def test_stateful_double_lstm_mode():
# 仿真数据
batch_size = 30
step_num = 10000
traj_batch = np.zeros(shape=(batch_size, step_num, 3))
length = 10 * 1.13
width = 6 * 1.13
high = 3.5
z_high = 2.0
anchors_loc = np.array([[-3.29, 1.13, 1.66], [3.57, -1.13, 0.925],
[3.57, 2.26, 1.950], [-2.26, 3.39, 2.230]])
origin_coordinate = np.array([4 * 1.13, 2 * 1.13, 0])
los_sd = 20e-3
nlos_bias = 0.6
nlos_sd = 45e-3
ranging_batch = np.zeros(shape=(batch_size, step_num, len(anchors_loc)))
for i in range(batch_size):
traj_batch[i] = generator_3d_trajectory(step_num=step_num,
step_mode=i // 2,
length=length,
width=width,
high=high,
z_high=z_high)
ranging_batch[i], traj_batch[i] = generator_3d_ranging_data(
traj_batch[i], anchors_loc, origin_coordinate, los_sd, nlos_bias,
nlos_sd, 1)
print(ranging_batch)
# model
model = ModelFactory.product_model(ModelType.STATEFUL_DOUBLE_LSTM_MODEL)
# TODO: batch_size和time_step不匹配,需要进行调整
model.fit(x=ranging_batch, y=traj_batch, batch_size=batch_size, epochs=100)
def test_stateful_double_lstm_mode_2():
"""
test_stateful_double_lstm_mode()函数的batch_size有问题,重新生成.
:return: 无返回值
"""
# 仿真数据
batch_size = 30
step_num = 10_020
model_time_step = 30
traj_batch = np.zeros(shape=(batch_size, step_num, 3))
length = 10 * 1.13
width = 6 * 1.13
high = 3.5
z_high = 2.0
anchors_loc = np.array([[-3.29, 1.13, 1.66], [3.57, -1.13, 0.925],
[3.57, 2.26, 1.950], [-2.26, 3.39, 2.230]])
origin_coordinate = np.array([4 * 1.13, 2 * 1.13, 0])
los_sd = 20e-3
nlos_bias = 0.6
nlos_sd = 45e-3
ranging_batch = np.zeros(shape=(batch_size, step_num, len(anchors_loc)))
for i in range(batch_size):
traj_batch[i] = generator_3d_trajectory(step_num=step_num,
step_mode=i // 2,
length=length,
width=width,
high=high,
z_high=z_high)
ranging_batch[i], traj_batch[i] = generator_3d_ranging_data(
traj_batch[i], anchors_loc, origin_coordinate, los_sd, nlos_bias,
nlos_sd, 1)
ranging_batch = ranging_batch.reshape(
(step_num // model_time_step * batch_size, model_time_step,
len(anchors_loc)))
traj_batch = traj_batch.reshape(
(step_num // model_time_step * batch_size, model_time_step, 3))
# 当前x-(batch_size, step_num, anchors_loc), y-(batch_size, step_num, 3)
# 每个x输入维度与基站个数有关
# model
model = ModelFactory.product_model(ModelType.STATEFUL_DOUBLE_LSTM_MODEL_2,
input_dim=4)
model.fit(x=ranging_batch, y=traj_batch, batch_size=batch_size, epochs=100)
# 测试数据生成, batch_size = 1, step_num = 30, 三维坐标
traj_test = np.zeros(shape=(1, 30, 3))
traj_test = generator_3d_trajectory(step_num=30,
step_mode=1,
length=length,
width=width,
high=high,
z_high=z_high)
ranging_test, traj_test = generator_3d_ranging_data(
traj_test, anchors_loc, origin_coordinate, los_sd, nlos_bias, nlos_sd,
1)
model.save("./save_model/stateful_double_lstm_mode_2_1.h5")
def test_double_lstm_mode_line_traj_nlos():
"""
210311测试网络训练直线性能。加入nlos误差， 更改了基站z（扩展）
:return: 无返回值
"""
# 仿真数据
batch_size = 30
step_num = 10_020
model_time_step = 30
traj_batch = np.zeros(shape=(batch_size, step_num, 3)) # 30 10_020 3
length = 10 * 1.13
width = 6 * 1.13
high = 3.5
z_high = 2.0
anchors_loc = np.array([[-3, 1, 0.5], [4, -1., 0.], [3, 3, 2],
[-2., 3, 2.5]])
origin_coordinate = np.array([4, 2, 0])
los_sd = 50e-3
# los_sd = 0
nlos_bias = 0.4
nlos_sd = 80e-3
ranging_batch = np.zeros(shape=(batch_size, step_num, len(anchors_loc)))
for i in range(batch_size):
traj_batch[i] = generator_3d_trajectory_2(step_num=step_num,
length=length,
width=width,
high=high,
z_high=z_high)
ranging_batch[i], traj_batch[i] = generator_3d_ranging_data(
traj_batch[i],
anchors_loc,
origin_coordinate,
los_sd,
nlos_bias,
nlos_sd,
mode=1)
ranging_batch = ranging_batch.reshape(
(step_num // model_time_step * batch_size, model_time_step,
len(anchors_loc)))
traj_batch = traj_batch.reshape(
(step_num // model_time_step * batch_size, model_time_step, 3))
# 当前x-(batch_size, step_num, anchors_loc), y-(batch_size, step_num, 3)
# 每个x输入维度与基站个数有关
# model
model = ModelFactory.product_model(ModelType.DOUBLE_LSTM_MODEL,
input_dim=4)
model.fit(x=ranging_batch,
y=traj_batch,
batch_size=batch_size,
epochs=1000)
model.save(
"./save_model/double_lstm_model_epoch_1000_0318_line_traj_nlos_3.h5")
def test_double_lstm_mode_line_traj():
"""
210309测试网络训练直线性能。
:return: 无返回值
"""
# 仿真数据
batch_size = 30
step_num = 10_020
model_time_step = 30
traj_batch = np.zeros(shape=(batch_size, step_num, 3)) # 30 10_020 3
length = 10 * 1.13
width = 6 * 1.13
high = 3.5
z_high = 2.0
anchors_loc = np.array([[-3.29, 1.13, 1.66], [3.57, -1.13, 0.925],
[3.57, 2.26, 1.950], [-2.26, 3.39, 2.230]])
origin_coordinate = np.array([4 * 1.13, 2 * 1.13, 0])
# los_sd = 20e-3
los_sd = 0
nlos_bias = 0.2
nlos_sd = 45e-3
ranging_batch = np.zeros(shape=(batch_size, step_num, len(anchors_loc)))
for i in range(batch_size):
# traj_batch[i] = generator_3d_trajectory(step_num=step_num, step_mode=1, length=length, width=width,
# high=high,
# z_high=z_high)
traj_batch[i] = generator_3d_trajectory_2(step_num=step_num,
length=length,
width=width,
high=high,
z_high=z_high)
ranging_batch[i], traj_batch[i] = generator_3d_ranging_data(
traj_batch[i], anchors_loc, origin_coordinate, los_sd, nlos_bias,
nlos_sd, 0)
ranging_batch = ranging_batch.reshape(
(step_num // model_time_step * batch_size, model_time_step,
len(anchors_loc)))
traj_batch = traj_batch.reshape(
(step_num // model_time_step * batch_size, model_time_step, 3))
# 当前x-(batch_size, step_num, anchors_loc), y-(batch_size, step_num, 3)
# 每个x输入维度与基站个数有关
# model
model = ModelFactory.product_model(ModelType.DOUBLE_LSTM_MODEL,
input_dim=4)
model.fit(x=ranging_batch,
y=traj_batch,
batch_size=batch_size,
epochs=1000)
model.save("./save_model/double_lstm_model_epoch_1000_0309_line_traj_1.h5")