PairwiseCorefModel

abstract def accumulateObjectiveGradient(accumulator: WeightsMapAccumulator, input: la.Tensor1, objectiveByPredictionGradient: Double, weight: Double): Unit

Put gradient of objective with respect to parameters into the accumulator.

abstract def predict(input: la.Tensor1): Double

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

val MentionPairCrossFeaturesDomain: VectorDomain

val MentionPairFeaturesDomain: CategoricalVectorDomain[String]

val MentionPairLabelDomain: CategoricalDomain[String]

object MentionPairLabelThing

def Weights(t4: ⇒ Tensor4): Weights4

def Weights(t3: ⇒ Tensor3): Weights3

def Weights(t2: ⇒ Tensor2): Weights2

def Weights(t1: ⇒ Tensor1): Weights1

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def deserialize(filename: String): Unit

def deserialize(stream: DataInputStream): Unit

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def generateTrueMap(mentions: Seq[Mention]): GenericEntityMap[Mention]

final def getClass(): Class[_]

def getExample(label: MentionPairLabel, scale: Double): optimize.Example

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val parameters: WeightsSet

def serialize(stream: DataOutputStream): Unit

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