Package ivory.smrf.retrieval

Examples of ivory.smrf.retrieval.Accumulator

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      throw new RuntimeException(e);
    }
    ////System.out.println("Ranker created.");

    sortedAccumulators.clear();
    Accumulator a = new Accumulator(0, 0.0f);

    // NodeWeight that must be achieved to enter result set.
    double scoreThreshold = Double.NEGATIVE_INFINITY;

    int docno = Integer.MAX_VALUE;
    int nextDocno = structureReader.getNextCandidate(docno);
    if(nextDocno < docno){
      docno = nextDocno;
    }
    int cnt = 0;
    while (docno < Integer.MAX_VALUE) {
      float score = 0.0f;

      // Document-at-a-time scoring.
      //      try {
      //    LOG.info("Advance to docno " + docno+" => "+getDocnoMapping().getDocid(docno));
      //      } catch (IOException e) {
      //    e.printStackTrace();
      //      }
      NodeWeight sc = structureReader.computeScore(docno,0);
      score = sc.getBM25((int) env.getDocumentCount(), env.getDocumentLength(docno), env.getCollectionSize()/ (float) env.getDocumentCount());
//      LOG.info("Docno " + docno + ","+docnoMapping.getDocid(docno)+" scored: "+score);

      cnt++;
      // Keep track of numResults best accumulators.
      if (score > scoreThreshold) {
        a.docno = docno;
        a.score = score;
        sortedAccumulators.add(a);

        if (sortedAccumulators.size() == numResults + 1) {
          a = sortedAccumulators.poll();
          scoreThreshold = sortedAccumulators.peek().score;
        } else {
          a = new Accumulator(0, 0.0f);
        }
      }    

      // Advance to next document
      docno = Integer.MAX_VALUE;
      nextDocno = structureReader.getNextCandidate(docno);
      if(nextDocno < docno){
        docno = nextDocno;
      }
    }

    // Grab the accumulators off the stack, in (reverse) order.
    Accumulator[] accs = new Accumulator[Math.min(numResults, sortedAccumulators.size())];
    for (int i = 0; i < accs.length; i++) {
      Accumulator acc = sortedAccumulators.poll();
      //    LOG.info((accs.length - 1 - i)+"="+acc.docno+","+acc.score);
      accs[accs.length - 1 - i] = acc;
    }

    this.results.put(qid, accs);
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        int size = sortedConcepts.size();
        if (size < curFbTerms || sortedConcepts.peek().score < score) {
          if (size == curFbTerms) {
            sortedConcepts.poll(); // Remove worst concept.
          }
          sortedConcepts.add(new Accumulator(conceptID, score));
        }
      }

      // Compute the weights of the expanded terms.
      int numTerms = Math.min(curFbTerms, sortedConcepts.size());
      float totalWt = 0.0f;
      Accumulator[] bestConcepts = new Accumulator[numTerms];
      for (int i = 0; i < numTerms; i++) {
        Accumulator a = sortedConcepts.poll();
        bestConcepts[i] = a;
        totalWt += a.score;
      }

      // Add cliques corresponding to best expansion concepts.
      for (int i = 0; i < numTerms; i++) {
        Accumulator a = bestConcepts[i];

        // Construct the MRF corresponding to this concept.
        String[] concepts = vocab[a.docno].getKey().split(" ");
        MarkovRandomField conceptMRF = curBuilder.buildMRF(concepts);
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      int size = sortedConcepts.size();
      if (size < numFeedbackTerms || sortedConcepts.peek().score < score) {
        if (size == numFeedbackTerms) {
          sortedConcepts.poll(); // Remove worst concept.
        }
        sortedConcepts.add(new Accumulator(conceptID, score));
      }
    }

    // Compute the weights of the expanded terms.
    int numTerms = Math.min(numFeedbackTerms, sortedConcepts.size());
    float totalWt = 0.0f;
    Accumulator[] bestConcepts = new Accumulator[numTerms];
    for (int i = 0; i < numTerms; i++) {
      Accumulator a = sortedConcepts.poll();
      bestConcepts[i] = a;
      totalWt += a.score;
    }

    // Document node (shared across all expansion cliques).
    DocumentNode docNode = new DocumentNode();

    // Expression generator (shared across all expansion cliques).
    ExpressionGenerator generator = new TermExpressionGenerator();

    // Add cliques corresponding to best expansion concepts.
    for (int i = 0; i < numTerms; i++) {
      Accumulator a = bestConcepts[i];

      // Construct the MRF corresponding to this concept.
      String concept = vocab[a.docno].getKey();

      for (int j = 0; j < scoringFunctionNodes.size(); j++) {
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          sumSq += score * score;
          i++;
          String originalDocID = lines[i];
          docnoMapping.put(new Integer(docid), originalDocID);
          i++;
          results[j] = new Accumulator(docid, score);
          j++;

        }
        int n = results.length;
        float muo = sum / n;
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            min = score;
          i++;
          String originalDocID = lines[i];
          docnoMapping.put(new Integer(docid), originalDocID);
          i++;
          results[j] = new Accumulator(docid, score);
          j++;

        }
        float d = max - min;
        for (Accumulator a : results) {
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          float score = Float.parseFloat(lines[i]);
          i++;
          String originalDocID = lines[i];
          docnoMapping.put(new Integer(docid), originalDocID);
          i++;
          results[j] = new Accumulator(docid, score);
          j++;
        }
        sLogger.info("returning original scores.");
        return results;
      }
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        }
        continue;
      }
      String[] s = lines[i].split("\t");

      r.add(new Accumulator(Integer.parseInt(s[0]), Float.parseFloat(s[1])));
      docnoMapping.put(Integer.parseInt(s[0]), s[2]);
    }
    Accumulator[] results = new Accumulator[r.size()];
    for (int i = 0; i < r.size(); i++)
      results[i] = r.get(i);
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        int size = sortedConcepts.size();
        if (size < curFbTerms || sortedConcepts.peek().score < score) {
          if (size == curFbTerms) {
            sortedConcepts.poll(); // Remove worst concept.
          }
          sortedConcepts.add(new Accumulator(conceptID, score));
        }
      }

      // Compute the weights of the expanded terms.
      int numTerms = Math.min(curFbTerms, sortedConcepts.size());
      float totalWt = 0.0f;
      Accumulator[] bestConcepts = new Accumulator[numTerms];
      for (int i = 0; i < numTerms; i++) {
        Accumulator a = sortedConcepts.poll();
        bestConcepts[i] = a;
        totalWt += a.score;
      }

      // Add cliques corresponding to best expansion concepts.
      for (int i = 0; i < numTerms; i++) {
        Accumulator a = bestConcepts[i];

        // Construct the MRF corresponding to this concept.
        String[] concepts = vocab[a.docno].getKey().split(" ");
        MarkovRandomField conceptMRF = curBuilder.buildMRF(concepts);
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      int size = sortedConcepts.size();
      if (size < numFeedbackTerms || sortedConcepts.peek().score < score) {
        if (size == numFeedbackTerms) {
          sortedConcepts.poll(); // Remove worst concept.
        }
        sortedConcepts.add(new Accumulator(conceptID, score));
      }
    }

    // Compute the weights of the expanded terms.
    int numTerms = Math.min(numFeedbackTerms, sortedConcepts.size());
    float totalWt = 0.0f;
    Accumulator[] bestConcepts = new Accumulator[numTerms];
    for (int i = 0; i < numTerms; i++) {
      Accumulator a = sortedConcepts.poll();
      bestConcepts[i] = a;
      totalWt += a.score;
    }

    // Document node (shared across all expansion cliques).
    DocumentNode docNode = new DocumentNode();

    // Expression generator (shared across all expansion cliques).
    ExpressionGenerator generator = new TermExpressionGenerator();

    // Add cliques corresponding to best expansion concepts.
    for (int i = 0; i < numTerms; i++) {
      Accumulator a = bestConcepts[i];

      // Construct the MRF corresponding to this concept.
      String concept = vocab[a.docno].getKey();

      for (int j = 0; j < scoringFunctionNodes.size(); j++) {
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          sumSq += score * score;
          i++;
          String originalDocID = lines[i];
          docnoMapping.put(new Integer(docid), originalDocID);
          i++;
          results[j] = new Accumulator(docid, score);
          j++;

        }
        int n = results.length;
        float muo = sum / n;
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Related Classes of ivory.smrf.retrieval.Accumulator

  • ivory.server.RunRetrievalBroker$QueryServlet$ServerThread
  • ivory.smrf.model.expander.NGramLatentConceptExpander
  • ivory.smrf.model.expander.UnigramLatentConceptExpander
  • ivory.smrf.retrieval.distributed.BrokerQueryRunner
  • ivory.smrf.retrieval.distributed.RunRetrievalBroker$QueryServlet$ServerThread
  • ivory.sqe.retrieval.StructuredQueryRanker
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