Mikhail,

Thank you for your reply.

Even though the long is returned from this function, it is always encoded as a 
single byte lossy representation.  In order to change that and add other norms 
(for using other similarity functions on the same indexed data), there should 
be a support for multiple norms.  Imagine using two similarities side-by-side - 
a default and an LMSimilarity with discountOverlaps set to false, or trying a 
different doc length normalization where the length shouldn't be a reciprocal 
square root function like in the DefaultSimilarity.  The only way of doing it, 
is to have the multiple norms stored.

Here is the LUCENE-3687 Description:
"This removes the long standing limitation that norms are a single byte. Yet, 
we still need to expose this functionality to Similarity to write / encode 
norms in a different format."

I am wondering if there is a plan to roll this into a release.

Thanks,


Ivan




On Saturday, October 3, 2015 11:04 PM, Mikhail Khludnev 
<mkhlud...@griddynamics.com> wrote:



Hello,

Norms can be long, see 
org.apache.lucene.search.similarities.TFIDFSimilarity.encodeNormValue(float)
  /** Encodes a normalization factor for storage in an index. */
  public abstract long encodeNormValue(float f);



On Sun, Oct 4, 2015 at 6:39 AM, Ivan Provalov <iprov...@yahoo.com.invalid> 
wrote:

When does this 4.0-ALPHA feature going to be included in the released version?  
>https://issues.apache.org/jira/browse/LUCENE-3687  
>It's the "Allow similarity to encode norms other than a single byte".   
>
>
>I thought that it would be in the released versions, but it looks like it's 
>only on 4.0-alpha.  I am using 4.6.1, but also looked in 5.3.1 source, none of 
>these include the changes.  
>
>
>With these changes, the new API in the sim class is accepting the norms, like 
>so: computeNorm(FieldInvertState state, Norm norm). 
>
>
>Thank you,
>
>
>Ivan Provalov


-- 

Sincerely yours
Mikhail Khludnev
Principal Engineer,

Grid Dynamics

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