/**
* Gets the current settings of FuzzyRoughSubsetEval
*
* @return an array of strings suitable for passing to setOptions()
*/
public String[] getOptions() {
Vector<String> result;
result = new Vector<String>();
result.add("-Z");
result.add(
(m_FuzzyMeasure.getClass().getName() + " " + Utils.joinOptions(m_FuzzyMeasure.getOptions()))
.trim());
result.add("-I");
result.add(
(m_Implicator.getClass().getName() + " " + Utils.joinOptions(m_Implicator.getOptions()))
.trim());
result.add("-T");
result.add(
(m_TNorm.getClass().getName() + " " + Utils.joinOptions(m_TNorm.getOptions())).trim());
result.add("-R");
result.add(
(m_Similarity.getClass().getName() + " " + Utils.joinOptions(m_Similarity.getOptions()))
.trim());
return result.toArray(new String[result.size()]);
}
protected void resetOptions() {
m_trainInstances = null;
try {
m_Similarity.setTNorm(new TNormLukasiewicz());
} catch (Exception e) {
}
}
/**
* Generates an attribute evaluator. Has to initialise all fields of the evaluator that are not
* being set via options.
*
* @param data set of instances serving as training data
* @throws Exception if the evaluator has not been generated successfully
*/
public void buildEvaluator(Instances data) throws Exception {
// can evaluator handle data?
getCapabilities().testWithFail(data);
m_trainInstances = new Instances(data);
m_trainInstances.deleteWithMissingClass();
m_numAttribs = m_trainInstances.numAttributes();
m_numInstances = m_trainInstances.numInstances();
// if the data has no decision feature, m_classIndex is negative
m_classIndex = m_trainInstances.classIndex();
// supervised
if (m_classIndex >= 0) {
m_isNumeric = m_trainInstances.attribute(m_classIndex).isNumeric();
if (m_isNumeric) {
m_DecisionSimilarity = m_Similarity;
} else m_DecisionSimilarity = m_SimilarityEq;
}
m_Similarity.setInstances(m_trainInstances);
m_DecisionSimilarity.setInstances(m_trainInstances);
m_SimilarityEq.setInstances(m_trainInstances);
m_composition = m_Similarity.getTNorm();
m_FuzzyMeasure.set(
m_Similarity,
m_DecisionSimilarity,
m_TNorm,
m_composition,
m_Implicator,
m_SNorm,
m_numInstances,
m_numAttribs,
m_classIndex,
m_trainInstances);
}
/**
* Return a description of the fuzzy rough attribute evaluator.
*
* @return a description of the evaluator as a String.
*/
public String toString() {
StringBuffer text = new StringBuffer();
if (m_trainInstances == null) {
text.append("FRFS feature evaluator has not been built yet\n");
} else {
text.append("\nFuzzy rough feature selection\n\nMethod: " + m_FuzzyMeasure);
text.append("\nSimilarity measure: " + m_Similarity);
text.append("\nDecision similarity: " + m_DecisionSimilarity);
text.append("\nImplicator: " + m_Implicator);
text.append("\nT-Norm: " + m_TNorm + "\nRelation composition: " + m_Similarity.getTNorm());
text.append("\n(S-Norm: " + m_SNorm + ")\n\n");
}
return text.toString();
}
