Examples of weka.core.FastVector.elements()

weka.core.FastVector.elements()
Returns an enumeration of this vector. @return an enumeration of this vector

    FastVector[] rules;


    // Build rules
    for (int j = 1; j < m_Ls.size(); j++) {
      FastVector currentItemSets = (FastVector)m_Ls.elementAt(j);
      Enumeration enumItemSets = currentItemSets.elements();
      while (enumItemSets.hasMoreElements()) {
  AprioriItemSet currentItemSet = (AprioriItemSet)enumItemSets.nextElement();
        //AprioriItemSet currentItemSet = new AprioriItemSet((ItemSet)enumItemSets.nextElement());
  rules = currentItemSet.generateRules(m_minMetric, m_hashtables, j + 1);
  for (int k = 0; k < rules[0].size(); k++) {

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    FastVector[] rules;


    // Build rules
    for (int j = 0; j < m_Ls.size(); j++) {
      FastVector currentLabeledItemSets = (FastVector)m_Ls.elementAt(j);
      Enumeration enumLabeledItemSets = currentLabeledItemSets.elements();
      while (enumLabeledItemSets.hasMoreElements()) {
  LabeledItemSet currentLabeledItemSet = (LabeledItemSet)enumLabeledItemSets.nextElement();
  rules = currentLabeledItemSet.generateRules(m_minMetric,false);
  for (int k = 0; k < rules[0].size(); k++) {
    m_allTheRules[0].addElement(rules[0].elementAt(k));

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    newVector.addElement(new Option(string9, "A", 0,
            "-A"));
    newVector.addElement(new Option(string10, "c", 1,
            "-c <the class index>"));
    
    return newVector.elements();
  }


  /**
   * Parses a given list of options. <p/>
   *

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        newVector.addElement(
        new Option("\tSmoothing value to avoid zero WordGivenClass"+
                   " probabilities (default=1.0).\n",
                   "S", 1,"-S"));
        
        return newVector.elements();
    }
    
    /**
     * Gets the current settings of the classifier.
     *

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     newVector.addElement(new Option(
   "\tSets incremental loading", 
   "I", 0, "-I"));
     
     return  newVector.elements();
  }
  
  /** 
   * Sets the options.
   *

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     newVector.addElement(new Option(
           "\tInput file in arff format that should be saved in database.",
           "i", 1, "-i <input file name>"));
     
     return  newVector.elements();
  }
  
  /** 
   * Sets the options. <p/>
   *

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     newVector.addElement(new Option(
           "\tInput file in arff format that should be saved in database.",
           "i", 1, "-i <input file name>"));
     
     return  newVector.elements();
  }
  
  /** 
   * Sets the options. <p/>
   *

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                + "\n\tIf not specified, the key will be determined automatically, if possible with the used JDBC driver.\n\tThe auto ID column created by the DatabaseSaver won't be loaded.",
            "P", 1, "-P<list of column names>"));
    newVector.addElement(new Option("\tSets incremental loading", "I", 0,
        "-I"));


    return newVector.elements();
  }


  /**
   * Sets the options.
   *

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     newVector.addElement(new Option(
           "\tInput file in arff format that should be saved in database.",
           "i", 1, "-i <input file name>"));
     
     return  newVector.elements();
  }
  
  /** 
   * Sets the options. <p/>
   *

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        newVector.addElement(
        new Option("\tSmoothing value to avoid zero WordGivenClass"+
                   " probabilities (default=1.0).\n",
                   "S", 1,"-S"));
        
        return newVector.elements();
    }
    
    /**
     * Gets the current settings of the classifier.
     *

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