Package org.apache.spark.mllib.optimization

Class Updater

Object

org.apache.spark.mllib.optimization.Updater

All Implemented Interfaces:

Serializable, scala.Serializable

Direct Known Subclasses:

L1Updater, SimpleUpdater, SquaredL2Updater

public abstract class Updater
extends Object
implements scala.Serializable

Class used to perform steps (weight update) using Gradient Descent methods.

For general minimization problems, or for regularized problems of the form min L(w) + regParam * R(w), the compute function performs the actual update step, when given some (e.g. stochastic) gradient direction for the loss L(w), and a desired step-size (learning rate).

The updater is responsible to also perform the update coming from the regularization term R(w) (if any regularization is used).

See Also:

• Serialized Form

Constructor Summary

Constructors

Constructor	Description
Updater()

Method Summary

Modifier and Type	Method	Description
abstract scala.Tuple2<Vector,Object>	compute(Vector weightsOld, Vector gradient, double stepSize, int iter, double regParam)	Compute an updated value for weights given the gradient, stepSize, iteration number and regularization parameter.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

Updater

public Updater()

Method Details

compute

public abstract scala.Tuple2<Vector,Object> compute(Vector weightsOld,
 Vector gradient,
 double stepSize,
 int iter,
 double regParam)

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.

Parameters:

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

gradient - - 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.