Lazy Classifier - KNN
ML/FieldAggregates.ecl
- support KDTree endless right-node propagation
ML/Trees.ecl
- modify KDTree, changes support KNN Search:
  . use variance to select split feature (more balanced tree)
  . detect leaf branches: only one instance or same value for all instances
  . avoid endless right-node propagation
  . add FullTree and Boundaries, support KNN Search
ML/Lazy.ecl
- define instance-based learning (lazy) classifier (default, general)
- define KNN classifier (particular)
ML/NearestNeighborsSearch.ecl
- define NearestNeighborsSearch, general KNN search Algorithm (default)
- define KDTreeNNSearch, particular KNN search using KDTree
ML/Test/Explanatory/KNN_KDTree.ecl
- explanatory examples for KNN-KDTree classifier
Merge pull request #164 from DonVitorio/LazyClassifierKNN

Lazy Classifier - KNN
Corrected the Extrapolated function in both Dense and Sparse of Regression. And added test code to tests/Explanatory

Signed-off-by: Timothy Humphrey <timothy.humphrey@lexisnexis.com>
Corrected the Extrapolated function in both Dense and Sparse of Regression. And added test code to tests/Explanatory

Signed-off-by: Timothy Humphrey <timothy.humphrey@lexisnexis.com>
Added new text for updates to the ML Regression Library

Added text to the ML Regression module and 2 sections in the Useful Routines
section Parallel Block BLAS for ECL and The Dense Matrix Library

Signed-off-by: Lorraine Chapman <lorraine.chapman@lexisnexis.com>
Version numbers and pdf

Changed the version and copyright info to reflect this year and incremented
the version number. Fixed a few doc errors and generated the pdf.
Signed-off-by: Lorraine Chapman <lorraine.chapman@lexisnexis.com>
Minor edits to the regression walkthrough

Signed-off-by: Lorraine Chapman <lorraine.chapman@lexisnexis.com>
Fixed a column wrapping issue

Signed-off-by: Lorraine Chapman <lorraine.chapman@lexisnexis.com>
Fix bugs Issue #158 and RandomForest.ClassifyD
Issue #158 :
Trees.SplitsGiniImpurBased now pass "Purity" to Trees.PartitionGiniImpurityBased

RandomForest.ClassifyD:
LOCAL prevented getting all classified instances  from some trees.
Removing LOCAL option from gClass:=JOIN() fixed the problem.
Merge pull request #169 from lorraineachapman/MLRegressionUpdates

Ml regression updates to documentation
Merge pull request #170 from DonVitorio/FixingBugs

Fix bugs: Issue #158 and RandomForest.ClassifyD
Add Bundle.ecl file

Signed-off-by: Richard Chapman <rchapman@hpccsystems.com>
Merge pull request #171 from richardkchapman/bundle

Add Bundle.ecl file
Added IrisPlantDS.ecl to Deprecated

Signed-off-by: Timothy Humphrey <timothy.humphrey@lexisnexis.com>
Merge pull request #172 from tlhumphrey2/newdataset

Newdataset
Fixed ToField.ecl. Parameter 4 now handles spaces separating field names

Signed-off-by: Timothy L Humphrey <timothy.humphrey@lexisnexis.com>
Merge pull request #176 from tlhumphrey2/master

Fixed ToField.ecl. Parameter 4 now handles spaces separating field names
Binary C45 Decision Tree
Learn from continuous Independent data
Build Binary Decision Tree based on Info Gain Ratio
Configuration Input
minNumObj   minimum number of instances in a leaf node, used in splitting process
maxLevel    stop learning criteria, either tree's level reachs maxLevel depth or no more split can be done.
Module Files:
- Classify.ecl
- Trees.ecl
Example Files:
- BinaryC45Horizontal.ecl + lymphomaDS.ecl, high dimentional dataset 96 inst x 4026 attribs + class
- BinaryC45Vertical.ecl		, dataset 26733 inst x 100 attribs + class
- BinaryC45Iris.ecl , Iris dataset 150 inst x 4 attribs + class
Merge pull request #177 from DonVitorio/BinaryC45_DT

Binary C45 Decision Tree
Fixing Classification Metrics
- Classify.ecl
There was a bug in FP_Rate_ByClass when working with big datasets:
"Error: System error: 10099: Graph[329], workunitwrite[338]: Dataset too large to output to workunit (limit 10) megabytes, in result (name=spillI), Master exception"

Changed all the rate calculations to use CrossAssignments accumulated data instead of
accumulate J (DiffRec  recordset) with different conditions for each calculation.