# 维特比算法
def vtb(n, o, s, ps, pe, pt):
ret = {}
path = {}
if n == 0:
for x in s:
ret[x] = ps[x] * pe[x][o[n]]
path[x] = [x]
else:
lret, lp = vtb(n - 1, o, s, ps, pe, pt) # n-1天的,结果有3^n-1个
for x in s:
ret[x], mlx = max((lret[lx] * pt[lx][x] * pe[x][o[n]], lx) for lx in s)
path[x] = lp[mlx] + [x]
return ret, path # 返回最大值和路径
# 马尔科夫算法
def hmm(n, o, s, ps, pe, pt):
ret = {}
if n == 0:
for x in s:
ret[x] = ps[x] * pe[x][o[n]]
else:
lret = hmm(n - 1, o, s, ps, pe, pt) # n-1天的,结果有3^n-1个
for k, v in lret.items():
for x in s:
ret[k + "-" + x] = v * pt[k.split("-")[-1]][x] * pe[x][o[n]]
return ret
if __name__ == '__main__':
p_start = {'good': 0.2, 'normal': 0.6, 'bad': 0.2} # 初始概率矩阵
p_emit = {
'good': {'working': 0.05, 'travel': 0.35, 'shopping': 0.35, 'running': 0.25},
'normal': {'working': 0.25, 'travel': 0.25, 'shopping': 0.25, 'running': 0.25},
'bad': {'working': 0.6, 'travel': 0.2, 'shopping': 0.05, 'running': 0.15}
} # 发射概率矩阵
p_trans = {
'good': {'good': 0.5, 'normal': 0.375, 'bad': 0.125},
'normal': {'good': 0.25, 'normal': 0.125, 'bad': 0.625},
'bad': {'good': 0.25, 'normal': 0.375, 'bad': 0.375}} # 转移概率矩阵
stats = ['good', 'normal', 'bad'] # 隐性状态
obs = ["travel", "running"] # 显性状态
rvtb, mpath = vtb(len(obs) - 1, obs, stats, p_start, p_emit, p_trans)
rhmm = hmm(len(obs) - 1, obs, stats, p_start, p_emit, p_trans)
x = max(rhmm, key=rhmm.get) # 算出概率最大的key
print(rhmm)
print(x)
输出结果:
{'good-good': 0.008749999999999999, 'good-normal': 0.006562499999999999, 'good-bad': 0.0013124999999999999, 'normal-good': 0.009375, 'normal-normal': 0.0046875, 'normal-bad': 0.014062499999999999, 'bad-good': 0.0025000000000000005, 'bad-normal': 0.0037500000000000007, 'bad-bad': 0.0022500000000000003}
normal-bad
算出第一天是travel,第二天是running的心情最大概率对应的心情
normal-bad,第一天心情是一般,第二天心情是糟糕的。