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ASF GitHub Bot commented on FLINK-1807: --------------------------------------- Github user tillrohrmann commented on a diff in the pull request: https://github.com/apache/flink/pull/613#discussion_r28763749 --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/common/WeightVector.scala --- @@ -0,0 +1,45 @@ +/* + * 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.common + +import org.apache.flink.ml.math.Vector + +// TODO(tvas): This privdes an abstraction for the weights +// but at the same time it leads to the creation of many objects as we have to pack and unpack +// the weights and the intercept often during SGD. + +/** This class represents a weight vector with an intercept, as it is required for many supervised + * learning tasks + * @param weights The vector of weights + * @param intercept The intercept (bias) weight + */ +case class WeightVector(weights: Vector, intercept: Double) extends Serializable { + + override def equals(obj: Any): Boolean = { --- End diff -- This method should be automatically generated by Scala since we're using case classes. > 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)