def lookup_table(options, embed_map, worddict, word_idict, f_emb, use_norm=False):
 wordvecs = get_embeddings(options, word_idict, f_emb)
 clf = train_regressor(options, embed_map, wordvecs, worddict)
 table = apply_regressor(clf, embed_map, use_norm=use_norm)
 for i in range(options['n_words']):
  w = word_idict[i]
  table[w] = wordvecs[w]
  if use_norm:
   table[w] /= norm(table[w])
 return table

def lookup_table ( options , embed_map , worddict , word_idict , f_emb , use_norm = False ) : wordvecs = get_embeddings ( options , word_idict , f_emb ) clf = train_regressor ( options , embed_map , wordvecs , worddict ) table = apply_regressor ( clf , embed_map , use_norm = use_norm ) for i in range ( options [ 'n_words' ] ) : w = word_idict [ i ] table [ w ] = wordvecs [ w ] if use_norm : table [ w ] /= norm ( table [ w ] ) return table
create a lookup table from linear mapping of word2vec into rnn word space .