dpnp.logical_xor

dpnp.logical_xor(x1, x2, out=None, where=True, order='K', dtype=None, subok=True, **kwargs)

Computes the logical XOR 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.logical_xor.

Parameters:

x1 ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array. Both inputs x1 and x2 can not be scalars at the same time.
x2 ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array. Both inputs x1 and x2 can not be scalars at the same time. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).
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 ({"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 logical XOR results. The shape is determined by broadcasting.

Return type:

dpnp.ndarray

Limitations

Parameters where and subok are supported with their default values. Otherwise NotImplementedError exception will be raised.

See also

dpnp.logical_and: Compute the truth value of x1 AND x2 element-wise.
dpnp.logical_or: Compute the truth value of x1 OR x2 element-wise.
dpnp.logical_not: Compute the truth value of NOT x element-wise.
dpnp.bitwise_xor: Compute the bit-wise XOR of two arrays element-wise.

Examples

>>> import dpnp as np
>>> x1 = np.array([True, True, False, False])
>>> x2 = np.array([True, False, True, False])
>>> np.logical_xor(x1, x2)
array([False,  True,  True, False])

>>> x = np.arange(5)
>>> np.logical_xor(x < 1, x > 3)
array([ True, False, False, False,  True])

Simple example showing support of broadcasting

>>> np.logical_xor(0, np.eye(2))
array([[ True, False],
       [False,  True]])