Exemplo n.º 1
0
  /**
   * Saves the feature divide model settings .fsm file.
   *
   * @throws MaltChainedException
   */
  protected void save() throws MaltChainedException {
    try {
      final BufferedWriter out =
          new BufferedWriter(
              getGuide()
                  .getConfiguration()
                  .getConfigurationDir()
                  .getOutputStreamWriter(getModelName() + ".dsm"));
      out.write(masterModel.getIndex() + "\t" + masterModel.getFrequency() + "\n");

      if (divideModels != null) {
        for (AtomicModel divideModel : divideModels.values()) {
          out.write(divideModel.getIndex() + "\t" + divideModel.getFrequency() + "\n");
        }
      }
      out.close();
    } catch (IOException e) {
      throw new GuideException(
          "Could not write to the guide model settings file '"
              + getModelName()
              + ".dsm"
              + "', when "
              + "saving the guide model settings to file. ",
          e);
    }
  }
Exemplo n.º 2
0
  public boolean predict(SingleDecision decision) throws MaltChainedException {
    if (getGuide().getGuideMode() == ClassifierGuide.GuideMode.BATCH) {
      throw new GuideException("Can only predict during parsing. ");
    } else if (!(divideFeature.getFeatureValue() instanceof SingleFeatureValue)) {
      throw new GuideException("The divide feature does not have a single value. ");
    }

    // divideFeature.update();
    if (divideModels != null
        && divideModels.containsKey(
            ((SingleFeatureValue) divideFeature.getFeatureValue()).getCode())) {
      return divideModels
          .get(((SingleFeatureValue) divideFeature.getFeatureValue()).getCode())
          .predict(decision);
    } else if (masterModel != null && masterModel.getFrequency() > 0) {
      return masterModel.predict(decision);
    } else {
      getGuide()
          .getConfiguration()
          .getConfigLogger()
          .info(
              "Could not predict the next parser decision because there is "
                  + "no divide or master model that covers the divide value '"
                  + ((SingleFeatureValue) divideFeature.getFeatureValue()).getCode()
                  + "', as default"
                  + " class code '1' is used. ");

      decision.addDecision(1); // default prediction
      // classCodeTable.getEmptyKBestList().addKBestItem(1);
    }
    return true;
  }
Exemplo n.º 3
0
 public void terminate() throws MaltChainedException {
   if (divideModels != null) {
     for (AtomicModel divideModel : divideModels.values()) {
       divideModel.terminate();
     }
   }
   if (masterModel != null) {
     masterModel.terminate();
   }
 }
Exemplo n.º 4
0
  public void finalizeSentence(DependencyStructure dependencyGraph) throws MaltChainedException {
    //		if (getGuide().getGuideMode() == Guide.GuideMode.CLASSIFY) {
    //			throw new GuideException("Can only finish sentence during learning. ");
    //		}

    if (divideModels != null) {
      for (AtomicModel divideModel : divideModels.values()) {
        divideModel.finalizeSentence(dependencyGraph);
      }
    } else {
      throw new GuideException("The feature divide models cannot be found. ");
    }
  }
Exemplo n.º 5
0
  public void noMoreInstances() throws MaltChainedException {
    //		if (getGuide().getGuideMode() == Guide.GuideMode.CLASSIFY) {
    //			throw new GuideException("Can only finish all data during learning. ");
    //		}

    if (divideModels != null) {
      divideFeature.updateCardinality();
      for (Integer index : divideModels.keySet()) {
        divideModels.get(index).noMoreInstances();
      }
      final TreeSet<Integer> removeSet = new TreeSet<Integer>();
      for (Integer index : divideModels.keySet()) {
        if (divideModels.get(index).getFrequency() <= divideThreshold) {
          divideModels
              .get(index)
              .moveAllInstances(masterModel, divideFeature, divideFeatureIndexVector);
          removeSet.add(index);
        }
      }
      for (Integer index : removeSet) {
        divideModels.remove(index);
      }
      masterModel.noMoreInstances();

    } else {
      throw new GuideException("The feature divide models cannot be found. ");
    }
  }
Exemplo n.º 6
0
  private AtomicModel getAtomicModel() throws MaltChainedException {
    if (getGuide().getGuideMode() == ClassifierGuide.GuideMode.BATCH) {
      throw new GuideException("Can only predict during parsing. ");
    } else if (!(divideFeature.getFeatureValue() instanceof SingleFeatureValue)) {
      throw new GuideException("The divide feature does not have a single value. ");
    }

    if (divideModels != null
        && divideModels.containsKey(
            ((SingleFeatureValue) divideFeature.getFeatureValue()).getCode())) {
      return divideModels.get(((SingleFeatureValue) divideFeature.getFeatureValue()).getCode());
    } else if (masterModel != null && masterModel.getFrequency() > 0) {
      return masterModel;
    } else {
      getGuide()
          .getConfiguration()
          .getConfigLogger()
          .info(
              "Could not predict the next parser decision because there is "
                  + "no divide or master model that covers the divide value '"
                  + ((SingleFeatureValue) divideFeature.getFeatureValue()).getCode()
                  + "', as default"
                  + " class code '1' is used. ");
    }
    return null;
  }
Exemplo n.º 7
0
 public void train() throws MaltChainedException {
   for (AtomicModel divideModel : divideModels.values()) {
     divideModel.train();
   }
   masterModel.train();
   save();
   for (AtomicModel divideModel : divideModels.values()) {
     divideModel.terminate();
   }
   masterModel.terminate();
 }
Exemplo n.º 8
0
 /**
  * Moves all instance from this atomic model into the destination atomic model and add the divide
  * feature. This method is used by the feature divide model to sum up all model below a certain
  * threshold.
  *
  * @param model the destination atomic model
  * @param divideFeature the divide feature
  * @param divideFeatureIndexVector the divide feature index vector
  * @throws MaltChainedException
  */
 public void moveAllInstances(
     AtomicModel model, FeatureFunction divideFeature, ArrayList<Integer> divideFeatureIndexVector)
     throws MaltChainedException {
   if (method == null) {
     throw new GuideException("The learner cannot be found. ");
   } else if (model == null) {
     throw new GuideException("The guide model cannot be found. ");
   } else if (divideFeature == null) {
     throw new GuideException("The divide feature cannot be found. ");
   } else if (divideFeatureIndexVector == null) {
     throw new GuideException("The divide feature index vector cannot be found. ");
   }
   ((Modifiable) divideFeature).setFeatureValue(index);
   method.moveAllInstances(model.getMethod(), divideFeature, divideFeatureIndexVector);
   method.terminate();
   method = null;
 }
Exemplo n.º 9
0
 /**
  * Loads the feature divide model settings .fsm file.
  *
  * @throws MaltChainedException
  */
 protected void load() throws MaltChainedException {
   try {
     final BufferedReader in =
         new BufferedReader(
             getGuide()
                 .getConfiguration()
                 .getConfigurationDir()
                 .getInputStreamReader(getModelName() + ".dsm"));
     final Pattern tabPattern = Pattern.compile("\t");
     while (true) {
       String line = in.readLine();
       if (line == null) break;
       String[] cols = tabPattern.split(line);
       if (cols.length != 2) {
         throw new GuideException("");
       }
       int code = -1;
       int freq = 0;
       try {
         code = Integer.parseInt(cols[0]);
         freq = Integer.parseInt(cols[1]);
       } catch (NumberFormatException e) {
         throw new GuideException(
             "Could not convert a string value into an integer value when loading the feature divide model settings (.fsm). ",
             e);
       }
       if (code == -1) {
         masterModel = new AtomicModel(-1, masterFeatureVector, this);
         masterModel.setFrequency(freq);
       } else if (divideModels != null) {
         divideModels.put(code, new AtomicModel(code, divideFeatureVector, this));
         divideModels.get(code).setFrequency(freq);
       }
       setFrequency(getFrequency() + freq);
     }
     in.close();
   } catch (IOException e) {
     throw new GuideException(
         "Could not read from the guide model settings file '"
             + getModelName()
             + ".dsm"
             + "', when "
             + "loading the guide model settings. ",
         e);
   }
 }