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Remove redundant tests
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pymc3/tests/test_distributions_random.py

Lines changed: 1 addition & 34 deletions
Original file line numberDiff line numberDiff line change
@@ -33,7 +33,7 @@
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from pymc3.distributions.multivariate import quaddist_matrix
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from pymc3.distributions.shape_utils import to_tuple
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from pymc3.exceptions import ShapeError
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from pymc3.tests.helpers import SeededTest, select_by_precision
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from pymc3.tests.helpers import SeededTest
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from pymc3.tests.test_distributions import (
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Domain,
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I,
@@ -1758,36 +1758,3 @@ def test_with_cov_rv(self, sample_shape, dist_shape, mu_shape):
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prior = pm.sample_prior_predictive(samples=sample_shape)
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assert prior["mv"].shape == to_tuple(sample_shape) + dist_shape
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def test_exponential_parameterization():
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test_lambda = floatX(10.0)
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exp_pymc = pm.Exponential.dist(lam=test_lambda)
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(rv_scale,) = exp_pymc.owner.inputs[3:]
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npt.assert_almost_equal(rv_scale.eval(), 1 / test_lambda)
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def test_gamma_parameterization():
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test_alpha = floatX(10.0)
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test_beta = floatX(100.0)
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gamma_pymc = pm.Gamma.dist(alpha=test_alpha, beta=test_beta)
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rv_alpha, rv_inv_beta = gamma_pymc.owner.inputs[3:]
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assert np.array_equal(rv_alpha.eval(), test_alpha)
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decimal = select_by_precision(float64=6, float32=3)
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npt.assert_almost_equal(rv_inv_beta.eval(), 1.0 / test_beta, decimal)
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test_mu = test_alpha / test_beta
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test_sigma = np.sqrt(test_mu / test_beta)
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gamma_pymc = pm.Gamma.dist(mu=test_mu, sigma=test_sigma)
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rv_alpha, rv_inv_beta = gamma_pymc.owner.inputs[3:]
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npt.assert_almost_equal(rv_alpha.eval(), test_alpha, decimal)
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npt.assert_almost_equal(rv_inv_beta.eval(), 1.0 / test_beta, decimal)

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