maxframe.tensor.nan_to_num#

maxframe.tensor.nan_to_num(x, copy=True, **kwargs)[source]#

Replace nan with zero and inf with large finite numbers.

If x is inexact, NaN is replaced by zero, and infinity and -infinity replaced by the respectively largest and most negative finite floating point values representable by x.dtype.

For complex dtypes, the above is applied to each of the real and imaginary components of x separately.

If x is not inexact, then no replacements are made.

Parameters:

x (array_like) – Input data.
copy (bool, optional) – Whether to create a copy of x (True) or to replace values in-place (False). The in-place operation only occurs if casting to an array does not require a copy. Default is True.

Returns:

out – x, with the non-finite values replaced. If copy is False, this may be x itself.

Return type:

Tensor

See also

isinf: Shows which elements are positive or negative infinity.
isneginf: Shows which elements are negative infinity.
isposinf: Shows which elements are positive infinity.
isnan: Shows which elements are Not a Number (NaN).
isfinite: Shows which elements are finite (not NaN, not infinity)

Notes

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

Examples

>>> import maxframe.tensor as mt

>>> x = mt.array([mt.inf, -mt.inf, mt.nan, -128, 128])
>>> mt.nan_to_num(x).execute()
array([  1.79769313e+308,  -1.79769313e+308,   0.00000000e+000,
        -1.28000000e+002,   1.28000000e+002])
>>> y = mt.array([complex(mt.inf, mt.nan), mt.nan, complex(mt.nan, mt.inf)])
>>> mt.nan_to_num(y).execute()
array([  1.79769313e+308 +0.00000000e+000j,
         0.00000000e+000 +0.00000000e+000j,
         0.00000000e+000 +1.79769313e+308j])

maxframe.tensor.nan_to_num#

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