def setUp(self):
self.asl = AslDb()
self.training_set = self.asl.build_training(FEATURES)
self.test_set = self.asl.build_test(FEATURES)
self.models = train_all_words(self.training_set, SelectorConstant)
def setUp(self):
self.asl = AslDb()
self.training_set = self.asl.build_training(FEATURES)
self.test_set = self.asl.build_test(FEATURES)
self.models = train_all_words(self.training_set, SelectorConstant)
test_SLM = BasicSLM("SLM_data/corpus_sentences.txt", verbose=False)
feature_set = features_custom
selector = SelectorCV
training_set = asl.build_training(feature_set)
testing_set = asl.build_test(feature_set)
train_words = training_set.words
test_words = testing_set.wordlist
#train_words = ['FISH', 'BOOK', 'VEGETABLE']
#test_words = ['FISH', 'BOOK', 'VEGETABLE']
sentences = testing_set.sentences_index
sentences = [sentences[i] for i in sentences]
models_dict = train_all_words(training_set,
selector,
train_words,
verbose=False,
features=feature_set)
test_probs, test_guesses = recognize_words(models_dict,
testing_set,
test_words,
verbose=False)
acc_before = report_recognizer_results(test_words, test_probs, test_guesses,
selector, test_SLM, feature_set)
with open("recognizer_results/raw_results.txt", 'w') as file:
json.dump((test_probs, test_guesses, test_words, sentences), file)
#test_SLM_probs = get_SLM_probs(test_guesses, test_probs, test_SLM)
#new_probs, new_guesses = normalise_and_combine(test_words, test_probs, test_SLM_probs, test_guesses, 1)
