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Object org.apache.spark.mllib.optimization.Updater
public abstract class Updater
:: DeveloperApi :: 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).
Constructor Summary | |
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Updater()
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Method Summary | |
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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 Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public Updater()
Method Detail |
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public abstract scala.Tuple2<Vector,Object> compute(Vector weightsOld, Vector gradient, double stepSize, int iter, double regParam)
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 iterationsiter
- - Iteration numberregParam
- - Regularization parameter
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