# Copyright (c) 2012 - 2014 the GPy Austhors (see AUTHORS.txt)
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from ..core import GP
from .. import likelihoods
from .. import kern
from .. import util
[docs]class GPCoregionalizedRegression(GP):
"""
Gaussian Process model for heteroscedastic multioutput regression
This is a thin wrapper around the models.GP class, with a set of sensible defaults
:param X_list: list of input observations corresponding to each output
:type X_list: list of numpy arrays
:param Y_list: list of observed values related to the different noise models
:type Y_list: list of numpy arrays
:param kernel: a GPy kernel ** Coregionalized, defaults to RBF ** Coregionalized
:type kernel: None | GPy.kernel defaults
:likelihoods_list: a list of likelihoods, defaults to list of Gaussian likelihoods
:type likelihoods_list: None | a list GPy.likelihoods
:param name: model name
:type name: string
:param W_rank: number tuples of the corregionalization parameters 'W' (see coregionalize kernel documentation)
:type W_rank: integer
:param kernel_name: name of the kernel
:type kernel_name: string
"""
def __init__(self, X_list, Y_list, kernel=None, likelihoods_list=None, name='GPCR',W_rank=1,kernel_name='coreg'):
#Input and Output
X,Y,self.output_index = util.multioutput.build_XY(X_list,Y_list)
Ny = len(Y_list)
#Kernel
if kernel is None:
kernel = kern.RBF(X.shape[1]-1)
kernel = util.multioutput.ICM(input_dim=X.shape[1]-1, num_outputs=Ny, kernel=kernel, W_rank=W_rank,name=kernel_name)
#Likelihood
likelihood = util.multioutput.build_likelihood(Y_list,self.output_index,likelihoods_list)
super(GPCoregionalizedRegression, self).__init__(X,Y,kernel,likelihood, Y_metadata={'output_index':self.output_index})