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.