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ASF GitHub Bot commented on FLINK-1807: --------------------------------------- Github user thvasilo commented on a diff in the pull request: https://github.com/apache/flink/pull/613#discussion_r29922199 --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/optimization/Regularization.scala --- @@ -0,0 +1,207 @@ +/* + * 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.{Vector => FlinkVector, BLAS} +import org.apache.flink.ml.math.Breeze._ + +import breeze.numerics._ +import breeze.linalg.{norm => BreezeNorm} + + + --- End diff -- Found this from the Scalastyle website: ```xml <check enabled="true" class="org.scalastyle.file.RegexChecker" level="warning"> <parameters> <parameter name="regex">(?m)^\s\*$(\r|)\n^\s*$(\r|)\n</parameter> </parameters> <customMessage>No double blank lines</customMessage> </check> ``` > Stochastic gradient descent optimizer for ML library > ---------------------------------------------------- > > Key: FLINK-1807 > URL: https://issues.apache.org/jira/browse/FLINK-1807 > Project: Flink > Issue Type: Improvement > Components: Machine Learning Library > Reporter: Till Rohrmann > Assignee: Theodore Vasiloudis > Labels: ML > > Stochastic gradient descent (SGD) is a widely used optimization technique in > different ML algorithms. Thus, it would be helpful to provide a generalized > SGD implementation which can be instantiated with the respective gradient > computation. Such a building block would make the development of future > algorithms easier. -- This message was sent by Atlassian JIRA (v6.3.4#6332)