[SPARK-8613] [ML] [TRIVIAL] add param to disable linear feature scaling

Add a param to disable linear feature scaling (to be implemented later in linear & logistic regression). Done as a seperate PR so we can use same param & not conflict while working on the sub-tasks.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #7024 from holdenk/SPARK-8522-Disable-Linear_featureScaling-Spark-8613-Add-param and squashes the following commits:

ce8931a [Holden Karau] Regenerate the sharedParams code
fa6427e [Holden Karau] update text for standardization param.
7b24a2b [Holden Karau] generate the new standardization param
3c190af [Holden Karau] Add the standardization param to sharedparamscodegen

mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala

@@ -53,6 +53,9 @@ private[shared] object SharedParamsCodeGen {
       ParamDesc[Int]("checkpointInterval", "checkpoint interval (>= 1)",
         isValid = "ParamValidators.gtEq(1)"),
       ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")),
+      ParamDesc[Boolean]("standardization", "whether to standardize the training features" +
+        " prior to fitting the model sequence. Note that the coefficients of models are" +
+        " always returned on the original scale.", Some("true")),
       ParamDesc[Long]("seed", "random seed", Some("this.getClass.getName.hashCode.toLong")),
       ParamDesc[Double]("elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]." +
         " For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.",

mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala

@@ -233,6 +233,23 @@ private[ml] trait HasFitIntercept extends Params {
   final def getFitIntercept: Boolean = $(fitIntercept)
 }
 
+/**
+ * (private[ml]) Trait for shared param standardization (default: true).
+ */
+private[ml] trait HasStandardization extends Params {
+
+  /**
+   * Param for whether to standardize the training features prior to fitting the model sequence. Note that the coefficients of models are always returned on the original scale..
+   * @group param
+   */
+  final val standardization: BooleanParam = new BooleanParam(this, "standardization", "whether to standardize the training features prior to fitting the model sequence. Note that the coefficients of models are always returned on the original scale.")
+
+  setDefault(standardization, true)
+
+  /** @group getParam */
+  final def getStandardization: Boolean = $(standardization)
+}
+
 /**
  * (private[ml]) Trait for shared param seed (default: this.getClass.getName.hashCode.toLong).
  */