Source code for infpy.gp.feature_kernel

#
# Copyright John Reid 2006
#

from kernel import *
import numpy


class AttributeExtractor( Kernel ):
    """
    Changes the type of object a kernel can act on

    For example our data might be of type X that aggregate several features:

    X.vec: a numpy.array
    X.is_true: a boolean

    We may have a kernel, Kvec, that acts on arrays and a kernel, Kbool,
    that acts on boolean data. We could combine them as follows::

        K = SumKernel(
            AttributeExtractor( 'vec', Kvec ),
            AttributeExtractor( 'is_true', Kbool )
        )

    The AttributeExtractor will find the vec and is_true attributes of
    each x and pass them to the kernels Kvec and Kbool
    """

    def __init__( self, attribute_name, sub_kernel ):
        """Creates a kernel that extracts attributes with the given name and pass
        them to the sub kernel
        """
        Kernel.__init__( self, sub_kernel.params, sub_kernel.param_priors )
        self.k = sub_kernel
        self.attribute_name = attribute_name

    def __str__( self ):
        return """AttributeExtractorKernel( %s )""" % self.attribute_name

    def __call__( self, x1, x2, identical = False ):
        return self.k(
                getattr( x1, self.attribute_name ),
                getattr( x2, self.attribute_name ),
                identical
        )

    class Derivative( object ):
        def __init__( self, k, i ):
            self.k = k
            self.i = i
        def __call__( self, x1, x2, identical = False ):
            return self.k.k.derivative_wrt_param( self.i )(
                    x1.__dict__[ self.k.attribute_name ],
                    x2.__dict__[ self.k.attribute_name ],
                    identical
            )