Remove dead scaladoc links

bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala

@@ -27,7 +27,7 @@ object Bagel extends Logging {
 
   /**
    * Runs a Bagel program.
-   * @param sc [[org.apache.spark.SparkContext]] to use for the program.
+   * @param sc org.apache.spark.SparkContext to use for the program.
    * @param vertices vertices of the graph represented as an RDD of (Key, Vertex) pairs. Often the
    *                 Key will be the vertex id.
    * @param messages initial set of messages represented as an RDD of (Key, Message) pairs. Often
@@ -38,12 +38,12 @@ object Bagel extends Logging {
    * @param aggregator [[org.apache.spark.bagel.Aggregator]] performs a reduce across all vertices
    *                  after each superstep and provides the result to each vertex in the next
    *                  superstep.
-   * @param partitioner [[org.apache.spark.Partitioner]] partitions values by key
+   * @param partitioner org.apache.spark.Partitioner partitions values by key
    * @param numPartitions number of partitions across which to split the graph.
    *                      Default is the default parallelism of the SparkContext
-   * @param storageLevel [[org.apache.spark.storage.StorageLevel]] to use for caching of
+   * @param storageLevel org.apache.spark.storage.StorageLevel to use for caching of
    *                    intermediate RDDs in each superstep. Defaults to caching in memory.
-   * @param compute function that takes a Vertex, optional set of (possibly combined) messages to
+   *@param compute function that takes a Vertex, optional set of (possibly combined) messages to
    *                the Vertex, optional Aggregator and the current superstep,
    *                and returns a set of (Vertex, outgoing Messages) pairs
    * @tparam K key
@@ -131,7 +131,7 @@ object Bagel extends Logging {
 
   /**
    * Runs a Bagel program with no [[org.apache.spark.bagel.Aggregator]], default
-   * [[org.apache.spark.HashPartitioner]] and default storage level
+   * org.apache.spark.HashPartitioner and default storage level
    */
   def run[K: Manifest, V <: Vertex : Manifest, M <: Message[K] : Manifest, C: Manifest](
     sc: SparkContext,
@@ -146,7 +146,7 @@ object Bagel extends Logging {
 
   /**
    * Runs a Bagel program with no [[org.apache.spark.bagel.Aggregator]] and the
-   * default [[org.apache.spark.HashPartitioner]]
+   * default org.apache.spark.HashPartitioner
    */
   def run[K: Manifest, V <: Vertex : Manifest, M <: Message[K] : Manifest, C: Manifest](
     sc: SparkContext,
@@ -166,7 +166,7 @@ object Bagel extends Logging {
 
   /**
    * Runs a Bagel program with no [[org.apache.spark.bagel.Aggregator]],
-   * default [[org.apache.spark.HashPartitioner]],
+   * default org.apache.spark.HashPartitioner,
    * [[org.apache.spark.bagel.DefaultCombiner]] and the default storage level
    */
   def run[K: Manifest, V <: Vertex : Manifest, M <: Message[K] : Manifest](
@@ -180,7 +180,7 @@ object Bagel extends Logging {
 
   /**
    * Runs a Bagel program with no [[org.apache.spark.bagel.Aggregator]],
-   * the default [[org.apache.spark.HashPartitioner]]
+   * the default org.apache.spark.HashPartitioner
    * and [[org.apache.spark.bagel.DefaultCombiner]]
    */
   def run[K: Manifest, V <: Vertex : Manifest, M <: Message[K] : Manifest](

core/src/main/scala/org/apache/spark/SparkContext.scala

@@ -351,7 +351,7 @@ class SparkContext(
    * using the older MapReduce API (`org.apache.hadoop.mapred`).
    *
    * @param conf JobConf for setting up the dataset
-   * @param inputFormatClass Class of the [[InputFormat]]
+   * @param inputFormatClass Class of the InputFormat
    * @param keyClass Class of the keys
    * @param valueClass Class of the values
    * @param minSplits Minimum number of Hadoop Splits to generate.

core/src/main/scala/org/apache/spark/util/IndestructibleActorSystem.scala

@@ -23,9 +23,9 @@ import scala.util.control.{ControlThrowable, NonFatal}
 import com.typesafe.config.Config
 
 /**
- * An [[akka.actor.ActorSystem]] which refuses to shut down in the event of a fatal exception.
+ * An akka.actor.ActorSystem which refuses to shut down in the event of a fatal exception
  * This is necessary as Spark Executors are allowed to recover from fatal exceptions
- * (see [[org.apache.spark.executor.Executor]]).
+ * (see org.apache.spark.executor.Executor)
  */
 object IndestructibleActorSystem {
   def apply(name: String, config: Config): ActorSystem =

core/src/main/scala/org/apache/spark/util/StatCounter.scala

@@ -20,8 +20,7 @@ package org.apache.spark.util
 /**
  * A class for tracking the statistics of a set of numbers (count, mean and variance) in a
  * numerically robust way. Includes support for merging two StatCounters. Based on 
- * [[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
- * Welford and Chan's algorithms for running variance]].
+ * [[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance Welford and Chan's algorithms for running variance]].
  *
  * @constructor Initialize the StatCounter with the given values.
  */

core/src/main/scala/org/apache/spark/util/Vector.scala

@@ -136,7 +136,7 @@ object Vector {
 
   /**
    * Creates this [[org.apache.spark.util.Vector]] of given length containing random numbers 
-   * between 0.0 and 1.0. Optional [[scala.util.Random]] number generator can be provided.
+   * between 0.0 and 1.0. Optional scala.util.Random number generator can be provided.
    */
   def random(length: Int, random: Random = new XORShiftRandom()) =
     Vector(length, _ => random.nextDouble())

streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala

@@ -127,7 +127,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
   /**
    * Return a new DStream by applying `groupByKey` on each RDD of `this` DStream.
    * Therefore, the values for each key in `this` DStream's RDDs are grouped into a
-   * single sequence to generate the RDDs of the new DStream. [[org.apache.spark.Partitioner]]
+   * single sequence to generate the RDDs of the new DStream. org.apache.spark.Partitioner
    * is used to control the partitioning of each RDD.
    */
   def groupByKey(partitioner: Partitioner): JavaPairDStream[K, JList[V]] =
@@ -151,7 +151,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
 
   /**
    * Return a new DStream by applying `reduceByKey` to each RDD. The values for each key are
-   * merged using the supplied reduce function. [[org.apache.spark.Partitioner]] is used to control
+   * merged using the supplied reduce function. org.apache.spark.Partitioner is used to control
    * thepartitioning of each RDD.
    */
   def reduceByKey(func: JFunction2[V, V, V], partitioner: Partitioner): JavaPairDStream[K, V] = {
@@ -161,7 +161,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
   /**
    * Combine elements of each key in DStream's RDDs using custom function. This is similar to the
    * combineByKey for RDDs. Please refer to combineByKey in
-   * [[org.apache.spark.rdd.PairRDDFunctions]] for more information.
+   * org.apache.spark.rdd.PairRDDFunctions for more information.
    */
   def combineByKey[C](createCombiner: JFunction[V, C],
       mergeValue: JFunction2[C, V, C],
@@ -176,7 +176,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
   /**
    * Combine elements of each key in DStream's RDDs using custom function. This is similar to the
    * combineByKey for RDDs. Please refer to combineByKey in
-   * [[org.apache.spark.rdd.PairRDDFunctions]] for more information.
+   * org.apache.spark.rdd.PairRDDFunctions for more information.
    */
   def combineByKey[C](createCombiner: JFunction[V, C],
       mergeValue: JFunction2[C, V, C],
@@ -479,7 +479,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
   /**
    * Return a new "state" DStream where the state for each key is updated by applying
    * the given function on the previous state of the key and the new values of the key.
-   * [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD.
+   * org.apache.spark.Partitioner is used to control the partitioning of each RDD.
    * @param updateFunc State update function. If `this` function returns None, then
    *                   corresponding state key-value pair will be eliminated.
    * @param partitioner Partitioner for controlling the partitioning of each RDD in the new
@@ -579,7 +579,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
 
   /**
    * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream.
-   * The supplied [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD.
+   * The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.
    */
   def join[W](
       other: JavaPairDStream[K, W],
@@ -619,7 +619,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
 
   /**
    * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream.
-   * The supplied [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD.
+   * The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.
    */
   def leftOuterJoin[W](
       other: JavaPairDStream[K, W],
@@ -660,7 +660,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
 
   /**
    * Return a new DStream by applying 'right outer join' between RDDs of `this` DStream and
-   * `other` DStream. The supplied [[org.apache.spark.Partitioner]] is used to control
+   * `other` DStream. The supplied org.apache.spark.Partitioner is used to control
    * the partitioning of each RDD.
    */
   def rightOuterJoin[W](

streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala

@@ -406,7 +406,7 @@ class JavaStreamingContext(val ssc: StreamingContext) {
    * JavaPairDStream in the list of JavaDStreams, convert it to a JavaDStream using
    * [[org.apache.spark.streaming.api.java.JavaPairDStream]].toJavaDStream().
    * In the transform function, convert the JavaRDD corresponding to that JavaDStream to
-   * a JavaPairRDD using [[org.apache.spark.api.java.JavaPairRDD]].fromJavaRDD().
+   * a JavaPairRDD using org.apache.spark.api.java.JavaPairRDD.fromJavaRDD().
    */
   def transform[T](
       dstreams: JList[JavaDStream[_]],
@@ -429,7 +429,7 @@ class JavaStreamingContext(val ssc: StreamingContext) {
    * JavaPairDStream in the list of JavaDStreams, convert it to a JavaDStream using
    * [[org.apache.spark.streaming.api.java.JavaPairDStream]].toJavaDStream().
    * In the transform function, convert the JavaRDD corresponding to that JavaDStream to
-   * a JavaPairRDD using [[org.apache.spark.api.java.JavaPairRDD]].fromJavaRDD().
+   * a JavaPairRDD using org.apache.spark.api.java.JavaPairRDD.fromJavaRDD().
    */
   def transform[K, V](
       dstreams: JList[JavaDStream[_]],

streaming/src/main/scala/org/apache/spark/streaming/dstream/PairDStreamFunctions.scala

@@ -65,7 +65,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
 
   /**
    * Return a new DStream by applying `groupByKey` on each RDD. The supplied
-   * [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD.
+   * org.apache.spark.Partitioner is used to control the partitioning of each RDD.
    */
   def groupByKey(partitioner: Partitioner): DStream[(K, Seq[V])] = {
     val createCombiner = (v: V) => ArrayBuffer[V](v)
@@ -95,7 +95,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
 
   /**
    * Return a new DStream by applying `reduceByKey` to each RDD. The values for each key are
-   * merged using the supplied reduce function. [[org.apache.spark.Partitioner]] is used to control
+   * merged using the supplied reduce function. org.apache.spark.Partitioner is used to control
    * the partitioning of each RDD.
    */
   def reduceByKey(reduceFunc: (V, V) => V, partitioner: Partitioner): DStream[(K, V)] = {
@@ -376,7 +376,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
   /**
    * Return a new "state" DStream where the state for each key is updated by applying
    * the given function on the previous state of the key and the new values of the key.
-   * [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD.
+   * org.apache.spark.Partitioner is used to control the partitioning of each RDD.
    * @param updateFunc State update function. If `this` function returns None, then
    *                   corresponding state key-value pair will be eliminated.
    * @param partitioner Partitioner for controlling the partitioning of each RDD in the new
@@ -396,7 +396,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
   /**
    * Return a new "state" DStream where the state for each key is updated by applying
    * the given function on the previous state of the key and the new values of each key.
-   * [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD.
+   * org.apache.spark.Partitioner is used to control the partitioning of each RDD.
    * @param updateFunc State update function. If `this` function returns None, then
    *                   corresponding state key-value pair will be eliminated. Note, that
    *                   this function may generate a different a tuple with a different key
@@ -453,7 +453,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
 
   /**
    * Return a new DStream by applying 'cogroup' between RDDs of `this` DStream and `other` DStream.
-   * The supplied [[org.apache.spark.Partitioner]] is used to partition the generated RDDs.
+   * The supplied org.apache.spark.Partitioner is used to partition the generated RDDs.
    */
   def cogroup[W: ClassTag](
       other: DStream[(K, W)],
@@ -483,7 +483,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
 
   /**
    * Return a new DStream by applying 'join' between RDDs of `this` DStream and `other` DStream.
-   * The supplied [[org.apache.spark.Partitioner]] is used to control the partitioning of each RDD.
+   * The supplied org.apache.spark.Partitioner is used to control the partitioning of each RDD.
    */
   def join[W: ClassTag](
       other: DStream[(K, W)],
@@ -518,7 +518,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
 
   /**
    * Return a new DStream by applying 'left outer join' between RDDs of `this` DStream and
-   * `other` DStream. The supplied [[org.apache.spark.Partitioner]] is used to control
+   * `other` DStream. The supplied org.apache.spark.Partitioner is used to control
    * the partitioning of each RDD.
    */
   def leftOuterJoin[W: ClassTag](
@@ -554,7 +554,7 @@ class PairDStreamFunctions[K: ClassTag, V: ClassTag](self: DStream[(K,V)])
 
   /**
    * Return a new DStream by applying 'right outer join' between RDDs of `this` DStream and
-   * `other` DStream. The supplied [[org.apache.spark.Partitioner]] is used to control
+   * `other` DStream. The supplied org.apache.spark.Partitioner is used to control
    * the partitioning of each RDD.
    */
   def rightOuterJoin[W: ClassTag](