Parity in PySpark for ml.features

mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala

@@ -31,7 +31,7 @@ import org.apache.spark.sql.types.DataType
  * which is available at [[http://en.wikipedia.org/wiki/Polynomial_expansion]], "In mathematics, an
  * expansion of a product of sums expresses it as a sum of products by using the fact that
  * multiplication distributes over addition". Take a 2-variable feature vector as an example:
- * `(x, y)`, if we want to expand it with degree 2, then we get `(x, y, x * x, x * y, y * y)`.
+ * `(x, y)`, if we want to expand it with degree 2, then we get `(x, x * x, y, x * y, y * y)`.
  */
 @AlphaComponent
 class PolynomialExpansion extends UnaryTransformer[Vector, Vector, PolynomialExpansion] {

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

@@ -58,7 +58,8 @@ private[shared] object SharedParamsCodeGen {
         " For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.",
         isValid = "ParamValidators.inRange(0, 1)"),
       ParamDesc[Double]("tol", "the convergence tolerance for iterative algorithms"),
-      ParamDesc[Double]("stepSize", "Step size to be used for each iteration of optimization."))
+      ParamDesc[Double]("stepSize", "Step size to be used for each iteration of optimization."),
+      ParamDesc[Double]("p", "the p norm value.", isValid = "ParamValidators.gtEq(1)"))
 
     val code = genSharedParams(params)
     val file = "src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala"