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Update hmm.py #27

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Update hmm.py #27

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20 changes: 12 additions & 8 deletions models/hmm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -104,7 +104,7 @@ def decoding(self, word_list, word2id, tag2id):
backpointer[:, 0] = -1

# 递推公式：
# viterbi[tag_id, step] = max(viterbi[:, step-1]* self.A.t()[tag_id] * Bt[word])
# viterbi[tag_id, step] = max(viterbi[:, step-1]* self.A.t()[tag_id]) * Bt[wordid] # 这里取最大值的范围应该是viterbi * A.t()的，不包括Bt[wordid]
# 其中word是step时刻对应的字
# 由上述递推公式求后续各步
for step in range(1, seq_len):
Expand All @@ -116,13 +116,17 @@ def decoding(self, word_list, word2id, tag2id):
bt = torch.log(torch.ones(self.N) / self.N)
else:
bt = Bt[wordid] # 否则从观测概率矩阵中取bt
for tag_id in range(len(tag2id)):
max_prob, max_id = torch.max(
viterbi[:, step-1] + A[:, tag_id],
dim=0
)
viterbi[tag_id, step] = max_prob + bt[tag_id]
backpointer[tag_id, step] = max_id
# for tag_id in range(len(tag2id)):
# max_prob, max_id = torch.max(
# viterbi[:, step-1] + A[:, tag_id],
# dim=0
# )
# viterbi[tag_id, step] = max_prob + bt[tag_id]
# backpointer[tag_id, step] = max_id
# 修改为直接使用矩阵加法运算，使程序时间复杂度从O(n^2)到O(n)
max_probs, max_ids = torch.max(viterbi[:, step-1] + A.t(), dim=1)
viterbi[:, step] = max_probs + Bt[wordid]
backpointer[:, step] = max_ids

# 终止， t=seq_len 即 viterbi[:, seq_len]中的最大概率，就是最优路径的概率
best_path_prob, best_path_pointer = torch.max(
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