/**
* 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 - 5) + ")";
FastVector newVector = new FastVector(1);
newVector.addElement(new Option(string1, "N", 1, "-N <required number of rules output>"));
return newVector.elements();
}
/**
* Method that finds all association rules.
*
* @exception Exception if an attribute is numeric
*/
private void findRulesQuickly() throws Exception {
FastVector[] rules;
RuleGeneration currentItemSet;
// Build rules
for (int j = 0; j < m_Ls.size(); j++) {
FastVector currentItemSets = (FastVector) m_Ls.elementAt(j);
Enumeration enumItemSets = currentItemSets.elements();
while (enumItemSets.hasMoreElements()) {
currentItemSet = new RuleGeneration((ItemSet) enumItemSets.nextElement());
m_best =
currentItemSet.generateRules(
m_numRules, m_midPoints, m_priors, m_expectation, m_instances, m_best, m_count);
m_count = currentItemSet.m_count;
if (!m_bestChanged && currentItemSet.m_change) m_bestChanged = true;
// update minimum expected predictive accuracy to get into the n best
if (m_best.size() > 0) m_expectation = ((RuleItem) m_best.first()).accuracy();
else m_expectation = 0;
}
}
}