Examples with DependencyDerivator de.lmu.ifi.dbs.elki.algorithm.DependencyDerivator used on opensource projects

Example 1 with DependencyDerivator

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);
}

Example 2 with DependencyDerivator

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);
    }
}