change SLR to experimental and update the doc

apache · Feb 4, 2015 · 8aca7ee · 8aca7ee
1 parent ebbb2e9
commit 8aca7ee
Showing 1 changed file with 11 additions and 5 deletions.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
@@ -21,15 +21,16 @@ import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
 import org.apache.spark.SparkContext._
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{Experimental, DeveloperApi}
 import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.streaming.dstream.DStream
 import org.apache.spark.util.Utils
 import org.apache.spark.util.random.XORShiftRandom
 
 /**
- * :: DeveloperApi ::
+ * :: Experimental ::
+ *
  * StreamingKMeansModel extends MLlib's KMeansModel for streaming
  * algorithms, so it can keep track of a continuously updated weight
  * associated with each cluster, and also update the model by
@@ -39,8 +40,10 @@ import org.apache.spark.util.random.XORShiftRandom
  * generalized to incorporate forgetfullness (i.e. decay).
  * The update rule (for each cluster) is:
  *
+ * {{{
  * c_t+1 = [(c_t * n_t * a) + (x_t * m_t)] / [n_t + m_t]
  * n_t+t = n_t * a + m_t
+ * }}}
  *
  * Where c_t is the previously estimated centroid for that cluster,
  * n_t is the number of points assigned to it thus far, x_t is the centroid
@@ -61,7 +64,7 @@ import org.apache.spark.util.random.XORShiftRandom
  * as batches or points.
  *
  */
-@DeveloperApi
+@Experimental
 class StreamingKMeansModel(
     override val clusterCenters: Array[Vector],
     val clusterWeights: Array[Double]) extends KMeansModel(clusterCenters) with Logging {
@@ -140,7 +143,8 @@ class StreamingKMeansModel(
 }
 
 /**
- * :: DeveloperApi ::
+ * :: Experimental ::
+ *
  * StreamingKMeans provides methods for configuring a
  * streaming k-means analysis, training the model on streaming,
  * and using the model to make predictions on streaming data.
@@ -149,13 +153,15 @@ class StreamingKMeansModel(
  * Use a builder pattern to construct a streaming k-means analysis
  * in an application, like:
  *
+ * {{{
  *  val model = new StreamingKMeans()
  *    .setDecayFactor(0.5)
  *    .setK(3)
  *    .setRandomCenters(5, 100.0)
  *    .trainOn(DStream)
+ * }}}
  */
-@DeveloperApi
+@Experimental
 class StreamingKMeans(
     var k: Int,
     var decayFactor: Double,