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I need to find a numpy.float64 value that is as close to zero as possible.
Numpy offers several constants that allow to do something similar:
np.finfo(np.float64).eps = 2.2204460492503131e-16np.finfo(np.float64).tiny = 2.2250738585072014e-308These are both reasonably small, but when I do this
>>> x = np.finfo(np.float64).tiny >>> x / 2 6.9533558078350043e-310 the result is even smaller. When using an impromptu binary search I can get down to about 1e-323, before the value is rounded down to 0.0.
Is there a constant for this in numpy that I am missing? Alternatively, is there a right way to do this?
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float is an alias for python float type. np. float32 and np. float64 are numpy specific 32 and 64-bit float types.
Python float values are represented as 64-bit double-precision values. 1.8 X 10308 is an approximate maximum value for any floating-point number. If it exceeds or exceeds the max value, Python returns an error with string inf (infinity).
For example, 97.98, 32.3+e18, -32.54e100 all are floating point numbers. Python float values are represented as 64-bit double-precision values. The maximum value any floating-point number can be is approx 1.8 x 10308.
Use np.nextafter.
>>> import numpy as np >>> np.nextafter(0, 1) 4.9406564584124654e-324 >>> np.nextafter(np.float32(0), np.float32(1)) 1.4012985e-45 2^-1075 is the smallest positive float. 2^-1075 = 5.10^-324
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