def w2v_ic(word, buckets=20):
global ic_dict
if not ic_dict:
ic_dict = LoadData.load_ic()
im = [0]*buckets
cn = [0]*buckets
if word in ic_dict:
if ic_dict[word].IMAGEABILITY != None:
im = bucket(ic_dict[word].IMAGEABILITY, 7., buckets)
elif word in model:
for w2 in model.most_similar(word, topn=20):
if w2[0] in ic_dict and ic_dict[w2[0]].IMAGEABILITY != None:
im = bucket(ic_dict[w2[0]].IMAGEABILITY, 7., buckets)
break
if ic_dict[word].CONCRETENESS != None:
cn = bucket(ic_dict[word].CONCRETENESS, 5., buckets)
elif word in model:
for w2 in model.most_similar(word, topn=20):
if w2[0] in ic_dict and ic_dict[w2[0]].CONCRETENESS != None:
cn = bucket(ic_dict[w2[0]].CONCRETENESS, 5., buckets)
break
return cn + im
def ic(word, buckets=5):
if type(word) == tuple:
word = word[0]
global ic_dict
if not ic_dict:
ic_dict = LoadData.load_ic()
im = [0]*buckets
cn = [0]*buckets
if word in ic_dict:
if ic_dict[word].IMAGEABILITY != None:
im = bucket(ic_dict[word].IMAGEABILITY, 7., buckets)
if ic_dict[word].CONCRETENESS != None:
cn = bucket(ic_dict[word].CONCRETENESS, 5., buckets)
return cn + im
