Examples of edu.umd.cloud9.io.map.HMapSFW.keySet()

edu.umd.cloud9.io.map.HMapSFW.keySet()
Returns a {@link Set} view of the keys contained in this map. The set's iterator returns thekeys in ascending order. The set is backed by the map, so changes to the map are reflected in the set, and vice-versa. If the map is modified while an iteration over the set is in progress (except through the iterator's own remove operation), the results of the iteration are undefined. The set supports element removal, which removes the corresponding mapping from the map, via the Iterator.remove, Set.remove, removeAll, retainAll, and clear operations. It does not support the add or addAll operations.

      // double logNormalizer = Math.log(hashMap.getNormalizeFactor());


      // logNormalizer = LogMath.add(pairOfIntFloat.getRightElement(),
      // Settings.DEFAULT_LOG_ETA + Math.log(numberOfTerms));


      Iterator<Integer> itr = hashMap.keySet().iterator();
      while (itr.hasNext()) {
        int termIndex = itr.next();
        double logBetaValue = hashMap.get(termIndex);
        // double logBetaValue = Math.log(hashMap.get(termIndex));

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        System.out.println("==============================");
        System.out.println("Top ranked " + topDisplay + " terms for Topic "
            + pairOfIntFloat.getLeftElement());
        System.out.println("==============================");


        Iterator<Integer> itr1 = hmap.keySet().iterator();
        int temp1 = 0;
        while (itr1.hasNext()) {
          temp1 = itr1.next();
          treeMap.put(-hmap.get(temp1), temp1);
          if (treeMap.size() > topDisplay) {

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      JsonArray queryJsonArr = new JsonArray();


      HMapSFW scaledPhrase2Weight = null;
      if (phraseWeight > 0) {
        scaledPhrase2Weight = Utils.scaleProbMap(lexProbThreshold, phraseWeight, translation.getPhraseDist());      
        for (String phrase : scaledPhrase2Weight.keySet()) {
          queryJsonArr.add(new JsonPrimitive(scaledPhrase2Weight.get(phrase)));
          queryJsonArr.add(new JsonPrimitive(phrase));
        }
      }
      if (tokenWeight > 0) {

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      HMapSFW dist = probMaps.get(i).getMap();
      float weight = probMaps.get(i).getWeight();


      // don't add vocabulary from a distribution that has 0 weight
      if (weight > 0) {
        translationAlternatives.addAll(dist.keySet());
        sumWeights += weight;
      }
    }


    // normalize by sumWeights

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        break;
      }
    }


    // normalize weights
    for(String e : probDist.keySet()){
      probDist.put(e, probDist.get(e) / sumProbEF);
    }


    //    LOG.info("Translations of "+token+"="+probDist);

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      HMapSFW dist = probMaps.get(i).getMap();
      float weight = probMaps.get(i).getWeight();


      // don't add vocabulary from a distribution that has 0 weight
      if (weight > 0) {
        translationAlternatives.addAll(dist.keySet());
        sumWeights += weight;
      }
    }
    
    // normalize by sumWeights

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        break;
      }
    }


    // normalize weights
    for(String e : probDist.keySet()){
      probDist.put(e, probDist.get(e) / sumProbEF);
    }


    //    LOG.info("Translations of "+token+"="+probDist);

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    HMapSFW weightedVector = CLIRUtils.createTermDocVector(terms.length, transTermTf, eVocabTrg, fScoreFn, dict, dfTable, true, sLogger);


    // don't count numbers for the min #terms constraint since Wikipedia has "sentences" full of numbers that doesn't make any sense
    int numNonNumbers = 0;
    for(String term : weightedVector.keySet()){      
      if (!term.matches("\\d+")) {
        numNonNumbers++;
      }
    }
    if(numNonNumbers < MinVectorTerms){

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    }


    weightedVector = CLIRUtils.createTermDocVector(terms.length, term2Tf, eScoreFn, dict, dfTable, true, sLogger);
    // don't count numbers for the min #terms constraint since Wikipedia has "sentences" full of numbers that doesn't make any sense
    int numNonNumbers = 0;
    for(String term : weightedVector.keySet()){      
      if (!term.matches("\\d+")) {
        numNonNumbers++;
      }
    }
    if(numNonNumbers < MinVectorTerms){

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      HMapSFW dist = probMaps.get(i).getMap();
      float weight = probMaps.get(i).getWeight();


      // don't add vocabulary from a distribution that has 0 weight
      if (weight > 0) {
        translationAlternatives.addAll(dist.keySet());
        sumWeights += weight;
      }
    }
    
    // normalize by sumWeights

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