def test_reconstruct_b_unknown(self):
system = create_system_without_B()
dmdc = DMDc(svd_rank=-1, opt=True)
dmdc.fit(system['snapshots'], system['u'])
np.testing.assert_array_almost_equal(dmdc.reconstructed_data(),
system['snapshots'],
decimal=6)
def test_atilde_b_unknown(self):
system = create_system_without_B()
dmdc = DMDc(svd_rank=-1, opt=True)
dmdc.fit(system['snapshots'], system['u'])
expected_atilde = dmdc.basis.T.conj().dot(system['A']).dot(dmdc.basis)
np.testing.assert_array_almost_equal(
dmdc.atilde, expected_atilde, decimal=1)
def test_B_b_known(self):
system = create_system_with_B()
dmdc = DMDc(svd_rank=-1)
dmdc.fit(system['snapshots'], system['u'], system['B'])
np.testing.assert_array_almost_equal(dmdc.B, system['B'])
def test_modes_b_unknown(self):
system = create_system_without_B()
dmdc = DMDc(svd_rank=3, opt=False, svd_rank_omega=4)
dmdc.fit(system['snapshots'], system['u'])
self.assertEqual(dmdc.modes.shape[1], 3)
def test_eigs_b_known(self):
system = create_system_with_B()
dmdc = DMDc(svd_rank=-1)
dmdc.fit(system['snapshots'], system['u'], system['B'])
real_eigs = np.array([0.1, 1.5])
np.testing.assert_array_almost_equal(dmdc.eigs, real_eigs)
def create_system(n, m):
A = scipy.linalg.helmert(n, True)
B = np.random.rand(n, n) - .5
x0 = np.array([0.25] * n)
u = np.random.rand(n, m - 1) - .5
snapshots = [x0]
for i in range(m - 1):
snapshots.append(A.dot(snapshots[i]) + B.dot(u[:, i]))
snapshots = np.array(snapshots).T
return {'snapshots': snapshots, 'u': u, 'B': B, 'A': A}
s = create_system(25, 10)
print(s['snapshots'].shape)
dmdc = DMDc(svd_rank=-1)
dmdc.fit(s['snapshots'], s['u'])
plt.figure(figsize=(16, 6))
plt.subplot(121)
plt.title('Original system')
plt.pcolor(s['snapshots'].real)
plt.colorbar()
plt.subplot(122)
plt.title('Reconstructed system')
plt.pcolor(dmdc.reconstructed_data().real)
plt.colorbar()
plt.show()
new_u = np.exp(s['u'])
def test_btilde_b_unknown(self):
dmdc = DMDc(svd_rank=-1)
dmdc.fit(snapshots, control)
expected_btilde = np.array([[-0.05836184, 0.31070992]]).T
np.testing.assert_array_almost_equal(dmdc.btilde, expected_btilde)
def test_btilde_b_known(self):
dmdc = DMDc(svd_rank=-1)
dmdc.fit(snapshots, control, b)
np.testing.assert_array_almost_equal(dmdc.btilde, b)
def test_reconstruct_b_unknown(self):
dmdc = DMDc(svd_rank=-1)
dmdc.fit(snapshots, control)
np.testing.assert_array_almost_equal(dmdc.reconstructed_data,
snapshots[:, 1:])
def test_atilde_b_known(self):
dmdc = DMDc(svd_rank=-1)
dmdc.fit(snapshots, control, b)
real_atilde = np.array([[1.5, 0], [0, 0.1]])
np.testing.assert_array_almost_equal(dmdc.atilde, real_atilde)
def main(mode="train", sizes=[50], initflag=True):
if initflag:
env = gym.make("balancebot-v0")
if mode == "train":
model = deepq(policy=LnMlpPolicy,
env=env,
double_q=True,
prioritized_replay=True,
learning_rate=1e-3,
buffer_size=10000,
verbose=0,
tensorboard_log="./dqn_balancebot_tensorboard")
model.learn(total_timesteps=100000, callback=callback)
print("Saving model to balance_dqn.pkl")
model.save("balance_dqn.pkl")
del model # remove to demonstrate saving and loading
if mode == "test":
model = deepq.load("balance_dqn.pkl")
for size in sizes:
dmdc = DMDc(svd_rank=-1)
obs = env.reset(testmode=True)
done = False
env.set_done(2000)
error = []
fitflag = 0
while not done:
action, _states = model.predict(obs)
action = 7 if action > 4 else 1
obs, rewards, done, info = env.step(action)
# env.render()
# print(obs)
if len(env.state_queue) > size:
snapshots = env.get_states(size=size)
u = env.get_inputs(size=size)
if fitflag % 50 == 0:
dmdc.fit(snapshots, u)
# fitflag = False
# print(fitflag)
else:
dmdc._snapshots = snapshots
dmdc._controlin = u
fitflag += 1
diff = np.linalg.norm(
dmdc.reconstructed_data(u)[:, 2].real -
snapshots[:, 2].real)
error.append(diff)
if np.isnan(diff):
print(dmdc.reconstructed_data().real[0], dmdc.eigs,
np.log(dmdc.eigs))
# plt.figure(figsize=(16, 6))
# plt.figure()
#
# plt.subplot(311)
# plt.title('1')
# # plt.pcolor(snapshots.real[0, :])
# plt.plot(snapshots.real[:, 0])
# plt.plot(dmdc.reconstructed_data().real[:, 0])
# # plt.colorbar()
#
# plt.subplot(312)
# plt.title('2')
# plt.plot(snapshots.real[:, 1])
# plt.plot(dmdc.reconstructed_data().real[:, 1])
# # plt.pcolor(dmdc.reconstructed_data().real)
# # plt.colorbar()
#
# plt.subplot(313)
# plt.title('3')
# plt.plot(snapshots.real[:, 2])
# plt.plot(dmdc.reconstructed_data().real[:, 2])
#
# plt.show()
plt.plot(error)
print(error)