search for: get_sumpart

I was adding the calculations for a lower bound to get_sumpart() (DLH has no term independent component) when I realized that the same lower bound will be calculated for each term-docment pair that get_sumpart is called pair which basically reduces efficiency. How do I calculate the lower bound for a term only once and then use it ? -Regards -Aarsh On Fri,...

...3 patched to r15140 and using chert. This also happens with complex queries where groups of results are expected to have identical weights. FIX: I found a simple fix for this issue, at least for my test cases: I added if (param_k1 == 0) RETURN(termweight); to the beginning of BM25Weight::get_sumpart in trunk/xapian-core/weight/bm25weight.cc:166 This apparently prevents floating point precision issues in the last line of get_sumpart() [which calculates termweight * wdf_double * 1 / wdf_double]. It also speeds up my case slightly. ;-) In order to prevent more such issues, it might be a good...

...e the query to get a Xapian::Query 3. construct an Enquire for searching by calling get_mset method here is the function-time-cost for searching: samples % symbol name 75649 28.0401 ChertPostList::move_forward_in_chunk_to_at_least(unsigned int) 30118 11.1635 Xapian::BM25Weight::get_sumpart(unsigned int, unsigned int) const 21291 7.8917 AndMaybePostList::process_next_or_skip_to(double, Xapian::PostingIterator::Internal*) 17803 6.5989 OrPostList::next(double) 12481 4.6262 AndMaybePostList::get_weight() const 10729 3.9768 OrPostList::get_weight() const 10096 3.74...

Hello guys. I am currently working on the DLH weighting scheme .The formula for DLH is very complex and it ends up giving negative weights to some documents because of the formula.Due to this,inspite of having occurence/occurences of the keyword, the documents with negative weights don't show up in the results at all. Please can I get some help on how to deal with this ? Or should I just leave

Hi, I'm trying to use custom weight with python binding. My test code is like this. class TinkerWeight(xapian.Weight): def __init__(self): pass def name(self): return "Tinker" def serialize(self): return "" def get_sumpart(*args): return 1 def get_maxpart(*args): return 1 def get_sumextra(*args): return 0 def get_maxextra(*args): return 0 ... ... enquire.set_weighting_scheme(TinkerWeight()) But is throws this error: *in method 'Enquire_set_weighting_scheme', a...

Hi, I'm using xapian with c# and mono and i'm having a segfault in get_weight. When i print the index variable, the value is clearly too high. I think something write over it. Do you have any idea on how i could trace the beginning of the segmentation fault ? Thanks, -- Yann

...rough the code base particularly *xapian-core* and studied *Xapian::Weight* class thoroughly and looked at the implementations of some of the already defined weighting schemes. Currently, I've started to look at the ticket https://trac.xapian. org/ticket/744 and trying to devise a way so that *get_sumpart() *method in every weight subclass does not need to be updated after the merging. Also, I am going through *xapian-api *to get more essence of the code base. The project ideas which I would like to propose and get some feedback on are: *1)* Currently, Xapian supports the weighting schemes which...

...[.] 0x0000000000005531 0.03% perl perl [.] Perl_scalar 0.03% /mnt/btr/public perl [.] Perl_do_kv 0.03% /mnt/btr/public perl [.] PerlIO_openn 0.03% script/public-i libxapian.so.30.8.0 [.] Xapian::BM25Weight::get_sumpart 0.03% /mnt/btr/public libc-2.28.so [.] vfprintf 0.03% script/public-i perl [.] Perl_yyparse 0.03% script/public-i perl [.] Perl_pad_alloc 0.03% /mnt/btr/public perl [.] S_cleanup_regmatch_info_aux 0.03% script...

Going back to the "prioritizing aggregated DBs" thread from February 2020, I've got 390 Xapian shards for 130 public inboxes I want to search against(*). There's more on the horizon (we're expecting tens of thousands of public inboxes). After bumping RLIMIT_NOFILE and running ->add_database a bunch, the actual queries seem to be taking ~30s (not good :x). Now I'm