def test_simulate(self):
noisefunc = lambda: [1, 1] # use deterministic function instead of noise
num_samples = 100
b = np.array([[0.2, 0.1, 0.4, -0.1], [0.3, -0.2, 0.1, 0]])
var = VAR(2)
var.coef = b
x = var.simulate(num_samples, noisefunc)
# make sure we got expected values within reasonable accuracy
for n in range(10, num_samples):
self.assertTrue(
np.all(
np.abs(x[n, :] - 1 - np.dot(b[:, 0::2], x[n - 1, :]) - np.dot(b[:, 1::2], x[n - 2, :])) < epsilon
)
)
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
