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likelihood test
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1 changed files with 6 additions and 6 deletions
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@ -651,7 +651,7 @@ class LaplaceTests(unittest.TestCase):
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m2['.*white'].constrain_fixed(1e-6)
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m2['.*white'].constrain_fixed(1e-6)
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m2['.*rbf.variance'].constrain_bounded(1e-4, 10)
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m2['.*rbf.variance'].constrain_bounded(1e-4, 10)
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m2.randomize()
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m2.randomize()
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if debug:
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if debug:
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print m1
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print m1
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print m2
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print m2
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@ -663,9 +663,8 @@ class LaplaceTests(unittest.TestCase):
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if debug:
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if debug:
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print m1
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print m1
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print m2
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print m2
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m2.parameters_changed()
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m2[:] = m1[:]
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#m2._set_params(m1._get_params())
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#Predict for training points to get posterior mean and variance
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#Predict for training points to get posterior mean and variance
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post_mean, post_var, _, _ = m1.predict(X)
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post_mean, post_var, _, _ = m1.predict(X)
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@ -701,8 +700,9 @@ class LaplaceTests(unittest.TestCase):
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np.testing.assert_almost_equal(m1.log_likelihood(), m2.log_likelihood(), decimal=2)
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np.testing.assert_almost_equal(m1.log_likelihood(), m2.log_likelihood(), decimal=2)
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#Check marginals are the same with random
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#Check marginals are the same with random
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m1.randomize()
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m1.randomize()
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#m2._set_params(m1._get_params())
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import ipdb;ipdb.set_trace()
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m2.parameters_changed()
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m2[:] = m1[:]
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np.testing.assert_almost_equal(m1.log_likelihood(), m2.log_likelihood(), decimal=2)
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np.testing.assert_almost_equal(m1.log_likelihood(), m2.log_likelihood(), decimal=2)
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#Check they are checkgradding
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#Check they are checkgradding
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