Eigen::SparseVector<double> normProbVector(Eigen::SparseVector<double> P_vec)
{
	VectorXd P_dense_vec = (VectorXd)P_vec;
	Eigen::SparseVector<double> P_norm;
	if (P_dense_vec == VectorXd::Zero(P_vec.size()))
	{
		P_norm = P_vec;
	}
	else{
		double P_positive = 0; double P_negative = 0;

		for (int row_idx = 0; row_idx < P_vec.size(); row_idx++){
			P_positive = (P_vec.coeff(row_idx) > 0) ? (P_positive + P_vec.coeff(row_idx)) : P_positive;
			P_negative = (P_vec.coeff(row_idx) > 0) ? P_negative : (P_negative + P_vec.coeff(row_idx));
		}
		if (fabs(P_positive) < fabs(P_negative)){
			P_norm = -P_vec / fabs(P_negative);
		}
		else{
			P_norm = P_vec / fabs(P_positive);
		}

		for (int row_idx = 0; row_idx < P_vec.size(); row_idx++){
			P_norm.coeffRef(row_idx) = (P_norm.coeff(row_idx)<0) ? 0 : P_norm.coeff(row_idx);
		}
	}
	P_norm.prune(TOLERANCE);
	return P_norm;
}
Eigen::SparseVector<double> pinv_vector(Eigen::SparseVector<double> pinvvec)
{
	Eigen::SparseVector<double> singularValues_inv;
	singularValues_inv.resize(pinvvec.size());

	for (int i = 0; i<pinvvec.size(); ++i) {
		singularValues_inv.coeffRef(i) = (fabs(pinvvec.coeff(i)) > TOLERANCE) ? 1.0 / pinvvec.coeff(i) : 0;
	}
	singularValues_inv.prune(TOLERANCE);
	return singularValues_inv;
}
Exemple #3
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IGL_INLINE void igl::diag(
    const Eigen::SparseVector<T>& V,
    Eigen::SparseMatrix<T>& X)
{
    // clear and resize output
    Eigen::DynamicSparseMatrix<T, Eigen::RowMajor> dyn_X(V.size(),V.size());
    dyn_X.reserve(V.size());
    // loop over non-zeros
    for(typename Eigen::SparseVector<T>::InnerIterator it(V); it; ++it)
    {
        dyn_X.coeffRef(it.index(),it.index()) += it.value();
    }
    X = Eigen::SparseMatrix<T>(dyn_X);
}