mars.tensor.power#

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

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

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.

  • 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

float_power

power function that promotes integers to float

Examples

Cube each element in a list.

>>> import mars.tensor as mt

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