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
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]])