[SPARK-2850] [mllib] MLlib stats examples + small fixes

Added examples for statistical summarization:
* Scala: StatisticalSummary.scala
** Tests: correlation, MultivariateOnlineSummarizer
* python: statistical_summary.py
** Tests: correlation (since MultivariateOnlineSummarizer has no Python API)

Added examples for random and sampled RDDs:
* Scala: RandomAndSampledRDDs.scala
* python: random_and_sampled_rdds.py
* Both test:
** RandomRDDGenerators.normalRDD, normalVectorRDD
** RDD.sample, takeSample, sampleByKey

Added sc.stop() to all examples.

CorrelationSuite.scala
* Added 1 test for RDDs with only 1 value

RowMatrix.scala
* numCols(): Added check for numRows = 0, with error message.
* computeCovariance(): Added check for numRows <= 1, with error message.

Python SparseVector (pyspark/mllib/linalg.py)
* Added toDense() function

python/run-tests script
* Added stat.py (doc test)

CC: @mengxr @dorx  Main changes were examples to show usage across APIs.
I don't quite understand why putting random data generation and sampling in a single example file. We can demo generating random uniform/normal/guassian/poisson RDDs in one example, and then stratified sampling in another (e.g., sampling based on the label to re-balance positive/negative examples).