void indri::infnet::InferenceNetwork::_evaluateIndex( indri::index::Index& index ) {
// don't need to do anything unless there are some
// evaluators in the network that need full evaluation
if( _complexEvaluators.size() ) {
lemur::api::DOCID_T maximumDocument = index.documentMaximum();
if (maximumDocument == index.documentBase()) {
// empty memory index, nothing to score.
return;
}
lemur::api::DOCID_T lastCandidate = MAX_INT32; // 64
int scoredDocuments = 0;
lemur::api::DOCID_T candidate = 0;
indri::index::DeletedDocumentList::read_transaction* deleted;
deleted = _repository.deletedList().getReadTransaction();
while(1) {
// ask the root node for a candidate document
// this asks the whole inference network for the
// first document that might possibly produce a
// usable (above the max score threshold) score
candidate = _nextCandidateDocument( deleted );
if (candidate < index.documentBase()) {
std::cerr << candidate << " < index.documentBase()" << std::endl;
break;
}
assert( candidate >= index.documentBase() );
// if candidate is MAX_INT32, we're done
if( candidate == MAX_INT32 || candidate > maximumDocument ) {
break;
}
// move all the doc info lists to this new document
// in preparation for scoring
if( candidate != lastCandidate ) {
_moveToDocument( candidate );
}
// ask all the evaluators to evaluate this document
_evaluateDocument( index, candidate );
scoredDocuments++;
// if that was the last document, we can quit now
if( candidate+1 > maximumDocument )
break;
// move all candidate iterators to candidate+1
_moveToDocument( candidate+1 );
lastCandidate = candidate+1;
assert( candidate >= index.documentBase() );
}
delete deleted;
}
}
void indri::infnet::ContextSimpleCountAccumulator::indexChanged( indri::index::Index& index ) {
_computeCounts( index );
_maximumDocument = index.documentMaximum();
}