def test_beta_prior(self):
# Test input parameters
self.assertRaises(ValueError, pints.BetaLogPrior, 0, 0)
self.assertRaises(ValueError, pints.BetaLogPrior, 2, -2)
self.assertRaises(ValueError, pints.BetaLogPrior, -2, 2)
p1 = pints.BetaLogPrior(0.123, 2.34)
p2 = pints.BetaLogPrior(3.45, 4.56)
points = [-2., 0.001, 0.1, 0.3, 0.5, 0.7, 0.9, 0.999, 2.]
# Test means
self.assertAlmostEqual(p1.mean(), 0.04993909866017051)
self.assertAlmostEqual(p2.mean(), 0.4307116104868914)
# Test n_parameters
self.assertEqual(p1.n_parameters(), 1)
# Test specific points
for point in points:
to_test = [point]
self.assertAlmostEqual(scipy.stats.beta.logpdf(
to_test[0], 0.123, 2.34),
p1(to_test),
places=9)
self.assertAlmostEqual(scipy.stats.beta.logpdf(
to_test[0], 3.45, 4.56),
p2(to_test),
places=9)
# Test derivatives
p1_derivs = [
0., -878.341341341342, -10.25888888888889, -4.837619047619048,
-4.434, -5.719523809523809, -14.37444444444445, -1340.877877877876,
0.
]
p2_derivs = [
0., 2446.436436436437, 20.54444444444445, 3.080952380952382,
-2.219999999999999, -8.36666666666666, -32.87777777777778,
-3557.547547547544, 0.
]
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)
# Test pathological edge cases
p3 = pints.BetaLogPrior(1.0, 1.0)
calc_val, calc_deriv = p3.evaluateS1([0.0])
self.assertAlmostEqual(calc_deriv[0], 0.0)
calc_val, calc_deriv = p3.evaluateS1([1.0])
self.assertAlmostEqual(calc_deriv[0], 0.0)
def test_beta_prior_sampling(self):
p1 = pints.BetaLogPrior(0.123, 2.34)
self.assertEqual(len(p1.sample()), 1)
n = 100
samples1 = p1.sample(n)
self.assertEqual(len(samples1), n)
n = 10000
p1 = pints.BetaLogPrior(100, 100)
samples = p1.sample(n)
self.assertTrue(np.abs(np.mean(samples) - 0.5) < 0.01)
p1 = pints.BetaLogPrior(20, 30)
samples = p1.sample(n)
self.assertTrue(np.abs(np.mean(samples) - 0.4) < 0.01)
def test_beta_prior_sampling(self):
# Just returns samples from the numpy beta distribution so no utility
# in verifying shape params - just check it's working as expected
p1 = pints.BetaLogPrior(0.123, 2.34)
self.assertEqual(len(p1.sample()), 1)
n = 100
samples1 = p1.sample(n)
self.assertEqual(len(samples1), n)
def test_beta_prior(self):
# Test input parameters
self.assertRaises(ValueError, pints.BetaLogPrior, 0, 0)
self.assertRaises(ValueError, pints.BetaLogPrior, 2, -2)
self.assertRaises(ValueError, pints.BetaLogPrior, -2, 2)
p1 = pints.BetaLogPrior(0.123, 2.34)
p2 = pints.BetaLogPrior(3.45, 4.56)
points = [-2., 0.001, 0.1, 0.3, 0.5, 0.7, 0.9, 0.999, 2.]
# Test means
self.assertAlmostEqual(p1.mean(), 0.04993909866017051)
self.assertAlmostEqual(p2.mean(), 0.4307116104868914)
# Test CDFs
self.assertAlmostEqual(p1.cdf(0.2), 0.9161569551360381)
self.assertAlmostEqual(p1.cdf(0.5), 0.982186394491929)
self.assertAlmostEqual(p2.cdf(0.5), 0.6606214580849932)
self.assertAlmostEqual(p2.cdf(0.05), 0.001056893325194372)
# Test inverse-CDFs
self.assertAlmostEqual(p1.icdf(0.9), 0.1662966999241491)
self.assertAlmostEqual(p1.icdf(0.99), 0.593317052562366)
self.assertAlmostEqual(p2.icdf(0.3), 0.33292896683293627)
self.assertAlmostEqual(p2.icdf(0.9), 0.6538975170733259)
# Test n_parameters
self.assertEqual(p1.n_parameters(), 1)
# Test specific points
for point in points:
to_test = [point]
self.assertAlmostEqual(
scipy.stats.beta.logpdf(to_test[0], 0.123, 2.34),
p1(to_test),
places=9)
self.assertAlmostEqual(
scipy.stats.beta.logpdf(to_test[0], 3.45, 4.56),
p2(to_test),
places=9)
# Test derivatives
p1_derivs = [0., -878.341341341342, -10.25888888888889,
-4.837619047619048,
-4.434, -5.719523809523809, -14.37444444444445,
-1340.877877877876,
0.]
p2_derivs = [0., 2446.436436436437, 20.54444444444445,
3.080952380952382,
-2.219999999999999, -8.36666666666666, -32.87777777777778,
-3557.547547547544, 0.]
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)
# Test pathological edge cases
p3 = pints.BetaLogPrior(1.0, 1.0)
calc_val, calc_deriv = p3.evaluateS1([0.0])
self.assertAlmostEqual(calc_deriv[0], 0.0)
calc_val, calc_deriv = p3.evaluateS1([1.0])
self.assertAlmostEqual(calc_deriv[0], 0.0)