The purpose of this programming assignment is to:

• help you get up-to-speed with the Python programming language
• provides an introduction to some simple algorithms for statistics-based natural language processing (NLP).

Component	Description
text_gen.py	Starter code
huck.txt	Sample text for learning word distribution
text_gen_tests.py	Code to test your solution

Get started by completing one or both of the following online tutorials:

• https://www.programiz.com/python-programming/tutorial
• http://www.learnpython.org

Once you have completed the tutorials above, log into Coding Bat.

After you log in, follow the "prefs" link to enter your name and my email address (molloykp@jmu.edu) in the "Teacher Share" box. You must complete at least one exercise from each of the non-warmup categories (at least 6 in all).

Feel free to skim the tutorials above if you are already comfortable programming in Python. If you are new to Python, I strongly encourage you to work through each step of the tutorials carefully. It will probably take a couple of hours, but the time spent will pay dividends throughout the semester.

Natural language processing (NLP) is an area of machine learning that involves understanding and generating human language. It is a big area, so, we will just graze the surface this semester. The goal for this PA is to take a tiny step into NLP by using n-grams for the purpose of generating random text in the style of particular authors or documents. Take a minute to read over the Wikipedia pages on n-grams and bigrams:

• http://en.wikipedia.org/wiki/N-gram
• http://en.wikipedia.org/wiki/Bigram

Your objective for this part of the PA is to complete the four unfinished functions in text_gen.py. Expected results are included in the documentation preceeding each method (in Python, these are called docstrings). You can use the unittests in text_gen_tests.py to help test your code. A text generation method based on unigrams is already completed implemented in text_gen.py. You can test this by copying the file huck.txt into the same folder as text_gen.py and executing the code. From the command prompt, this would be:

• python3 text_gen.py --infile huck.txt

Running this command should generate output like the following:

tell shot up finn man if unloads on wonderful go know swear s myself no to good in and no home times a pick inside janeero s warn misunderstood sometimes sweat wouldn sakes and i away didn i as next furnish two the it put his dick take scared nor i on said we a was i blankets up poor bull him and asked what mary old and you that night en and comfortable all from and re it running we a lonesome but bible he up hitched a t a i telling says yarter hot call can if then

Text generated using bigrams and trigrams should look significantly more English-like.

Note that the code in text_gen.py uses the terms "bigram" and "trigram" in slightly non-standard ways. Typically, bigrams encode the probability of particular word pairs. For example, bigrams could tell us that the probability of the sequence ("pineapple", "extrapolate") is relatively low, while the probability of the sequence ("going", "home") is relatively high. Since we are interested in generating text, it is more useful for us to store the probability that one word will follow another: the probability of observing "home" after "going" is much higher than the probability of observing "cup" after "going". It turns out that the two representations are interchangeable, as described on the Wikipedia bigram page.

You should submit the completed version of and text_gen.py to Autolab by the deadline.

This project will be graded on the following scale:

CodingBat exercises	20%
Code in text_gen.py more or less conforms to PEP8	10%
Functionality of bigram functions in text_gen.py	60%
Functionality of trigram functions in text_gen.py	10%

Keep in mind that testing does not prove the correctness of code. It is possible to write incorrect code that passes the provided unit tests. As always, it is your responsibility to ensure that your code functions correctly.