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]])