Java FastVector.elements方法代碼示例

import weka.core.FastVector; //導入方法依賴的package包/類
/** 
  * Method that finds all association rules.
  *
  * @throws Exception if an attribute is numeric
  */
 private void findRulesQuickly() throws Exception {

   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++) {
  m_allTheRules[0].addElement(rules[0].elementAt(k));
  m_allTheRules[1].addElement(rules[1].elementAt(k));
  m_allTheRules[2].addElement(rules[2].elementAt(k));
  
  if (rules.length > 3) {
    m_allTheRules[3].addElement(rules[3].elementAt(k));
    m_allTheRules[4].addElement(rules[4].elementAt(k));
    m_allTheRules[5].addElement(rules[5].elementAt(k));
  }
}
     }
   }
 }
 

import weka.core.FastVector; //導入方法依賴的package包/類
/** 
  * Method that finds all class association rules.
  *
  * @throws Exception if an attribute is numeric
  */
  private void findCarRulesQuickly() throws Exception {

   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));
  m_allTheRules[1].addElement(rules[1].elementAt(k));
  m_allTheRules[2].addElement(rules[2].elementAt(k));
}
     }
   }
 }
 

import weka.core.FastVector; //導入方法依賴的package包/類
/**
  * Updates counter of a specific item set
  * @param itemSets an item sets
  * @param instancesNoClass instances without the class attribute
  * @param instancesClass the values of the class attribute sorted according to instances
  */  
  public static void upDateCounters(FastVector itemSets, Instances instancesNoClass, Instances instancesClass){

   for (int i = 0; i < instancesNoClass.numInstances(); i++) {
       Enumeration enu = itemSets.elements();
while (enu.hasMoreElements())
           ((LabeledItemSet)enu.nextElement()).upDateCounter(instancesNoClass.instance(i),instancesClass.instance(i));
   }

 }
 

import weka.core.FastVector; //導入方法依賴的package包/類
/** 
  * Method that finds all association rules and performs significance test.
  *
  * @throws Exception if an attribute is numeric
  */
 private void findRulesBruteForce() throws Exception {

   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.generateRulesBruteForce(m_minMetric,m_metricType,
			  m_hashtables,j+1,
			  m_instances.numInstances(),
			  m_significanceLevel);
for (int k = 0; k < rules[0].size(); k++) {
  m_allTheRules[0].addElement(rules[0].elementAt(k));
  m_allTheRules[1].addElement(rules[1].elementAt(k));
  m_allTheRules[2].addElement(rules[2].elementAt(k));

  m_allTheRules[3].addElement(rules[3].elementAt(k));
  m_allTheRules[4].addElement(rules[4].elementAt(k));
  m_allTheRules[5].addElement(rules[5].elementAt(k));
}
     }
   }
 }
 

import weka.core.FastVector; //導入方法依賴的package包/類
/**
  * Updates counters for a set of item sets and a set of instances.
  *
  * @param itemSets the set of item sets which are to be updated
  * @param instances the instances to be used for updating the counters
  */
 public static void upDateCounters(FastVector itemSets, Instances instances) {

   for (int i = 0; i < instances.numInstances(); i++) {
     Enumeration enu = itemSets.elements();
     while (enu.hasMoreElements()) 
((ItemSet)enu.nextElement()).upDateCounter(instances.instance(i));
   }
 }
 

import weka.core.FastVector; //導入方法依賴的package包/類
/**
 * Returns an enumeration describing the available options.
 *
 * @return an enumeration of all the available options.
 */
public Enumeration listOptions() {

  String string1 = "\tThe required number of rules. (default = " + m_numRules + ")",
    string2 = 
    "\tThe minimum confidence of a rule. (default = " + m_minMetric + ")",
    string3 = "\tThe delta by which the minimum support is decreased in\n",
    string4 = "\teach iteration. (default = " + m_delta + ")",
    string5 = 
    "\tThe lower bound for the minimum support. (default = " + 
    m_lowerBoundMinSupport + ")",
    string6 = "\tIf used, rules are tested for significance at\n",
    string7 = "\tthe given level. Slower. (default = no significance testing)",
    string8 = "\tIf set the itemsets found are also output. (default = no)",
    string9 = "\tIf set class association rules are mined. (default = no)",
    string10 = "\tThe class index. (default = last)",
    stringType = "\tThe metric type by which to rank rules. (default = "
    +"confidence)",
    stringZeroAsMissing = "\tTreat zero (i.e. first value of nominal attributes) as " +
    		"missing";
  

  FastVector newVector = new FastVector(11);

  newVector.addElement(new Option(string1, "N", 1, 
		    "-N <required number of rules output>"));
  newVector.addElement(new Option(stringType, "T", 1,
		    "-T <0=confidence | 1=lift | "
		    +"2=leverage | 3=Conviction>"));
  newVector.addElement(new Option(string2, "C", 1, 
		    "-C <minimum metric score of a rule>"));
  newVector.addElement(new Option(string3 + string4, "D", 1,
		    "-D <delta for minimum support>"));
  newVector.addElement(new Option("\tUpper bound for minimum support. "
		    +"(default = 1.0)", "U", 1,
		     "-U <upper bound for minimum support>"));
  newVector.addElement(new Option(string5, "M", 1,
		    "-M <lower bound for minimum support>"));
  newVector.addElement(new Option(string6 + string7, "S", 1,
		    "-S <significance level>"));
  newVector.addElement(new Option(string8, "I", 0,
		    "-I"));
  newVector.addElement(new Option("\tRemove columns that contain "
		    +"all missing values (default = no)"
		    , "R", 0,
		    "-R"));
  newVector.addElement(new Option("\tReport progress iteratively. (default "
		    +"= no)", "V", 0,
		    "-V"));
  newVector.addElement(new Option(string9, "A", 0,
		    "-A"));
  newVector.addElement(new Option(stringZeroAsMissing, "Z", 0,
      "-Z"));
  newVector.addElement(new Option(string10, "c", 1,
		    "-c <the class index>"));
  
  return newVector.elements();
}