Skip to main content
`Ctrl`+`K`

# numpy.vecmat

numpy.vecmat(x1, x2, /, out=None, *, casting='same_kind', order='K', dtype=None, subok=True[, signature, axes, axis]) = <ufunc 'vecmat'>

  Vector-matrix dot product of two arrays.

  Given a vector (or stack of vector) \(\mathbf{v}\) in `x1` and a matrix (or stack of matrices) \(\mathbf{A}\) in `x2`, the vector-matrix product is defined as:

  \[\mathbf{v} \cdot \mathbf{A} = \sum_{i=0}^{n-1} \overline{v_i}A_{ij}\]

  where the sum is over the last dimension of `x1` and the one-but-last dimensions in `x2` (unless axes is specified) and where \(\overline{v_i}\) denotes the complex conjugate if \(v\) is complex and the identity otherwise. (For a non-conjugated vector-matrix product, use `np.matvec(x2.mT, x1)`.)

  New in version 2.2.0.

  Parameters:
    x1, x2 array_like

      Input arrays, scalars not allowed.

    out ndarray, optional

      A location into which the result is stored. If provided, it must have the broadcasted shape of `x1` and `x2` with the summation axis removed. If not provided or None, a freshly-allocated array is used.

    **kwargs

      For other keyword-only arguments, see the ufunc docs.

  Returns:
    y ndarray

      The vector-matrix product of the inputs.

  Raises:
    ValueError

      If the last dimensions of `x1` and the one-but-last dimension of `x2` are not the same size.

      If a scalar value is passed in.

  See also

  `vecdot`

    Vector-vector product.

  `matvec`

    Matrix-vector product.

  `matmul`

    Matrix-matrix product.

  `einsum`

    Einstein summation convention.

  Examples

  Project a vector along X and Y.

      >>> v = np.array([0., 4., 2.])
      >>> a = np.array([[1., 0., 0.],
      ...               [0., 1., 0.],
      ...               [0., 0., 0.]])
      >>> np.vecmat(v, a)
      array([ 0.,  4., 0.])