<gh_stars>0
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  X,lengths in test_set.get_all_Xlengths().values():
        guess = None
        maxLogL = float("-inf")
        wordprobabilities = {}
        for word,model in models.items():
          try:
            LogL = model.score(X,lengths)
          except Exception as error:
            LogL = float("-inf") 
          wordprobabilities[word] = LogL
          if LogL > maxLogL:
            maxLogL = LogL
            guess = word
        guesses.append(guess)
        probabilities.append(wordprobabilities)

    

    # TODO implement the recognizer
    return probabilities, guesses
   

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  X,lengths in test_set.get_all_Xlengths().values():
        guess = None
        maxLogL = float("-inf")
        wordprobabilities = {}
        for word,model in models.items():
          try:
            LogL = model.score(X,lengths)
          except Exception as error:
            LogL = float("-inf") 
          wordprobabilities[word] = LogL
          if LogL > maxLogL:
            maxLogL = LogL
            guess = word
        guesses.append(guess)
        probabilities.append(wordprobabilities)

    

    # TODO implement the recognizer
    return probabilities, guesses
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',...] # TODO implement the recognizer