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From Mark Miller <markrmil...@gmail.com>
Subject Re: custom similarity based on tf but greater than 1.0
Date Fri, 19 Jan 2007 01:30:29 GMT
Ahhhh...I brushed over your example too fast...looked like normal 
counting to me...I see now what you mean. So OMIT_NORMS probably did 
work. Are you getting the results through hits? Hits will normalize. Use 
topdocs or a hitcollector.

- Mark

Vagelis Kotsonis wrote:
> But i don't want to get the frequency of each term in the doc.
>
> what I want is 1 if the term exists in the doc and 0 if it doesn't. After
> this, I want all thes 1s and 0s to be summed and give me a number to use as
> a score.
>
> If I set the TF value as 1 or 0, as I described above, I get the right
> number, but this number is normalized to 1.0 and smaller numbers.
>
> It is the normalization that I want to avoid.
>
> Thanks again!
> Vagelis
>
>
> markrmiller wrote:
>   
>> Dont return 1 for tf...just return the tf straight with no 
>> changes...return freq. For everything else return 1. After that 
>> OMIT_NORMS should work. If you want to try a custom reader:
>>
>> public class FakeNormsIndexReader extends FilterIndexReader {
>>     byte[] ones = SegmentReader.createFakeNorms(maxDoc());
>>
>>     public FakeNormsIndexReader(IndexReader in) {
>>         super(in);
>>     }
>>     public synchronized byte[] norms(String field) throws IOException {
>>           System.out.println("returning fake norms...");
>>         return ones;
>>     }
>>
>>     public synchronized void norms(String field, byte[] result, int 
>> offset) {
>>           System.out.println("writing fake norms...");
>>         System.arraycopy(ones, 0, result, offset, maxDoc());
>>     }
>> }
>>
>> The beauty of this reader is that you can flip between it and your 
>> custom similarity and Lucene's default implementations live on the same 
>> index.
>>
>> - Mark
>>
>>
>>     
>
>   

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