class KNN_strings(object):
'''
classdocs
'''
def __init__(self, n_neighbors=1):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
self.data = []
self.knn = KNeighborsClassifier(n_neighbors=n_neighbors, algorithm='auto', metric=self.lev_metric)
def lev_metric(self, x, y):
i, j = int(x[0]), int(y[0]) # extract indices
# if self.data[i] == self.data[j]:
# print self.data[i], self.data[j], edit_dist(self.data[i], self.data[j])
return edit_dist(self.data[i], self.data[j])
def knn_train(self, dataset, cv=1, datasplit=0.7):
images_dataset= self.dsr.read_dataset_images(dataset)
freeman_code_dict = self.fenc.encode_freeman_dataset(images_dataset)
_, codes, labels = self.dsr.gen_labelled_arrays(freeman_code_dict)
self.data = codes
X = np.arange(len(self.data)).reshape(-1, 1)
if cv <= 1:
self.knn.fit(X, labels)
elif cv > 1:
cv_result = cross_validation.cross_val_score(self.knn, X, labels, cv=cv)
print cv_result
print 'Training Done!'
def knn_predict(self, test_data, score=False):
images_dataset= self.dsr.read_dataset_images(test_data)
freeman_code_dict = self.fenc.encode_freeman_dataset(images_dataset)
_, codes, labels = self.dsr.gen_labelled_arrays(freeman_code_dict)
X_pred = np.arange(len(codes)).reshape(-1, 1)
predictions = self.knn.predict(X_pred)
if score == True:
accuracy = self.knn.score(X_pred, labels)
print "Test Accuracy: ", accuracy
return predictions
def knn_predict_one(self, test_image):
image_code = self.fenc.encode_freeman(test_image)
print image_code
data = [image_code]
X_pred = np.arange(len(data)).reshape(-1, 1)
prediction = self.knn.predict(X_pred)
return prediction
class NaiveBayes(ml_alg_base):
'''
classdocs
'''
def __init__(self):
'''
Constructor
'''
ml_alg_base.__init__(self)
self.dsr = DatasetReader()
self.learning_model = naive_bayes.GaussianNB()
def get_data(self, dataset_path="./teams_dataset"):
data_dict = self.dsr.read_dataset_images(dataset_path)
_, data_set_x, data_set_y = self.dsr.gen_labelled_arrays(data_dict)
data_set_x = data_set_x.reshape(len(data_set_x), -1)
return data_set_x, data_set_y
def training(self, dataset_path, cv=1):
dataset = self.dsr.read_dataset_images(dataset_path)
_, images, labels = self.dsr.gen_labelled_arrays(dataset)
images = numpy.array(images)
#reshape images for input
data = images.reshape(len(images), -1)
if cv <= 1:
self.learning_model.fit(data, labels)
elif cv > 1:
cv_result = cross_validation.cross_val_score(self.learning_model,
data,
labels,
cv=cv)
return cv_result
pickle.dump(self.learning_model,
open("./Models/naivebayes_model.p", "wb"))
def predict(self, image_path):
try:
self.learning_model = pickle.load(
open("./Models/naivebayes_model.p", "rb"))
except:
print "Please train the Naive Bayes model first"
if isbasestring(image_path):
image = self.dsr.read_img_bw(image_path)
else:
image = image_path
image = image.reshape(-1, image.shape[0] * image.shape[1])
result = self.learning_model.predict(image)
return result
# from NaiveBayes import NaiveBayes
# NB = NaiveBayes()
# # NB.training('I:\\eclipse_workspace\\CharacterRecognition\\digits_dataset_clean', cv=5)
# # print NB.predict('I:\\eclipse_workspace\\CharacterRecognition\\test1.jpg')
# data_x, data_y = NB.get_data()
# print data_x.shape, data_y.shape
# NB.first_exp(data_x, data_y, NB.learning_model, algorithm_name='NaiveBayes' ,num_iter=50)
class KNN_statistic(object):
'''
classdocs
'''
def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
self.training_data = []
def generate_labelled_sequences(self, freeman_codes_dict):
labelled_sequences = []
codes_list = freeman_codes_dict.items()
for tup in codes_list:
for code in tup[1]:
labelled_sequences.append((tup[0],code))
return labelled_sequences
def prepare_data(self, arrays_data=[], arrays_labels=[], split=0.2):
# Separate data into 2 sets, 1 is training and 1 is test,split is the ratio (the default is 0.20)
ad_train, ad_test, al_train, al_test = train_test_split(arrays_data, arrays_labels, test_size=split, random_state=42)
return ad_train, ad_test, al_train, al_test
def get_neighbors(self, data, data_label, test_instance, k):
# Get the list of nearest neighbors to a test instance
distances = []
for i in range(len(data)):
dist = edit_dist(test_instance, data[i])
distances.append((data[i], data_label[i], dist))
distances.sort(key=operator.itemgetter(2))
neighbors = []
for x in range(0, k):
neighbors.append([distances[x][0], distances[x][1]])
return neighbors
def get_label(self, neighbors):
# Determine the label of a test instance base on its nearest neighbors
labels = {}
for neighbor in neighbors:
if neighbor[1] not in labels:
labels[neighbor[1]] = 1
else:
labels[neighbor[1]] += 1
sorted_labels = sorted(labels.items(), key=operator.itemgetter(1), reverse=True)
return sorted_labels[0][0]
def evaluation(self, data, data_for_distance_caculation, data_label, data_for_distance_calculation_label, k=3):
# Evaluate the accuracy of knn
correct_count = 0
for instance in range(0, len(data)-1):
neighbors = self.get_neighbors(data_for_distance_caculation,data_for_distance_calculation_label, data[instance], k)
label = self.get_label(neighbors)
if int(label) == int(data_label[instance]):
correct_count += 1
return (float(correct_count)/len(data))
def knn_train(self, dataset_path, train_test_split=0.2):
dataset = self.dsr.read_dataset_images(dataset_path)
freeman_codes_dict = self.fenc.encode_freeman_dataset(dataset)
_, arrays_data, arrays_label = self.dsr.gen_labelled_arrays(freeman_codes_dict)
arrays_data, arrays_label = shuffle(arrays_data, arrays_label)
ad_train, ad_test, al_train, al_test = self.prepare_data(arrays_data, arrays_label, split=train_test_split)
# Cross validation with 5 folds
kf = KFold(len(ad_train), 5)
result = 0
for train_index, test_index in kf:
ad_train_kfold, ad_test_kfold = ad_train[train_index], ad_train[test_index]
al_train_kfold, al_test_kfold = al_train[train_index], al_train[test_index]
result += self.evaluation(ad_test_kfold, ad_train_kfold, al_test_kfold, al_train_kfold, k=2)
result_average = result/5
# Result with the training
result_training = self.evaluation(ad_train, ad_train, al_train, al_train, k=2)
# Result with the test
result_test = self.evaluation(ad_test, ad_train, al_test, al_train, k=2)
return result_average, result_training, result_test
# knn = KNN_strings(n_neighbors=1)
# knn = KNN_statistic()
# results = []
# for x in range(50):
# result_average, result_training, result_test = knn.knn_train("/home/thovo/PycharmProjects/CharacterRecognition/digits_dataset", 0.2)
# text = result_average.__str__() + " , " + result_training.__str__() + " , " + result_test.__str__() + "\n"
# results.append(text)
#
#
# f = open("Results/knn.txt", "w")
# for item in results:
# f.write(item)
#
# f.close()
class KNN_statistic(object):
'''
classdocs
'''
def __init__(self):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
self.training_data = []
def generate_labelled_sequences(self, freeman_codes_dict):
labelled_sequences = []
codes_list = freeman_codes_dict.items()
for tup in codes_list:
for code in tup[1]:
labelled_sequences.append((tup[0], code))
return labelled_sequences
def prepare_data(self, arrays_data=[], arrays_labels=[], split=0.2):
# Separate data into 2 sets, 1 is training and 1 is test,split is the ratio (the default is 0.20)
ad_train, ad_test, al_train, al_test = train_test_split(
arrays_data, arrays_labels, test_size=split, random_state=42)
return ad_train, ad_test, al_train, al_test
def get_neighbors(self, data, data_label, test_instance, k):
# Get the list of nearest neighbors to a test instance
distances = []
for i in range(len(data)):
dist = edit_dist(test_instance, data[i])
distances.append((data[i], data_label[i], dist))
distances.sort(key=operator.itemgetter(2))
neighbors = []
for x in range(0, k):
neighbors.append([distances[x][0], distances[x][1]])
return neighbors
def get_label(self, neighbors):
# Determine the label of a test instance base on its nearest neighbors
labels = {}
for neighbor in neighbors:
if neighbor[1] not in labels:
labels[neighbor[1]] = 1
else:
labels[neighbor[1]] += 1
sorted_labels = sorted(labels.items(),
key=operator.itemgetter(1),
reverse=True)
return sorted_labels[0][0]
def evaluation(self,
data,
data_for_distance_caculation,
data_label,
data_for_distance_calculation_label,
k=3):
# Evaluate the accuracy of knn
correct_count = 0
for instance in range(0, len(data) - 1):
neighbors = self.get_neighbors(
data_for_distance_caculation,
data_for_distance_calculation_label, data[instance], k)
label = self.get_label(neighbors)
if int(label) == int(data_label[instance]):
correct_count += 1
return (float(correct_count) / len(data))
def knn_train(self, dataset_path, train_test_split=0.2):
dataset = self.dsr.read_dataset_images(dataset_path)
freeman_codes_dict = self.fenc.encode_freeman_dataset(dataset)
_, arrays_data, arrays_label = self.dsr.gen_labelled_arrays(
freeman_codes_dict)
arrays_data, arrays_label = shuffle(arrays_data, arrays_label)
ad_train, ad_test, al_train, al_test = self.prepare_data(
arrays_data, arrays_label, split=train_test_split)
# Cross validation with 5 folds
kf = KFold(len(ad_train), 5)
result = 0
for train_index, test_index in kf:
ad_train_kfold, ad_test_kfold = ad_train[train_index], ad_train[
test_index]
al_train_kfold, al_test_kfold = al_train[train_index], al_train[
test_index]
result += self.evaluation(ad_test_kfold,
ad_train_kfold,
al_test_kfold,
al_train_kfold,
k=2)
result_average = result / 5
# Result with the training
result_training = self.evaluation(ad_train,
ad_train,
al_train,
al_train,
k=2)
# Result with the test
result_test = self.evaluation(ad_test,
ad_train,
al_test,
al_train,
k=2)
return result_average, result_training, result_test
# knn = KNN_strings(n_neighbors=1)
# knn = KNN_statistic()
# results = []
# for x in range(50):
# result_average, result_training, result_test = knn.knn_train("/home/thovo/PycharmProjects/CharacterRecognition/digits_dataset", 0.2)
# text = result_average.__str__() + " , " + result_training.__str__() + " , " + result_test.__str__() + "\n"
# results.append(text)
#
#
# f = open("Results/knn.txt", "w")
# for item in results:
# f.write(item)
#
# f.close()
class KNN_strings(object):
'''
classdocs
'''
def __init__(self, n_neighbors=1):
'''
Constructor
'''
self.dsr = DatasetReader()
self.fenc = FreemanEncoder()
self.data = []
self.knn = KNeighborsClassifier(n_neighbors=n_neighbors,
algorithm='auto',
metric=self.lev_metric)
def lev_metric(self, x, y):
i, j = int(x[0]), int(y[0]) # extract indices
# if self.data[i] == self.data[j]:
# print self.data[i], self.data[j], edit_dist(self.data[i], self.data[j])
return edit_dist(self.data[i], self.data[j])
def knn_train(self, dataset, cv=1, datasplit=0.7):
images_dataset = self.dsr.read_dataset_images(dataset)
freeman_code_dict = self.fenc.encode_freeman_dataset(images_dataset)
_, codes, labels = self.dsr.gen_labelled_arrays(freeman_code_dict)
self.data = codes
X = np.arange(len(self.data)).reshape(-1, 1)
if cv <= 1:
self.knn.fit(X, labels)
elif cv > 1:
cv_result = cross_validation.cross_val_score(self.knn,
X,
labels,
cv=cv)
print cv_result
print 'Training Done!'
def knn_predict(self, test_data, score=False):
images_dataset = self.dsr.read_dataset_images(test_data)
freeman_code_dict = self.fenc.encode_freeman_dataset(images_dataset)
_, codes, labels = self.dsr.gen_labelled_arrays(freeman_code_dict)
X_pred = np.arange(len(codes)).reshape(-1, 1)
predictions = self.knn.predict(X_pred)
if score == True:
accuracy = self.knn.score(X_pred, labels)
print "Test Accuracy: ", accuracy
return predictions
def knn_predict_one(self, test_image):
image_code = self.fenc.encode_freeman(test_image)
print image_code
data = [image_code]
X_pred = np.arange(len(data)).reshape(-1, 1)
prediction = self.knn.predict(X_pred)
return prediction