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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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24
GPy/models_modules/gp_classification.py
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@ -1,11 +1,10 @@
# Copyright (c) 2013, Ricardo Andrade
# Copyright (c) 2013, the GPy Authors (see AUTHORS.txt)
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from ..core import GP
from .. import likelihoods
from .. import kern
from ..inference.latent_function_inference.expectation_propagation import EP
class GPClassification(GP):
"""
@ -15,27 +14,16 @@ class GPClassification(GP):
:param X: input observations
:param Y: observed values, can be None if likelihood is not None
:param likelihood: a GPy likelihood, defaults to Bernoulli with Probit link_function
:param kernel: a GPy kernel, defaults to rbf
:param normalize_X: whether to normalize the input data before computing (predictions will be in original scales)
:type normalize_X: False|True
:param normalize_Y: whether to normalize the input data before computing (predictions will be in original scales)
:type normalize_Y: False|True
.. Note:: Multiple independent outputs are allowed using columns of Y
"""
def __init__(self,X,Y=None,likelihood=None,kernel=None,normalize_X=False,normalize_Y=False):
def __init__(self, X, Y, kernel=None,Y_metadata=None):
if kernel is None:
kernel = kern.rbf(X.shape[1])
kernel = kern.RBF(X.shape[1])
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.'
likelihood = likelihoods.Bernoulli()
GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
self.ensure_default_constraints()
GP.__init__(self, X=X, Y=Y, kernel=kernel, likelihood=likelihood, inference_method=EP(), name='gp_classification')