breeze.optimize

StochasticAveragedGradient

class StochasticAveragedGradient[T] extends FirstOrderMinimizer[T, BatchDiffFunction[T]]

Linear Supertypes
FirstOrderMinimizer[T, BatchDiffFunction[T]], SerializableLogging, Serializable, Serializable, Minimizer[T, BatchDiffFunction[T]], AnyRef, Any
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Inherited
  1. StochasticAveragedGradient
  2. FirstOrderMinimizer
  3. SerializableLogging
  4. Serializable
  5. Serializable
  6. Minimizer
  7. AnyRef
  8. Any
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  1. Public
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Instance Constructors

  1. new StochasticAveragedGradient(maxIter: Int = -1, initialStepSize: Double = 0.25, tuneStepFrequency: Int = 10, l2Regularization: Double = 0.0)(implicit vs: MutableInnerProductModule[T, Double])

Type Members

  1. case class History(stepSize: Double, range: IndexedSeq[Int], currentSum: T, previousGradients: IndexedSeq[T], nextPos: Int) extends Product with Serializable

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

  2. case class State(x: T, value: Double, grad: T, adjustedValue: Double, adjustedGradient: T, iter: Int, initialAdjVal: Double, history: History, fVals: IndexedSeq[Double] = ..., numImprovementFailures: Int = 0, searchFailed: Boolean = false) extends Product with Serializable

    Tracks the information about the optimizer, including the current point, its value, gradient, and then any history.

Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

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

    Attributes
    protected
    Definition Classes
    StochasticAveragedGradient → FirstOrderMinimizer

  7. def adjustFunction(f: BatchDiffFunction[T]): BatchDiffFunction[T]

    Attributes
    protected
    Definition Classes
    FirstOrderMinimizer

  8. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  9. def calculateObjective(f: BatchDiffFunction[T], x: T, history: History): (Double, T)

    Attributes
    protected
    Definition Classes
    StochasticAveragedGradient → FirstOrderMinimizer

  10. def chooseDescentDirection(state: State, f: BatchDiffFunction[T]): T

    Attributes
    protected
    Definition Classes
    StochasticAveragedGradient → FirstOrderMinimizer

  11. def clone(): AnyRef

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

  12. def determineStepSize(state: State, f: BatchDiffFunction[T], direction: T): Double

    Attributes
    protected
    Definition Classes
    StochasticAveragedGradient → FirstOrderMinimizer

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

    Definition Classes
    AnyRef

  14. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  15. def finalize(): Unit

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

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

    Definition Classes
    AnyRef → Any

  17. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  18. def initialHistory(f: BatchDiffFunction[T], init: T): History

    Attributes
    protected
    Definition Classes
    StochasticAveragedGradient → FirstOrderMinimizer

  19. def initialState(f: BatchDiffFunction[T], init: T): State

    Attributes
    protected
    Definition Classes
    FirstOrderMinimizer

  20. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  21. def iterations(f: BatchDiffFunction[T], init: T): Iterator[State]

    Definition Classes
    FirstOrderMinimizer

  22. def logger: LazyLogger

    Attributes
    protected
    Definition Classes
    SerializableLogging

  23. val minImprovementWindow: Int

    Definition Classes
    FirstOrderMinimizer

  24. def minimize(f: BatchDiffFunction[T], init: T): T

    Definition Classes
    FirstOrderMinimizer → Minimizer

  25. def minimizeAndReturnState(f: BatchDiffFunction[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

  28. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  29. val numberOfImprovementFailures: Int

    Definition Classes
    FirstOrderMinimizer

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

    Definition Classes
    AnyRef

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

    Attributes
    protected
    Definition Classes
    StochasticAveragedGradient → FirstOrderMinimizer

  32. def toString(): String

    Definition Classes
    AnyRef → Any

  33. def updateFValWindow(oldState: State, newAdjVal: Double): IndexedSeq[Double]

    Attributes
    protected
    Definition Classes
    FirstOrderMinimizer

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

    Attributes
    protected
    Definition Classes
    StochasticAveragedGradient → FirstOrderMinimizer

  35. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from FirstOrderMinimizer[T, BatchDiffFunction[T]]

Inherited from SerializableLogging

Inherited from Serializable

Inherited from Serializable

Inherited from Minimizer[T, BatchDiffFunction[T]]

Inherited from AnyRef

Inherited from Any

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