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make IR experimental and update its doc
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mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala

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@@ -23,10 +23,13 @@ import java.util.Arrays.binarySearch
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import scala.collection.mutable.ArrayBuffer
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import org.apache.spark.annotation.Experimental
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import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD}
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import org.apache.spark.rdd.RDD
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/**
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* :: Experimental ::
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*
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* Regression model for isotonic regression.
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*
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* @param boundaries Array of boundaries for which predictions are known.
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* Results of isotonic regression and therefore monotone.
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* @param isotonic indicates whether this is isotonic or antitonic.
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*/
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@Experimental
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class IsotonicRegressionModel (
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val boundaries: Array[Double],
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val predictions: Array[Double],
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}
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/**
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* :: Experimental ::
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*
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* Isotonic regression.
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* Currently implemented using parallelized pool adjacent violators algorithm.
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* Only univariate (single feature) algorithm supported.
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*
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* Sequential PAV implementation based on:
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* Tibshirani, Ryan J., Holger Hoefling, and Robert Tibshirani.
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* "Nearly-isotonic regression." Technometrics 53.1 (2011): 54-61.
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* Available from http://www.stat.cmu.edu/~ryantibs/papers/neariso.pdf
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* Available from [[http://www.stat.cmu.edu/~ryantibs/papers/neariso.pdf]]
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*
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* Sequential PAV parallelization based on:
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* Kearsley, Anthony J., Richard A. Tapia, and Michael W. Trosset.
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* "An approach to parallelizing isotonic regression."
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* Applied Mathematics and Parallel Computing. Physica-Verlag HD, 1996. 141-147.
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* Available from http://softlib.rice.edu/pub/CRPC-TRs/reports/CRPC-TR96640.pdf
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* Available from [[http://softlib.rice.edu/pub/CRPC-TRs/reports/CRPC-TR96640.pdf]]
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*
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* @see [[http://en.wikipedia.org/wiki/Isotonic_regression Isotonic regression (Wikipedia)]]
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*/
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@Experimental
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class IsotonicRegression private (private var isotonic: Boolean) extends Serializable {
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/**

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