cc.mallet.types.InstanceList.setFeatureSelection()方法的使用及代码示例

x33g5p2x  于2022-01-21 转载在 其他  
字(11.4k)|赞(0)|评价(0)|浏览(84)

本文整理了Java中cc.mallet.types.InstanceList.setFeatureSelection()方法的一些代码示例，展示了InstanceList.setFeatureSelection()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。InstanceList.setFeatureSelection()方法的具体详情如下：
包路径：cc.mallet.types.InstanceList
类名称：InstanceList
方法名：setFeatureSelection

InstanceList.setFeatureSelection介绍

暂无

代码示例

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

/** When the CRF has done feature induction, these new feature conjunctions must be 
 * created in the test or validation data in order for them to take effect. */
public void induceFeaturesFor (InstanceList instances) {
  instances.setFeatureSelection(this.globalFeatureSelection);
  for (int i = 0; i < featureInducers.size(); i++) {
    FeatureInducer klfi = featureInducers.get(i);
    klfi.induceFeaturesFor (instances, false, false);
  }
}

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

/** When the CRF has done feature induction, these new feature conjunctions must be 
 * created in the test or validation data in order for them to take effect. */
public void induceFeaturesFor (InstanceList instances) {
  instances.setFeatureSelection(this.globalFeatureSelection);
  for (int i = 0; i < featureInducers.size(); i++) {
    FeatureInducer klfi = featureInducers.get(i);
    klfi.induceFeaturesFor (instances, false, false);
  }
}

代码示例来源：origin: cc.mallet/mallet

/** When the CRF has done feature induction, these new feature conjunctions must be 
 * created in the test or validation data in order for them to take effect. */
public void induceFeaturesFor (InstanceList instances) {
  instances.setFeatureSelection(this.globalFeatureSelection);
  for (int i = 0; i < featureInducers.size(); i++) {
    FeatureInducer klfi = featureInducers.get(i);
    klfi.induceFeaturesFor (instances, false, false);
  }
}

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

/** This method is deprecated. */
// But it is here as a reminder to do something about induceFeaturesFor(). */
@Deprecated
public Sequence[] predict (InstanceList testing) {
  testing.setFeatureSelection(this.globalFeatureSelection);
  for (int i = 0; i < featureInducers.size(); i++) {
    FeatureInducer klfi = (FeatureInducer)featureInducers.get(i);
    klfi.induceFeaturesFor (testing, false, false);
  }
  Sequence[] ret = new Sequence[testing.size()];
  for (int i = 0; i < testing.size(); i++) {
    Instance instance = testing.get(i);
    Sequence input = (Sequence) instance.getData();
    Sequence trueOutput = (Sequence) instance.getTarget();
    assert (input.size() == trueOutput.size());
    Sequence predOutput = new MaxLatticeDefault(this, input).bestOutputSequence();
    assert (predOutput.size() == trueOutput.size());
    ret[i] = predOutput;
  }
  return ret;
}

代码示例来源：origin: cc.mallet/mallet

/** This method is deprecated. */
// But it is here as a reminder to do something about induceFeaturesFor(). */
@Deprecated
public Sequence[] predict (InstanceList testing) {
  testing.setFeatureSelection(this.globalFeatureSelection);
  for (int i = 0; i < featureInducers.size(); i++) {
    FeatureInducer klfi = (FeatureInducer)featureInducers.get(i);
    klfi.induceFeaturesFor (testing, false, false);
  }
  Sequence[] ret = new Sequence[testing.size()];
  for (int i = 0; i < testing.size(); i++) {
    Instance instance = testing.get(i);
    Sequence input = (Sequence) instance.getData();
    Sequence trueOutput = (Sequence) instance.getTarget();
    assert (input.size() == trueOutput.size());
    Sequence predOutput = new MaxLatticeDefault(this, input).bestOutputSequence();
    assert (predOutput.size() == trueOutput.size());
    ret[i] = predOutput;
  }
  return ret;
}

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

/** This method is deprecated. */
// But it is here as a reminder to do something about induceFeaturesFor(). */
@Deprecated
public Sequence[] predict (InstanceList testing) {
  testing.setFeatureSelection(this.globalFeatureSelection);
  for (int i = 0; i < featureInducers.size(); i++) {
    FeatureInducer klfi = (FeatureInducer)featureInducers.get(i);
    klfi.induceFeaturesFor (testing, false, false);
  }
  Sequence[] ret = new Sequence[testing.size()];
  for (int i = 0; i < testing.size(); i++) {
    Instance instance = testing.get(i);
    Sequence input = (Sequence) instance.getData();
    Sequence trueOutput = (Sequence) instance.getTarget();
    assert (input.size() == trueOutput.size());
    Sequence predOutput = new MaxLatticeDefault(this, input).bestOutputSequence();
    assert (predOutput.size() == trueOutput.size());
    ret[i] = predOutput;
  }
  return ret;
}

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

public void selectFeaturesForAllLabels (InstanceList ilist)
  
{
  RankedFeatureVector ranking = ranker.newRankedFeatureVector (ilist);
  FeatureSelection fs = new FeatureSelection (ilist.getDataAlphabet());
  if (numFeatures != -1) { // Select by number of features.
    int nf = Math.min (numFeatures, ranking.singleSize());
    for (int i = 0; i < nf; i++) {
      logger.info ("adding feature "+i+" word="+ilist.getDataAlphabet().lookupObject(ranking.getIndexAtRank(i)));
      fs.add (ranking.getIndexAtRank(i));
    }
  } else { // Select by threshold.
    for (int i = 0; i < ranking.singleSize(); i++) {
      if (ranking.getValueAtRank(i) > minThreshold)
        fs.add (ranking.getIndexAtRank(i));
    }
  }
  logger.info("Selected " + fs.cardinality() + " features from " +
              ilist.getDataAlphabet().size() + " features");
  ilist.setPerLabelFeatureSelection (null);
  ilist.setFeatureSelection (fs);
}

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

trainingData.setFeatureSelection(crf.globalFeatureSelection);
validationData.setFeatureSelection(crf.globalFeatureSelection);
testingData.setFeatureSelection(crf.globalFeatureSelection);
          1 - trainingProportions[featureInductionIteration] });
  theTrainingData = sampledTrainingData[0];
  theTrainingData.setFeatureSelection(crf.globalFeatureSelection); // xxx necessary?
  logger.info("  which is " + theTrainingData.size() + " instances");
errorInstances.setFeatureSelection(crf.globalFeatureSelection);
ArrayList errorLabelVectors = new ArrayList();
InstanceList clusteredErrorInstances[][] = new InstanceList[numLabels][numLabels];
    clusteredErrorInstances[i][j] = new InstanceList(trainingData.getDataAlphabet(),
        trainingData.getTargetAlphabet());
    clusteredErrorInstances[i][j].setFeatureSelection(crf.globalFeatureSelection);
    clusteredErrorLabelVectors[i][j] = new ArrayList();

代码示例来源：origin: cc.mallet/mallet

public void selectFeaturesForAllLabels (InstanceList ilist)
  
{
  RankedFeatureVector ranking = ranker.newRankedFeatureVector (ilist);
  FeatureSelection fs = new FeatureSelection (ilist.getDataAlphabet());
  if (numFeatures != -1) { // Select by number of features.
    int nf = Math.min (numFeatures, ranking.singleSize());
    for (int i = 0; i < nf; i++) {
      logger.info ("adding feature "+i+" word="+ilist.getDataAlphabet().lookupObject(ranking.getIndexAtRank(i)));
      fs.add (ranking.getIndexAtRank(i));
    }
  } else { // Select by threshold.
    for (int i = 0; i < ranking.singleSize(); i++) {
      if (ranking.getValueAtRank(i) > minThreshold)
        fs.add (ranking.getIndexAtRank(i));
    }
  }
  logger.info("Selected " + fs.cardinality() + " features from " +
              ilist.getDataAlphabet().size() + " features");
  ilist.setPerLabelFeatureSelection (null);
  ilist.setFeatureSelection (fs);
}

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

public void selectFeaturesForAllLabels (InstanceList ilist)
  
{
  RankedFeatureVector ranking = ranker.newRankedFeatureVector (ilist);
  FeatureSelection fs = new FeatureSelection (ilist.getDataAlphabet());
  if (numFeatures != -1) { // Select by number of features.
    int nf = Math.min (numFeatures, ranking.singleSize());
    for (int i = 0; i < nf; i++) {
      logger.info ("adding feature "+i+" word="+ilist.getDataAlphabet().lookupObject(ranking.getIndexAtRank(i)));
      fs.add (ranking.getIndexAtRank(i));
    }
  } else { // Select by threshold.
    for (int i = 0; i < ranking.singleSize(); i++) {
      if (ranking.getValueAtRank(i) > minThreshold)
        fs.add (ranking.getIndexAtRank(i));
    }
  }
  logger.info("Selected " + fs.cardinality() + " features from " +
              ilist.getDataAlphabet().size() + " features");
  ilist.setPerLabelFeatureSelection (null);
  ilist.setFeatureSelection (fs);
}

代码示例来源：origin: cc.mallet/mallet

public void selectFeaturesForPerLabel (InstanceList ilist)
{
  RankedFeatureVector[] rankings = perLabelRanker.newRankedFeatureVectors (ilist);
  int numClasses = rankings.length;
  FeatureSelection[] fs = new FeatureSelection[numClasses];
  for (int i = 0; i < numClasses; i++) {
    fs[i] = new FeatureSelection (ilist.getDataAlphabet());
    RankedFeatureVector ranking = rankings[i];
    int nf = Math.min (numFeatures, ranking.singleSize());
    if (nf >= 0) {
      for (int j = 0; j < nf; j++)
        fs[i].add (ranking.getIndexAtRank(j));
    } else {
      for (int j = 0; j < ranking.singleSize(); j++) {
        if (ranking.getValueAtRank(j) > minThreshold)
          fs[i].add (ranking.getIndexAtRank(j));
        else
          break;
      }
    }
  }
  ilist.setFeatureSelection (null);
  ilist.setPerLabelFeatureSelection (fs);
}

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

public void selectFeaturesForPerLabel (InstanceList ilist)
{
  RankedFeatureVector[] rankings = perLabelRanker.newRankedFeatureVectors (ilist);
  int numClasses = rankings.length;
  FeatureSelection[] fs = new FeatureSelection[numClasses];
  for (int i = 0; i < numClasses; i++) {
    fs[i] = new FeatureSelection (ilist.getDataAlphabet());
    RankedFeatureVector ranking = rankings[i];
    int nf = Math.min (numFeatures, ranking.singleSize());
    if (nf >= 0) {
      for (int j = 0; j < nf; j++)
        fs[i].add (ranking.getIndexAtRank(j));
    } else {
      for (int j = 0; j < ranking.singleSize(); j++) {
        if (ranking.getValueAtRank(j) > minThreshold)
          fs[i].add (ranking.getIndexAtRank(j));
        else
          break;
      }
    }
  }
  ilist.setFeatureSelection (null);
  ilist.setPerLabelFeatureSelection (fs);
}

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

public void selectFeaturesForPerLabel (InstanceList ilist)
{
  RankedFeatureVector[] rankings = perLabelRanker.newRankedFeatureVectors (ilist);
  int numClasses = rankings.length;
  FeatureSelection[] fs = new FeatureSelection[numClasses];
  for (int i = 0; i < numClasses; i++) {
    fs[i] = new FeatureSelection (ilist.getDataAlphabet());
    RankedFeatureVector ranking = rankings[i];
    int nf = Math.min (numFeatures, ranking.singleSize());
    if (nf >= 0) {
      for (int j = 0; j < nf; j++)
        fs[i].add (ranking.getIndexAtRank(j));
    } else {
      for (int j = 0; j < ranking.singleSize(); j++) {
        if (ranking.getValueAtRank(j) > minThreshold)
          fs[i].add (ranking.getIndexAtRank(j));
        else
          break;
      }
    }
  }
  ilist.setFeatureSelection (null);
  ilist.setPerLabelFeatureSelection (fs);
}

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

trainingData.setFeatureSelection (crf.globalFeatureSelection);
if (validationData != null) validationData.setFeatureSelection (crf.globalFeatureSelection);
if (testingData != null) testingData.setFeatureSelection (crf.globalFeatureSelection);
      1-trainingProportions[featureInductionIteration]});
    theTrainingData = sampledTrainingData[0];
    theTrainingData.setFeatureSelection (crf.globalFeatureSelection); // xxx necessary?
        logger.info ("  which is "+theTrainingData.size()+" instances");
  errorInstances.setFeatureSelection (crf.globalFeatureSelection);
  ArrayList errorLabelVectors = new ArrayList();
  InstanceList clusteredErrorInstances[][] = new InstanceList[numLabels][numLabels];
      clusteredErrorInstances[i][j] = new InstanceList (trainingData.getDataAlphabet(),
          trainingData.getTargetAlphabet());
      clusteredErrorInstances[i][j].setFeatureSelection (crf.globalFeatureSelection);
      clusteredErrorLabelVectors[i][j] = new ArrayList();

代码示例来源：origin: cc.mallet/mallet

trainingData.setFeatureSelection (crf.globalFeatureSelection);
if (validationData != null) validationData.setFeatureSelection (crf.globalFeatureSelection);
if (testingData != null) testingData.setFeatureSelection (crf.globalFeatureSelection);
      1-trainingProportions[featureInductionIteration]});
    theTrainingData = sampledTrainingData[0];
    theTrainingData.setFeatureSelection (crf.globalFeatureSelection); // xxx necessary?
        logger.info ("  which is "+theTrainingData.size()+" instances");
  errorInstances.setFeatureSelection (crf.globalFeatureSelection);
  ArrayList errorLabelVectors = new ArrayList();
  InstanceList clusteredErrorInstances[][] = new InstanceList[numLabels][numLabels];
      clusteredErrorInstances[i][j] = new InstanceList (trainingData.getDataAlphabet(),
          trainingData.getTargetAlphabet());
      clusteredErrorInstances[i][j].setFeatureSelection (crf.globalFeatureSelection);
      clusteredErrorLabelVectors[i][j] = new ArrayList();

内容来源于网络，如有侵权，请联系作者删除！

InstanceList

相关文章

    查看更多
    微信公众号

    热门标签

    更多
    JavaquerypythonNode开发语言requestUtil数据库Table后端算法LoggerMessageElementParser

    最新文章

    更多

    InstanceList类方法

    • 蜀ICP备13028337号-1 大数据知识库 https://www.saoniuhuo.com © All rights reserved
    • 本站内容来源互联网,如果侵犯您的权益请联系我们删除, 联系方式：448109455@qq.com