paulkoerbitz / linfa

A Rust machine learning framework.

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linfa

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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.

Documentation: latest Community chat: Zulip

Current state

Where does linfa stand right now? Are we learning yet?

linfa currently provides sub-packages with the following algorithms:

Name Purpose Status Category Notes
clustering Data clustering Tested / Benchmarked Unsupervised learning Clustering of unlabeled data; contains K-Means 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)
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

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!

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.

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A Rust machine learning framework.

License:Apache License 2.0


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