def test_residuals(self):
np.random.seed(31415)
x, var0 = self.generate_data()
var = VAR(2)
var.fit(x)
self.assertEqual(x.shape, var.residuals.shape)
self.assertTrue(np.allclose(var.rescov, np.eye(var.rescov.shape[0]), 0.005, 0.005))
def test_fit(self):
np.random.seed(12345)
x, var0 = self.generate_data()
y = x.copy()
var = VAR(2)
var.fit(x)
# make sure the input remains unchanged
self.assertTrue(np.all(x == y))
self.assertTrue(np.all(np.abs(var0.coef - var.coef) < 0.005))
def func(self):
x, var0 = self.generate_data()
y = x.copy()
var = VAR(10, o)
var.fit(x)
# make sure the input remains unchanged
self.assertTrue(np.all(x == y))
b0 = np.zeros((2, 20))
b0[:, 0:2] = var0.coef[:, 0:2]
b0[:, 10:12] = var0.coef[:, 2:4]
# that limit is rather generous, but we don't want tests to fail due to random variation
self.assertTrue(np.all(np.abs(b0 - var.coef) < 0.02))
def generate_data(self):
var = VAR(2)
var.coef = np.array([[0.2, 0.1, 0.4, -0.1], [0.3, -0.2, 0.1, 0]])
l = (1000, 100)
x = var.simulate(l)
return x, var
