def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
states = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
symbols = ['0', '1', '2', '3', '4', '5', '6', '7']
self.learning_model = HiddenMarkovModelTrainer(states=states,
symbols=symbols)
self.model = None
def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
states = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
symbols = ['0', '1', '2', '3', '4', '5', '6', '7']
self.learning_model = HiddenMarkovModelTrainer(states=states, symbols=symbols)
self.model = None
class HMM(object):
'''
classdocs
'''
def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
states = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
symbols = ['0', '1', '2', '3', '4', '5', '6', '7']
self.learning_model = HiddenMarkovModelTrainer(states=states,
symbols=symbols)
self.model = None
def generate_labelled_sequences(self, freeman_codes_dict):
labeled_sequences = []
labeled_symbols = []
codes_list = freeman_codes_dict.items()
for tup in codes_list:
for code in tup[1]:
temp = []
for symbol in code:
temp.append((symbol, tup[0]))
labeled_symbols.append(temp)
for tup in codes_list:
for code in tup[1]:
labeled_sequences.append((code, tup[0]))
codes = numpy.array([x[0] for x in labeled_sequences])
labels = numpy.array([y[1] for y in labeled_sequences])
return labeled_symbols, labeled_sequences, codes, labels
def learning_curve(self,
dataset,
n_iter,
train_sizes=numpy.linspace(0.1, 1.0, 5)):
cv_scores = []
train_scores = []
for i in train_sizes:
data = dataset[:int(len(dataset) * i)]
cv_score = []
t_score = []
for j in range(n_iter):
cv_score.extend(self.training(dataset, cv=10, n_iter=1))
train_score, test_score = self.training(dataset, n_iter=1)
t_score.extend(train_score)
cv_scores.append(cv_score)
train_scores.append(t_score)
cv_scores = numpy.array(cv_scores)
train_scores = numpy.array(train_scores)
print cv_scores.shape
print train_scores.shape
return train_scores, cv_scores
def get_data(self, dataset_path):
dataset = self.dsr.read_dataset_images(dataset_path)
freeman_codes_dict = self.fenc.encode_freeman_dataset(dataset)
labeled_symbols, labeled_sequence, codes, labels = self.generate_labelled_sequences(
freeman_codes_dict)
return labeled_symbols, labeled_sequence, codes, labels
def training(self, dataset, cv=1, n_iter=1):
if isinstance(dataset, basestring):
labeled_symbols, labeled_sequence, codes, labels = self.get_data(
dataset)
else:
labeled_symbols, labeled_sequence, codes, labels = dataset
self.model = self.learning_model.train(labeled_symbols)
if cv > 1:
cv_scores = []
for i in range(n_iter):
skf = cross_validation.KFold(len(labels),
n_folds=10,
shuffle=True)
iter_score = []
for train_index, test_index in skf:
train_data = list(
numpy.array(labeled_symbols)[train_index])
test_data = list(numpy.array(labeled_symbols)[test_index])
self.model = self.learning_model.train(train_data)
fold_score = self.model.evaluate(test_data)
iter_score.append(fold_score)
cv_scores.append(numpy.mean(iter_score))
return cv_scores
else:
skf = cross_validation.ShuffleSplit(len(labels),
n_iter=n_iter,
test_size=0.2,
random_state=0)
training_score = []
test_score = []
for train_index, test_index in skf:
train_data = list(numpy.array(labeled_symbols)[train_index])
test_data = list(numpy.array(labeled_symbols)[test_index])
self.model = self.learning_model.train(train_data)
training_score.append(self.model.evaluate(train_data))
test_score.append(self.model.evaluate(test_data))
if n_iter == 1:
predict_labels = []
for i in range(len(list(codes[test_index]))):
predicted_states = self.model.tag(
list(codes[test_index])[i])
predict_labels.append(predicted_states[0][1])
self.ConfusionMatrix = ConfusionMatrix(
list(labels[test_index]), predict_labels)
return training_score, test_score
def predict(self, image_path):
if os.path.isfile(image_path):
image_array = self.dsr.read_img_bw(image_path)
freeman_code = self.fenc.encode_freeman(image_array)
else:
freeman_code = image_path
predicted_states = self.model.tag(freeman_code)
predicted_states = [x[1] for x in predicted_states]
if len(set(predicted_states)) == 1:
predicted_class = list(set(predicted_states))[0]
return predicted_class
## TESTING CODE (WILL BE REMOVED) ##
# from HMM import HMM
# hmm = HMM()
# cv_scores = hmm.training('I:\\eclipse_workspace\\CharacterRecognition\\teams_dataset', cv=10, n_iter=50)
# train_score, test_score = hmm.training('I:\\eclipse_workspace\\CharacterRecognition\\teams_dataset', n_iter=1)
# with open('hmm_confusion_matrix.txt', 'w') as fp:
# fp.write(hmm.ConfusionMatrix.__str__())
#
# with open("./Results/hmm.txt", 'w') as fp:
# for i in range(len(cv_scores)):
# text = str(cv_scores[i]) + ',' + str(train_score[i]) + ',' + str(test_score[i]) + '\n'
# print text
# print '--------------------------------'
# fp.write(text)
class HMM(object):
'''
classdocs
'''
def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
states = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
symbols = ['0', '1', '2', '3', '4', '5', '6', '7']
self.learning_model = HiddenMarkovModelTrainer(states=states, symbols=symbols)
self.model = None
def generate_labelled_sequences(self, freeman_codes_dict):
labeled_sequences = []
labeled_symbols = []
codes_list = freeman_codes_dict.items()
for tup in codes_list:
for code in tup[1]:
temp = []
for symbol in code:
temp.append((symbol, tup[0]))
labeled_symbols.append(temp)
for tup in codes_list:
for code in tup[1]:
labeled_sequences.append((code,tup[0]))
codes = numpy.array([x[0] for x in labeled_sequences])
labels = numpy.array([y[1] for y in labeled_sequences])
return labeled_symbols, labeled_sequences, codes, labels
def learning_curve(self, dataset, n_iter, train_sizes=numpy.linspace(0.1, 1.0, 5)):
cv_scores = []
train_scores = []
for i in train_sizes:
data = dataset[:int(len(dataset)*i)]
cv_score = []
t_score = []
for j in range(n_iter):
cv_score.extend(self.training(dataset, cv=10, n_iter=1))
train_score, test_score = self.training(dataset, n_iter=1)
t_score.extend(train_score)
cv_scores.append(cv_score)
train_scores.append(t_score)
cv_scores = numpy.array(cv_scores)
train_scores = numpy.array(train_scores)
print cv_scores.shape
print train_scores.shape
return train_scores, cv_scores
def get_data(self, dataset_path):
dataset = self.dsr.read_dataset_images(dataset_path)
freeman_codes_dict = self.fenc.encode_freeman_dataset(dataset)
labeled_symbols, labeled_sequence, codes, labels = self.generate_labelled_sequences(freeman_codes_dict)
return labeled_symbols, labeled_sequence, codes, labels
def training(self, dataset, cv=1, n_iter=1):
if isinstance(dataset, basestring):
labeled_symbols, labeled_sequence, codes, labels = self.get_data(dataset)
else:
labeled_symbols, labeled_sequence, codes, labels = dataset
self.model = self.learning_model.train(labeled_symbols)
if cv > 1:
cv_scores = []
for i in range(n_iter):
skf = cross_validation.KFold(len(labels), n_folds=10, shuffle=True)
iter_score = []
for train_index, test_index in skf:
train_data = list(numpy.array(labeled_symbols)[train_index])
test_data = list(numpy.array(labeled_symbols)[test_index])
self.model = self.learning_model.train(train_data)
fold_score = self.model.evaluate(test_data)
iter_score.append(fold_score)
cv_scores.append(numpy.mean(iter_score))
return cv_scores
else:
skf = cross_validation.ShuffleSplit(len(labels), n_iter=n_iter, test_size=0.2, random_state=0)
training_score = []
test_score = []
for train_index, test_index in skf:
train_data = list(numpy.array(labeled_symbols)[train_index])
test_data = list(numpy.array(labeled_symbols)[test_index])
self.model = self.learning_model.train(train_data)
training_score.append(self.model.evaluate(train_data))
test_score.append(self.model.evaluate(test_data))
if n_iter==1:
predict_labels = []
for i in range(len(list(codes[test_index]))):
predicted_states = self.model.tag(list(codes[test_index])[i])
predict_labels.append(predicted_states[0][1])
self.ConfusionMatrix = ConfusionMatrix(list(labels[test_index]), predict_labels)
return training_score, test_score
def predict(self, image_path):
if os.path.isfile(image_path):
image_array = self.dsr.read_img_bw(image_path)
freeman_code = self.fenc.encode_freeman(image_array)
else:
freeman_code = image_path
predicted_states = self.model.tag(freeman_code)
predicted_states = [x[1] for x in predicted_states]
if len(set(predicted_states)) == 1:
predicted_class = list(set(predicted_states))[0]
return predicted_class
## TESTING CODE (WILL BE REMOVED) ##
# from HMM import HMM
# hmm = HMM()
# cv_scores = hmm.training('I:\\eclipse_workspace\\CharacterRecognition\\teams_dataset', cv=10, n_iter=50)
# train_score, test_score = hmm.training('I:\\eclipse_workspace\\CharacterRecognition\\teams_dataset', n_iter=1)
# with open('hmm_confusion_matrix.txt', 'w') as fp:
# fp.write(hmm.ConfusionMatrix.__str__())
#
# with open("./Results/hmm.txt", 'w') as fp:
# for i in range(len(cv_scores)):
# text = str(cv_scores[i]) + ',' + str(train_score[i]) + ',' + str(test_score[i]) + '\n'
# print text
# print '--------------------------------'
# fp.write(text)
