Adds SVD solver, code refactoring

src/regression/linear_regression.rs

@@ -26,10 +26,14 @@ impl<M: Matrix> LinearRegression<M> {
             panic!("Number of rows of X doesn't match number of rows of Y");
         }
 
-        let b = y.v_stack(&M::ones(1, 1));
+        // let b = y.v_stack(&M::ones(1, 1));
+        let b = y.clone();
         let mut a = x.h_stack(&M::ones(x_nrows, 1));
 
-        let w  = a.qr_solve_mut(b); 
+        let w = match solver {
+            LinearRegressionSolver::QR => a.qr_solve_mut(b),
+            LinearRegressionSolver::SVD => a.svd_solve_mut(b)
+        };
 
         let wights = w.slice(0..num_attributes, 0..1);  
 
@@ -45,7 +49,7 @@ impl<M: Matrix> LinearRegression<M> {
 impl<M: Matrix> Regression<M> for LinearRegression<M> {
 
 
-    fn predict(&self, x: M) -> M {
+    fn predict(&self, x: &M) -> M {
         let (nrows, _) = x.shape();
         let mut y_hat = x.dot(&self.coefficients);
         y_hat.add_mut(&M::fill(nrows, 1, self.intercept));
@@ -81,10 +85,13 @@ mod tests {
                 &[554.894, 400.7, 282.7, 130.081, 1962., 70.551]]);
             let y = DenseMatrix::from_array(16, 1, &[83.0,  88.5,  88.2,  89.5,  96.2,  98.1,  99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9]);        
 
-            let lr = LinearRegression::fit(&x, &y, LinearRegressionSolver::QR);
+            let y_hat_qr = LinearRegression::fit(&x, &y, LinearRegressionSolver::QR).predict(&x);                        
+
+            let y_hat_svd = LinearRegression::fit(&x, &y, LinearRegressionSolver::SVD).predict(&x);
+
+            assert!(y.approximate_eq(&y_hat_qr, 5.));
+            assert!(y.approximate_eq(&y_hat_svd, 5.));
 
-            let y_hat = lr.predict(x);
 
-            assert!(y.approximate_eq(&y_hat, 5.));
     }
 }