def generate_all(strings, max_length=75, descr_depth=2):
"""
:param strings: (all_titles, all_authors, all_descr)
:return: (new_title, new_author, new_descr)
Generate the random title/author/description using the Markov chains
"""
m1 = Markov(prob=True, level=1)
m2 = Markov(prob=True, level=1)
m3 = Markov(prob=True, level=descr_depth)
m1.parse(strings[0])
m2.parse(strings[1])
m3.parse(strings[2])
t_string = kill_chars(generate_title(m1))
a_string = kill_chars(generate_author(m2))
d_string = kill_chars(generate_description(m3, max_length))
#t_string = generate_title(m1)
#a_string = generate_author(m2)
#d_string = generate_description(m3)
t_list = t_string.split()
while t_list[-1] in stop_words:
t_list = t_list[0:-1]
t_string = " ".join(t_list)
return (t_string,a_string,d_string)
def handle(self, *args, **options):
"""Create fake users and statuses for Mowdie."""
from faker import Faker
import random
from django.conf import settings
from PyMarkovTextGenerator import Markov
fake = Faker()
textgen = Markov(prob=True, level=3)
with open(settings.BASE_DIR + "/../john_carter.txt") as file:
textgen.parse(file.read())
def update_text():
return textgen.generate(
startf=lambda db: random.choice([x for x in db
if x[0][0].isupper()]),
endf=lambda s: len(s) > 120)
Favorite.objects.all().delete()
Update.objects.all().delete()
User.objects.all().delete()
users = []
for _ in range(20):
user = User(username=fake.user_name(),
email=fake.email())
user.set_password("password")
user.save()
Profile(user=user).save()
users.append(user)
user = User(username="******",
email="*****@*****.**",
is_staff=True,
is_superuser=True)
user.set_password("password")
user.save()
Profile(user=user).save()
updates = []
for _ in range(100):
update = Update(text=update_text(),
posted_at=make_aware(fake.date_time_this_year()),
user=random.choice(users))
update.save()
updates.append(update)
combos = random.sample([(user, update)
for user in users
for update in updates], 200)
for user, update in combos:
favorite = Favorite(user=user, update=update)
favorite.save()
def handle(self, *args, **options):
"""Create fake users and statuses for Mowdie."""
from faker import Faker
import random
from django.conf import settings
from PyMarkovTextGenerator import Markov
fake = Faker()
textgen = Markov(prob=True, level=3)
with open(settings.BASE_DIR + "/../john_carter.txt") as file:
textgen.parse(file.read())
def update_text():
return textgen.generate(
startf=lambda db: random.choice([x for x in db if x[0][0].isupper()]), endf=lambda s: len(s) > 120
)
Favorite.objects.all().delete()
Update.objects.all().delete()
User.objects.all().delete()
users = []
for _ in range(20):
user = User(username=fake.user_name(), email=fake.email())
user.set_password("password")
user.save()
Profile(user=user).save()
users.append(user)
user = User(username="******", email="*****@*****.**", is_staff=True, is_superuser=True)
user.set_password("password")
user.save()
Profile(user=user).save()
updates = []
for _ in range(100):
update = Update(
text=update_text(), posted_at=make_aware(fake.date_time_this_year()), user=random.choice(users)
)
update.save()
updates.append(update)
combos = random.sample([(user, update) for user in users for update in updates], 200)
for user, update in combos:
favorite = Favorite(user=user, update=update)
favorite.save()
class TweetBot(object):
def __init__(self, text_file):
self.markov_dictionary = Markov()
with open(text_file) as file:
self.markov_dictionary.parse(file.read())
def _end(self, sentance):
last_word = sentance.split()[-1:]
if (sentance[-1:] in punctuation and len(sentance.split()) > 10
and last_word not in ("Mr.", "St.")):
return True
else:
return False
def cleanup_tweet(self, tweet):
"""
Does a variety of things to beautify the markov-ed text.
"""
# Fix quotes
# Remove two quotes separated by a space.
tweet = tweet.replace('" "', ' ')
# If the first quote has a space after, put a quote at the beginning of the line.
if search(r'^[^"]+" ', tweet):
tweet = '"' + tweet
# If the last quote on the line has a space before it put a quote at the end of the line.
if search(r' "[^"]+$', tweet):
tweet = tweet + '"'
return tweet
def generate_tweet(self):
tweet = ""
while tweet == "" or len(tweet) > 135:
tweet = self.markov_dictionary.generate(endf=self._end)
tweet = self.cleanup_tweet(tweet)
return tweet
def __init__(self, text_file):
self.markov_dictionary = Markov()
with open(text_file) as file:
self.markov_dictionary.parse(file.read())
from PyMarkovTextGenerator import Markov
def end(s):
interpunction = (".", "?", "!")
if s[len(s)-1] in interpunction and len(s.split()) > 10:
return True
else:
return False
m = Markov(prob=True, level=2)
with open("text-test") as file:
m.parse(file.read())
print m.generate(endf=end)
def generate_all(strings, max_length=75, descr_depth=2):
"""
:param strings: (all_titles, all_authors, all_descr)
:return: (new_title, new_author, new_descr)
Generate the random title/author/description using the Markov chains
"""
m1 = Markov(prob=True, level=1)
m2 = Markov(prob=True, level=1)
m3 = Markov(prob=True, level=descr_depth)
m1.parse(strings[0])
m2.parse(strings[1])
m3.parse(strings[2])
t_string = kill_chars(generate_title(m1))
a_string = kill_chars(generate_author(m2))
d_string = kill_chars(generate_description(m3, max_length))
#t_string = generate_title(m1)
#a_string = generate_author(m2)
#d_string = generate_description(m3)
t_list = t_string.split()
while t_list[-1] in stop_words:
t_list = t_list[0:-1]
t_string = " ".join(t_list)
return (t_string, a_string, d_string)
