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Clang-format tests, examples, libraries, benchmarks, etc.

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This commit is contained in:
Antonio Sánchez
2023-12-05 21:22:55 +00:00
committed by Rasmus Munk Larsen
parent 3252ecc7a4
commit 46e9cdb7fe
876 changed files with 33453 additions and 37795 deletions
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96
test/random_matrix_helper.h
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@@ -13,38 +13,35 @@
#define EIGEN_RANDOM_MATRIX_HELPER
#include <typeinfo>
#include <Eigen/QR> // required for createRandomPIMatrixOfRank and generateRandomMatrixSvs
#include <Eigen/QR> // required for createRandomPIMatrixOfRank and generateRandomMatrixSvs
// Forward declarations to avoid ICC warnings
#if EIGEN_COMP_ICC
namespace Eigen {
template<typename MatrixType>
template <typename MatrixType>
void createRandomPIMatrixOfRank(Index desired_rank, Index rows, Index cols, MatrixType& m);
template<typename PermutationVectorType>
template <typename PermutationVectorType>
void randomPermutationVector(PermutationVectorType& v, Index size);
template<typename MatrixType>
template <typename MatrixType>
MatrixType generateRandomUnitaryMatrix(const Index dim);
template<typename MatrixType, typename RealScalarVectorType>
void generateRandomMatrixSvs(const RealScalarVectorType &svs, const Index rows, const Index cols, MatrixType& M);
template <typename MatrixType, typename RealScalarVectorType>
void generateRandomMatrixSvs(const RealScalarVectorType& svs, const Index rows, const Index cols, MatrixType& M);
template<typename VectorType, typename RealScalar>
template <typename VectorType, typename RealScalar>
VectorType setupRandomSvs(const Index dim, const RealScalar max);
template<typename VectorType, typename RealScalar>
template <typename VectorType, typename RealScalar>
VectorType setupRangeSvs(const Index dim, const RealScalar min, const RealScalar max);
} // end namespace Eigen
} // end namespace Eigen
#endif // EIGEN_COMP_ICC
namespace Eigen {
/**
@@ -59,9 +56,8 @@ namespace Eigen {
* @param cols column dimension of requested random partial isometry matrix
* @param m random partial isometry matrix
*/
template<typename MatrixType>
void createRandomPIMatrixOfRank(Index desired_rank, Index rows, Index cols, MatrixType& m)
{
template <typename MatrixType>
void createRandomPIMatrixOfRank(Index desired_rank, Index rows, Index cols, MatrixType& m) {
typedef typename internal::traits<MatrixType>::Scalar Scalar;
enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime };
@@ -69,27 +65,25 @@ void createRandomPIMatrixOfRank(Index desired_rank, Index rows, Index cols, Matr
typedef Matrix<Scalar, Rows, Rows> MatrixAType;
typedef Matrix<Scalar, Cols, Cols> MatrixBType;
if(desired_rank == 0)
{
m.setZero(rows,cols);
if (desired_rank == 0) {
m.setZero(rows, cols);
return;
}
if(desired_rank == 1)
{
if (desired_rank == 1) {
// here we normalize the vectors to get a partial isometry
m = VectorType::Random(rows).normalized() * VectorType::Random(cols).normalized().transpose();
return;
}
MatrixAType a = MatrixAType::Random(rows,rows);
MatrixType d = MatrixType::Identity(rows,cols);
MatrixBType b = MatrixBType::Random(cols,cols);
MatrixAType a = MatrixAType::Random(rows, rows);
MatrixType d = MatrixType::Identity(rows, cols);
MatrixBType b = MatrixBType::Random(cols, cols);
// set the diagonal such that only desired_rank non-zero entries remain
const Index diag_size = (std::min)(d.rows(),d.cols());
if(diag_size != desired_rank)
d.diagonal().segment(desired_rank, diag_size-desired_rank) = VectorType::Zero(diag_size-desired_rank);
const Index diag_size = (std::min)(d.rows(), d.cols());
if (diag_size != desired_rank)
d.diagonal().segment(desired_rank, diag_size - desired_rank) = VectorType::Zero(diag_size - desired_rank);
HouseholderQR<MatrixAType> qra(a);
HouseholderQR<MatrixBType> qrb(b);
@@ -103,18 +97,17 @@ void createRandomPIMatrixOfRank(Index desired_rank, Index rows, Index cols, Matr
* @param v permutation vector
* @param size length of permutation vector
*/
template<typename PermutationVectorType>
void randomPermutationVector(PermutationVectorType& v, Index size)
{
template <typename PermutationVectorType>
void randomPermutationVector(PermutationVectorType& v, Index size) {
typedef typename PermutationVectorType::Scalar Scalar;
v.resize(size);
for(Index i = 0; i < size; ++i) v(i) = Scalar(i);
if(size == 1) return;
for(Index n = 0; n < 3 * size; ++n)
{
Index i = internal::random<Index>(0, size-1);
for (Index i = 0; i < size; ++i) v(i) = Scalar(i);
if (size == 1) return;
for (Index n = 0; n < 3 * size; ++n) {
Index i = internal::random<Index>(0, size - 1);
Index j;
do j = internal::random<Index>(0, size-1); while(j==i);
do j = internal::random<Index>(0, size - 1);
while (j == i);
std::swap(v(i), v(j));
}
}
@@ -129,16 +122,14 @@ void randomPermutationVector(PermutationVectorType& v, Index size)
* @param dim row and column dimension of the requested square matrix
* @return random unitary matrix
*/
template<typename MatrixType>
MatrixType generateRandomUnitaryMatrix(const Index dim)
{
template <typename MatrixType>
MatrixType generateRandomUnitaryMatrix(const Index dim) {
typedef typename internal::traits<MatrixType>::Scalar Scalar;
typedef Matrix<Scalar, Dynamic, 1> VectorType;
MatrixType v = MatrixType::Identity(dim, dim);
VectorType h = VectorType::Zero(dim);
for (Index i = 0; i < dim; ++i)
{
for (Index i = 0; i < dim; ++i) {
v.col(i).tail(dim - i - 1) = VectorType::Random(dim - i - 1);
h(i) = 2 / v.col(i).tail(dim - i).squaredNorm();
}
@@ -174,9 +165,8 @@ MatrixType generateRandomUnitaryMatrix(const Index dim)
* @param cols column dimension of requested random matrix
* @param M generated matrix with prescribed singular values
*/
template<typename MatrixType, typename RealScalarVectorType>
void generateRandomMatrixSvs(const RealScalarVectorType &svs, const Index rows, const Index cols, MatrixType& M)
{
template <typename MatrixType, typename RealScalarVectorType>
void generateRandomMatrixSvs(const RealScalarVectorType& svs, const Index rows, const Index cols, MatrixType& M) {
enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime };
typedef typename internal::traits<MatrixType>::Scalar Scalar;
typedef Matrix<Scalar, Rows, Rows> MatrixAType;
@@ -206,9 +196,8 @@ void generateRandomMatrixSvs(const RealScalarVectorType &svs, const Index rows,
* @param max upper bound for singular values
* @return vector of singular values
*/
template<typename VectorType, typename RealScalar>
VectorType setupRandomSvs(const Index dim, const RealScalar max)
{
template <typename VectorType, typename RealScalar>
VectorType setupRandomSvs(const Index dim, const RealScalar max) {
VectorType svs = max / RealScalar(2) * (VectorType::Random(dim) + VectorType::Ones(dim));
std::sort(svs.begin(), svs.end(), std::greater<RealScalar>());
return svs;
@@ -232,14 +221,11 @@ VectorType setupRandomSvs(const Index dim, const RealScalar max)
* @param max largest singular value to use
* @return vector of singular values
*/
template<typename VectorType, typename RealScalar>
VectorType setupRangeSvs(const Index dim, const RealScalar min, const RealScalar max)
{
template <typename VectorType, typename RealScalar>
VectorType setupRangeSvs(const Index dim, const RealScalar min, const RealScalar max) {
VectorType svs = VectorType::Random(dim);
if(dim == 0)
return svs;
if(dim == 1)
{
if (dim == 0) return svs;
if (dim == 1) {
svs(0) = min;
return svs;
}
@@ -251,6 +237,6 @@ VectorType setupRangeSvs(const Index dim, const RealScalar min, const RealScalar
return min * (VectorType::Ones(dim) - svs) + max * svs;
}
} // end namespace Eigen
} // end namespace Eigen
#endif // EIGEN_RANDOM_MATRIX_HELPER
#endif // EIGEN_RANDOM_MATRIX_HELPER