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From m...@apache.org
Subject [2/3] spark git commit: [SPARK-9864] [DOC] [MLlib] [SQL] Replace since in scaladoc to Since annotation
Date Fri, 21 Aug 2015 21:19:39 GMT
http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
index 7f4de77..ba3b447 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
@@ -20,7 +20,7 @@ import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
 import org.apache.spark.mllib.fpm.AssociationRules.Rule
@@ -33,24 +33,22 @@ import org.apache.spark.rdd.RDD
  * Generates association rules from a [[RDD[FreqItemset[Item]]]. This method only generates
  * association rules which have a single item as the consequent.
  *
- * @since 1.5.0
  */
+@Since("1.5.0")
 @Experimental
 class AssociationRules private[fpm] (
     private var minConfidence: Double) extends Logging with Serializable {
 
   /**
    * Constructs a default instance with default parameters {minConfidence = 0.8}.
-   *
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def this() = this(0.8)
 
   /**
    * Sets the minimal confidence (default: `0.8`).
-   *
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def setMinConfidence(minConfidence: Double): this.type = {
     require(minConfidence >= 0.0 && minConfidence <= 1.0)
     this.minConfidence = minConfidence
@@ -62,8 +60,8 @@ class AssociationRules private[fpm] (
    * @param freqItemsets frequent itemset model obtained from [[FPGrowth]]
    * @return a [[Set[Rule[Item]]] containing the assocation rules.
    *
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def run[Item: ClassTag](freqItemsets: RDD[FreqItemset[Item]]): RDD[Rule[Item]] = {
     // For candidate rule X => Y, generate (X, (Y, freq(X union Y)))
     val candidates = freqItemsets.flatMap { itemset =>
@@ -102,8 +100,8 @@ object AssociationRules {
    *                   instead.
    * @tparam Item item type
    *
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   @Experimental
   class Rule[Item] private[fpm] (
       val antecedent: Array[Item],
@@ -114,8 +112,8 @@ object AssociationRules {
     /**
      * Returns the confidence of the rule.
      *
-     * @since 1.5.0
      */
+    @Since("1.5.0")
     def confidence: Double = freqUnion.toDouble / freqAntecedent
 
     require(antecedent.toSet.intersect(consequent.toSet).isEmpty, {
@@ -127,8 +125,8 @@ object AssociationRules {
     /**
      * Returns antecedent in a Java List.
      *
-     * @since 1.5.0
      */
+    @Since("1.5.0")
     def javaAntecedent: java.util.List[Item] = {
       antecedent.toList.asJava
     }
@@ -136,8 +134,8 @@ object AssociationRules {
     /**
      * Returns consequent in a Java List.
      *
-     * @since 1.5.0
      */
+    @Since("1.5.0")
     def javaConsequent: java.util.List[Item] = {
       consequent.toList.asJava
     }

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
index e2370a5..e37f806 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
@@ -25,7 +25,7 @@ import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.spark.{HashPartitioner, Logging, Partitioner, SparkException}
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
 import org.apache.spark.mllib.fpm.FPGrowth._
@@ -39,15 +39,15 @@ import org.apache.spark.storage.StorageLevel
  * @param freqItemsets frequent itemset, which is an RDD of [[FreqItemset]]
  * @tparam Item item type
  *
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) extends Serializable {
   /**
    * Generates association rules for the [[Item]]s in [[freqItemsets]].
    * @param confidence minimal confidence of the rules produced
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def generateAssociationRules(confidence: Double): RDD[AssociationRules.Rule[Item]] = {
     val associationRules = new AssociationRules(confidence)
     associationRules.run(freqItemsets)
@@ -71,8 +71,8 @@ class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) ex
  * @see [[http://en.wikipedia.org/wiki/Association_rule_learning Association rule learning
  *       (Wikipedia)]]
  *
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class FPGrowth private (
     private var minSupport: Double,
@@ -82,15 +82,15 @@ class FPGrowth private (
    * Constructs a default instance with default parameters {minSupport: `0.3`, numPartitions: same
    * as the input data}.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def this() = this(0.3, -1)
 
   /**
    * Sets the minimal support level (default: `0.3`).
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setMinSupport(minSupport: Double): this.type = {
     this.minSupport = minSupport
     this
@@ -99,8 +99,8 @@ class FPGrowth private (
   /**
    * Sets the number of partitions used by parallel FP-growth (default: same as input data).
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setNumPartitions(numPartitions: Int): this.type = {
     this.numPartitions = numPartitions
     this
@@ -111,8 +111,8 @@ class FPGrowth private (
    * @param data input data set, each element contains a transaction
    * @return an [[FPGrowthModel]]
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def run[Item: ClassTag](data: RDD[Array[Item]]): FPGrowthModel[Item] = {
     if (data.getStorageLevel == StorageLevel.NONE) {
       logWarning("Input data is not cached.")
@@ -213,8 +213,8 @@ class FPGrowth private (
 /**
  * :: Experimental ::
  *
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 object FPGrowth {
 
@@ -224,15 +224,15 @@ object FPGrowth {
    * @param freq frequency
    * @tparam Item item type
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   class FreqItemset[Item](val items: Array[Item], val freq: Long) extends Serializable {
 
     /**
      * Returns items in a Java List.
      *
-     * @since 1.3.0
      */
+    @Since("1.3.0")
     def javaItems: java.util.List[Item] = {
       items.toList.asJava
     }

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index dfa8910..28b5b46 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -23,7 +23,7 @@ import scala.collection.mutable.{ArrayBuilder => MArrayBuilder, HashSet => MHash
 
 import breeze.linalg.{CSCMatrix => BSM, DenseMatrix => BDM, Matrix => BM}
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
@@ -227,8 +227,8 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
  * @param values matrix entries in column major if not transposed or in row major otherwise
  * @param isTransposed whether the matrix is transposed. If true, `values` stores the matrix in
  *                     row major.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @SQLUserDefinedType(udt = classOf[MatrixUDT])
 class DenseMatrix(
     val numRows: Int,
@@ -253,8 +253,8 @@ class DenseMatrix(
    * @param numRows number of rows
    * @param numCols number of columns
    * @param values matrix entries in column major
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def this(numRows: Int, numCols: Int, values: Array[Double]) =
     this(numRows, numCols, values, false)
 
@@ -278,9 +278,7 @@ class DenseMatrix(
 
   private[mllib] def apply(i: Int): Double = values(i)
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def apply(i: Int, j: Int): Double = values(index(i, j))
 
   private[mllib] def index(i: Int, j: Int): Int = {
@@ -291,9 +289,7 @@ class DenseMatrix(
     values(index(i, j)) = v
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def copy: DenseMatrix = new DenseMatrix(numRows, numCols, values.clone())
 
   private[spark] def map(f: Double => Double) = new DenseMatrix(numRows, numCols, values.map(f),
@@ -309,9 +305,7 @@ class DenseMatrix(
     this
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def transpose: DenseMatrix = new DenseMatrix(numCols, numRows, values, !isTransposed)
 
   private[spark] override def foreachActive(f: (Int, Int, Double) => Unit): Unit = {
@@ -342,21 +336,17 @@ class DenseMatrix(
     }
   }
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def numNonzeros: Int = values.count(_ != 0)
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def numActives: Int = values.length
 
   /**
    * Generate a `SparseMatrix` from the given `DenseMatrix`. The new matrix will have isTransposed
    * set to false.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toSparse: SparseMatrix = {
     val spVals: MArrayBuilder[Double] = new MArrayBuilder.ofDouble
     val colPtrs: Array[Int] = new Array[Int](numCols + 1)
@@ -383,8 +373,8 @@ class DenseMatrix(
 
 /**
  * Factory methods for [[org.apache.spark.mllib.linalg.DenseMatrix]].
- * @since 1.3.0
  */
+@Since("1.3.0")
 object DenseMatrix {
 
   /**
@@ -392,8 +382,8 @@ object DenseMatrix {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `DenseMatrix` with size `numRows` x `numCols` and values of zeros
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def zeros(numRows: Int, numCols: Int): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
             s"$numRows x $numCols dense matrix is too large to allocate")
@@ -405,8 +395,8 @@ object DenseMatrix {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `DenseMatrix` with size `numRows` x `numCols` and values of ones
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def ones(numRows: Int, numCols: Int): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
             s"$numRows x $numCols dense matrix is too large to allocate")
@@ -417,8 +407,8 @@ object DenseMatrix {
    * Generate an Identity Matrix in `DenseMatrix` format.
    * @param n number of rows and columns of the matrix
    * @return `DenseMatrix` with size `n` x `n` and values of ones on the diagonal
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def eye(n: Int): DenseMatrix = {
     val identity = DenseMatrix.zeros(n, n)
     var i = 0
@@ -435,8 +425,8 @@ object DenseMatrix {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `DenseMatrix` with size `numRows` x `numCols` and values in U(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def rand(numRows: Int, numCols: Int, rng: Random): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
             s"$numRows x $numCols dense matrix is too large to allocate")
@@ -449,8 +439,8 @@ object DenseMatrix {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `DenseMatrix` with size `numRows` x `numCols` and values in N(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def randn(numRows: Int, numCols: Int, rng: Random): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
             s"$numRows x $numCols dense matrix is too large to allocate")
@@ -462,8 +452,8 @@ object DenseMatrix {
    * @param vector a `Vector` that will form the values on the diagonal of the matrix
    * @return Square `DenseMatrix` with size `values.length` x `values.length` and `values`
    *         on the diagonal
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def diag(vector: Vector): DenseMatrix = {
     val n = vector.size
     val matrix = DenseMatrix.zeros(n, n)
@@ -498,8 +488,8 @@ object DenseMatrix {
  * @param isTransposed whether the matrix is transposed. If true, the matrix can be considered
  *                     Compressed Sparse Row (CSR) format, where `colPtrs` behaves as rowPtrs,
  *                     and `rowIndices` behave as colIndices, and `values` are stored in row major.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @SQLUserDefinedType(udt = classOf[MatrixUDT])
 class SparseMatrix(
     val numRows: Int,
@@ -536,8 +526,8 @@ class SparseMatrix(
    * @param rowIndices the row index of the entry. They must be in strictly increasing
    *                   order for each column
    * @param values non-zero matrix entries in column major
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def this(
       numRows: Int,
       numCols: Int,
@@ -560,8 +550,8 @@ class SparseMatrix(
   }
 
   /**
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def apply(i: Int, j: Int): Double = {
     val ind = index(i, j)
     if (ind < 0) 0.0 else values(ind)
@@ -585,9 +575,7 @@ class SparseMatrix(
     }
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def copy: SparseMatrix = {
     new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values.clone())
   }
@@ -605,9 +593,7 @@ class SparseMatrix(
     this
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def transpose: SparseMatrix =
     new SparseMatrix(numCols, numRows, colPtrs, rowIndices, values, !isTransposed)
 
@@ -641,28 +627,24 @@ class SparseMatrix(
   /**
    * Generate a `DenseMatrix` from the given `SparseMatrix`. The new matrix will have isTransposed
    * set to false.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toDense: DenseMatrix = {
     new DenseMatrix(numRows, numCols, toArray)
   }
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def numNonzeros: Int = values.count(_ != 0)
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def numActives: Int = values.length
 
 }
 
 /**
  * Factory methods for [[org.apache.spark.mllib.linalg.SparseMatrix]].
- * @since 1.3.0
  */
+@Since("1.3.0")
 object SparseMatrix {
 
   /**
@@ -673,8 +655,8 @@ object SparseMatrix {
    * @param numCols number of columns of the matrix
    * @param entries Array of (i, j, value) tuples
    * @return The corresponding `SparseMatrix`
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def fromCOO(numRows: Int, numCols: Int, entries: Iterable[(Int, Int, Double)]): SparseMatrix = {
     val sortedEntries = entries.toSeq.sortBy(v => (v._2, v._1))
     val numEntries = sortedEntries.size
@@ -722,8 +704,8 @@ object SparseMatrix {
    * Generate an Identity Matrix in `SparseMatrix` format.
    * @param n number of rows and columns of the matrix
    * @return `SparseMatrix` with size `n` x `n` and values of ones on the diagonal
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def speye(n: Int): SparseMatrix = {
     new SparseMatrix(n, n, (0 to n).toArray, (0 until n).toArray, Array.fill(n)(1.0))
   }
@@ -792,8 +774,8 @@ object SparseMatrix {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `SparseMatrix` with size `numRows` x `numCols` and values in U(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): SparseMatrix = {
     val mat = genRandMatrix(numRows, numCols, density, rng)
     mat.update(i => rng.nextDouble())
@@ -806,8 +788,8 @@ object SparseMatrix {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `SparseMatrix` with size `numRows` x `numCols` and values in N(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): SparseMatrix = {
     val mat = genRandMatrix(numRows, numCols, density, rng)
     mat.update(i => rng.nextGaussian())
@@ -818,8 +800,8 @@ object SparseMatrix {
    * @param vector a `Vector` that will form the values on the diagonal of the matrix
    * @return Square `SparseMatrix` with size `values.length` x `values.length` and non-zero
    *         `values` on the diagonal
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def spdiag(vector: Vector): SparseMatrix = {
     val n = vector.size
     vector match {
@@ -835,8 +817,8 @@ object SparseMatrix {
 
 /**
  * Factory methods for [[org.apache.spark.mllib.linalg.Matrix]].
- * @since 1.0.0
  */
+@Since("1.0.0")
 object Matrices {
 
   /**
@@ -845,8 +827,8 @@ object Matrices {
    * @param numRows number of rows
    * @param numCols number of columns
    * @param values matrix entries in column major
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def dense(numRows: Int, numCols: Int, values: Array[Double]): Matrix = {
     new DenseMatrix(numRows, numCols, values)
   }
@@ -859,8 +841,8 @@ object Matrices {
    * @param colPtrs the index corresponding to the start of a new column
    * @param rowIndices the row index of the entry
    * @param values non-zero matrix entries in column major
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def sparse(
      numRows: Int,
      numCols: Int,
@@ -893,8 +875,8 @@ object Matrices {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `Matrix` with size `numRows` x `numCols` and values of zeros
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def zeros(numRows: Int, numCols: Int): Matrix = DenseMatrix.zeros(numRows, numCols)
 
   /**
@@ -902,24 +884,24 @@ object Matrices {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `Matrix` with size `numRows` x `numCols` and values of ones
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def ones(numRows: Int, numCols: Int): Matrix = DenseMatrix.ones(numRows, numCols)
 
   /**
    * Generate a dense Identity Matrix in `Matrix` format.
    * @param n number of rows and columns of the matrix
    * @return `Matrix` with size `n` x `n` and values of ones on the diagonal
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def eye(n: Int): Matrix = DenseMatrix.eye(n)
 
   /**
    * Generate a sparse Identity Matrix in `Matrix` format.
    * @param n number of rows and columns of the matrix
    * @return `Matrix` with size `n` x `n` and values of ones on the diagonal
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def speye(n: Int): Matrix = SparseMatrix.speye(n)
 
   /**
@@ -928,8 +910,8 @@ object Matrices {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in U(0, 1)
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def rand(numRows: Int, numCols: Int, rng: Random): Matrix =
     DenseMatrix.rand(numRows, numCols, rng)
 
@@ -940,8 +922,8 @@ object Matrices {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in U(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix =
     SparseMatrix.sprand(numRows, numCols, density, rng)
 
@@ -951,8 +933,8 @@ object Matrices {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in N(0, 1)
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def randn(numRows: Int, numCols: Int, rng: Random): Matrix =
     DenseMatrix.randn(numRows, numCols, rng)
 
@@ -963,8 +945,8 @@ object Matrices {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in N(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix =
     SparseMatrix.sprandn(numRows, numCols, density, rng)
 
@@ -973,8 +955,8 @@ object Matrices {
    * @param vector a `Vector` that will form the values on the diagonal of the matrix
    * @return Square `Matrix` with size `values.length` x `values.length` and `values`
    *         on the diagonal
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def diag(vector: Vector): Matrix = DenseMatrix.diag(vector)
 
   /**
@@ -983,8 +965,8 @@ object Matrices {
    * a sparse matrix. If the Array is empty, an empty `DenseMatrix` will be returned.
    * @param matrices array of matrices
    * @return a single `Matrix` composed of the matrices that were horizontally concatenated
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def horzcat(matrices: Array[Matrix]): Matrix = {
     if (matrices.isEmpty) {
       return new DenseMatrix(0, 0, Array[Double]())
@@ -1042,8 +1024,8 @@ object Matrices {
    * a sparse matrix. If the Array is empty, an empty `DenseMatrix` will be returned.
    * @param matrices array of matrices
    * @return a single `Matrix` composed of the matrices that were vertically concatenated
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def vertcat(matrices: Array[Matrix]): Matrix = {
     if (matrices.isEmpty) {
       return new DenseMatrix(0, 0, Array[Double]())

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
index 8f504f6..a37aca9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
@@ -17,13 +17,13 @@
 
 package org.apache.spark.mllib.linalg
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 
 /**
  * :: Experimental ::
  * Represents singular value decomposition (SVD) factors.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 case class SingularValueDecomposition[UType, VType](U: UType, s: Vector, V: VType)
 

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 52ef7be..3d577ed 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -26,7 +26,7 @@ import scala.collection.JavaConverters._
 import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, Vector => BV}
 
 import org.apache.spark.SparkException
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.{AlphaComponent, Since}
 import org.apache.spark.mllib.util.NumericParser
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
@@ -240,14 +240,14 @@ class VectorUDT extends UserDefinedType[Vector] {
  * Factory methods for [[org.apache.spark.mllib.linalg.Vector]].
  * We don't use the name `Vector` because Scala imports
  * [[scala.collection.immutable.Vector]] by default.
- * @since 1.0.0
  */
+@Since("1.0.0")
 object Vectors {
 
   /**
    * Creates a dense vector from its values.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @varargs
   def dense(firstValue: Double, otherValues: Double*): Vector =
     new DenseVector((firstValue +: otherValues).toArray)
@@ -255,8 +255,8 @@ object Vectors {
   // A dummy implicit is used to avoid signature collision with the one generated by @varargs.
   /**
    * Creates a dense vector from a double array.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def dense(values: Array[Double]): Vector = new DenseVector(values)
 
   /**
@@ -265,8 +265,8 @@ object Vectors {
    * @param size vector size.
    * @param indices index array, must be strictly increasing.
    * @param values value array, must have the same length as indices.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def sparse(size: Int, indices: Array[Int], values: Array[Double]): Vector =
     new SparseVector(size, indices, values)
 
@@ -275,8 +275,8 @@ object Vectors {
    *
    * @param size vector size.
    * @param elements vector elements in (index, value) pairs.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def sparse(size: Int, elements: Seq[(Int, Double)]): Vector = {
     require(size > 0, "The size of the requested sparse vector must be greater than 0.")
 
@@ -297,8 +297,8 @@ object Vectors {
    *
    * @param size vector size.
    * @param elements vector elements in (index, value) pairs.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def sparse(size: Int, elements: JavaIterable[(JavaInteger, JavaDouble)]): Vector = {
     sparse(size, elements.asScala.map { case (i, x) =>
       (i.intValue(), x.doubleValue())
@@ -310,16 +310,16 @@ object Vectors {
    *
    * @param size vector size
    * @return a zero vector
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def zeros(size: Int): Vector = {
     new DenseVector(new Array[Double](size))
   }
 
   /**
    * Parses a string resulted from [[Vector.toString]] into a [[Vector]].
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def parse(s: String): Vector = {
     parseNumeric(NumericParser.parse(s))
   }
@@ -362,8 +362,8 @@ object Vectors {
    * @param vector input vector.
    * @param p norm.
    * @return norm in L^p^ space.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def norm(vector: Vector, p: Double): Double = {
     require(p >= 1.0, "To compute the p-norm of the vector, we require that you specify a p>=1. " +
       s"You specified p=$p.")
@@ -415,8 +415,8 @@ object Vectors {
    * @param v1 first Vector.
    * @param v2 second Vector.
    * @return squared distance between two Vectors.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def sqdist(v1: Vector, v2: Vector): Double = {
     require(v1.size == v2.size, s"Vector dimensions do not match: Dim(v1)=${v1.size} and Dim(v2)" +
       s"=${v2.size}.")
@@ -529,33 +529,25 @@ object Vectors {
 
 /**
  * A dense vector represented by a value array.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @SQLUserDefinedType(udt = classOf[VectorUDT])
 class DenseVector(val values: Array[Double]) extends Vector {
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def size: Int = values.length
 
   override def toString: String = values.mkString("[", ",", "]")
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def toArray: Array[Double] = values
 
   private[spark] override def toBreeze: BV[Double] = new BDV[Double](values)
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def apply(i: Int): Double = values(i)
 
-  /**
-   * @since 1.1.0
-   */
+  @Since("1.1.0")
   override def copy: DenseVector = {
     new DenseVector(values.clone())
   }
@@ -587,14 +579,10 @@ class DenseVector(val values: Array[Double]) extends Vector {
     result
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def numActives: Int = size
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def numNonzeros: Int = {
     // same as values.count(_ != 0.0) but faster
     var nnz = 0
@@ -606,9 +594,7 @@ class DenseVector(val values: Array[Double]) extends Vector {
     nnz
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def toSparse: SparseVector = {
     val nnz = numNonzeros
     val ii = new Array[Int](nnz)
@@ -624,9 +610,7 @@ class DenseVector(val values: Array[Double]) extends Vector {
     new SparseVector(size, ii, vv)
   }
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def argmax: Int = {
     if (size == 0) {
       -1
@@ -646,9 +630,7 @@ class DenseVector(val values: Array[Double]) extends Vector {
   }
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object DenseVector {
   /** Extracts the value array from a dense vector. */
   def unapply(dv: DenseVector): Option[Array[Double]] = Some(dv.values)
@@ -660,8 +642,8 @@ object DenseVector {
  * @param size size of the vector.
  * @param indices index array, assume to be strictly increasing.
  * @param values value array, must have the same length as the index array.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @SQLUserDefinedType(udt = classOf[VectorUDT])
 class SparseVector(
     override val size: Int,
@@ -677,9 +659,7 @@ class SparseVector(
   override def toString: String =
     s"($size,${indices.mkString("[", ",", "]")},${values.mkString("[", ",", "]")})"
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def toArray: Array[Double] = {
     val data = new Array[Double](size)
     var i = 0
@@ -691,9 +671,7 @@ class SparseVector(
     data
   }
 
-  /**
-   * @since 1.1.0
-   */
+  @Since("1.1.0")
   override def copy: SparseVector = {
     new SparseVector(size, indices.clone(), values.clone())
   }
@@ -734,14 +712,10 @@ class SparseVector(
     result
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def numActives: Int = values.length
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def numNonzeros: Int = {
     var nnz = 0
     values.foreach { v =>
@@ -752,9 +726,7 @@ class SparseVector(
     nnz
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def toSparse: SparseVector = {
     val nnz = numNonzeros
     if (nnz == numActives) {
@@ -774,9 +746,7 @@ class SparseVector(
     }
   }
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def argmax: Int = {
     if (size == 0) {
       -1
@@ -847,9 +817,7 @@ class SparseVector(
   }
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object SparseVector {
   def unapply(sv: SparseVector): Option[(Int, Array[Int], Array[Double])] =
     Some((sv.size, sv.indices, sv.values))

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
index cfb6680..94376c2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
@@ -22,7 +22,7 @@ import scala.collection.mutable.ArrayBuffer
 import breeze.linalg.{DenseMatrix => BDM}
 
 import org.apache.spark.{Logging, Partitioner, SparkException}
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg.{DenseMatrix, Matrices, Matrix, SparseMatrix}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
@@ -128,9 +128,8 @@ private[mllib] object GridPartitioner {
  *              the number of rows will be calculated when `numRows` is invoked.
  * @param nCols Number of columns of this matrix. If the supplied value is less than or equal to
  *              zero, the number of columns will be calculated when `numCols` is invoked.
- * @since 1.3.0
- *
  */
+@Since("1.3.0")
 @Experimental
 class BlockMatrix(
     val blocks: RDD[((Int, Int), Matrix)],
@@ -151,10 +150,8 @@ class BlockMatrix(
    *                     rows are not required to have the given number of rows
    * @param colsPerBlock Number of columns that make up each block. The blocks forming the final
    *                     columns are not required to have the given number of columns
-   *
-   * @since 1.3.0
-   *
    */
+  @Since("1.3.0")
   def this(
       blocks: RDD[((Int, Int), Matrix)],
       rowsPerBlock: Int,
@@ -162,20 +159,13 @@ class BlockMatrix(
     this(blocks, rowsPerBlock, colsPerBlock, 0L, 0L)
   }
 
-  /**
-   * @since 1.3.0
-   * */
-
+  @Since("1.3.0")
   override def numRows(): Long = {
     if (nRows <= 0L) estimateDim()
     nRows
   }
 
-  /**
-   *
-   * @since 1.3.0
-   */
-
+  @Since("1.3.0")
   override def numCols(): Long = {
     if (nCols <= 0L) estimateDim()
     nCols
@@ -206,8 +196,8 @@ class BlockMatrix(
   /**
    * Validates the block matrix info against the matrix data (`blocks`) and throws an exception if
    * any error is found.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def validate(): Unit = {
     logDebug("Validating BlockMatrix...")
     // check if the matrix is larger than the claimed dimensions
@@ -243,25 +233,22 @@ class BlockMatrix(
     logDebug("BlockMatrix is valid!")
   }
 
-  /** Caches the underlying RDD.
-    * @since 1.3.0
-    * */
+  /** Caches the underlying RDD. */
+  @Since("1.3.0")
   def cache(): this.type = {
     blocks.cache()
     this
   }
 
-  /** Persists the underlying RDD with the specified storage level.
-    * @since 1.3.0
-    * */
+  /** Persists the underlying RDD with the specified storage level. */
+  @Since("1.3.0")
   def persist(storageLevel: StorageLevel): this.type = {
     blocks.persist(storageLevel)
     this
   }
 
-  /** Converts to CoordinateMatrix.
-    * @since 1.3.0
-    * */
+  /** Converts to CoordinateMatrix. */
+  @Since("1.3.0")
   def toCoordinateMatrix(): CoordinateMatrix = {
     val entryRDD = blocks.flatMap { case ((blockRowIndex, blockColIndex), mat) =>
       val rowStart = blockRowIndex.toLong * rowsPerBlock
@@ -275,9 +262,8 @@ class BlockMatrix(
     new CoordinateMatrix(entryRDD, numRows(), numCols())
   }
 
-  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range.
-    * @since 1.3.0
-    * */
+  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range. */
+  @Since("1.3.0")
   def toIndexedRowMatrix(): IndexedRowMatrix = {
     require(numCols() < Int.MaxValue, "The number of columns must be within the integer range. " +
       s"numCols: ${numCols()}")
@@ -285,9 +271,8 @@ class BlockMatrix(
     toCoordinateMatrix().toIndexedRowMatrix()
   }
 
-  /** Collect the distributed matrix on the driver as a `DenseMatrix`.
-    * @since 1.3.0
-    * */
+  /** Collect the distributed matrix on the driver as a `DenseMatrix`. */
+  @Since("1.3.0")
   def toLocalMatrix(): Matrix = {
     require(numRows() < Int.MaxValue, "The number of rows of this matrix should be less than " +
       s"Int.MaxValue. Currently numRows: ${numRows()}")
@@ -312,11 +297,11 @@ class BlockMatrix(
     new DenseMatrix(m, n, values)
   }
 
-  /** Transpose this `BlockMatrix`. Returns a new `BlockMatrix` instance sharing the
-    * same underlying data. Is a lazy operation.
-    * @since 1.3.0
-    *
-    * */
+  /**
+   * Transpose this `BlockMatrix`. Returns a new `BlockMatrix` instance sharing the
+   * same underlying data. Is a lazy operation.
+   */
+  @Since("1.3.0")
   def transpose: BlockMatrix = {
     val transposedBlocks = blocks.map { case ((blockRowIndex, blockColIndex), mat) =>
       ((blockColIndex, blockRowIndex), mat.transpose)
@@ -330,13 +315,14 @@ class BlockMatrix(
     new BDM[Double](localMat.numRows, localMat.numCols, localMat.toArray)
   }
 
-  /** Adds two block matrices together. The matrices must have the same size and matching
-    * `rowsPerBlock` and `colsPerBlock` values. If one of the blocks that are being added are
-    * instances of [[SparseMatrix]], the resulting sub matrix will also be a [[SparseMatrix]], even
-    * if it is being added to a [[DenseMatrix]]. If two dense matrices are added, the output will
-    * also be a [[DenseMatrix]].
-    * @since 1.3.0
-    */
+  /**
+   * Adds two block matrices together. The matrices must have the same size and matching
+   * `rowsPerBlock` and `colsPerBlock` values. If one of the blocks that are being added are
+   * instances of [[SparseMatrix]], the resulting sub matrix will also be a [[SparseMatrix]], even
+   * if it is being added to a [[DenseMatrix]]. If two dense matrices are added, the output will
+   * also be a [[DenseMatrix]].
+   */
+  @Since("1.3.0")
   def add(other: BlockMatrix): BlockMatrix = {
     require(numRows() == other.numRows(), "Both matrices must have the same number of rows. " +
       s"A.numRows: ${numRows()}, B.numRows: ${other.numRows()}")
@@ -364,14 +350,14 @@ class BlockMatrix(
     }
   }
 
-  /** Left multiplies this [[BlockMatrix]] to `other`, another [[BlockMatrix]]. The `colsPerBlock`
-    * of this matrix must equal the `rowsPerBlock` of `other`. If `other` contains
-    * [[SparseMatrix]], they will have to be converted to a [[DenseMatrix]]. The output
-    * [[BlockMatrix]] will only consist of blocks of [[DenseMatrix]]. This may cause
-    * some performance issues until support for multiplying two sparse matrices is added.
-    *
-    * @since 1.3.0
-    */
+  /**
+   * Left multiplies this [[BlockMatrix]] to `other`, another [[BlockMatrix]]. The `colsPerBlock`
+   * of this matrix must equal the `rowsPerBlock` of `other`. If `other` contains
+   * [[SparseMatrix]], they will have to be converted to a [[DenseMatrix]]. The output
+   * [[BlockMatrix]] will only consist of blocks of [[DenseMatrix]]. This may cause
+   * some performance issues until support for multiplying two sparse matrices is added.
+   */
+  @Since("1.3.0")
   def multiply(other: BlockMatrix): BlockMatrix = {
     require(numCols() == other.numRows(), "The number of columns of A and the number of rows " +
       s"of B must be equal. A.numCols: ${numCols()}, B.numRows: ${other.numRows()}. If you " +

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
index 2b751e4..4bb27ec 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.linalg.distributed
 
 import breeze.linalg.{DenseMatrix => BDM}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.{Matrix, SparseMatrix, Vectors}
 
@@ -29,8 +29,8 @@ import org.apache.spark.mllib.linalg.{Matrix, SparseMatrix, Vectors}
  * @param i row index
  * @param j column index
  * @param value value of the entry
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 case class MatrixEntry(i: Long, j: Long, value: Double)
 
@@ -43,22 +43,20 @@ case class MatrixEntry(i: Long, j: Long, value: Double)
  *              be determined by the max row index plus one.
  * @param nCols number of columns. A non-positive value means unknown, and then the number of
  *              columns will be determined by the max column index plus one.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 class CoordinateMatrix(
     val entries: RDD[MatrixEntry],
     private var nRows: Long,
     private var nCols: Long) extends DistributedMatrix {
 
-  /** Alternative constructor leaving matrix dimensions to be determined automatically.
-    * @since 1.0.0
-    * */
+  /** Alternative constructor leaving matrix dimensions to be determined automatically. */
+  @Since("1.0.0")
   def this(entries: RDD[MatrixEntry]) = this(entries, 0L, 0L)
 
-  /** Gets or computes the number of columns.
-    * @since 1.0.0
-    * */
+  /** Gets or computes the number of columns. */
+  @Since("1.0.0")
   override def numCols(): Long = {
     if (nCols <= 0L) {
       computeSize()
@@ -66,9 +64,8 @@ class CoordinateMatrix(
     nCols
   }
 
-  /** Gets or computes the number of rows.
-    * @since 1.0.0
-    * */
+  /** Gets or computes the number of rows. */
+  @Since("1.0.0")
   override def numRows(): Long = {
     if (nRows <= 0L) {
       computeSize()
@@ -76,16 +73,14 @@ class CoordinateMatrix(
     nRows
   }
 
-  /** Transposes this CoordinateMatrix.
-    * @since 1.3.0
-    * */
+  /** Transposes this CoordinateMatrix. */
+  @Since("1.3.0")
   def transpose(): CoordinateMatrix = {
     new CoordinateMatrix(entries.map(x => MatrixEntry(x.j, x.i, x.value)), numCols(), numRows())
   }
 
-  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range.
-    * @since 1.0.0
-    * */
+  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range. */
+  @Since("1.0.0")
   def toIndexedRowMatrix(): IndexedRowMatrix = {
     val nl = numCols()
     if (nl > Int.MaxValue) {
@@ -104,15 +99,14 @@ class CoordinateMatrix(
   /**
    * Converts to RowMatrix, dropping row indices after grouping by row index.
    * The number of columns must be within the integer range.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def toRowMatrix(): RowMatrix = {
     toIndexedRowMatrix().toRowMatrix()
   }
 
-  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024.
-    * @since 1.3.0
-    * */
+  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024. */
+  @Since("1.3.0")
   def toBlockMatrix(): BlockMatrix = {
     toBlockMatrix(1024, 1024)
   }
@@ -124,8 +118,8 @@ class CoordinateMatrix(
    * @param colsPerBlock The number of columns of each block. The blocks at the right edge may have
    *                     a smaller value. Must be an integer value greater than 0.
    * @return a [[BlockMatrix]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toBlockMatrix(rowsPerBlock: Int, colsPerBlock: Int): BlockMatrix = {
     require(rowsPerBlock > 0,
       s"rowsPerBlock needs to be greater than 0. rowsPerBlock: $rowsPerBlock")

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
index 98e90af..e51327e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
@@ -19,10 +19,12 @@ package org.apache.spark.mllib.linalg.distributed
 
 import breeze.linalg.{DenseMatrix => BDM}
 
+import org.apache.spark.annotation.Since
+
 /**
  * Represents a distributively stored matrix backed by one or more RDDs.
- * @since 1.0.0
  */
+@Since("1.0.0")
 trait DistributedMatrix extends Serializable {
 
   /** Gets or computes the number of rows. */

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
index a09f88c..6d2c05a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.linalg.distributed
 
 import breeze.linalg.{DenseMatrix => BDM}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.linalg.SingularValueDecomposition
@@ -27,8 +27,8 @@ import org.apache.spark.mllib.linalg.SingularValueDecomposition
 /**
  * :: Experimental ::
  * Represents a row of [[org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix]].
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 case class IndexedRow(index: Long, vector: Vector)
 
@@ -42,23 +42,19 @@ case class IndexedRow(index: Long, vector: Vector)
  *              be determined by the max row index plus one.
  * @param nCols number of columns. A non-positive value means unknown, and then the number of
  *              columns will be determined by the size of the first row.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 class IndexedRowMatrix(
     val rows: RDD[IndexedRow],
     private var nRows: Long,
     private var nCols: Int) extends DistributedMatrix {
 
-  /** Alternative constructor leaving matrix dimensions to be determined automatically.
-    * @since 1.0.0
-    * */
+  /** Alternative constructor leaving matrix dimensions to be determined automatically. */
+  @Since("1.0.0")
   def this(rows: RDD[IndexedRow]) = this(rows, 0L, 0)
 
-  /**
-   *
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def numCols(): Long = {
     if (nCols <= 0) {
       // Calling `first` will throw an exception if `rows` is empty.
@@ -67,10 +63,7 @@ class IndexedRowMatrix(
     nCols
   }
 
-  /**
-   *
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def numRows(): Long = {
     if (nRows <= 0L) {
       // Reduce will throw an exception if `rows` is empty.
@@ -82,15 +75,14 @@ class IndexedRowMatrix(
   /**
    * Drops row indices and converts this matrix to a
    * [[org.apache.spark.mllib.linalg.distributed.RowMatrix]].
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def toRowMatrix(): RowMatrix = {
     new RowMatrix(rows.map(_.vector), 0L, nCols)
   }
 
-  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024.
-    * @since 1.3.0
-    * */
+  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024. */
+  @Since("1.3.0")
   def toBlockMatrix(): BlockMatrix = {
     toBlockMatrix(1024, 1024)
   }
@@ -102,8 +94,8 @@ class IndexedRowMatrix(
    * @param colsPerBlock The number of columns of each block. The blocks at the right edge may have
    *                     a smaller value. Must be an integer value greater than 0.
    * @return a [[BlockMatrix]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toBlockMatrix(rowsPerBlock: Int, colsPerBlock: Int): BlockMatrix = {
     // TODO: This implementation may be optimized
     toCoordinateMatrix().toBlockMatrix(rowsPerBlock, colsPerBlock)
@@ -112,8 +104,8 @@ class IndexedRowMatrix(
   /**
    * Converts this matrix to a
    * [[org.apache.spark.mllib.linalg.distributed.CoordinateMatrix]].
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toCoordinateMatrix(): CoordinateMatrix = {
     val entries = rows.flatMap { row =>
       val rowIndex = row.index
@@ -149,8 +141,8 @@ class IndexedRowMatrix(
    * @param rCond the reciprocal condition number. All singular values smaller than rCond * sigma(0)
    *              are treated as zero, where sigma(0) is the largest singular value.
    * @return SingularValueDecomposition(U, s, V)
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeSVD(
       k: Int,
       computeU: Boolean = false,
@@ -176,8 +168,8 @@ class IndexedRowMatrix(
    *
    * @param B a local matrix whose number of rows must match the number of columns of this matrix
    * @return an IndexedRowMatrix representing the product, which preserves partitioning
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def multiply(B: Matrix): IndexedRowMatrix = {
     val mat = toRowMatrix().multiply(B)
     val indexedRows = rows.map(_.index).zip(mat.rows).map { case (i, v) =>
@@ -188,8 +180,8 @@ class IndexedRowMatrix(
 
   /**
    * Computes the Gramian matrix `A^T A`.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeGramianMatrix(): Matrix = {
     toRowMatrix().computeGramianMatrix()
   }

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index b2e94f2..78036eb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -28,7 +28,7 @@ import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
 import org.apache.spark.Logging
 import org.apache.spark.SparkContext._
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.stat.{MultivariateOnlineSummarizer, MultivariateStatisticalSummary}
 import org.apache.spark.rdd.RDD
@@ -44,22 +44,20 @@ import org.apache.spark.storage.StorageLevel
  *              be determined by the number of records in the RDD `rows`.
  * @param nCols number of columns. A non-positive value means unknown, and then the number of
  *              columns will be determined by the size of the first row.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 class RowMatrix(
     val rows: RDD[Vector],
     private var nRows: Long,
     private var nCols: Int) extends DistributedMatrix with Logging {
 
-  /** Alternative constructor leaving matrix dimensions to be determined automatically.
-    * @since 1.0.0
-    * */
+  /** Alternative constructor leaving matrix dimensions to be determined automatically. */
+  @Since("1.0.0")
   def this(rows: RDD[Vector]) = this(rows, 0L, 0)
 
-  /** Gets or computes the number of columns.
-    * @since 1.0.0
-    * */
+  /** Gets or computes the number of columns. */
+  @Since("1.0.0")
   override def numCols(): Long = {
     if (nCols <= 0) {
       try {
@@ -74,9 +72,8 @@ class RowMatrix(
     nCols
   }
 
-  /** Gets or computes the number of rows.
-    * @since 1.0.0
-    * */
+  /** Gets or computes the number of rows. */
+  @Since("1.0.0")
   override def numRows(): Long = {
     if (nRows <= 0L) {
       nRows = rows.count()
@@ -114,8 +111,8 @@ class RowMatrix(
 
   /**
    * Computes the Gramian matrix `A^T A`.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeGramianMatrix(): Matrix = {
     val n = numCols().toInt
     checkNumColumns(n)
@@ -185,8 +182,8 @@ class RowMatrix(
    * @param rCond the reciprocal condition number. All singular values smaller than rCond * sigma(0)
    *              are treated as zero, where sigma(0) is the largest singular value.
    * @return SingularValueDecomposition(U, s, V). U = null if computeU = false.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeSVD(
       k: Int,
       computeU: Boolean = false,
@@ -326,8 +323,8 @@ class RowMatrix(
   /**
    * Computes the covariance matrix, treating each row as an observation.
    * @return a local dense matrix of size n x n
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeCovariance(): Matrix = {
     val n = numCols().toInt
     checkNumColumns(n)
@@ -380,8 +377,8 @@ class RowMatrix(
    *
    * @param k number of top principal components.
    * @return a matrix of size n-by-k, whose columns are principal components
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computePrincipalComponents(k: Int): Matrix = {
     val n = numCols().toInt
     require(k > 0 && k <= n, s"k = $k out of range (0, n = $n]")
@@ -399,8 +396,8 @@ class RowMatrix(
 
   /**
    * Computes column-wise summary statistics.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeColumnSummaryStatistics(): MultivariateStatisticalSummary = {
     val summary = rows.treeAggregate(new MultivariateOnlineSummarizer)(
       (aggregator, data) => aggregator.add(data),
@@ -415,8 +412,8 @@ class RowMatrix(
    * @param B a local matrix whose number of rows must match the number of columns of this matrix
    * @return a [[org.apache.spark.mllib.linalg.distributed.RowMatrix]] representing the product,
    *         which preserves partitioning
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def multiply(B: Matrix): RowMatrix = {
     val n = numCols().toInt
     val k = B.numCols
@@ -448,8 +445,8 @@ class RowMatrix(
    *
    * @return An n x n sparse upper-triangular matrix of cosine similarities between
    *         columns of this matrix.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def columnSimilarities(): CoordinateMatrix = {
     columnSimilarities(0.0)
   }
@@ -492,8 +489,8 @@ class RowMatrix(
    *                  with the cost vs estimate quality trade-off described above.
    * @return An n x n sparse upper-triangular matrix of cosine similarities
    *         between columns of this matrix.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def columnSimilarities(threshold: Double): CoordinateMatrix = {
     require(threshold >= 0, s"Threshold cannot be negative: $threshold")
 
@@ -671,9 +668,7 @@ class RowMatrix(
   }
 }
 
-/**
- * @since 1.0.0
- */
+@Since("1.0.0")
 @Experimental
 object RowMatrix {
 

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index 56c549e..b27ef1b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.recommendation
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.ml.recommendation.{ALS => NewALS}
 import org.apache.spark.rdd.RDD
@@ -26,8 +26,8 @@ import org.apache.spark.storage.StorageLevel
 
 /**
  * A more compact class to represent a rating than Tuple3[Int, Int, Double].
- * @since 0.8.0
  */
+@Since("0.8.0")
 case class Rating(user: Int, product: Int, rating: Double)
 
 /**
@@ -255,8 +255,8 @@ class ALS private (
 
 /**
  * Top-level methods for calling Alternating Least Squares (ALS) matrix factorization.
- * @since 0.8.0
  */
+@Since("0.8.0")
 object ALS {
   /**
    * Train a matrix factorization model given an RDD of ratings given by users to some products,
@@ -271,8 +271,8 @@ object ALS {
    * @param lambda     regularization factor (recommended: 0.01)
    * @param blocks     level of parallelism to split computation into
    * @param seed       random seed
-   * @since 0.9.1
    */
+  @Since("0.9.1")
   def train(
       ratings: RDD[Rating],
       rank: Int,
@@ -296,8 +296,8 @@ object ALS {
    * @param iterations number of iterations of ALS (recommended: 10-20)
    * @param lambda     regularization factor (recommended: 0.01)
    * @param blocks     level of parallelism to split computation into
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       ratings: RDD[Rating],
       rank: Int,
@@ -319,8 +319,8 @@ object ALS {
    * @param rank       number of features to use
    * @param iterations number of iterations of ALS (recommended: 10-20)
    * @param lambda     regularization factor (recommended: 0.01)
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double)
     : MatrixFactorizationModel = {
     train(ratings, rank, iterations, lambda, -1)
@@ -336,8 +336,8 @@ object ALS {
    * @param ratings    RDD of (userID, productID, rating) pairs
    * @param rank       number of features to use
    * @param iterations number of iterations of ALS (recommended: 10-20)
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(ratings: RDD[Rating], rank: Int, iterations: Int)
     : MatrixFactorizationModel = {
     train(ratings, rank, iterations, 0.01, -1)
@@ -357,8 +357,8 @@ object ALS {
    * @param blocks     level of parallelism to split computation into
    * @param alpha      confidence parameter
    * @param seed       random seed
-   * @since 0.8.1
    */
+  @Since("0.8.1")
   def trainImplicit(
       ratings: RDD[Rating],
       rank: Int,
@@ -384,8 +384,8 @@ object ALS {
    * @param lambda     regularization factor (recommended: 0.01)
    * @param blocks     level of parallelism to split computation into
    * @param alpha      confidence parameter
-   * @since 0.8.1
    */
+  @Since("0.8.1")
   def trainImplicit(
       ratings: RDD[Rating],
       rank: Int,
@@ -409,8 +409,8 @@ object ALS {
    * @param iterations number of iterations of ALS (recommended: 10-20)
    * @param lambda     regularization factor (recommended: 0.01)
    * @param alpha      confidence parameter
-   * @since 0.8.1
    */
+  @Since("0.8.1")
   def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, alpha: Double)
     : MatrixFactorizationModel = {
     trainImplicit(ratings, rank, iterations, lambda, -1, alpha)
@@ -427,8 +427,8 @@ object ALS {
    * @param ratings    RDD of (userID, productID, rating) pairs
    * @param rank       number of features to use
    * @param iterations number of iterations of ALS (recommended: 10-20)
-   * @since 0.8.1
    */
+  @Since("0.8.1")
   def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int)
     : MatrixFactorizationModel = {
     trainImplicit(ratings, rank, iterations, 0.01, -1, 1.0)

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
index 261ca9c..ba4cfdc 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -30,6 +30,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.{Logging, SparkContext}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.{JavaPairRDD, JavaRDD}
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.rdd.MLPairRDDFunctions._
@@ -49,8 +50,8 @@ import org.apache.spark.storage.StorageLevel
  *                     the features computed for this user.
  * @param productFeatures RDD of tuples where each tuple represents the productId
  *                        and the features computed for this product.
- * @since 0.8.0
  */
+@Since("0.8.0")
 class MatrixFactorizationModel(
     val rank: Int,
     val userFeatures: RDD[(Int, Array[Double])],
@@ -74,9 +75,8 @@ class MatrixFactorizationModel(
     }
   }
 
-  /** Predict the rating of one user for one product.
-   * @since 0.8.0
-   */
+  /** Predict the rating of one user for one product. */
+  @Since("0.8.0")
   def predict(user: Int, product: Int): Double = {
     val userVector = userFeatures.lookup(user).head
     val productVector = productFeatures.lookup(product).head
@@ -114,8 +114,8 @@ class MatrixFactorizationModel(
    *
    * @param usersProducts  RDD of (user, product) pairs.
    * @return RDD of Ratings.
-   * @since 0.9.0
    */
+  @Since("0.9.0")
   def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating] = {
     // Previously the partitions of ratings are only based on the given products.
     // So if the usersProducts given for prediction contains only few products or
@@ -146,8 +146,8 @@ class MatrixFactorizationModel(
 
   /**
    * Java-friendly version of [[MatrixFactorizationModel.predict]].
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def predict(usersProducts: JavaPairRDD[JavaInteger, JavaInteger]): JavaRDD[Rating] = {
     predict(usersProducts.rdd.asInstanceOf[RDD[(Int, Int)]]).toJavaRDD()
   }
@@ -162,8 +162,8 @@ class MatrixFactorizationModel(
    *  by score, decreasing. The first returned is the one predicted to be most strongly
    *  recommended to the user. The score is an opaque value that indicates how strongly
    *  recommended the product is.
-   *  @since 1.1.0
    */
+  @Since("1.1.0")
   def recommendProducts(user: Int, num: Int): Array[Rating] =
     MatrixFactorizationModel.recommend(userFeatures.lookup(user).head, productFeatures, num)
       .map(t => Rating(user, t._1, t._2))
@@ -179,8 +179,8 @@ class MatrixFactorizationModel(
    *  by score, decreasing. The first returned is the one predicted to be most strongly
    *  recommended to the product. The score is an opaque value that indicates how strongly
    *  recommended the user is.
-   *  @since 1.1.0
    */
+  @Since("1.1.0")
   def recommendUsers(product: Int, num: Int): Array[Rating] =
     MatrixFactorizationModel.recommend(productFeatures.lookup(product).head, userFeatures, num)
       .map(t => Rating(t._1, product, t._2))
@@ -199,8 +199,8 @@ class MatrixFactorizationModel(
    * @param sc  Spark context used to save model data.
    * @param path  Path specifying the directory in which to save this model.
    *              If the directory already exists, this method throws an exception.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     MatrixFactorizationModel.SaveLoadV1_0.save(this, path)
   }
@@ -212,8 +212,8 @@ class MatrixFactorizationModel(
    * @return [(Int, Array[Rating])] objects, where every tuple contains a userID and an array of
    * rating objects which contains the same userId, recommended productID and a "score" in the
    * rating field. Semantics of score is same as recommendProducts API
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def recommendProductsForUsers(num: Int): RDD[(Int, Array[Rating])] = {
     MatrixFactorizationModel.recommendForAll(rank, userFeatures, productFeatures, num).map {
       case (user, top) =>
@@ -230,8 +230,8 @@ class MatrixFactorizationModel(
    * @return [(Int, Array[Rating])] objects, where every tuple contains a productID and an array
    * of rating objects which contains the recommended userId, same productID and a "score" in the
    * rating field. Semantics of score is same as recommendUsers API
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def recommendUsersForProducts(num: Int): RDD[(Int, Array[Rating])] = {
     MatrixFactorizationModel.recommendForAll(rank, productFeatures, userFeatures, num).map {
       case (product, top) =>
@@ -241,9 +241,7 @@ class MatrixFactorizationModel(
   }
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] {
 
   import org.apache.spark.mllib.util.Loader._
@@ -326,8 +324,8 @@ object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] {
    * @param sc  Spark context used for loading model files.
    * @param path  Path specifying the directory to which the model was saved.
    * @return  Model instance
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): MatrixFactorizationModel = {
     val (loadedClassName, formatVersion, _) = loadMetadata(sc, path)
     val classNameV1_0 = SaveLoadV1_0.thisClassName

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
index 2980b94..509f6a2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.regression
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.feature.StandardScaler
 import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.rdd.RDD
@@ -35,8 +35,8 @@ import org.apache.spark.storage.StorageLevel
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 @DeveloperApi
 abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double)
   extends Serializable {
@@ -56,8 +56,8 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
    * @param testData RDD representing data points to be predicted
    * @return RDD[Double] where each entry contains the corresponding prediction
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(testData: RDD[Vector]): RDD[Double] = {
     // A small optimization to avoid serializing the entire model. Only the weightsMatrix
     // and intercept is needed.
@@ -76,8 +76,8 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
    * @param testData array representing a single data point
    * @return Double prediction from the trained model
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(testData: Vector): Double = {
     predictPoint(testData, weights, intercept)
   }
@@ -95,8 +95,8 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
  * GeneralizedLinearAlgorithm implements methods to train a Generalized Linear Model (GLM).
  * This class should be extended with an Optimizer to create a new GLM.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 @DeveloperApi
 abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   extends Logging with Serializable {
@@ -106,8 +106,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /**
    * The optimizer to solve the problem.
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def optimizer: Optimizer
 
   /** Whether to add intercept (default: false). */
@@ -143,8 +143,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /**
    * The dimension of training features.
    *
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getNumFeatures: Int = this.numFeatures
 
   /**
@@ -168,16 +168,16 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /**
    * Get if the algorithm uses addIntercept
    *
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def isAddIntercept: Boolean = this.addIntercept
 
   /**
    * Set if the algorithm should add an intercept. Default false.
    * We set the default to false because adding the intercept will cause memory allocation.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def setIntercept(addIntercept: Boolean): this.type = {
     this.addIntercept = addIntercept
     this
@@ -186,8 +186,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /**
    * Set if the algorithm should validate data before training. Default true.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def setValidateData(validateData: Boolean): this.type = {
     this.validateData = validateData
     this
@@ -197,8 +197,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
    * Run the algorithm with the configured parameters on an input
    * RDD of LabeledPoint entries.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def run(input: RDD[LabeledPoint]): M = {
     if (numFeatures < 0) {
       numFeatures = input.map(_.features.size).first()
@@ -231,8 +231,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
    * Run the algorithm with the configured parameters on an input RDD
    * of LabeledPoint entries starting from the initial weights provided.
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def run(input: RDD[LabeledPoint], initialWeights: Vector): M = {
 
     if (numFeatures < 0) {

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
index 8995591..31ca7c2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
@@ -29,7 +29,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD}
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.{Loader, Saveable}
@@ -47,8 +47,8 @@ import org.apache.spark.sql.SQLContext
  *                    Results of isotonic regression and therefore monotone.
  * @param isotonic indicates whether this is isotonic or antitonic.
  *
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class IsotonicRegressionModel (
     val boundaries: Array[Double],
@@ -64,8 +64,8 @@ class IsotonicRegressionModel (
   /**
    * A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter.
    *
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def this(boundaries: java.lang.Iterable[Double],
       predictions: java.lang.Iterable[Double],
       isotonic: java.lang.Boolean) = {
@@ -90,8 +90,8 @@ class IsotonicRegressionModel (
    * @param testData Features to be labeled.
    * @return Predicted labels.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def predict(testData: RDD[Double]): RDD[Double] = {
     testData.map(predict)
   }
@@ -103,8 +103,8 @@ class IsotonicRegressionModel (
    * @param testData Features to be labeled.
    * @return Predicted labels.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def predict(testData: JavaDoubleRDD): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(predict(testData.rdd.retag.asInstanceOf[RDD[Double]]))
   }
@@ -125,8 +125,8 @@ class IsotonicRegressionModel (
    *           as piecewise linear function and interpolated value is returned. In case there are
    *           multiple values with the same boundary then the same rules as in 2) are used.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def predict(testData: Double): Double = {
 
     def linearInterpolation(x1: Double, y1: Double, x2: Double, y2: Double, x: Double): Double = {
@@ -160,9 +160,7 @@ class IsotonicRegressionModel (
   /** A convenient method for boundaries called by the Python API. */
   private[mllib] def predictionVector: Vector = Vectors.dense(predictions)
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def save(sc: SparkContext, path: String): Unit = {
     IsotonicRegressionModel.SaveLoadV1_0.save(sc, path, boundaries, predictions, isotonic)
   }
@@ -170,9 +168,7 @@ class IsotonicRegressionModel (
   override protected def formatVersion: String = "1.0"
 }
 
-/**
- * @since 1.4.0
- */
+@Since("1.4.0")
 object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
 
   import org.apache.spark.mllib.util.Loader._
@@ -219,8 +215,8 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
   }
 
   /**
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   override def load(sc: SparkContext, path: String): IsotonicRegressionModel = {
     implicit val formats = DefaultFormats
     val (loadedClassName, version, metadata) = loadMetadata(sc, path)

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
index 8b51011..f7fe1b7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
@@ -19,6 +19,7 @@ package org.apache.spark.mllib.regression
 
 import scala.beans.BeanInfo
 
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
 import org.apache.spark.mllib.util.NumericParser
 import org.apache.spark.SparkException
@@ -29,8 +30,8 @@ import org.apache.spark.SparkException
  * @param label Label for this data point.
  * @param features List of features for this data point.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 @BeanInfo
 case class LabeledPoint(label: Double, features: Vector) {
   override def toString: String = {
@@ -41,15 +42,15 @@ case class LabeledPoint(label: Double, features: Vector) {
 /**
  * Parser for [[org.apache.spark.mllib.regression.LabeledPoint]].
  *
- * @since 1.1.0
  */
+@Since("1.1.0")
 object LabeledPoint {
   /**
    * Parses a string resulted from `LabeledPoint#toString` into
    * an [[org.apache.spark.mllib.regression.LabeledPoint]].
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def parse(s: String): LabeledPoint = {
     if (s.startsWith("(")) {
       NumericParser.parse(s) match {

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
index 03eb589..556411a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.regression
 
 import org.apache.spark.SparkContext
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.pmml.PMMLExportable
@@ -31,8 +32,8 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 class LassoModel (
     override val weights: Vector,
     override val intercept: Double)
@@ -46,9 +47,7 @@ class LassoModel (
     weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     GLMRegressionModel.SaveLoadV1_0.save(sc, path, this.getClass.getName, weights, intercept)
   }
@@ -56,14 +55,10 @@ class LassoModel (
   override protected def formatVersion: String = "1.0"
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object LassoModel extends Loader[LassoModel] {
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): LassoModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -118,8 +113,8 @@ class LassoWithSGD private (
 /**
  * Top-level methods for calling Lasso.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 object LassoWithSGD {
 
   /**
@@ -137,8 +132,8 @@ object LassoWithSGD {
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -162,8 +157,8 @@ object LassoWithSGD {
    * @param regParam Regularization parameter.
    * @param miniBatchFraction Fraction of data to be used per iteration.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -185,8 +180,8 @@ object LassoWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LassoModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -205,8 +200,8 @@ object LassoWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LassoModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int): LassoModel = {

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
index fb5c220..00ab06e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.regression
 
 import org.apache.spark.SparkContext
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.pmml.PMMLExportable
@@ -31,8 +32,8 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 class LinearRegressionModel (
     override val weights: Vector,
     override val intercept: Double)
@@ -46,9 +47,7 @@ class LinearRegressionModel (
     weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     GLMRegressionModel.SaveLoadV1_0.save(sc, path, this.getClass.getName, weights, intercept)
   }
@@ -56,14 +55,10 @@ class LinearRegressionModel (
   override protected def formatVersion: String = "1.0"
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object LinearRegressionModel extends Loader[LinearRegressionModel] {
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): LinearRegressionModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -117,8 +112,8 @@ class LinearRegressionWithSGD private[mllib] (
 /**
  * Top-level methods for calling LinearRegression.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 object LinearRegressionWithSGD {
 
   /**
@@ -135,8 +130,8 @@ object LinearRegressionWithSGD {
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -158,8 +153,8 @@ object LinearRegressionWithSGD {
    * @param stepSize Step size to be used for each iteration of gradient descent.
    * @param miniBatchFraction Fraction of data to be used per iteration.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -179,8 +174,8 @@ object LinearRegressionWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LinearRegressionModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -198,8 +193,8 @@ object LinearRegressionWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LinearRegressionModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int): LinearRegressionModel = {

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
index b097fd3..0e72d65 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
@@ -19,14 +19,12 @@ package org.apache.spark.mllib.regression
 
 import org.json4s.{DefaultFormats, JValue}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.rdd.RDD
 
-/**
- * @since 0.8.0
- */
+@Since("0.8.0")
 @Experimental
 trait RegressionModel extends Serializable {
   /**
@@ -35,8 +33,8 @@ trait RegressionModel extends Serializable {
    * @param testData RDD representing data points to be predicted
    * @return RDD[Double] where each entry contains the corresponding prediction
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(testData: RDD[Vector]): RDD[Double]
 
   /**
@@ -45,8 +43,8 @@ trait RegressionModel extends Serializable {
    * @param testData array representing a single data point
    * @return Double prediction from the trained model
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(testData: Vector): Double
 
   /**
@@ -54,8 +52,8 @@ trait RegressionModel extends Serializable {
    * @param testData JavaRDD representing data points to be predicted
    * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] =
     predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]]
 }

http://git-wip-us.apache.org/repos/asf/spark/blob/e7db8761/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
index 5bced6b..21a791d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.regression
 
 import org.apache.spark.SparkContext
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.pmml.PMMLExportable
@@ -32,8 +33,8 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 class RidgeRegressionModel (
     override val weights: Vector,
     override val intercept: Double)
@@ -47,9 +48,7 @@ class RidgeRegressionModel (
     weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     GLMRegressionModel.SaveLoadV1_0.save(sc, path, this.getClass.getName, weights, intercept)
   }
@@ -57,14 +56,10 @@ class RidgeRegressionModel (
   override protected def formatVersion: String = "1.0"
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object RidgeRegressionModel extends Loader[RidgeRegressionModel] {
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): RidgeRegressionModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -120,8 +115,8 @@ class RidgeRegressionWithSGD private (
 /**
  * Top-level methods for calling RidgeRegression.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 object RidgeRegressionWithSGD {
 
   /**
@@ -138,8 +133,8 @@ object RidgeRegressionWithSGD {
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -162,8 +157,8 @@ object RidgeRegressionWithSGD {
    * @param regParam Regularization parameter.
    * @param miniBatchFraction Fraction of data to be used per iteration.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -184,8 +179,8 @@ object RidgeRegressionWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a RidgeRegressionModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -203,8 +198,8 @@ object RidgeRegressionWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a RidgeRegressionModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int): RidgeRegressionModel = {


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