Packages

class SimpleSGD[T] extends StochasticGradientDescent[T]

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Inherited
  1. SimpleSGD
  2. StochasticGradientDescent
  3. FirstOrderMinimizer
  4. SerializableLogging
  5. Serializable
  6. Serializable
  7. Minimizer
  8. AnyRef
  9. Any
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Visibility
  1. Public
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Instance Constructors

  1. new SimpleSGD(initialStepSize: Double = 4, maxIter: Int = 100)(implicit vs: NormedModule[T, Double])

Type Members

  1. type History = Unit

    Any history the derived minimization function needs to do its updates.

    Any history the derived minimization function needs to do its updates. typically an approximation to the second derivative/hessian matrix.

    Definition Classes
    SimpleSGD → FirstOrderMinimizer
  2. type State = FirstOrderMinimizer.State[T, Info, History]
    Definition Classes
    FirstOrderMinimizer

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
    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
    @native() @HotSpotIntrinsicCandidate() @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
    StochasticGradientDescent → FirstOrderMinimizer
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  16. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  17. def infiniteIterations(f: StochasticDiffFunction[T], state: State): Iterator[State]
    Definition Classes
    FirstOrderMinimizer
  18. def initialHistory(f: StochasticDiffFunction[T], init: T): Unit
    Definition Classes
    SimpleSGD → FirstOrderMinimizer
  19. def initialState(f: StochasticDiffFunction[T], init: T): State
    Attributes
    protected
    Definition Classes
    FirstOrderMinimizer
  20. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  21. def iterations(f: StochasticDiffFunction[T], init: T): Iterator[State]
    Definition Classes
    FirstOrderMinimizer
  22. def logger: LazyLogger
    Attributes
    protected
    Definition Classes
    SerializableLogging
  23. val maxIter: Int
    Definition Classes
    StochasticGradientDescent
  24. def minimize(f: StochasticDiffFunction[T], init: T): T
    Definition Classes
    FirstOrderMinimizer → Minimizer
  25. def minimizeAndReturnState(f: StochasticDiffFunction[T], init: T): State
    Definition Classes
    FirstOrderMinimizer
  26. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  28. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  29. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  30. 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
    StochasticGradientDescent → FirstOrderMinimizer
  31. def toString(): String
    Definition Classes
    AnyRef → Any
  32. def updateHistory(newX: T, newGrad: T, newValue: Double, f: StochasticDiffFunction[T], oldState: State): Unit
    Definition Classes
    SimpleSGD → FirstOrderMinimizer
  33. implicit val vspace: NormedModule[T, Double]
    Attributes
    protected
    Definition Classes
    StochasticGradientDescent
  34. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  35. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  36. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @Deprecated @deprecated @throws( classOf[java.lang.Throwable] )
    Deprecated

    (Since version ) see corresponding Javadoc for more information.

Inherited from StochasticGradientDescent[T]

Inherited from FirstOrderMinimizer[T, StochasticDiffFunction[T]]

Inherited from SerializableLogging

Inherited from Serializable

Inherited from Serializable

Inherited from Minimizer[T, StochasticDiffFunction[T]]

Inherited from AnyRef

Inherited from Any

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