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

      if (output != null && output.size() > 0) {
        // Do it for the paths consistent with the labels...
        sumLatticeFactory.newSumLattice (this, input, output, new Transducer.Incrementor() {
          public void incrementTransition (Transducer.TransitionIterator ti, double count) {
            State source = (CRF.State)ti.getSourceState();
            FeatureVector input = (FeatureVector)ti.getInput();
            int index = ti.getIndex();
            int nwi = source.weightsIndices[index].length;
            for (int wi = 0; wi < nwi; wi++) {
              int weightsIndex = source.weightsIndices[index][wi];
              for (int i = 0; i < input.numLocations(); i++) {
                int featureIndex = input.indexAtLocation(i);
                if ((globalFeatureSelection == null || globalFeatureSelection.contains(featureIndex))
                    && (featureSelections == null
                        || featureSelections[weightsIndex] == null
                        || featureSelections[weightsIndex].contains(featureIndex)))
                  weightsPresent[weightsIndex].set (featureIndex);
              }
            }
          }
          public void incrementInitialState (Transducer.State s, double count) {  }
          public void incrementFinalState (Transducer.State s, double count) {  }
        });
      }
      // ...and also do it for the paths selected by the current model (so we will get some negative weights)
      if (useSomeUnsupportedTrick && this.getParametersAbsNorm() > 0) {
        if (i == 0)
          logger.info ("CRF: Incremental training detected.  Adding weights for some unsupported features...");
        // (do this once some training is done)
        sumLatticeFactory.newSumLattice (this, input, null, new Transducer.Incrementor() {
          public void incrementTransition (Transducer.TransitionIterator ti, double count) {
            if (count < 0.2) // Only create features for transitions with probability above 0.2 
              return;  // This 0.2 is somewhat arbitrary -akm
            State source = (CRF.State)ti.getSourceState();
            FeatureVector input = (FeatureVector)ti.getInput();
            int index = ti.getIndex();
            int nwi = source.weightsIndices[index].length;
            for (int wi = 0; wi < nwi; wi++) {
              int weightsIndex = source.weightsIndices[index][wi];
              for (int i = 0; i < input.numLocations(); i++) {
                int featureIndex = input.indexAtLocation(i);
                if ((globalFeatureSelection == null || globalFeatureSelection.contains(featureIndex))
                    && (featureSelections == null
                        || featureSelections[weightsIndex] == null
                        || featureSelections[weightsIndex].contains(featureIndex)))
                  weightsPresent[weightsIndex].set (featureIndex);

View Full Code Here

    fs.add (dict.lookupIndex ("l"));
    fs.add (dict.lookupIndex ("l"));
    fs.add (dict.lookupIndex ("u"));
    fs.add (dict.lookupIndex ("m"));
    //System.out.println (fs.toString());
    fv = new FeatureVector (fs);
    //System.out.println (fs.toString());
    //System.out.println (fv.toString());
  }

View Full Code Here

     * @throws IOException 
     */
    private void serializeObject (ObjectOutputStream out, Object obj)
    throws IOException {
        if (obj instanceof FeatureVector) {
            FeatureVector features = (FeatureVector) obj;
            out.writeChar (TYPE_FEATURE_VECTOR);
            out.writeObject (features.getIndices ());
            out.writeObject (features.getValues ());
        }
        else if (obj instanceof Label) {
                out.writeChar (TYPE_LABEL);
                out.writeObject (((Label) obj).toString ());
        } else {

View Full Code Here

            obj = ldict.lookupLabel (name);
            break;
        case TYPE_FEATURE_VECTOR:
            int[] indices = (int[]) in.readObject ();
            double[] values = (double[]) in.readObject ();
            obj = new FeatureVector(getDataAlphabet (), indices, values);
            break;
        case TYPE_OBJECT:
            obj = in.readObject ();
            break;
        default:

View Full Code Here

    assert(fvs.size() == ls.size());


    Object instName = (inst.getName() == null ? "NONAME" : inst.getName());
    
    for (int j = 0; j < fvs.size(); j++) {
      FeatureVector fv = fvs.getFeatureVector(j);
      int[] indices = fv.getIndices();
      FeatureVector data = new AugmentableFeatureVector (alphabetsPipe.getDataAlphabet(),
          indices, fv.getValues(), indices.length); 
      Labeling target = ls.getLabelAtPosition(j);
      String name = instName.toString() + "_@_POS_" + (j + 1);
      Object source = inst.getSource();
      Instance toAdd = alphabetsPipe.pipe(new Instance(data, target, name, source));

View Full Code Here

    crf.setParameter(1, 1, 0, 1.0); // state1 self-transition
    crf.setParameter(1, 0, 0, Transducer.IMPOSSIBLE_WEIGHT); // state1->state0


    FeatureVectorSequence fvs = new FeatureVectorSequence(
        new FeatureVector[] {
            new FeatureVector((Alphabet) crf.getInputAlphabet(),
                new double[] { 1 }),
            new FeatureVector((Alphabet) crf.getInputAlphabet(),
                new double[] { 1 }),
            new FeatureVector((Alphabet) crf.getInputAlphabet(),
                new double[] { 1 }), });


    SumLattice lattice = new SumLatticeDefault(crf, fvs, true);
    // We start in state0
    assertTrue(lattice.getGammaProbability(0, crf.getState(0)) == 1.0);

View Full Code Here

    crf.setParameter(1, 1, 0, 1.0); // state1 self-transition
    crf.setParameter(1, 0, 0, Transducer.IMPOSSIBLE_WEIGHT); // state1->state0


    FeatureVectorSequence fvs = new FeatureVectorSequence(
        new FeatureVector[] {
            new FeatureVector((Alphabet) crf.getInputAlphabet(),
                new double[] { 1 }),
            new FeatureVector((Alphabet) crf.getInputAlphabet(),
                new double[] { 1 }),
            new FeatureVector((Alphabet) crf.getInputAlphabet(),
                new double[] { 1 }), });


    MaxLattice lattice = new MaxLatticeDefault(crf, fvs);
    Sequence<Transducer.State> viterbiPath = lattice.bestStateSequence();
    // We start in state0

View Full Code Here

    CRF crf = new CRF(inputAlphabet, outputAlphabet);
    CRF saveCRF = crf;
    // inputAlphabet = (Feature.Alphabet) crf.getInputAlphabet();
    FeatureVectorSequence fvs = new FeatureVectorSequence(
        new FeatureVector[] {
            new FeatureVector(crf.getInputAlphabet(), new int[] {
                1, 2, 3 }),
            new FeatureVector(crf.getInputAlphabet(), new int[] {
                1, 2, 3 }),
            new FeatureVector(crf.getInputAlphabet(), new int[] {
                1, 2, 3 }),
            new FeatureVector(crf.getInputAlphabet(), new int[] {
                1, 2, 3 }), });
    FeatureSequence ss = new FeatureSequence(crf.getOutputAlphabet(),
        new int[] { 0, 1, 2, 3 });
    InstanceList ilist = new InstanceList(new Noop(inputAlphabet,
        outputAlphabet));

View Full Code Here

        }


        for (int j = 0; j < s.trainingSet.size(); j++) {
          Instance instance = s.trainingSet.get (j);
          double instWeight = s.trainingSet.getInstanceWeight (j);
          FeatureVector fv = (FeatureVector) instance.getData ();
          String labelString = (String) instance.getTarget ();
          TransitionIterator iter = new TransitionIterator (s, fv, gatherConstraints?labelString:null, memm);
          while (iter.hasNext ()) {
            // gsc
            iter.nextState(); // advance the iterator

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

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.