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fix normalizer

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This commit is contained in:
Antoine Blanchard 2020-01-14 15:32:25 -05:00
parent 5a907bd013
commit 7550b1e5ef
2 changed files with 100 additions and 4 deletions
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43
GPy/testing/model_tests.py
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@ -1168,7 +1168,7 @@ class GradientTests(np.testing.TestCase):
Y = np.array([[1], [2]])
m = GPy.models.GPRegression(X1, Y, kernel=k)
result = m.posterior_covariance_between_points(X1, X2)
result = m._raw_posterior_covariance_between_points(X1, X2)
expected = np.array([[0.4, 2.2], [1.0, 1.0]]) / 3.0
self.assertTrue(np.allclose(result, expected))
@ -1179,7 +1179,7 @@ class GradientTests(np.testing.TestCase):
m = _create_missing_data_model(k, Q)
with self.assertRaises(RuntimeError):
m.posterior_covariance_between_points(np.array([[1], [2]]), np.array([[3], [4]]))
m._raw_posterior_covariance_between_points(np.array([[1], [2]]), np.array([[3], [4]]))
def test_multioutput_model_with_derivative_observations(self):
f = lambda x: np.sin(x)+0.1*(x-2.)**2-0.005*x**3
@ -1242,6 +1242,45 @@ class GradientTests(np.testing.TestCase):
self.assertTrue(m.checkgrad())
def test_predictive_gradients_with_normalizer(self):
"""
Check that model.predictive_gradients returns the gradients of
model.predict when normalizer=True
"""
N, M, Q = 10, 15, 3
X = np.random.rand(M,Q)
Y = np.random.rand(M,1)
x = np.random.rand(N, Q)
model = GPy.models.GPRegression(X=X, Y=Y, normalizer=True)
from GPy.models import GradientChecker
gm = GradientChecker(lambda x: model.predict(x)[0],
lambda x: model.predictive_gradients(x)[0],
x, 'x')
gc = GradientChecker(lambda x: model.predict(x)[1],
lambda x: model.predictive_gradients(x)[1],
x, 'x')
assert(gm.checkgrad())
assert(gc.checkgrad())
def test_posterior_covariance_between_points_with_normalizer(self):
"""
Check that model.posterior_covariance_between_points returns
the covariance from model.predict when normalizer=True
"""
np.random.seed(3)
N, M, Q = 10, 15, 3
X = np.random.rand(M,Q)
Y = np.random.rand(M,1)
x = np.random.rand(2, Q)
model = GPy.models.GPRegression(X=X, Y=Y, normalizer=True)
c1 = model.posterior_covariance_between_points(x,x)
c2 = model.predict(x, full_cov=True)[1]
np.testing.assert_allclose(c1,c2)
def _create_missing_data_model(kernel, Q):
D1, D2, D3, N, num_inducing = 13, 5, 8, 400, 3
_, _, Ylist = GPy.examples.dimensionality_reduction._simulate_matern(D1, D2, D3, N, num_inducing, False)