Examples of weka.core.SerializedObject

weka.core.SerializedObject
Class for storing an object in serialized form in memory. It can be used to make deep copies of objects, and also allows compression to conserve memory.
@author Richard Kirkby (rbk1@cs.waikato.ac.nz) @version $Revision: 1.12 $

   * @param source the object to copy
   * @return a copy of the source object
   * @exception Exception if the copy fails
   */
  public static Object makeCopy(Object source) throws Exception {
    SerializedObject so = new SerializedObject(source);
    Object result = so.getObject();
    return result;
  }

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   * @param model the clusterer to copy
   * @return a deep copy of the clusterer
   * @exception Exception if an error occurs
   */
  public static Clusterer makeCopy(Clusterer model) throws Exception {
    return (Clusterer) new SerializedObject(model).getObject();
  }

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          int num) throws Exception {
     if (model == null) {
      throw new Exception("No model clusterer set");
    }
    Clusterer [] clusterers = new Clusterer [num];
    SerializedObject so = new SerializedObject(model);
    for(int i = 0; i < clusterers.length; i++) {
      clusterers[i] = (Clusterer) so.getObject();
    }
    return clusterers;
  }

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   * @return a deep copy of the classifier
   * @exception Exception if an error occurs
   */
  public static Classifier makeCopy(Classifier model) throws Exception {


    return (Classifier)new SerializedObject(model).getObject();
  }

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    if (model == null) {
      throw new Exception("No model classifier set");
    }
    Classifier [] classifiers = new Classifier [num];
    SerializedObject so = new SerializedObject(model);
    for(int i = 0; i < classifiers.length; i++) {
      classifiers[i] = (Classifier) so.getObject();
    }
    return classifiers;
  }

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      clone.m_trainInstances = new Instances(m_trainInstances); // copy training instances
      
      // deep copy the random object
      if (m_random != null) { 
  SerializedObject randomSerial = null;
  try {
    randomSerial = new SerializedObject(m_random);
  } catch (Exception ignored) {} // we know that Random is serializable
  clone.m_random = (Random) randomSerial.getObject();
      }


      clone.m_lastAddedSplitNum = m_lastAddedSplitNum;
      clone.m_numericAttIndices = m_numericAttIndices;
      clone.m_nominalAttIndices = m_nominalAttIndices;

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        // unbiased predictions
        insts.randomize(random);
        insts.stratify(numFolds);
        for (int i = 0; i < numFolds; i++) {
          Instances train = insts.trainCV(numFolds, i, random);
          SerializedObject so = new SerializedObject(this);
          BinaryMISMO smo = (BinaryMISMO)so.getObject();
          smo.buildClassifier(train, cl1, cl2, false, -1, -1);
          Instances test = insts.testCV(numFolds, i);
          for (int j = 0; j < test.numInstances(); j++) {
            double[] vals = new double[2];
            vals[0] = smo.SVMOutput(-1, test.instance(j));

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   * The environment variables.
   */
  protected transient Environment m_env;
  
  private weka.core.converters.Saver makeCopy() throws Exception {
    return (weka.core.converters.Saver)new SerializedObject(m_SaverTemplate).getObject();
  }

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    DeleteNodeAction(int nTargetNode) {
      m_nTargetNode = nTargetNode;
      m_att = m_Instances.attribute(nTargetNode);
      try {
        SerializedObject so = new SerializedObject(m_Distributions[nTargetNode]);
        m_CPT = (Estimator[]) so.getObject();
        ;
        so = new SerializedObject(m_ParentSets[nTargetNode]);
        m_ParentSet = (ParentSet) so.getObject();
      } catch (Exception e) {
        e.printStackTrace();
      }
      m_deleteArcActions = new FastVector();
      for (int iNode = 0; iNode < getNrOfNodes(); iNode++) {

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        // update parentsets
        ParentSet[] parentSets = new ParentSet[nAtts];
        int nX = 0;
        for (int iParentSet = 0; iParentSet < nAtts; iParentSet++) {
          if (iParentSet == m_nTargetNode) {
            SerializedObject so = new SerializedObject(m_ParentSet);
            parentSets[iParentSet] = (ParentSet) so.getObject();
            nX = 1;
          } else {
            parentSets[iParentSet] = m_ParentSets[iParentSet - nX];
            for (int iParent = 0; iParent < parentSets[iParentSet].getNrOfParents(); iParent++) {
              int nParent = parentSets[iParentSet].getParent(iParent);
              if (nParent >= m_nTargetNode) {
                parentSets[iParentSet].SetParent(iParent, nParent + 1);
              }
            }
          }
        }
        m_ParentSets = parentSets;
        // update distributions
        Estimator[][] distributions = new Estimator[nAtts][];
        nX = 0;
        for (int iNode = 0; iNode < nAtts; iNode++) {
          if (iNode == m_nTargetNode) {
            SerializedObject so = new SerializedObject(m_CPT);
            distributions[iNode] = (Estimator[]) so.getObject();
            nX = 1;
          } else {
            distributions[iNode] = m_Distributions[iNode - nX];
          }
        }

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