Commit my long sought after *cough*Skorobeus*cough* randomSpeaking updates

This commit is contained in:
James Vega 2004-11-09 18:43:27 +00:00
parent 2fb7041627
commit 59d306598a
1 changed files with 74 additions and 26 deletions

View File

@ -28,7 +28,7 @@
### ###
""" """
Silently listens to a channel, building an SQL database of Markov Chains for Silently listens to a channel, building a database of Markov Chains for
later hijinks. To read more about Markov Chains, check out later hijinks. To read more about Markov Chains, check out
<http://www.cs.bell-labs.com/cm/cs/pearls/sec153.html>. When the database is <http://www.cs.bell-labs.com/cm/cs/pearls/sec153.html>. When the database is
large enough, you can have it make fun little random messages from it. large enough, you can have it make fun little random messages from it.
@ -39,18 +39,46 @@ __revision__ = "$Id$"
import supybot.plugins as plugins import supybot.plugins as plugins
import sets import sets
import time
import Queue import Queue
import anydbm import anydbm
import random import random
import os.path import os.path
import threading import threading
import supybot.conf as conf
import supybot.world as world import supybot.world as world
from supybot.commands import *
import supybot.ircmsgs as ircmsgs import supybot.ircmsgs as ircmsgs
import supybot.ircutils as ircutils import supybot.ircutils as ircutils
import supybot.privmsgs as privmsgs import supybot.registry as registry
import supybot.schedule as schedule
import supybot.callbacks as callbacks import supybot.callbacks as callbacks
class Probability(registry.Float):
"""Value must be a floating-point number between 0 and 1."""
def setValue(self, v):
if v < 0 or v > 1:
self.error()
else:
registry.Float.setValue(self, float(v))
conf.registerPlugin('Markov')
conf.registerGroup(conf.supybot.plugins.Markov, 'randomSpeaking')
conf.registerChannelValue(conf.supybot.plugins.Markov.randomSpeaking,
'probability', Probability(0, """Determines the probability that will be
checked against to determine whether the bot should randomly say
something. If 0, the bot will never say anything on it's own. If 1, the
bot will speak every time we make a check."""))
conf.registerChannelValue(conf.supybot.plugins.Markov.randomSpeaking,
'maxDelay', registry.PositiveInteger(10, """Determines the upper bound for
how long the bot will wait before randomly speaking. The delay is a
randomly generated number of seconds below the value of this config
variable."""))
conf.registerChannelValue(conf.supybot.plugins.Markov.randomSpeaking,
'throttleTime', registry.PositiveInteger(300, """Determines the minimum
number of seconds between the bot randomly speaking."""))
class MarkovDBInterface(object): class MarkovDBInterface(object):
def close(self): def close(self):
pass pass
@ -249,10 +277,13 @@ class MarkovWorkQueue(threading.Thread):
class Markov(callbacks.Privmsg): class Markov(callbacks.Privmsg):
def __init__(self): def __init__(self):
self.q = MarkovWorkQueue() self.q = MarkovWorkQueue()
callbacks.Privmsg.__init__(self) self.__parent = super(Markov, self)
self.__parent.__init__()
self.lastSpoke = time.time()
def die(self): def die(self):
self.q.die() self.q.die()
self.__parent.die()
def tokenize(self, m): def tokenize(self, m):
if ircmsgs.isAction(m): if ircmsgs.isAction(m):
@ -264,7 +295,20 @@ class Markov(callbacks.Privmsg):
def doPrivmsg(self, irc, msg): def doPrivmsg(self, irc, msg):
channel = msg.args[0] channel = msg.args[0]
if ircutils.isChannel(channel): if irc.isChannel(channel):
canSpeak = False
now = time.time()
throttle = self.registryValue('randomSpeaking.throttleTime',
channel)
prob = self.registryValue('randomSpeaking.probability', channel)
delay = self.registryValue('randomSpeaking.maxDelay', channel)
irc = callbacks.SimpleProxy(irc, msg)
if now > self.lastSpoke + throttle:
canSpeak = True
if canSpeak and random.random() < prob:
f = self._markov(channel, irc, private=True, to=channel)
schedule.addEvent(lambda: self.q.enqueue(f), now + delay)
self.lastSpoke = now + delay
words = self.tokenize(msg) words = self.tokenize(msg)
words.insert(0, '\n') words.insert(0, '\n')
words.insert(0, '\n') words.insert(0, '\n')
@ -277,17 +321,8 @@ class Markov(callbacks.Privmsg):
db.addPair(channel, first, second, follower) db.addPair(channel, first, second, follower)
self.q.enqueue(doPrivmsg) self.q.enqueue(doPrivmsg)
def markov(self, irc, msg, args): def _markov(self, channel, irc, word1=None, word2=None, **kwargs):
"""[<channel>] [word1 word2] def f(db):
Returns a randomly-generated Markov Chain generated sentence from the
data kept on <channel> (which is only necessary if not sent in the
channel itself). If word1 and word2 are specified, they will be used
to start the Markov chain.
"""
channel = privmsgs.getChannel(msg, args)
(word1, word2) = privmsgs.getArgs(args, required=0, optional=2)
def markov(db):
if word1 and word2: if word1 and word2:
givenArgs = True givenArgs = True
words = [word1, word2] words = [word1, word2]
@ -314,58 +349,71 @@ class Markov(callbacks.Privmsg):
return return
words.append(follower) words.append(follower)
if givenArgs: if givenArgs:
irc.reply(' '.join(words[:-1])) irc.reply(' '.join(words[:-1]), **kwargs)
else: else:
irc.reply(' '.join(resp)) irc.reply(' '.join(resp), **kwargs)
self.q.enqueue(markov) return f
def firsts(self, irc, msg, args): def markov(self, irc, msg, args, channel, word1, word2):
"""[<channel>] [word1 word2]
Returns a randomly-generated Markov Chain generated sentence from the
data kept on <channel> (which is only necessary if not sent in the
channel itself). If word1 and word2 are specified, they will be used
to start the Markov chain.
"""
f = self._markov(channel, irc, word1, word2)
self.q.enqueue(f)
markov = wrap(markov, ['channel', optional('something'),
additional('something')])
def firsts(self, irc, msg, args, channel):
"""[<channel>] """[<channel>]
Returns the number of Markov's first links in the database for Returns the number of Markov's first links in the database for
<channel>. <channel>.
""" """
channel = privmsgs.getChannel(msg, args)
def firsts(db): def firsts(db):
s = 'There are %s firsts in my Markov database for %s.' s = 'There are %s firsts in my Markov database for %s.'
irc.reply(s % (db.firsts(channel), channel)) irc.reply(s % (db.firsts(channel), channel))
self.q.enqueue(firsts) self.q.enqueue(firsts)
firsts = wrap(firsts, ['channel'])
def lasts(self, irc, msg, args): def lasts(self, irc, msg, args, channel):
"""[<channel>] """[<channel>]
Returns the number of Markov's last links in the database for Returns the number of Markov's last links in the database for
<channel>. <channel>.
""" """
channel = privmsgs.getChannel(msg, args)
def lasts(db): def lasts(db):
s = 'There are %s lasts in my Markov database for %s.' s = 'There are %s lasts in my Markov database for %s.'
irc.reply(s % (db.lasts(channel), channel)) irc.reply(s % (db.lasts(channel), channel))
self.q.enqueue(lasts) self.q.enqueue(lasts)
lasts = wrap(lasts, ['channel'])
def pairs(self, irc, msg, args): def pairs(self, irc, msg, args, channel):
"""[<channel>] """[<channel>]
Returns the number of Markov's chain links in the database for Returns the number of Markov's chain links in the database for
<channel>. <channel>.
""" """
channel = privmsgs.getChannel(msg, args)
def pairs(db): def pairs(db):
s = 'There are %s pairs in my Markov database for %s.' s = 'There are %s pairs in my Markov database for %s.'
irc.reply(s % (db.pairs(channel), channel)) irc.reply(s % (db.pairs(channel), channel))
self.q.enqueue(pairs) self.q.enqueue(pairs)
pairs = wrap(pairs, ['channel'])
def follows(self, irc, msg, args): def follows(self, irc, msg, args, channel):
"""[<channel>] """[<channel>]
Returns the number of Markov's third links in the database for Returns the number of Markov's third links in the database for
<channel>. <channel>.
""" """
channel = privmsgs.getChannel(msg, args)
def follows(db): def follows(db):
s = 'There are %s follows in my Markov database for %s.' s = 'There are %s follows in my Markov database for %s.'
irc.reply(s % (db.follows(channel), channel)) irc.reply(s % (db.follows(channel), channel))
self.q.enqueue(follows) self.q.enqueue(follows)
follows = wrap(follows, ['channel'])
Class = Markov Class = Markov