- mars.tensor.isinf(x, out=None, where=None, **kwargs)#
Test element-wise for positive or negative infinity.
Returns a boolean array of the same shape as x, True where
x == +/-inf, otherwise False.
x (array_like) – Input values
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 – For scalar input, the result is a new boolean with value True if the input is positive or negative infinity; otherwise the value is False.
For tensor input, the result is a boolean tensor with the same shape as the input and the values are True where the corresponding element of the input is positive or negative infinity; elsewhere the values are False. If a second argument was supplied the result is stored there. If the type of that array is a numeric type the result is represented as zeros and ones, if the type is boolean then as False and True, respectively. The return value y is then a reference to that tensor.
- Return type
bool (scalar) or boolean Tensor
Mars uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754).
Errors result if the second argument is supplied when the first argument is a scalar, or if the first and second arguments have different shapes.
>>> import mars.tensor as mt
>>> mt.isinf(mt.inf).execute() True >>> mt.isinf(mt.nan).execute() False >>> mt.isinf(mt.NINF).execute() True >>> mt.isinf([mt.inf, -mt.inf, 1.0, mt.nan]).execute() array([ True, True, False, False])
>>> x = mt.array([-mt.inf, 0., mt.inf]) >>> y = mt.array([2, 2, 2]) >>> mt.isinf(x, y).execute() array([1, 0, 1]) >>> y.execute() array([1, 0, 1])