def test_log_normal_prior_sampling(self):
mu = -1.234
sig = 0.456
p1 = pints.LogNormalLogPrior(mu, sig)
self.assertEqual(len(p1.sample()), 1)
n = 1000
samples1 = p1.sample(n)
self.assertEqual(len(samples1), n)
# Mean should be exp(mu + 0.5*sig^2), so we check that this is very
# roughly the case, to ensure the parameterisation is correct
sample_mean = np.mean(samples1).item()
analyt_mean = np.exp(mu + 0.5 * sig * sig)
self.assertTrue(0.9 * analyt_mean < sample_mean < 1.1 * analyt_mean)
def test_log_normal_prior_cdf_icdf(self):
p1 = pints.LogNormalLogPrior(-3.5, 7.7)
self.assertAlmostEqual(p1.cdf(1.1), 0.6797226585187124)
self.assertAlmostEqual(p1.icdf(0.4), 0.004292986243507321)
def test_log_normal_prior(self):
# Test input parameters
self.assertRaises(ValueError, pints.LogNormalLogPrior, 0, 0)
self.assertRaises(ValueError, pints.LogNormalLogPrior, 2, -2)
mu1 = 0.123
mu2 = -4.567
sd1 = 2.345
sd2 = 0.356
p1 = pints.LogNormalLogPrior(mu1, sd1)
p2 = pints.LogNormalLogPrior(mu2, sd2)
points = [-2., 0.001, 0.1, 1.0, 2.45, 6.789]
# Test means
self.assertAlmostEqual(p1.mean(), 17.68138692293243)
self.assertAlmostEqual(p2.mean(), 0.01106872184593001)
# Test n_parameters
self.assertEqual(p1.n_parameters(), 1)
# Test specific points
for point in points:
pints_val_1 = p1([point])
scipy_val_1 = scipy.stats.lognorm.logpdf(point, scale=np.exp(mu1),
s=sd1)
pints_val_2 = p2([point])
scipy_val_2 = scipy.stats.lognorm.logpdf(point, scale=np.exp(mu2),
s=sd2)
self.assertAlmostEqual(pints_val_1, scipy_val_1)
self.assertAlmostEqual(pints_val_2, scipy_val_2)
# Test derivatives
p1_derivs = [0., 278.54579293277163, -5.589063346695011,
-0.977632398470638, -0.4655454616904081,
-0.19530581390245802]
p2_derivs = [0., 17469.53729786411, -188.67179862122487,
-37.03553844211642, -18.00246833062188,
-7.681261547066602]
for point, hand_calc_deriv in zip(points, p1_derivs):
pints_val, pints_deriv = p1.evaluateS1([point])
scipy_val = scipy.stats.lognorm.logpdf(point, scale=np.exp(mu1),
s=sd1)
self.assertAlmostEqual(pints_val, scipy_val)
self.assertAlmostEqual(pints_deriv[0], hand_calc_deriv)
for point, hand_calc_deriv in zip(points, p2_derivs):
pints_val, pints_deriv = p2.evaluateS1([point])
scipy_val = scipy.stats.lognorm.logpdf(point, scale=np.exp(mu2),
s=sd2)
self.assertAlmostEqual(pints_val, scipy_val)
self.assertAlmostEqual(pints_deriv[0], hand_calc_deriv)
