AdaDeltaGradientDescent

Value Members

1. final def !=(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

   AnyRef → Any

3. final def ==(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

4. def adjust(newX: T, newGrad: T, newVal: Double): (Double, T)

   

   Attributes

   protected

   Definition Classes

   AdaDeltaGradientDescent → FirstOrderMinimizer

5. def adjustFunction(f: StochasticDiffFunction[T]): StochasticDiffFunction[T]

   

   Attributes

   protected

   Definition Classes

   FirstOrderMinimizer

6. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

7. def calculateObjective(f: StochasticDiffFunction[T], x: T, history: History): (Double, T)

   

   Attributes

   protected

   Definition Classes

   FirstOrderMinimizer

8. def chooseDescentDirection(state: State, fn: StochasticDiffFunction[T]): T

   

   Attributes

   protected

   Definition Classes

   StochasticGradientDescent → FirstOrderMinimizer

9. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

10. val convergenceCheck: ConvergenceCheck[T]

    

    Definition Classes

    FirstOrderMinimizer

11. val defaultStepSize: Double

    

    Definition Classes

    StochasticGradientDescent

12. def determineStepSize(state: State, f: StochasticDiffFunction[T], dir: T): Double

    

    Choose a step size scale for this iteration.

    Choose a step size scale for this iteration.

    Default is eta / math.pow(state.iter + 1,2.0 / 3.0)

    Definition Classes

    AdaDeltaGradientDescent → StochasticGradientDescent → FirstOrderMinimizer

13. val epsilon: Double

    

14. final def eq(arg0: AnyRef): Boolean

    

    Definition Classes

    AnyRef

15. def equals(arg0: Any): Boolean

    

    Definition Classes

    AnyRef → Any

16. def finalize(): Unit

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

17. final def getClass(): Class[_]

    

    Definition Classes

    AnyRef → Any

18. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

19. def infiniteIterations(f: StochasticDiffFunction[T], state: State): Iterator[State]

    

    Definition Classes

    FirstOrderMinimizer

20. def initialHistory(f: StochasticDiffFunction[T], init: T): History

    

    Attributes

    protected

    Definition Classes

    AdaDeltaGradientDescent → FirstOrderMinimizer

21. def initialState(f: StochasticDiffFunction[T], init: T): State

    

    Attributes

    protected

    Definition Classes

    FirstOrderMinimizer

22. final def isInstanceOf[T0]: Boolean

    

    Definition Classes

    Any

23. def iterations(f: StochasticDiffFunction[T], init: T): Iterator[State]

    

    Definition Classes

    FirstOrderMinimizer

24. def logger: LazyLogger

    

    Attributes

    protected

    Definition Classes

    SerializableLogging

25. val maxIter: Int

    

    Definition Classes

    StochasticGradientDescent

26. def minimize(f: StochasticDiffFunction[T], init: T): T

    

    Definition Classes

    FirstOrderMinimizer → Minimizer

27. def minimizeAndReturnState(f: StochasticDiffFunction[T], init: T): State

    

    Definition Classes

    FirstOrderMinimizer

28. final def ne(arg0: AnyRef): Boolean

    

    Definition Classes

    AnyRef

29. final def notify(): Unit

    

    Definition Classes

    AnyRef

30. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

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

    

    Definition Classes

    AnyRef

32. def takeStep(state: State, dir: T, stepSize: Double): T

    

    Projects the vector x onto whatever ball is needed.

    Projects the vector x onto whatever ball is needed. Can also incorporate regularization, or whatever.

    Default just takes a step

    Attributes

    protected

    Definition Classes

    AdaDeltaGradientDescent → StochasticGradientDescent → FirstOrderMinimizer

33. def toString(): String

    

    Definition Classes

    AnyRef → Any

34. def updateHistory(newX: T, newGrad: T, newVal: Double, f: StochasticDiffFunction[T], oldState: State): History

    

    Attributes

    protected

    Definition Classes

    AdaDeltaGradientDescent → FirstOrderMinimizer

35. implicit val vspace: NormedModule[T, Double]

    

    Attributes

    protected

    Definition Classes

    StochasticGradientDescent

36. final def wait(): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

38. final def wait(arg0: Long): Unit

    

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )