# mars.tensor.isnan¶

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

Test element-wise for NaN and return result as a boolean tensor.

x : array_like
Input tensor.
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

y : Tensor or bool

For scalar input, the result is a new boolean with value True if the input is NaN; otherwise the value is False.

For array input, the result is a boolean tensor of the same dimensions as the input and the values are True if the corresponding element of the input is NaN; otherwise the values are False.

isinf, isneginf, isposinf, isfinite, isnat

Mars uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity.

>>> import mars.tensor as mt

>>> mt.isnan(mt.nan).execute()
True
>>> mt.isnan(mt.inf).execute()
False
>>> mt.isnan([mt.log(-1.).execute(),1.,mt.log(0).execute()]).execute()
array([ True, False, False])