Make it work on stable! 🎉🎉🎉

.cargo/config.toml

@@ -0,0 +1,2 @@
+[build]
+rustflags = ["-C", "target-cpu=native"]

Cargo.toml

@@ -1,26 +1,27 @@
 [package]
 name = "ffsvm"
-description="A libSVM compatible support vector machine, but up to 10x faster, for games or VR."
-version = "0.10.1"
+description = "A libSVM compatible support vector machine, but up to 10x faster, for games or VR."
+version = "0.11.0"
 repository = "https://github.com/ralfbiedert/ffsvm-rust"
 authors = ["Ralf Biedert <[email protected]>"]
 readme = "README.md"
 categories = ["science", "algorithms"]
 keywords = ["svm", "libsvm", "machine-learning"]
 license = "MIT"
-edition = "2018"
+edition = "2021"
+rust-version = "1.83"
 exclude = [
     "docs/*",
 ]
 
 [lib]
 name = "ffsvm"
 path = "src/lib.rs"
-crate-type = [ "rlib" ]
+crate-type = ["rlib"]
 
 [dependencies]
-simd_aligned = "0.4"
-#simd_aligned = { path = "../simd_aligned_rust" }
+#simd_aligned = "0.5"
+simd_aligned = { path = "../simd_aligned" }
 
 [dev-dependencies]
 rand = "0.8.5"

README.md

@@ -6,81 +6,89 @@
 
 ## 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.
-
+You trained an 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)
+* 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)
+* 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.
-
+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`.
+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 svm = DenseSVM::try_from(SAMPLE_MODEL) ?;
 
-let mut problem = Problem::from(&svm);
+let mut problem = Problem::from( & svm);
 let features = problem.features();
 
 features[0] = 0.55838;
-features[1] = -0.157895;
+features[1] = - 0.157895;
 features[2] = 0.581292;
-features[3] = -0.221184;
+features[3] = - 0.221184;
 
-svm.predict_value(&mut problem)?;
+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.
+  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).
+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`.
-
+* 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

benches/svm_dense.rs

@@ -9,16 +9,16 @@ mod util;
 
 mod svm_dense {
     use crate::test::Bencher;
-    use ffsvm::{DenseSVM, Predict, Problem};
+    use ffsvm::{DenseSVM, FeatureVector, Predict};
     use std::convert::TryFrom;
 
     /// Produces a test case run for benchmarking
     #[allow(dead_code)]
     fn produce_testcase(svm_type: &str, kernel_type: &str, total_sv: u32, num_attributes: u32) -> impl FnMut() {
         let raw_model = super::util::random_dense(svm_type, kernel_type, total_sv, num_attributes);
         let svm = DenseSVM::try_from(&raw_model).unwrap();
-        let mut problem = Problem::from(&svm);
-        let problem_mut = problem.features().as_slice_mut();
+        let mut problem = FeatureVector::from(&svm);
+        let problem_mut = problem.features();
 
         for i in 0 .. num_attributes {
             problem_mut[i as usize] = i as f32;
@@ -70,5 +70,4 @@ mod svm_dense {
 
     #[bench]
     fn predict_sigmoid_sv1024_attr1024(b: &mut Bencher) { b.iter(produce_testcase("c_svc", "sigmoid", 1024, 1024)); }
-
 }

benches/svm_sparse.rs

@@ -9,15 +9,15 @@ mod util;
 
 mod svm_sparse {
     use crate::test::Bencher;
-    use ffsvm::{Predict, Problem, SparseSVM};
+    use ffsvm::{Predict, FeatureVector, SparseSVM};
     use std::convert::TryFrom;
 
     /// Produces a test case run for benchmarking
     #[allow(dead_code)]
     fn produce_testcase(svm_type: &str, kernel_type: &str, total_sv: u32, num_attributes: u32) -> impl FnMut() {
         let raw_model = super::util::random_dense(svm_type, kernel_type, total_sv, num_attributes);
         let svm = SparseSVM::try_from(&raw_model).unwrap();
-        let mut problem = Problem::from(&svm);
+        let mut problem = FeatureVector::from(&svm);
         let problem_mut = problem.features();
 
         for i in 0 .. num_attributes {

benches/util.rs

@@ -4,31 +4,32 @@ use rand::Rng;
 pub fn random_dense<'b>(svm_type: &'b str, kernel_type: &'b str, total_sv: u32, attr: u32) -> ModelFile<'b> {
     let mut rng = rand::thread_rng();
 
-    ModelFile {
-        header: Header {
-            svm_type,
-            kernel_type,
-            total_sv,
-            gamma: Some(rng.gen::<f32>()),
-            coef0: Some(rng.gen::<f32>()),
-            degree: Some(rng.gen_range(1..10)),
-            nr_class: 2,
-            rho: vec![rng.gen::<f64>()],
-            label: vec![0, 1],
-            prob_a: Some(vec![rng.gen::<f64>(), rng.gen::<f64>()]),
-            prob_b: Some(vec![rng.gen::<f64>(), rng.gen::<f64>()]),
-            nr_sv: vec![total_sv / 2, total_sv / 2],
-        },
-        vectors: (0 .. total_sv)
-            .map(|_| SupportVector {
-                coefs: vec![rng.gen::<f32>()],
-                features: (0 .. attr)
-                    .map(|i| Attribute {
-                        index: i,
-                        value: rng.gen::<f32>(),
-                    })
-                    .collect(),
-            })
-            .collect(),
-    }
+    let header = Header {
+        svm_type,
+        kernel_type,
+        total_sv,
+        gamma: Some(rng.gen::<f32>()),
+        coef0: Some(rng.gen::<f32>()),
+        degree: Some(rng.gen_range(1 .. 10)),
+        nr_class: 2,
+        rho: vec![rng.gen::<f64>()],
+        label: vec![0, 1],
+        prob_a: Some(vec![rng.gen::<f64>(), rng.gen::<f64>()]),
+        prob_b: Some(vec![rng.gen::<f64>(), rng.gen::<f64>()]),
+        nr_sv: vec![total_sv / 2, total_sv / 2],
+    };
+
+    let vectors = (0 .. total_sv)
+        .map(|_| SupportVector {
+            coefs: vec![rng.gen::<f32>()],
+            features: (0 .. attr)
+                .map(|i| Attribute {
+                    index: i,
+                    value: rng.gen::<f32>(),
+                })
+                .collect(),
+        })
+        .collect();
+
+    ModelFile::new(header, vectors)
 }

examples/basic.rs

@@ -4,17 +4,17 @@ use std::convert::TryFrom;
 fn main() -> Result<(), Error> {
     let svm = DenseSVM::try_from(SAMPLE_MODEL)?;
 
-    let mut problem = Problem::from(&svm);
-    let features = problem.features();
+    let mut fv = FeatureVector::from(&svm);
+    let features = fv.features();
 
     features[0] = 0.558_382;
     features[1] = -0.157_895;
     features[2] = 0.581_292;
     features[3] = -0.221_184;
 
-    svm.predict_value(&mut problem)?;
+    svm.predict_value(&mut fv)?;
 
-    assert_eq!(problem.solution(), Solution::Label(42));
+    assert_eq!(fv.label(), Label::Class(42));
 
     Ok(())
 }

src/errors.rs

@@ -1,11 +1,9 @@
-use std::{
-    num::{ParseFloatError, ParseIntError},
-};
+use std::num::{ParseFloatError, ParseIntError};
 
 /// Possible error types when classifying with one of the SVMs.
 #[derive(Debug)]
 pub enum Error {
-    /// This can be emitted when creating a SVM from a [`ModelFile`](crate::ModelFile). For models generated by
+    /// This can be emitted when creating an SVM from a [`ModelFile`](crate::ModelFile). For models generated by
     /// libSVM's `svm-train`, the most common reason this occurs is skipping attributes.
     /// All attributes must be in sequential order 0, 1, 2, ..., n. If they are not, this
     /// error will be emitted. For more details see the documentation provided in [`ModelFile`](crate::ModelFile).