def __full_mapping(self):
"""
Mapping bags by using all instances.
@Note:
The size of data set instance space will greatly affect the running time.
"""
self.full_mapping = np.zeros((self.num_bags, self.num_ins))
if not os.path.exists(self.mapping_path) or os.path.getsize(
self.mapping_path) == 0:
print("Full mapping starting...")
open(self.mapping_path, 'a').close()
for i in range(self.num_bags):
print_progress_bar(i, self.num_bags)
# print("%d-th bag mapping..." % i)
for j in range(self.num_ins):
self.full_mapping[i, j] = max_similarity(
self.bags[i, 0][:, :self.dimensions], self.ins[j],
'rbf2', self.gamma)
pd.DataFrame.to_csv(pd.DataFrame(self.full_mapping),
self.mapping_path,
index=False,
header=False,
float_format='%.6f')
print("Full mapping end...")
else:
temp_data = load_file(self.mapping_path)
for i in range(self.num_bags):
self.full_mapping[i] = [
float(value) for value in temp_data[i].strip().split(',')
]
def __full_mapping(self):
"""
Mapping bags by using all instances.
@Note:
The size of data set instance space will greatly affect the running time.
"""
self.full_mapping = np.zeros((self.num_bags, self.num_ins))
if not os.path.exists(self.save_mapping) or os.path.getsize(
self.save_mapping) == 0:
print("Full mapping starting...")
open(self.save_mapping, 'a').close()
for i in range(self.num_bags):
print_progress_bar(i, self.num_bags)
for j in range(self.num_ins):
self.full_mapping[i, j] = self.get_bag_ins_sim(i, j)
pd.DataFrame.to_csv(pd.DataFrame(self.full_mapping),
self.save_mapping,
index=False,
header=False,
float_format='%.6f')
print("Full mapping end...")
else:
temp_data = load_file(self.save_mapping)
for i in range(self.num_bags):
self.full_mapping[i] = [
float(value) for value in temp_data[i].strip().split(',')
]
def __generate_ot_bag(self, bag_num):
print("Generate other class bag...")
for i in range(bag_num):
print_progress_bar(i, bag_num)
bag = []
bag_size = np.random.randint(self.bag_size[0],
self.bag_size[1] + 1)
for j in range(bag_size):
ins = self.data_space[np.random.choice(
self.ot_idx)].tolist() + [0]
bag.append(ins)
bag = np.array(bag)
bag = np.array([bag, np.array([[0]])])
self.bag_space.append(bag)
print()
def __load_data(self, train):
flag = "train" if train else "test"
print("Loading MNIST %s data..." % flag)
data_loader = mnist_bag_loader(train, self.mnist_path)
num_data = len(data_loader)
ret_data, ret_label = [], []
for i, (data, label) in enumerate(data_loader):
print_progress_bar(i, num_data)
data, label = data.reshape(-1).numpy().tolist(), int(
label.numpy()[0])
ret_data.append(data)
ret_label.append(label)
print()
return ret_data, ret_label