$ git clone https://gitlab.com/antoinehonore/hq.git
$ git clone -b api https://gitlab.com/antoinehonore/gm_hmm.git

In hq/ and gm_hmm/ folders Run:

$ cd [FOLDER]
$ virtualenv -p python3 pyenv
$ . pyenv/bin/activate
$ python [FOLDER]/setup.py develop

The last line adds the folder containing hq/ and gm_hmm/ to PYTHONPATH.

Place the train and test pickle files corresponding to 61 classes and 39 features and clean test with the names: train.feat0.pkl and test.feat0.pkl under exp/split\_c61f39clean/data. Follow the same naming notations for the rest of the possible datasets.

• feat0 is not used here but might be necessary if we were to compute different sets of features.

From hq/, run:

$ make model=gmmhmm splits=_c61f39clean feats=0

Once the previous test runs and gives results, we can try more advanced calls:

$ ./gmmhmm_submodels
$ ./gmmhmm_submodels.sh print
Models:  gmmhmm
Number of states (ns):  3 6 9
Number of iterations (niter):  2 10 20
Number of mixtures (nmix):  2 4 6 8 10 12
gmmhmm-ns\{3,6,9\}-niter\{2,10,20\}-nmix\{2,4,6,8,10,12\}

Copy the last line and use it in the make call

$ make gmmhmm-ns\{3,6,9\}-niter\{2,10,20\}-nmix\{2,4,6,8,10,12\} splits=_c61f39clean feats=0 -j 5

This allows to train all combinations of hyper parameters for gmmhmms on the data called c61f39clean. Once you have set up more split_ folders, you can run things like:

$ make gmmhmm-ns\{3,6,9\}-niter\{2,10,20\}-nmix\{2,4,6,8,10,12\} splits=_c61f\{39,13\}clean feats=0 -j 5

This repos is forked from hq