This change adds Java examples and unit tests for all GLM algorithms
to make sure the MLLib interface works from Java. Changes include
- Introduce LabeledPoint and avoid using Doubles in train arguments
- Rename train to run in class methods
- Make the optimizer a member variable of GLM to make sure the builder
  pattern works

- Changes ALS to accept RDD[Rating] instead of (Int, Int, Double) making it
  easier to call from Java
- Renames class methods from `train` to `run` to enable static methods to be
  called from Java.
- Add unit tests which check if both static / class methods can be called.
- Also add examples which port the main() function in ALS, KMeans to the
  examples project.

Couple of minor changes to existing code:
- Add a toJavaRDD method in RDD to convert scala RDD to java RDD easily
- Workaround a bug where using double[] from Java leads to class cast exception in
  KMeans init

Also don't build an assembly jar for them

Also add jblas dependency to mllib pom.xml

requiring Spark to be installed. Using 'make_distribution.sh' a user
can put a Spark distribution at a URI supported by Mesos (e.g.,
'hdfs://...') and then set that when launching their job. Also added
SPARK_EXECUTOR_URI for the REPL.

This is used to find methods in the Scala API that
need to be ported to the Java API.  To use it:

  ./run spark.tools.JavaAPICompletenessChecker
Conflicts:
	project/SparkBuild.scala
	run
	run2.cmd

this :s.

Conflicts:
	run