mars.tensor.sqrt#

mars.tensor.sqrt(x, out=None, where=None, **kwargs)[source]#

Return the positive square-root of an tensor, element-wise.

Parameters

  • 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.

  • **kwargs –

Returns

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

Tensor

Notes

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.

Examples

>>> 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])