TRAINING: MAKING A HYPO FROM AN EXAMPLES.

As mentioned above, some learners (e.g. naive
nearest neighbor) may require no training.
But any learner that does require training
must implement two functions: mk_xxx_hypo and
free_xxx_hypo, where xxx is the name of the
learner.

   char *mk_xxx_hypo(examples *exams,
                     ivec *in_att_nums,
                     ivec *out_att_nums,
                     char *params,
                     ivec *train_rows,
                     ivec *holdout_rows,
                     expo **r_error_expo);

mk_xxx_hypo trains the learner on the data in
examples, considering just the specified
input and output attributes (the data may
have additional attributes not included in
either in_att_nums or out_att_nums).  If xxx
is a learner that needs an internal
holdout-set, this holdout-set is defined by
holdout_rows. The rows that may be used for
training are specified by train_rows.  If the
learner uses internal parameters, these will
be specified by the params argument.

mk_xxx_hypo should return a pointer to the
result of training (called a "hypo"). This
lump of memory will have been AM_MALLOCKED,
and will eventually be freed with
free_xxx_hypo.

If the learner is unable to learn using the
given set of att_nums, params, and rows, it
returns NULL and sets *r_error_expo to a
non-NULL error message explaining the
problem. If the learner is able to learn,
*r_error_expo should be returned as NULL.