org.apache.spark.mllib.optimization

GradientDescent

class GradientDescent extends Optimizer with Logging

Class used to solve an optimization problem using Gradient Descent.

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Logging, Optimizer, AnyRef, Any
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Instance Constructors

  1. new GradientDescent(gradient: Gradient, updater: Updater)

    gradient

    Gradient function to be used.

    updater

    Updater to be used to update weights after every iteration.

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. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  10. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  12. var gradient: Gradient

    Gradient function to be used.

  13. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  14. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  15. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  16. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging
  17. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  18. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  19. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  20. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  21. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  22. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  23. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  24. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  25. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  26. def logWarning(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  27. final def ne(arg0: AnyRef): Boolean

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

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

    Definition Classes
    AnyRef
  30. def optimize(data: RDD[(Double, Array[Double])], initialWeights: Array[Double]): Array[Double]

    Solve the provided convex optimization problem.

    Solve the provided convex optimization problem.

    Definition Classes
    GradientDescentOptimizer
  31. def setGradient(gradient: Gradient): GradientDescent.this.type

    Set the gradient function to be used for SGD.

  32. def setMiniBatchFraction(fraction: Double): GradientDescent.this.type

    Set fraction of data to be used for each SGD iteration.

    Set fraction of data to be used for each SGD iteration. Default 1.0.

  33. def setNumIterations(iters: Int): GradientDescent.this.type

    Set the number of iterations for SGD.

    Set the number of iterations for SGD. Default 100.

  34. def setRegParam(regParam: Double): GradientDescent.this.type

    Set the regularization parameter used for SGD.

    Set the regularization parameter used for SGD. Default 0.0.

  35. def setStepSize(step: Double): GradientDescent.this.type

    Set the step size per-iteration of SGD.

    Set the step size per-iteration of SGD. Default 1.0.

  36. def setUpdater(updater: Updater): GradientDescent.this.type

    Set the updater function to be used for SGD.

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

    Definition Classes
    AnyRef
  38. def toString(): String

    Definition Classes
    AnyRef → Any
  39. var updater: Updater

    Updater to be used to update weights after every iteration.

  40. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Logging

Inherited from Optimizer

Inherited from AnyRef

Inherited from Any

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