Update readmes, changelog

Update `Cargo.toml`

README.md

@@ -0,0 +1,10 @@
+[![Latest Version]][crates.io]
+![MIT]
+
+# liblinear-rs
+
+This is the official repository for the [liblinear](crates/liblinear/) Rust library and its support crates.
+
+[latest version]: https://img.shields.io/crates/v/liblinear.svg
+[crates.io]: https://crates.io/crates/liblinear
+[mit]: https://img.shields.io/badge/license-MIT-blue.svg

crates/liblinear-macros/Cargo.toml

@@ -1,12 +1,20 @@
 [package]
 name = "liblinear-macros"
 version = "0.1.0"
+authors = ["Madeesh Kannan"]
 edition = "2021"
 
+readme = "README.md"
+description = "Macros for the `liblinear` crate"
+repository = "https://github.com/shadeMe/liblinear-rs"
+keywords = ["machine-learning", "statistics", "svm", "linear-model"]
+categories = ["api-bindings", "science", "simulation"]
+license = "MIT"
+
 [lib]
 proc-macro = true
 
 [dependencies]
 proc-macro2 = "1.0"
 quote = "1.0"
-syn = { version = "1.0", features = ["full"] }
+syn = { version = "2.0", features = ["full"] }

crates/liblinear-macros/README.md

@@ -1,5 +1,4 @@
 [![Latest Version]][crates.io]
-[![deps.svg]][deps]
 ![MIT]
 
 # liblinear-macros
@@ -13,4 +12,3 @@ Macros for the [liblinear](https://github.com/shadeMe/liblinear-rs) Rust library
 [latest version]: https://img.shields.io/crates/v/liblinear-macros.svg
 [crates.io]: https://crates.io/crates/liblinear-macros
 [mit]: https://img.shields.io/badge/license-MIT-blue.svg
-[deps.svg]: https://deps.rs/repo/github/shademe/liblinear-rs/status.svg

crates/liblinear/CHANGELOG.md

@@ -0,0 +1,38 @@
+# Changelog
+
+## 2.0.0
+
+### Breaking
+
+- Rewrite public API to utilize compile-time validation of models and parameters.
+- Replace `failure` with `thiserror`.
+- Update LIBLINEAR to `v246`.
+- Expose one-class SVM solver.
+- Reorganize as `cargo` workspace.
+  - Add `liblinear-macros` crate.
+
+### Non-breaking
+
+- Use `-O2` optimizations when compiling LIBLINEAR.
+
+## 1.0.0
+
+### Breaking
+
+- Update LIBLINEAR to `v230`.
+- `ParameterError` - Add variant `IllegalArgument`.
+- `LibLinearCrossValidator` - Change `find_optimal_constraints_violation_cost` to `find_optimal_constraints_violation_cost_and_loss_sensitivity` signature.
+
+### Non-breaking
+
+- Fix build failures on macOS by automatically selecting the C++ standard library when compiling LIBLINEAR.
+- Update dependencies.
+
+## 0.1.1
+
+- Added readme.
+- Minor documentation fixes.
+
+## 0.1.0
+
+- Initial release.

crates/liblinear/README.md

@@ -5,54 +5,45 @@
 
 # liblinear
 
-Rust bindings for the [liblinear](https://github.com/cjlin1/liblinear) C/C++ library.
+Rust bindings for the [LIBLINEAR](https://github.com/cjlin1/liblinear) C/C++ library.
 Provides a thin (but rustic) wrapper around the original C-interface exposed by the library.
 
 # Usage
 
-Use the `liblinear::Builder` API to train a model on sparse features and
-predict the class of a new instance.
-
 ```rust
-use liblinear::*;
+use liblinear::{
+    Model,
+    Parameters,
+    TrainingInput,
+    PredictionInput,
+    solver::L2R_LR,
+    model::traits::*,
+    parameter::traits::*,
+    solver::traits::*,
+};
 
 let x: Vec<Vec<(u32, f64)>> = vec![
-        vec![(1, 0.1), (3, 0.2)],
-        vec![(3, 9.9)],
-        vec![(1, 0.2), (2, 3.2)],
-    ];
+    vec![(1, 0.1), (3, 0.2)],
+    vec![(3, 9.9)],
+    vec![(1, 0.2), (2, 3.2)],
+];
 let y = vec![0.0, 1.0, 0.0];
 
-let mut model_builder = liblinear::Builder::default();
-model_builder
-    .problem()
-    .input_data(util::TrainingInput::from_sparse_features(y, x).unwrap())
-    .bias(0f64);
-model_builder
-    .parameters()
-    .solver_type(SolverType::L2R_LR)
+let mut params = Parameters::<L2R_LR>::default();
+params
+    .bias(0f64)
     .stopping_tolerance(0.1f64)
-    .constraints_violation_cost(0.1f64)
-    .regression_loss_sensitivity(1f64);
+    .constraints_violation_cost(0.1f64);
 
-let model = model_builder.build_model().unwrap();
-assert_eq!(model.num_classes(), 2);
+let model = Model::train(&TrainingInput::from_sparse_features(y, x).unwrap(), &params).unwrap();
 
 let predicted_class = model
-    .predict(util::PredictionInput::from_sparse_features(vec![(1u32, 2.2f64)]).unwrap())
+    .predict(&PredictionInput::from_sparse_features(vec![(3u32, 9.9f64)]).unwrap())
     .unwrap();
-println!(predicted_class);
+println!("{}", predicted_class);
 ```
 
-More examples can be found in the bundled unit tests.
-
-# Changelog
-
-1.0.0 - Update liblinear to v230 (breaking changes), minor changes and fixes.
-
-0.1.1 - Added readme, minor documentation fixes.
-
-0.1.0 - Initial release.
+Refer to the [API docs][docs.rs] for more information.
 
 [latest version]: https://img.shields.io/crates/v/liblinear.svg
 [crates.io]: https://crates.io/crates/liblinear