BatchDiffFunction

abstract def calculate(x: T, batch: IndexedSeq[Int]): (Double, T)

Calculates the value and gradient of the function on a subset of the data

abstract def fullRange: IndexedSeq[Int]

The full size of the data

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): (T) ⇒ A

def apply(x: T, batch: IndexedSeq[Int]): Double

final def apply(x: T): Double

final def asInstanceOf[T0]: T0

def calculate(x: T): (Double, T)

Calculates both the value and the gradient at a point

def clone(): AnyRef

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

def curried: (T) ⇒ (IndexedSeq[Int]) ⇒ Double

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def gradientAt(x: T): T

calculates the gradient at a point

def gradientAt(x: T, batch: IndexedSeq[Int]): T

Calculates the gradient of the function on a subset of the data

def groupItems(groupSize: Int): BatchDiffFunction[T]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def throughLens[U](implicit l: Isomorphism[T, 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 tupled: ((T, IndexedSeq[Int])) ⇒ Double

def valueAt(x: T): Double

calculates the value at a point

def valueAt(x: T, batch: IndexedSeq[Int]): Double

Calculates the value of the function on a subset of the data

final def wait(): Unit

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

final def wait(arg0: Long): Unit

def withRandomBatches(size: Int): StochasticDiffFunction[T]

def withScanningBatches(size: Int): StochasticDiffFunction[T]