Added missing classes from final year at OSU

OSU Coursework/CS 331 - Intro to Artificial Intelligence/Programming Assignment 3/reference/lib.py

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+import re
+from math import log
+from collections import defaultdict
+
+non_alphanum = re.compile(r'[\W_]+')
+flatten = lambda l: [item for sublist in l for item in sublist]
+
+
+def process_word(word):
+    return non_alphanum.sub('', word.lower())
+
+
+def process_document(document):
+    """ Returns a tuple. The first element is the review split into array of words.
+        The second element is an int indicating whether the review was positive. """
+    return (
+        [process_word(word) for word in document.split()][:-1],
+        int(document.split()[-1])
+    )
+
+
+def format_document(document, vocab):
+    return (
+            ','.join(['1' if word in document[0] else '0' for word in vocab])
+            + ',' + str(document[1])
+    )
+
+
+def preprocess(infilename, outfilename):
+    """ Returns a tuple in the form of (vocab, documents).
+        Vocab: a set of words, e.g. { 'great', 'steak', 'gross', ... }
+        Documents: a tuple containing the array of words in the document followed
+        by the sentiment of the document, represented by a 1 or 0, e.g.
+        (['i', 'liked', 'the', 'food'], 1) """
+
+    # Process input file
+    infile = open(infilename, 'r')
+    raw_documents = infile.read().splitlines()
+    documents = [process_document(document) for document in raw_documents]
+    vocab = {process_word(word) for document in raw_documents for word in document.split()[:-1]}
+    vocab.discard('')
+    infile.close()
+
+    # Format text for outfile and write
+    outfile = open(outfilename, 'w')
+    formatted_vocab = ','.join(sorted(list(vocab)))
+    formatted_documents = '\n'.join([format_document(document, vocab) for document in documents])
+    outfile.write(formatted_vocab + '\n' + formatted_documents)
+    outfile.close()
+
+    return vocab, documents
+
+
+def train_naive_bayes(vocab, documents, classes):
+    logprior = {}
+    loglikelihood = defaultdict(dict)
+    big_doc = {}
+
+    def get_logprior(documents, c):
+        class_document_count = len([doc for doc in documents if doc[1] == c])
+        total_document_count = len(documents)
+        return log(class_document_count / total_document_count)
+
+    def get_bigdoc(documents, c):
+        return flatten([doc[0] for doc in documents if doc[1] == c])
+
+    for c in classes:
+        logprior[c] = get_logprior(documents, c)
+        big_doc[c] = get_bigdoc(documents, c)
+        words_in_class_count = sum([big_doc[c].count(w) for w in vocab])
+
+        for word in vocab:
+            word_count = big_doc[c].count(word)
+            loglikelihood[word][c] = log((word_count + 1) / (words_in_class_count + 1))
+
+    return logprior, loglikelihood
+
+
+def test_naive_bayes(document, logprior, loglikelihood, classes, vocab):
+    # Filter words not in the vocab
+    document = [word for word in document[0] if word in vocab]
+    prob = {}
+
+    for c in classes:
+        prob[c] = logprior[c]
+        for word in document:
+            prob[c] = prob[c] + loglikelihood[word][c]
+
+    return max(prob.keys(), key=(lambda key: prob[key]))