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https://issues.apache.org/jira/browse/FLINK-1901?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14649332#comment-14649332
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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_r35984232
--- Diff:
flink-core/src/main/java/org/apache/flink/api/common/operators/util/BernoulliSampler.java
---
@@ -0,0 +1,105 @@
+/*
+ * 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 com.google.common.base.Preconditions;
+
+import java.util.Iterator;
+import java.util.Random;
+
+/**
+ * A sampler implementation built upon Bernoulli Trail. For sample without
replacement, each element sample choice is just a bernoulli trail.
+ *
+ * @param <T> The type of sample.
+ */
+public class BernoulliSampler<T> extends RandomSampler<T> {
+
+ private final double fraction;
+ private final Random random;
+
+ /**
+ * Create a bernoulli sampler sample fraction and default random number
generator.
+ *
+ * @param fraction sample fraction, aka the bernoulli sampler
possibility.
+ */
+ public BernoulliSampler(double fraction) {
+ this(fraction, new Random());
+ }
+
+ /**
+ * Create a bernoulli sampler sample fraction and random number
generator seed.
+ *
+ * @param fraction sample fraction, aka the bernoulli sampler
possibility.
+ * @param seed random number generator seed.
+ */
+ public BernoulliSampler(double fraction, long seed) {
+ this(fraction, new Random(seed));
+ }
+
+ /**
+ * Create a bernoulli sampler sample fraction and random number
generator.
+ *
+ * @param fraction sample fraction, aka the bernoulli sampler
possibility.
+ * @param random the random number generator.
+ */
+ public BernoulliSampler(double fraction, Random random) {
+ Preconditions.checkArgument(fraction >= 0 && fraction <= 1.0d,
"fraction fraction must between [0, 1].");
+ this.fraction = fraction;
+ this.random = random;
+ }
+
+ /**
+ * Sample the input elements, for each input element, take a Bernoulli
Trail for sample.
+ *
+ * @param input elements to be sampled.
+ * @return the sampled result which is lazy computed upon input
elements.
+ */
+ @Override
+ public Iterator<T> sample(final Iterator<T> input) {
+ if (fraction == 0) {
+ return EMPTY_ITERABLE;
+ }
+
+ return new SampledIterator<T>() {
+ T current;
+
+ @Override
+ public boolean hasNext() {
+ if (current == null) {
+ while (input.hasNext()) {
+ T element = input.next();
+ if (random.nextDouble() <=
fraction) {
+ current = element;
+ return true;
+ }
+ }
+ current = null;
+ return false;
+ }
+ return false;
--- End diff --
I think, if I'm not mistaken, that `hasNext` has to be idempotent. Thus it
should return `true` if `current != null`.
> 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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