LinearCRFTraining

abstract val sGen: TrSeqGen

A SeqGen object specialized for training

abstract def trainModel(dCrf: Trainable[AbstractInstance], seqs: IndexedSeq[InstanceSequence], modelIterFn: Option[(CoreModel, Int) ⇒ Unit] = None): Unit

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

val adjust: 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 getCrf(empDist: Boolean = false): Crf

def getNumPs: Int

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val opts: Options

def printHeader(seqs: Seq[InstanceSequence]): Unit

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

def toString(): String

def train(): Unit

Train and return a model.

def trainFromSeqs(seqs: IndexedSeq[SourceSequence[Obs]]): Unit

def trainingRoutine(seqs: IndexedSeq[InstanceSequence]): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit

def xValidate(): Unit

def xValidate[Obs](sGen: SeqGen[Obs], crf: Crf, accessSeq: MemoryAccessSeq, num: Int = 200, folds: Int = 10): Unit

def xValidateFromSeqs(seqs: IndexedSeq[SourceSequence[Obs]]): Unit