feat: documents matrix methods

src/linear/linear_regression.rs

@@ -23,7 +23,7 @@
 //! use smartcore::linear::linear_regression::*;
 //!
 //! // Longley dataset (https://www.statsmodels.org/stable/datasets/generated/longley.html)
-//! let x = DenseMatrix::from_array(&[
+//! let x = DenseMatrix::from_2d_array(&[
 //!               &[234.289, 235.6, 159.0, 107.608, 1947., 60.323],
 //!               &[259.426, 232.5, 145.6, 108.632, 1948., 61.122],
 //!               &[258.054, 368.2, 161.6, 109.773, 1949., 60.171],
@@ -125,7 +125,7 @@ impl<T: RealNumber, M: Matrix<T>> LinearRegression<T, M> {
             panic!("Number of rows of X doesn't match number of rows of Y");
         }
 
-        let a = x.v_stack(&M::ones(x_nrows, 1));
+        let a = x.h_stack(&M::ones(x_nrows, 1));
 
         let w = match parameters.solver {
             LinearRegressionSolverName::QR => a.qr_solve_mut(b),
@@ -145,7 +145,7 @@ impl<T: RealNumber, M: Matrix<T>> LinearRegression<T, M> {
     /// * `x` - _KxM_ data where _K_ is number of observations and _M_ is number of features.
     pub fn predict(&self, x: &M) -> M::RowVector {
         let (nrows, _) = x.shape();
-        let mut y_hat = x.dot(&self.coefficients);
+        let mut y_hat = x.matmul(&self.coefficients);
         y_hat.add_mut(&M::fill(nrows, 1, self.intercept));
         y_hat.transpose().to_row_vector()
     }
@@ -168,7 +168,7 @@ mod tests {
 
     #[test]
     fn ols_fit_predict() {
-        let x = DenseMatrix::from_array(&[
+        let x = DenseMatrix::from_2d_array(&[
             &[234.289, 235.6, 159.0, 107.608, 1947., 60.323],
             &[259.426, 232.5, 145.6, 108.632, 1948., 61.122],
             &[258.054, 368.2, 161.6, 109.773, 1949., 60.171],
@@ -215,7 +215,7 @@ mod tests {
 
     #[test]
     fn serde() {
-        let x = DenseMatrix::from_array(&[
+        let x = DenseMatrix::from_2d_array(&[
             &[234.289, 235.6, 159.0, 107.608, 1947., 60.323],
             &[259.426, 232.5, 145.6, 108.632, 1948., 61.122],
             &[258.054, 368.2, 161.6, 109.773, 1949., 60.171],