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https://issues.apache.org/jira/browse/FLINK-1994?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15089529#comment-15089529
]
ASF GitHub Bot commented on FLINK-1994:
---------------------------------------
Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/1397#discussion_r49211433
--- Diff:
flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/optimization/GradientDescent.scala
---
@@ -54,14 +54,15 @@ abstract class GradientDescent extends IterativeSolver {
*/
override def optimize(
data: DataSet[LabeledVector],
- initialWeights: Option[DataSet[WeightVector]]): DataSet[WeightVector]
= {
+ initialWeights: Option[DataSet[WeightVector]]
+ ): DataSet[WeightVector] = {
--- End diff --
Indentation:
```
override def optimize(
data: ...
initialWeights: ...)
: DataSet[WeightVector] = {
}
```
> Add different gain calculation schemes to SGD
> ---------------------------------------------
>
> Key: FLINK-1994
> URL: https://issues.apache.org/jira/browse/FLINK-1994
> Project: Flink
> Issue Type: Improvement
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Trevor Grant
> Priority: Minor
> Labels: ML, Starter
>
> The current SGD implementation uses as gain for the weight updates the
> formula {{stepsize/sqrt(iterationNumber)}}. It would be good to make the gain
> calculation configurable and to provide different strategies for that. For
> example:
> * stepsize/(1 + iterationNumber)
> * stepsize*(1 + regularization * stepsize * iterationNumber)^(-3/4)
> See also how to properly select the gains [1].
> Resources:
> [1] http://arxiv.org/pdf/1107.2490.pdf
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