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https://issues.apache.org/jira/browse/LUCENE-6896?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15007103#comment-15007103
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Adrien Grand commented on LUCENE-6896:
--------------------------------------
+1
I'm curious what the reasoning is for
{code}NORM_TABLE[0] = 1.0f / NORM_TABLE[255];{code}
Is it just a way to get a high float value that would be unlikely to overflow
to Infinity (eg. when multiplied) or is it more than that?
> Fix/document various Similarity bugs around extreme norm values
> ---------------------------------------------------------------
>
> Key: LUCENE-6896
> URL: https://issues.apache.org/jira/browse/LUCENE-6896
> Project: Lucene - Core
> Issue Type: Bug
> Reporter: Robert Muir
> Fix For: 6.0, 5.4
>
> Attachments: LUCENE-6896.patch
>
>
> Spinoff from LUCENE-6818:
> [~iorixxx] found problems with every Similarity (except ClassicSimilarity)
> when trying to test how they behave on every possible norm value, to ensure
> they are robust for all index-time boosts.
> There are several problems:
> 1. buggy normalization decode that causes the smallest possible norm value
> (0) to be treated as an infinitely long document. These values are intended
> to be encoded as non-negative finite values, but going to infinity breaks
> everything.
> 2. various problems in the less practical functions that already have
> documented warnings that they do bad things for extreme values. These impact
> DFR models D, Be, and P and IB distribution SPL.
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