Source code for GPy.models.sparse_gp_regression

# 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()