linfa-hierarchical
| Crates.io | linfa-hierarchical |
| lib.rs | linfa-hierarchical |
| version | |
| source | src |
| created_at | 2020-11-29 16:43:15.273085+00 |
| updated_at | 2025-01-14 15:51:30.819647+00 |
| description | Agglomerative Hierarchical clustering |
| homepage | |
| repository | https://github.com/rust-ml/linfa |
| max_upload_size | |
| id | 317812 |
| Cargo.toml error: | TOML parse error at line 18, column 1 | 18 | autolib = false | ^^^^^^^ unknown field `autolib`, expected one of `name`, `version`, `edition`, `authors`, `description`, `readme`, `license`, `repository`, `homepage`, `documentation`, `build`, `resolver`, `links`, `default-run`, `default_dash_run`, `rust-version`, `rust_dash_version`, `rust_version`, `license-file`, `license_dash_file`, `license_file`, `licenseFile`, `license_capital_file`, `forced-target`, `forced_dash_target`, `autobins`, `autotests`, `autoexamples`, `autobenches`, `publish`, `metadata`, `keywords`, `categories`, `exclude`, `include` |
| size | 0 |
wg (github:rust-ml:wg)
documentation
README
Clustering
linfa-hierarchical provides an implementation of agglomerative hierarchical clustering.
In this clustering algorithm, each point is first considered as a separate cluster. During each
step, two points are merged into new clusters, until a stopping criterion is reached. The distance
between the points is computed as the negative-log transform of the similarity kernel.
Documentation: latest.
The big picture
linfa-hierarchical is a crate in the linfa ecosystem, a wider effort to bootstrap a toolkit for classical Machine Learning implemented in pure Rust, akin in spirit to Python's scikit-learn.
Current state
linfa-hierarchical implements agglomerative hierarchical clustering with support of the kodama crate.
License
Dual-licensed to be compatible with the Rust project.
Licensed under the Apache License, Version 2.0 http://www.apache.org/licenses/LICENSE-2.0 or the MIT license http://opensource.org/licenses/MIT, at your option. This file may not be copied, modified, or distributed except according to those terms.