maxframe.tensor.isinf#

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

Test element-wise 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 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 – 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

See also

isneginf, isposinf, isnan, isfinite

Notes

MaxFrame 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.

Examples

>>> import maxframe.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])
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