linfa-reduction
| Crates.io | linfa-reduction |
| lib.rs | linfa-reduction |
| version | |
| source | src |
| created_at | 2020-11-29 16:51:18.98146+00 |
| updated_at | 2025-01-14 15:59:14.389942+00 |
| description | A collection of dimensionality reduction techniques |
| homepage | |
| repository | https://github.com/rust-ml/linfa |
| max_upload_size | |
| id | 317820 |
| Cargo.toml error: | TOML parse error at line 21, column 1 | 21 | 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
Dimensional Reduction
linfa-reduction aims to provide pure Rust implementations of dimensional reduction algorithms.
The Big Picture
linfa-reduction is a crate in the linfa ecosystem, an effort to create a toolkit for classical Machine Learning implemented in pure Rust, akin to Python's scikit-learn.
Current state
linfa-reduction currently provides an implementation of the following dimensional reduction methods:
- Diffusion Mapping
- Principal Component Analysis (PCA)
- Gaussian random projections
- Sparse random projections
Examples
There is an usage example in the examples/ directory. To run, use:
$ cargo run --release --example diffusion_map
$ cargo run --release --example pca
$ cargo run --release --example gaussian_projection
$ cargo run --release --example sparse_projection
BLAS/LAPACK backend
See this section to enable an external BLAS/LAPACK backend.
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.