[
https://issues.apache.org/jira/browse/FLINK-1807?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14519590#comment-14519590
]
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_r29348685
--- Diff:
flink-staging/flink-ml/src/test/scala/org/apache/flink/ml/optimization/GradientDescentITSuite.scala
---
@@ -0,0 +1,212 @@
+/*
+ * 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.common.{LabeledVector, WeightVector,
ParameterMap}
+import org.apache.flink.ml.math.DenseVector
+import org.apache.flink.ml.regression.RegressionData._
+import org.scalatest.{Matchers, FlatSpec}
+
+import org.apache.flink.api.scala._
+import org.apache.flink.test.util.FlinkTestBase
+
+
+class GradientDescentITSuite extends FlatSpec with Matchers with
FlinkTestBase {
+
+ behavior of "The Stochastic Gradient Descent implementation"
+
+ it should "correctly solve an L1 regularized regression problem" in {
+ val env = ExecutionEnvironment.getExecutionEnvironment
+
+ env.setParallelism(2)
+
+ val parameters = ParameterMap()
+
+ parameters.add(IterativeSolver.Stepsize, 0.01)
+ parameters.add(IterativeSolver.Iterations, 2000)
+ parameters.add(Solver.LossFunction, new SquaredLoss)
+ parameters.add(Solver.RegularizationType, new L1Regularization)
+ parameters.add(Solver.RegularizationParameter, 0.3)
+
--- End diff --
I agree. We'll fix this soon :-)
> 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)
