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for ( int i=0; i < data.cols(); i++) {
    Deg(i,i)=1/(sqrt((data.row(i).sum())) );
}
Eigen::MatrixXd Lapla = Deg * data * Deg;

Eigen::SelfAdjointEigenSolver<Eigen::MatrixXd> s(Lapla, true);
    // Change ends here
Eigen::VectorXd val = s.eigenvalues();
Eigen::MatrixXd vec = s.eigenvectors();

//sort eigenvalues/vectors
int n = data.cols();
for (int i = 0; i < n - 1; ++i) {
    int k;
    val.segment(i, n - i).maxCoeff(&k);
    if (k > 0) {
        std::swap(val[i], val[k + i]);
        vec.col(i).swap(vec.col(k + i));
    }
}

//choose the number of eigenvectors to consider
if (mNumDims < vec.cols()) {
    mEigenVectors = vec.block(0,0,vec.rows(),mNumDims);
} else {
    mEigenVectors = vec;
}