petal-decomposition
petal-decomposition provides matrix decomposition algorithms including PCA (principal component analysis) and ICA (independent component analysis).
Requirements
- Rust ≥ 1.38
- BLAS/LAPACK backend (OpenBLAS, Netlib, or Intel MKL)
Features
- PCA with exact, full SVD (singular value decomposition)
- PCA with randomized, truncated SVD
- FastICA
Crate Features
intel-mkl
,netlib
, andopenblas
are used to select a BLAS/LAPACK backend. By default,intel-mkl
is used.serialization
enables serialization/deserialization using serde.
Examples
The following example shows how to apply PCA to an array of three samples, and obtain singular values as well as how much variance each component explains.
use arr2;
use Pca;
let x = arr2;
let mut pca = new; // Keep two dimensions.
pca.fit.unwrap;
let s = pca.singular_values; // [2_f64, 0_f64]
let v = pca.explained_variance_ratio; // [1_f64, 0_f64]
let y = pca.transform.unwrap; // [-2_f64.sqrt(), 0_f64, 2_f64.sqrt()]
License
Copyright 2020 Petabi, Inc.
Licensed under Apache License, Version 2.0 (the "License"); you may not use this crate except in compliance with the License.
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See LICENSE for the specific language governing permissions and limitations under the License.
Contribution
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be licensed as above, without any additional terms or conditions.