www.pudn.com > VAD-DTW-HMM.rar > getparam.m

function param = getparam(hmm, O) 
%给定输出序列O, 计算前向概率alpha, 后向概率beta, 标定系数c, 及ksai,gama 
%输入: 
%  hmm -- HMM模型参数 
%  O   -- n*d 观察序列 
%输出: 
%  param -- 包含各种参数的结构 
 
T = size(O,1);	%序列的长度 
 
init  = hmm.init;	%初始概率 
trans = hmm.trans;	%转移概率 
mix   = hmm.mix;	%高斯混合 
N     = hmm.N;		%HMM状态数 
 
% 给定观察序列O, 计算前向概率alpha 
alpha = zeros(T,N); 
 
% t=1的前向概率 
x = O(1,:); 
for i = 1:N 
	alpha(1,i) = init(i) * mixture(mix(i),x);	 
end 
 
% 标定t=1的前向概率 
c    = zeros(T,1); 
c(1) = 1/sum(alpha(1,:));		 
alpha(1,:) = c(1) * alpha(1,:); 
 
% t=2:T的前向概率和标定 
for t = 2:T 
	for i = 1:N 
		temp = 0; 
		for j = 1:N 
			temp = temp + alpha(t-1,j) * trans(j,i); 
		end 
		alpha(t,i) = temp * mixture(mix(i),O(t,:)); 
	end 
	c(t) = 1/sum(alpha(t,:)); 
	alpha(t,:) = c(t)*alpha(t,:); 
end 
 
% 给定观察序列O, 计算后向概率beta 
beta = zeros(T,N); 
 
% t=T的后向概率及标定 
for l = 1:N 
	beta(T,l) = c(T);	 
end 
 
% t=T-1:1的后向概率和标定 
for t = T-1:-1:1 
	x = O(t+1,:); 
	for i = 1:N 
	for j = 1:N 
		beta(t,i) = beta(t,i) + beta(t+1,j) * mixture(mix(j),x) * trans(i,j); 
	end 
	end 
	beta(t,:) = c(t) * beta(t,:); 
end 
 
%过渡概率ksai 
ksai = zeros(T-1,N,N); 
for t = 1:T-1 
	denom = sum(alpha(t,:).*beta(t,:)); 
	for i = 1:N-1 
	for j = i:i+1 
		nom = alpha(t,i) * trans(i,j) * mixture(mix(j),O(t+1,:)) * beta(t+1,j); 
		ksai(t,i,j) = c(t) * nom/denom; 
	end 
	end 
end 
 
%混合输出概率:gama 
gama = zeros(T,N,max(hmm.M)); 
for t = 1:T 
	pab = zeros(N,1); 
	for l = 1:N 
		pab(l) = alpha(t,l) * beta(t,l); 
	end 
	x = O(t,:); 
	for l = 1:N 
		prob = zeros(mix(l).M,1); 
		for j = 1:mix(l).M 
			m = mix(l).mean(j,:); 
			v = mix(l).var (j,:); 
			prob(j) = mix(l).weight(j) * pdf(m, v, x); 
		end 
		tmp  = pab(l)/sum(pab); 
		for j = 1:mix(l).M 
			gama(t,l,j) = tmp * prob(j)/sum(prob); 
		end 
	end 
end 
 
param.c     = c; 
param.alpha = alpha; 
param.beta  = beta; 
param.ksai  = ksai; 
param.gama  = gama;