# 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

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})

1cfb16d5a4fcf4ab578c3ee8d99dfd2ce2ba10ad
<mask token>


class GPCoregionalizedRegression(GP):
    <mask token>
    <mask token>

<mask token>


class GPCoregionalizedRegression(GP):
    <mask token>

    def __init__(self, X_list, Y_list, kernel=None, likelihoods_list=None,
        name='GPCR', W_rank=1, kernel_name='coreg'):
        X, Y, self.output_index = util.multioutput.build_XY(X_list, Y_list)
        Ny = len(Y_list)
        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 = 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})

<mask token>


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'):
        X, Y, self.output_index = util.multioutput.build_XY(X_list, Y_list)
        Ny = len(Y_list)
        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 = 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})

import numpy as np
from ..core import GP
from .. import likelihoods
from .. import kern
from .. import util


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'):
        X, Y, self.output_index = util.multioutput.build_XY(X_list, Y_list)
        Ny = len(Y_list)
        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 = 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})

# 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

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})
