dpnp.float_power

dpnp.float_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.

This differs from the power function in that boolean, integers, and float16 are promoted to floats with a minimum precision of float32 so that the result is always inexact. The intent is that the function will return a usable result for negative powers and seldom overflow for positive powers.

Negative values raised to a non-integral value will return NaN. To get complex results, cast the input to complex, or specify the dtype to be one of complex dtype.

For full documentation refer to numpy.float_power.

Parameters:

x1 ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array, expected to have floating-point data types. 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 floating-point data types. 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 bases in x1 raised to the exponents in x2 element-wise.

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.power: Power function that preserves type.

Examples

>>> import dpnp as np

Cube each element in an array:

>>> x1 = np.arange(6)
>>> x1
array([0, 1, 2, 3, 4, 5])
>>> np.float_power(x1, 3)
array([  0.,   1.,   8.,  27.,  64., 125.])

Raise the bases to different exponents:

>>> x2 = np.array([1.0, 2.0, 3.0, 3.0, 2.0, 1.0])
>>> np.float_power(x1, x2)
array([ 0.,  1.,  8., 27., 16.,  5.])

The effect of broadcasting:

>>> x2 = np.array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]])
>>> x2
array([[1, 2, 3, 3, 2, 1],
       [1, 2, 3, 3, 2, 1]])
>>> np.float_power(x1, x2)
array([[ 0.,  1.,  8., 27., 16.,  5.],
       [ 0.,  1.,  8., 27., 16.,  5.]])

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

>>> x3 = np.array([-1, -4])
>>> np.float_power(x3, 1.5)
array([nan, nan])

To get complex results, give the argument one of complex dtype, i.e. dtype=np.complex64:

>>> np.float_power(x3, 1.5, dtype=np.complex64)
array([1.1924881e-08-1.j, 9.5399045e-08-8.j], dtype=complex64)