This assignment is due Wednesday, November 23. There are two points of extra credit for passing the test_setup test case, due early on November 16 .

Your work must be your own. Do not submit other people's work as your own, and do not allow others to submit your work as theirs. You may talk with other students about the homework, but you may not share code with them in any way. If you have a question about an error message or about why a numpy function returns what it does, post it on Piazza. If you need help debugging your code, make a private post on Piazza or come to office hours. If you're unsure of the academic integrity policies, ask for help; we can help you avoid breaking the rules, but we can't un-report a suspected violation.

By pushing your code to GitHub, you agree to these rules, and understand that there may be severe consequences for violating them.

• Nothing much: the autograder should work the same as with HW3.

As before, your work will be graded and aggregated using an autograder that will download the code and free response questions from each student's repository. If you don't follow the instructions, you run the risk of getting zero points. The test_setup test case gives you extra credit for following these instructions and will make it possible to grade your work easily.

You should be able to use the same cs349hw2 environment that you used for HW2. If you deleted that environment, please refer to the HW2 readme to recreate it.

The detailed instructions for the work you need to do are in problems.md. You will also find it very helpful to read included naive_bayes.pdf writeup.

For the coding portion of the assignment, you will:

• Solve some simple practice problems with sparse matrices
• Write a stable softmax and log sum functions
• Implement a fully-supervised NaiveBayes classifier
• Implement a semi-supervised NaiveBayes classifier

You will also write up answers to the free response questions.

In every function where you need to write code, there is a raise NotImplementedError in the code. The test cases will guide you through the work you need to do and tell you how many points you've earned. The test cases can be run from the root directory of this repository with:

python -m pytest

To run a single test, you can call e.g., python -m pytest -s -k test_setup. The -s means that any print statements you include will in fact be printed; the default behavior (python -m pytest) will suppress everything but the pytest output.

We will use these test cases to grade your work! Even if you change the test cases such that you pass the tests on your computer, we're still going to use the original test cases to grade your assignment.

Simply post on Piazza, and we'll get back to you.