def load_test_data(self, m_5=6000, k=1000):
test_bgs_data = model_utils.load_test_bg_data(self.path_dataset)
test_signal_data = model_utils.load_test_signal_data(self.path_dataset,
m_5=m_5,
k=k)
factor = -1 * np.log(0.01)
#
# factor = 1
norm_test_bgs_data = model_utils.normalize(test_bgs_data, factor)
norm_test_signal_data = model_utils.normalize(test_signal_data, factor)
reshape_norm_test_bg = np.reshape(norm_test_bgs_data, (
norm_test_bgs_data.shape[0],
norm_test_bgs_data.shape[1],
norm_test_bgs_data.shape[2],
1))
reshape_norm_train_bg = np.reshape(norm_test_signal_data, (
norm_test_signal_data.shape[0],
norm_test_signal_data.shape[1],
norm_test_signal_data.shape[2],
1))
return reshape_norm_test_bg, reshape_norm_train_bg
def load_test_data(self, signal_id=1):
test_bgs_data = model_utils.load_test_bgs_data(self.path_dataset)
test_signal_data = model_utils.load_test_signal_data(
self.path_dataset, signal_id=signal_id)
factor = -1 * np.log(0.01)
norm_test_bgs_data = model_utils.normalize(test_bgs_data, factor)
norm_test_signal_data = model_utils.normalize(test_signal_data, factor)
return norm_test_bgs_data, norm_test_signal_data
def load_test_data(self, signal_id=1):
test_bgs_data = model_utils.load_test_bgs_data(self.path_dataset)
test_signal_data = model_utils.load_test_signal_data(self.path_dataset, signal_id=signal_id)
test_bgs_shape = (len(test_bgs_data), self.shape)
test_signal_shape = (len(test_signal_data), self.shape)
print(test_signal_data.shape)
factor = -1 * np.log(0.01)
norm_test_bgs_data = model_utils.normalize(test_bgs_data.reshape(test_bgs_shape), factor)
norm_test_signal_data = model_utils.normalize(test_signal_data.reshape(test_signal_shape), factor)
return norm_test_bgs_data, norm_test_signal_data