Area Under the ROC Curve
- Separate ensemble functions from Trees.ecl and create Ensemble.ecl
- Modify Classify.ecl to use Ensemble.ecl (Random Forest)
- Calculate Class Probability Distribution in Random Forest
- Calculate Area Under ROC Curve given Class Probaiblity Distribution, class of interest,
  and actual class values.
- Modify test/explanatory/randomforest, add area under ROC performance to results
ML-213 KMeans Clusters twice

 - Use INDEPENDENT workflow service to prevent clustering repetitions

Signed-off-by: Edin Muharemagic <edin.muharemagic@lexisnexis.com>
Merge pull request #182 from emuharemagic/ml213_clustering_done_twice

ML-213 KMeans Clusters twice
ML-215 Field Length Issue

 - Clarify issues associated with using native ML library datatypes

Signed-off-by: Edin Muharemagic <edin.muharemagic@lexisnexis.com>
Merge pull request #183 from emuharemagic/ML212_clustering_field_length_issue

ML-215 Field Length Issue
Fix of Cluster.ecl, issue ML-214

Signed-off-by: Timothy L Humphrey <timothy.humphrey@lexisnexis.com>
Merge pull request #184 from tlhumphrey2/ML-214

Fix of Cluster.ecl, issue ML-214
Modification based on Pull Request comments.
Final changes to RandomForest example
Adding explanatory comments in RandomForest example
changing learner's number of features to 4 (better classifier performance)
GROUPing by "group_id and id " before dedup0 and dedup1
Merge pull request #181 from DonVitorio/AreaUnderROCCurve

Area Under the ROC Curve
ML-224 SVD decomposition does not produce original matrix

https://track.hpccsystems.com/i#browse/ML-224
https://track.hpccsystems.com/i#browse/ML-226

- Remove Lanczos optimization for calculation of eigen vectors and values

There's a number of optimizations that can be made in the ecl-ml matrix
libary whose implementation is based on sprase matrix representation.
For example, in the 'strict' mode of operation, matrix dimenssion should always
be exlicitely set, so that matrix dimensionality requirements can be enforced.
Once this is done, it should be possible to perform matrix thining and
value rounding to prevent unnecessarily carrying very small real numbers
through iterations, but without possibility of losing matrix
dimensionality during that process. This in turn will prevent error
propagation, and it will make it possible to re-introduce other matrix
operations optimizations, such as using Lanczos algorithm in calculation
of eigen vectors.



Signed-off-by: Edin Muharemagic <edin.muharemagic@lexisnexis.com>
Merge pull request #185 from emuharemagic/ML224_SVD

ML-224 SVD decomposition does not produce original matrix
Merge remote-tracking branch 'upstream/master' into arima/initial