apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-04-30 23:36:23 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

changes according to new definitions

Browse source
This commit is contained in:
Ricardo 2014-04-16 11:04:16 +01:00
parent b12438c0af
commit 5d4e727521
1 changed files with 11 additions and 13 deletions
Download patch file Download diff file Expand all files Collapse all files

24
GPy/models/sparse_gp_classification.py
View file

@ -7,6 +7,7 @@ from ..core import SparseGP
from .. import likelihoods
from .. import kern
from ..likelihoods import likelihood
from ..inference.latent_function_inference import expectation_propagation_dtc
class SparseGPClassification(SparseGP):
"""
@ -26,16 +27,14 @@ class SparseGPClassification(SparseGP):
"""
def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, num_inducing=10):
if kernel is None:
kernel = kern.rbf(X.shape[1])# + kern.white(X.shape[1],1e-3)
#def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, num_inducing=10):
def __init__(self, X, Y=None, likelihood=None, kernel=None, Z=None, num_inducing=10, Y_metadata=None):
if likelihood is None:
noise_model = likelihoods.binomial()
likelihood = likelihoods.EP(Y, noise_model)
elif Y is not None:
if not all(Y.flatten() == likelihood.data.flatten()):
raise Warning, 'likelihood.data and Y are different.'
if kernel is None:
kernel = kern.RBF(X.shape[1])
likelihood = likelihoods.Bernoulli()
if Z is None:
i = np.random.permutation(X.shape[0])[:num_inducing]
@ -43,8 +42,9 @@ class SparseGPClassification(SparseGP):
else:
assert Z.shape[1] == X.shape[1]
SparseGP.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
self.ensure_default_constraints()
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method=expectation_propagation_dtc.EPDTC(), name='SparseGPClassification',Y_metadata=Y_metadata)
#def __init__(self, X, Y, Z, kernel, likelihood, inference_method=None, name='sparse gp', Y_metadata=None):
def _getstate(self):
return SparseGP._getstate(self)
@ -52,5 +52,3 @@ class SparseGPClassification(SparseGP):
def _setstate(self, state):
return SparseGP._setstate(self, state)
pass