Additions and changes from theJenix/ABAGAIL

- `func.nn.feedwd`: Packages for a FeedForward (not backprop!) neural network
- `shared.reader`: DataSetReader is now an abstract class (and in its own package), and we have readers for two file formats so far (ARFF and CSV) built that follow it's API
- `shared.tester`: package for implementing testers and metrics. Includes things such as Confusion Matrices and Accuracy testing
- An updated ant build file, including a target for Javadoc documentation (javadoc updating needs to happen however)

Those are the big changes we've implemented right now; mostly things we needed/wanted in order to do P2

Contributors:
@theJenix
@lexlinton
@darkzeroman
@Noirbot
and myself, @chronoslynx.
Added Abalone Test example
Merge from theJenix/ABAGAIL:

DataSetFilter implemented to split a dataset into a testing and training set
Added code to recombine separated binary labels into discrete labels (for the
test metrics)

Fixed bugs in metrics introduced by splitting labels into binary labels

Added a framework for defining experiment runners, which expose a standard set
of test metrics, and support writing to a CSV file

Added a CSV writer and writer framework, to write data to a file
Merge pull request #2 from theJenix/master

Merge from theJenix
Solves an old bug in SimpleSupportVectorMachineClassifier
Merge remote-tracking branch 'upstream/master'

* upstream/master:
  Solves an old bug in SimpleSupportVectorMachineClassifier
  Merge from theJenix/ABAGAIL:
  Added Abalone Test example
  Additions and changes from theJenix/ABAGAIL

Conflicts:
	build.xml
	manifest.mf
	src/func/SimpleSupportVectorMachineClassifier.java
	src/func/nn/backprop/BackPropagationLayer.java
	src/shared/filt/ContinuousToDiscreteFilter.java
	src/shared/filt/LabelSplitFilter.java
	src/shared/reader/DataSetLabelBinarySeperator.java
	src/shared/tester/AccuracyTestMetric.java
	src/shared/tester/Comparison.java
	src/shared/tester/ConfusionMatrixTestMetric.java
	src/shared/tester/NeuralNetworkTester.java
added filter for choosing an arbitrary DataSet feature to use as label