Classifier

abstract def classOf(d: Datum[L, F]): L

Returns the argmax for this datum

abstract def saveTo(writer: Writer): Unit

Saves to writer.

abstract def scoresOf(d: Datum[L, F]): Counter[L]

Returns the scores of all possible labels for this datum Convention: if the classifier can return probabilities, these must be probabilities

abstract def train(dataset: Dataset[L, F], indices: Array[Int]): Unit

Trains a classifier, using only the datums specified in indices indices is useful for bagging

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): 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 hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def saveTo(fileName: String): Unit

Saves the current model to a file

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

def toString(): String

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

Trains the classifier on the given dataset spans is useful during cross validation

final def wait(): Unit

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

final def wait(arg0: Long): Unit