feat: + cross_validate, trait Predictor, refactoring

src/linear/linear_regression.rs

@@ -64,11 +64,12 @@ use std::fmt::Debug;
 
 use serde::{Deserialize, Serialize};
 
+use crate::base::Predictor;
 use crate::error::Failed;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 
-#[derive(Serialize, Deserialize, Debug)]
+#[derive(Serialize, Deserialize, Debug, Clone)]
 /// Approach to use for estimation of regression coefficients. QR is more efficient but SVD is more stable.
 pub enum LinearRegressionSolverName {
     /// QR decomposition, see [QR](../../linalg/qr/index.html)
@@ -78,7 +79,7 @@ pub enum LinearRegressionSolverName {
 }
 
 /// Linear Regression parameters
-#[derive(Serialize, Deserialize, Debug)]
+#[derive(Serialize, Deserialize, Debug, Clone)]
 pub struct LinearRegressionParameters {
     /// Solver to use for estimation of regression coefficients.
     pub solver: LinearRegressionSolverName,
@@ -107,6 +108,12 @@ impl<T: RealNumber, M: Matrix<T>> PartialEq for LinearRegression<T, M> {
     }
 }
 
+impl<T: RealNumber, M: Matrix<T>> Predictor<M, M::RowVector> for LinearRegression<T, M> {
+    fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
+        self.predict(x)
+    }
+}
+
 impl<T: RealNumber, M: Matrix<T>> LinearRegression<T, M> {
     /// Fits Linear Regression to your data.
     /// * `x` - _NxM_ matrix with _N_ observations and _M_ features in each observation.