Source code for GPy.models.gp_heteroscedastic_regression

# 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)