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 $

      if (instances.classIndex() > -1)
  values[values.length - 1] = inst.value(instances.classIndex());


      // generate and add instance
      if (inst instanceof SparseInstance)
  newInst = new SparseInstance(instances.instance(i).weight(), values);
      else
  newInst = new Instance(instances.instance(i).weight(), values);
      result.add(newInst);
    }

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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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  int current = m_SelectedAttributes[i];
  newVals[i] = instance.value(current);
      }
    }
    if (instance instanceof SparseInstance) {
      push(new SparseInstance(instance.weight(), newVals));
    } else {
      push(new Instance(instance.weight(), newVals));
    }
  }

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      }
    }
    
    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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  start++;
      }
      
      // create new instance
      if (oldInstance instanceof SparseInstance)
  newInstance = new SparseInstance(oldInstance.weight(), newValues);
      else
  newInstance = new Instance(oldInstance.weight(), newValues);


      // copy string/relational values from input to output
      copyValues(newInstance, false, oldInstance.dataset(), getOutputFormat());

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  }
      }
    }
    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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  attSoFar += att.numValues() - 1;
      }
    }
    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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        vals[i] = dest.value(i);
      }
    }
    Instance inst = null;
    if (dest instanceof SparseInstance) {
      inst = new SparseInstance(dest.weight(), vals);
    } else {
      inst = new Instance(dest.weight(), vals);
    }
    inst.setDataset(dest.dataset());
    return inst;

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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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  }
      }
    }
    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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