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[huge merge] trying to merge old master and master

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Max Zwiessele 2014-11-21 16:17:03 +00:00
commit 180650ec85
308 changed files with 27071 additions and 24550 deletions
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32
GPy/models_modules/sparse_gp_classification.py
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@ -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):
"""
@ -16,7 +17,7 @@ class SparseGPClassification(SparseGP):
:param X: input observations
:param Y: observed values
:param likelihood: a GPy likelihood, defaults to Bernoulli with probit link_function
:param likelihood: a GPy likelihood, defaults to Binomial with probit link_function
:param kernel: a GPy kernel, defaults to rbf+white
:param normalize_X: whether to normalize the input data before computing (predictions will be in original scales)
:type normalize_X: False|True
@ -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.bernoulli()
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]
@ -43,14 +42,5 @@ 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()
def getstate(self):
return SparseGP.getstate(self)
def setstate(self, state):
return SparseGP.setstate(self, state)
pass
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):