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https://issues.apache.org/jira/browse/LUCENE-1260?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12586588#action_12586588
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Karl Wettin commented on LUCENE-1260:
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I suppose it would be possible to implement a NormCodec that would listen to
encodeNorm(float) while one is creating a subset of the index in order to find
all norm resolution sweetspots for that corpus using some appropriate
algorithm. Mean shift?.
Perhaps it even would be possible to compress it down to n bags from the start
and then allow for it to grow in case new documents with other norm
requirements are added to the store.
I haven't thought too much about it yet, but it seems to me that norm codec has
more to do with the physical store (Directory) than Similarity and should
perhaps be moved there instead? I have no idea how, but I also want to move it
to the instance scope so I can have multiple indices with unique norm
span/resolutions created from the same classloader.
> Norm codec strategy in Similarity
> ---------------------------------
>
> Key: LUCENE-1260
> URL: https://issues.apache.org/jira/browse/LUCENE-1260
> Project: Lucene - Java
> Issue Type: Improvement
> Components: Search
> Affects Versions: 2.3.1
> Reporter: Karl Wettin
> Attachments: LUCENE-1260.txt
>
>
> The static span and resolution of the 8 bit norms codec might not fit with
> all applications.
> My use case requires that 100f-250f is discretized in 60 bags instead of the
> default.. 10?
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