use of de.lmu.ifi.dbs.elki.algorithm.DependencyDerivator in project elki by elki-project.
the class CASH method runDerivator.
/**
* Runs the derivator on the specified interval and assigns all points having
* a distance less then the standard deviation of the derivator model to the
* model to this model.
*
* @param relation the database containing the parameterization functions
* @param interval the interval to build the model
* @param dim the dimensionality of the database
* @param ids an empty set to assign the ids
* @return a basis of the found subspace
*/
private double[][] runDerivator(Relation<ParameterizationFunction> relation, int dim, CASHInterval interval, ModifiableDBIDs ids) {
Database derivatorDB = buildDerivatorDB(relation, interval);
PCARunner pca = new PCARunner(new StandardCovarianceMatrixBuilder());
EigenPairFilter filter = new FirstNEigenPairFilter(dim - 1);
DependencyDerivator<DoubleVector> derivator = new DependencyDerivator<>(null, FormatUtil.NF4, pca, filter, 0, false);
CorrelationAnalysisSolution<DoubleVector> model = derivator.run(derivatorDB);
double[][] weightMatrix = model.getSimilarityMatrix();
double[] centroid = model.getCentroid();
double eps = .25;
ids.addDBIDs(interval.getIDs());
// Search for nearby vectors in original database
for (DBIDIter iditer = relation.iterDBIDs(); iditer.valid(); iditer.advance()) {
double[] v = relation.get(iditer).getColumnVector();
double d = mahalanobisDistance(weightMatrix, v, centroid);
if (d <= eps) {
ids.add(iditer);
}
}
double[][] basis = model.getStrongEigenvectors();
return getMatrix(basis, 0, basis.length, 0, dim - 1);
}
use of de.lmu.ifi.dbs.elki.algorithm.DependencyDerivator in project elki by elki-project.
the class CASH method runDerivator.
/**
* Runs the derivator on the specified interval and assigns all points having
* a distance less then the standard deviation of the derivator model to the
* model to this model.
*
* @param relation the database containing the parameterization functions
* @param ids the ids to build the model
* @param dimensionality the dimensionality of the subspace
* @return a basis of the found subspace
*/
private LinearEquationSystem runDerivator(Relation<ParameterizationFunction> relation, int dimensionality, DBIDs ids) {
try {
// build database for derivator
Database derivatorDB = buildDerivatorDB(relation, ids);
PCARunner pca = new PCARunner(new StandardCovarianceMatrixBuilder());
EigenPairFilter filter = new FirstNEigenPairFilter(dimensionality);
DependencyDerivator<DoubleVector> derivator = new DependencyDerivator<>(null, FormatUtil.NF4, pca, filter, 0, false);
CorrelationAnalysisSolution<DoubleVector> model = derivator.run(derivatorDB);
LinearEquationSystem les = model.getNormalizedLinearEquationSystem(null);
return les;
} catch (NonNumericFeaturesException e) {
throw new IllegalStateException("Error during normalization" + e);
}
}
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