Today, when I was writing the sinc function using numpy, I accidentally found that the function could actually get 1 when x=0. It was incredible. Logically, the function was meaningless when x=0. I studied it and found that numpy automatically replaced 0 with a very small number when generating an array.
In[2]: import numpy as np
In[3]: (-1, 1, 0.1)
Out[3]:
array([ -1.00000000e+00, -9.00000000e-01, -8.00000000e-01,
-7.00000000e-01, -6.00000000e-01, -5.00000000e-01,
-4.00000000e-01, -3.00000000e-01, -2.00000000e-01,
-1.00000000e-01, -2.22044605e-16, 1.00000000e-01,
2.00000000e-01, 3.00000000e-01, 4.00000000e-01,
5.00000000e-01, 6.00000000e-01, 7.00000000e-01,
8.00000000e-01, 9.00000000e-01])
In[4]: (-1, 0.9, 20)
Out[4]:
array([ -1.00000000e+00, -9.00000000e-01, -8.00000000e-01,
-7.00000000e-01, -6.00000000e-01, -5.00000000e-01,
-4.00000000e-01, -3.00000000e-01, -2.00000000e-01,
-1.00000000e-01, -1.11022302e-16, 1.00000000e-01,
2.00000000e-01, 3.00000000e-01, 4.00000000e-01,
5.00000000e-01, 6.00000000e-01, 7.00000000e-01,
8.00000000e-01, 9.00000000e-01])
The two functions aparte and linspace are replaced by a very small number of e-16 where they should be 0.
The above article briefly discusses the zero value issue of numpy generating arrays is all the content I have shared with you. I hope you can give you a reference and I hope you can support me more.