Source code for GPy.models.gp_var_gauss

# Copyright (c) 2014, James Hensman, Alan Saul
# Licensed under the BSD 3-clause license (see LICENSE.txt)

import numpy as np
from ..core import GP
from ..core.parameterization.param import Param
from ..inference.latent_function_inference import VarGauss

log_2_pi = np.log(2*np.pi)

[docs]class GPVariationalGaussianApproximation(GP): """ The Variational Gaussian Approximation revisited .. rubric:: References .. [opper_archambeau_2009] Opper, M.; Archambeau, C.; The Variational Gaussian Approximation Revisited. Neural Comput. 2009, pages 786-792. """ def __init__(self, X, Y, kernel, likelihood, Y_metadata=None): num_data = Y.shape[0] self.alpha = Param('alpha', np.zeros((num_data,1))) # only one latent fn for now. self.beta = Param('beta', np.ones(num_data)) inf = VarGauss(self.alpha, self.beta) super(GPVariationalGaussianApproximation, self).__init__(X, Y, kernel, likelihood, name='VarGP', inference_method=inf, Y_metadata=Y_metadata) self.link_parameter(self.alpha) self.link_parameter(self.beta)