|0.3.0||Jan 20, 2021|
|0.2.1||Nov 29, 2020|
|0.1.2||Nov 25, 2019|
|0.1.1||Nov 23, 2019|
|0.1.0||Apr 5, 2018|
#5 in Machine learning
99 downloads per month
Used in less than 12 crates
linfa (Italian) / sap (English):
The vital circulating fluid of a plant.
linfa aims to provide a comprehensive toolkit to build Machine Learning applications with Rust.
Kin in spirit to Python's
scikit-learn, it focuses on common preprocessing tasks and classical ML algorithms for your everyday ML tasks.
linfa stand right now? Are we learning yet?
linfa currently provides sub-packages with the following algorithms:
|clustering||Data clustering||Tested / Benchmarked||Unsupervised learning||Clustering of unlabeled data; contains K-Means, Gaussian-Mixture-Model and DBSCAN|
|kernel||Kernel methods for data transformation||Tested||Pre-processing||Maps feature vector into higher-dimensional space|
|linear||Linear regression||Tested||Partial fit||Contains Ordinary Least Squares (OLS), Generalized Linear Models (GLM)|
|elasticnet||Elastic Net||Tested||Supervised learning||Linear regression with elastic net constraints|
|logistic||Logistic regression||Tested||Partial fit||Builds two-class logistic regression models|
|reduction||Dimensionality reduction||Tested||Pre-processing||Diffusion mapping and Principal Component Analysis (PCA)|
|trees||Decision trees||Experimental||Supervised learning||Linear decision trees|
|svm||Support Vector Machines||Tested||Supervised learning||Classification or regression analysis of labeled datasets|
|hierarchical||Agglomerative hierarchical clustering||Tested||Unsupervised learning||Cluster and build hierarchy of clusters|
|bayes||Naive Bayes||Tested||Supervised learning||Contains Gaussian Naive Bayes|
|ica||Independent component analysis||Tested||Supervised learning||Contains FastICA implementation|
We believe that only a significant community effort can nurture, build, and sustain a machine learning ecosystem in Rust - there is no other way forward.
If this strikes a chord with you, please take a look at the roadmap and get involved!
At the moment you can choose between the following BLAS/LAPACK backends:
For example if you want to use the system IntelMKL library for the PCA example, then pass the corresponding feature:
cd linfa-reduction && cargo run --release --example pca --features linfa/intel-mkl-system
This selects the
intel-mkl system library as BLAS/LAPACK backend. On the other hand if you want to compile the library and link it with the generated artifacts, pass
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.