Examples of edu.umd.cloud9.util.FibonacciHeapInt

edu.umd.cloud9.io.pair.PairOfStrings
This class implements a Fibonacci heap data structure. Much of the code in this class is based on the algorithms in Chapter 21 of the "Introduction to Algorithms" by Cormen, Leiserson, Rivest, and Stein. The amortized running time of most of these methods is O(1), making it a very fast data structure. Several have an actual running time of O(1). removeMin() and delete() have O(log n) amortized running times because they do the heap consolidation.
Note that this implementation is not synchronized. If multiple threads access a set concurrently, and at least one of the threads modifies the set, it must be synchronized externally. This is typically accomplished by synchronizing on some object that naturally encapsulates the set.
@author Nathan Fiedler

          int e2 = eVocabTrg.get(eTerm);         


          float prob2 = f2e_Probs.get(f2, e2);
          float prob = prob1*prob2;
          sumOfProbs += prob;
          topTrans.add(new PairOfFloatString(prob, fTerm));
        }
        logger.info("Adding "+eTerm);
        addToTable(e1, topTrans, sumOfProbs, table, fVocabTrg, 1.0f, stats);      
      }
      logger.info(stats);

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    if (featSet > 2) {
      // uppercase token matching features : find uppercased tokens that exactly appear on both sides
      // lack of this evidence does not imply anything, but its existence might indicate parallel
//      fSentence.replaceAll("([',:;.?%!])", " $1 ");
//      eSentence.replaceAll("([',:;.?%!])", " $1 ");
      PairOfFloats pair = getUppercaseRatio(fTokenizer.processContent(fSentence), eTokenizer.processContent(eSentence));
      features.add("uppercaseratio1=" + pair.getLeftElement() );
      features.add("uppercaseratio2=" + pair.getRightElement() );
    }


    if (featSet > 3) {
      // future work = count number of single/double letter words in src and trg side

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    // now, read tokens in first sentence and keep track of sequences of uppercased tokens in buffer
    HashSet<String> upperCaseMap1 = getUppercaseParts(tokens1);
    HashSet<String> upperCaseMap2 = getUppercaseParts(tokens2);
    float cntUpperRatio1 = getRatio(upperCaseMap1, upperCaseMap2);
    float cntUpperRatio2 = getRatio(upperCaseMap2, upperCaseMap1);
    PairOfFloats result = new PairOfFloats(cntUpperRatio1, cntUpperRatio2);
    return result;
  }

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                String term = m2.group(1);
                if ( !term.equals("NULL") ) {
                  float prob = Float.parseFloat(m2.group(2));
                  int engIndex = trgVocab.addOrGet(term);
                  logger.debug("Added: "+term+" with index: "+engIndex+" and prob:"+prob);
                  indexProbPairs.add(new PairOfIntFloat(engIndex, prob));
                  sumOfProbs += prob;
                }
              }
            }
            // if number of translations not set, we never cut-off, so all cases are long tails

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                String term = m2.group(1);
                if (!term.equals("NULL")) {
                  float prob = Float.parseFloat(m2.group(2));
                  int engIndex = trgVocab.addOrGet(term);
                  logger.debug("Added: "+term+" with index: "+engIndex+" and prob:"+prob);
                  indexProbPairs.add(new PairOfIntFloat(engIndex, prob));
                  sumOfProbs+=prob;
                }
              }
            }
            if(sumOfProbs > probThreshold){

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    @Override
    public void reduce(Text key, Iterable<PairOfIntLong> values, Context context)
        throws IOException, InterruptedException {
      String term = key.toString();
      Iterator<PairOfIntLong> iter = values.iterator();
      PairOfIntLong p = iter.next();
      int df = p.getLeftElement();
      long cf = p.getRightElement();
      WritableUtils.writeVInt(dfByTermOut, df);
      WritableUtils.writeVLong(cfByTermOut, cf);
      if (iter.hasNext()) {
        throw new RuntimeException("More than one record for term: " + term);
      }

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      // map from the id back to text
      // sLogger.info("termid: " + key);
      String term = mTermIdMap.getTerm(key.get());
      // sLogger.info("term: " + term);
      PairOfIntLong pair = gs.getStats(term);


      if (pair == null) {
        p.setCf(-1);
        p.setDf(-1);
      } else {
        p.setCf(pair.getRightElement());
        p.setDf(pair.getLeftElement());
      }


      output.collect(key, p);
    }

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      float sumProb2 = 0;
      for (Entry<String> entry : probDist.entrySet()) {
        float pr = entry.getValue() / sumProb;
        if (pr > lexProbThreshold) {
          sumProb2 += pr;
          sortedFilteredProbDist.add(new PairOfStringFloat(entry.getKey(), pr));
        }
      }


      // re-normalize values after removal of low-prob entries
      float cumProb = 0;
      int cnt = 0;
      while (cnt < maxNumTrans && cumProb < cumProbThreshold && !sortedFilteredProbDist.isEmpty()) {
        PairOfStringFloat entry = sortedFilteredProbDist.pollLast();
        float pr = entry.getValue() / sumProb2;
        cumProb += pr;
        normProbDist.put(entry.getKey(), pr);
        cnt++;
      }


      probMap.put(sourceTerm, normProbDist);
    }

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        String[] parts = rule.split("\\|\\|\\|");
        String[] lhs = parts[0].trim().split(" ");
        String[] rhs = parts[1].trim().split(" ");;
        for (String l : lhs) {
          for (String r : rhs) {
            pairsInSCFG.add(new PairOfStrings(l, r));
          }
        }
      }
    } catch (UnsupportedEncodingException e) {
      e.printStackTrace();

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      int e = entry.getRightElement();
      String eTerm = eVocab_f2e.get(e);


      //      LOG.info("Pr("+eTerm+"|"+token+")="+probEF);


      if (probEF > 0 && e > 0 && !docLangTokenizer.isStopWord(eTerm) && (translateOnly == null || !translateOnly.equals("indri") || indriPuncPattern.matcher(eTerm).matches()) && (pairsInSCFG == null || pairsInSCFG.contains(new PairOfStrings(token,eTerm)))) {      
        // assuming our bilingual dictionary is learned from normally segmented text, but we want to use bigram tokenizer for CLIR purposes
        // then we need to convert the translations of each source token into a sequence of bigrams
        // we can distribute the translation probability equally to the each bigram
        if (bigramSegment) {
          String[] eTokens = docLangTokenizer.processContent(eTerm);

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