# Copyright (c) 2012-2014, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
# Kurt Cutajar
from ..core import GpGrid
from .. import likelihoods
from .. import kern
[docs]class GPRegressionGrid(GpGrid):
"""
Gaussian Process model for grid inputs using Kronecker products
This is a thin wrapper around the models.GpGrid class, with a set of sensible defaults
:param X: input observations
:param Y: observed values
:param kernel: a GPy kernel, defaults to the kron variation of SqExp
:param Norm normalizer: [False]
Normalize Y with the norm given.
If normalizer is False, no normalization will be done
If it is None, we use GaussianNorm(alization)
.. Note:: Multiple independent outputs are allowed using columns of Y
"""
def __init__(self, X, Y, kernel=None, Y_metadata=None, normalizer=None):
if kernel is None:
kernel = kern.RBF(1) # no other kernels implemented so far
likelihood = likelihoods.Gaussian()
super(GPRegressionGrid, self).__init__(X, Y, kernel, likelihood, name='GP Grid regression', Y_metadata=Y_metadata, normalizer=normalizer)