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From "Joseph K. Bradley (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-19110) DistributedLDAModel returns different logPrior for original and loaded model
Date Sat, 07 Jan 2017 19:42:58 GMT

     [ https://issues.apache.org/jira/browse/SPARK-19110?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Joseph K. Bradley resolved SPARK-19110.
---------------------------------------
       Resolution: Fixed
    Fix Version/s: 2.2.0
                   2.0.3
                   2.1.1

Issue resolved by pull request 16491
[https://github.com/apache/spark/pull/16491]

> DistributedLDAModel returns different logPrior for original and loaded model
> ----------------------------------------------------------------------------
>
>                 Key: SPARK-19110
>                 URL: https://issues.apache.org/jira/browse/SPARK-19110
>             Project: Spark
>          Issue Type: Bug
>          Components: ML, MLlib
>    Affects Versions: 1.3.1, 1.4.1, 1.5.2, 1.6.3, 2.0.2, 2.1.0, 2.2.0
>            Reporter: Miao Wang
>            Assignee: Miao Wang
>             Fix For: 2.1.1, 2.0.3, 2.2.0
>
>
> While adding DistributedLDAModel training summary for SparkR, I found that the logPrior
for original and loaded model is different.
> For example, in the test("read/write DistributedLDAModel"), I add the test:
> val logPrior = model.asInstanceOf[DistributedLDAModel].logPrior
>       val logPrior2 = model2.asInstanceOf[DistributedLDAModel].logPrior
>       assert(logPrior === logPrior2)
> The test fails:
> -4.394180878889078 did not equal -4.294290536919573



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