pub fn dot_owned<T2>(self, other: Matrix<N, N1, T2>) -> T1
        where for<'a, 'b> &'a T1: Mul<&'b T2, Output = T1>,
              T1: Add<T1, Output = T1> + std::num::Zero
    {
        let tp = self.transpose();
        (tp * other).data.into_iter().next().unwrap()
    }
/// 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_owned<T2>(self, other: Matrix<N, N1, T2>) -> T1
        where for<'a, 'b> &'a T1: Mul<&'b T2, Output = T1>,
              T1: Add<T1, Output = T1> + std::num::Zero
    {
        let tp = self.transpose();
        (tp * other).data.into_iter().next().unwrap()
    }
pub fn dot_owned < T2 > ( self , other : Matrix < N , N1 , T2 > ) -> T1 where for < ' a , ' b > & ' a T1 : Mul < & ' b T2 , Output = T1 > , T1 : Add < T1 , Output = T1 > + std :: num :: Zero { let tp = self . transpose ( ) ; ( tp * other ) . data . into_iter ( ) . next ( ) . unwrap ( ) }
Treating `n x 1` matrices as column vectors, take the dot product.
Treating ` n x 1 ` matrices as column vectors take the dot product .
self
other
Matrix<N, N1, T2>
self
other
Matrix<N, N1, T2>