def test_init_method_different_parameters(self): test_dir = File("../../character_examples").getPath() nr_of_training_examples = 90 nr_of_test_examples = 10 for size_classification_factor in drange(0.7, 6.0, 0.3): print str(size_classification_factor) + ' &', for nr_of_segs in range(4,13): #print(nr_of_segs) test_scores = [] for test_nr in range(10): #print(test_nr) extracor = SimpleImageFeatureExtractor(nr_of_divisions=nr_of_segs, size_classification_factor=size_classification_factor) training_examples, test_examples = extracor.extract_training_and_test_examples(test_dir, nr_of_training_examples, nr_of_test_examples) classifier = CharacterClassifier(training_examples, nr_of_hmms_to_try=1, fraction_of_examples_for_test=0, train_with_examples=False, initialisation_method=SpecializedHMM.InitMethod.count_based) test_scores.append(classifier.test(test_examples)) score = sum(test_scores) / len(test_scores) print ' $' + str(score) +'$ ', if nr_of_segs == 12: print '\\\\' else: print '&',
def test_init_method_different_parameters(self): test_dir = File("../../character_examples").getPath() nr_of_training_examples = 90 nr_of_test_examples = 10 for size_classification_factor in drange(0.7, 6.0, 0.3): print str(size_classification_factor) + ' &', for nr_of_segs in range(4, 13): #print(nr_of_segs) test_scores = [] for test_nr in range(10): #print(test_nr) extracor = SimpleImageFeatureExtractor( nr_of_divisions=nr_of_segs, size_classification_factor=size_classification_factor) training_examples, test_examples = extracor.extract_training_and_test_examples( test_dir, nr_of_training_examples, nr_of_test_examples) classifier = CharacterClassifier( training_examples, nr_of_hmms_to_try=1, fraction_of_examples_for_test=0, train_with_examples=False, initialisation_method=SpecializedHMM.InitMethod. count_based) test_scores.append(classifier.test(test_examples)) score = sum(test_scores) / len(test_scores) print ' $' + str(score) + '$ ', if nr_of_segs == 12: print '\\\\' else: print '&',
def create_character_classifier(save_to_file_path): example_dir = File("../../character_examples").getPath() nr_of_training_examples = 90 nr_of_test_examples = 10 extractor = SimpleImageFeatureExtractor(nr_of_divisions=11, size_classification_factor=4.6) training_examples, test_examples = extractor.extract_training_and_test_examples(example_dir, nr_of_training_examples, nr_of_test_examples) classifier = CharacterClassifier(training_examples, nr_of_hmms_to_try=1, fraction_of_examples_for_test=0, train_with_examples=True, initialisation_method=SpecializedHMM.InitMethod.count_based, feature_extractor=extractor) test_result = str(classifier.test(test_examples)) print('Prediction ratio:', test_result)