dpnp.ufunc.outer

ufunc.outer(x1, x2, out=None, where=True, order='K', dtype=None, subok=True, **kwargs)[source]

Apply the ufunc op to all pairs (a, b) with a in A and b in B.

Parameters:

  • x1 ({dpnp.ndarray, usm_ndarray}) -- First input array.

  • x2 ({dpnp.ndarray, usm_ndarray}) -- Second input array.

  • out ({None, dpnp.ndarray, usm_ndarray}, optional) -- Output array to populate. Array must have the correct shape and the expected data type.

  • **kwargs -- For other keyword-only arguments, see the dpnp.ufunc.

Returns:

out -- Output array. The data type of the returned array is determined by the Type Promotion Rules.

Return type:

dpnp.ndarray

Limitations

Parameters where and subok are supported with their default values. Keyword argument kwargs is currently unsupported. Otherwise NotImplementedError exception will be raised.

See also

dpnp.outer

A less powerful version of dpnp.multiply.outer that ravels all inputs to 1D. This exists primarily for compatibility with old code.

dpnp.tensordot

dpnp.tensordot(a, b, axes=((), ())) and dpnp.multiply.outer(a, b) behave same for all dimensions of a and b.

Examples

>>> import dpnp as np
>>> A = np.array([1, 2, 3])
>>> B = np.array([4, 5, 6])
>>> np.multiply.outer(A, B)
array([[ 4,  5,  6],
       [ 8, 10, 12],
       [12, 15, 18]])

A multi-dimensional example:

>>> A = np.array([[1, 2, 3], [4, 5, 6]])
>>> A.shape
(2, 3)
>>> B = np.array([[1, 2, 3, 4]])
>>> B.shape
(1, 4)
>>> C = np.multiply.outer(A, B)
>>> C.shape; C
(2, 3, 1, 4)
array([[[[ 1,  2,  3,  4]],
        [[ 2,  4,  6,  8]],
        [[ 3,  6,  9, 12]]],
       [[[ 4,  8, 12, 16]],
        [[ 5, 10, 15, 20]],
        [[ 6, 12, 18, 24]]]])