cc.mallet.types.Alphabet.clone()方法的使用及代码示例

x33g5p2x  于2022-01-16 转载在 其他  
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本文整理了Java中cc.mallet.types.Alphabet.clone()方法的一些代码示例，展示了Alphabet.clone()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Alphabet.clone()方法的具体详情如下：
包路径：cc.mallet.types.Alphabet
类名称：Alphabet
方法名：clone

Alphabet.clone介绍

暂无

代码示例

代码示例来源：origin: cc.mallet/mallet

private void copyStatesAndWeightsFrom (CRF initialCRF)
{
  this.parameters = new Factors (initialCRF.parameters, true);  // This will copy all the transition weights
  this.parameters.weightAlphabet = (Alphabet) initialCRF.parameters.weightAlphabet.clone();
  //weightAlphabet = (Alphabet) initialCRF.weightAlphabet.clone ();
  //weights = new SparseVector [initialCRF.weights.length];
  
  states.clear ();
  // Clear these, because they will be filled by this.addState()
  this.parameters.initialWeights = new double[0];
  this.parameters.finalWeights = new double[0];

  for (int i = 0; i < initialCRF.states.size(); i++) {
    State s = (State) initialCRF.getState (i);
    String[][] weightNames = new String[s.weightsIndices.length][];
    for (int j = 0; j < weightNames.length; j++) {
      int[] thisW = s.weightsIndices[j];
      weightNames[j] = (String[]) initialCRF.parameters.weightAlphabet.lookupObjects(thisW, new String [s.weightsIndices[j].length]);
    }
    addState (s.name, initialCRF.parameters.initialWeights[i], initialCRF.parameters.finalWeights[i], 
        s.destinationNames, s.labels, weightNames);
  }
  featureSelections = initialCRF.featureSelections.clone ();
  // yyy weightsFrozen = (boolean[]) initialCRF.weightsFrozen.clone();
}

代码示例来源：origin: com.github.steveash.mallet/mallet

/** Construct new Factors by copying the other one. */
public Factors (Factors other, boolean cloneAlphabet) {
  weightAlphabet = cloneAlphabet ? (Alphabet) other.weightAlphabet.clone() : other.weightAlphabet;
  weights = new SparseVector[other.weights.length];
  for (int i = 0; i < weights.length; i++)
    weights[i] = (SparseVector) other.weights[i].cloneMatrix();
  defaultWeights = other.defaultWeights.clone();
  weightsFrozen = other.weightsFrozen;
  initialWeights = other.initialWeights.clone();
  finalWeights = other.finalWeights.clone();
}

代码示例来源：origin: cc.mallet/mallet

/** Construct new Factors by copying the other one. */
public Factors (Factors other, boolean cloneAlphabet) {
  weightAlphabet = cloneAlphabet ? (Alphabet) other.weightAlphabet.clone() : other.weightAlphabet;
  weights = new SparseVector[other.weights.length];
  for (int i = 0; i < weights.length; i++)
    weights[i] = (SparseVector) other.weights[i].cloneMatrix();
  defaultWeights = other.defaultWeights.clone();
  weightsFrozen = other.weightsFrozen;
  initialWeights = other.initialWeights.clone();
  finalWeights = other.finalWeights.clone();
}

代码示例来源：origin: de.julielab/jcore-mallet-2.0.9

/** Construct new Factors by copying the other one. */
public Factors (Factors other, boolean cloneAlphabet) {
  weightAlphabet = cloneAlphabet ? (Alphabet) other.weightAlphabet.clone() : other.weightAlphabet;
  weights = new SparseVector[other.weights.length];
  for (int i = 0; i < weights.length; i++)
    weights[i] = (SparseVector) other.weights[i].cloneMatrix();
  defaultWeights = other.defaultWeights.clone();
  weightsFrozen = other.weightsFrozen;
  initialWeights = other.initialWeights.clone();
  finalWeights = other.finalWeights.clone();
}

代码示例来源：origin: de.julielab/jcore-mallet-2.0.9

private void copyStatesAndWeightsFrom (CRF initialCRF)
{
  this.parameters = new Factors (initialCRF.parameters, true);  // This will copy all the transition weights
  this.parameters.weightAlphabet = (Alphabet) initialCRF.parameters.weightAlphabet.clone();
  //weightAlphabet = (Alphabet) initialCRF.weightAlphabet.clone ();
  //weights = new SparseVector [initialCRF.weights.length];
  
  states.clear ();
  // Clear these, because they will be filled by this.addState()
  this.parameters.initialWeights = new double[0];
  this.parameters.finalWeights = new double[0];

  for (int i = 0; i < initialCRF.states.size(); i++) {
    State s = (State) initialCRF.getState (i);
    String[][] weightNames = new String[s.weightsIndices.length][];
    for (int j = 0; j < weightNames.length; j++) {
      int[] thisW = s.weightsIndices[j];
      weightNames[j] = (String[]) initialCRF.parameters.weightAlphabet.lookupObjects(thisW, new String [s.weightsIndices[j].length]);
    }
    addState (s.name, initialCRF.parameters.initialWeights[i], initialCRF.parameters.finalWeights[i], 
        s.destinationNames, s.labels, weightNames);
  }
  featureSelections = initialCRF.featureSelections.clone ();
  // yyy weightsFrozen = (boolean[]) initialCRF.weightsFrozen.clone();
}

代码示例来源：origin: com.github.steveash.mallet/mallet

private void copyStatesAndWeightsFrom (CRF initialCRF)
{
  this.parameters = new Factors (initialCRF.parameters, true);  // This will copy all the transition weights
  this.parameters.weightAlphabet = (Alphabet) initialCRF.parameters.weightAlphabet.clone();
  //weightAlphabet = (Alphabet) initialCRF.weightAlphabet.clone ();
  //weights = new SparseVector [initialCRF.weights.length];
  
  states.clear ();
  // Clear these, because they will be filled by this.addState()
  this.parameters.initialWeights = new double[0];
  this.parameters.finalWeights = new double[0];

  for (int i = 0; i < initialCRF.states.size(); i++) {
    State s = (State) initialCRF.getState (i);
    String[][] weightNames = new String[s.weightsIndices.length][];
    for (int j = 0; j < weightNames.length; j++) {
      int[] thisW = s.weightsIndices[j];
      weightNames[j] = (String[]) initialCRF.parameters.weightAlphabet.lookupObjects(thisW, new String [s.weightsIndices[j].length]);
    }
    addState (s.name, initialCRF.parameters.initialWeights[i], initialCRF.parameters.finalWeights[i], 
        s.destinationNames, s.labels, weightNames);
  }
  featureSelections = initialCRF.featureSelections.clone ();
  // yyy weightsFrozen = (boolean[]) initialCRF.weightsFrozen.clone();
}

代码示例来源：origin: cc.mallet/mallet

public AddClassifierTokenPredictions(TokenClassifiers tokenClassifiers, int[] predRanks2add, 
    boolean binary, InstanceList testList)
{
  m_predRanks2add = predRanks2add;
  m_binary = binary;
  m_tokenClassifiers = tokenClassifiers;
  m_inProduction = false;
  m_dataAlphabet = (Alphabet) tokenClassifiers.getAlphabet().clone();
  Alphabet labelAlphabet = tokenClassifiers.getLabelAlphabet();
  
  // add the token prediction features to the alphabet
  for (int i = 0; i < m_predRanks2add.length; i++) {
    for (int j = 0; j < labelAlphabet.size(); j++) {
      String featName = "TOK_PRED=" + labelAlphabet.lookupObject(j).toString() + "_@_RANK_" + m_predRanks2add[i];
      m_dataAlphabet.lookupIndex(featName, true);
    }
  }
  
  // evaluate token classifier  
  if (testList != null) {
    Trial trial = new Trial(m_tokenClassifiers, testList);
    logger.info("Token classifier accuracy on test set = " + trial.getAccuracy());
  }
}

代码示例来源：origin: de.julielab/jcore-mallet-2.0.9

public AddClassifierTokenPredictions(TokenClassifiers tokenClassifiers, int[] predRanks2add, 
    boolean binary, InstanceList testList)
{
  m_predRanks2add = predRanks2add;
  m_binary = binary;
  m_tokenClassifiers = tokenClassifiers;
  m_inProduction = false;
  m_dataAlphabet = (Alphabet) tokenClassifiers.getAlphabet().clone();
  Alphabet labelAlphabet = tokenClassifiers.getLabelAlphabet();
  
  // add the token prediction features to the alphabet
  for (int i = 0; i < m_predRanks2add.length; i++) {
    for (int j = 0; j < labelAlphabet.size(); j++) {
      String featName = "TOK_PRED=" + labelAlphabet.lookupObject(j).toString() + "_@_RANK_" + m_predRanks2add[i];
      m_dataAlphabet.lookupIndex(featName, true);
    }
  }
  
  // evaluate token classifier  
  if (testList != null) {
    Trial trial = new Trial(m_tokenClassifiers, testList);
    logger.info("Token classifier accuracy on test set = " + trial.getAccuracy());
  }
}

代码示例来源：origin: com.github.steveash.mallet/mallet

public AddClassifierTokenPredictions(TokenClassifiers tokenClassifiers, int[] predRanks2add, 
    boolean binary, InstanceList testList)
{
  m_predRanks2add = predRanks2add;
  m_binary = binary;
  m_tokenClassifiers = tokenClassifiers;
  m_inProduction = false;
  m_dataAlphabet = (Alphabet) tokenClassifiers.getAlphabet().clone();
  Alphabet labelAlphabet = tokenClassifiers.getLabelAlphabet();
  
  // add the token prediction features to the alphabet
  for (int i = 0; i < m_predRanks2add.length; i++) {
    for (int j = 0; j < labelAlphabet.size(); j++) {
      String featName = "TOK_PRED=" + labelAlphabet.lookupObject(j).toString() + "_@_RANK_" + m_predRanks2add[i];
      m_dataAlphabet.lookupIndex(featName, true);
    }
  }
  
  // evaluate token classifier  
  if (testList != null) {
    Trial trial = new Trial(m_tokenClassifiers, testList);
    logger.info("Token classifier accuracy on test set = " + trial.getAccuracy());
  }
}

代码示例来源：origin: cc.mallet/mallet

return;
Alphabet tmpDV = (Alphabet) ilist.getDataAlphabet().clone();
FeatureSelection featuresSelected = ilist.getFeatureSelection();
InstanceList tmpilist = new InstanceList (tmpDV, ilist.getTargetAlphabet());

代码示例来源：origin: de.julielab/jcore-mallet-2.0.9

return;
Alphabet tmpDV = (Alphabet) ilist.getDataAlphabet().clone();
FeatureSelection featuresSelected = ilist.getFeatureSelection();
InstanceList tmpilist = new InstanceList (tmpDV, ilist.getTargetAlphabet());

代码示例来源：origin: com.github.steveash.mallet/mallet

return;
Alphabet tmpDV = (Alphabet) ilist.getDataAlphabet().clone();
FeatureSelection featuresSelected = ilist.getFeatureSelection();
InstanceList tmpilist = new InstanceList (tmpDV, ilist.getTargetAlphabet());

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