FeaturizedClassifier

abstract def apply(v1: Array[(Int, Double)]): Array[Double]

abstract val featurizer: Featurizer[I, String]

abstract def indexOfFeature(feature: String): Option[Int]

abstract def indexOfLabel(label: L): Int

abstract def labelOfIndex(index: Int): L

abstract def labels: Seq[L]

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def andThen[A](g: (Array[Double]) ⇒ A): (Array[(Int, Double)]) ⇒ A

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def compose[A](g: (A) ⇒ Array[(Int, Double)]): (A) ⇒ Array[Double]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def evalIndexed(observations: Seq[FeatureObservation[Int]]): Array[Double]

Evaluate the given indexed feature observations.

def evalRaw(content: I): Array[Double]

Evaluate a raw observation by featurizing it and then calling evalUnindexed of IndexedClassifier.

def evalUnindexed(observations: Seq[FeatureObservation[String]]): Array[Double]

Evaluate the un-indexed feature observations.

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 predict(content: I): L

Evaluate a raw observation by using evalRaw, identify the highest scoring label, and then return it.

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