dpnp.not_equal

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

Calculates inequality test results 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.not_equal.

Parameters:

• x1 ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array, expected to have numeric data type. Both inputs x1 and x2 can not be scalars at the same time.

• x2 ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array, also expected to have numeric data type. Both inputs x1 and x2 can not be scalars at the same time.

• 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 result of element-wise inequality comparison. The returned array has a data type of bool.

Return type:

dpnp.ndarray

Limitations

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

See also

dpnp.equal

Return (x1 == x2) element-wise.

dpnp.greater

Return the truth value of (x1 > x2) element-wise.

dpnp.greater_equal

Return the truth value of (x1 >= x2) element-wise.

dpnp.less

Return the truth value of (x1 < x2) element-wise.

dpnp.less_equal

Return the truth value of (x1 =< x2) element-wise.

Examples

>>> import dpnp as np
>>> x1 = np.array([1., 2.])
>>> x2 = np.arange(1., 3.)
>>> np.not_equal(x1, x2)
array([False, False])

The != operator can be used as a shorthand for not_equal on dpnp.ndarray.

>>> a = np.array([1., 2.])
>>> b = np.array([1., 3.])
>>> a != b
array([False,  True])