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/algorithm/neighbour/fastpair.rs

@@ -27,9 +27,10 @@ use std::collections::HashMap;
 
 use crate::algorithm::neighbour::distances::PairwiseDistance;
 use crate::error::{Failed, FailedError};
-use crate::linalg::Matrix;
-use crate::math::distance::euclidian::Euclidian;
-use crate::math::num::RealNumber;
+use crate::linalg::basic::arrays::Array2;
+use crate::metrics::distance::euclidian::Euclidian;
+use crate::numbers::realnum::RealNumber;
+use crate::numbers::floatnum::FloatNumber;
 
 ///
 /// Inspired by Python implementation:
@@ -39,7 +40,7 @@ use crate::math::num::RealNumber;
 /// affinity used is Euclidean so to allow linkage with single, ward, complete and average
 ///
 #[derive(Debug, Clone)]
-pub struct FastPair<'a, T: RealNumber, M: Matrix<T>> {
+pub struct FastPair<'a, T: RealNumber + FloatNumber, M: Array2<T>> {
     /// initial matrix
     samples: &'a M,
     /// closest pair hashmap (connectivity matrix for closest pairs)
@@ -48,7 +49,7 @@ pub struct FastPair<'a, T: RealNumber, M: Matrix<T>> {
     pub neighbours: Vec<usize>,
 }
 
-impl<'a, T: RealNumber, M: Matrix<T>> FastPair<'a, T, M> {
+impl<'a, T: RealNumber + FloatNumber, M: Array2<T>> FastPair<'a, T, M> {
     ///
     /// Constructor
     /// Instantiate and inizialise the algorithm
@@ -72,7 +73,7 @@ impl<'a, T: RealNumber, M: Matrix<T>> FastPair<'a, T, M> {
     }
 
     ///
-    /// Initialise `FastPair` by passing a `Matrix`.
+    /// Initialise `FastPair` by passing a `Array2`.
     /// Build a FastPairs data-structure from a set of (new) points.
     ///
     fn init(&mut self) {
@@ -96,8 +97,8 @@ impl<'a, T: RealNumber, M: Matrix<T>> FastPair<'a, T, M> {
                 index_row_i,
                 PairwiseDistance {
                     node: index_row_i,
-                    neighbour: None,
-                    distance: Some(T::max_value()),
+                    neighbour: Option::None,
+                    distance: Some(T::MAX),
                 },
             );
         }
@@ -142,7 +143,7 @@ impl<'a, T: RealNumber, M: Matrix<T>> FastPair<'a, T, M> {
         // compute sparse matrix (connectivity matrix)
         let mut sparse_matrix = M::zeros(len, len);
         for (_, p) in distances.iter() {
-            sparse_matrix.set(p.node, p.neighbour.unwrap(), p.distance.unwrap());
+            sparse_matrix.set((p.node, p.neighbour.unwrap()), p.distance.unwrap());
         }
 
         self.distances = distances;
@@ -180,7 +181,7 @@ impl<'a, T: RealNumber, M: Matrix<T>> FastPair<'a, T, M> {
 
         let mut closest_pair = PairwiseDistance {
             node: 0,
-            neighbour: None,
+            neighbour: Option::None,
             distance: Some(T::max_value()),
         };
         for pair in (0..m).combinations(2) {
@@ -549,7 +550,7 @@ mod tests_fastpair {
 
         let mut min_dissimilarity = PairwiseDistance {
             node: 0,
-            neighbour: None,
+            neighbour: Option::None,
             distance: Some(f64::MAX),
         };
         for p in dissimilarities.iter() {