def load_bigrams():
tokens, vocab = read_data('Dataset2.txt')
tokens = preprocess_text(tokens)
tr_tokens = tokens[math.floor(len(tokens) / 2):]
te_tokens = tokens[:math.floor(len(tokens) / 2)]
# get the bigrams by sentence and the corresponding vocabulary
tr_bigrms, vocab2 = find_bigrams(tr_tokens)
te_bigrms, vocab2 = find_bigrams(te_tokens)
# find frequency of bigrams and add to the data
trY, trX = find_cumFrequency(tr_tokens, tr_bigrms, vocab, vocab2)
teY, teX = find_cumFrequency(te_tokens, te_bigrms, vocab, vocab2)
return trX, teX, trY, teY
def load_unigrams():
# tokenize data by sentences
tokens, vocab = read_data('Dataset2.txt')
# clean up data
tokens = preprocess_text(tokens)
# split into training and test sets
tr_tokens = tokens[math.floor(len(tokens) / 2):]
te_tokens = tokens[:math.floor(len(tokens) / 2)]
# find frequency: positive: 0; negative: 1
trY, trX = find_frequency(tr_tokens, vocab)
teY, teX = find_frequency(te_tokens, vocab)
return trX, teX, trY, teY
def main():
data = data_utilities.read_data('course_data.json')
formatted_data = format_data(data)
data_utilities.write_data(formatted_data, 'parse_formatted_course_data.json')
titles.append(class_datum.get('full_title'))
completer = MyCompleter(titles)
top = completer.complete(query, range(5))
print top
class MyCompleter(object): # Custom completer
def __init__(self, options):
self.options = sorted(options)
def complete(self, text, states):
if text: # cache matches (entries that start with entered text)
self.matches = [s for s in self.options
if s and s.startswith(text)]
# return match indexed by state
try:
complete = []
print len(self.matches)
for state in states:
complete.append(self.matches[state])
return complete
except IndexError as e:
print e
return None
if __name__ == '__main__':
class_data = data_utilities.read_data('all_class_data.json')['results']
query = raw_input("Class query: ")
parse_query_search(class_data, query)
