Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.SortedEigenPairs

Package de.lmu.ifi.dbs.elki.math.linearalgebra

Examples of de.lmu.ifi.dbs.elki.math.linearalgebra.SortedEigenPairs

de.lmu.ifi.dbs.elki.math.linearalgebra.SortedEigenPairs
Helper class which encapsulates an array of eigenpairs (i.e. an array of eigenvectors and their corresponding eigenvalues). This class is used to sort eigenvectors (and -values). @author Elke Achtert @apiviz.composedOf de.lmu.ifi.dbs.elki.math.linearalgebra.EigenPair

        Matrix covmat = clus.getModel().getCovarianceMatrix();
        NV centroid = clus.getModel().getMean();
        Vector cent = new Vector(proj.fastProjectDataToRenderSpace(centroid));


        // Compute the eigenvectors
        SortedEigenPairs eps = pcarun.processCovarMatrix(covmat).getEigenPairs();


        Vector[] pc = new Vector[eps.size()];
        for(int i = 0; i < eps.size(); i++) {
          EigenPair ep = eps.getEigenPair(i);
          Vector sev = ep.getEigenvector().times(Math.sqrt(ep.getEigenvalue()));
          pc[i] = new Vector(proj.fastProjectRelativeDataToRenderSpace(sev.getArrayRef()));
        }
        if(drawStyle != 0 || eps.size() == 2) {
          drawSphere2D(cnum, cent, pc);
        }
        else {
          Polygon chres = makeHullComplex(pc);
          drawHullLines(cnum, cent, chres);

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      DistanceResultPair<DoubleDistance> qr = new GenericDistanceResultPair<DoubleDistance>(distance, id);
      results.add(qr);
    }
    Collections.sort(results);
    PCAResult pcares = pca.processQueryResult(results, database);
    SortedEigenPairs eigenPairs = pcares.getEigenPairs();
    return eigenPairs.reverseEigenVectors(dim);


    // Used to be just this:


    // Matrix pcaMatrix = pca.pcaMatrixResults(database, results);
    // pca.determineEigenPairs(pcaMatrix);

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  @Override
  public FilteredEigenPairs filter(SortedEigenPairs eigenPairs) {
    FilteredEigenPairs result = null;
    for(EigenPairFilter f : filters) {
      result = f.filter(eigenPairs);
      eigenPairs = new SortedEigenPairs(result.getStrongEigenPairs());
    }
    return result;
  }

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   * 
   * @param evd eigenvalue decomposition to use
   * @return PCA result
   */
  public PCAResult processEVD(EigenvalueDecomposition evd) {
    SortedEigenPairs eigenPairs = new SortedEigenPairs(evd, false);
    return new PCAResult(eigenPairs);
  }

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   * 
   * @param evd eigenvalue decomposition to use
   */
  @Override
  public PCAFilteredResult processEVD(EigenvalueDecomposition evd) {
    SortedEigenPairs eigenPairs = new SortedEigenPairs(evd, false);
    FilteredEigenPairs filteredEigenPairs = eigenPairFilter.filter(eigenPairs);
    return new PCAFilteredResult(eigenPairs, filteredEigenPairs, big, small);
  }

View Full Code Here

        Matrix covmat = clus.getModel().getCovarianceMatrix();
        NV centroid = clus.getModel().getMean();
        Vector cent = new Vector(proj.fastProjectDataToRenderSpace(centroid));


        // Compute the eigenvectors
        SortedEigenPairs eps = pcarun.processCovarMatrix(covmat).getEigenPairs();


        Vector[] pc = new Vector[eps.size()];
        for(int i = 0; i < eps.size(); i++) {
          EigenPair ep = eps.getEigenPair(i);
          Vector sev = ep.getEigenvector().times(Math.sqrt(ep.getEigenvalue()));
          pc[i] = new Vector(proj.fastProjectRelativeDataToRenderSpace(sev));
        }
        if(drawStyle != 0 || eps.size() == 2) {
          drawSphere2D(cnum, cent, pc);
        }
        else {
          Polygon chres = makeHullComplex(pc);
          drawHullLines(cnum, cent, chres);

View Full Code Here

   * 
   * @param evd eigenvalue decomposition to use
   * @return PCA result
   */
  public PCAResult processEVD(EigenvalueDecomposition evd) {
    SortedEigenPairs eigenPairs = new SortedEigenPairs(evd, false);
    return new PCAResult(eigenPairs);
  }

View Full Code Here

   * 
   * @param evd eigenvalue decomposition to use
   */
  @Override
  public PCAFilteredResult processEVD(EigenvalueDecomposition evd) {
    SortedEigenPairs eigenPairs = new SortedEigenPairs(evd, false);
    FilteredEigenPairs filteredEigenPairs = eigenPairFilter.filter(eigenPairs);
    return new PCAFilteredResult(eigenPairs, filteredEigenPairs, big, small);
  }

View Full Code Here

  @Override
  public FilteredEigenPairs filter(SortedEigenPairs eigenPairs) {
    FilteredEigenPairs result = null;
    for(EigenPairFilter f : filters) {
      result = f.filter(eigenPairs);
      eigenPairs = new SortedEigenPairs(result.getStrongEigenPairs());
    }
    return result;
  }

View Full Code Here

      DistanceResultPair<DoubleDistance> qr = new GenericDistanceResultPair<DoubleDistance>(distance, id);
      results.add(qr);
    }
    Collections.sort(results);
    PCAResult pcares = pca.processQueryResult(results, database);
    SortedEigenPairs eigenPairs = pcares.getEigenPairs();
    return eigenPairs.reverseEigenVectors(dim);


    // Used to be just this:


    // Matrix pcaMatrix = pca.pcaMatrixResults(database, results);
    // pca.determineEigenPairs(pcaMatrix);

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de.lmu.ifi.dbs.elki.math.linearalgebra.pca.CompositeEigenPairFilter

de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredRunner

de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCARunner

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