def get_word_embedding(self, path_to_vec,orthonormalized=True):
samples = self.data['train']['X'] + self.data['dev']['X'] + \
self.data['test']['X']
id2word, word2id = data.create_dictionary(samples, threshold=0)
word_vec = data.get_wordvec(path_to_vec, word2id,orthonormalized=orthonormalized)
wvec_dim = len(word_vec[next(iter(word_vec))])
#stores the value of theta for each word
word_complex_phase = data.set_wordphase(word2id)
params = {'word2id':word2id, 'word_vec':word_vec, 'wvec_dim':wvec_dim,'word_complex_phase':word_complex_phase,'id2word':id2word}
return params
def prepare(params, samples):
_, params.word2id = data.create_dictionary(samples)
params.word_vec = data.get_wordvec(PATH_TO_VEC, params.word2id)
params.wvec_dim = 300
return
def prepare(params, samples):
_, params.word2id = data.create_dictionary(samples)
params.word_vec = data.get_wordvec(PATH_TO_GLOVE, params.word2id)
return
def prepare(params, samples):
_, params.word2id = data.create_dictionary(samples)
params.word_vec = data.get_wordvec(PATH_TO_VEC, params.word2id)
# Make sure this matches the embedding size
params.wvec_dim = emb_size
return