def __data_preparation(self, path_input_dir, N):
assert os.path.exists(path_input_dir)
assert isinstance(N, int)
list_of_path = data_loader.make_path_pic_list(path_input_dir)
index_datapath_mapper, dataset = data_loader.make_data_matrix(list_of_input_files=list_of_path)
self.dataset = dataset
self.index_datapath_mapper = index_datapath_mapper
train_test_object = data_loader.split_data_train_and_test(self.dataset, N)
self.y_train = train_test_object['train']
self.y_test = train_test_object['test']
self.N_test = train_test_object['N_test']
if self.is_add_noise==True:
x_train = self.add_noise(train_set=train_test_object['train'], noise_ratio=self.noise_rate)
x_test = self.add_noise(train_set=train_test_object['test'], noise_ratio=self.noise_rate)
n_dimension = x_train.shape[1]
else:
x_train = train_test_object['train']
x_test = train_test_object['test']
n_dimension = x_train.shape[1]
assert isinstance(x_train, np.ndarray)
assert isinstance(x_test, np.ndarray)
assert len(x_train.shape) == 2
assert len(x_test.shape) == 2
self.x_train = x_train
self.x_test = x_test
self.n_dimension = n_dimension
def exp_interface():
"""make dataset for pylearn2 from girls' face gray scaled pictures
:return:
"""
path_index_path = '../../extracted/miss_collection/gray'
input_files_list = data_loader.make_path_pic_list(path_input_dir=path_index_path)
path_to_save_directory = 'intermediate_files_pylearn2'
project_name = 'toy_train'
main(input_files_list, path_to_save_directory, project_name)
