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
Should be faster if we branch on the vector type here. If the vector is sparse, we only need to copy its value array. Also, the `activeIterator` is not very efficient.