fix a hmmRecSmoother

greengrass2015 · Sep 12, 2016 · ad6c784 · ad6c784
1 parent 90d4783
commit ad6c784
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Showing 3 changed files with 10 additions and 7 deletions.
diff --git a/chapter13/HMM/hmmRecSmoother_.m b/chapter13/HMM/hmmRecSmoother_.m
@@ -13,11 +13,11 @@
 At = A';
 c = zeros(1,T); % normalization constant
 gamma = zeros(K,T);
-[gamma(:,1),c(1)] = nml(s.*M(:,1),1);
+[gamma(:,1),c(1)] = normalize(s.*M(:,1),1);
 for t = 2:T
-    [gamma(:,t),c(t)] = nml((At*gamma(:,t-1)).*M(:,t),1);  % 13.59
+    [gamma(:,t),c(t)] = normalize((At*gamma(:,t-1)).*M(:,t),1);  % 13.59
 end
 for t = T-1:-1:1
-    gamma(:,t) = nml(bsxfun(@times,A,gamma(:,t)),1)*gamma(:,t+1);
+    gamma(:,t) = normalize(bsxfun(@times,A,gamma(:,t)),1)*gamma(:,t+1);
 end
 
diff --git a/chapter13/HMM/hmmSmoother.m b/chapter13/HMM/hmmSmoother.m
@@ -1,7 +1,8 @@
 function [gamma, alpha, beta, c] = hmmSmoother(x, model)
 % HMM smoothing alogrithm (normalized forward-backward or normalized alpha-beta algorithm). This is a wrapper function which transform input and call underlying algorithm
-% Unlike the method described in the book of PRML, the alpha returned is the normalized version: gamma(t)=p(z_t|x_{1:T})
-% Computing unnormalized version gamma(t)=p(z_t,x_{1:T}) is numerical unstable, which grows exponential fast to infinity.
+% Unlike the method described in the book of PRML, the alpha and beta
+% returned is the normalized.
+% Computing unnormalized version alpha and beta is numerical unstable, which grows exponential fast to infinity.
 % Input:
 %   x: 1 x n integer vector which is the sequence of observations
 %   model:  model structure
@@ -20,3 +21,4 @@
 X = sparse(x,1:n,1,d,n);
 M = E*X;
 [gamma, alpha, beta, c] = hmmSmoother_(M, A, s);
+% [gamma,c] = hmmRecSmoother_(M, A, s);
diff --git a/chapter13/HMM/hmmSmoother_.m b/chapter13/HMM/hmmSmoother_.m
@@ -1,7 +1,8 @@
 function [gamma, alpha, beta, c] = hmmSmoother_(M, A, s)
 % Implmentation function HMM smoothing alogrithm.
-% Unlike the method described in the book of PRML, the alpha returned is the normalized version: gamma(t)=p(z_t|x_{1:T})
-% Computing unnormalized version gamma(t)=p(z_t,x_{1:T}) is numerical unstable, which grows exponential fast to infinity.
+% Unlike the method described in the book of PRML, the alpha and beta
+% returned is the normalized.
+% Computing unnormalized version alpha and beta is numerical unstable, which grows exponential fast to infinity.
 % Input:
 %   M: k x n emmision data matrix M=E*X
 %   A: k x k transition matrix