Examples of weka.core.SparseInstance

weka.core.SparseInstance
Class for storing an instance as a sparse vector. A sparse instance only requires storage for those attribute values that are non-zero. Since the objective is to reduce storage requirements for datasets with large numbers of default values, this also includes nominal attributes -- the first nominal value (i.e. that which has index 0) will not require explicit storage, so rearrange your nominal attribute value orderings if necessary. Missing values will be stored explicitly. @author Eibe Frank @version $Revision: 5970 $

                sparseValues[count] = histClassCounts[i][j];
                sparseIndices[count++] = j;
              }
            }
            
            SparseInstance tempS = 
              new SparseInstance(1.0, sparseValues, sparseIndices, 
                  histClassCounts[i].length);
            histClassCountsSparse[i] = tempS;
            
          }

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      int current = m_SelectedAttributes[i];
      vals[i] = instance.value(current);
    }
    Instance inst = null;
    if (instance instanceof SparseInstance)
      inst = new SparseInstance(instance.weight(), vals);
    else
      inst = new Instance(instance.weight(), vals);


    inst.setDataset(getOutputFormat());
    copyValues(inst, false, instance.dataset(), getOutputFormat());

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      Double value = (Double)contained.get(index);
      values[i] = value.doubleValue();
      indices[i] = index.intValue();
    }


    Instance inst = new SparseInstance(instance.weight(), values, indices, 
  outputFormatPeek().numAttributes());
    inst.setDataset(outputFormatPeek());


    v.addElement(inst);


    return firstCopy;    
  }

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  break;
    }


    // create instance
    if (instance instanceof SparseInstance)
      result = new SparseInstance(instance.weight(), newVals);
    else
      result = new Instance(instance.weight(), newVals);
    
    return result;
  }

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    m_attributeExpression.evaluateExpression(vals);


    Instance inst = null;
    if (instance instanceof SparseInstance) {
      inst = new SparseInstance(instance.weight(), vals);
    } else {
      inst = new Instance(instance.weight(), vals);
    }


    inst.setDataset(getOutputFormat());

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  values[n] = result.attribute(n).indexOfValue(value);
      }
      
      // generate new instance
      if (inst instanceof SparseInstance)
  newInst = new SparseInstance(inst.weight(), values);
      else
  newInst = new Instance(inst.weight(), values);
      
      // copy possible string, relational values
      newInst.setDataset(getOutputFormat());

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        newValues[m_outputNumAttributes - 1] = currentInstance.classValue();
      }
      //create new instance with recorded values and add to output dataset
      Instance newInstance;
      if (currentInstance instanceof SparseInstance) {
        newInstance = new SparseInstance(currentInstance.weight(), newValues);
      } else {
        newInstance = new Instance(currentInstance.weight(), newValues);
      }
      output.add(newInstance);
    }

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      newValues[i] = instanceVector.get(0, i);
    }
    
    // return newly transformed instance
    if (instance instanceof SparseInstance) {
      return new SparseInstance(instance.weight(), newValues);
    } else {
      return new Instance(instance.weight(), newValues);
    }
  }

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       }
      newVals[i] = tempval;
    }
    
    if (inst instanceof SparseInstance) {
      return new SparseInstance(inst.weight(), newVals);
    } else {
      return new Instance(inst.weight(), newVals);
    }      
  }

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      }
    }
    
    if (!m_transBackToOriginal) {
      if (instance instanceof SparseInstance) {
      return new SparseInstance(instance.weight(), newVals);
      } else {
        return new Instance(instance.weight(), newVals);
      }      
    } else {
      if (instance instanceof SparseInstance) {
        return convertInstanceToOriginal(new SparseInstance(instance.weight(), 
                                                            newVals));
      } else {
        return convertInstanceToOriginal(new Instance(instance.weight(),
                                                      newVals));
      }

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