def test():
vocab_path = '../../data/people_char_vocab.pkl'
vocabs = load_vocab(vocab_path)
train_data_path = '../../data/people.txt'
gen = train_generator(train_data_path, vocabs=vocabs)
states = ['B', 'M', 'E', 'S']
hmm = HMM(vocabs=vocabs, states=states)
#hmm.train(train_generator=gen)
model_dir = '../../models/hmm'
#hmm.save_model(model_dir=model_dir)
hmm.load_model(model_dir=model_dir)
sentence = "我是中国人，我爱我的祖国"
decode_states={0: 'B', 1: 'M', 2: 'E', 3: 'S'}
hiddens = hmm.decode(outputs=sentence, decode_states=decode_states)
words = hmm.format_hiddens(hiddens, sentence)
print(hiddens)
print('/ '.join(words))
sentence = '4月29日，雄浑悠长的钟声响起，关闭了近百日的武汉黄鹤楼重新开门迎客。这钟声，传递出中华民族从磨难中奋起的昂扬斗志，彰显出伟大民族精神在新时代焕发出的熠熠光辉。'
hiddens = hmm.decode(outputs=sentence, decode_states=decode_states)
words= hmm.format_hiddens(hiddens, sentence)
print('/ '.join(words))
def test_hmm():
vocab_path = '../../data/people_char_vocab.pkl'
model_dir = '../../models/hmm'
states = ['B', 'M', 'E', 'S']
decode_states = {0: 'B', 1: 'M', 2: 'E', 3: 'S'}
vocabs = load_vocab(vocab_path)
hmm = HMM(vocabs=vocabs, states=states)
hmm.load_model(model_dir=model_dir)
sentence = "我是中国人，我爱我的祖国"
hiddens = hmm.decode(outputs=sentence, decode_states=decode_states)
words = hmm.format_hiddens(hiddens, sentence)
print(hiddens)
print('/ '.join(words))
sentence = '4月29日，雄浑悠长的钟声响起，关闭了近百日的武汉黄鹤楼重新开门迎客。' \
'这钟声，传递出中华民族从磨难中奋起的昂扬斗志，彰显出伟大民族精神在新时代焕发出的熠熠光辉。'
sentence = 'I love you china'
hiddens = hmm.decode(outputs=sentence, decode_states=decode_states)
words = hmm.format_hiddens(hiddens, sentence)
print('/ '.join(words))
def lihang_example():
T = np.array([[0.5, 0.2, 0.3], [0.3, 0.5, 0.2], [0.2, 0.3, 0.5]])
E = np.array([[0.5, 0.5], [0.4, 0.6], [0.7, 0.3]])
pi = np.array([0.2, 0.4, 0.4])
#states = [0, 1, 2]
states = {'a': 0, 'b': 1, 'c': 2}
vocabs = {'red': 0, 'white': 1}
hmm = HMM(states=states, vocabs=vocabs, pi=pi, trans_p=T, emit_p=E)
O = ['red', 'white', 'red']
f_prob = hmm.forward_evaluate(O)
print('forward prob', f_prob)
b_prob = hmm.backward_evaluate(O)
print('backward prob', b_prob)
decode_states = {0: 'a', 1: 'b', 2: 'c'}
hiddens = hmm.decode(O, decode_states=decode_states)
print('optimal hiddens', hiddens)