Examples of FeatureVector

cc.mallet.types.FeatureVector
A subset of an {@link cc.mallet.types.Alphabet} in which each element of the subset has an associated value.The subset is represented as a {@link cc.mallet.types.SparseVector}
A SparseVector represents only the non-zero locations of a vector. In the case of a FeatureVector, a location represents the index of an entry in the Alphabet that is contained in the FeatureVector.
To loop over the elements of a feature vector, one loops over the consecutive integers between 0 and the number of locations in the feature vector. From these locations one can cheaply obtain the index of the entry in the underlying Alphabet, the entry itself, and the value in this feature vector associated the entry.
A SparseVector (or FeatureVector) can be sparse or dense depending on whether or not an array if indices is specified at construction time. If the FeatureVector is dense, the mapping from location to index is the identity mapping.
The associated value of an element in a SparseVector (or FeatureVector) can be a double or binary (0.0 or 1.0), depending on whether an array of doubles is specified at contruction time. @see SparseVector @see Alphabet @author Andrew McCallum mccallum@cs.umass.edu
edu.gslis.textrepresentation.FeatureVector
edu.illinois.lis.document.FeatureVector
Simple container mapping term->count pairs grabbed from an input text. @author Miles Efron
edu.stanford.nlp.sempre.FeatureVector
A FeatureVector represents a mapping from feature (string) to value (double). We enforce the convention that each feature is (domain, name), so that the key space isn't a free-for-all. @author Percy Liang
org.maltparserx.core.feature.FeatureVector
@author Johan Hall

Examples of edu.illinois.lis.document.FeatureVector

import edu.illinois.lis.document.FeatureVector;
import edu.illinois.lis.query.GQuery;


public class LuceneQuery {
  public static String gQueryToLucene(GQuery gQuery, int k) {
    FeatureVector mainVector = new FeatureVector(gQuery.getText(), null);
    mainVector.normalizeToOne();
    FeatureVector fbVector = gQuery.getFeatureVector();
    fbVector.pruneToSize(k);
    fbVector.normalizeToOne();
    FeatureVector finalVector = FeatureVector.interpolate(mainVector, fbVector, 0.5);
    StringBuilder b = new StringBuilder();
    Iterator<String> terms = finalVector.iterator();
    while(terms.hasNext()) {
      String term = terms.next();
      double weight = finalVector.getFeaturetWeight(term);
      b.append(term + "^" + weight + " ");
    }
    return b.toString().trim();
  }

View Full Code Here

Examples of edu.illinois.lis.document.FeatureVector

      
      hitIterator = relDocs.iterator();
      while(hitIterator.hasNext()) {
        TResult hit = hitIterator.next();
        String text = hit.getText().toLowerCase();
        FeatureVector docVector = new FeatureVector(text, stopper);
        vocab.addAll(docVector.getFeatures());
        fbDocVectors.add(docVector);
      }


      features = new LinkedList<KeyValuePair>();


      
      Iterator<String> it = vocab.iterator();
      while(it.hasNext()) {
        String term = it.next();        
        double fbWeight = 0.0;


        Iterator<FeatureVector> docIT = fbDocVectors.iterator();
        k=0;
        while(docIT.hasNext()) {
          double docWeight = 1.0;
          if(docWeights != null)
            docWeight = docWeights[k];
          FeatureVector docVector = docIT.next();
          double docProb = docVector.getFeaturetWeight(term) / docVector.getLength();
          docProb *= rsvs[k++] * docWeight;


          fbWeight += docProb;
        }

View Full Code Here

Examples of edu.illinois.lis.document.FeatureVector

  public GQuery asGquery() {
    GQuery newQuery = new GQuery();
    newQuery.setTitle(originalQuery.getTitle());
    newQuery.setText(originalQuery.getText());
    
    FeatureVector finalVector = new FeatureVector(stopper);
    
    ScorableComparator comparator = new ScorableComparator(true);
    Collections.sort(features, comparator);
    Iterator<KeyValuePair> it = features.iterator();
    
    int i=0;
    while(it.hasNext() && i++ < fbTermCount) {      
      KeyValuePair tuple = it.next();
      finalVector.addTerm(tuple.getKey(), tuple.getScore());
    }
    
    newQuery.setFeatureVector(finalVector);
    
    return newQuery;

View Full Code Here

Examples of edu.illinois.lis.document.FeatureVector

    
    return newQuery;
  }


  public FeatureVector asFeatureVector() {
    FeatureVector f = new FeatureVector(stopper);
    Iterator<KeyValuePair> it = features.iterator();
    
    while(it.hasNext()) {      
      KeyValuePair tuple = it.next();
      f.addTerm(tuple.getKey(), tuple.getScore());
    }  
    
    return f;
  }

View Full Code Here

Examples of edu.illinois.lis.document.FeatureVector

      String title = queryObject.get("title").getAsString();
      String text  = queryObject.get("text").getAsString();
      double epoch = queryObject.get("epoch").getAsDouble();
      long querytweettime = queryObject.get("querytweettime").getAsLong();
      nameToIndex.put(title, k++);
      FeatureVector featureVector = new FeatureVector(null);
      JsonArray modelObjectArray = queryObject.getAsJsonArray("model");
      Iterator<JsonElement> featureIterator = modelObjectArray.iterator();
      while(featureIterator.hasNext()) {
        JsonObject featureObject = (JsonObject)featureIterator.next();
        double weight  = featureObject.get("weight").getAsDouble();
        String feature = featureObject.get("feature").getAsString();
        featureVector.addTerm(feature, weight);
      }
      
      
      GQuery gQuery = new GQuery();
      gQuery.setTitle(title);

View Full Code Here

Examples of edu.illinois.lis.document.FeatureVector

    return null;
  }
  
  public FeatureVector getDocVector(String docId) {
    if(seenDocs.containsKey(docId))
      return new FeatureVector(seenDocs.get(docId), null);


    // we should also be able to ping the API to get docs we haven't already seen
    return null;
  }

View Full Code Here

Examples of edu.stanford.nlp.sempre.FeatureVector

              (token2.equals("czech") || token2.equals("republic")))
            continue;


          double[] targetTokenVec = wordVectors.get(paraExample.targetInfo.tokens.get(j));
          if(targetTokenVec!=null) {
            FeatureVector fv;
            if(vsmSimilarityFunc==SimilarityFunc.FULL_MATRIX)
              fv = getFullMatrixFeatures(sourceTokenVec, targetTokenVec);
            else if(vsmSimilarityFunc==SimilarityFunc.DIAGNONAL)
              fv = getDiagonalMatrixFeatures(sourceTokenVec, targetTokenVec);
            else
              fv = getDotProductFeature(sourceTokenVec, targetTokenVec);
            double score = fv.dotProduct(params);
            scoreList.add(Pair.newPair(paraExample.sourceInfo.tokens.get(i)+","+paraExample.targetInfo.tokens.get(j), alpha*score));
          }
        }
      }
    }

View Full Code Here

Examples of org.maltparserx.core.feature.FeatureVector

    final SingleDecision singleDecision = ((MultipleDecision)decision).getSingleDecision(decisionIndex);
    if (instanceModel == null) {
      initInstanceModel(singleDecision.getTableContainer().getTableContainerName());
    }


    FeatureVector fv = instanceModel.predictExtract(singleDecision);
    if (singleDecision.continueWithNextDecision() && decisionIndex+1 < decision.numberOfDecisions()) {
      if (nextDecisionModel == null) {
        initNextDecisionModel(((MultipleDecision)decision).getSingleDecision(decisionIndex+1), branchedDecisionSymbols);
      }
      nextDecisionModel.predictExtract(decision);

View Full Code Here

Examples of org.maltparserx.core.feature.FeatureVector

  private void setGuide(ClassifierGuide guide) {
    this.guide = guide;
  }
  
  private void initInstanceModel(String subModelName) throws MaltChainedException {
    FeatureVector fv = featureModel.getFeatureVector(branchedDecisionSymbols+"."+subModelName);
    if (fv == null) {
      fv = featureModel.getFeatureVector(subModelName);
    }
    if (fv == null) {
      fv = featureModel.getMainFeatureVector();

View Full Code Here

Examples of org.maltparserx.core.feature.FeatureVector

  public DecisionModel getPrevDecisionModel() {
    return prevDecisionModel;
  }


  private final void initInstanceModel(String subModelName) throws MaltChainedException {
    FeatureVector fv = featureModel.getFeatureVector(branchedDecisionSymbols+"."+subModelName);
    if (fv == null) {
      fv = featureModel.getFeatureVector(subModelName);
    }
    if (fv == null) {
      fv = featureModel.getMainFeatureVector();

View Full Code Here

0 1 2 3 4 5

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.