mars.tensor.isinf¶

mars.tensor.
isinf
(x, out=None, where=None, **kwargs)[source]¶ Test elementwise for positive or negative infinity.
Returns a boolean array of the same shape as x, True where
x == +/inf
, otherwise False. Parameters
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 freshlyallocated 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 – 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
Notes
Mars uses the IEEE Standard for Binary FloatingPoint 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.
Examples
>>> 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])