9.2+dfsg-2
Fast Library for Approximate Nearest Neighbors - documentation
http://www.cs.ubc.ca/research/flann/
sudo apt install flann-doclibflann-dev 1.9.2+dfsg-2+b2
Fast Library for Approximate Nearest Neighbors - development
http://www.cs.ubc.ca/research/flann/
sudo apt install libflann-devlibflann1.9 1.9.2+dfsg-2+b2
Fast Library for Approximate Nearest Neighbors - runtime
http://www.cs.ubc.ca/research/flann/
sudo apt install libflann1.9flann
nix profile install nixpkgs#flannflann-doc 1.9.2+dfsg-2build1
Fast Library for Approximate Nearest Neighbors - documentation
http://www.cs.ubc.ca/research/flann/
sudo apt install flann-doclibflann-dev 1.9.2+dfsg-2build1
Fast Library for Approximate Nearest Neighbors - development
http://www.cs.ubc.ca/research/flann/
sudo apt install libflann-devlibflann1.9 1.9.2+dfsg-2build1
Fast Library for Approximate Nearest Neighbors - runtime
http://www.cs.ubc.ca/research/flann/
sudo apt install libflann1.9flann 1.9.2-r1
Fast Library for Approximate Nearest Neighbors
https://github.com/flann-lib/flann
sudo apk add flannflann-dev 1.9.2-r1
Fast Library for Approximate Nearest Neighbors (development files)
https://github.com/flann-lib/flann
sudo apk add flann-devflann-doc 1.9.2-r1
Fast Library for Approximate Nearest Neighbors (documentation)
https://github.com/flann-lib/flann
sudo apk add flann-docflann 1.9.2-16.fc45
Fast Library for Approximate Nearest Neighbors
http://www.cs.ubc.ca/research/flann
sudo dnf install flannflann-devel 1.9.2-16.fc45
Development headers and libraries for flann
http://www.cs.ubc.ca/research/flann
sudo dnf install flann-develflann-static 1.9.2-16.fc45
Static libraries for flann
http://www.cs.ubc.ca/research/flann
sudo dnf install flann-staticpython3-flann 1.9.2-16.fc45
Python bindings for flann
http://www.cs.ubc.ca/research/flann
sudo dnf install python3-flannflann 1.9.2-7
A library for performing fast approximate nearest neighbor searches in high dimensional spaces
https://github.com/flann-lib/flann
sudo pacman -S flannflann-devel 1.9.2-3.10
Development files for flann
https://www.cs.ubc.ca/research/flann/
sudo zypper install flann-develソース経路
このページは scripts/generate-pkg-sqlite.py が生成した非公開のパッケージ SQLite アーティファクトから av-web によって提供されます。
View the package source record on GitHub.