org.apache.spark.mllib.optimization

L1Updater

class L1Updater extends Updater

Updater that adjusts learning rate and performs L1 regularization.

The corresponding proximal operator used is the soft-thresholding function. That is, each weight component is shrunk towards 0 by shrinkageVal.

If w > shrinkageVal, set weight component to w-shrinkageVal. If w < -shrinkageVal, set weight component to w+shrinkageVal. If -shrinkageVal < w < shrinkageVal, set weight component to 0.

Equivalently, set weight component to signum(w) * max(0.0, abs(w) - shrinkageVal)

Linear Supertypes
Updater, Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. L1Updater
  2. Updater
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Instance Constructors

  1. new L1Updater()

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. def compute(weightsOld: DoubleMatrix, gradient: DoubleMatrix, stepSize: Double, iter: Int, regParam: Double): (DoubleMatrix, Double)

    Compute an updated value for weights given the gradient, stepSize, iteration number and regularization parameter.

    Compute an updated value for weights given the gradient, stepSize, iteration number and regularization parameter. Also returns the regularization value computed using the *updated* weights.

    weightsOld

    - Column matrix of size nx1 where n is the number of features.

    gradient

    - Column matrix of size nx1 where n is the number of features.

    stepSize

    - step size across iterations

    iter

    - Iteration number

    regParam

    - Regularization parameter

    returns

    A tuple of 2 elements. The first element is a column matrix containing updated weights, and the second element is the regularization value computed using updated weights.

    Definition Classes
    L1Updater → Updater

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

    Definition Classes
    AnyRef

  10. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  11. def finalize(): Unit

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

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

    Definition Classes
    AnyRef → Any

  13. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  14. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  15. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  16. final def notify(): Unit

    Definition Classes
    AnyRef

  17. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  19. def toString(): String

    Definition Classes
    AnyRef → Any

  20. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Updater

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped