for i in features[l2]: characters[i] = 1.0 test.append((lex_col[lang1][l1], lex_col[lang2][l2], embedding, characters)) model.reset() model.train = train before = model.eval(test)[0:2] #print size, experiment C_best = 0 err, hmm, mistakes = float("inf"), float("inf"), None # grid search for C in [1.0]: #0.0, 0.1, 0.5, 1.0, 2.0, 3.0, 4.0]: model.C = C model.learn(disp=0) err_tmp, hmm_tmp, mistakes_tmp = model.eval(test) if hmm_tmp < hmm: err, hmm, mistakes = err_tmp, hmm_tmp, mistakes_tmp C_best = C after = (err, hmm) print size, experiment, before, after, C_best for iter in xrange(100): train2 = [] for datum in train: train2.append(datum)