mirror of
https://gitlab.com/libeigen/eigen.git
synced 2026-04-10 11:34:33 +08:00
Clang-format tests, examples, libraries, benchmarks, etc.
This commit is contained in:
Antonio Sánchez
committed by
Rasmus Munk Larsen
Rasmus Munk Larsen
parent
3252ecc7a4
commit
46e9cdb7fe
@@ -7,68 +7,62 @@
|
||||
#include <sstream>
|
||||
using namespace Eigen;
|
||||
|
||||
std::map<std::string,Array<float,1,8,DontAlign|RowMajor> > results;
|
||||
std::map<std::string, Array<float, 1, 8, DontAlign | RowMajor> > results;
|
||||
std::vector<std::string> labels;
|
||||
std::vector<Array2i> sizes;
|
||||
|
||||
template<typename Solver,typename MatrixType>
|
||||
EIGEN_DONT_INLINE
|
||||
void compute_norm_equation(Solver &solver, const MatrixType &A) {
|
||||
if(A.rows()!=A.cols())
|
||||
solver.compute(A.transpose()*A);
|
||||
template <typename Solver, typename MatrixType>
|
||||
EIGEN_DONT_INLINE void compute_norm_equation(Solver &solver, const MatrixType &A) {
|
||||
if (A.rows() != A.cols())
|
||||
solver.compute(A.transpose() * A);
|
||||
else
|
||||
solver.compute(A);
|
||||
}
|
||||
|
||||
template<typename Solver,typename MatrixType>
|
||||
EIGEN_DONT_INLINE
|
||||
void compute(Solver &solver, const MatrixType &A) {
|
||||
template <typename Solver, typename MatrixType>
|
||||
EIGEN_DONT_INLINE void compute(Solver &solver, const MatrixType &A) {
|
||||
solver.compute(A);
|
||||
}
|
||||
|
||||
template<typename Scalar,int Size>
|
||||
void bench(int id, int rows, int size = Size)
|
||||
{
|
||||
typedef Matrix<Scalar,Dynamic,Size> Mat;
|
||||
typedef Matrix<Scalar,Dynamic,Dynamic> MatDyn;
|
||||
typedef Matrix<Scalar,Size,Size> MatSquare;
|
||||
Mat A(rows,size);
|
||||
template <typename Scalar, int Size>
|
||||
void bench(int id, int rows, int size = Size) {
|
||||
typedef Matrix<Scalar, Dynamic, Size> Mat;
|
||||
typedef Matrix<Scalar, Dynamic, Dynamic> MatDyn;
|
||||
typedef Matrix<Scalar, Size, Size> MatSquare;
|
||||
Mat A(rows, size);
|
||||
A.setRandom();
|
||||
if(rows==size)
|
||||
A = A*A.adjoint();
|
||||
if (rows == size) A = A * A.adjoint();
|
||||
BenchTimer t_llt, t_ldlt, t_lu, t_fplu, t_qr, t_cpqr, t_cod, t_fpqr, t_jsvd, t_bdcsvd;
|
||||
|
||||
int tries = 5;
|
||||
int rep = 1000/size;
|
||||
if(rep==0) rep = 1;
|
||||
// rep = rep*rep;
|
||||
|
||||
int rep = 1000 / size;
|
||||
if (rep == 0) rep = 1;
|
||||
// rep = rep*rep;
|
||||
|
||||
LLT<MatSquare> llt(size);
|
||||
LDLT<MatSquare> ldlt(size);
|
||||
PartialPivLU<MatSquare> lu(size);
|
||||
FullPivLU<MatSquare> fplu(size,size);
|
||||
HouseholderQR<Mat> qr(A.rows(),A.cols());
|
||||
ColPivHouseholderQR<Mat> cpqr(A.rows(),A.cols());
|
||||
CompleteOrthogonalDecomposition<Mat> cod(A.rows(),A.cols());
|
||||
FullPivHouseholderQR<Mat> fpqr(A.rows(),A.cols());
|
||||
JacobiSVD<MatDyn, ComputeThinU|ComputeThinV> jsvd(A.rows(),A.cols());
|
||||
BDCSVD<MatDyn, ComputeThinU|ComputeThinV> bdcsvd(A.rows(),A.cols());
|
||||
|
||||
BENCH(t_llt, tries, rep, compute_norm_equation(llt,A));
|
||||
BENCH(t_ldlt, tries, rep, compute_norm_equation(ldlt,A));
|
||||
BENCH(t_lu, tries, rep, compute_norm_equation(lu,A));
|
||||
if(size<=1000)
|
||||
BENCH(t_fplu, tries, rep, compute_norm_equation(fplu,A));
|
||||
BENCH(t_qr, tries, rep, compute(qr,A));
|
||||
BENCH(t_cpqr, tries, rep, compute(cpqr,A));
|
||||
BENCH(t_cod, tries, rep, compute(cod,A));
|
||||
if(size*rows<=10000000)
|
||||
BENCH(t_fpqr, tries, rep, compute(fpqr,A));
|
||||
if(size<500) // JacobiSVD is really too slow for too large matrices
|
||||
FullPivLU<MatSquare> fplu(size, size);
|
||||
HouseholderQR<Mat> qr(A.rows(), A.cols());
|
||||
ColPivHouseholderQR<Mat> cpqr(A.rows(), A.cols());
|
||||
CompleteOrthogonalDecomposition<Mat> cod(A.rows(), A.cols());
|
||||
FullPivHouseholderQR<Mat> fpqr(A.rows(), A.cols());
|
||||
JacobiSVD<MatDyn, ComputeThinU | ComputeThinV> jsvd(A.rows(), A.cols());
|
||||
BDCSVD<MatDyn, ComputeThinU | ComputeThinV> bdcsvd(A.rows(), A.cols());
|
||||
|
||||
BENCH(t_llt, tries, rep, compute_norm_equation(llt, A));
|
||||
BENCH(t_ldlt, tries, rep, compute_norm_equation(ldlt, A));
|
||||
BENCH(t_lu, tries, rep, compute_norm_equation(lu, A));
|
||||
if (size <= 1000) BENCH(t_fplu, tries, rep, compute_norm_equation(fplu, A));
|
||||
BENCH(t_qr, tries, rep, compute(qr, A));
|
||||
BENCH(t_cpqr, tries, rep, compute(cpqr, A));
|
||||
BENCH(t_cod, tries, rep, compute(cod, A));
|
||||
if (size * rows <= 10000000) BENCH(t_fpqr, tries, rep, compute(fpqr, A));
|
||||
if (size < 500) // JacobiSVD is really too slow for too large matrices
|
||||
BENCH(t_jsvd, tries, rep, jsvd.compute(A));
|
||||
// if(size*rows<=20000000)
|
||||
BENCH(t_bdcsvd, tries, rep, bdcsvd.compute(A));
|
||||
|
||||
// if(size*rows<=20000000)
|
||||
BENCH(t_bdcsvd, tries, rep, bdcsvd.compute(A));
|
||||
|
||||
results["LLT"][id] = t_llt.best();
|
||||
results["LDLT"][id] = t_ldlt.best();
|
||||
results["PartialPivLU"][id] = t_lu.best();
|
||||
@@ -81,9 +75,7 @@ void bench(int id, int rows, int size = Size)
|
||||
results["BDCSVD"][id] = t_bdcsvd.best();
|
||||
}
|
||||
|
||||
|
||||
int main()
|
||||
{
|
||||
int main() {
|
||||
labels.push_back("LLT");
|
||||
labels.push_back("LDLT");
|
||||
labels.push_back("PartialPivLU");
|
||||
@@ -95,48 +87,48 @@ int main()
|
||||
labels.push_back("JacobiSVD");
|
||||
labels.push_back("BDCSVD");
|
||||
|
||||
for(int i=0; i<labels.size(); ++i)
|
||||
results[labels[i]].fill(-1);
|
||||
for (int i = 0; i < labels.size(); ++i) results[labels[i]].fill(-1);
|
||||
|
||||
const int small = 8;
|
||||
sizes.push_back(Array2i(small,small));
|
||||
sizes.push_back(Array2i(100,100));
|
||||
sizes.push_back(Array2i(1000,1000));
|
||||
sizes.push_back(Array2i(4000,4000));
|
||||
sizes.push_back(Array2i(10000,small));
|
||||
sizes.push_back(Array2i(10000,100));
|
||||
sizes.push_back(Array2i(10000,1000));
|
||||
sizes.push_back(Array2i(10000,4000));
|
||||
sizes.push_back(Array2i(small, small));
|
||||
sizes.push_back(Array2i(100, 100));
|
||||
sizes.push_back(Array2i(1000, 1000));
|
||||
sizes.push_back(Array2i(4000, 4000));
|
||||
sizes.push_back(Array2i(10000, small));
|
||||
sizes.push_back(Array2i(10000, 100));
|
||||
sizes.push_back(Array2i(10000, 1000));
|
||||
sizes.push_back(Array2i(10000, 4000));
|
||||
|
||||
using namespace std;
|
||||
|
||||
for(int k=0; k<sizes.size(); ++k)
|
||||
{
|
||||
for (int k = 0; k < sizes.size(); ++k) {
|
||||
cout << sizes[k](0) << "x" << sizes[k](1) << "...\n";
|
||||
bench<float,Dynamic>(k,sizes[k](0),sizes[k](1));
|
||||
bench<float, Dynamic>(k, sizes[k](0), sizes[k](1));
|
||||
}
|
||||
|
||||
cout.width(32);
|
||||
cout << "solver/size";
|
||||
cout << " ";
|
||||
for(int k=0; k<sizes.size(); ++k)
|
||||
{
|
||||
for (int k = 0; k < sizes.size(); ++k) {
|
||||
std::stringstream ss;
|
||||
ss << sizes[k](0) << "x" << sizes[k](1);
|
||||
cout.width(10); cout << ss.str(); cout << " ";
|
||||
cout.width(10);
|
||||
cout << ss.str();
|
||||
cout << " ";
|
||||
}
|
||||
cout << endl;
|
||||
|
||||
|
||||
for(int i=0; i<labels.size(); ++i)
|
||||
{
|
||||
cout.width(32); cout << labels[i]; cout << " ";
|
||||
ArrayXf r = (results[labels[i]]*100000.f).floor()/100.f;
|
||||
for(int k=0; k<sizes.size(); ++k)
|
||||
{
|
||||
for (int i = 0; i < labels.size(); ++i) {
|
||||
cout.width(32);
|
||||
cout << labels[i];
|
||||
cout << " ";
|
||||
ArrayXf r = (results[labels[i]] * 100000.f).floor() / 100.f;
|
||||
for (int k = 0; k < sizes.size(); ++k) {
|
||||
cout.width(10);
|
||||
if(r(k)>=1e6) cout << "-";
|
||||
else cout << r(k);
|
||||
if (r(k) >= 1e6)
|
||||
cout << "-";
|
||||
else
|
||||
cout << r(k);
|
||||
cout << " ";
|
||||
}
|
||||
cout << endl;
|
||||
@@ -145,25 +137,20 @@ int main()
|
||||
// HTML output
|
||||
cout << "<table class=\"manual\">" << endl;
|
||||
cout << "<tr><th>solver/size</th>" << endl;
|
||||
for(int k=0; k<sizes.size(); ++k)
|
||||
cout << " <th>" << sizes[k](0) << "x" << sizes[k](1) << "</th>";
|
||||
for (int k = 0; k < sizes.size(); ++k) cout << " <th>" << sizes[k](0) << "x" << sizes[k](1) << "</th>";
|
||||
cout << "</tr>" << endl;
|
||||
for(int i=0; i<labels.size(); ++i)
|
||||
{
|
||||
for (int i = 0; i < labels.size(); ++i) {
|
||||
cout << "<tr";
|
||||
if(i%2==1) cout << " class=\"alt\"";
|
||||
if (i % 2 == 1) cout << " class=\"alt\"";
|
||||
cout << "><td>" << labels[i] << "</td>";
|
||||
ArrayXf r = (results[labels[i]]*100000.f).floor()/100.f;
|
||||
for(int k=0; k<sizes.size(); ++k)
|
||||
{
|
||||
if(r(k)>=1e6) cout << "<td>-</td>";
|
||||
else
|
||||
{
|
||||
ArrayXf r = (results[labels[i]] * 100000.f).floor() / 100.f;
|
||||
for (int k = 0; k < sizes.size(); ++k) {
|
||||
if (r(k) >= 1e6)
|
||||
cout << "<td>-</td>";
|
||||
else {
|
||||
cout << "<td>" << r(k);
|
||||
if(i>0)
|
||||
cout << " (x" << numext::round(10.f*results[labels[i]](k)/results["LLT"](k))/10.f << ")";
|
||||
if(i<4 && sizes[k](0)!=sizes[k](1))
|
||||
cout << " <sup><a href=\"#note_ls\">*</a></sup>";
|
||||
if (i > 0) cout << " (x" << numext::round(10.f * results[labels[i]](k) / results["LLT"](k)) / 10.f << ")";
|
||||
if (i < 4 && sizes[k](0) != sizes[k](1)) cout << " <sup><a href=\"#note_ls\">*</a></sup>";
|
||||
cout << "</td>";
|
||||
}
|
||||
}
|
||||
@@ -171,14 +158,15 @@ int main()
|
||||
}
|
||||
cout << "</table>" << endl;
|
||||
|
||||
// cout << "LLT (ms) " << (results["LLT"]*1000.).format(fmt) << "\n";
|
||||
// cout << "LDLT (%) " << (results["LDLT"]/results["LLT"]).format(fmt) << "\n";
|
||||
// cout << "PartialPivLU (%) " << (results["PartialPivLU"]/results["LLT"]).format(fmt) << "\n";
|
||||
// cout << "FullPivLU (%) " << (results["FullPivLU"]/results["LLT"]).format(fmt) << "\n";
|
||||
// cout << "HouseholderQR (%) " << (results["HouseholderQR"]/results["LLT"]).format(fmt) << "\n";
|
||||
// cout << "ColPivHouseholderQR (%) " << (results["ColPivHouseholderQR"]/results["LLT"]).format(fmt) << "\n";
|
||||
// cout << "CompleteOrthogonalDecomposition (%) " << (results["CompleteOrthogonalDecomposition"]/results["LLT"]).format(fmt) << "\n";
|
||||
// cout << "FullPivHouseholderQR (%) " << (results["FullPivHouseholderQR"]/results["LLT"]).format(fmt) << "\n";
|
||||
// cout << "JacobiSVD (%) " << (results["JacobiSVD"]/results["LLT"]).format(fmt) << "\n";
|
||||
// cout << "BDCSVD (%) " << (results["BDCSVD"]/results["LLT"]).format(fmt) << "\n";
|
||||
// cout << "LLT (ms) " << (results["LLT"]*1000.).format(fmt) << "\n";
|
||||
// cout << "LDLT (%) " << (results["LDLT"]/results["LLT"]).format(fmt) << "\n";
|
||||
// cout << "PartialPivLU (%) " << (results["PartialPivLU"]/results["LLT"]).format(fmt) << "\n";
|
||||
// cout << "FullPivLU (%) " << (results["FullPivLU"]/results["LLT"]).format(fmt) << "\n";
|
||||
// cout << "HouseholderQR (%) " << (results["HouseholderQR"]/results["LLT"]).format(fmt) <<
|
||||
// "\n"; cout << "ColPivHouseholderQR (%) " <<
|
||||
// (results["ColPivHouseholderQR"]/results["LLT"]).format(fmt) << "\n"; cout << "CompleteOrthogonalDecomposition (%)
|
||||
// " << (results["CompleteOrthogonalDecomposition"]/results["LLT"]).format(fmt) << "\n"; cout <<
|
||||
// "FullPivHouseholderQR (%) " << (results["FullPivHouseholderQR"]/results["LLT"]).format(fmt) << "\n";
|
||||
// cout << "JacobiSVD (%) " << (results["JacobiSVD"]/results["LLT"]).format(fmt) << "\n";
|
||||
// cout << "BDCSVD (%) " << (results["BDCSVD"]/results["LLT"]).format(fmt) << "\n";
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user