def test_run_default(self):
_cos = lambda l: l[2] * np.cos(l[0] * t + l[1])
theta = [
[15, 0, 1, 0],
[35, 2, 3, 0],
]
theta_std = [
[1.0, 0.1, 0.8, 0.01],
[1.5, 0.1, 1.5, 0.01],
]
theta, theta_std = np.array(theta), np.array(theta_std)
t = np.linspace(0, 1, 100)
c1 = _cos(theta[0])
c2 = _cos(theta[1])
S = c1 + c2
theta_init = np.array(theta, dtype=np.float64)
theta_init[:, 0] += 2 - np.random.random(2) * 0.5
theta_init[:, 2] += 1 - np.random.random(2) * 0.5
mcmc = KurslMCMC(theta_init, theta_std, nwalkers=40, niter=200)
mcmc.set_threshold(0.001)
mcmc.set_sampler(t, S)
mcmc.run()
# After simulation is finished check for correctness
theta_computed = mcmc.get_theta()
self.assertTrue(
np.allclose(theta, theta_computed, atol=0.5),
"Expected:\n{}\nReceived:\n{}".format(theta, theta_computed))
def test_set_threshold(self):
theta_init = self.random_theta(5, 1)
mcmc = KurslMCMC(theta_init)
self.assertEqual(mcmc.THRESHOLD, 0.05)
self.assertEqual(mcmc.model.THRESHOLD, 0.05)
mcmc.set_threshold(0.1)
self.assertEqual(mcmc.THRESHOLD, 0.1)
self.assertEqual(mcmc.model.THRESHOLD, 0.1)