L1Updater

class L1Updater extends Updater

:: DeveloperApi :: Updater for L1 regularized problems. R(w) = ||w||_1 Uses a step-size decreasing with the square root of the number of iterations.

Instead of subgradient of the regularizer, the proximal operator for the L1 regularization is applied after the gradient step. This is known to result in better sparsity of the intermediate solution.

The corresponding proximal operator for the L1 norm 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)

Annotations
()
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
Visibility
1. Public
2. All

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: Vector, gradient: Vector, stepSize: Double, iter: Int, regParam: Double): (Vector, 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 regParam * R(w) computed using the *updated* weights.

weightsOld

- Column matrix of size dx1 where d is the number of features.

- Column matrix of size dx1 where d 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
L1UpdaterUpdater
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( ... )