[SPARK-2852][MLLIB] Separate model from IDF/StandardScaler algorithms

[mengxr]

This is part of SPARK-2828:

1. separate IDF model from IDF algorithm (which generates a model)
2. separate StandardScaler model from StandardScaler

CC: @dbtsai

[SparkQA]

QA tests have started for PR 1814. This patch merges cleanly.
View progress: https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/18063/consoleFull

[dbtsai]

Since users may want to know the variance of the training set, should we have constructor

class StandardScalerModel private[mllib] (
    val withMean: Boolean,
    val withStd: Boolean,
    val mean: BV[Double],
    val variance: BV[Double]) {

  lazy val factor = { 
    val temp = variance.clone
    while (i < temp.size) {
    temp(i) = if (temp(i) != 0.0) 1.0 / math.sqrt(temp(i)) else 0.0
     i += 1
     temp
    }
  }
}

[mengxr]

done.

[SparkQA]

QA results for PR 1814:
- This patch PASSES unit tests.
- This patch merges cleanly
- This patch adds the following public classes (experimental):
class StandardScaler(withMean: Boolean, withStd: Boolean) {

For more information see test ouptut:
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/18063/consoleFull

[SparkQA]

QA tests have started for PR 1814. This patch merges cleanly.
View progress: https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/18111/consoleFull

[SparkQA]

QA results for PR 1814:
- This patch PASSES unit tests.
- This patch merges cleanly
- This patch adds the following public classes (experimental):
class StandardScaler(withMean: Boolean, withStd: Boolean) extends Logging {

For more information see test ouptut:
https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/18111/consoleFull

[dbtsai]

LGTM. Merged into both master and branch-1.1. Thanks!