breeze.optimize

StochasticGradientDescent

abstract class StochasticGradientDescent[T] extends FirstOrderMinimizer[T, StochasticDiffFunction[T]] with Logging

Minimizes a function using stochastic gradient descent

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  1. StochasticGradientDescent
  2. FirstOrderMinimizer
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Instance Constructors

  1. new StochasticGradientDescent(defaultStepSize: Double, maxIter: Int, tolerance: Double = 1.0E-5, improvementTol: Double = 1.0E-4, minImprovementWindow: Int = 50)(implicit vspace: MutableCoordinateSpace[T, Double])

Type Members

  1. abstract type History

    Definition Classes
    FirstOrderMinimizer
  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

    Definition Classes
    FirstOrderMinimizer

Abstract Value Members

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

    Attributes
    protected
    Definition Classes
    FirstOrderMinimizer
  2. abstract def updateHistory(newX: T, newGrad: T, newVal: Double, f: StochasticDiffFunction[T], oldState: State): History

    Attributes
    protected
    Definition Classes
    FirstOrderMinimizer

Concrete 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
    FirstOrderMinimizer
  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  8. def calculateObjective(f: StochasticDiffFunction[T], x: T, history: History): (Double, T)

    Attributes
    protected
    Definition Classes
    FirstOrderMinimizer
  9. def chooseDescentDirection(state: State, fn: StochasticDiffFunction[T]): T

    Attributes
    protected
    Definition Classes
    StochasticGradientDescentFirstOrderMinimizer
  10. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  11. val defaultStepSize: Double

  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
    StochasticGradientDescentFirstOrderMinimizer
  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 initialState(f: StochasticDiffFunction[T], init: T): State

    Attributes
    protected
    Definition Classes
    FirstOrderMinimizer
  19. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  20. def iteratingShouldStop(state: State): Boolean

    Definition Classes
    FirstOrderMinimizer
  21. def iterations(f: StochasticDiffFunction[T], init: T): Iterator[State]

    Definition Classes
    FirstOrderMinimizer
  22. lazy val logger: Logger

    Attributes
    protected
    Definition Classes
    Logging
  23. val maxIter: Int

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

    Definition Classes
    FirstOrderMinimizerMinimizer
  25. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  26. final def notify(): Unit

    Definition Classes
    AnyRef
  27. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  28. val numberOfImprovementFailures: Int

    Definition Classes
    FirstOrderMinimizer
  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
    StochasticGradientDescentFirstOrderMinimizer
  31. def toString(): String

    Definition Classes
    AnyRef → Any
  32. def updateFValWindow(oldState: State, newAdjVal: Double): IndexedSeq[Double]

    Attributes
    protected
    Definition Classes
    StochasticGradientDescentFirstOrderMinimizer
  33. implicit val vspace: MutableCoordinateSpace[T, Double]

    Attributes
    protected
  34. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Logging

Inherited from Minimizer[T, StochasticDiffFunction[T]]

Inherited from AnyRef

Inherited from Any

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