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https://issues.apache.org/jira/browse/FLINK-1979?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15287019#comment-15287019
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ASF GitHub Bot commented on FLINK-1979:
---------------------------------------
Github user skavulya commented on a diff in the pull request:
https://github.com/apache/flink/pull/1985#discussion_r63562423
--- Diff:
flink-libraries/flink-ml/src/test/scala/org/apache/flink/ml/optimization/RegularizationPenaltyITSuite.scala
---
@@ -0,0 +1,65 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.flink.ml.optimization
+
+import org.apache.flink.ml.math.DenseVector
+import org.apache.flink.test.util.FlinkTestBase
+import org.scalatest.{FlatSpec, Matchers}
+
+
+class RegularizationPenaltyITSuite extends FlatSpec with Matchers with
FlinkTestBase {
--- End diff --
Thanks @tillrohrmann @thvasilo for all your comments. I'll make the
changes for passing the regression penalty as a parameter, fix the formatting
issue and update the unit tests
> 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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