/**
* Calculates the difference
*
* @return difference
*/
public double prototypeDifference(CombStat stat) {
double sumdiff = 0;
double weight;
// Numeric atts: abs difference
for (int i = 0; i < m_RegStat.getNbNumericAttributes(); i++) {
weight = m_StatManager.getClusteringWeights().getWeight(m_RegStat.getAttribute(i));
sumdiff += Math.abs(prototypeNum(i) - stat.prototypeNum(i)) * weight;
// System.err.println("sumdiff: " + Math.abs(prototypeNum(i) - stat.prototypeNum(i)) *
// weight);
}
// Nominal atts: Manhattan distance
for (int i = 0; i < m_ClassStat.getNbNominalAttributes(); i++) {
weight = m_StatManager.getClusteringWeights().getWeight(m_ClassStat.getAttribute(i));
double sum = 0;
double[] proto1 = prototypeNom(i);
double[] proto2 = stat.prototypeNom(i);
for (int j = 0; j < proto1.length; j++) {
sum += Math.abs(proto1[j] - proto2[j]);
}
sumdiff += sum * weight;
// System.err.println("sumdiff: " + (sum * weight));
}
// System.err.println("sumdiff-total: " + sumdiff);
return sumdiff != 0 ? sumdiff : 0.0;
}
// TODO: Move to ClassificationStat
public double meanEntropy() {
double sent = 0;
int nbNominal = m_ClassStat.getNbNominalAttributes();
for (int i = 0; i < nbNominal; i++) {
sent += entropy(i);
}
return sent / nbNominal;
}
public double getSVarSDiff(ClusAttributeWeights scale, ClusStatistic other) {
int nbTargetNom = m_ClassStat.getNbNominalAttributes();
int nbTargetNum = m_RegStat.getNbNumericAttributes();
ClassificationStat ocls = ((CombStat) other).getClassificationStat();
RegressionStat oreg = ((CombStat) other).getRegressionStat();
return (m_ClassStat.getSVarSDiff(scale, ocls) * nbTargetNom
+ m_RegStat.getSVarSDiff(scale, oreg) * nbTargetNum)
/ (nbTargetNom + nbTargetNum);
}
// TODO: Move to ClassificationStat
public double meanDistNom(int use) {
double sumdist = 0;
double weight = 0;
for (int i = 0; i < m_ClassStat.getNbNominalAttributes(); i++) {
if (use == IN_HEURISTIC) {
weight = m_StatManager.getClusteringWeights().getWeight(m_ClassStat.getAttribute(i));
} else { // use == IN_OUTPUT
weight = m_StatManager.getDispersionWeights().getWeight(m_ClassStat.getAttribute(i));
}
sumdist += meanDistNomOne(i) * weight;
}
return sumdist;
}
/**
* Checks weather values of a target attribute are significantly different
*
* @return
*/
public boolean targetSignDifferent() {
boolean res = false;
int att = -1;
String att_name;
String att_name2;
ClusStatistic targetStat = m_StatManager.getStatistic(ClusAttrType.ATTR_USE_TARGET);
if (targetStat instanceof ClassificationStat) {
for (int i = 0; i < targetStat.getNbNominalAttributes(); i++) {
att_name = ((ClassificationStat) targetStat).getAttribute(i).getName();
for (int j = 0; j < m_ClassStat.getNbNominalAttributes(); j++) {
att_name2 = m_ClassStat.getAttribute(j).getName();
if (att_name.equals(att_name2)) {
att = j;
break;
}
}
if (SignDifferentNom(att)) {
res = true;
break; // TODO: If one target att significant, the whole rule significant!?
}
}
// System.out.println("Target sign. testing: " + res);
return res;
} else if (targetStat instanceof RegressionStat) {
for (int i = 0; i < targetStat.getNbNumericAttributes(); i++) {
att_name = ((RegressionStat) targetStat).getAttribute(i).getName();
for (int j = 0; j < m_RegStat.getNbNumericAttributes(); j++) {
att_name2 = m_RegStat.getAttribute(j).getName();
if (att_name.equals(att_name2)) {
att = j;
break;
}
}
try {
if (SignDifferentNum(att)) {
res = true;
break; // TODO: If one target att significant, the whole rule significant!?
}
} catch (IllegalArgumentException e) {
e.printStackTrace();
} catch (MathException e) {
e.printStackTrace();
}
}
return res;
} else {
// TODO: Classification and regression
return true;
}
}
public int getNbNominalAttributes() {
return m_ClassStat.getNbNominalAttributes();
}
// TODO: Not sure this makes sense in CombStat - Check!
public double getSVarS(ClusAttributeWeights scale) {
int nbTargetNom = m_ClassStat.getNbNominalAttributes();
int nbTargetNum = m_RegStat.getNbNumericAttributes();
return (m_ClassStat.getSVarS(scale) * nbTargetNom + m_RegStat.getSVarS(scale) * nbTargetNum)
/ (nbTargetNom + nbTargetNum);
}