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 $

    }
    for(int j=0, numInsts=list1.length()+list2.length(); 
        j < attrVals.length; j++) {
      attrVals[j] /= numInsts;
    }
    temp = new DenseInstance(1.0, attrVals);
    return temp;
  }

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    for(int k=0; k<node2.anchor.numValues(); k++) {
      if(node2.anchor.index(k)==classIdx)
  continue;
      attrVals[k] += node2.anchor.valueSparse(k)*anchr2Ratio;
    }
    temp = new DenseInstance(1.0, attrVals);
    return temp;
  }

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    if (m_RP != rp) {
      throw new Error("Unrecognized ResultProducer sending results!!");
    }
    
    Instance newInst = new DenseInstance(m_AttributeTypes.length);
    for(int i = 0; i < m_AttributeTypes.length; i++) {
      Object val = null;
      if (i < key.length) {
  val = key[i];
      } else {
  val = result[i - key.length];
      }
      if (val == null) {
  newInst.setValue(i, Utils.missingValue());
      } else {
  switch (m_AttributeTypes[i]) {
  case Attribute.NOMINAL:
    String str = (String) val;
    Double index = (Double)m_NominalIndexes[i].get(str);
    if (index == null) {
      index = new Double(m_NominalStrings[i].size());
      m_NominalIndexes[i].put(str, index);
      m_NominalStrings[i].addElement(str);
    }
    newInst.setValue(i, index.doubleValue());
    break;
  case Attribute.NUMERIC:
    double dou = ((Double) val).doubleValue();
    newInst.setValue(i, (double)dou);
    break;
  default:
    newInst.setValue(i, Utils.missingValue());
  }
      }
    }
    m_Instances.addElement(newInst);
  }

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            }
        }
        
        for (int iInstance = 0; iInstance < bayesNet.m_Instances.numInstances(); iInstance++) {
            Instance instanceOrig = bayesNet.m_Instances.instance(iInstance);
            Instance instance = new DenseInstance(instances.numAttributes());
            for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
                if (iAttribute != instances.classIndex()) {
                    if (instanceOrig.value(iAttribute) > 0) {
                        instance.setValue(iAttribute, 1);
                    }
                } else {
                    instance.setValue(iAttribute, instanceOrig.value(iAttribute));
                }
            }
        }
        // ok, now all data is binary, except the class attribute
        // now learn the empty and tree network

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            vals2[j] = Math.sqrt(tempI[i].variance(j));
          } else {
            vals2[j] = Utils.missingValue();
          }  
        }    
        m_ClusterStdDevs.add(new DenseInstance(1.0, vals2));
      }
      m_ClusterSizes[i] = tempI[i].numInstances();
    }
  }

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          }
        }
      }
    }
    if (updateClusterInfo)
      m_ClusterCentroids.add(new DenseInstance(1.0, vals));
    return vals;
  }

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   */
  public void generateInstances () throws Exception {
      int [] order = getOrder();
    for (int iInstance = 0; iInstance < m_nNrOfInstances; iInstance++) {
        int nNrOfAtts = m_Instances.numAttributes();
      Instance instance = new DenseInstance(nNrOfAtts);
      instance.setDataset(m_Instances);
      for (int iAtt2 = 0; iAtt2 < nNrOfAtts; iAtt2++) {
          int iAtt = order[iAtt2];


        double iCPT = 0;


        for (int iParent = 0; iParent < m_ParentSets[iAtt].getNrOfParents(); iParent++) {
          int nParent = m_ParentSets[iAtt].getParent(iParent);
          iCPT = iCPT * m_Instances.attribute(nParent).numValues() + instance.value(nParent);
        } 
  
        double fRandom = random.nextInt(1000) / 1000.0f;
        int iValue = 0;
        while (fRandom > m_Distributions[iAtt][(int) iCPT].getProbability(iValue)) {
          fRandom = fRandom - m_Distributions[iAtt][(int) iCPT].getProbability(iValue);
          iValue++ ;
        }
        instance.setValue(iAtt, iValue);
      }
      m_Instances.add(instance);
    }
  } // GenerateInstances

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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());
    push(inst);

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    }
    Instance inst = null;
    if (dest instanceof SparseInstance) {
      inst = new SparseInstance(dest.weight(), vals);
    } else {
      inst = new DenseInstance(dest.weight(), vals);
    }
    inst.setDataset(dest.dataset());
    return inst;
  }

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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());
    push(inst);

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