Due to this change in HDFS:
https://issues.apache.org/jira/browse/HADOOP-7549

there is a bug when using the new assembly builds. The symptom is that any HDFS access
results in an exception saying "No filesystem for scheme 'hdfs'". This adds a merge
strategy in the assembly build which fixes the problem.

This includes the following changes:
- The "assembly" package now builds in Maven by default, and creates an
  assembly containing both hadoop-client and Spark, unlike the old
  BigTop distribution assembly that skipped hadoop-client
- There is now a bigtop-dist package to build the old BigTop assembly
- The repl-bin package is no longer built by default since the scripts
  don't reply on it; instead it can be enabled with -Prepl-bin
- Py4J is now included in the assembly/lib folder as a local Maven repo,
  so that the Maven package can link to it
- run-example now adds the original Spark classpath as well because the
  Maven examples assembly lists spark-core and such as provided
- The various Maven projects add a spark-yarn dependency correctly

This commit makes Spark invocation saner by using an assembly JAR to
find all of Spark's dependencies instead of adding all the JARs in
lib_managed. It also packages the examples into an assembly and uses
that as SPARK_EXAMPLES_JAR. Finally, it replaces the old "run" script
with two better-named scripts: "run-examples" for examples, and
"spark-class" for Spark internal classes (e.g. REPL, master, etc). This
is also designed to minimize the confusion people have in trying to use
"run" to run their own classes; it's not meant to do that, but now at
least if they look at it, they can modify run-examples to do a decent
job for them.

As part of this, Bagel's examples are also now properly moved to the
examples package instead of bagel.

This reverts commit 1fb1b099, reversing
changes made to c69c4894.

Fixes SBT build under Hadoop 0.23.9 and 2.0.4

It made the JSON creation slightly more complicated, but reduces one external dependency. The scala library also properly escape "/" (which lift-json doesn't).

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