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https://issues.apache.org/jira/browse/FLINK-1901?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14649351#comment-14649351
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ASF GitHub Bot commented on FLINK-1901:
---------------------------------------
Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/949#discussion_r35984921
--- Diff:
flink-core/src/main/java/org/apache/flink/api/common/operators/util/RandomSampler.java
---
@@ -0,0 +1,51 @@
+/*
+ * 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.api.common.operators.util;
+
+import java.util.Iterator;
+
+public abstract class RandomSampler<T> {
--- End diff --
JavaDocs are missing
> Create sample operator for Dataset
> ----------------------------------
>
> Key: FLINK-1901
> URL: https://issues.apache.org/jira/browse/FLINK-1901
> Project: Flink
> Issue Type: Improvement
> Components: Core
> Reporter: Theodore Vasiloudis
> Assignee: Chengxiang Li
>
> In order to be able to implement Stochastic Gradient Descent and a number of
> other machine learning algorithms we need to have a way to take a random
> sample from a Dataset.
> We need to be able to sample with or without replacement from the Dataset,
> choose the relative size of the sample, and set a seed for reproducibility.
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