dpnp.add

dpnp.add(x1, x2, out=None, order='K', dtype=None, casting='same_kind', **kwargs)

Calculates the sum for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

For full documentation refer to numpy.add.

Parameters:

x1 ({dpnp.ndarray, usm_ndarray}) -- Input arrays, expected to have numeric data type.
x2 ({dpnp.ndarray, usm_ndarray}) -- Input arrays, expected to have numeric data type.
out ({None, dpnp.ndarray, usm_ndarray}, optional) -- Output array to populate. Array must have the correct shape and the expected data type.
order ({None, "C", "F", "A", "K"}, optional) -- Memory layout of the newly output array, Cannot be provided together with out. Default: "K".
dtype ({None, dtype}, optional) -- If provided, the destination array will have this dtype. Cannot be provided together with out. Default: None.
casting ({"no", "equiv", "safe", "same_kind", "unsafe"}, optional) -- Controls what kind of data casting may occur. Cannot be provided together with out. Default: "safe".

Returns:

out -- An array containing the element-wise sums. The data type of the returned array is determined by the Type Promotion Rules.

Return type:

dpnp.ndarray

Limitations

Keyword arguments where and subok are supported with their default values. Other keyword arguments is currently unsupported. Otherwise NotImplementedError exception will be raised.

Notes

Equivalent to x1 + x2 in terms of array broadcasting.

Examples

>>> import dpnp as np
>>> a = np.array([1, 2, 3])
>>> b = np.array([1, 2, 3])
>>> np.add(a, b)
array([2, 4, 6])

>>> x1 = np.arange(9.0).reshape((3, 3))
>>> x2 = np.arange(3.0)
>>> np.add(x1, x2)
array([[  0.,   2.,   4.],
       [  3.,   5.,   7.],
       [  6.,   8.,  10.]])

The + operator can be used as a shorthand for add on dpnp.ndarray.

>>> x1 + x2
array([[  0.,   2.,   4.],
       [  3.,   5.,   7.],
       [  6.,   8.,  10.]])