Examples of cc.mallet.types.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

    
    double[][] featureLabelCounts = new double[numFeatures][numLabels];
    
    for (int ii = 0; ii < list.size(); ii++) {
      Instance instance = list.get(ii);
      FeatureVector featureVector = (FeatureVector)instance.getData();
      
      // this handles distributions over labels
      for (int li = 0; li < numLabels; li++) {
        double py = instance.getLabeling().value(li);
        for (int loc = 0; loc < featureVector.numLocations(); loc++) {
          int fi = featureVector.indexAtLocation(loc);
          double val = featureVector.valueAtLocation(loc);
          featureLabelCounts[fi][li] += py * val;
        }
      }
    }
    return featureLabelCounts;

View Full Code Here

    assert(Maths.almostEquals(MatrixOps.sum(dist),1));
    return dist;
  }
  
  private static void labelByVoting(HashMap<Integer,ArrayList<Integer>> labeledFeatures, Instance instance, double[] scores) {
    FeatureVector fv = (FeatureVector)instance.getData();
    int numFeatures = instance.getDataAlphabet().size() + 1;
    
    int[] numLabels = new int[instance.getTargetAlphabet().size()];
    Iterator<Integer> keyIterator = labeledFeatures.keySet().iterator();
    while (keyIterator.hasNext()) {
      ArrayList<Integer> majorityClassList = labeledFeatures.get(keyIterator.next());
      for (int i = 0; i < majorityClassList.size(); i++) {
        int li = majorityClassList.get(i);
        numLabels[li]++;
      } 
    }
    
    keyIterator = labeledFeatures.keySet().iterator();
    while (keyIterator.hasNext()) {
      int next = keyIterator.next();
      assert(next < numFeatures);
      int loc = fv.location(next);
      if (loc < 0) {
        continue;
      }
      
      ArrayList<Integer> majorityClassList = labeledFeatures.get(next);

View Full Code Here

        int[] featureIndicesArr = new int[featureIndices.size()];
        for (int index = 0; index < featureIndices.size(); index++) {
          featureIndicesArr[index] = featureIndices.get(index);
        }
         fvs[l] = featureInductionOption.value ? new AugmentableFeatureVector(features, featureIndicesArr, null, featureIndicesArr.length) : 
          new FeatureVector(features, featureIndicesArr);
      }
      carrier.setData(new FeatureVectorSequence(fvs));
      if (isTargetProcessing())
        carrier.setTarget(target);
      else

View Full Code Here

            }
          }
        }


        if (viterbiOutputStream != null) {
          FeatureVector fv = (FeatureVector) input.get(j);
          //viterbiOutputStream.println (tokens.charAt(j)+" "+trueOutput.get(j).toString()+
          //'/'+predOutput.get(j).toString()+"  "+ fv.toString(true));
          if (sourceTokenSequence != null)
            viterbiOutputStream.print (sourceTokenSequence.get(j).getText()+": ");
          viterbiOutputStream.println (trueOutput.get(j).toString()+
                                       '/'+predOutput.get(j).toString()+"  "+ fv.toString(true));
            
        }
      }
    }
    DecimalFormat f = new DecimalFormat ("0.####");

View Full Code Here

      double instanceWeight = trainingList.getInstanceWeight(inst);
      Labeling labeling = inst.getLabeling ();
      if (labeling == null)
        continue;
      //logger.fine ("Instance "+ii+" labeling="+labeling);
      FeatureVector fv = (FeatureVector) inst.getData ();
      Alphabet fdict = fv.getAlphabet();
      assert (fv.getAlphabet() == fd);


      // Here is the difference between this code and the single label 
      //  version: rather than only picking out the "best" index, 
      //  loop over all label indices.
      
      for (int pos = 0; pos < labeling.numLocations(); pos++){
        MatrixOps.rowPlusEquals (constraints, numFeatures,
                     labeling.indexAtLocation(pos),
                     fv,
                     instanceWeight*labeling.valueAtLocation(pos));
      }


      assert(!Double.isNaN(instanceWeight)) : "instanceWeight is NaN";


      boolean hasNaN = false;
      for (int i = 0; i < fv.numLocations(); i++) {
        if (Double.isNaN(fv.valueAtLocation(i))) {
          logger.info("NaN for feature " + fdict.lookupObject(fv.indexAtLocation(i)).toString()); 
          hasNaN = true;
        }
      }
      if (hasNaN)
        logger.info("NaN in instance: " + inst.getName());

View Full Code Here

        if (labeling == null)
          continue;
        //System.out.println("L Now "+inputAlphabet.size()+" regular features.");


        this.theClassifier.getClassificationScores (instance, scores);
        FeatureVector fv = (FeatureVector) instance.getData ();


            value = 0.0;
        for(int pos = 0; pos < labeling.numLocations(); pos++) { //loop, added by Limin Yao
          int ll = labeling.indexAtLocation(pos);
          value -= (instanceWeight * labeling.valueAtLocation(pos) * Math.log (scores[ll]));

View Full Code Here

    //        int numFeatures = getAlphabet().size() + 1;
    int numFeatures = this.defaultFeatureIndex + 1;


    int numLabels = getLabelAlphabet().size();
    assert (scores.length == numLabels);
    FeatureVector fv = (FeatureVector) instance.getData ();
    // Make sure the feature vector's feature dictionary matches
    // what we are expecting from our data pipe (and thus our notion
    // of feature probabilities.
    assert (fv.getAlphabet ()
        == this.instancePipe.getDataAlphabet ());


    // Include the feature weights according to each label
    for (int li = 0; li < numLabels; li++) {
      scores[li] = parameters[li*numFeatures + defaultFeatureIndex]

View Full Code Here

0 1 2 3 4 5 6 7 8 9

TOP

Related Classes of cc.mallet.types.FeatureVector

cc.mallet.classify.BalancedWinnow

cc.mallet.classify.BalancedWinnowTrainer

cc.mallet.classify.C45

cc.mallet.classify.C45$Node

cc.mallet.classify.Classification

cc.mallet.classify.constraints.ge.MaxEntFLGEConstraints

cc.mallet.classify.constraints.ge.MaxEntRangeL2FLGEConstraints

cc.mallet.classify.constraints.pr.MaxEntFLPRConstraints

cc.mallet.classify.DecisionTree

cc.mallet.classify.DecisionTree$Node

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.