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])

    Alias of runMiniBatchSGD with convergenceTol set to default value of 0.

    Alias of runMiniBatchSGD with convergenceTol set to default value of 0.001.

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

    convergenceTol

    Minibatch iteration will end before numIterations if the relative difference between the current weight and the previous weight is less than this value. In measuring convergence, L2 norm is calculated. Default value 0.001. Must be between 0.0 and 1.0 inclusively.

    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.

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

    Definition Classes

    AnyRef

33. def toString(): String

    Definition Classes

    AnyRef → Any

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( ... )