[SPARK-10236] [MLLIB] update since versions in mllib.feature

mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala

@@ -68,8 +68,6 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
     private[clustering]
     val thisClassName = "org.apache.spark.mllib.clustering.PowerIterationClusteringModel"
 
-    /**
-     */
     @Since("1.4.0")
     def save(sc: SparkContext, model: PowerIterationClusteringModel, path: String): Unit = {
       val sqlContext = new SQLContext(sc)

mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala

@@ -33,7 +33,7 @@ import org.apache.spark.rdd.RDD
  */
 @Since("1.3.0")
 @Experimental
-class ChiSqSelectorModel (
+class ChiSqSelectorModel @Since("1.3.0") (
   @Since("1.3.0") val selectedFeatures: Array[Int]) extends VectorTransformer {
 
   require(isSorted(selectedFeatures), "Array has to be sorted asc")
@@ -112,7 +112,7 @@ class ChiSqSelectorModel (
  */
 @Since("1.3.0")
 @Experimental
-class ChiSqSelector (
+class ChiSqSelector @Since("1.3.0") (
   @Since("1.3.0") val numTopFeatures: Int) extends Serializable {
 
   /**

mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala

@@ -29,7 +29,8 @@ import org.apache.spark.mllib.linalg._
  */
 @Since("1.4.0")
 @Experimental
-class ElementwiseProduct(val scalingVec: Vector) extends VectorTransformer {
+class ElementwiseProduct @Since("1.4.0") (
+    @Since("1.4.0") val scalingVec: Vector) extends VectorTransformer {
 
   /**
    * Does the hadamard product transformation.

mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala

@@ -39,8 +39,9 @@ import org.apache.spark.rdd.RDD
  */
 @Since("1.1.0")
 @Experimental
-class IDF(val minDocFreq: Int) {
+class IDF @Since("1.2.0") (@Since("1.2.0") val minDocFreq: Int) {
 
+  @Since("1.1.0")
   def this() = this(0)
 
   // TODO: Allow different IDF formulations.
@@ -162,7 +163,8 @@ private object IDF {
  * Represents an IDF model that can transform term frequency vectors.
  */
 @Experimental
-class IDFModel private[spark] (val idf: Vector) extends Serializable {
+@Since("1.1.0")
+class IDFModel private[spark] (@Since("1.1.0") val idf: Vector) extends Serializable {
 
   /**
    * Transforms term frequency (TF) vectors to TF-IDF vectors.

mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala

@@ -33,7 +33,7 @@ import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors
  */
 @Since("1.1.0")
 @Experimental
-class Normalizer(p: Double) extends VectorTransformer {
+class Normalizer @Since("1.1.0") (p: Double) extends VectorTransformer {
 
   @Since("1.1.0")
   def this() = this(2)

mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala

@@ -29,7 +29,7 @@ import org.apache.spark.rdd.RDD
  * @param k number of principal components
  */
 @Since("1.4.0")
-class PCA(val k: Int) {
+class PCA @Since("1.4.0") (@Since("1.4.0") val k: Int) {
   require(k >= 1, s"PCA requires a number of principal components k >= 1 but was given $k")
 
   /**
@@ -74,7 +74,10 @@ class PCA(val k: Int) {
  * @param k number of principal components.
  * @param pc a principal components Matrix. Each column is one principal component.
  */
-class PCAModel private[spark] (val k: Int, val pc: DenseMatrix) extends VectorTransformer {
+@Since("1.4.0")
+class PCAModel private[spark] (
+    @Since("1.4.0") val k: Int,
+    @Since("1.4.0") val pc: DenseMatrix) extends VectorTransformer {
   /**
    * Transform a vector by computed Principal Components.
    *

mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala

@@ -34,7 +34,7 @@ import org.apache.spark.rdd.RDD
  */
 @Since("1.1.0")
 @Experimental
-class StandardScaler(withMean: Boolean, withStd: Boolean) extends Logging {
+class StandardScaler @Since("1.1.0") (withMean: Boolean, withStd: Boolean) extends Logging {
 
   @Since("1.1.0")
   def this() = this(false, true)
@@ -74,11 +74,11 @@ class StandardScaler(withMean: Boolean, withStd: Boolean) extends Logging {
  */
 @Since("1.1.0")
 @Experimental
-class StandardScalerModel (
-    val std: Vector,
-    val mean: Vector,
-    var withStd: Boolean,
-    var withMean: Boolean) extends VectorTransformer {
+class StandardScalerModel @Since("1.3.0") (
+    @Since("1.3.0") val std: Vector,
+    @Since("1.1.0") val mean: Vector,
+    @Since("1.3.0") var withStd: Boolean,
+    @Since("1.3.0") var withMean: Boolean) extends VectorTransformer {
 
   /**
    */

mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala

@@ -436,6 +436,7 @@ class Word2Vec extends Serializable with Logging {
  *                    (i * vectorSize, i * vectorSize + vectorSize)
  */
 @Experimental
+@Since("1.1.0")
 class Word2VecModel private[mllib] (
     private val wordIndex: Map[String, Int],
     private val wordVectors: Array[Float]) extends Serializable with Saveable {