- mars.tensor.sqrt(x, out=None, where=None, **kwargs)#
Return the positive square-root of an tensor, element-wise.
x (array_like) – The values whose square-roots are required.
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.
y – An tensor of the same shape as x, containing the positive square-root of each element in x. If any element in x is complex, a complex tensor is returned (and the square-roots of negative reals are calculated). If all of the elements in x are real, so is y, with negative elements returning
nan. If out was provided, y is a reference to it.
- Return type
sqrt has–consistent with common convention–as its branch cut the real “interval” [-inf, 0), and is continuous from above on it. A branch cut is a curve in the complex plane across which a given complex function fails to be continuous.
>>> import mars.tensor as mt
>>> mt.sqrt([1,4,9]).execute() array([ 1., 2., 3.])
>>> mt.sqrt([4, -1, -3+4J]).execute() array([ 2.+0.j, 0.+1.j, 1.+2.j])
>>> mt.sqrt([4, -1, mt.inf]).execute() array([ 2., NaN, Inf])