Source code for GPy.kern.src.multioutput_derivative_kern

# Copyright (c) 2018, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)

from .kern import Kern, CombinationKernel
from .multioutput_kern import MultioutputKern, ZeroKern
import numpy as np
from functools import partial

[docs]class KernWrapper(Kern): def __init__(self, fk, fug, fg, base_kern): = fk self.fug = fug self.fg = fg self.base_kern = base_kern super(KernWrapper, self).__init__(base_kern.active_dims.size, base_kern.active_dims, name='KernWrapper',useGPU=False)
[docs] def K(self, X, X2=None): return,X2=X2)
[docs] def update_gradients_full(self,dL_dK, X, X2=None): return self.fug(dL_dK, X, X2=X2)
[docs] def gradients_X(self,dL_dK, X, X2=None): return self.fg(dL_dK, X, X2=X2)
@property def gradient(self): return self.base_kern.gradient @gradient.setter def gradient(self, gradient): self.base_kern.gradient = gradient
[docs]class MultioutputDerivativeKern(MultioutputKern): """ Multioutput derivative kernel is a meta class for combining different kernels for multioutput GPs. Multioutput derivative kernel is only a thin wrapper for Multioutput kernel for user not having to define cross covariances. """ def __init__(self, kernels, cross_covariances={}, name='MultioutputDerivativeKern'): #kernels contains a list of kernels as input, if not isinstance(kernels, list): self.single_kern = True self.kern = kernels kernels = [kernels] else: self.single_kern = False self.kern = kernels # The combination kernel ALLWAYS puts the extra dimension last. # Thus, the index dimension of this kernel is always the last dimension # after slicing. This is why the index_dim is just the last column: self.index_dim = -1 super(MultioutputKern, self).__init__(kernels=kernels, extra_dims=[self.index_dim], name=name, link_parameters=False) nl = len(kernels) #build covariance structure covariance = [[None for i in range(nl)] for j in range(nl)] linked = [] for i in range(0,nl): unique=True for j in range(0,nl): if i==j or (kernels[i] is kernels[j]): kern = kernels[i] if i>j: unique=False elif cross_covariances.get((i,j)) is not None: #cross covariance is given kern = cross_covariances.get((i,j)) elif kernels[i].name == 'DiffKern' and kernels[i].base_kern == kernels[j]: # one is derivative of other kern = KernWrapper(kernels[i].dK_dX_wrap,kernels[i].update_gradients_dK_dX,kernels[i].gradients_X, kernels[j]) unique=False elif kernels[j].name == 'DiffKern' and kernels[j].base_kern == kernels[i]: # one is derivative of other kern = KernWrapper(kernels[j].dK_dX2_wrap,kernels[j].update_gradients_dK_dX2,kernels[j].gradients_X2, kernels[i]) elif kernels[i].name == 'DiffKern' and kernels[j].name == 'DiffKern' and kernels[i].base_kern == kernels[j].base_kern: #both are partial derivatives kern = KernWrapper(partial(kernels[i].K, dimX2=kernels[j].dimension), partial(kernels[i].update_gradients_full, dimX2=kernels[j].dimension),None, kernels[i].base_kern) if i>j: unique=False else: kern = ZeroKern() covariance[i][j] = kern if unique is True: linked.append(i) self.covariance = covariance self.link_parameters(*[kernels[i] for i in linked])