Function matmul

numpy/linalg/_linalg.py:3318–3398

Computes the matrix product. This function is Array API compatible, contrary to :func:`numpy.matmul`. Parameters ---------- x1 : array_like The first input array. x2 : array_like The second input array. Returns ------- out : ndarray

(x1, x2, /)

@array_function_dispatch(_matmul_dispatcher)
def matmul(x1, x2, /):
    """
    Computes the matrix product.

    This function is Array API compatible, contrary to
    :func:`numpy.matmul`.

    Parameters
    ----------
    x1 : array_like
        The first input array.
    x2 : array_like
        The second input array.

    Returns
    -------
    out : ndarray
        The matrix product of the inputs.
        This is a scalar only when both ``x1``, ``x2`` are 1-d vectors.

    Raises
    ------
    ValueError
        If the last dimension of ``x1`` is not the same size as
        the second-to-last dimension of ``x2``.

        If a scalar value is passed in.

    See Also
    --------
    numpy.matmul

    Examples
    --------
    For 2-D arrays it is the matrix product:

    >>> a = np.array([[1, 0],
    ...               [0, 1]])
    >>> b = np.array([[4, 1],
    ...               [2, 2]])
    >>> np.linalg.matmul(a, b)
    array([[4, 1],
           [2, 2]])

    For 2-D mixed with 1-D, the result is the usual.

    >>> a = np.array([[1, 0],
    ...               [0, 1]])
    >>> b = np.array([1, 2])
    >>> np.linalg.matmul(a, b)
    array([1, 2])
    >>> np.linalg.matmul(b, a)
    array([1, 2])


    Broadcasting is conventional for stacks of arrays

    >>> a = np.arange(2 * 2 * 4).reshape((2, 2, 4))