# Copyright (c) 2012, James Hensman
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from ..core.sparse_gp_mpi import SparseGP_MPI
from .. import likelihoods
from .. import kern
from ..inference.latent_function_inference import VarDTC
from GPy.core.parameterization.variational import NormalPosterior
[docs]class SparseGPRegression(SparseGP_MPI):
"""
Gaussian Process model for regression
This is a thin wrapper around the SparseGP class, with a set of sensible defalts
:param X: input observations
:param X_variance: input uncertainties, one per input X
:param Y: observed values
:param kernel: a GPy kernel, defaults to rbf+white
:param Z: inducing inputs (optional, see note)
:type Z: np.ndarray (num_inducing x input_dim) | None
:param num_inducing: number of inducing points (ignored if Z is passed, see note)
:type num_inducing: int
:rtype: model object
.. Note:: If no Z array is passed, num_inducing (default 10) points are selected from the data. Other wise num_inducing is ignored
.. Note:: Multiple independent outputs are allowed using columns of Y
"""
def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, X_variance=None, mean_function=None, normalizer=None, mpi_comm=None, name='sparse_gp'):
num_data, input_dim = X.shape
# kern defaults to rbf (plus white for stability)
if kernel is None:
kernel = kern.RBF(input_dim)# + kern.white(input_dim, variance=1e-3)
# Z defaults to a subset of the data
if Z is None:
i = np.random.permutation(num_data)[:min(num_inducing, num_data)]
Z = X.view(np.ndarray)[i].copy()
else:
assert Z.shape[1] == input_dim
likelihood = likelihoods.Gaussian()
if not (X_variance is None):
X = NormalPosterior(X,X_variance)
if mpi_comm is not None:
from ..inference.latent_function_inference.var_dtc_parallel import VarDTC_minibatch
infr = VarDTC_minibatch(mpi_comm=mpi_comm)
else:
infr = VarDTC()
super(SparseGPRegression, self).__init__(X, Y, Z, kernel, likelihood, mean_function=mean_function,
inference_method=infr, normalizer=normalizer, mpi_comm=mpi_comm, name=name)
[docs] def parameters_changed(self):
from ..inference.latent_function_inference.var_dtc_parallel import update_gradients_sparsegp,VarDTC_minibatch
if isinstance(self.inference_method,VarDTC_minibatch):
update_gradients_sparsegp(self, mpi_comm=self.mpi_comm)
else:
super(SparseGPRegression, self).parameters_changed()