HMM-前向后向算法
作者：金良（[email protected]） csdn博客： http://blog.csdn.net/u012176591
状态转移概率分布矩阵
观测概率分布矩阵
初始概率分布
αt(i)=P(o1,o2,⋯,ot,it=qi|λ)
βt(i)=P(ot+1,ot+2,⋯,oT|it=qi,λ)
P(O|λ)=∑Ni=1∑Nj=1αt(i)aijbj(ot+1)βt+1(j)
前向算法
def frontforward(A,B,PI,O,t):
# 0<=t<T时，输出向量，t=T时输出最终概率
# n是状态数，m是观测数
n,m = np.shape(B)
T = np.shape(O)[0]
if 0< t < T: # 当t小于等于0时，按t等于0计算
front = frontforward(A,B,PI,O,t-1)
return [np.dot(front,A[:,i])*B[i][O[t]] for i in range(n)]
elif t <= 0:
return [PI[i]*B[i][O[t]] for i in range(n)]
else: #当t大于等于T时，按t等于length计算
return np.sum(frontforward(A,B,PI,O,t-1))后向算法
def backforward(A,B,PI,O,t):
# 0<=t<=T-1时输出向量，t=-1时输出最终概率
n,m = np.shape(B)
T = np.shape(O)[0]
if 0<=t<=T-2:
back = backforward(A,B,PI,O,t+1)
return [np.dot(back*B[:,O[t+1]],A[i,:]) for i in range(n)]
elif t >= T-1:
return [1]*n
else:
return np.sum(backforward(A,B,PI,O,0)*PI*B[:,O[0]])概率计算
def backfront(A,B,PI,O,t):
n,m = np.shape(B)
T = np.shape(O)[0]
if 0<=t<=T-2:
return np.sum([frontforward(A,B,PI,O,t)[i]*backforward(A,B,PI,O,t+1)[j]*A[i,j]*B[j,O[t+1]] for i in range(n) for j in range(n)])
elif t >= T-1:
return frontforward(A,B,PI,O,T)
else:
return backforward(A,B,PI,O,-1)测试
print '前向算法frontforward的测试：'
for t in range(6):
print 't=%d:'%t,frontforward(A,B,PI,O,t)前向算法frontforward的测试：
t=0: [0.10000000000000001, 0.16000000000000003, 0.27999999999999997]
t=1: [0.10000000000000001, 0.094799999999999995, 0.054599999999999996]
t=2: [0.04514, 0.02572, 0.066373999999999989]
t=3: [0.026160799999999998, 0.020828519999999996, 0.015059459999999998]
t=4: [0.011368329999999999, 0.0092791955999999981, 0.0071540381999999989]
t=5: 0.0278015638
print '后向算法backforward的测试：'
for t in range(5,-2,-1):
print 't=%d:'%t,backforward(A,B,PI,O,t)后向算法backforward的测试：
t=5: [1, 1, 1]
t=4: [1, 1, 1]
t=3: [0.41000000000000003, 0.47999999999999998, 0.46999999999999997]
t=2: [0.18130000000000002, 0.219, 0.2107]
t=1: [0.11876500000000001, 0.10648200000000001, 0.10678700000000001]
t=0: [0.046159970000000002, 0.052981260000000002, 0.052530590000000002]
t=-1: 0.0278015638
print '计算概率backfront的测试：'
for t in range(-1,6):
print 't=%d:'%t,backfront(A,B,PI,O,t)计算概率backfront的测试：
t=-1: 0.0278015638
t=0: 0.0278015638
t=1: 0.0278015638
t=2: 0.0278015638
t=3: 0.0278015638
t=4: 0.0278015638
t=5: 0.0278015638
一些概率和期望值的计算
def gammat(A,B,PI,O,t,i):
prosingle = frontforward(A,B,PI,O,t)[i]*backforward(A,B,PI,O,t)[i]
prosum = np.sum([frontforward(A,B,PI,O,t)[i]*backforward(A,B,PI,O,t)[i] for j in range(n)])
return 1.0*prosingle/prosumdef xit(A,B,PI,O,t,i,j):
prosingle = frontforward(A,B,PI,O,t)[i]*backforward(A,B,PI,O,t+1)[j]*A[i,j]*B[j][O[t+1]]
prosum = np.sum([frontforward(A,B,PI,O,t)[i]*backforward(A,B,PI,O,t+1)[j]*A[i,j]*B[j][O[t+1]] for i in range(n) for j in range(n)])
return 1.0*prosingle/prosum对数空间的运算
定义了一个类，用于对数空间的数值计算
class Logspace:
def __init__(self):
self.LOGZERO = 'LOGZERO'
def eexp(self,x):
if x == self.LOGZERO:
return 0
else:
return np.exp(x)
def eln(self,x):
if x == 0:
return self.LOGZERO
elif x>0:
return np.log(x)
else:
print 'Wrong!!!\n\t negative input error'
return np.nan
def elnsum(self,elnx,elny):
if elnx == self.LOGZERO:
return elny
elif elny == self.LOGZERO:
return elnx
elif elnx > elny:
return elnx + self.eln(1+np.exp(elny-elnx))
else:
return elny + self.eln(1+np.exp(elnx-elny))
def elnproduct(self,elnx,elny):
if elnx == self.LOGZERO or elny == self.LOGZERO:
return self.LOGZERO
else:
return elnx + elny下面是对该类Logspace 的测试语句和测试结果：
logspace = Logspace()
elnx = logspace.eln(np.exp(60))
elny = logspace.eln(np.exp(-10))
elnx_plus_y = logspace.elnsum(elnx,elny)
print elnx_plus_y
elnx_prod_y = logspace.elnproduct(elnx,elny)
print elnx_prod_y
logzero = logspace.eln(0)
elnx_plus_y = logspace.elnsum(elnx,logzero)
print elnx_plus_y
elnx_prod_y = logspace.elnproduct(elnx,logzero)
print elnx_prod_y
print logspace.eln(-1)测试结果：
60.0
50.0
60.0
LOGZERO
Wrong!!!
negative input error
nan
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http://www.zhihu.com/question/20962240 - 隐马尔科夫模型
http://www.52nlp.cn/category/hidden-markov-model/page/4 - 发现一篇不错的学习隐马尔可夫模型的文章
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http://www.cs.colostate.edu/~anderson/cs440/index.html/doku.php?id=notes:hmm2 - 经典论文 http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=18626
《A tutorial on Hidden Markov Models and selected applications in speech recognition》 - http://www.cnblogs.com/CheeseZH/p/4229910.html