Requirements:
- {doc}
string_formatting.
There are an infinite number of real {{ -- }} er {{ -- }} numbers, for example, there are an infinite number of real numbers between 0 and 1.
The computer stores real numbers as floating point numbers. The typical format the computer uses for floating point numbers uses 8 bytes (64 bits) of memory per number. That means the computer can only represent some of the numbers between 0 and 1 (for example).
One number that standard floating point numbers cannot represent exactly is
0.1. Here we print the value with 20 digits after the decimal point to show
that (see {doc}string_formatting):
print('{:.20f}'.format(0.1)) 0.10000000000000000555
This means that when we do mathematical operations on floating point numbers, the exact result of the operation may be a number that the computer cannot represent. In that case the software implementing the calculation can at best give us the nearest floating point number that it can represent:
print('{:1.20f}'.format(1 / 10)) 0.10000000000000000555
For this reason, sometimes the results of doing floating point calculations are not exactly accurate. This is called [floating point error]. In the case above the floating point error is very small {{ -- }} 0.00000000000000000555.
Floating point error means that we have to be careful comparing results of floating point calculations, because the result may be incorrect by some small amount:
import numpy as np c = np.cos(1)
np.arccos(c) == 1 False np.arccos(c) 0.99999999999999989
In this case, we probably want to check that the result is equal within some
reasonable error due to the limited precision of floating point. For example,
np.allclose checks whether two numbers are almost equal within some
default tolerance levels:
np.allclose(np.arccos(c), 1) True