PerceptronRankingClassifier

Instance Constructors

new PerceptronRankingClassifier(props: Properties)
new PerceptronRankingClassifier(epochs: Int = 2, burnInIterations: Int = 0, marginRatio: Double = 1.0)

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def addToAvg(): Unit
final def asInstanceOf[T0]: T0

Definition Classes
Any
var avgWeights: Array[Double]
val burnInIterations: Int
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def computeAverageVectorLength(dataset: RankingDataset[F], indices: Array[Int]): Double
def datumDotProduct(c: Counter[F]): Double
def displayModel(pw: PrintWriter): Unit

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

Definition Classes
PerceptronRankingClassifier → RankingClassifier
val epochs: Int
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
var featureLexicon: Lexicon[F]
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
var margin: Double
val marginRatio: Double
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
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
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
queryDatums
All datums for one query
returns

Definition Classes
RankingClassifier
def saveTo(fileName: String): Unit

Saves the current model to a file
Saves the current model to a file

Definition Classes
PerceptronRankingClassifier → RankingClassifier
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!
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!
queryDatums
All datums for one query
returns

Definition Classes
PerceptronRankingClassifier → RankingClassifier
var survivedIterations: Int
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
var totalQueries: Int
var totalUpdates: Int
def train(dataset: RankingDataset[F], spans: Option[Iterable[(Int, Int)]] = None): Unit

Definition Classes
PerceptronRankingClassifier → RankingClassifier
def update(better: Counter[Int], worse: Counter[Int]): Unit
def updateWeights(v: Counter[Int], w: Double): Unit
var updatesPerEpoch: Int
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
var weights: Array[Double]

Related Docs: object PerceptronRankingClassifier | package learning

class PerceptronRankingClassifier[F] extends RankingClassifier[F] with Serializable

Instance Constructors

new PerceptronRankingClassifier(props: Properties)

new PerceptronRankingClassifier(epochs: Int = 2, burnInIterations: Int = 0, marginRatio: Double = 1.0)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def addToAvg(): Unit

final def asInstanceOf[T0]: T0

var avgWeights: Array[Double]

val burnInIterations: Int

def clone(): AnyRef

def computeAverageVectorLength(dataset: RankingDataset[F], indices: Array[Int]): Double

def datumDotProduct(c: Counter[F]): Double

def displayModel(pw: PrintWriter): Unit

val epochs: Int

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

var featureLexicon: Lexicon[F]

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

var margin: Double

val marginRatio: Double

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]

def saveTo(fileName: String): Unit

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

var survivedIterations: Int

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

def toString(): String

var totalQueries: Int

var totalUpdates: Int

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

def update(better: Counter[Int], worse: Counter[Int]): Unit

def updateWeights(v: Counter[Int], w: Double): Unit

var updatesPerEpoch: Int

final def wait(): Unit

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

final def wait(arg0: Long): Unit

var weights: Array[Double]

Inherited from Serializable

Inherited from Serializable

Inherited from RankingClassifier[F]

Inherited from AnyRef

Inherited from Any

Ungrouped