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)
