Python pymarkovchain.MarkovChain类代码示例

class TextGenerator:
	def __init__(self, generatorName, trainString, prefixLength):
		self.generatorName = generatorName
		self.chain = MarkovChain()
		self.chain.generateDatabase(trainString, n=prefixLength)
		self.currState = []
		self.hyphenator = Hyphenator('en_US')
		self.syllableQ = Queue()
		self.stripPattern = re.compile('[\W_]+')
		while (len(self.currState) < prefixLength):
			self.currState = self.chain.generateString().split()[-(prefixLength+1):-1]
	
	def load_next_word(self):
		nextword = ""
		try:
			while nextword == "":
				nextword = self.stripPattern.sub('', self.chain._nextWord(self.currState))
				self.currState = self.currState[1:]
				self.currState.append(nextword)
			if len(nextword) < 4: # because hyphenator doesnt work for words less than 4 letters
				self.syllableQ.put(nextword)
			else: 
				for syllable in self.hyphenator.syllables(nextword):
					self.syllableQ.put(syllable)
		except UnicodeEncodeError:
			print("unicode error")
		
	def get_next_syllable(self):
		if (self.syllableQ.empty()):
			self.load_next_word()
		return self.syllableQ.get()

def lyrics():
    artist = request.form['artist']
    lines = int(request.form['lines'])

    if not artist:
        return redirect(url_for('index'))

    # Get a response of sample lyrics from the provided artist
    uri = "http://api.lyricsnmusic.com/songs"
    params = {
        'api_key': API_KEY,
        'artist': artist,
    }
    response = requests.get(uri, params=params)
    lyric_list = response.json()
    # Parse results into a long string of lyrics
    lyrics = ''
    for lyric_dict in lyric_list:
        lyrics += lyric_dict['snippet'].replace('...', '') + ' '

    # Generate a Markov model
    mc = MarkovChain()
    mc.generateDatabase(lyrics)

    result = []
    for line in range(0, lines):
        result.append(mc.generateString())

    return render_template('lyrics.html', result=result, artist=artist)

class BeerMarkov():

    def __init__(self, reviews_file, markov_dir):
        self._reviews_file = reviews_file
        self._markov_dir = markov_dir
        self._markov = MarkovChain(markov_dir + '/beer_desc')
        self._name_markov = MarkovChain(markov_dir + '/beer_name')
        self.refresh_database()

    def refresh_database(self):
        with open(self._reviews_file, 'r') as review_data:
            reviews = json.load(review_data)

        reviews_string = [r['desc'] for r in reviews]
        names_string = [r['name'] for r in reviews]

        new_markov = MarkovChain(self._markov_dir + '/beer_desc')
        new_markov.generateDatabase(' '.join(reviews_string))

        new_name_markov = MarkovChain(self._markov_dir + '/beer_name')
        new_name_markov.generateDatabase('.'.join(names_string))

        self._markov = new_markov
        self._name_markov = new_name_markov

    def get_review(self):
        return  self._markov.generateString() + '. ' + \
            self._markov.generateString()

def main():
    import config
    from pymarkovchain import MarkovChain

    optp = ArgumentParser()

    optp.add_argument("-d", "--database", dest="database", help="Where to store the database")
    optp.add_argument("-f", "--file", dest="file", help="File source to use for generating the database")
    opts = optp.parse_args()

    # Setup logging.
    logformat = "%(levelname)-8s %(name)s %(message)s"
    logging.basicConfig(level=logging.INFO, format=logformat)

    if opts.database is None:
        try:
            opts.database = config.markovbrainfile
        except NameError:
            log.critical("I require a brainfile to write into!")
            exit(1)
    if opts.file is None:
        log.critical("I require an imput file to learn from!")
        exit(1)

    mc = MarkovChain(opts.database)

    mc.generateDatabase(opts.file)

def fetch_lyrics(artist, lines):
    API_KEY = os.environ.get('API_KEY')

    uri = "http://api.lyricsnmusic.com/songs"
    params = {
        'api_key': API_KEY,
        'artist': artist,
    }
    response = requests.get(uri, params=params)
    lyric_list = response.json()

    lyrics = ''
    for lyric_dict in lyric_list:
        lyrics += lyric_dict['snippet'].replace('...', '') + ' '

    # Generate a Markov model
    mc = MarkovChain('./markov')
    mc.generateDatabase(lyrics)

    # Add lines of lyrics
    result = []
    for line in range(0, lines):
        line_string = mc.generateString()
        result.append(line_string)
    return result

class Michiov(object):
  def __init__(self, autogen=True, markovdb=os.path.expanduser("~/markov"), twcreds=os.path.expanduser("~/.michiov_twitter_credentials"),twappcreds=os.path.expanduser("~/.michiov_twitter_appdata")):
    self.mc = MarkovChain(markovdb)
    self.reload()
    if not os.path.exists(twappcreds):
      print("Lack of app creds")
      sys.exit(1)
    twcons = json.loads(open(twappcreds).read())
    conskey = twcons['key']
    conssec = twcons['secret']
    while not os.path.exists(twcreds):
      twitter.oauth_dance("MPRZ Tech Labs", conskey, conssec, twcreds)
    oauth_token, oauth_secret = twitter.read_token_file(twcreds)
    self.t = twitter.Twitter(auth=twitter.OAuth(oauth_token, oauth_secret, conskey, conssec))
  def should(self):
    ret = input("Should I send it? (y/N) ")
    return ("y" in ret or "Y" in ret)
  def qas(self):
    idea = self.mc.generateString()
    print("Generated: %s" % idea)
    if self.should():
      self.t.statuses.update(status=idea)
  def loop(self):
    try:
      while True:
        self.qas()
        #self.reload()
    except KeyboardInterrupt:
      pass
  def reload(self):
    with open("markovpredb.txt") as file:
      self.mc.generateDatabase(file.read())

def mkdb():
  mc = MarkovChain('./markov')
  with open('yaks.txt') as input:
    mc.generateDatabase(input.read())
  with open('yaks.txt') as input:
    yaks = [l.strip() for l in input]
  return mc, yaks

class EuroMarkov:
    def __init__(self):
        self.mc = MarkovChain("./markovdata")

    def generateCountryList(self):
        countryList = []
        for filename in os.listdir("json_lyrics/2015"):
            countryList.append(os.path.splitext(filename)[0])
        return countryList

    def loadFiles(self,startYear,endYear,countryList):
        model = ""
        for year in range(startYear,endYear+1):
            for country in countryList:
                fname = "json_lyrics/"+str(year)+"/"+country+".json"
                if os.path.isfile((fname)):
                    with open (fname,"r") as myfile:
                        data = json.load(myfile)
                        model += (data['lyrics']) + '\n';
        return model

    def runMarkov(self,model):
        self.mc.generateDatabase(model)

    def generateString(self):
        return self.mc.generateString()

def markov(msg, botName, channel, db):
  if msg.rawMatchRe('!markov (?P<source>#?[a-zA-Z]\S*)\s*$') or msg.rawMatchRe('what (would|does) (the )?(?P<source>#?[a-zA-Z]\S+) say\??'):
    m = msg.getRegExpResult()
    source = m.group('source')

    if source[0] == '#':
      logsList = db.getLogs(chan=source, lines=2000)
    else:
      logsList = db.getLogs(nick=source, lines=2000)
    
    if len(logsList) < 100:
      hexchat.command("msg %s Not enough data for %s" % (channel, source))
      
    else:
      mc = MarkovChain("./markov_db")
      ircText = ''
      
      for line in logsList:
        # disqualify lines that are too short or are certain bot functions that start with '!'
        if len(line.split(' ')) >= 5 and line[0] != '!':
          ircText += line.replace('.','') + '. '
          
      mc.generateDatabase(ircText)
      markovOutput = mc.generateString().capitalize()
      hexchat.command('msg %s "%s"  --%s' % (channel, markovOutput, source))
      
    return True
  return False

def markov():
    """A simple markov function"""
    mc = MarkovChain("./tempchain")

    with open(CORPUS, 'r') as f:
        data = f.read()

    mc.generateDatabase(data)

    return mc.generateString()

def markov(messages):
    # 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("./markov")

    # To generate the markov chain's language model, in case it's not present
    # mc.generateDatabase("\n".join(messages))

    # To let the markov chain generate some text, execute
    for i in xrange(100):
        print mc.generateString()

    def prepare_dict(self):
        if self.dictfile is None:
            print "error: no dictfile"
            return
        # now build the markov database. just using pymarkovchain's default settings for now. will fail if it doesn't
        # have write access to $PWD.
        chain = MarkovChain("./markov")

        source = self.build_source()
        chain.generateDatabase(source)

        # seem to need to do this to reload the database after generating it
        self.chain = MarkovChain("./markov")

def main(args):
	markov_filename = "./" + args.subreddit + ".mcd"
	new_chain = os.path.isfile(markov_filename) == False # this must come before the creation of the Markov Chain
	mc = MarkovChain(markov_filename)

	if args.new or new_chain:
		titles = getTitles(getSubmissions(100, args.subreddit))
		training_data = str.join('.', titles)
		mc.generateDatabase(training_data)

	N = args.num_submissions
	while N > 0:
		print(mc.generateString())
		N -= 1

class markovbuild(object):
    '''Builds a markov chain DB and outputs data'''
    def __init__(self, target, data, lines=5):
        self.database = '/tmp/markov_%s.db' % target
        self.lines = lines
        self.data = '\n'.join(data)
        self.mchain = MarkovChain(self.database)

    def build(self):
        '''Builds a markov chain'''
        self.mchain.generateDatabase(self.data)

    def output(self):
        '''Outputs markov chain data'''
        self.build()
        return [ self.mchain.generateString() for x in xrange(0, self.lines) ]

def poem():
    story = str(request.form['story'].encode('ascii', 'ignore'))
    lines = int(request.form['lines'])

    if not story:
        return redirect(url_for('index'))

    mc = MarkovChain()
    mc.generateDatabase(story)

    result = []
    for line in range(0, lines):
        new_line = mc.generateString()
        if new_line not in result:
            result.append(new_line)

    return render_template('poem.html', result=result, story=story)