维特比算法手写

def hmm_viterbi(A,B,pi,O):
δ = np.zeros((len(O),len(A)))   #第一个局部
Ψ = np.zeros((len(O),len(A)))   #第二个局部
# 1、初始化t=1时刻维特比的两个局部变量
δ[0] = pi*B[:,O[0]]
# 2、递归求序列每一步的两个局部变量
for index in range(1,len(δ)):
δ[index] = np.max(δ[index-1]*A.T,1)*B[:,O[index]]
Ψ[index] = np.argmax(δ[index-1]*A.T,1)
# 3、求最后一个概率最大对应的隐含标签
label = [δ[-1].argmax()]
# 4、回溯求整个序列的隐含标签
for index,tag in enumerate(Ψ[::-1]):
if index<len(Ψ)-1:
label.append(int(tag[int(label[-1])]))
return label[::-1]

A = np.array([[0.5,0.2,0.3],[0.3,0.5,0.2],[0.2,0.3,0.5]])
B = np.array([[0.5,0.5],[0.4,0.6],[0.7,0.3]])
pi = np.array([0.2,0.4,0.4])
O = '维特比算法是一个分词方法'
O = np.array([0,1,0])
hmm_viterbi(A, B, pi, O)