def __init__(self):
#self.tensor = get_picklecached_thing('tensor.gz')
#self.svd = self.tensor.svd(k=50)
self.en_nl = get_nl('en')
self.normalizer = LemmatizedEuroNL('en')
self.cnet = conceptnet_2d_from_db('en')
self.analogyspace = self.cnet.svd(k=100)
class Analogies:
def __init__(self):
#self.tensor = get_picklecached_thing('tensor.gz')
#self.svd = self.tensor.svd(k=50)
self.en_nl = get_nl('en')
self.normalizer = LemmatizedEuroNL('en')
self.cnet = conceptnet_2d_from_db('en')
self.analogyspace = self.cnet.svd(k=100)
def similar(self, word_container):
untagged = []
last = []
for words in word_container.tagged_words:
normal = self.normalizer.normalize(words[0])
untagged.append(normal)
whole_sentence = ' '.join(untagged)
lemma_d = self.en_nl.lemma_split(whole_sentence)
print lemma_d
lemma = lemma_d[0].split(' ')
for x in lemma:
try:
debug(x)
obj = self.analogyspace.weighted_u[x, :]
last.append(obj)
if len(last) > 1:
print obj.hat() * last[-2].hat()
#cur = self.analogyspace.u_dotproducts_with(obj)
#print cur.top_items()
#analogyspace.weighted_v.label_list(0)[:10]
except Exception, E:
print E
