Source code for GPy.models.gp_coregionalized_regression

# 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})