RankingClassifier

abstract def displayModel(pw: PrintWriter): Unit

Displays the learned model in a human-readable format, for debug purposes

abstract def saveTo(fileName: String): Unit

Saves the current model to a file

abstract def scoresOf(queryDatums: Iterable[Datum[Int, F]]): Iterable[Double]

Returns scores that can be used for ranking for a group of datums, from the same query These scores do NOT have to be normalized, they are NOT probabilities!

abstract def train(dataset: RankingDataset[F], spans: Option[Iterable[(Int, Int)]] = None): Unit

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def probabilitiesOf(queryDatums: Iterable[Datum[Int, F]], gamma: Double = 1.0): Iterable[Double]

Returns probabilities that can be used for ranking for a group of datums, from the same query These probabilities are obtained here from scoresOf() using softmax

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit