Generic feature extraction from XML documents.

For machine learning problems, we often need our inputs to be the same, fixed size.
When we have a recursive structure, like a tree, we can fold over the structure to
obtain a single value.

This is a very basic implementation of this idea: we take arbitrary XML documents,
which are tree structured, and assign each element a value based on the md5 of its
name and attributes concatenated together. We fold sub-trees together using bitwise
circular convolution, to obtain a value for the whole tree.

Circular convolution is a linear operation, so it can't preserve as much
information as, for example, auto-encoding, but it is reasonably fast, requires no
learning and is largely non-commutative/associative, so sub-trees should be
distinguishable to a certain extent.