Python MarkovChain.generateStringWithSeed方法代码示例

# 需要导入模块: from pymarkovchain import MarkovChain [as 别名]
# 或者: from pymarkovchain.MarkovChain import generateStringWithSeed [as 别名]
class MarkovBot(BotPlugin):

    def __init__(self):
        self.markov = MarkovChain()

    @botcmd
    def talk(self, mess, args):
        """ Generate a sentence based on database """
        return self.markov.generateString()

    @botcmd
    def complete(self, mess, args):
        """ Try to complete a sentence """
        return self.markov.generateStringWithSeed(args)

    @botcmd
    def gendbfromfile(self, mess, args):
        """ Generate markov chain word database """
        try:
            with open(args) as txtFile:
                txt = txtFile.read()
        except IOError as e:
            return 'Error: could not open text file'
        # At this point, we've got the file contents
        if self.markov.generateDatabase(txt):
            return 'Done.'
        else:
            return 'Error: Could not generate database'

    @botcmd
    def gendbfromstring(self, mess, args):
        if self.markov.generateDatabase(args):
            return 'Done.'
        else:
            return 'Error: Could not generate database from String'

    @botcmd
    def gendbfromurl(self, mess, args):
        req = requests.get(args)
        if req.ok and self.markov.generateDatabase(req.content):
            return 'Done.'
        else:
            return 'Error: Could not generate database from URL'

# 需要导入模块: from pymarkovchain import MarkovChain [as 别名]
# 或者: from pymarkovchain.MarkovChain import generateStringWithSeed [as 别名]
    for root, dirs, files in os.walk(_dir, topdown=False):
        random.shuffle(files)
        for name in files:
            num_files -= 1
            if num_files > 0:
                _path = "{}/{}".format(_dir,name)
                train_text += file_get_contents(_path)


#scrub train_text
train_text = re.sub('<[^<]+?>', '', train_text) #remove html
train_text = re.sub(r'[^a-zA-Z. ]+', ' ', train_text).replace('\n', '') #remove non-alphanumeric chars
for word in remove_words:
    train_text = train_text.replace(word,'') #remove words

#create markov db
mc.generateDatabase(train_text)

#tweet
for x in range(0,num_tweets):
    random.shuffle(seed_words)
    status = (ucfirst(mc.generateStringWithSeed(seed_words[0])) + ".  ")
    if not validate_tweet(status):
        continue;
    try:
        status = api.PostUpdate(status)
    except:
        pass
    time.sleep(wait_time_between_tweets_in_secs)

# 需要导入模块: from pymarkovchain import MarkovChain [as 别名]
# 或者: from pymarkovchain.MarkovChain import generateStringWithSeed [as 别名]
class MarkovBot(BotPlugin):
    def __init__(self):
        super(MarkovBot, self).__init__()
        self.sentenceSep = None
        self.markov = MarkovChain(dbFilePath="./markovdb")

    @botcmd
    def talk(self, mess, args):
        """ Generate a sentence based on database """
        return self.markov.generateString()

    @botcmd
    def complete(self, mess, args):
        """ Try to complete a sentence """
        return self.markov.generateStringWithSeed(args)

    @botcmd
    def gendbfromfile(self, mess, args):
        """ Generate markov chain word database based on local file """
        try:
            with open(args) as txtFile:
                txt = txtFile.read()
        except IOError as e:
            return "Error: could not open text file"
        # At this point, we've got the file contents
        if self.sentenceSep:
            result = self.markov.generateDatabase(txt, self.sentenceSep)
        else:
            result = self.markov.generateDatabase(txt)
        if result:
            return "Done."
        else:
            return "Error: Could not generate database"

    @botcmd
    def setsentencesep(self, mess, args):
        """ Specify how to detect sentence borders """
        self.sentenceSep = args

    @botcmd
    def gendbfromstring(self, mess, args):
        """ Generate markov chain word database based on given string """
        if self.sentenceSep:
            result = self.markov.generateDatabase(args, self.sentenceSep)
        else:
            result = self.markov.generateDatabase(args)
        if result:
            return "Done."
        else:
            return "Error: Could not generate database from String"

    @botcmd
    def gendbfromurl(self, mess, args):
        """ Generate markov chain word database based on contents of url """
        response, content = httplib2.Http().request(args, "GET")
        if response["status"] == "200":
            if self.sentenceSep:
                result = self.markov.generateDatabase(content.decode("utf-8"), self.sentenceSep)
            else:
                result = self.markov.generateDatabase(content.decode("utf-8"))
            if result:
                return "Done."
            else:
                return "Error: Could not generate database from URL"

# 需要导入模块: from pymarkovchain import MarkovChain [as 别名]
# 或者: from pymarkovchain.MarkovChain import generateStringWithSeed [as 别名]
from pymarkovchain import MarkovChain
# Create an instance of the markov chain. By default, it uses MarkovChain.py's location to
# store and load its database files to. You probably want to give it another location, like so:
mc = MarkovChain("./markovdb.txt")

with open("../formatted.txt", "r") as myfile:
    data=myfile.read()

# To generate the markov chain's language model, in case it's not present
mc.generateDatabase(data)

# To let the markov chain generate some text, execute
#print( mc.generateString() )
print( mc.generateStringWithSeed("the") )

# 需要导入模块: from pymarkovchain import MarkovChain [as 别名]
# 或者: from pymarkovchain.MarkovChain import generateStringWithSeed [as 别名]
			final += ts
			sd = True
			seed = ts.split()[-1]
			seed = seed.translate(string.maketrans("",""), string.punctuation)
			c += 1
		print ("\n" + final + "\n")
		raw_input("press enter to continue...")
if raw_input("press enter to begin.") == "beta":
	pgraph()

sd = False
s = ""
while True:
	if not sd:
		ts = mc.generateString()
	else:
		ts = mc.generateStringWithSeed(s)
	if countString(ts) >= msl:
		os.system("clear")
		print ("\n" + ts + "\n")
		sd = False
		s = raw_input("\npress enter to generate string. : ")
		if s == '!pg':
			pgraph()
		if (len(s) > 0):
			if f.find(s) != -1:
				sd = True
			else:
				raw_input('could not find "' + s + '" in database\npress enter to continue')
				sd = False

# 需要导入模块: from pymarkovchain import MarkovChain [as 别名]
# 或者: from pymarkovchain.MarkovChain import generateStringWithSeed [as 别名]
regex = re.compile('[%s]' % re.escape(string.punctuation))
text = regex.sub(" b", text)

# generate MC data
mc = MarkovChain("./markov")
mc.generateDatabase(text)
f = open("potential_tweets.txt", "a")

# generate and evaluate tweets
while 1:
  try:
    seed = sys.argv[1]
  except:
    seed = None
  if seed is not None:
    tweet = mc.generateStringWithSeed(seed).title()
  else:
    tweet = mc.generateString().title()
  print tweet
  answer = raw_input("Tweet this text? (yes|no|edit) ")
  if answer == "yes":
    f.write(tweet)
    break
  elif answer == "edit":
    tweet = raw_input("Enter in the edited text: ")
    f.write(tweet)
    break