Hi I have been using Murphy's HMM toolbox with output of Gaussian Mixture. In brief, I have 2 datasets for training. Each dataset comprises of 2000 observations with 11 dimensions per observation. I implemented the following steps to observe the path sequence output.
N_states=2
N_Gaussian_Mixture=1
For each of the dataset, a HMM model was generated. The steps are:
Step 1: mixgauss_init() was used to generated GMM signature for my training data. Step 2: After declaring the matrices for Prior and Transmat, mhmm_em() was used to generate HMM model for the training dataset.
Testing: 2 test data from each of the dataset are used for testing using mhm_logprob(). The output were correctly predicted using loglikelihood scores in every run.
However, when I tried to observe the sequence of the HMM modelling (Dataset_123 with testdata_123) via mixgauss_prob() followed by viterbi_path(), the output sequences were inconsistent. For example, for the first run, the output sequence can be 2221111111111. But when I rerun the program again, the sequence can change to 1111111111111 or 1111111111222. Initially I thought it could be due to my Prior matrix. I fixed the Prior value but it is not helping.
Secondly, it there a possibility when I can assigned labels to the states and sequence? Like Matlab function:
hmmgenerate(...,'Symbols',SYMBOLS) specifies the symbols that are emitted. SYMBOLS can be a numeric array or a cell array of the names of the symbols. The default symbols are integers 1 through N, where N is the number of possible emissions.
`hmmgenerate(...,'Statenames',STATENAMES) specifies the names of the states. STATENAMES can be a numeric array or a cell array of the names of the states. The default state names are 1 through M, where M is the number of states.?
Thank you for your time and hope to hear from the expert sharing.
