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Test for BGPLVM predictions, for linear case which is possible to do analytically

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Alan Saul 2016-03-24 14:56:42 +00:00
parent d8447a1c65
commit 9defc07672
1 changed files with 22 additions and 0 deletions
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GPy/testing/model_tests.py
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@ -148,6 +148,28 @@ class MiscTests(unittest.TestCase):
assert(gc.checkgrad())
assert(gc2.checkgrad())
def test_predict_uncertain_inputs(self):
""" Projection of Gaussian through a linear function is still gaussian, and moments are analytical to compute, so we can check this case for predictions easily """
X = np.linspace(-5,5, 10)[:, None]
Y = 2*X + np.random.randn(*X.shape)*1e-3
m = GPy.models.BayesianGPLVM(Y, 1, X=X, kernel=GPy.kern.Linear(1), num_inducing=1)
m.Gaussian_noise[:] = 1e-4
m.X.mean[:] = X[:]
m.X.variance[:] = 1e-5
m.X.fix()
m.optimize()
X_pred_mu = np.random.randn(5, 1)
X_pred_var = np.random.rand(5, 1) + 1e-5
from GPy.core.parameterization.variational import NormalPosterior
X_pred = NormalPosterior(X_pred_mu, X_pred_var)
# mu = \int f(x)q(x|mu,S) dx = \int 2x.q(x|mu,S) dx = 2.mu
# S = \int (f(x) - m)^2q(x|mu,S) dx = \int f(x)^2 q(x) dx - mu**2 = 4(mu^2 + S) - (2.mu)^2 = 4S
Y_mu_true = 2*X_pred_mu
Y_var_true = 4*X_pred_var
Y_mu_pred, Y_var_pred = m._raw_predict(X_pred)
np.testing.assert_allclose(Y_mu_true, Y_mu_pred, rtol=1e-4)
np.testing.assert_allclose(Y_var_true, Y_var_pred, rtol=1e-4)
def test_sparse_raw_predict(self):
k = GPy.kern.RBF(1)
m = GPy.models.SparseGPRegression(self.X, self.Y, kernel=k)