Peter M. Groen / openbr

For non-leaf transforms, add a separate train method that takes
QList<TemplateList> as arguments, where the separate list items correspond to
the separate calls to project that would be made by parent transforms during
enrollment. This is basically to deal with the effects of distribute on
untrainable meta transforms such as flatten, we have to maintain a consistent
grouping with project, otherwise the intermediate projection results used
during training won't be consistent with what they would be during enrollment.

Conflicts:
	openbr/plugins/algorithms.cpp

resolved conflicts:
	app/br/br.cpp
	openbr/core/bee.cpp
	openbr/core/classify.cpp
	openbr/core/cluster.cpp
	openbr/core/eval.h
	openbr/openbr.cpp
	openbr/openbr.h
	openbr/plugins/algorithms.cpp
	openbr/plugins/independent.cpp
	openbr/plugins/output.cpp
	openbr/plugins/svm.cpp

Remove variables related to subsampling training data from Transform, move them
to a new transform called DownsampleTraining, and perform subsampling in that
transform, rather than as part of IndependentTransform (which depended on the
subsampling variables in Transform).

The prior approach was incompatible with using explicit class variable names,
rather than assuming a fixed label variable. This is because the actual
downsampling was carried out as part of IndependentTransform, which is not a
visible part of the algorithm grammar. Removing this logic from
Independent/Transform is generally desirable if only to avoid cluttering
Transform with member variables that are only used some of the time, and used
in a (fairly) restrictive way.

The current approach of makring DownsampleTraining as an indepent transform
is still limited since the downsample logic (still) cannot be applied without
the split used in Independent, and also DownsampleTraining can only be
implemented as a wrapper for another transform (because the argument to train
is const, and performing the donwsample logic in project would also impact the
testing case (since we have no way to exclude a transform used in training from
use in testing, for a fixed algorithm)).

Move implmeentation of getEvents to the .cpp file, make getChildren const and
public.

Look for properties with type Transform * or QList<Transform *> and for
getEvent, follow the tree using those, rather than having separate methods for
different transforms with children.

These names mirroring File functionality.
Fixed SVMDistance
dropped unnecessary second relabel in LDATransform::train

Remove global label/subject lookup table
Consistently use "Subject" rather than "Label", subject is assumed to be
convertable to QString. When desirable, map discrete subject values to ints.

For classifiers such as svm that require numeric labels, generate a string->int
mapping for the training data, and store it (local to the transform).

Utility functions for collecting all values of a given property (on a template
list), and mapping discrete property values to 0-based integers

Some outstanding issues include use of label/subject in matrix output

Remove subject/label methods from the API, replace some methods with general
methods taking a property name as an argument.

This breaks quite a few things