Method Derive

Filters/Statistics/vtkPCAStatistics.cxx:611–732

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//------------------------------------------------------------------------------
void vtkPCAStatistics::Derive(vtkStatisticalModel* inMeta)
{
  if (!inMeta)
  {
    return;
  }

  // Use the parent class to compute a covariance matrix for each request.
  this->Superclass::Derive(inMeta);

  // Now that we have the covariance matrices, compute the SVD of each.
  vtkIdType nb = static_cast<vtkIdType>(inMeta->GetNumberOfTables(vtkStatisticalModel::Derived));
  for (vtkIdType b = 0; b < nb; ++b)
  {
    vtkTable* reqModel = inMeta->GetTable(vtkStatisticalModel::Derived, b);
    if (!reqModel)
    {
      continue;
    }
    vtkIdType m = reqModel->GetNumberOfColumns() - 2;
    Eigen::MatrixXd cov(m, m);
    // Fill the cov array with values from the vtkTable
    vtkIdType i, j;
    for (j = 2; j < 2 + m; ++j)
    {
      for (i = 0; i < j - 1; ++i)
      {
        cov(i, j - 2) = reqModel->GetValue(i, j).ToDouble();
      }
    }
    // Fill in the lower triangular portion of the matrix
    for (j = 0; j < m - 1; ++j)
    {
      for (i = j; i < m; ++i)
      {
        cov(i, j) = cov(j, i);
      }
    }
    // If normalization of the covariance array is requested, perform it:
    vtkVariantArray* normData = vtkVariantArray::New();
    switch (this->NormalizationScheme)
    {
      case TRIANGLE_SPECIFIED:
      case DIAGONAL_SPECIFIED:
        vtkPCAStatisticsNormalizeSpec(normData, cov, this->GetSpecifiedNormalization(), reqModel,
          this->NormalizationScheme == TRIANGLE_SPECIFIED);
        break;
      case DIAGONAL_VARIANCE:
        vtkPCAStatisticsNormalizeVariance(normData, cov);
        break;
      case NONE:
      case NUM_NORMALIZATION_SCHEMES:
      default:
        // do nothing
        break;
    }
    Eigen::BDCSVD<Eigen::MatrixXd> svd(cov, Eigen::ComputeFullU);
    const Eigen::MatrixXd& u = svd.matrixU();