# 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 SparseGP
from ..inference.latent_function_inference import VarDTC
from .. import kern
from .. import util
[docs]class SparseGPCoregionalizedRegression(SparseGP):
"""
Sparse Gaussian Process model for heteroscedastic multioutput regression
This is a thin wrapper around the SparseGP 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 Z_list: list of inducing inputs (optional)
:type Z_list: empty 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 num_inducing: number of inducing inputs, defaults to 10 per output (ignored if Z_list is not empty)
:type num_inducing: integer | list of integers
: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, Z_list=[], kernel=None, likelihoods_list=None, num_inducing=10, X_variance=None, name='SGPCR',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)
#Inducing inputs list
if len(Z_list):
assert len(Z_list) == Ny, 'Number of outputs do not match length of inducing inputs list.'
else:
if isinstance(num_inducing,np.int):
num_inducing = [num_inducing] * Ny
num_inducing = np.asarray(num_inducing)
assert num_inducing.size == Ny, 'Number of outputs do not match length of inducing inputs list.'
for ni,Xi in zip(num_inducing,X_list):
i = np.random.permutation(Xi.shape[0])[:ni]
Z_list.append(Xi[i].copy())
Z, _, Iz = util.multioutput.build_XY(Z_list)
super(SparseGPCoregionalizedRegression, self).__init__(X, Y, Z, kernel, likelihood, inference_method=VarDTC(), Y_metadata={'output_index':self.output_index})
self['.*inducing'][:,-1].fix()