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## In One Sentence

You trained a SVM using [libSVM](https://github.com/cjlin1/libsvm), now you want the highest possible performance during (real-time) classification, like games or VR.


## Highlights

* loads almost all [libSVM](https://github.com/cjlin1/libsvm) types (C-SVC, ν-SVC, ε-SVR,  ν-SVR) and kernels (linear, poly, RBF and sigmoid)
* produces practically same classification results as libSVM
* optimized for [SIMD](https://github.com/rust-lang/rfcs/pull/2366) and can be mixed seamlessly with [Rayon](https://github.com/rayon-rs/rayon)
* written in 100% Rust
* allocation-free during classification for dense SVMs
* **2.5x - 14x faster than libSVM for dense SVMs**
* extremely low classification times for small models (e.g., 128 SV, 16 dense attributes, linear ~ 500ns)
* successfully used in **Unity and VR** projects (Windows & Android)


Note: Currently **requires Rust nightly** (March 2019 and later), because we depend on RFC 2366 (portable SIMD). Once that stabilizes we'll also go stable.


## Usage

Train with [libSVM](https://github.com/cjlin1/libsvm) (e.g., using the tool `svm-train`), then classify with `ffsvm-rust`.

From Rust:

```rust
// Replace `SAMPLE_MODEL` with a `&str` to your model.
let svm = DenseSVM::try_from(SAMPLE_MODEL)?;

let mut problem = Problem::from(&svm);
let features = problem.features();

features[0] = 0.55838;
features[1] = -0.157895;
features[2] = 0.581292;
features[3] = -0.221184;

svm.predict_value(&mut problem)?;

assert_eq!(problem.solution(), Solution::Label(42));

```

## Status
* **March 10, 2023**: Reactivated for latest Rust nightly.
* **June 7, 2019**: Gave up on 'no `unsafe`', but gained runtime SIMD selection.
* **March 10, 2019**: As soon as we can move away from nightly we'll go beta.
* **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

![performance](https://raw.githubusercontent.com/ralfbiedert/ffsvm-rust/master/docs/performance_relative.v3.png)

All performance numbers reported for the `DenseSVM`. We also have support for `SparseSVM`s, which are slower for "mostly dense" models, and faster for "mostly sparse" models (and generally on the performance level of libSVM).

[See here for details.](https://github.com/ralfbiedert/ffsvm-rust/blob/master/docs/performance.md)


#### Tips

* For an x-fold performance increase, create a number of `Problem` structures, and process them with [Rayon's](https://docs.rs/rayon/1.0.3/rayon/) `par_iter`.


## FAQ

[See here for details.](https://github.com/ralfbiedert/ffsvm-rust/blob/master/docs/FAQ.md)

[Latest Version]: https://img.shields.io/crates/v/ffsvm.svg
[crates.io]: https://crates.io/crates/ffsvm
[MIT]: https://img.shields.io/badge/license-MIT-blue.svg
[docs]: https://docs.rs/ffsvm/badge.svg
[docs.rs]: https://docs.rs/ffsvm/
[deps]: https://deps.rs/repo/github/ralfbiedert/ffsvm-rust
[deps.svg]: https://deps.rs/repo/github/ralfbiedert/ffsvm-rust/status.svg