Source code for GPy.models.sparse_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 SparseGP
from ..inference.latent_function_inference import VarDTC
from .. import kern
from .. import util

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()