Class

breeze.optimize.StochasticGradientDescent

SimpleSGD

Related Doc: package StochasticGradientDescent

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class SimpleSGD[T] extends StochasticGradientDescent[T]

Linear Supertypes
StochasticGradientDescent[T], FirstOrderMinimizer[T, StochasticDiffFunction[T]], SerializableLogging, Serializable, Serializable, Minimizer[T, StochasticDiffFunction[T]], AnyRef, Any
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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
  2. All

Instance Constructors

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

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Type Members

  1. type History = Unit

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    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]

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    Definition Classes
    FirstOrderMinimizer

Value Members

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

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

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

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    Definition Classes
    AnyRef → Any

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

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    Attributes
    protected
    Definition Classes
    FirstOrderMinimizer

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

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    Attributes
    protected
    Definition Classes
    FirstOrderMinimizer

  6. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

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

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    Attributes
    protected
    Definition Classes
    FirstOrderMinimizer

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

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    Attributes
    protected
    Definition Classes
    StochasticGradientDescent → FirstOrderMinimizer

  9. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  10. val convergenceCheck: ConvergenceCheck[T]

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    Definition Classes
    FirstOrderMinimizer

  11. val defaultStepSize: Double

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    Definition Classes
    StochasticGradientDescent

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

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    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

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    Definition Classes
    AnyRef

  14. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  15. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

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    Definition Classes
    AnyRef → Any

  17. def hashCode(): Int

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    Definition Classes
    AnyRef → Any

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

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    Definition Classes
    FirstOrderMinimizer

  19. def initialHistory(f: StochasticDiffFunction[T], init: T): Unit

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    Definition Classes
    SimpleSGD → FirstOrderMinimizer

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

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    Attributes
    protected
    Definition Classes
    FirstOrderMinimizer

  21. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

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

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    Definition Classes
    FirstOrderMinimizer

  23. def logger: LazyLogger

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    Attributes
    protected
    Definition Classes
    SerializableLogging

  24. val maxIter: Int

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    Definition Classes
    StochasticGradientDescent

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

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    Definition Classes
    FirstOrderMinimizer → Minimizer

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

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    Definition Classes
    FirstOrderMinimizer

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

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    Definition Classes
    AnyRef

  28. final def notify(): Unit

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    Definition Classes
    AnyRef

  29. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

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

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    Definition Classes
    AnyRef

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

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    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

  32. def toString(): String

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    Definition Classes
    AnyRef → Any

  33. def updateHistory(newX: T, newGrad: T, newValue: Double, f: StochasticDiffFunction[T], oldState: State): Unit

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    Definition Classes
    SimpleSGD → FirstOrderMinimizer

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

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    Attributes
    protected
    Definition Classes
    StochasticGradientDescent

  35. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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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