I don't want to use -inf with np.log

TL;DR

Specify the minimum value (maximum value) with np.clip!

Execution environment

What's going on?

In the float type of python, the value less than 1e-323 will be 0.0. The python float type can range from -1.7976931348623157e + 308 to 1.9796931348623157e + 308 in many environments.

$ python
>>> a = 1e-323
>>> b = 1e-324
>>> print(a)
 1e-323
>>> print(b)
 0.0

Since b becomes 0.0, if you log for this b, an error will occur and it will become -inf. Also, a 0% warning will appear.

>>> import numpy as np
>>> b = 1e-324
>>> np.log(b)
 __main__:1: RuntimeWarning: divide by zero encountered in log
 -inf

solution

Specify the minimum value (maximum value) with np.clip.

>>> import numpy as np
>>> b = 1e-324
>>> np.log(np.clip(a=b, a_min=1e-323, a_max=1e+10))
 -743.7469247408213

By specifying the minimum value (maximum value), it will be possible to calculate even if a value outside the float range is entered. However, values that exceed the minimum value (maximum value) are rounded to the set values, which has the disadvantage of increasing the error.

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