PositionalNeuralGrammar

final def !=(arg0: Any): Boolean

final def ##(): Int

def *(refined: Grammar[L, W]): Grammar[L, W]

final def ==(arg0: Any): Boolean

val BinaryLayerIdx: Int

val SpanLayerIdx: Int

val UnaryLayerIdx: Int

def anchor(w: IndexedSeq[W], cons: ChartConstraints[L]): GrammarAnchoring[L, W]

final def asInstanceOf[T0]: T0

def clone(): AnyRef

val dcBinaryFeatOffset: Int

val dcSpanFeatOffset: Int

val dcUnaryFeatOffset: Int

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def extractEcounts(m: ParseMarginal[L, W], deriv: DenseVector[Double], scale: Double): Unit

N.B.

final def getClass(): Class[_]

def hashCode(): Int

def index: Index[Rule[L]]

final def isInstanceOf[T0]: Boolean

def labelEncoder: Encoder[L]

def labelIndex: Index[L]

val layers: IndexedSeq[OutputLayer]

val lexicon: Lexicon[L, W]

val maybeSparseSurfaceFeaturizer: Option[IndexedSpanFeaturizer[L, L2, W]]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val origPTModel: PositionalNeuralModel[L, L2, W]

val refinedTopology: RuleTopology[L2]

val refinements: GrammarRefinements[L, L2]

def root: L

val surfaceFeaturizer: Word2VecSurfaceFeaturizerIndexed[W]

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

def toString(): String

val topology: RuleTopology[L]

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

final def wait(arg0: Long): Unit

final def wait(): Unit

val weights: DenseVector[Double]

def withPermissiveLexicon: Grammar[L, W]

def finalize(): Unit