Source code for GPy.models.gp_grid_regression

# 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)