def test_bisection_invalid(self):
np.random.seed(42)
x = np.random.randn(10, 100, 10)
var = VAR(1)
var.optimize_delta_bisection(x)
# totally ugly data, should be unable to find reasonable regularization.
self.assertEqual(var.delta, 0)
def test_bisection_invalid(self):
np.random.seed(42)
x = np.random.randn(10, 100, 10)
var = VAR(1)
var.optimize_delta_bisection(x)
# totally ugly data, should be unable to find reasonable regularization.
self.assertEqual(var.delta, 0)
def test_bisection_overdetermined(self):
np.random.seed(42)
var0 = VAR(2)
var0.coef = np.array([[0.2, 0.1, 0.4, -0.1], [0.3, -0.2, 0.1, 0]])
l = (100, 10)
x = var0.simulate(l)
var = VAR(2)
var.optimize_delta_bisection(x)
# nice data, so the regularization should not be too strong.
self.assertLess(var.delta, 10)
def test_bisection_overdetermined(self):
np.random.seed(42)
var0 = VAR(2)
var0.coef = np.array([[0.2, 0.1, 0.4, -0.1], [0.3, -0.2, 0.1, 0]])
l = (100, 10)
x = var0.simulate(l)
var = VAR(2)
var.optimize_delta_bisection(x)
# nice data, so the regularization should not be too strong.
self.assertLess(var.delta, 10)
def test_bisection_underdetermined(self):
n_trials, n_samples = 10, 10
np.random.seed(42)
var0 = VAR(2)
var0.coef = np.array([[0.2, 0.1, 0.4, -0.1], [0.3, -0.2, 0.1, 0]])
x = var0.simulate((n_samples, n_trials))
x = np.concatenate([x, np.random.randn(n_trials, 8, n_samples)], axis=1)
var = VAR(7)
var.optimize_delta_bisection(x)
# nice data, so the regularization should not be too weak.
self.assertGreater(var.delta, 10)
def test_bisection_underdetermined(self):
n_trials, n_samples = 10, 10
np.random.seed(42)
var0 = VAR(2)
var0.coef = np.array([[0.2, 0.1, 0.4, -0.1], [0.3, -0.2, 0.1, 0]])
x = var0.simulate((n_samples, n_trials))
x = np.concatenate([x, np.random.randn(n_trials, 8, n_samples)],
axis=1)
var = VAR(7)
var.optimize_delta_bisection(x)
# nice data, so the regularization should not be too weak.
self.assertGreater(var.delta, 10)
