Esempio n. 1
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    def test_gamma_prior(self):

        # Test input parameters
        self.assertRaises(ValueError, pints.GammaLogPrior, 0, 0)
        self.assertRaises(ValueError, pints.GammaLogPrior, 2, -2)
        self.assertRaises(ValueError, pints.GammaLogPrior, -2, 2)

        a1 = 0.123
        a2 = 4.567

        b1 = 2.345
        b2 = 0.356

        p1 = pints.GammaLogPrior(a1, b1)
        p2 = pints.GammaLogPrior(a2, b2)

        points = [-2., 0.001, 0.1, 1.0, 2.45, 6.789]

        # Test means
        self.assertAlmostEqual(p1.mean(), 0.05245202558635395)
        self.assertAlmostEqual(p2.mean(), 12.82865168539326)

        # Test n_parameters
        self.assertEqual(p1.n_parameters(), 1)

        # Test specific points
        for point in points:
            to_test = [point]
            self.assertAlmostEqual(
                scipy.stats.gamma.logpdf(to_test[0], a=a1, scale=1. / b1),
                p1(to_test), places=9)
            self.assertAlmostEqual(
                scipy.stats.gamma.logpdf(to_test[0], a=a2, scale=1. / b2),
                p2(to_test), places=9)

        # Test derivatives
        p1_derivs = [0., -879.345, -11.115, -3.222, -2.70295918367347,
                     -2.474179555162763]

        p2_derivs = [0., 3566.643999999999, 35.314, 3.211, 1.099918367346939,
                     0.1694087494476359]

        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 case
        p3 = pints.GammaLogPrior(1.0, 1.0)
        calc_val, calc_deriv = p3.evaluateS1([0.0])
        self.assertAlmostEqual(calc_deriv[0], -1.)
Esempio n. 2
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    def test_gamma_prior_sampling(self):
        # Just returns samples from the numpy gamma 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.GammaLogPrior(5.0, 0.25)
        self.assertEqual(len(p1.sample()), 1)

        n = 1000
        samples1 = p1.sample(n)
        self.assertEqual(len(samples1), n)

        # Mean should be ~ 5/0.25 = 20, so we check that this is very roughly
        # the case, but we can be very relaxed as we only check it's not ~1.25
        mean = np.mean(samples1).item()
        self.assertTrue(19. < mean < 20.)
Esempio n. 3
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 def test_gamma_prior_cdf_icdf(self):
     p1 = pints.GammaLogPrior(5.0, 0.25)
     self.assertAlmostEqual(p1.cdf(3.4), 0.0018346464720195225)
     self.assertAlmostEqual(p1.icdf(0.05), 7.880598272238121)