def test_exponential_prior(self):
# Test input parameter
self.assertRaises(ValueError, pints.ExponentialLogPrior, 0.0)
self.assertRaises(ValueError, pints.ExponentialLogPrior, -1.0)
r1 = 0.123
r2 = 4.567
p1 = pints.ExponentialLogPrior(r1)
p2 = pints.ExponentialLogPrior(r2)
points = [-2., 0.001, 0.1, 1.0, 2.45, 6.789]
# Test means
self.assertAlmostEqual(p1.mean(), 8.13008130081301)
self.assertAlmostEqual(p2.mean(), 0.2189621195533173)
# Test n_parameters
self.assertEqual(p1.n_parameters(), 1)
# Test specific points
for point in points:
to_test = [point]
self.assertAlmostEqual(scipy.stats.expon.logpdf(to_test[0],
scale=1. / r1),
p1(to_test),
places=9)
self.assertAlmostEqual(scipy.stats.expon.logpdf(to_test[0],
scale=1. / r2),
p2(to_test),
places=9)
# Test derivatives
p1_derivs = [0., -r1, -r1, -r1, -r1]
p2_derivs = [0., -r2, -r2, -r2, -r2]
for point, deriv in zip(points, p1_derivs):
calc_val, calc_deriv = p1.evaluateS1([point])
self.assertAlmostEqual(calc_deriv[0], deriv)
for point, deriv in zip(points, p2_derivs):
calc_val, calc_deriv = p2.evaluateS1([point])
self.assertAlmostEqual(calc_deriv[0], deriv)
def test_exponential_prior_sampling(self):
# Just returns samples from the numpy exponential distribution, but
# because we are parameterising it with rate not shape, we check the
# first moment to be sure we're doing the right thing
p1 = pints.ExponentialLogPrior(0.25)
self.assertEqual(len(p1.sample()), 1)
n = 1000
samples1 = p1.sample(n)
self.assertEqual(len(samples1), n)
# Mean should be ~ 1/0.25 = 4, so we check that this is very roughly
# the case, but we can be very relaxed as we only check it's not ~0.25
mean = np.mean(samples1).item()
self.assertTrue(3. < mean < 4.)
def test_exponential_prior_cdf_icdf(self):
p = pints.ExponentialLogPrior(4.11)
self.assertAlmostEqual(p.cdf(0.25), 0.6420994054523911)
self.assertAlmostEqual(p.icdf(0.25), 0.06999563806612673)
