In One Sentence

You trained a non-sparse RBF-C-SVM using libSVM, now you want the highest possible performance during (real-time) classification, like games or VR.

Highlights

• can load trained libSVM models (currently RBF-CSVM without sparse attributes)
• optimized for SIMD and can be mixed seamlessly with Rayon.
• allocation-free during classification
• written in 100% Rust, but can be loaded from any language (via FFI)
• 2.5x - 14x faster than libSVM
• Free of unsafe code ;)

Principal Usage

Train with libSVM (e.g., using the tool svm-train), then classify with ffsvm-rust.

From Rust:

// Load model file / SVM.
let model_str: &str = include_str!("model.libsvm");
let model = ModelFile::try_from(model_str)?;
let csvm = RbfCSVM::try_from(&model)?;

// Produce problem we want to classify.
let mut problem = Problem::from(&csvm);

// Set features
problem.features = vec![ 0.3093766, 0.0, 0.0, 0.0, 0.0, 0.1764706, 0.1137485 ];

// Can be trivially parallelized (e.g., with Rayon) ...
csvm.predict_value(&mut problem);

// Results should match libSVM
assert_eq!(42, problem.label);

From C / FFI:

Please see 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

Classification time vs. libSVM.

Performance milestones during development.

See here for details.

FAQ

See here for details.