def compute(obs, states, start_p, trans_p, emit_p): # max_p（3*2）每一列存储第一列不同隐状态的最大概率 max_p = np.zeros((len(obs), len(states))) path = np.zeros((len(states), len(obs))) # 初始化 for i in range(len(states)): max_p[0][i] = start_p[i] * emit_p[i][obs[0]] path[i][0] = i for t in range(1, len(obs)): newpath = np.zeros((len(states), len(obs))) for y in range(len(states)): prob = -1 for y0 in range(len(states)): nprob = max_p[t-1][y0] * trans_p[y0][y] * emit_p[y][obs[t]] if nprob > prob: prob = nprob state = y0 # 记录路径 max_p[t][y] = prob for m in range(t): newpath[y][m] = path[state][m] newpath[y][t] = y请给这段代码每行代码加上注释

# 导入需要的库
import numpy as np

# 定义函数compute，传入参数obs观测序列、states隐状态、start_p初始概率、trans_p转移概率、emit_p发射概率
def compute(obs, states, start_p, trans_p, emit_p):
# max_p（3*2）每一列存储第一列不同隐状态的最大概率
max_p = np.zeros((len(obs), len(states)))
# path记录每个隐状态的路径
path = np.zeros((len(states), len(obs)))

# 初始化
for i in range(len(states)):
# 计算第一列不同隐状态的最大概率
max_p[0][i] = start_p[i] * emit_p[i][obs[0]]
# 记录第一列的路径
path[i][0] = i

# 对于后面的每个时刻
for t in range(1, len(obs)):
# 新建一个路径
newpath = np.zeros((len(states), len(obs)))
# 对于每个隐状态
for y in range(len(states)):
prob = -1
# 对于上一个时刻的每个隐状态
for y0 in range(len(states)):
# 计算概率
nprob = max_p[t-1][y0] * trans_p[y0][y] * emit_p[y][obs[t]]
# 如果概率更大
if nprob > prob:
prob = nprob
state = y0
# 记录路径
max_p[t][y] = prob
for m in range(t):
newpath[y][m] = path[state][m]
newpath[y][t] = y
# 更新路径
path = newpath

# 返回结果
return path, max_p