def createWordsObjects(self, dataDict, language, translateTo): wordObjects = [] for k in dataDict.keys(): if int(k) < 7: wordObjects.append(Words(dataDict[k][language], dataDict[k][translateTo])) elif int(k) < 12: wordObjects.append(Words(dataDict[k][language], dataDict[k][translateTo], 1)) else: wordObjects.append(Words(dataDict[k][language], dataDict[k][translateTo], 2)) return wordObjects
def __init__(self):
emotion_list = ["Angry", "Fear", "Happy", "Sad", "Surprise"]
self.feeling = random.choice(emotion_list)
emotion_list.remove(self.feeling)
self.opposite = random.choice(emotion_list)
self.read_rate = random.randint(1, 10) # number of sentences to read
#self.current_power = random.randint(0,30) # starting power?
self.sentences = []
self.words = Words(self.feeling, self.opposite)
self.markov_blob = None
def __init__(self):
# initialize all "global" data
logger.debug('loading...')
logger.debug(' corpus...')
# FIXME: using absolute paths is the easiest way to make us work from cmdline and invoked
# in a web app. perhaps we could set up softlinks in /var/ to make this slightly more respectable.
self.g = GramsBin(
'/home/pizza/proj/spill-chick/data/corpus/google-ngrams/word.bin',
'/home/pizza/proj/spill-chick/data/corpus/google-ngrams/ngram3.bin'
)
self.w = Words(NGram3BinWordCounter(self.g.ng))
logger.debug(' phon')
self.p = Phon(self.w, self.g)
logger.debug('done.')
# sanity-check junk
"""
t = ' '.join(snd[:j])
words = self.phon.get(t)
if words:
for s in self.soundsToWords(snd[j:]):
yield [words] + s
if __name__ == '__main__':
def words(str):
return re.findall('[a-z\']+', str.lower())
def pron(wl, wd):
print(' '.join(
[str(wd[w][0]) if w in wd else '<%s>' % (w, ) for w in wl]))
P = Phon(Words())
for a in sys.argv[1:]:
pron(words(a), P.W)
print(P.word['there'])
print(P.phon[P.word['there'][0]])
P.phraseSound(['making', 'mistake'])
P.phraseSound(['may', 'king', 'mist', 'ache'])
x = P.phraseSound(['making', 'miss', 'steak'])
from itertools import product
for f in P.soundsToWords(x):
print(f)
#print(list(product(*f)))
if x == y:
return 0
damlev = ngd.diff.damlev
sx, sy = p.phraseSound([x]), p.phraseSound([y])
if sx == sy and sx:
# sound the same, e.g. there/their. consider these equal.
return damlev
# otherwise, calculate phonic/edit difference
return max(damlev,
min(NGramDiffScore.overlap(sx, sy), abs(len(x) - len(y))))
if __name__ == '__main__':
import sys
sys.path.append('..')
from grambin import GramsBin
from word import Words, NGram3BinWordCounter
from phon import Phon
import logging
logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
logging.debug('loading...')
g = GramsBin(
'/home/pizza/proj/spill-chick/data/corpus/google-ngrams/word.bin',
'/home/pizza/proj/spill-chick/data/corpus/google-ngrams/ngram3.bin')
w = Words(NGram3BinWordCounter(g.ng))
p = Phon(w, g)
logging.debug('loaded.')
pass
