mars.tensor.float_power#

mars.tensor.float_power(x1, x2, out=None, where=None, **kwargs)[source]#

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

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.

Parameters

x1 (array_like) – The bases.
x2 (array_like) – The exponents.
out (Tensor, None, or tuple of Tensor and None, optional) – A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated tensor is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
where (array_like, optional) – Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.
**kwargs –

Returns

y – The bases in x1 raised to the exponents in x2.

Return type

Tensor

See also

power: power function that preserves type

Examples

Cube each element in a list.

>>> import mars.tensor as mt

>>> x1 = range(6)
>>> x1
[0, 1, 2, 3, 4, 5]
>>> mt.float_power(x1, 3).execute()
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]
>>> mt.float_power(x1, x2).execute()
array([  0.,   1.,   8.,  27.,  16.,   5.])

The effect of broadcasting.

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