/**
* @param ex the given test exemplar
* @return the classification
* @throws Exception if the exemplar could not be classified successfully
*/
public double classifyInstance(Instance ex) throws Exception {
// Instance ex = new Exemplar(e);
Instances exi = ex.relationalValue(1);
double[] n = new double[m_Dimension];
double[] xBar = new double[m_Dimension];
for (int i = 0; i < exi.numAttributes(); i++) xBar[i] = exi.meanOrMode(i);
for (int w = 0, t = 0; w < m_Dimension; w++, t++) {
// if((t==m_ClassIndex) || (t==m_IdIndex))
// t++;
for (int u = 0; u < exi.numInstances(); u++)
if (!exi.instance(u).isMissing(t)) n[w] += exi.instance(u).weight();
}
double logOdds = likelihoodRatio(n, xBar);
return (logOdds > m_Cutoff) ? 1 : 0;
}
/**
* Computes the distribution for a given exemplar
*
* @param ex the exemplar for which distribution is computed
* @return the distribution
* @throws Exception if the distribution can't be computed successfully
*/
public double[] distributionForInstance(Instance ex) throws Exception {
double[] distribution = new double[2];
Instances exi = ex.relationalValue(1);
double[] n = new double[m_Dimension];
double[] xBar = new double[m_Dimension];
for (int i = 0; i < exi.numAttributes(); i++) xBar[i] = exi.meanOrMode(i);
for (int w = 0, t = 0; w < m_Dimension; w++, t++) {
for (int u = 0; u < exi.numInstances(); u++)
if (!exi.instance(u).isMissing(t)) n[w] += exi.instance(u).weight();
}
double logOdds = likelihoodRatio(n, xBar);
// returned logOdds value has been divided by m_Dimension to avoid
// Math.exp(logOdds) getting too large or too small,
// that may result in two fixed distribution value (1 or 0).
distribution[0] = 1 / (1 + Math.exp(logOdds)); // Prob. for class 0 (negative)
distribution[1] = 1 - distribution[0];
return distribution;
}
