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:ext-relative: {outfilesuffix}
image:https://travis-ci.org/ralfbiedert/ffsvm-rust.svg?branch=master["Build Status", link="https://travis-ci.org/ralfbiedert/ffsvm-rust"] image:https://img.shields.io/badge/license-MIT-blue.svg["License", link="LICENSE"]
= In One Sentence
You trained a binary non-sparse RBF-C-SVM using https://github.com/cjlin1/libsvm[libSVM], now you want the highest possible performance during (real-time) classification, like games or VR.
= Highlights
- can load trained https://github.com/cjlin1/libsvm[libSVM] models (currently binary RBF-CSVM without sparse attributes)
- optimized for SIMD and can be mixed seamlessly with https://github.com/rayon-rs/rayon[Rayon].
- allocation-free during classification
- written in 100% Rust, but can be loaded from any language (via FFI)
- 2.5x - 14x faster than libSVM
= Principal Usage
Train with https://github.com/cjlin1/libsvm[libSVM] (e.g., using the tool svm-train), then classify with ffsvm-rust.
From Rust:
[source,rust]
// Get your libSVM model string from wherever and parse it. let model_str: &str = include_str!("model.libsvm"); let model = ModelFile::try_from(model_str).unwrap();
// Produce actual SVM from raw model, and a problem let csvm = RbfCSVM::try_from(&model).unwrap(); let mut problem = Problem::from(&csvm);
// Set the features of this problem we want to classify. problem.features = vec![ 0.3093766, 0.0, 0.0, 0.0, 0.0, 0.1764706, 0.1137485 ];
// (We also have methods to classify multiple in parallel w. Rayon ...) csvm.predict_value(&mut problem);
// Results should match libSVM assert_eq!(0, problem.label);
From C / FFI:
Please see https://github.com/ralfbiedert/ffsvm-rust[FFSVM-FFI]
= Status
- Aug 5, 2018: Still in alpha, but finally on crates.io.
- May 27, 2018: We're in alpha. Successfully used internally on Windows, Mac, Android and Linux on various machines and devices. Once SIMD stabilizes and we can cross-compile to WASM we'll move to beta.
- December 16, 2017: We're in pre-alpha. It will probably not even work on your machine.
= Performance
image::docs/performance_relative.v3.png[]
Classification time vs. libSVM.
image::docs/performance_history.v3.png[]
Performance milestones during development.
link:docs/performance{ext-relative}[See here for details.]
= FAQ
link:docs/FAQ{ext-relative}[See here for details.]