MaxEntObjectiveFunction

abstract val contextIndex: Index[Context]

abstract val decisionIndex: Index[Decision]

abstract def expectedCounts(logThetas: IndexedSeq[Vector[Double]]): (Double, IndexedSeq[Vector[Double]])

abstract def features(d: Decision, c: Context): IndexedSeq[Feature]

abstract val indexedDecisionsForContext: IndexedSeq[IndexedSeq[Int]]

abstract def initialValueForFeature(f: Feature): Double

final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

def andThen[A](g: (Double) ⇒ A): (DenseVector[Double]) ⇒ A

def apply(x: DenseVector[Double]): Double

final def asInstanceOf[T0]: T0

def calculate(weights: DenseVector[Double]): (Double, DenseVector[Double])

Calculates both the value and the gradient at a point

def clone(): AnyRef

def compose[A](g: (A) ⇒ DenseVector[Double]): (A) ⇒ Double

def computeGradient(featureWeights: Vector[Double], logThetas: IndexedSeq[Vector[Double]], eCounts: IndexedSeq[Vector[Double]], eTotals: IndexedSeq[Double]): (Double, DenseVector[Double])

def computeValue(featureWeights: Vector[Double], logThetas: IndexedSeq[Vector[Double]], eCounts: IndexedSeq[Vector[Double]], eTotals: IndexedSeq[Double]): Double

lazy val contextBroker: Encoder[Context]

lazy val decisionBroker: Encoder[Decision]

def decodeThetas(m: IndexedSeq[Vector[Double]]): Counter2[Context, Decision, Double]

lazy val defaultInitWeights: Counter[Feature, Double]

lazy val encodedInitialWeights: DenseVector[Double]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

lazy val featureEncoder: Encoder[Feature]

lazy val featureGrid: Array[SparseArray[Array[Int]]]

lazy val featureIndex: Index[Feature]

def finalize(): Unit

final def getClass(): Class[_]

def gradientAt(x: DenseVector[Double]): DenseVector[Double]

calculates the gradient at a point

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def sumWeights(indices: Array[Int], weights: DenseVector[Double]): Double

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

def throughLens[U](implicit l: Isomorphism[DenseVector[Double], U]): DiffFunction[U]

Lenses provide a way of mapping between two types, which we typically use to convert something to a DenseVector or other Tensor for optimization purposes.

def toString(): String

def valueAt(weights: DenseVector[Double]): Double

calculates the value at a point

final def wait(): Unit

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

final def wait(arg0: Long): Unit