Dataset

abstract def +=(datum: Datum[L, F]): Unit

abstract def featuresCounter(datumOffset: Int): Counter[Int]

abstract def keepOnly(featuresToKeep: Set[Int]): Dataset[L, F]

Creates a new dataset keeping only the features in the given set

abstract def mkDatum(row: Int): Datum[L, F]

Returns the Datum for given row

abstract def removeFeaturesByFrequency(threshold: Int): Dataset[L, F]

Removes features that appear less than threshold times in this dataset.

abstract def removeFeaturesByInformationGain(pctToKeep: Double): Dataset[L, F]

Removes features by information gain.

abstract def toCounterDataset: CounterDataset[L, F]

Convert this dataset to a CounterDataset

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

val featureLexicon: Lexicon[F]

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

def indices: Range

final def isInstanceOf[T0]: Boolean

val labelLexicon: Lexicon[L]

val labels: ArrayBuffer[Int]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def numFeatures: Int

def numLabels: Int

def size: Int

number of training examples

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