Examples of weka.core.Instances.compactify()

weka.core.Instances.compactify()
Compactifies the set of instances. Decreases the capacity of the set so that it matches the number of instances in the set.

      }
      // free memory
      subsets[i] = null;
    }
    train.compactify();
    test.compactify();
    
    // randomize the final sets
    train.randomize(rand);
    test.randomize(rand);
  } else {

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    data.add(subsets[i].instance(k));
  }
  for (int k = 0; k < subsets[j].numInstances(); k++) {
    data.add(subsets[j].instance(k));
  }
  data.compactify();
  data.randomize(rand);
  m_classifiers[i][j].buildClassifier(data, i, j, 
              m_fitLogisticModels,
              m_numFolds, m_randomSeed);
      }

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              }
            }
          }
        }
      }
      sub.compactify();
      return sub;
    }


    /**
     * Evaluate a numeric attribute for a potential split

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    } else {
      rightSubset.add(m_instances.instance(i));
    } 
  } 


  leftSubset.compactify();
  rightSubset.compactify();


  // build left and right nodes
  m_left = new RuleNode(m_globalDeviation, m_globalAbsDeviation, this);
  m_left.setMinNumInstances(m_splitNum);

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          data.add(subsets[i].instance(k));
        }
        for (int k = 0; k < subsets[j].numInstances(); k++) {
          data.add(subsets[j].instance(k));
        }  
        data.compactify(); 
        data.randomize(rand);
        m_classifiers[i][j].buildClassifier(data, i, j, 
            m_fitLogisticModels,
            m_numFolds, m_randomSeed);
      }

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    data.add(subsets[i].instance(k));
  }
  for (int k = 0; k < subsets[j].numInstances(); k++) {
    data.add(subsets[j].instance(k));
  }
  data.compactify();
  data.randomize(rand);
  m_classifiers[i][j].buildClassifier(data, i, j, 
              m_fitLogisticModels,
              m_numFolds, m_randomSeed);
      }

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      // create the new Instances
      Instances dataSet = new Instances(m_structure, m_batch.size());
      for (Instance i : m_batch) {
        dataSet.add(i);
      }
      dataSet.compactify();
      
      // save memory
      m_batch = null;
      
      if (m_log != null) {

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    } else {
      rightSubset.add(m_instances.instance(i));
    } 
  } 


  leftSubset.compactify();
  rightSubset.compactify();


  // build left and right nodes
  m_left = new RuleNode(m_globalDeviation, m_globalAbsDeviation, this);
  m_left.setMinNumInstances(m_splitNum);

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      }
      // free memory
      subsets[i] = null;
    }
    train.compactify();
    test.compactify();
    
    // randomize the final sets
    train.randomize(rand);
    test.randomize(rand);
  } else {

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        if (passesMustContain(data.instance(i), 
            transactionsMustContainIndexes, numInTransactionsMustContainList)) {
          newInsts.add(data.instance(i));
        }
      }
      newInsts.compactify();
      return newInsts;
    }
  }
  
  private ArrayList<Item> parseRulesMustContain(Instances data) {

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