import sqlite3
from sqlite3 import Error
from markov_chain import MarkovChain
chain = MarkovChain()
database = '/home/drue/Deployment/star_trek_club/star_trek_db.sqlite3'
connection = sqlite3.connect(database)
cursor = connection.cursor()
char_name = 'PICARD'
cursor.execute('SELECT id FROM characters WHERE name=?', (char_name, ))
char_id = cursor.fetchone()[0]
cursor.execute('SELECT line FROM lines WHERE character_id=?', (char_id, ))
for result in cursor.fetchall():
chain.train(result[0].replace('...', '').replace('--', ''))
chain.save_training(f'bin/star_trek/{char_name}.bin')
max_id=next_id,
lang='en')
for t in tweets['statuses']:
if EXCLUDE_WORDS.search(t['full_text']) is None:
tweet = TEXT_ONLY.sub(' ', t['full_text'])
tweet = RETWEET.sub(' ', tweet)
tweet = USER_NAME.sub(' ', tweet)
tweet = LINKS.sub(' ', tweet)
tweet = AMPERSAND.sub('and', tweet)
tweet = TYPO_HASHTAGS.sub(fix_hashtag, tweet)
tweet = TYPO_PERIOD.sub(fix_period, tweet)
tweet = TYPO_QUESTION.sub(fix_question, tweet)
tweet = TYPO_EXCLAMATION.sub(fix_exclamation, tweet)
tweet = LONE_PUNCTUATION.sub(' ', tweet)
tweet = GT.sub('>', tweet)
tweet = LT.sub('<', tweet)
chain.train(tweet)
print(f'len(chain.tree): {len(chain.tree)}')
chain.bulk_adjust_weights(fitness_functions=[
aw_mult(aw_favor_complexity, .001),
aw_mult(aw_favor_punctuation, .00015),
aw_mult(aw_favor_alternating_complexity, .1)
],
iterations=len(chain.tree))
chain.save_training('bin/twitter/trending.bin')
print(
f'Sample tweet: {chain.generate_tweet(append_tag="Category: #trending")}')
break
print(f'# of tweets: {len(tweets)}')
for t in tweets:
# if 'retweeted_status' in t:
# continue
# tweet = USER_NAME.sub(' ', t['full_text'])
tweet = LINKS.sub(' ', t['full_text'])
tweet = AMPERSAND.sub('&', tweet)
tweet = GT.sub('>', tweet)
tweet = LT.sub('<', tweet)
chain.train(tweet)
print(f'length of chain: {len(chain.tree)}\n')
# chain.bulk_adjust_weights(fitness_functions=[aw_mult(aw_favor_complexity, .001), aw_mult(aw_favor_punctuation, .00015), aw_mult(aw_favor_alternating_complexity, .1)], iterations=len(chain.tree))
chain.save_training(f'bin/twitter/{user}.bin')
tweets = [tweets[i] for i in range(100)]
tweets.reverse()
print(f'{"—"*64}\n')
replies = 0
for t in tweets:
replies += 1
print(f'___tweet #{replies}___\n')
print(f'___original tweet:___\n{t["full_text"]}\n')
words = sorted(re.split(r'[^a-zA-Z#]', t['full_text']),
key=lambda w: len(w),
reverse=True)
begin = None
print(f'search_term: {user}')
tweets = twit.statuses.user_timeline(screen_name=user, count=200, tweet_mode='extended', include_rts=False, trim_user=True)
for t in tweets:
if EXCLUDE_WORDS.search(t['full_text']) is None:
tweet = TEXT_ONLY.sub(' ', t['full_text'])
tweet = USER_NAME.sub(' ', tweet)
tweet = LINKS.sub(' ', tweet)
tweet = TYPO_HASHTAGS.sub(fix_hashtag, tweet)
tweet = TYPO_PERIOD.sub(fix_period, tweet)
tweet = TYPO_QUESTION.sub(fix_question, tweet)
tweet = TYPO_EXCLAMATION.sub(fix_exclamation, tweet)
tweet = LONE_PUNCTUATION.sub(' ', tweet)
tweet = AMPERSAND.sub('and', tweet)
tweet = GT.sub('>', tweet)
tweet = LT.sub('<', tweet)
chain.train(tweet)
# chain.train(t['full_text'])
print(f'len(chain.tree): {len(chain.tree)}')
chain.bulk_adjust_weights(fitness_functions=[aw_mult(aw_favor_complexity, .001), aw_mult(aw_favor_punctuation, .00015), aw_mult(dg_disfavor_consecutive_hashtags, .001)], iterations=len(chain.tree))
print('Sample tweet:', chain.generate_tweet())
# chain.save_training('bin/twitter/apologists.bin')
# chain.save_training('bin/twitter/atheists.bin')
chain.save_training('bin/twitter/news.bin')
# chain.save_training('bin/twitter/newagers.bin')
# chain.save_training('bin/twitter/churches.bin')
# chain.save_training('bin/twitter/trumpsterfire.bin')
# chain.save_training('bin/twitter/meta.bin')
next_id = re.split(r'\D+', tweets['search_metadata']['next_results'])[1]
try:
tweets = twit.search.tweets(q=query,
count=100,
lang='en',
result_type='recent',
tweet_mode='extended',
include_entities=False,
max_id=next_id)
except Exception as e:
print('____an error occurred____')
print(f'____search ended at i = {i+1}____')
break
for t in tweets['statuses']:
if EXCLUDE_WORDS.search(t['full_text']) is None:
tweet = clean_tweet(t['full_text'])
chain.train(tweet)
print(f'____len(chain.tree) = {len(chain.tree)}____')
print('____adjusting weights, this may take a moment____')
chain.bulk_adjust_weights(fitness_functions=[
aw_mult(aw_favor_complexity, .001),
aw_mult(dg_disfavor_consecutive_hashtags, .001)
],
iterations=len(chain.tree))
print('____done____')
chain.save_training('bin/twitter/beliefs.bin')
print('____sample tweet____:\n', chain.generate_tweet())
from markov_chain import MarkovChain
from markov_algorithms import *
chain = MarkovChain()
chain.train_on_file(filename='training_txt/quran.txt', verbose=True)
chain.train_on_file(filename='training_txt/new_testament.csv', verbose=True)
print(f'len(chain.tree): {len(chain.tree)}\n')
# print('Adjusting weights. This may take a while.\n_\n')
# chain.bulk_adjust_weights(fitness_functions=[aw_mult(aw_favor_complexity, .001), aw_mult(aw_favor_punctuation, .00015), aw_mult(aw_favor_alternating_complexity, .1)], iterations=len(chain.tree))
chain.save_training('bin/quran_testament.bin')
for i in range(8):
print(chain.generate_tweet(append_tag=None, follow=False), '\n_\n')
