本文整理汇总了Python中pymarkovchain.MarkovChain.dumpdb方法的典型用法代码示例。如果您正苦于以下问题：Python MarkovChain.dumpdb方法的具体用法？Python MarkovChain.dumpdb怎么用？Python MarkovChain.dumpdb使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pymarkovchain.MarkovChain的用法示例。
在下文中一共展示了MarkovChain.dumpdb方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from pymarkovchain import MarkovChain [as 别名]
# 或者: from pymarkovchain.MarkovChain import dumpdb [as 别名]
def main():
args = parser.parse_args()
dirname=os.path.split(__file__)[0]
filename=os.path.join(dirname,"phil.txt")
title_filename=os.path.join(dirname,"phil_titles.txt")
dbname1 = "database.pkl"
dbname2 = "database_title.pkl"
new_db = not os.path.exists(dbname1)
body_maker = MarkovChain(dbname1)
title_maker = MarkovChain(dbname2)
if new_db:
title_maker.generateDatabase(open(title_filename).read())
title_maker.dumpdb()
body_maker.generateDatabase(open(filename).read())
body_maker.dumpdb()
name = title_maker.generateString()
body = ' '.join([body_maker.generateString()+'.' for i in xrange(3)])
if args.repo:
if args.token:
token = args.token
else:
token_filename = os.path.join(dirname, "token.txt")
if not os.path.exists(token_filename):
sys.stderr.write("Please either specify --token=XXX on the command line or put a github API token in token.txt\n")
sys.stderr.write("You can generate a token here: https://github.com/settings/tokens\n")
sys.exit(1)
token = open(token_filename).read().strip()
import github
gh=github.Github(token)
user=gh.get_user()
repo=user.get_repo(args.repo)
issue = repo.create_issue(title=name, body=body)
print issue.html_url
else:
print
print name
print "-"*len(name)
print body示例2: MarkovChain
# 需要导入模块: from pymarkovchain import MarkovChain [as 别名]
# 或者: from pymarkovchain.MarkovChain import dumpdb [as 别名]
if not os.path.isfile(DB_FILE):
# Handle common user errors
if not os.path.isfile(SOURCE_FILE):
if os.path.isfile(DB_FILE + '.7z'):
sys.exit("NOTICE: Please extract the archive containing the Markov database before use.")
sys.exit("NOTICE: You can't regenerate the Markov database without the source text.");
# Moving this in here avoids an annoying warning message if either of the
# above two sys.exit() calls would be triggered
mc = MarkovChain(DB_FILE)
# Generate the database
with open(SOURCE_FILE, 'r') as f:
mc.generateDatabase(f.read(), sentenceSep='[.!?"\n]', n=2)
mc.dumpdb()
else:
mc = MarkovChain(DB_FILE)
def generate_string(max_length):
# Generate the string
# We could be a bit smarter about this, but it works fairly well
gen_string = ''
short_counter = 0
while len(gen_string) < max_length:
new_str = mc.generateString().strip()
new_str = re.sub(r' , ?', ', ', new_str)
# Too short or too long to be meaningful
if len(new_str) < 4 or len(new_str) > 100:
continue