def test_run_optimize_no_prior(self):
"Tests for exception when there is no default theta_init"
t = np.arange(0, 1, 0.01)
S = np.random.random(t.size)
kursl = KurslMethod()
with self.assertRaises(ValueError) as error:
kursl.run_optimize(t, S)
self.assertEqual("No prior parameters were assigned.",
str(error.exception))
def test_run_optimize_default(self):
"""Tests optmization with default scipy optimization method,
which is L-BFGS and so it takes a while.
"""
t = np.arange(0, 1, 0.01)
theta = np.array([
[10, 0, 1, 0],
[18, 0, 2, 0],
])
c1 = theta[0, 2] * np.cos(theta[0, 0] * t + theta[0, 1])
c2 = theta[1, 2] * np.cos(theta[1, 0] * t + theta[1, 1])
S = c1 + c2
theta_init = theta.copy()
theta_init[0, 0] -= 1
theta_init[1, 0] += 1
kursl = KurslMethod()
_theta = kursl.run_optimize(t, S, theta_init=theta_init, maxiter=20)
self.assertEqual(_theta.shape, theta.shape, "Results in same shape")
self.assertTrue(np.allclose(theta, _theta, rtol=1e-1, atol=1e-1),
"Expecting fit to be similar to theta initial value")