if __name__ == "__main__":
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
neural_net_ang.load_weights(ang_model_path+"/weights/BNN_weights") # load NN parameters
neural_net_vel.load_weights(vel_model_path+"/weights/BNN_weights")
angs = [] # prediction in angle space
vels = [] # prediction in velocity space
angs.append(validation_data[0][:2])
vels.append(validation_data[0][2:4])
state = np.array(validation_data[0])
nor_state = z_score_normalize(np.asarray([state]), x_nor_arr[0], x_nor_arr[1])
print(nor_state) #EDIT
for i in range(len(validation_data)-1):
(ang_delta, vel_delta) = sess.run((y_ang_delta_pre, y_vel_delta_pre), feed_dict={x: nor_state})
ang_delta = z_score_denormalize(ang_delta, y_nor_arr_ang[0], y_nor_arr_ang[1])[0] # denormalize
vel_delta = z_score_denormalize(vel_delta, y_nor_arr_vel[0], y_nor_arr_vel[1])[0]
next_ang = state[:2] + ang_delta
next_vel = state[2:4] + vel_delta
angs.append(next_ang)
vels.append(next_vel)
state = np.append(np.append(next_ang, next_vel), validation_data[i + 1][4:5])
state = map_angle(state)
nor_state = z_score_normalize(np.asarray([state]), x_nor_arr[0], x_nor_arr[1])
angs = np.asarray(angs)
vels = np.asarray(vels)
plt.figure(1)
plt.scatter(validation_data[0, 0], validation_data[0, 1], marker="*", label='start')
plt.plot(validation_data[:, 0], validation_data[:, 1], color='blue', label='Ground Truth', marker='.')
neural_net_pos.load_weights(
pos_model_path + "/weights/BNN_weights") # load NN parameters
neural_net_load.load_weights(load_model_path + "/weights/BNN_weights")
poses = [] # prediction in angle space
loads = [] # prediction in velocity space
poses.append(validation_data[0][:2])
loads.append(validation_data[0][2:4])
state = np.array(validation_data[0])
nor_state = z_score_normalize(np.asarray([state]), x_nor_arr[0],
x_nor_arr[1])
#Normalize data for the neural net, makes it easier to learn
print(nor_state)
for i in range(len(validation_data) - 1):
(pos_delta, load_delta) = sess.run(
(y_pos_delta_pre, y_load_delta_pre), feed_dict={x: nor_state})
pos_delta = z_score_denormalize(pos_delta, y_nor_arr_ang[0],
y_nor_arr_ang[1])[0] # denormalize
load_delta = z_score_denormalize(load_delta, y_nor_arr_vel[0],
y_nor_arr_vel[1])[0]
#And now we turn it back
next_pos = state[:2] + pos_delta
next_load = state[2:4] + load_delta
poses.append(next_pos)
loads.append(next_load)
state = np.append(np.append(next_pos, next_load),
validation_data[i + 1][4:6])
nor_state = z_score_normalize(np.asarray([state]), x_nor_arr[0],
x_nor_arr[1])
poses = np.asarray(poses)
loads = np.asarray(loads)
