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https://issues.apache.org/jira/browse/FLINK-1979?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15286480#comment-15286480
]
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_r63510204
--- Diff:
flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/optimization/PartialLossFunction.scala
---
@@ -47,21 +47,106 @@ object SquaredLoss extends PartialLossFunction {
/** Calculates the loss depending on the label and the prediction
*
- * @param prediction
- * @param label
- * @return
+ * @param prediction The predicted value
+ * @param label The true value
+ * @return The loss
*/
override def loss(prediction: Double, label: Double): Double = {
0.5 * (prediction - label) * (prediction - label)
}
/** Calculates the derivative of the [[PartialLossFunction]]
*
- * @param prediction
- * @param label
- * @return
+ * @param prediction The predicted value
+ * @param label The true value
+ * @return The derivative of the loss function
*/
override def derivative(prediction: Double, label: Double): Double = {
(prediction - label)
}
}
+
+/** Logistic loss function which can be used with the
[[GenericLossFunction]]
+ *
+ *
+ * The [[LogisticLoss]] function implements `log(1 +
exp(prediction*label))`
--- End diff --
A minus is missing in the exponent of e.
> 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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