if line == '':
percept.train_greedy(sentence, poslist, true_tags)
sentence = []
poslist = []
true_tags = []
else:
line = line.split(' ')
sentence.append(line[0])
poslist.append(line[1])
true_tags.append(line[-1])
training_data.seek(0)
percept.finish_round()
training_data.close()
#percept.finish_training()
# Step 3 - classify
percept.finish_training()
test_data = open("data/eng.testb")
id_str = '_'.join([f.id for f in fs.features])
results_file = "eng_leftout_greedy" + "_" + str(ROUNDS) + "_" + id_str + ".out"
fnum = 0
while results_file in os.listdir("results"):
results_file = results_file.split('.')[0] + str(fnum) + ".out"
fnum += 1
results_file = "results/" + results_file
output = open(results_file, 'w')
test_lines = []
for line in test_data:
line = line.strip()
if line == '':
tags_star = percept.test_greedy([obj[0] for obj in test_lines], [obj[1] for obj in test_lines])
#print "classified and result:"
