Source code for GPy.mappings.kernel

# Copyright (c) 2013, GPy authors (see AUTHORS.txt).
# Copyright (c) 2015, James Hensman
# Licensed under the BSD 3-clause license (see LICENSE.txt)

import numpy as np
from ..core.mapping import Mapping
from ..core import Param

[docs]class Kernel(Mapping): """ Mapping based on a kernel/covariance function. .. math:: f(\mathbf{x}) = \sum_i \alpha_i k(\mathbf{z}_i, \mathbf{x}) or for multple outputs .. math:: f_i(\mathbf{x}) = \sum_j \alpha_{i,j} k(\mathbf{z}_i, \mathbf{x}) :param input_dim: dimension of input. :type input_dim: int :param output_dim: dimension of output. :type output_dim: int :param Z: input observations containing :math:`\mathbf{Z}` :type Z: ndarray :param kernel: a GPy kernel, defaults to GPy.kern.RBF :type kernel: GPy.kern.kern """ def __init__(self, input_dim, output_dim, Z, kernel, name='kernmap'): super(Kernel, self).__init__(input_dim=input_dim, output_dim=output_dim, name=name) self.kern = kernel self.Z = Z self.num_bases, Zdim = Z.shape assert Zdim == self.input_dim self.A = Param('A', np.random.randn(self.num_bases, self.output_dim)) self.link_parameter(self.A)
[docs] def f(self, X): return, self.Z), self.A)
[docs] def update_gradients(self, dL_dF, X): self.kern.update_gradients_full(, self.A.T), X, self.Z) self.A.gradient = self.kern.K(self.Z, X), dL_dF)
[docs] def gradients_X(self, dL_dF, X): return self.kern.gradients_X(, self.A.T), X, self.Z)