dpnp.add

dpnp.add(x1, x2, out=None, where=True, order='K', dtype=None, subok=True, **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, scalar}) -- First input array, may have any data type.

  • x2 ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array, also may have any data type.

  • out ({None, dpnp.ndarray, usm_ndarray}, optional) --

    Output array to populate. Array must have the correct shape and the expected data type.

    Default: None.

  • order ({None, "C", "F", "A", "K"}, optional) --

    Memory layout of the newly output array, if parameter out is None.

    Default: "K".

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

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

Notes

At least one of x1 or x2 must be an array.

If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

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.]])