def test_lnprior(self):
t = np.arange(0, 1, 0.01)
theta = self.random_theta(3, 2)
kursl = KurSL(theta)
model = ModelWrapper(kursl)
lnprob = KurslMCMC.lnprior(theta, model)
# Given default uniform probablities it's either 0 or -np.inf
self.assertEqual(np.round(lnprob, 10), 0,
"Theta within max ranges results in 0")
def test_lnprior_theta_outside_ranges(self):
t = np.arange(0, 1, 0.01)
S = np.random.random(t.size)
theta = self.random_theta(3, 2)
kursl = KurSL(theta)
model = ModelWrapper(kursl)
new_theta = theta.copy()
new_theta[0, 2] = model.MIN_R - 1
self.assertEqual(KurslMCMC.lnprior(new_theta, model), -np.inf)
new_theta[0, 2] = model.MAX_R + 1
self.assertEqual(KurslMCMC.lnprior(new_theta, model), -np.inf)
new_theta = theta.copy()
new_theta[1, 0] = model.MIN_W - 1
self.assertEqual(KurslMCMC.lnprior(new_theta, model), -np.inf)
new_theta[1, 0] = model.MAX_W + 1
self.assertEqual(KurslMCMC.lnprior(new_theta, model), -np.inf)
# Check for W_i < sum_j (|k_ij|)
new_theta = theta.copy()
new_theta[2, 0] = np.sum(new_theta[2, 3:]) * 0.6
self.assertEqual(KurslMCMC.lnprior(new_theta, model), -np.inf)