Handle Floating-Point Precision
In this step, we will discuss how to handle floating-point precision when comparing floating-point numbers with zero. Due to the way floating-point numbers are represented in computers, they are often subject to small rounding errors. This can lead to unexpected results when comparing them directly with zero.
For example, a calculation that should theoretically result in zero might actually produce a very small non-zero number like 1e-16
(0.0000000000000001). Directly comparing this number with zero using ==
will return False
, which might not be the desired outcome.
To handle this, we typically use a tolerance value (also known as epsilon) to check if a floating-point number is "close enough" to zero.
Let's create a Python script to demonstrate this.
-
Open the VS Code editor in the LabEx environment.
-
Create a new file named float_precision.py
in the ~/project
directory.
~/project/float_precision.py
-
Add the following code to float_precision.py
:
## Import the math module
import math
## Define a tolerance value (epsilon)
tolerance = 1e-9 ## A small value like 0.000000001
## A floating-point number that is very close to zero due to calculation
number = 1e-10
## Direct comparison with zero (may fail)
if number == 0:
print("Direct comparison: number is zero")
else:
print("Direct comparison: number is not zero")
## Using tolerance to compare with zero
if math.isclose(number, 0, abs_tol=tolerance):
print("Comparison with tolerance: number is close to zero")
else:
print("Comparison with tolerance: number is not close to zero")
## Another example with a slightly larger number
number = 1e-7
## Direct comparison with zero (will fail)
if number == 0:
print("Direct comparison: number is zero")
else:
print("Direct comparison: number is not zero")
## Using tolerance to compare with zero
if math.isclose(number, 0, abs_tol=tolerance):
print("Comparison with tolerance: number is close to zero")
else:
print("Comparison with tolerance: number is not close to zero")
This script demonstrates how to use the math.isclose()
function with a tolerance value to compare floating-point numbers with zero.
-
Save the file.
-
Run the script using the python
command in the terminal:
python float_precision.py
You should see the following output:
Direct comparison: number is not zero
Comparison with tolerance: number is close to zero
Direct comparison: number is not zero
Comparison with tolerance: number is not close to zero
As you can see, the direct comparison fails because the number is not exactly zero. However, when using a tolerance value with math.isclose()
, the comparison correctly identifies that the number is close to zero.
The math.isclose()
function checks if two floating-point values are close to each other within a specified tolerance. The abs_tol
argument specifies the absolute tolerance. If the absolute difference between the two numbers is less than or equal to the tolerance, the function returns True
.
By using a tolerance value, you can avoid issues caused by floating-point precision and ensure that your comparisons with zero are accurate and reliable.