def test_half_cauchy_prior(self):
# Test two specific function values
p1 = pints.HalfCauchyLogPrior(0, 10)
self.assertEqual(p1([0]), -float('Inf'))
self.assertAlmostEqual(p1([10]), -3.447314978843445)
p2 = pints.HalfCauchyLogPrior(10, 5)
self.assertAlmostEqual(p2([10]), -2.594487638427916)
# Test exceptions
self.assertRaises(ValueError, pints.HalfCauchyLogPrior, 0, 0)
self.assertRaises(ValueError, pints.HalfCauchyLogPrior, 10, -1)
# Test means
self.assertTrue(np.isnan(p1.mean()))
self.assertTrue(np.isnan(p2.mean()))
# Test other function calls
self.assertEqual(p1.n_parameters(), 1)
self.assertEqual(p2.n_parameters(), 1)
# Test sensitivities
p = pints.HalfCauchyLogPrior(-3, 5)
val = p([3.3])
val1, dp = p.evaluateS1([3.3])
self.assertEqual(val, val1)
self.assertAlmostEqual(dp[0], -0.19477508115628384, places=6)
def test_half_cauchy_prior_sampling(self):
p1 = pints.HalfCauchyLogPrior(0, 1000)
self.assertEqual(len(p1.sample()), 1)
n = 1000
v_samples = p1.sample(n)
self.assertEqual(len(v_samples), n)
self.assertTrue(np.all(v_samples > 0))
# test medians
p1 = pints.HalfCauchyLogPrior(-3, 10)
n = 1000000
v_samples = p1.sample(n)
self.assertTrue(np.abs(np.median(v_samples) - 10.45) < 0.1)
def __init__(self, centered=True):
self._n_parameters = 10
self._y_j = [28, 8, -3, 7, -1, 1, 18, 12]
self._sigma_j = [15, 10, 16, 11, 9, 11, 10, 18]
# priors
self._mu_log_pdf = pints.GaussianLogPrior(0, 5)
self._tau_log_pdf = pints.HalfCauchyLogPrior(0, 5)
self._centered = bool(centered)
def test_half_cauchy_prior_sampling(self):
# Aren't many tests for Cauchy distributions
# because they have no mean or variance!
p1 = pints.HalfCauchyLogPrior(0, 1000)
self.assertEqual(len(p1.sample()), 1)
n = 1000
v_samples = p1.sample(n)
self.assertEqual(len(v_samples), n)
self.assertTrue(np.all(v_samples > 0))
def test_half_cauchy_prior(self):
# Test two specific function values
p1 = pints.HalfCauchyLogPrior(0, 10)
self.assertEqual(p1([0]), -float('Inf'))
self.assertAlmostEqual(p1([10]), -3.447314978843445)
p2 = pints.HalfCauchyLogPrior(10, 5)
self.assertAlmostEqual(p2([10]), -2.594487638427916)
# Test exceptions
self.assertRaises(ValueError, pints.HalfCauchyLogPrior, 0, 0)
self.assertRaises(ValueError, pints.HalfCauchyLogPrior, 10, -1)
# Test means
self.assertTrue(np.isnan(p1.mean()))
self.assertTrue(np.isnan(p2.mean()))
# Test other function calls
self.assertEqual(p1.n_parameters(), 1)
self.assertEqual(p2.n_parameters(), 1)
def test_half_cauchy_cdf_icdf(self):
p1 = pints.HalfCauchyLogPrior(-3, 4.5)
self.assertAlmostEqual(p1.cdf(5.5), 0.504576372137924)
self.assertAlmostEqual(p1.icdf(0.72), 12.937927031237367)