maxframe.tensor.log1p#

maxframe.tensor.log1p(x, out=None, where=None, **kwargs)[source]#

Return the natural logarithm of one plus the input tensor, element-wise.

Calculates log(1 + x).

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 – Natural logarithm of 1 + x, element-wise.

Return type:

Tensor

See also

expm1

exp(x) - 1, the inverse of log1p.

Notes

For real-valued input, log1p is accurate also for x so small that 1 + x == 1 in floating-point accuracy.

Logarithm is a multivalued function: for each x there is an infinite number of z such that exp(z) = 1 + x. The convention is to return the z whose imaginary part lies in [-pi, pi].

For real-valued input data types, log1p always returns real output. For each value that cannot be expressed as a real number or infinity, it yields nan and sets the invalid floating point error flag.

For complex-valued input, log1p is a complex analytical function that has a branch cut [-inf, -1] and is continuous from above on it. log1p handles the floating-point negative zero as an infinitesimal negative number, conforming to the C99 standard.

References

Examples

>>> import maxframe.tensor as mt

>>> mt.log1p(1e-99).execute()
1e-99
>>> mt.log(1 + 1e-99).execute()
0.0
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