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https://issues.apache.org/jira/browse/FLINK-1979?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15286464#comment-15286464
]
ASF GitHub Bot commented on FLINK-1979:
---------------------------------------
Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/1985#discussion_r63509514
--- Diff:
flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/optimization/GradientDescent.scala
---
@@ -321,19 +318,8 @@ class GradientDescentL1 extends GradientDescent {
regularizationConstant: Double,
learningRate: Double)
: Vector = {
- // Update weight vector with gradient. L1 regularization has no
gradient, the proximal operator
- // does the job.
- BLAS.axpy(-learningRate, gradient, weightVector)
-
- // Apply proximal operator (soft thresholding)
- val shrinkageVal = regularizationConstant * learningRate
- var i = 0
- while (i < weightVector.size) {
- val wi = weightVector(i)
- weightVector(i) = scala.math.signum(wi) *
- scala.math.max(0.0, scala.math.abs(wi) - shrinkageVal)
- i += 1
- }
+
+ L1Regularization.takeStep(weightVector, gradient,
regularizationConstant,learningRate)
--- End diff --
whitespace missing
> Implement Loss Functions
> ------------------------
>
> Key: FLINK-1979
> URL: https://issues.apache.org/jira/browse/FLINK-1979
> Project: Flink
> Issue Type: Improvement
> Components: Machine Learning Library
> Reporter: Johannes Günther
> Assignee: Johannes Günther
> Priority: Minor
> Labels: ML
>
> For convex optimization problems, optimizer methods like SGD rely on a
> pluggable implementation of a loss function and its first derivative.
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