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my_recognizer.py
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my_recognizer.py
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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 = []
# DONE implement the recognizer
#
# Recognizer Implementation
# https://discussions.udacity.com/t/recognizer-implementation/234793
for (X, lengths) in test_set.get_all_Xlengths().values():
probability = {}
select_logL = float("-inf")
guess = None
for word, model in models.items():
try:
logL = model.score(X, lengths)
probability[word] = logL
except:
probability[word] = float("-inf")
# continue
if logL > select_logL:
select_logL = logL
guess = word
probabilities.append(probability)
guesses.append(guess)
return probabilities, guesses