def prepare_data(dt, num_steps, num_observers):
y_recs = []
for i in range(num_observers):
print_flush("Generating simulation sequences {}/{}".format(
i, num_observers))
sm = StateSpaceModel2Dim(n_dim=2,
A=A0,
g=fn.LinearFn(utils.zeros(2)),
sigma_w=0.1,
sigma_z=0.1,
x=mu0[0],
y=utils.zeros((1, 2)))
sm.dt = dt
(x_rec, y_rec) = sm.simulation(num_steps, dt)
y_recs.append(y_rec)
return y_recs
def test_init_val(self):
actual_mu = self.s.data["mu"]
expected_mu = utils.zeros(self.n)
self.assertIsNone(np.testing.assert_array_equal(expected_mu, actual_mu))
actual_sigma = self.s.data["sigma"]
expected_sigma = np.diag(np.eye(self.n, dtype=np.float32))
self.assertIsNone(np.testing.assert_array_equal(expected_sigma, actual_sigma))
# print("===Starting UnitTest01===")
# print("-- A simple test of Bundle with no Matcher")
# plt.figure(1)
# test_BundleEKFContinuousTime01(dt, n_steps)
# plt.pause(0.2)
dt = 0.02
n_steps = 500
# preparing a list of three simulation sequences
yrecs = []
for i in range(1):
sm = StateSpaceModel2Dim(n_dim=2,
A=np.array([[-0.1, 2], [-2, -0.1]],
dtype=np.float32),
g=fn.LinearFn(utils.zeros(2)),
sigma_w=0.1,
sigma_z=0.1,
x=np.array([0, 0], dtype=np.float32),
y=utils.zeros((1, 2)))
sm.A = A0
sm.x = mu0
sm.dt = dt
(x_rec, y_rec) = sm.simulation(n_steps, dt)
yrecs.append(y_rec)
plt.figure(0)
for i in range(1):
plt.subplot(2, 2, i + 1)
plt.scatter(yrecs[i][:, 0], yrecs[i][:, 1], s=2)
plt.title("sequence {}".format(i))
def test_init_val(self):
actual = self.b0.state.data["mu"]
expected = utils.zeros(self.n)
self.assertIsNone(np.testing.assert_array_equal(expected, actual))