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/metrics/r2.rs

@@ -10,59 +10,70 @@
 //!
 //! ```
 //! use smartcore::metrics::mean_absolute_error::MeanAbsoluteError;
+//! use smartcore::metrics::Metrics;
 //! let y_pred: Vec<f64> = vec![3., -0.5, 2., 7.];
 //! let y_true: Vec<f64> = vec![2.5, 0.0, 2., 8.];
 //!
-//! let mse: f64 = MeanAbsoluteError {}.get_score(&y_pred, &y_true);
+//! let mse: f64 = MeanAbsoluteError::new().get_score(&y_pred, &y_true);
 //! ```
 //!
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+use std::marker::PhantomData;
+
 #[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
-use crate::linalg::BaseVector;
-use crate::math::num::RealNumber;
+use crate::linalg::basic::arrays::ArrayView1;
+use crate::numbers::basenum::Number;
+
+use crate::metrics::Metrics;
 
 /// Coefficient of Determination (R2)
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug)]
-pub struct R2 {}
+pub struct R2<T> {
+    _phantom: PhantomData<T>,
+}
 
-impl R2 {
+impl<T: Number> Metrics<T> for R2<T> {
+    /// create a typed object to call R2 functions
+    fn new() -> Self {
+        Self {
+            _phantom: PhantomData,
+        }
+    }
+    fn new_with(_parameter: f64) -> Self {
+        Self {
+            _phantom: PhantomData,
+        }
+    }
     /// Computes R2 score
     /// * `y_true` - Ground truth (correct) target values.
     /// * `y_pred` - Estimated target values.
-    pub fn get_score<T: RealNumber, V: BaseVector<T>>(&self, y_true: &V, y_pred: &V) -> T {
-        if y_true.len() != y_pred.len() {
+    fn get_score(&self, y_true: &dyn ArrayView1<T>, y_pred: &dyn ArrayView1<T>) -> f64 {
+        if y_true.shape() != y_pred.shape() {
             panic!(
                 "The vector sizes don't match: {} != {}",
-                y_true.len(),
-                y_pred.len()
+                y_true.shape(),
+                y_pred.shape()
             );
         }
 
-        let n = y_true.len();
-
-        let mut mean = T::zero();
-
-        for i in 0..n {
-            mean += y_true.get(i);
-        }
-
-        mean /= T::from_usize(n).unwrap();
+        let n = y_true.shape();
 
+        let mean: f64 = y_true.mean_by();
         let mut ss_tot = T::zero();
         let mut ss_res = T::zero();
 
         for i in 0..n {
-            let y_i = y_true.get(i);
-            let f_i = y_pred.get(i);
-            ss_tot += (y_i - mean).square();
-            ss_res += (y_i - f_i).square();
+            let y_i = *y_true.get(i);
+            let f_i = *y_pred.get(i);
+            ss_tot += (y_i - T::from(mean).unwrap()) * (y_i - T::from(mean).unwrap());
+            ss_res += (y_i - f_i) * (y_i - f_i);
         }
 
-        T::one() - (ss_res / ss_tot)
+        (T::one() - ss_res / ss_tot).to_f64().unwrap()
     }
 }
 
@@ -76,8 +87,8 @@ mod tests {
         let y_true: Vec<f64> = vec![3., -0.5, 2., 7.];
         let y_pred: Vec<f64> = vec![2.5, 0.0, 2., 8.];
 
-        let score1: f64 = R2 {}.get_score(&y_true, &y_pred);
-        let score2: f64 = R2 {}.get_score(&y_true, &y_true);
+        let score1: f64 = R2::new().get_score(&y_true, &y_pred);
+        let score2: f64 = R2::new().get_score(&y_true, &y_true);
 
         assert!((score1 - 0.948608137).abs() < 1e-8);
         assert!((score2 - 1.0).abs() < 1e-8);