def test_mrd(self):
from GPy.inference.latent_function_inference import InferenceMethodList, VarDTC
from GPy.likelihoods import Gaussian
Y1 = np.random.normal(0, 1, (40, 13))
Y2 = np.random.normal(0, 1, (40, 6))
Y3 = np.random.normal(0, 1, (40, 8))
Q = 5
m = GPy.models.MRD(dict(data1=Y1, data2=Y2, data3=Y3), Q,
)
m.randomize()
self.assertTrue(m.checkgrad())
m = GPy.models.MRD(dict(data1=Y1, data2=Y2, data3=Y3), Q, initx='PCA_single',
initz='random',
kernel=[GPy.kern.RBF(Q, ARD=1) for _ in range(3)],
inference_method=InferenceMethodList([VarDTC() for _ in range(3)]),
likelihoods = [Gaussian(name='Gaussian_noise'.format(i)) for i in range(3)])
m.randomize()
self.assertTrue(m.checkgrad())
m = GPy.models.MRD(dict(data1=Y1, data2=Y2, data3=Y3), Q, initx='random',
initz='random',
kernel=GPy.kern.RBF(Q, ARD=1),
)
m.randomize()
self.assertTrue(m.checkgrad())
m = GPy.models.MRD(dict(data1=Y1, data2=Y2, data3=Y3), Q, X=np.random.normal(0,1,size=(40,Q)),
X_variance=False,
kernel=GPy.kern.RBF(Q, ARD=1),
likelihoods = [Gaussian(name='Gaussian_noise'.format(i)) for i in range(3)])
m.randomize()
self.assertTrue(m.checkgrad())
def __init__(self, X, Y, kernel, Y_variance, Z=None, num_inducing=25):
# likelihood defaults to Gaussian
likelihood = Gaussian(Y, variance=Y_variance)
# Z defaults to a subset of the data
if Z is None:
i = np.random.permutation(X.shape[0])[:num_inducing]
Z = X[i].copy()
else:
assert Z.shape[1] == X.shape[1]
SparseGP.__init__(self, X, likelihood, kernel, Z)
self.ensure_default_constraints()
def __init__(self, X, Y, kernel, Y_variance):
# likelihood defaults to Gaussian
likelihood = Gaussian(Y, variance=Y_variance)
GP.__init__(self, X, likelihood, kernel)
self.ensure_default_constraints()