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/preprocessing/categorical.rs

@@ -5,7 +5,7 @@
 //!
 //! ### Usage Example
 //! ```
-//! use smartcore::linalg::naive::dense_matrix::DenseMatrix;
+//! use smartcore::linalg::basic::matrix::DenseMatrix;
 //! use smartcore::preprocessing::categorical::{OneHotEncoder, OneHotEncoderParams};
 //! let data = DenseMatrix::from_2d_array(&[
 //!         &[1.5, 1.0, 1.5, 3.0],
@@ -27,10 +27,10 @@
 use std::iter;
 
 use crate::error::Failed;
-use crate::linalg::Matrix;
+use crate::linalg::basic::arrays::Array2;
 
-use crate::preprocessing::data_traits::{CategoricalFloat, Categorizable};
 use crate::preprocessing::series_encoder::CategoryMapper;
+use crate::preprocessing::traits::{CategoricalFloat, Categorizable};
 
 /// OneHotEncoder Parameters
 #[derive(Debug, Clone)]
@@ -106,7 +106,7 @@ impl OneHotEncoder {
     pub fn fit<T, M>(data: &M, params: OneHotEncoderParams) -> Result<OneHotEncoder, Failed>
     where
         T: Categorizable,
-        M: Matrix<T>,
+        M: Array2<T>,
     {
         match (params.col_idx_categorical, params.infer_categorical) {
             (None, false) => Err(Failed::fit(
@@ -157,7 +157,7 @@ impl OneHotEncoder {
     pub fn transform<T, M>(&self, x: &M) -> Result<M, Failed>
     where
         T: Categorizable,
-        M: Matrix<T>,
+        M: Array2<T>,
     {
         let (nrows, p) = x.shape();
         let additional_params: Vec<usize> = self
@@ -174,7 +174,7 @@ impl OneHotEncoder {
 
         for (pidx, &old_cidx) in self.col_idx_categorical.iter().enumerate() {
             let cidx = new_col_idx[old_cidx];
-            let col_iter = (0..nrows).map(|r| x.get(r, old_cidx).to_category());
+            let col_iter = (0..nrows).map(|r| x.get((r, old_cidx)).to_category());
             let sencoder = &self.category_mappers[pidx];
             let oh_series = col_iter.map(|c| sencoder.get_one_hot::<T, Vec<T>>(&c));
 
@@ -188,7 +188,7 @@ impl OneHotEncoder {
                     Some(v) => {
                         // copy one hot vectors to their place in the data matrix;
                         for (col_ofst, &val) in v.iter().enumerate() {
-                            res.set(row, cidx + col_ofst, val);
+                            res.set((row, cidx + col_ofst), val);
                         }
                     }
                 }
@@ -209,8 +209,8 @@ impl OneHotEncoder {
             }
 
             for r in 0..nrows {
-                let val = x.get(r, old_p);
-                res.set(r, new_p, val);
+                let val = x.get((r, old_p));
+                res.set((r, new_p), *val);
             }
         }
 
@@ -221,7 +221,7 @@ impl OneHotEncoder {
 #[cfg(test)]
 mod tests {
     use super::*;
-    use crate::linalg::naive::dense_matrix::DenseMatrix;
+    use crate::linalg::basic::matrix::DenseMatrix;
     use crate::preprocessing::series_encoder::CategoryMapper;
 
     #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]