Merge potential next release v0.4 (#187) Breaking Changes

* First draft of the new n-dimensional arrays + NB use case
* Improves default implementation of multiple Array methods
* Refactors tree methods
* Adds matrix decomposition routines
* Adds matrix decomposition methods to ndarray and nalgebra bindings
* Refactoring + linear regression now uses array2
* Ridge & Linear regression
* LBFGS optimizer & logistic regression
* LBFGS optimizer & logistic regression
* Changes linear methods, metrics and model selection methods to new n-dimensional arrays
* Switches KNN and clustering algorithms to new n-d array layer
* Refactors distance metrics
* Optimizes knn and clustering methods
* Refactors metrics module
* Switches decomposition methods to n-dimensional arrays
* Linalg refactoring - cleanup rng merge (#172)
* Remove legacy DenseMatrix and BaseMatrix implementation. Port the new Number, FloatNumber and Array implementation into module structure.
* Exclude AUC metrics. Needs reimplementation
* Improve developers walkthrough

New traits system in place at `src/numbers` and `src/linalg`
Co-authored-by: Lorenzo <tunedconsulting@gmail.com>

* Provide SupervisedEstimator with a constructor to avoid explicit dynamical box allocation in 'cross_validate' and 'cross_validate_predict' as required by the use of 'dyn' as per Rust 2021
* Implement getters to use as_ref() in src/neighbors
* Implement getters to use as_ref() in src/naive_bayes
* Implement getters to use as_ref() in src/linear
* Add Clone to src/naive_bayes
* Change signature for cross_validate and other model_selection functions to abide to use of dyn in Rust 2021
* Implement ndarray-bindings. Remove FloatNumber from implementations
* Drop nalgebra-bindings support (as decided in conf-call to go for ndarray)
* Remove benches. Benches will have their own repo at smartcore-benches
* Implement SVC
* Implement SVC serialization. Move search parameters in dedicated module
* Implement SVR. Definitely too slow
* Fix compilation issues for wasm (#202)

Co-authored-by: Luis Moreno <morenol@users.noreply.github.com>
* Fix tests (#203)

* Port linalg/traits/stats.rs
* Improve methods naming
* Improve Display for DenseMatrix

Co-authored-by: Montana Low <montanalow@users.noreply.github.com>
Co-authored-by: VolodymyrOrlov <volodymyr.orlov@gmail.com>

src/api.rs

@@ -16,8 +16,12 @@ pub trait UnsupervisedEstimator<X, P> {
         P: Clone;
 }
 
-/// An estimator for supervised learning, , that provides method `fit` to learn from data and training values
-pub trait SupervisedEstimator<X, Y, P> {
+/// An estimator for supervised learning, that provides method `fit` to learn from data and training values
+pub trait SupervisedEstimator<X, Y, P>: Predictor<X, Y> {
+    /// Empty constructor, instantiate an empty estimator. Object is dropped as soon as `fit()` is called.
+    /// used to pass around the correct `fit()` implementation.
+    /// by calling `::fit()`. mostly used to be used with `model_selection::cross_validate(...)`
+    fn new() -> Self;
     /// Fit a model to a training dataset, estimate model's parameters.
     /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
     /// * `y` - target training values of size _N_.
@@ -28,6 +32,24 @@ pub trait SupervisedEstimator<X, Y, P> {
         P: Clone;
 }
 
+/// An estimator for supervised learning.
+/// In this one parameters are borrowed instead of moved, this is useful for parameters that carry
+/// references. Also to be used when there is no predictor attached to the estimator.
+pub trait SupervisedEstimatorBorrow<'a, X, Y, P> {
+    /// Empty constructor, instantiate an empty estimator. Object is dropped as soon as `fit()` is called.
+    /// used to pass around the correct `fit()` implementation.
+    /// by calling `::fit()`. mostly used to be used with `model_selection::cross_validate(...)`
+    fn new() -> Self;
+    /// Fit a model to a training dataset, estimate model's parameters.
+    /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
+    /// * `y` - target training values of size _N_.
+    /// * `&parameters` - hyperparameters of an algorithm
+    fn fit(x: &'a X, y: &'a Y, parameters: &'a P) -> Result<Self, Failed>
+    where
+        Self: Sized,
+        P: Clone;
+}
+
 /// Implements method predict that estimates target value from new data
 pub trait Predictor<X, Y> {
     /// Estimate target values from new data.
@@ -35,9 +57,19 @@ pub trait Predictor<X, Y> {
     fn predict(&self, x: &X) -> Result<Y, Failed>;
 }
 
+/// Implements method predict that estimates target value from new data, with borrowing
+pub trait PredictorBorrow<'a, X, T> {
+    /// Estimate target values from new data.
+    /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
+    fn predict(&self, x: &'a X) -> Result<Vec<T>, Failed>;
+}
+
 /// Implements method transform that filters or modifies input data
 pub trait Transformer<X> {
     /// Transform data by modifying or filtering it
     /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.
     fn transform(&self, x: &X) -> Result<X, Failed>;
 }
+
+/// empty parameters for an estimator, see `BiasedEstimator`
+pub trait NoParameters {}