vecdot

Computes the (vector) dot product of two arrays.

Parameters:

Name	Type	Description	Default
x1	array_like	Input sparse arrays	required
x2	array_like	Input sparse arrays	required
axis	int	The axis to reduce over.	-1

Returns:

Name	Type	Description
out	Union[SparseArray, ndarray]	Sparse or 0-D array containing dot product.

Source code in sparse/numba_backend/_common.py

def vecdot(x1, x2, /, *, axis=-1):
    """
    Computes the (vector) dot product of two arrays.

    Parameters
    ----------
    x1, x2 : array_like
        Input sparse arrays
    axis : int
        The axis to reduce over.

    Returns
    -------
    out : Union[SparseArray, numpy.ndarray]
        Sparse or 0-D array containing dot product.
    """
    ndmin = builtins.min((x1.ndim, x2.ndim))
    if not (-ndmin <= axis < ndmin) or x1.shape[axis] != x2.shape[axis]:
        raise ValueError("Shapes must match along `axis`.")

    if np.issubdtype(x1.dtype, np.complexfloating):
        x1 = np.conjugate(x1)

    return np.sum(x1 * x2, axis=axis, dtype=np.result_type(x1, x2))