private void computeSymmEigSVD()

in src/main/java/org/apache/datasketches/vector/decomposition/MatrixOpsImplOjAlgo.java [211:231]

• 16 lines of code
• 6 McCabe index (conditional complexity)

  private void computeSymmEigSVD(final MatrixStore<Double> A, final boolean computeVectors) {
    if (evd_ == null) {
      evd_ = Eigenvalue.PRIMITIVE.make(n_, true);
    }

    // want left singular vectors U, aka eigenvectors of AA^T -- so compute that
    evd_.decompose(A.transpose().premultiply(A));

    // TODO: can we only use k_ values?
    final double[] ev = new double[n_];
    evd_.getEigenvalues(ev, Optional.empty());
    for (int i = 0; i < ev.length; ++i) {
      final double val = Math.sqrt(ev[i]);
      sv_[i] = val;
      if (computeVectors && (val > 0)) { S_.set(i, i, 1 / val); }
    }

    if (computeVectors) {
      S_.multiply(evd_.getV().transpose()).multiply(A, Vt_);
    }
  }