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renaming: posterior_variationa -> variational_posterior

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This commit is contained in:
James Hensman 2014-02-24 19:31:13 +00:00
parent 17f9764a55
commit da4686dd3c
9 changed files with 58 additions and 63 deletions
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7
GPy/models/sparse_gp_regression.py
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@ -7,6 +7,7 @@ from ..core import SparseGP
from .. import likelihoods
from .. import kern
from ..inference.latent_function_inference import VarDTC
from ..util.misc import param_to_array
class SparseGPRegression(SparseGP):
"""
@ -33,18 +34,18 @@ class SparseGPRegression(SparseGP):
# kern defaults to rbf (plus white for stability)
if kernel is None:
kernel = kern.rbf(input_dim)# + kern.white(input_dim, variance=1e-3)
kernel = kern.RBF(input_dim)# + kern.white(input_dim, variance=1e-3)
# Z defaults to a subset of the data
if Z is None:
i = np.random.permutation(num_data)[:min(num_inducing, num_data)]
Z = X[i].copy()
Z = param_to_array(X)[i].copy()
else:
assert Z.shape[1] == input_dim
likelihood = likelihoods.Gaussian()
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, X_variance=X_variance, inference_method=VarDTC())
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method=VarDTC())
def _getstate(self):
return SparseGP._getstate(self)