CODEWITHSUNDEEP
matlabhidden-markov-models
user1132254
user1132254

Reputation: 141

keving murphy's hmm matlab toolbox assertion error

I am working on a project that needs to use hidden markov models. I downloaded Kevin Murphy's toolbox. I have some problems about the usage. In the toolbox webpage, he says that first input of dhmm_em and dhmm_logprob are symbol sequence data. On their examples, they give row vectors as data. So, when I give my symbol sequence as row vector, I get error;

??? Error using ==> assert at 9
assertion violated:

Error in ==> fwdback at 105
assert(approxeq(sum(alpha(:,t)),1))

Error in ==> dhmm_logprob at 17
  [alpha, beta, gamma, ll] = fwdback(prior,
  transmat, obslik, 'fwd_only', 1);

Error in ==> mainCourseProject at 110
            loglik(train_act) =
            dhmm_logprob(orderedSymbols,
            hmm{train_act}.prior,
            hmm{train_act}.trans,
            hmm{act}.emiss);

However, before giving this error, code works for some symbol vectors. When I give my data as column vector, functions work fine, no errors. So why exactly am I getting this error?

You might say that I should be giving not single vectors, but vector sets, I also tried to collect my feature vectors in a struct and give row vectors as such, but nothing changed, I still get assertion error.

By the way, my symbol sequence does not have any zeros, I am doing everything almost the same as they showed in their examples, so I would be greatful if anyone could help me please.

Upvotes: 1

Views: 771

Answers (2)

Amro
Amro

Reputation: 124563

Im not sure, but from the function call stack shown above, shouldn't the last line be hmm{train_act}.emiss instead of hmm{act}.emiss. In other words when you computing the log-probability of a sequence, you should pass components that belong to the same HMM model (transition matrix, emission matrix, and prior probabilities).

By the way, the ASSERT in the code is a sanity check that a vector of probabilities should sum to 1. Oftentimes, when working with very small values (log-probabilities), numerical stability issues can creep in... You could edit the APPROXEQ function to relax the comparison a bit, by giving it a bigger margin of error

Upvotes: 2

Peter
Peter

Reputation: 14937

This error message and the code it refers to are human-readable. An assertion is a guard put in by the programmer, to ensure that certain conditions are met. In this case, what is the condition? approxeq(sum(alpha(:,t)),1) I'd venture to say that approxeq wants the values to be approximately equal, so this boils down to: sum(alpha(:,t)) ~= 1

Without knowing anything about the code, I'd also guess that these refer to probabilities. The probabilities of a node's edges must sum to one. Hopefully this starts you down a productive debugging path. If you can't figure out what's wrong with your input that produces this condition, start wading into the code a bit to see where this alpha vector comes from, and how it ended up invalid.

Upvotes: 1

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