jax.numpy.power¶

jax.numpy.power(x1, x2)[source]¶

First array elements raised to powers from second array, element-wise.

LAX-backend implementation of power(). Original docstring below.

power(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj])

Raise each base in x1 to the positionally-corresponding power in x2. x1 and x2 must be broadcastable to the same shape. Note that an integer type raised to a negative integer power will raise a ValueError.

Parameters

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

Returns

y – The bases in x1 raised to the exponents in x2. This is a scalar if both x1 and x2 are scalars.

Return type

ndarray

See also

float_power(): power function that promotes integers to float

Examples

Cube each element in a list.

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

Raise the bases to different exponents.

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