# Copyright (c) 2012 - 2014 the GPy Austhors (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 .. import util
[docs]class GPHeteroscedasticRegression(GP):
"""
Gaussian Process model for heteroscedastic regression
This is a thin wrapper around the models.GP class, with a set of sensible defaults
:param X: input observations
:param Y: observed values
:param kernel: a GPy kernel, defaults to rbf
NB: This model does not make inference on the noise outside the training set
"""
def __init__(self, X, Y, kernel=None, Y_metadata=None):
Ny = Y.shape[0]
if Y_metadata is None:
Y_metadata = {'output_index':np.arange(Ny)[:,None]}
else:
assert Y_metadata['output_index'].shape[0] == Ny
if kernel is None:
kernel = kern.RBF(X.shape[1])
#Likelihood
likelihood = likelihoods.HeteroscedasticGaussian(Y_metadata)
super(GPHeteroscedasticRegression, self).__init__(X,Y,kernel,likelihood, Y_metadata=Y_metadata)