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

  }
  
  
  public Instance pipe (Instance carrier)
  {
    FeatureVector fv = (FeatureVector) carrier.getData();
    carrier.setData(new FeatureVector (fv, fv.getAlphabet(), null, null));
    return carrier;
  }

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    for (int i = 0; i < numStates; i++)
      stateNames[i] = "state" + i;
    MEMM saveCRF = crf;
    //inputAlphabet = (Feature.Alphabet) crf.getInputAlphabet();
    FeatureVectorSequence fvs = new FeatureVectorSequence(new FeatureVector[]{
      new FeatureVector(crf.getInputAlphabet(), new int[]{1, 2, 3}, new double[]{1, 1, 1}),
      new FeatureVector(crf.getInputAlphabet(), new int[]{1, 2, 3}, new double[]{1, 1, 1}),
      new FeatureVector(crf.getInputAlphabet(), new int[]{1, 2, 3}, new double[]{1, 1, 1}),
      new FeatureVector(crf.getInputAlphabet(), new int[]{1, 2, 3}, new double[]{1, 1, 1}),
    });
    FeatureSequence ss = new FeatureSequence(crf.getOutputAlphabet(), new int[]{0, 1, 2, 3});
    InstanceList ilist = new InstanceList(null);
    ilist.add(fvs, ss, null, null);

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    LogNumber[] oneStateValueCache = new LogNumber[numStates];
    LogNumber nuAlpha = new LogNumber(Transducer.IMPOSSIBLE_WEIGHT,true);
    LogNumber temp = new LogNumber(Transducer.IMPOSSIBLE_WEIGHT,true);
    
    for (int ip = 0; ip < latticeLength-1; ++ip) {
      FeatureVector fv = fvs.get(ip);
      // speed things up by giving the constraints an 
      // opportunity to cache, for example, which 
      // constrained input features appear in this 
      // FeatureVector
      for (GEConstraint constraint : constraints1) {

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            {
               StringBuffer buf = new StringBuffer();
              for (int a = 0; a < k; a++)
                 buf.append(outputs[a].get(j).toString()).append(" ");
              if (includeInput) {
                FeatureVector fv = (FeatureVector)input.get(j);
                buf.append(fv.toString(true));                
              }
              System.out.println(buf.toString());
            }
            System.out.println();
          }

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              StringBuffer buf = new StringBuffer();
              for (int a = 0; a < k; a++) {
                buf.append(outputs[a].get(j).toString()).append(" ");
              }
              if (includeInput) {
                FeatureVector fv = (FeatureVector)input.get(j);
                buf.append(fv.toString(true));                
              }
              System.out.println(buf.toString());
            }
            System.out.println();
          }

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        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);
      }

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      }
        }


    // [removed code that sorted indices but NOT values -DM]


        FeatureVector fv = new FeatureVector(dataAlphabet, keys, values);
        carrier.setData( fv );
        return carrier;
    }

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        featureNames[i] = fields[i];
        featureValues[i] = 1.0;
      }
    }


    carrier.setData(new FeatureVector(getDataAlphabet(), featureNames, featureValues));
    
    return carrier;
  }

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    t.setFeatureValue ("length", 3);
    t.setFeatureValue ("containsVowel", 1);
    t.setFeatureValue ("in /usr/dict/words", 0);


    Alphabet dict = new Alphabet();
    FeatureVector fv = t.toFeatureVector (dict, false);
    assertTrue (fv.numLocations() == 2);
    assertTrue (fv.value (dict.lookupIndex("length")) == 3);
  }

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    for (int i = 0; i < indicesArr.length; i++) {
      indicesArr[i] = indices.get(i);
      valuesArr[i] = values.get(i);
    }
    
    FeatureVector fv = new FeatureVector(getDataAlphabet(), indicesArr, valuesArr);
    carrier.setData(fv);
    return carrier;
  }

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