Assignment: Confidence Interval

📅 Deadline: 6.11.2024 21:59

🏦 Points: 2

In this assignment, you are tasked with computing a confidence interval using the Hoeffding inequality. You can find the complete description of the assignment in the Assignment PDF.

You are provided with a template containing the following files:

• main.py: This file includes the function confidence_interval that you are required to implement. It loads the input, verifies the correctness of the confidence_interval output, and writes the results to a file readable by the BRUTE system.
• utils.py: Contains helper functions for loading and saving data. You do not need to modify this file.
• test-cases: A folder containing public test cases to help you verify your implementation before submitting to BRUTE.

Your objective is to implement the function confidence_interval in main.py.

After completing your implementation, you can test your solution using the following commands before submitting it to BRUTE:

python main.py test-cases/public/instances/instance_1.json

Expected output:

The true risk is in the interval [-0.30, 0.30] with probability atleast 0.95
Comparing with reference solution
Test OK

python main.py test-cases/public/instances/instance_2.json

Expected output:

The true risk is in the interval [-0.61, 0.61] with probability atleast 0.95
Comparing with reference solution
Test OK

python main.py test-cases/public/instances/instance_3.json

Expected output:

The true risk is in the interval [0.13, 0.37] with probability atleast 0.9
Comparing with reference solution
Test OK

python main.py test-cases/public/instances/instance_4.json

Expected output:

The true risk is in the interval [-0.23, 0.75] with probability atleast 0.9
Comparing with reference solution
Test OK

python main.py test-cases/public/instances/instance_5.json

Expected output:

The true risk is in the interval [0.06, 0.07] with probability atleast 0.95
Comparing with reference solution
Test OK

python main.py test-cases/public/instances/instance_6.json

Expected output:

The true risk is in the interval [0.74, 0.82] with probability atleast 0.95
Comparing with reference solution
Test OK

• Submit the completed code as a .zip via BRUTE.
• Make sure your implementation passes the test cases provided above. Good luck! 😊