jax.numpy.float_power

jax.numpy.float_power(x1, x2)

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

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

float_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. This differs from the power function in that integers, float16, and float32 are promoted to floats with a minimum precision of float64 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.

New in version 1.12.0.

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

power()

power function that preserves type

Examples

Cube each element in a list.

>>> x1 = range(6)
>>> x1
[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 = [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.]])