GradientDescent

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. def hashCode(): Int

    Definition Classes

    AnyRef → Any

13. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

14. def isTraceEnabled(): Boolean

    Attributes

    protected

    Definition Classes

    Logging

15. def log: Logger

    Attributes

    protected

    Definition Classes

    Logging

16. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

17. def logDebug(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

18. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

19. def logError(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

20. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

21. def logInfo(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

22. def logName: String

    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 runMiniBatchSGD(data: RDD[(Double, Vector)], gradient: Gradient, updater: Updater, stepSize: Double, numIterations: Int, regParam: Double, miniBatchFraction: Double, initialWeights: Vector): (Vector, Array[Double])

    Run stochastic gradient descent (SGD) in parallel using mini batches.

    Run stochastic gradient descent (SGD) in parallel using mini batches. In each iteration, we sample a subset (fraction miniBatchFraction) of the total data in order to compute a gradient estimate. Sampling, and averaging the subgradients over this subset is performed using one standard spark map-reduce in each iteration.

    data

    - Input data for SGD. RDD of the set of data examples, each of the form (label, [feature values]).

    gradient

    - Gradient object (used to compute the gradient of the loss function of one single data example)

    updater

    - Updater function to actually perform a gradient step in a given direction.

    stepSize

    - initial step size for the first step

    numIterations

    - number of iterations that SGD should be run.

    regParam

    - regularization parameter

    miniBatchFraction

    - fraction of the input data set that should be used for one iteration of SGD. Default value 1.0.

    returns

    A tuple containing two elements. The first element is a column matrix containing weights for every feature, and the second element is an array containing the stochastic loss computed for every iteration.

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

    Definition Classes

    AnyRef

32. def toString(): String

    Definition Classes

    AnyRef → Any

33. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    @throws( ... )