/**
* Evaluate a clustering result.
*
* @param db Database
* @param c Clustering
* @param refc Reference clustering
*/
protected void evaluteResult(Database db, Clustering<?> c, Clustering<?> refc) {
ClusterContingencyTable contmat =
new ClusterContingencyTable(selfPairing, noiseSpecialHandling);
contmat.process(refc, c);
ScoreResult sr = new ScoreResult(contmat);
sr.addHeader(c.getLongName());
db.getHierarchy().add(c, sr);
}
@Override
public ScoreResult score(SparseMatrix m) {
ScoreResult hitsResult = hits.score(m);
double[] colScore = hitsResult.getColScore();
SingularValueDecompositionLibJ svd = new SingularValueDecompositionLibJ();
svd.factorize(m, Math.min(20, Math.min(m.rows(), m.columns()) * 7 / 11));
Matrix u = svd.dataClasses();
Matrix v = new TransposedMatrix(svd.classFeatures());
if (hasNaN(u) || hasNaN(v)) return hitsResult;
double[] svs = svd.singularValues();
PriorityQueue<Double, Integer> cos2dim =
PriorityQueue.make(
Math.min(5, svs.length),
SortUtils.reverse(Double.class),
SortUtils.comp(Integer.class));
for (int j = 0; j < v.columns(); j++) {
double cos = MatrixUtils.cosine(colScore, v.getColumn(j));
cos2dim.add(cos, j);
}
List<Integer> dims = cos2dim.values();
double[] hub = new double[u.rows()];
for (int k = 0; k < u.rows(); k++) {
double sum = 0;
for (int i : dims) sum += u.get(k, i) * u.get(k, i) * svs[i] * svs[i];
hub[k] = Math.sqrt(sum);
}
MatrixUtils.normalize(hub);
double[] authority = new double[v.rows()];
for (int k = 0; k < v.rows(); k++) {
double sum = 0;
for (int i : dims) sum += v.get(k, i) * v.get(k, i) * svs[i] * svs[i];
authority[k] = Math.sqrt(sum);
}
MatrixUtils.normalize(authority);
return new ScoreResult(hub, authority);
}