Skip to content

[SPARK-24300][ML] change the way to set seed in ml.cluster.LDASuite.generateLDAData #21492

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
lu-wang-dl wants to merge 2 commits into from
Closed

[SPARK-24300][ML] change the way to set seed in ml.cluster.LDASuite.generateLDAData #21492

Changes from 1 commit
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
91fc116
change the way to generate the random seed in LDASuite
lu-wang-dl Jun 4, 2018
fa2c422
minnor fix for setting random seed
lu-wang-dl Jun 4, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Next Next commit
change the way to generate the random seed in LDASuite
  • Loading branch information
@lu-wang-dl
lu-wang-dl committed Jun 4, 2018
commit 91fc11639d56cc3f283e383e589808b6ab9bd0f7
4 changes: 2 additions & 2 deletions mllib/src/test/scala/org/apache/spark/ml/clustering/LDASuite.scala
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -36,9 +36,9 @@ object LDASuite {
vocabSize: Int): DataFrame = {
val avgWC = 1 // average instances of each word in a doc
val sc = spark.sparkContext
val rng = new java.util.Random()
rng.setSeed(1)
val rdd = sc.parallelize(1 to rows).map { i =>
val rng = new java.util.Random()
Copy link
Contributor

@mengxr mengxr Jun 4, 2018

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

minor: you can use new java.util.random(i) direct

rng.setSeed(i)
Vectors.dense(Array.fill(vocabSize)(rng.nextInt(2 * avgWC).toDouble))
}.map(v => new TestRow(v))
spark.createDataFrame(rdd)
Expand Down