def generate_status(tweet_list):
'''(list) -> str,
returns markov-generated status'''
tweet_text = ' '.join(tweet_list)
try:
mc = MarkovGenerator(tweet_text, 90, tokenize_fun=twitter_tokenize)
status = mc.generate_words().lower()
return status
except ValueError as e:
print e
pass
def test_train(self):
generator = MarkovGenerator(
MarkovGenerator.TrainStrategy(database=self.db,
text_path='test_texts/test.txt',
window_size=2))
manual_model = {
'end бои': {
'у': 1
},
'бои у': {
'сопоцкина': 1
},
'у сопоцкина': {
'и': 1
},
'сопоцкина и': {
'друскеник': 1
},
'и друскеник': {
'закончились': 1
},
'друскеник закончились': {
'отступлением': 1
},
'закончились отступлением': {
'германцев': 1
},
'отступлением германцев': {
'.': 1
},
'германцев .': {
'end': 1
},
}
model = dict()
tokenizer = generator.strategy.tokenizer
for line in self.db['model'].find():
line_key = ' '.join(
[tokenizer.idx2word(idx) for idx in line['key'].split()])
line_value = {
tokenizer.idx2word(key): value
for key, value in line['value'].items()
}
model[line_key] = line_value
self.assertEqual(manual_model, model)
def __init__(self, context, config):
self.db = context.db.get('MarkovPlugin')
self.tables = defaultdict(MarkovGenerator)
for item in self.db.getAll():
self.tables[item.key] = MarkovGenerator.from_dict(item.value)
print self.tables
class TextGrid(object): def __init__(self, path): # pp = pprint.PrettyPrinter(indent=4) self.generator = MarkovGenerator(n=1, max=3) self.path = path + '/textGrids/' #let's get the text from the textGrids, save the annotations in a dict, key=filename self.annotations = dict() for tgFile in os.listdir(self.path): if tgFile[-9:] == '.TextGrid': #print tgFile tg = tgt.read_textgrid(self.path + tgFile) file_annotations = [i for t in tg.tiers for i in t] for i in range(len(file_annotations)): a1 = file_annotations[i] filename = tgFile[:-9] self.annotations[a1.text] = (filename, a1) if i == len(file_annotations)-1: continue else: a2 = file_annotations[i+1] self.feedMarkov(a1,a2) # pp.pprint(self.annotations) def generateText(self, numLines): output = list() for i in range(numLines): output.append(self.generator.generate()) return output def feedMarkov(self, a1, a2): a1.text = a1.text.strip() a2.text = a2.text.strip() endFirst = a1.text[-1] startSecond = a2.text[0] first = (endFirst, a1) second = (startSecond, a2) self.generator.feed(first, second)
def generate_random_content():
global contents
global titles
def random_content():
return ".\n\n".join([(mg.say().strip()) for _i in range(4)])
start = time.time()
print("Generating random content...", end="")
sys.stdout.flush()
mg = MarkovGenerator(2)
txt = file("/rinjani/var/data/milton-paradise.txt").read()
txt = re.sub('["*]*-', "", txt)
mg.learn(txt)
contents = [random_content() for _i in range(200)]
titles = [re.sub(r"[^\w\s]+", "", truncate_words(mg.say().strip(), 10)) for _i in range(100)]
print(" finished in %ds." % (time.time() - start))
sys.stdout.flush()
def generate_random_content():
global contents
global titles
def random_content():
return ".\n\n".join([(mg.say().strip()) for _i in range(4)])
start = time.time()
print("Generating random content...", end="")
sys.stdout.flush()
mg = MarkovGenerator(2)
txt = file('/rinjani/var/data/milton-paradise.txt').read()
txt = re.sub('["*]*-', '', txt)
mg.learn(txt)
contents = [random_content() for _i in range(200)]
titles = [
re.sub(r'[^\w\s]+', '', truncate_words(mg.say().strip(), 10))
for _i in range(100)
]
print(" finished in %ds." % (time.time() - start))
sys.stdout.flush()
def __init__(self, path): # pp = pprint.PrettyPrinter(indent=4) self.generator = MarkovGenerator(n=1, max=3) self.path = path + '/textGrids/' #let's get the text from the textGrids, save the annotations in a dict, key=filename self.annotations = dict() for tgFile in os.listdir(self.path): if tgFile[-9:] == '.TextGrid': #print tgFile tg = tgt.read_textgrid(self.path + tgFile) file_annotations = [i for t in tg.tiers for i in t] for i in range(len(file_annotations)): a1 = file_annotations[i] filename = tgFile[:-9] self.annotations[a1.text] = (filename, a1) if i == len(file_annotations)-1: continue else: a2 = file_annotations[i+1] self.feedMarkov(a1,a2)
24 : [0,0,0,1,1,0,0,0],\
25 : [0,0,0,1,1,0,0,1],\
26 : [0,0,0,1,1,0,1,0],\
27 : [0,0,0,1,1,0,1,1],\
28 : [0,0,0,1,1,1,0,0],\
29 : [0,0,0,1,1,1,0,1],\
30 : [0,0,0,1,1,1,1,0],\
54 : [0,0,1,1,0,1,1,0],\
60 : [0,0,1,1,1,1,0,0],\
62 : [0,0,1,1,1,1,1,0],\
102 : [0,1,1,0,0,1,1,0],\
126 : [0,1,1,1,1,1,1,0],\
188 : [1,0,1,1,1,1,0,0]\
}
generator = MarkovGenerator(n=3,max=2) # initialize the generator.
for line in sys.stdin:
line = line.lower()
line = line.strip()
generator.feed(line)
def wordFromRule(lines,j): # inputs list and gets an n-gram.
if j > 0:
m = generator.generate(lines,j)
# print m
m = m.split(" ")
return m[len(m)-1]
else:
return ""
def newgen(x, y, rule):
import random
from markov import MarkovGenerator
from markov_by_char import CharacterMarkovGenerator
# word MarkovGenerator
generator = MarkovGenerator(n=2, max=500)
# character MarkovGenerator
#generator = CharacterMarkovGenerator(n=3, max=100)
for line in open('white-album.txt'):
line = line.strip()
generator.feed(line)
generator.feed(line)
for line in open('black-album.txt'):
line = line.strip()
generator.feed(line)
for i in range(3):
print generator.generate()
import sys
reload(sys)
sys.setdefaultencoding('utf8')
from markov import MarkovGenerator
file = open("input/" + sys.argv[1])
lines = file.readlines()
generator = MarkovGenerator(n=2, max=3000)
for line in lines:
generator.feed(line)
text = generator.generate()
print text
lines = file.readlines() nountypes = ["NN", "NNP", "NNS", "NNPS"] punc = [".",",",";","?","-",] newlines = "" for line in lines: tokens = nltk.word_tokenize(line) tagged = nltk.pos_tag(tokens) newline = line for idx, tag in enumerate(tagged): if any(tag[1] in n for n in nountypes): newword = random.choice(prefixes).rstrip().lower() + tag[0] newline = newline.replace(tag[0], newword) newlines += newline newlines += "\n" generator = MarkovGenerator(n=1, max=1000) generator.feed(newlines) genpoem = generator.generate() print newlines f = open(text+"-pref.txt", "w") f.write(newlines) f.close() p = open(text+"-markov.txt", "w") p.write(genpoem) p.close()
import random
import sys
from textblob import TextBlob, Word
from nplc import NPLC #this object creates noun phrases from texts
from rhymebot import rhyme_set, rhyming_word #
#### add markov generator named underground and feed it all the source text ####
from markov import MarkovGenerator
underground = MarkovGenerator(5, 6)
utext = []
for line in open('notesfromunderground.txt', 'r'):
utext.append(line + ' ')
utext_sentences = TextBlob(" ".join(utext).decode('ascii', errors='replace')).sentences
for sentence in utext_sentences:
underground.feed(sentence)
print 'loaded 1/3'
sailortext = []
for line in open('sailorswordbook.txt', 'r'):
sailortext.append(line + ' ')
sailor_sentences = TextBlob(" ".join(sailortext).decode('ascii', errors='replace')).sentences
for sentence in sailor_sentences:
underground.feed(sentence)
print 'loaded 2/3'
posttext = []
for line in open('etiquette.txt', 'r'):
posttext.append(line + ' ')