Move to JGraphx version 2.3.0.5

Signed-off-by: Jean-Yves Tinevez <jean-yves.tinevez@pasteur.fr>
Move to JGrapht version 0.9.0

Signed-off-by: Jean-Yves Tinevez <jean-yves.tinevez@pasteur.fr>
Merge branch 'master' of git://github.com/fiji/fiji
Revert "Move to JGrapht version 0.9.0"

This reverts commit e5d54c619b3f90142a744fb9cf2d203d46776826.
Revert "Move to JGraphx version 2.3.0.5"

This reverts commit 6fc6e0e5f8af7ca7ffe6e2453969f7be617c22eb.
Merge branch 'master' of git://github.com/fiji/fiji
Merge branch 'master' of git://github.com/fiji/fiji
Trainable Weka Segmentation: add input error control.
Trainable Weka Segmentation: when creating training instances from a shape roi, use in-place method.
Trainable Weka Segmentation: clean code to try to identify bug that is causing huge delay when adding instances whose attributes are all 0.0.
gitignore: add more dependency JARs

They were copied by a MiniMaven build. However, I could not easily find
where they came from; "mvn dependency:tree" does not show them anywhere.
Trainable Weka Segmentation: add warning in javadoc to limit the use of getProcessor in FeatureStack.
Trainable Weka Segmentation: remove calls to getProcessor() in the FeatureStack for more reliable methods.

Maybe the ImageStack field of FeatureStack should be substituted by an array of ImageProcessor to prevent heavy duplications of the data.
Merge branch 'master' into trainable-seg-gui
Trainable Weka Segmentation: comment redundant log message.
Trainable Weka Segmentation: cleanup code.
Trainable Weka Segmentation: remove commented code.
Trainable Weka Segmentation: remove calls to createInstanceInPlace.

Surprisingly, setting the values of DenseInstance is more expensive than creating a new one.

TODO: create a new Instance class that prevents so many calls to new() and memory duplications.
Trainable Weka Segmentation: update fastRandomForest package to version 0.99 by Frank Supek.

--- VERSION 0.99 ---

Multiple speed and memory optimizations, for more details, please refer to
javadoc of the following:
- in the class FastRandomTree, please see function buildTree(), and the new
functions splitDataNew() and distributionSequentialAtt()
- in the FastRfBagging, see function computeOOBError( DataCache data, ... ) and
its helper class VotesCollectorDataCache, and also the buildClassifier() where
the feature importances are computed

Nominal attributes with more than 2 levels are treated differently than before;
in 0.98 and previous, as well as Weka's Random Forest, for such attributes,
splits with many branches are created. In 0.99, all splits are binary, and the
best value of the nominal attribute (in a one-vs-rest fashion) is chosen for the
split. Thus, the accuracy of 0.99 in datasets with multilevel nominal attributes
may differ somewhat (in either direction) from Weka's standard implementation.

The feature to compute RF attribute importances is accessible via the Explorer
GUI and is off by default, as it incurs a significant computational cost.
Results of the feature importance calculation are now displayed in the GUI; they
are output by the FastRandomForest toString() function.

Small changes in the output format of the Benchmark class.
Trainable Weka Segmentation: delete redundant log messages.