def test_two_dimensional_series(self):
""" Is it possible to run 2D series? """
int1_reg = InterceptRegressor()
int2_reg = InterceptRegressor()
lb = Lava(nominal_model=int2_reg, latent_model=int1_reg)
ret = lb.step([1, 1], [2, 2])
self.assertTrue(ret)
def test_get_regressor(self):
li = InterceptRegressor()
y_history = np.array([[1, 1], [3, 4]])
u_history = np.array([[1, 1], [3, 4]])
one = li.update_regressor(y_history, u_history)
self.assertEqual(np.array([1]), one)
one = li.update_regressor(y_history, u_history, nominal_regressor=None)
self.assertEqual(np.array([1]), one)
def test_step_stacking(self):
lb = Lava(InterceptRegressor(), InterceptRegressor())
y = np.array([1, 4, 6]).T
u = np.array([5, 4]).T
lb.step(y, u)
lb.step(y, u)
self.assertEqual((2, 2), lb.u_history.shape,
f"the u history has shape {lb.u_history.shape}")
self.assertEqual((3, 2), lb.y_history.shape,
f"the y history has shape {lb.y_history.shape}")
def test_simulate(self):
"""Make sure that the simulation loop works with the simplest of regressor models"""
from lava import RegressorModel
from numpy.testing import assert_array_almost_equal
# noinspection PyAbstractClass
i1 = InterceptRegressor()
i2 = InterceptRegressor()
lb = Lava(i1, i2)
# inject trained parameters
lb.Theta = np.array([[2], [0]])
lb.Z = np.array([[1], [1]])
y_true = np.array([[3, 3, 3], [1, 1, 1]])
y_hat, *_ = lb.simulate(np.array([1, 2, 3]))
assert_array_almost_equal(y_true, y_hat)
def test_step_regularized_ARX(self):
"""Can we step with some actual regressor models?"""
arx_regressor = ARXRegressor(y_lag_max=2, u_lag_max=1)
intercept_regressor = InterceptRegressor()
lb = Lava(nominal_model=intercept_regressor,
latent_model=arx_regressor)
ret = lb.step(np.array([3, 3]), np.array([3, 3]))
self.assertFalse(ret)
lb.step(np.array([1, 1]), np.array([2, 2]))
ret = lb.step(np.array([1, 1]), np.array([2, 2]))
self.assertTrue(ret)
def test_get_regressor_stepwise(self):
li = InterceptRegressor()
one = li.update_regressor(None, None, None)
self.assertEqual(np.array([1]), one)
def test_init(self):
lb = Lava(InterceptRegressor(), InterceptRegressor())
self.assertEqual(1, 1)