GitHub - diop/kebetu: A Markov Chain generator for "believable" African proverbs.

Make School CS 1.2: How Data Structures Work - Winter 2019

Project goals:
- Use a Python script to randomly generate words from a dictionary.
- Build sentences by sampling these words using a Markov language model.
- Implement grammar rules parsed from the text of a large document set.
- Build data structures including linked lists, hash tables, stacks, queues, and heaps to store the words and sentences.
- Analyze the inner workings and performance tradeoffs of each data structure.
- Deploy your language model to a Flask web server on Heroku and connect it to Twitter to let users tweet their favorite results.
Learning outcomes:
- Master Python data structures and data processing algorithms
- Implement data structures including linked lists, hash tables, stacks, queues and heaps.
- Parse text documents and store information in data structures
- Implement sampling methods and Markov chains on data sets

Name		Name	Last commit message	Last commit date
Latest commit History 59 Commits
bin		bin
include		include
lib/python3.7		lib/python3.7
static		static
templates		templates
.DS_Store		.DS_Store
.Python		.Python
.gitignore		.gitignore
Procfile		Procfile
README.md		README.md
app.py		app.py
cleanup.py		cleanup.py
corpus.txt		corpus.txt
corpus_original.txt		corpus_original.txt
corpus_with_period.txt		corpus_with_period.txt
dictogram.py		dictogram.py
dictogram_test.py		dictogram_test.py
fishes.txt		fishes.txt
hashtable.py		hashtable.py
hashtable_test.py		hashtable_test.py
linkedlist.py		linkedlist.py
linkedlist_test.py		linkedlist_test.py
listogram.py		listogram.py
listogram_test.py		listogram_test.py
random_dictionary_words.py		random_dictionary_words.py
requirements.txt		requirements.txt
sample.py		sample.py
screenshot.png		screenshot.png
sentence.py		sentence.py
snippet.txt		snippet.txt
tokenized.py		tokenized.py
word_count.py		word_count.py

diop/kebetu