/// Treating `n x 1` matrices as column vectors, take the dot product.
Treating `n x 1` matrices as column vectors, take the dot product.
Treating ` n x 1 ` matrices as column vectors take the dot product .
pub fn dot<'a, 'b, T2>(&'a self, other: &'b Matrix<N, N1, T2>) -> T1
        where for<'c> &'c T1: Mul<&'b T2, Output = T1>,
              T1: Add<T1, Output = T1> + std::num::Zero,
              T1: Clone + 'b
    {
        let tp: Matrix<_, _, T1> = self.transposed().map(|x| (*x).clone());
        (tp * other).get(0, 0).clone()
    }
pub fn dot < ' a , ' b , T2 > ( & ' a self , other : & ' b Matrix < N , N1 , T2 > ) -> T1 where for < ' c > & ' c T1 : Mul < & ' b T2 , Output = T1 > , T1 : Add < T1 , Output = T1 > + std :: num :: Zero , T1 : Clone + ' b { let tp : Matrix < _ , _ , T1 > = self . transposed ( ) . map ( | x | ( * x ) . clone ( ) ) ; ( tp * other ) . get ( 0 , 0 ) . clone ( ) }
Treating `n x 1` matrices as column vectors, take the dot product.
Treating ` n x 1 ` matrices as column vectors take the dot product .
&'a self
other
&'b Matrix<N, N1, T2>