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

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

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    List<EigenPair> weakEigenPairs = new ArrayList<EigenPair>();

    // determine sum of eigenvalues
    double totalSum = 0;
    for(int i = 0; i < eigenPairs.size(); i++) {
      EigenPair eigenPair = eigenPairs.getEigenPair(i);
      totalSum += eigenPair.getEigenvalue();
    }
    if(logger.isDebugging()) {
      msg.append("\ntotalSum = ").append(totalSum);
    }

    // determine strong and weak eigenpairs
    double currSum = 0;
    boolean found = false;
    for(int i = 0; i < eigenPairs.size(); i++) {
      EigenPair eigenPair = eigenPairs.getEigenPair(i);
      currSum += eigenPair.getEigenvalue();
      if(currSum / totalSum >= alpha) {
        if(!found) {
          found = true;
          strongEigenPairs.add(eigenPair);
        }
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      limit = delta;
    }
    else {
      double max = Double.NEGATIVE_INFINITY;
      for(int i = 0; i < eigenPairs.size(); i++) {
        EigenPair eigenPair = eigenPairs.getEigenPair(i);
        double eigenValue = Math.abs(eigenPair.getEigenvalue());
        if(max < eigenValue) {
          max = eigenValue;
        }
      }
      limit = max * delta;
    }
    if(logger.isDebugging()) {
      msg.append("\nlimit = ").append(limit);
    }

    // init strong and weak eigenpairs
    List<EigenPair> strongEigenPairs = new ArrayList<EigenPair>();
    List<EigenPair> weakEigenPairs = new ArrayList<EigenPair>();

    // determine strong and weak eigenpairs
    for(int i = 0; i < eigenPairs.size(); i++) {
      EigenPair eigenPair = eigenPairs.getEigenPair(i);
      double eigenValue = Math.abs(eigenPair.getEigenvalue());
      if(eigenValue >= limit) {
        strongEigenPairs.add(eigenPair);
      }
      else {
        weakEigenPairs.add(eigenPair);
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        // 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);
        }
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    List<EigenPair> weakEigenPairs = new ArrayList<EigenPair>();

    // determine sum of eigenvalues
    double totalSum = 0;
    for(int i = 0; i < eigenPairs.size(); i++) {
      EigenPair eigenPair = eigenPairs.getEigenPair(i);
      totalSum += eigenPair.getEigenvalue();
    }
    double expectEigenvalue = totalSum / eigenPairs.size() * walpha;

    // determine strong and weak eigenpairs
    for(int i = 0; i < eigenPairs.size(); i++) {
      EigenPair eigenPair = eigenPairs.getEigenPair(i);
      if(eigenPair.getEigenvalue() > expectEigenvalue) {
        strongEigenPairs.add(eigenPair);
      }
      else {
        weakEigenPairs.add(eigenPair);
      }
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    List<EigenPair> weakEigenPairs = new ArrayList<EigenPair>();

    // determine sum of eigenvalues
    double totalSum = 0;
    for(int i = 0; i < eigenPairs.size(); i++) {
      EigenPair eigenPair = eigenPairs.getEigenPair(i);
      totalSum += eigenPair.getEigenvalue();
    }
    double expectedVariance = totalSum / eigenPairs.size() * walpha;

    // determine strong and weak eigenpairs
    double currSum = 0;
    boolean found = false;
    int i;
    for(i = 0; i < eigenPairs.size(); i++) {
      EigenPair eigenPair = eigenPairs.getEigenPair(i);
      // weak Eigenvector?
      if(eigenPair.getEigenvalue() < expectedVariance) {
        break;
      }
      currSum += eigenPair.getEigenvalue();
      // calculate progressive alpha level
      double alpha = 1.0 - (1.0 - palpha) * (1.0 - (i + 1) / eigenPairs.size());
      if(currSum / totalSum >= alpha || i == eigenPairs.size() - 1) {
        found = true;
        strongEigenPairs.add(eigenPair);
        break;
      }
    }
    // if we didn't hit our alpha level, we consider all vectors to be weak!
    if(!found) {
      assert (weakEigenPairs.size() == 0);
      weakEigenPairs = strongEigenPairs;
      strongEigenPairs = new ArrayList<EigenPair>();
    }
    for(; i < eigenPairs.size(); i++) {
      EigenPair eigenPair = eigenPairs.getEigenPair(i);
      weakEigenPairs.add(eigenPair);
    }

    // the code using this method doesn't expect an empty strong set,
    // if we didn't find any strong ones, we make all vectors strong
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    List<EigenPair> strongEigenPairs = new ArrayList<EigenPair>();
    List<EigenPair> weakEigenPairs = new ArrayList<EigenPair>();

    // determine strong and weak eigenpairs
    for(int i = 0; i < eigenPairs.size(); i++) {
      EigenPair eigenPair = eigenPairs.getEigenPair(i);
      if(i < n) {
        strongEigenPairs.add(eigenPair);
      }
      else {
        weakEigenPairs.add(eigenPair);
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      List<EigenPair> strongEigenPairs = filteredEigenPairs.getStrongEigenPairs();
      strongEigenvalues = new double[strongEigenPairs.size()];
      strongEigenvectors = new Matrix(dim, strongEigenPairs.size());
      int i = 0;
      for(Iterator<EigenPair> it = strongEigenPairs.iterator(); it.hasNext(); i++) {
        EigenPair eigenPair = it.next();
        strongEigenvalues[i] = eigenPair.getEigenvalue();
        strongEigenvectors.setColumnVector(i, eigenPair.getEigenvector());
        sumStrongEigenvalues += strongEigenvalues[i];
      }
    }

    {// weak eigenpairs
      List<EigenPair> weakEigenPairs = filteredEigenPairs.getWeakEigenPairs();
      weakEigenvalues = new double[weakEigenPairs.size()];
      weakEigenvectors = new Matrix(dim, weakEigenPairs.size());
      int i = 0;
      for(Iterator<EigenPair> it = weakEigenPairs.iterator(); it.hasNext(); i++) {
        EigenPair eigenPair = it.next();
        weakEigenvalues[i] = eigenPair.getEigenvalue();
        weakEigenvectors.setColumnVector(i, eigenPair.getEigenvector());
        sumWeakEigenvalues += weakEigenvalues[i];
      }
    }
    explainedVariance = sumStrongEigenvalues / (sumStrongEigenvalues + sumWeakEigenvalues);
    int localdim = strongEigenvalues.length;
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        // 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);
        }
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    int contrastMaximum = eigenPairs.size() - 1;
    double maxContrast = 0.0;
    // calc the eigenvalue sum.
    double eigenValueSum = 0.0;
    for(int i = 0; i < eigenPairs.size(); i++) {
      EigenPair eigenPair = eigenPairs.getEigenPair(i);
      eigenValueSum += eigenPair.getEigenvalue();
    }
    double weakEigenvalue = eigenValueSum / eigenPairs.size() * walpha;
    // now find the maximum contrast.
    double currSum = eigenPairs.getEigenPair(eigenPairs.size() - 1).getEigenvalue();
    for(int i = eigenPairs.size() - 2; i >= 0; i--) {
      EigenPair eigenPair = eigenPairs.getEigenPair(i);
      currSum += eigenPair.getEigenvalue();
      // weak?
      if(eigenPair.getEigenvalue() < weakEigenvalue) {
        continue;
      }
      double contrast = eigenPair.getEigenvalue() / (currSum / (eigenPairs.size() - i));
      if(contrast > maxContrast) {
        maxContrast = contrast;
        contrastMaximum = i;
      }
    }

    for(int i = 0; i <= contrastMaximum /* && i < eigenPairs.size() */; i++) {
      EigenPair eigenPair = eigenPairs.getEigenPair(i);
      strongEigenPairs.add(eigenPair);
    }
    for(int i = contrastMaximum + 1; i < eigenPairs.size(); i++) {
      EigenPair eigenPair = eigenPairs.getEigenPair(i);
      weakEigenPairs.add(eigenPair);
    }

    return new FilteredEigenPairs(weakEigenPairs, strongEigenPairs);
  }
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    // initialize strong and weak eigenpairs
    // all normalized eigenpairs are regarded as strong
    final List<EigenPair> strongEigenPairs = new ArrayList<EigenPair>();
    final List<EigenPair> weakEigenPairs = new ArrayList<EigenPair>();
    for(int i = 0; i < eigenPairs.size(); i++) {
      final EigenPair eigenPair = eigenPairs.getEigenPair(i);
      normalizeEigenPair(eigenPair);
      strongEigenPairs.add(eigenPair);
    }
    if(logger.isDebugging()) {
      final StringBuffer msg = new StringBuffer();
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Related Classes of de.lmu.ifi.dbs.elki.math.linearalgebra.EigenPair

  • de.lmu.ifi.dbs.elki.math.linearalgebra.pca.FirstNEigenPairFilter
  • de.lmu.ifi.dbs.elki.math.linearalgebra.pca.LimitEigenPairFilter
  • de.lmu.ifi.dbs.elki.math.linearalgebra.pca.NormalizingEigenPairFilter
  • de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredResult
  • de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PercentageEigenPairFilter
  • de.lmu.ifi.dbs.elki.math.linearalgebra.pca.ProgressiveEigenPairFilter
  • de.lmu.ifi.dbs.elki.math.linearalgebra.pca.RelativeEigenPairFilter
  • de.lmu.ifi.dbs.elki.math.linearalgebra.pca.SignificantEigenPairFilter
  • de.lmu.ifi.dbs.elki.math.linearalgebra.pca.WeakEigenPairFilter
  • de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.cluster.EMClusterVisualization
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