mirror of
https://github.com/Mikaela/Limnoria.git
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225 lines
7.4 KiB
Python
225 lines
7.4 KiB
Python
#!/usr/bin/env python
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###
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# Copyright (c) 2002, Jeremiah Fincher
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# * Redistributions of source code must retain the above copyright notice,
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# this list of conditions, and the following disclaimer.
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# * Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions, and the following disclaimer in the
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# documentation and/or other materials provided with the distribution.
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# * Neither the name of the author of this software nor the name of
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# contributors to this software may be used to endorse or promote products
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# derived from this software without specific prior written consent.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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# POSSIBILITY OF SUCH DAMAGE.
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###
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"""
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Silently listens to a channel, building an SQL database of Markov Chains for
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later hijinks. To read more about Markov Chains, check out
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<http://www.cs.bell-labs.com/cm/cs/pearls/sec153.html>. When the database is
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large enough, you can have it make fun little random messages from it.
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"""
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__revision__ = "$Id$"
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import supybot.plugins as plugins
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import Queue
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import anydbm
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import random
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import os.path
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import threading
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import supybot.ircmsgs as ircmsgs
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import supybot.ircutils as ircutils
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import supybot.privmsgs as privmsgs
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import supybot.callbacks as callbacks
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class MarkovDBInterface(object):
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def close(self):
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pass
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def addPair(self, channel, first, second, follower,
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isFirst=False, isLast=False):
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pass
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def getFirstPair(self, channel):
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pass
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def getPair(self, channel, first, second):
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# Returns (follower, last) tuple.
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pass
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class SqliteMarkovDB(object):
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def addPair(self, channel, first, second, follower,
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isFirst=False, isLast=False):
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pass
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def getFirstPair(self, channel):
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pass
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def getFollower(self, channel, first, second):
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# Returns (follower, last) tuple.
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pass
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class DbmMarkovDB(object):
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def __init__(self):
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self.dbs = ircutils.IrcDict()
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def close(self):
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for db in self.dbs.values():
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db.close()
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def _getDb(self, channel):
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if channel not in self.dbs:
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# Stupid anydbm seems to append .db to the end of this.
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filename = plugins.makeChannelFilename(channel, 'DbmMarkovDB')
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self.dbs[channel] = anydbm.open(filename, 'c')
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self.dbs[channel]['lasts'] = ''
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self.dbs[channel]['firsts'] = ''
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return self.dbs[channel]
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def _addFirst(self, db, combined):
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db['firsts'] = db['firsts'] + (combined + '\n')
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def _addLast(self, db, second, follower):
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combined = self._combine(second, follower)
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db['lasts'] = db['lasts'] + (combined + '\n')
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def addPair(self, channel, first, second, follower,
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isFirst=False, isLast=False):
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db = self._getDb(channel)
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combined = self._combine(first, second)
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if isFirst:
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self._addFirst(db, combined)
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elif isLast:
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self._addLast(db, second, follower)
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else:
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if db.has_key(combined): # EW!
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db[combined] = db[combined] + (' ' + follower)
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else:
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db[combined] = follower
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#db.flush()
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def getFirstPair(self, channel):
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db = self._getDb(channel)
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firsts = db['firsts'].splitlines()
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if firsts:
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firsts.pop() # Empty line.
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if firsts:
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return random.choice(firsts).split()
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else:
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raise KeyError, 'No firsts for %s.' % channel
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else:
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raise KeyError, 'No firsts for %s.' % channel
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def _combine(self, first, second):
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return '%s %s' % (first, second)
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def getFollower(self, channel, first, second):
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db = self._getDb(channel)
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followers = db[self._combine(first, second)]
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follower = random.choice(followers.split())
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if self._combine(second, follower) in db['lasts']:
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last = True
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else:
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last = False
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return (follower, last)
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def MarkovDB():
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return DbmMarkovDB()
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class MarkovWorkQueue(threading.Thread):
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def __init__(self, *args, **kwargs):
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name = 'Thread #%s (MarkovWorkQueue)' % world.threadsSpawned
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world.threadsSpawned += 1
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threading.Thread.__init__(self, name=name)
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self.db = MarkovDB(*args, **kwargs)
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self.q = Queue.Queue()
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self.killed = False
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self.setDaemon(True)
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self.start()
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def die(self):
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self.killed = True
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self.q.put(None)
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def enqueue(self, f):
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self.q.put(f)
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def run(self):
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while not self.killed:
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f = self.q.get()
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if f is not None:
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f(self.db)
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self.db.close()
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class Markov(callbacks.Privmsg):
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def __init__(self):
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self.q = MarkovWorkQueue()
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callbacks.Privmsg.__init__(self)
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def die(self):
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self.q.die()
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def tokenize(self, s):
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# XXX: Should this be smarter?
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return s.split()
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def doPrivmsg(self, irc, msg):
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channel = msg.args[0]
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if ircutils.isChannel(channel):
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words = self.tokenize(msg.args[1])
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if len(words) >= 3:
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def doPrivmsg(db):
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db.addPair(channel, words[0], words[1], words[2],
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isFirst=True)
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db.addPair(channel, words[-3], words[-2], words[-1],
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isLast=True)
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del words[0] # Remove first.
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del words[-1] # Remove last.
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for (first, second, follower) in window(words, 3):
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db.addPair(channel, first, second, follower)
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self.q.enqueue(doPrivmsg)
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def markov(self, irc, msg, args):
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"""[<channel>]
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Returns a randomly-generated Markov Chain generated sentence from the
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data kept on <channel> (which is only necessary if not sent in the
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channel itself).
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"""
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channel = privmsgs.getChannel(msg, args)
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def markov(db):
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try:
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words = list(db.getFirstPair(channel))
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except KeyError:
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irc.error('I don\'t have any first pairs for %s.' % channel)
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return
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last = False
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while not last:
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(follower,last) = db.getFollower(channel, words[-2], words[-1])
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words.append(follower)
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irc.reply(' '.join(words))
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self.q.enqueue(markov)
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Class = Markov
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