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vardtc mixed noise

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This commit is contained in:
Max Zwiessele 2014-11-05 14:58:04 +00:00
parent 79041770c5
commit 1822f59433
2 changed files with 23 additions and 7 deletions
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26
GPy/testing/model_tests.py
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@ -377,17 +377,14 @@ class GradientTests(np.testing.TestCase):
m = GPy.models.GPLVM(Y, input_dim, init='PCA', kernel=k)
self.assertTrue(m.checkgrad())
@unittest.expectedFailure
def test_GP_EP_probit(self):
N = 20
X = np.hstack([np.random.normal(5, 2, N / 2), np.random.normal(10, 2, N / 2)])[:, None]
Y = np.hstack([np.ones(N / 2), np.zeros(N / 2)])[:, None]
kernel = GPy.kern.RBF(1)
m = GPy.models.GPClassification(X, Y, kernel=kernel)
m.update_likelihood_approximation()
self.assertTrue(m.checkgrad())
@unittest.expectedFailure
def test_sparse_EP_DTC_probit(self):
N = 20
X = np.hstack([np.random.normal(5, 2, N / 2), np.random.normal(10, 2, N / 2)])[:, None]
@ -399,7 +396,6 @@ class GradientTests(np.testing.TestCase):
# likelihood = GPy.likelihoods.EP(Y, distribution)
# m = GPy.core.SparseGP(X, likelihood, kernel, Z)
# m.ensure_default_constraints()
m.update_likelihood_approximation()
self.assertTrue(m.checkgrad())
@unittest.expectedFailure
@ -412,7 +408,8 @@ class GradientTests(np.testing.TestCase):
m.update_likelihood_approximation()
self.assertTrue(m.checkgrad())
def multioutput_regression_1D(self):
@unittest.expectedFailure
def test_multioutput_regression_1D(self):
X1 = np.random.rand(50, 1) * 8
X2 = np.random.rand(30, 1) * 5
X = np.vstack((X1, X2))
@ -422,10 +419,12 @@ class GradientTests(np.testing.TestCase):
k1 = GPy.kern.RBF(1)
m = GPy.models.GPMultioutputRegression(X_list=[X1, X2], Y_list=[Y1, Y2], kernel_list=[k1])
import ipdb;ipdb.set_trace()
m.constrain_fixed('.*rbf_var', 1.)
self.assertTrue(m.checkgrad())
def multioutput_sparse_regression_1D(self):
@unittest.expectedFailure
def test_multioutput_sparse_regression_1D(self):
X1 = np.random.rand(500, 1) * 8
X2 = np.random.rand(300, 1) * 5
X = np.vstack((X1, X2))
@ -447,6 +446,21 @@ class GradientTests(np.testing.TestCase):
m = GPy.models.GPHeteroscedasticRegression(X, Y, kern)
self.assertTrue(m.checkgrad())
def test_sparse_gp_heteroscedastic_regression(self):
num_obs = 25
X = np.random.randint(0, 140, num_obs)
X = X[:, None]
Y = 25. + np.sin(X / 20.) * 2. + np.random.rand(num_obs)[:, None]
kern = GPy.kern.Bias(1) + GPy.kern.RBF(1)
Y_metadata = {'output_index':np.arange(num_obs)[:,None]}
noise_terms = np.unique(Y_metadata['output_index'].flatten())
likelihoods_list = [GPy.likelihoods.Gaussian(name="Gaussian_noise_%s" %j) for j in noise_terms]
likelihood = GPy.likelihoods.MixedNoise(likelihoods_list=likelihoods_list)
m = GPy.core.SparseGP(X, Y, X[np.random.choice(num_obs, 10)],
kern, likelihood,
GPy.inference.latent_function_inference.VarDTC(),
Y_metadata=Y_metadata)
self.assertTrue(m.checkgrad())
def test_gp_kronecker_gaussian(self):
N1, N2 = 30, 20