def test_batch_score():
model = Numila(PARSE='batch', CHUNK_THRESHOLD=2)
for _ in range(5):
model.parse('the dog ate the steak')
assert model.score('the dog ate the steak') > model.score('the ate dog steak the')
def main():
lang = 'English'
kind = 'word'
train_len = 4000
N = 20
model = Numila()
corpus = utils.get_corpus(lang, kind)
#import pcfg
#corpus = (s.split(' ') for s in pcfg.toy2())
train_corpus = [next(corpus) for _ in range(train_len)]
model.fit(train_corpus)
top_words, _ = zip(*Counter(utils.flatten(train_corpus)).most_common(N))
nodes = [model.graph[w] for w in top_words]
data = [[1-n1.similarity(n2) for n2 in nodes]
for n1 in nodes]
mds(data, top_words)
def foo():
model = Numila(GRAPH='vector')
model = Numila(GRAPH='graph', CHUNK_THRESHOLD=0.05)
corp = get_corpora('English', 'syl', 5000)
model.fit(corp['train'])
test = iter(corp['bleu_test'])
print(model.parse(next(test)))
print(model.speak(next(test)))
print(model.speak(next(test)))
print(model.speak(next(test)))
print(model._debug['speak_chunks'])
def get_model(train_len=1000, lang='english', kind='word', **params):
# for testing
model = Numila(**params)
corpus = corpora.get_corpus(lang, kind)
train_corpus = [next(corpus) for _ in range(train_len)]
return model.fit(train_corpus)
