Move HMMClassifier prior and posterior definition

[Edwin (Ed) Onuonga]

Move HMMClassifier prior creation in predict() to a separate function:

sequentia/lib/sequentia/classifiers/hmm/hmm_classifier.py

Lines 109 to 116 in b857c72

 # Create look-up for prior probabilities 
 if prior == 'frequency': 
     total_seqs = sum(model.n_seqs_ for model in self._models_) 
     prior = {model.label:(model.n_seqs_ / total_seqs) for model in self._models_} 
 elif prior == 'uniform': 
     prior = {model.label:(1. / len(self._models_)) for model in self._models_} 
 else: 
     prior = {model.label:prior[self._encoder_.transform([model.label]).item()] for model in self._models_} 

and similarly for the posterior calculation:

sequentia/lib/sequentia/classifiers/hmm/hmm_classifier.py

Lines 121 to 122 in b857c72

 # Lambda for calculating the log un-normalized posteriors as a sum of the log forward probabilities (likelihoods) and log priors 
 posteriors = lambda x: np.array([model.forward(x) + np.log(prior[model.label]) for model in self._models_]) 

[Edwin (Ed) Onuonga]

Fixed in #200.