dpnp.power

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

Calculates \(x1_i\) raised to \(x2_i\) for each element \(x1_i\) of the input array x1 with the respective element \(x2_i\) of the input array x2.

Note that dpnp.pow is an alias of dpnp.power.

For full documentation refer to numpy.power.

Parameters:

  • x1 ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array, expected to have a numeric data type.

  • x2 ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array, also expected to have a 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.

    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 bases in x1 raised to the exponents in x2 element-wise. 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.fmax

Element-wise maximum of array elements.

dpnp.fmin

Element-wise minimum of array elements.

dpnp.fmod

Calculate the element-wise remainder of division.

dpnp.float_power

Power function that promotes integers to floats.

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).

Examples

>>> import dpnp as dp
>>> a = dp.arange(6)
>>> dp.power(a, 3)
array([  0,   1,   8,  27,  64, 125])

Raise the bases to different exponents.

>>> b = dp.array([1.0, 2.0, 3.0, 3.0, 2.0, 1.0])
>>> dp.power(a, b)
array([ 0.,  1.,  8., 27., 16.,  5.])

The effect of broadcasting.

>>> c = dp.array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]])
>>> dp.power(a, c)
array([[ 0,  1,  8, 27, 16,  5],
        [ 0,  1,  8, 27, 16,  5]])

The ** operator can be used as a shorthand for power on dpnp.ndarray.

>>> b = dp.array([1, 2, 3, 3, 2, 1])
>>> a = dp.arange(6)
>>> a ** b
array([ 0,  1,  8, 27, 16,  5])

Negative values raised to a non-integral value will result in NaN.

>>> d = dp.array([-1.0, -4.0])
>>> dp.power(d, 1.5)
array([nan, nan])