Examples of weka.core.DenseInstance

weka.core.DenseInstance
te empty instance with three attribute values
Instance inst = new DenseInstance(3);

// Set instance's values for the attributes "length", "weight", and "position"
inst.setValue(length, 5.3);
inst.setValue(weight, 300);
inst.setValue(position, "first");

// Set instance's dataset to be the dataset "race"
inst.setDataset(race);

// Print the instance
System.out.println("The instance: " + inst);
...

All methods that change an instance's attribute values are safe, ie. a change of an instance's attribute values does not affect any other instances. All methods that change an instance's attribute values clone the attribute value vector before it is changed. If your application heavily modifies instance values, it may be faster to create a new instance from scratch. @author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5987 $

    
    values = new double[m_Indices.length];
    for (i = 0; i < m_Indices.length; i++)
      values[i] = instance.value(m_Indices[i]);


    result = new DenseInstance(instance.weight(), values);
    result.setDataset(getOutputFormat());
    
    copyValues(result, false, instance.dataset(), getOutputFormat());
    result.setDataset(getOutputFormat());

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    // generate new Instance
    values = instance.toDoubleArray();
    if (!instance.isMissing(m_AttIndex.getIndex()))
      values[m_AttIndex.getIndex()] = m_SortedIndices[(int) values[m_AttIndex.getIndex()]];
    newInstance = new DenseInstance(instance.weight(), values);


    // copy string values etc. from input to output
    copyValues(instance, false, instance.dataset(), getOutputFormat());
    
    push(newInstance);

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

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    // create new instance
    if (original instanceof SparseInstance) {
      processed = new SparseInstance(original.weight(), instanceVals);
    } else {
      processed = new DenseInstance(original.weight(), instanceVals);
    }


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

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      // generate new instance
      if (inst instanceof SparseInstance)
  newInst = new SparseInstance(inst.weight(), values);
      else
  newInst = new DenseInstance(inst.weight(), values);
      
      // copy possible string, relational values
      newInst.setDataset(getOutputFormat());
      copyValues(newInst, false, inst.dataset(), getOutputFormat());

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


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

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


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

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      }
      Instance newInst;
      if (adapter.getSparseData()) {
        newInst = new SparseInstance(1.0, vals);
      } else {
        newInst = new DenseInstance(1.0, vals);
      }
      instances.addElement(newInst);
      rowCount++;
    }
    //disconnectFromDatabase();  (perhaps other queries might be made)

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      }      
    } catch (Exception ex) {
      ex.printStackTrace();
      return;
    } finally {
      newInst = new DenseInstance(currentI.weight(), instanceVals);
      newInst.setDataset(m_format);
      m_instanceEvent.setInstance(newInst);
      m_instanceEvent.setStatus(status);
      // notify listeners
      notifyInstanceAvailable(m_instanceEvent);

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    desiredMag = rand.nextGaussian() * m_centroidStdDevs[centroid];
    scale      = desiredMag / magnitude;
    for (i = 0; i < getNumAttributes(); i++) {
      atts[i] *= scale;
      atts[i] += m_centroids[centroid][i];
      result   = new DenseInstance(1.0, atts);
    }


    // dataset reference
    result.setDataset(m_DatasetFormat);

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