|
33 | 33 | from pymc3.distributions.multivariate import quaddist_matrix
|
34 | 34 | from pymc3.distributions.shape_utils import to_tuple
|
35 | 35 | from pymc3.exceptions import ShapeError
|
36 |
| -from pymc3.tests.helpers import SeededTest, select_by_precision |
| 36 | +from pymc3.tests.helpers import SeededTest |
37 | 37 | from pymc3.tests.test_distributions import (
|
38 | 38 | Domain,
|
39 | 39 | I,
|
@@ -1758,36 +1758,3 @@ def test_with_cov_rv(self, sample_shape, dist_shape, mu_shape):
|
1758 | 1758 | prior = pm.sample_prior_predictive(samples=sample_shape)
|
1759 | 1759 |
|
1760 | 1760 | assert prior["mv"].shape == to_tuple(sample_shape) + dist_shape
|
1761 |
| - |
1762 |
| - |
1763 |
| -def test_exponential_parameterization(): |
1764 |
| - test_lambda = floatX(10.0) |
1765 |
| - |
1766 |
| - exp_pymc = pm.Exponential.dist(lam=test_lambda) |
1767 |
| - (rv_scale,) = exp_pymc.owner.inputs[3:] |
1768 |
| - |
1769 |
| - npt.assert_almost_equal(rv_scale.eval(), 1 / test_lambda) |
1770 |
| - |
1771 |
| - |
1772 |
| -def test_gamma_parameterization(): |
1773 |
| - |
1774 |
| - test_alpha = floatX(10.0) |
1775 |
| - test_beta = floatX(100.0) |
1776 |
| - |
1777 |
| - gamma_pymc = pm.Gamma.dist(alpha=test_alpha, beta=test_beta) |
1778 |
| - rv_alpha, rv_inv_beta = gamma_pymc.owner.inputs[3:] |
1779 |
| - |
1780 |
| - assert np.array_equal(rv_alpha.eval(), test_alpha) |
1781 |
| - |
1782 |
| - decimal = select_by_precision(float64=6, float32=3) |
1783 |
| - |
1784 |
| - npt.assert_almost_equal(rv_inv_beta.eval(), 1.0 / test_beta, decimal) |
1785 |
| - |
1786 |
| - test_mu = test_alpha / test_beta |
1787 |
| - test_sigma = np.sqrt(test_mu / test_beta) |
1788 |
| - |
1789 |
| - gamma_pymc = pm.Gamma.dist(mu=test_mu, sigma=test_sigma) |
1790 |
| - rv_alpha, rv_inv_beta = gamma_pymc.owner.inputs[3:] |
1791 |
| - |
1792 |
| - npt.assert_almost_equal(rv_alpha.eval(), test_alpha, decimal) |
1793 |
| - npt.assert_almost_equal(rv_inv_beta.eval(), 1.0 / test_beta, decimal) |
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