my_recognizer.py

import warnings
from asl_data import SinglesData


def recognize(models: dict, test_set: SinglesData):
    """ Recognize test word sequences from word models set
   :param models: dict of trained models
       {'SOMEWORD': GaussianHMM model object, 'SOMEOTHERWORD': GaussianHMM model object, ...}
   :param test_set: SinglesData object
   :return: (list, list)  as probabilities, guesses
       both lists are ordered by the test set word_id
       probabilities is a list of dictionaries where each key a word and value is Log Liklihood
           [{SOMEWORD': LogLvalue, 'SOMEOTHERWORD' LogLvalue, ... },
            {SOMEWORD': LogLvalue, 'SOMEOTHERWORD' LogLvalue, ... },
            ]
       guesses is a list of the best guess words ordered by the test set word_id
           ['WORDGUESS0', 'WORDGUESS1', 'WORDGUESS2',...]
   """
    warnings.filterwarnings("ignore", category=DeprecationWarning)
    probabilities = []
    guesses = []

    for ix, _ in test_set.get_all_Xlengths().items():
        X, Xlength = test_set.get_item_Xlengths(ix)
        
        scores = {}
        best_word = ""
        best_score = float("-inf")
        
        # Iterate over words and models
        for word, model in models.items():
            score = float("-inf")
            try: 
                # Get Score
                score = model.score(X, Xlength)
            except:
                pass

            # Select best score
            if score >= best_score:
                best_score = score
                best_word = word

            scores[word] = score

        probabilities.append(scores)
        guesses.append(best_word)
    return probabilities,guesses