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Add mean function functionality to dtc inference method

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This commit is contained in:
Mark Pullin 2017-11-13 21:15:38 +00:00
parent 754c67f71d
commit a24a9b3edc
5 changed files with 45 additions and 18 deletions
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11
GPy/testing/inference_tests.py
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@ -49,6 +49,7 @@ class InferenceXTestCase(unittest.TestCase):
m.optimize()
x, mi = m.infer_newX(m.Y, optimize=True)
np.testing.assert_array_almost_equal(m.X, mi.X, decimal=2)
class InferenceGPEP(unittest.TestCase):
def genData(self):
@ -132,6 +133,16 @@ class InferenceGPEP(unittest.TestCase):
np.sum(p._woodbury_vector - p0._woodbury_vector),
np.sum(p.woodbury_inv - p0.woodbury_inv)])) < 1e6)
class VarDtcTest(unittest.TestCase):
def test_var_dtc_inference_with_mean(self):
""" Check dL_dm in var_dtc is calculated correctly"""
np.random.seed(1)
x = np.linspace(0.,2*np.pi,100)[:,None]
y = -np.cos(x)+np.random.randn(*x.shape)*0.3+1
m = GPy.models.SparseGPRegression(x,y, mean_function=GPy.mappings.Linear(input_dim=1, output_dim=1))
self.assertTrue(m.checkgrad())
class HMCSamplerTest(unittest.TestCase):