Examples of kea.util.Counter

kea.util.Counter

      String str = getInputFormat().instance(i).stringValue(m_DocumentAtt);
      HashMap<String,Counter> hash = getPhrasesForDictionary(str);
      Iterator<String> it = hash.keySet().iterator();
      while (it.hasNext()) {
        String phrase = it.next();
        Counter counter = m_Dictionary.get(phrase);
        if (counter == null) {
          m_Dictionary.put(phrase, new Counter());
        } else {
          counter.increment();
        }
      }
    }


    if (m_KFused) {       
      


      // Build dictionary of n-grams that occur as keyphrases
      // with associated keyphrase frequencies
      m_KeyphraseDictionary = new HashMap<String,Counter>();
      for (int i = 0; i < getInputFormat().numInstances(); i++) {
        String str = getInputFormat().instance(i).stringValue(m_KeyphrasesAtt);
        HashMap<String,Counter> hash = getGivenKeyphrases(str, false);
        if (hash != null) {
          Iterator<String> it = hash.keySet().iterator();
          while (it.hasNext()) {
            String phrase = it.next();
            Counter counter = m_KeyphraseDictionary.get(phrase);
            if (counter == null) {
              m_KeyphraseDictionary.put(phrase, new Counter());
            } else {
              counter.increment();
            }
          }
        }
      }
    } else {

View Full Code Here

  private double[] featVals(String id, FastVector phraseInfo, 
      boolean training, HashMap<String,Counter> hashKeysEval,
      HashMap<String,Counter> hashKeyphrases, int length, HashMap<String,FastVector> hash) {


    // Compute feature values
    Counter counterLocal = (Counter)phraseInfo.elementAt(1);
    double[] newInst = new double[m_NumFeatures + 1];




    // Compute TFxIDF
    Counter counterGlobal = (Counter)m_Dictionary.get(id);
    double localVal = counterLocal.value(), globalVal = 0;
    if (counterGlobal != null) {
      globalVal = counterGlobal.value();
      if (training) {
        globalVal = globalVal - 1;
      }
    }


    // Just devide by length to get approximation of probability
    // that phrase in document is our phrase
    // newInst[m_TfidfIndex] = (localVal / ((double)length));
    newInst[m_TfidfIndex] = (localVal / ((double)length)) *
    (-Math.log((globalVal + 1)/ ((double)m_NumDocs + 1)));


    // Compute first occurrence
    Counter counterFirst = (Counter)phraseInfo.elementAt(0);
    newInst[m_FirstOccurIndex] = (double)counterFirst.value() /
    (double)length;




    // Is keyphrase frequency attribute being used?
    if (m_KFused) {
      Counter keyphraseC = (Counter)m_KeyphraseDictionary.get(id);
      if ((training) && (hashKeyphrases != null) &&
          (hashKeyphrases.containsKey(id))) {
        newInst[m_KeyFreqIndex] = keyphraseC.value() - 1;
      } else {
        if (keyphraseC != null) {
          newInst[m_KeyFreqIndex] = keyphraseC.value();
        } else {
          newInst[m_KeyFreqIndex] = 0;
        }
      }
    }

View Full Code Here

            } else {
              id = (String)m_Vocabulary.getID(orig);
            }


            if (id != null) {                
              Counter count = (Counter)hash.get(id);
              if (count == null) {
                hash.put(id, new Counter());
              } else {
                count.increment();
              }
              //  System.err.println(orig + "\t" + id);
            }            
          }
        }

View Full Code Here

                } else {
                  vec = new FastVector(2);
                }


                // Update hashtable with all the info
                vec.addElement(new Counter(pos + 1 - i)); //0
                vec.addElement(new Counter()); //1
                vec.addElement(orig); //2


                if (m_STDEVfeature) {
                  FastVector app = new FastVector();
                  app.addElement(new Counter(pos + 1 - i));
                  vec.addElement(app); 
                }
                hash.put(id, vec);
              } else {


                // If the phrase already was identified,
                // update its values in the old vector


                // Update number of occurrences
                ((Counter)((FastVector)vec).elementAt(1)).increment();


                if (m_STDEVfeature) {


                  FastVector app = (FastVector)vec.elementAt(3); 
                  app.addElement(new Counter(pos + 1 - i));
                  vec.addElement(app); 
                }


              }                
            }

View Full Code Here

          id = (String)m_Vocabulary.getID(orig);
        }
        if (id != null) {
          //System.err.println("\t" + id);
          if (!hash.containsKey(id)) {
            hash.put(id, new Counter());
          } else {  
            Counter c = (Counter)hash.get(id);
            c.increment();
            hash.put(id, c);
            if (forEval && m_Debug) {
              System.err.println("Skipping the phrase " + orig + ", which appears twice in the author-assigned keyphrase set.");
            }
          }

View Full Code Here

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