LossFunction

Abstract class that implements some of the functionality for common loss functions

A loss function determines the loss term L(w) of the objective function f(w) = L(w) + lambda*R(w) for prediction tasks, the other being regularization, R(w).

The regularization is specific to the used optimization algorithm and, thus, implemented there.

We currently only support differentiable loss functions, in the future this class could be changed to DiffLossFunction in order to support other types, such as absolute loss.

Linear Supertypes

Serializable, Serializable, AnyRef, Any

Known Subclasses

GenericLossFunction

Abstract Value Members

abstract def lossGradient(dataPoint: LabeledVector, weightVector: WeightVector): (Double, WeightVector)

Calculates the gradient as well as the loss given a data point and the weight vector

Concrete Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def gradient(dataPoint: LabeledVector, weightVector: WeightVector): WeightVector

Calculates the gradient of the loss function given a data point and weight vector
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
def loss(dataPoint: LabeledVector, weightVector: WeightVector): Double

Calculates the loss given the prediction and label value
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Doc: package optimization

trait LossFunction extends Serializable

Abstract Value Members

abstract def lossGradient(dataPoint: LabeledVector, weightVector: WeightVector): (Double, WeightVector)

Concrete Value Members

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 gradient(dataPoint: LabeledVector, weightVector: WeightVector): WeightVector

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def loss(dataPoint: LabeledVector, weightVector: WeightVector): Double

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped