Upcase FASTA/Phylip letters after reading in.
[FIX] Correctly place priors on log of parameters, in smodel.C
 * alignment-random.C
   + randomize( )
     - make this not depend on placement of gaps in initial alignment.
Increase the lambda::prior_median from -5 to -4, but shrink the prior_stddev.
Change initial values to epsilon=-0.25, invariant=0.05, prior_length=10.
Remove the undocumented "invariant" parameters from the RS07 model.

This was causing convergence/mixing problems.
Report prior_A.
Introduce 'training' state for IndelModel.

 * imodel.{H,C}
   + class IndelModel
     - void set_training(bool): new virtual function.
     - bool is_training() const: new virtual function.
   + class SimpleIndelModel
     - void set_training(bool): new virtual function.
     - bool is_training() const: new virtual function.
     - get_branch_HMM( ): keep delta < 0.005
   + class NewIndelModel
     - void set_training(bool): new virtual function.
     - bool is_training() const: new virtual function.
     - get_branch_HMM( ): keep A < 0.005.

Basically, we expect that |T| to be much larger at the beginning
of the run.  However, this means that lambda*|T| is ALSO large,
which can make everything unalign for no real reason.  Avoid this
by making delta/(1-delta) < 0.005 even when T[b] is large.
Make RS07 models have t=0.25 during training.
Revert to the old implementation of "training" for indel models.
Fix initial un-alignment problems by turning on alignment training.

Also change the training period from 5->10 iterations.
Also make this settable via the parameter alignment-burnin.
Only set necessary internal node characters to '+'.

 * alignment-util.{H,C}
   + alignment add_internal( )
     - set internal node sequences to - by default.
   + connect_all_characters( ): new function.
   + void connect_leaf_characters( ): new function.
   + check_leaf_sequences
     - indicate which sequences is the original one.

 * setup.C
   + link( ), link( )
     - run connect_leaf_characters( ) if there are internal node sequences.
Try to continue instead of crashing when allocating too much memory.
Change version # to 2.0.3.