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/model_selection/kfold.rs

@@ -1,11 +1,11 @@
 //! # KFold
 //!
 //! Defines k-fold cross validator.
+use std::fmt::{Debug, Display};
 
-use crate::linalg::Matrix;
-use crate::math::num::RealNumber;
+use crate::linalg::basic::arrays::Array2;
 use crate::model_selection::BaseKFold;
-use crate::rand::get_rng_impl;
+use crate::rand_custom::get_rng_impl;
 use rand::seq::SliceRandom;
 
 /// K-Folds cross-validator
@@ -20,7 +20,10 @@ pub struct KFold {
 }
 
 impl KFold {
-    fn test_indices<T: RealNumber, M: Matrix<T>>(&self, x: &M) -> Vec<Vec<usize>> {
+    fn test_indices<T: Debug + Display + Copy + Sized, M: Array2<T>>(
+        &self,
+        x: &M,
+    ) -> Vec<Vec<usize>> {
         // number of samples (rows) in the matrix
         let n_samples: usize = x.shape().0;
 
@@ -51,7 +54,7 @@ impl KFold {
         return_values
     }
 
-    fn test_masks<T: RealNumber, M: Matrix<T>>(&self, x: &M) -> Vec<Vec<bool>> {
+    fn test_masks<T: Debug + Display + Copy + Sized, M: Array2<T>>(&self, x: &M) -> Vec<Vec<bool>> {
         let mut return_values: Vec<Vec<bool>> = Vec::with_capacity(self.n_splits);
         for test_index in self.test_indices(x).drain(..) {
             // init mask
@@ -71,7 +74,7 @@ impl Default for KFold {
         KFold {
             n_splits: 3,
             shuffle: true,
-            seed: None,
+            seed: Option::None,
         }
     }
 }
@@ -134,7 +137,7 @@ impl BaseKFold for KFold {
         self.n_splits
     }
 
-    fn split<T: RealNumber, M: Matrix<T>>(&self, x: &M) -> Self::Output {
+    fn split<T: Debug + Display + Copy + Sized, M: Array2<T>>(&self, x: &M) -> Self::Output {
         if self.n_splits < 2 {
             panic!("Number of splits is too small: {}", self.n_splits);
         }
@@ -154,7 +157,7 @@ impl BaseKFold for KFold {
 mod tests {
 
     use super::*;
-    use crate::linalg::naive::dense_matrix::*;
+    use crate::linalg::basic::matrix::DenseMatrix;
 
     #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
     #[test]
@@ -162,7 +165,7 @@ mod tests {
         let k = KFold {
             n_splits: 3,
             shuffle: false,
-            seed: None,
+            seed: Option::None,
         };
         let x: DenseMatrix<f64> = DenseMatrix::rand(33, 100);
         let test_indices = k.test_indices(&x);
@@ -178,7 +181,7 @@ mod tests {
         let k = KFold {
             n_splits: 3,
             shuffle: false,
-            seed: None,
+            seed: Option::None,
         };
         let x: DenseMatrix<f64> = DenseMatrix::rand(34, 100);
         let test_indices = k.test_indices(&x);
@@ -194,7 +197,7 @@ mod tests {
         let k = KFold {
             n_splits: 2,
             shuffle: false,
-            seed: None,
+            seed: Option::None,
         };
         let x: DenseMatrix<f64> = DenseMatrix::rand(22, 100);
         let test_masks = k.test_masks(&x);
@@ -221,7 +224,7 @@ mod tests {
         let k = KFold {
             n_splits: 2,
             shuffle: false,
-            seed: None,
+            seed: Option::None,
         };
         let x: DenseMatrix<f64> = DenseMatrix::rand(22, 100);
         let train_test_splits: Vec<(Vec<usize>, Vec<usize>)> = k.split(&x).collect();
@@ -254,7 +257,7 @@ mod tests {
         let k = KFold {
             n_splits: 3,
             shuffle: false,
-            seed: None,
+            seed: Option::None,
         };
         let x: DenseMatrix<f64> = DenseMatrix::rand(10, 4);
         let expected: Vec<(Vec<usize>, Vec<usize>)> = vec![