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观测序列的生成(原生Python实现).py
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观测序列的生成(原生Python实现).py
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from bisect import bisect_left
from copy import deepcopy
from random import random
def build_markov_sequence(a, b, p, t):
"""隐马尔可夫模型观测序列的生成"""
# 计算前缀和
a = deepcopy(a)
for i in range(len(a)):
for j in range(1, len(a[0])):
a[i][j] += a[i][j - 1]
b = deepcopy(b)
for i in range(len(b)):
for j in range(1, len(b[0])):
b[i][j] += b[i][j - 1]
p = deepcopy(p)
for j in range(1, len(p)):
p[j] += p[j - 1]
# 按照初始状态分布p产生状态i
stat = [bisect_left(p, random())]
# 按照状态的观测概率分布生成观测o
res = [bisect_left(b[stat[-1]], random())]
for _ in range(1, t):
# 按照状态的状态转移概率分布产生下一个状态
stat.append(bisect_left(a[stat[-1]], random()))
# 按照状态的观测概率分布生成观测o
res.append(bisect_left(b[stat[-1]], random()))
return res
if __name__ == "__main__":
A = [[0.0, 1.0, 0.0, 0.0],
[0.4, 0.0, 0.6, 0.0],
[0.0, 0.4, 0.0, 0.6],
[0.0, 0.0, 0.5, 0.5]]
B = [[0.5, 0.5],
[0.3, 0.7],
[0.6, 0.4],
[0.8, 0.2]]
p = [0.25, 0.25, 0.25, 0.25]
print(build_markov_sequence(A, B, p, 200))