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 $

    } // c'tor


    public void undo() {
      try {
        m_Instances.insertAttributeAt(m_att, m_nTargetNode);
        SerializedObject so = new SerializedObject(m_CPT);
        m_Distributions[m_nTargetNode] = (Estimator[]) so.getObject();
        for (int iChild = 0; iChild < m_children.size(); iChild++) {
          int nChild = (Integer) m_children.elementAt(iChild);
          m_Instances.insertAttributeAt(m_att, m_nTargetNode);
          so = new SerializedObject(m_childAtts[iChild]);
          m_Distributions[nChild] = (Estimator[]) so.getObject();
        }
      } catch (Exception e) {
        e.printStackTrace();
      }
    } // undo

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

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

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                if (m_BayesNet.getEvidence(m_nCurrentNode) < 0 && iValue >= 0) {
                  m_BayesNet.setEvidence(m_nCurrentNode, iValue);
                  m_marginCalculatorWithEvidence.setEvidence(m_nCurrentNode, iValue);
                } else {
                  m_BayesNet.setEvidence(m_nCurrentNode, iValue);
                  SerializedObject so = new SerializedObject(m_marginCalculator);
                  m_marginCalculatorWithEvidence = (MarginCalculator) so.getObject();
                  for (int iNode = 0; iNode < m_BayesNet.getNrOfNodes(); iNode++) {
                    if (m_BayesNet.getEvidence(iNode) >= 0) {
                      m_marginCalculatorWithEvidence.setEvidence(iNode, m_BayesNet.getEvidence(iNode));
                    }
                  }

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    }


    try {
      m_marginCalculator = new MarginCalculator();
      m_marginCalculator.calcMargins(m_BayesNet);
      SerializedObject so = new SerializedObject(m_marginCalculator);
      m_marginCalculatorWithEvidence = (MarginCalculator) so.getObject();
      for (int iNode = 0; iNode < m_BayesNet.getNrOfNodes(); iNode++) {
        if (m_BayesNet.getEvidence(iNode) >= 0) {
          m_marginCalculatorWithEvidence.setEvidence(iNode, m_BayesNet.getEvidence(iNode));
        }
      }

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    grph = ((Drawable)clusterer).graph();
        } catch (Exception ex) {
        }
      }
      // copy full model for output
      SerializedObject so = new SerializedObject(clusterer);
      fullClusterer = (Clusterer) so.getObject();
      
      ClusterEvaluation eval = new ClusterEvaluation();
      eval.setClusterer(clusterer);
      switch (testMode) {
        case 3: case 5: // Test on training

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      grph = ((Drawable)classifier).graph();
    } catch (Exception ex) {
    }
        }
        // copy full model for output
        SerializedObject so = new SerializedObject(classifier);
        fullClassifier = (Classifier) so.getObject();
      }
      
      switch (testMode) {
        case 3: // Test on training
        m_Log.statusMessage("Evaluating on training data...");

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


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

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

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      m_Instances.stratify(numFolds);


      for (int k = 0; k < list.length; k++) {
        for (int i = 0; i < numFolds; i++) {
          Instances train = m_Instances.trainCV(numFolds, i, random);
          SerializedObject so = new SerializedObject(this);
          BayesianLogisticRegression blr = (BayesianLogisticRegression) so.getObject();
          //          blr.setHyperparameterSelection(3);
          blr.setHyperparameterSelection(new SelectedTag(SPECIFIC_VALUE, 
                                                         TAGS_HYPER_METHOD));
          blr.setHyperparameterValue(list[k]);
          //          blr.setPriorClass(PriorClass);

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Related Classes of weka.core.SerializedObject

trackerModule.weka.WekaInterface

weka.associations.AbstractAssociator

weka.attributeSelection.ASEvaluation

weka.attributeSelection.ASSearch

weka.attributeSelection.CheckAttributeSelection

weka.classifiers.AbstractClassifier

weka.classifiers.bayes.BayesianLogisticRegression

weka.classifiers.bayes.net.EditableBayesNet$AddArcAction

weka.classifiers.bayes.net.EditableBayesNet$DeleteArcAction

weka.classifiers.bayes.net.EditableBayesNet$DeleteNodeAction

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