SPARK-2272 [MLlib] Feature scaling which standardizes the range of independent variables or features of data

Feature scaling is a method used to standardize the range of independent variables or features of data. In data processing, it is generally performed during the data preprocessing step.

In this work, a trait called `VectorTransformer` is defined for generic transformation on a vector. It contains one method to be implemented, `transform` which applies transformation on a vector.

There are two implementations of `VectorTransformer` now, and they all can be easily extended with PMML transformation support.

1) `StandardScaler` - Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set.

2) `Normalizer` - Normalizes samples individually to unit L^n norm
This block could be simplified:

~~~
    assert((data, data1, data1RDD.collect()).zipped.forall {
      case (v1: DenseVector, v2: DenseVector, v3: DenseVector) => true
      case (v1: SparseVector, v2: SparseVector, v3: SparseVector) => true
      case _ => false
    }, "The vector type should be preserved after normalization.")
~~~