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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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123
test/visitor.cpp
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@@ -9,8 +9,8 @@
#include "main.h"
template<typename MatrixType> void matrixVisitor(const MatrixType& p)
{
template <typename MatrixType>
void matrixVisitor(const MatrixType& p) {
typedef typename MatrixType::Scalar Scalar;
Index rows = p.rows();
@@ -19,33 +19,30 @@ template<typename MatrixType> void matrixVisitor(const MatrixType& p)
// construct a random matrix where all coefficients are different
MatrixType m;
m = MatrixType::Random(rows, cols);
for(Index i = 0; i < m.size(); i++)
for(Index i2 = 0; i2 < i; i2++)
while(numext::equal_strict(m(i), m(i2))) // yes, strict equality
for (Index i = 0; i < m.size(); i++)
for (Index i2 = 0; i2 < i; i2++)
while (numext::equal_strict(m(i), m(i2))) // yes, strict equality
m(i) = internal::random<Scalar>();
Scalar minc = Scalar(1000), maxc = Scalar(-1000);
Index minrow=0,mincol=0,maxrow=0,maxcol=0;
for(Index j = 0; j < cols; j++)
for(Index i = 0; i < rows; i++)
{
if(m(i,j) < minc)
{
minc = m(i,j);
minrow = i;
mincol = j;
Index minrow = 0, mincol = 0, maxrow = 0, maxcol = 0;
for (Index j = 0; j < cols; j++)
for (Index i = 0; i < rows; i++) {
if (m(i, j) < minc) {
minc = m(i, j);
minrow = i;
mincol = j;
}
if (m(i, j) > maxc) {
maxc = m(i, j);
maxrow = i;
maxcol = j;
}
}
if(m(i,j) > maxc)
{
maxc = m(i,j);
maxrow = i;
maxcol = j;
}
}
Index eigen_minrow, eigen_mincol, eigen_maxrow, eigen_maxcol;
Scalar eigen_minc, eigen_maxc;
eigen_minc = m.minCoeff(&eigen_minrow,&eigen_mincol);
eigen_maxc = m.maxCoeff(&eigen_maxrow,&eigen_maxcol);
eigen_minc = m.minCoeff(&eigen_minrow, &eigen_mincol);
eigen_maxc = m.maxCoeff(&eigen_maxrow, &eigen_maxcol);
VERIFY(minrow == eigen_minrow);
VERIFY(maxrow == eigen_maxrow);
VERIFY(mincol == eigen_mincol);
@@ -55,9 +52,9 @@ template<typename MatrixType> void matrixVisitor(const MatrixType& p)
VERIFY_IS_APPROX(minc, m.minCoeff());
VERIFY_IS_APPROX(maxc, m.maxCoeff());
eigen_maxc = (m.adjoint()*m).maxCoeff(&eigen_maxrow,&eigen_maxcol);
Index maxrow2=0,maxcol2=0;
eigen_maxc = (m.adjoint()*m).eval().maxCoeff(&maxrow2,&maxcol2);
eigen_maxc = (m.adjoint() * m).maxCoeff(&eigen_maxrow, &eigen_maxcol);
Index maxrow2 = 0, maxcol2 = 0;
eigen_maxc = (m.adjoint() * m).eval().maxCoeff(&maxrow2, &maxcol2);
VERIFY(maxrow2 == eigen_maxrow);
VERIFY(maxcol2 == eigen_maxcol);
@@ -66,9 +63,8 @@ template<typename MatrixType> void matrixVisitor(const MatrixType& p)
bool stop = false;
for (Index j = 0; j < cols && !stop; ++j) {
for (Index i = 0; i < rows && !stop; ++i) {
if (!(j == mincol && i == minrow) &&
!(j == maxcol && i == maxrow)) {
m(i,j) = NumTraits<Scalar>::quiet_NaN();
if (!(j == mincol && i == minrow) && !(j == maxcol && i == maxrow)) {
m(i, j) = NumTraits<Scalar>::quiet_NaN();
stop = true;
break;
}
@@ -124,8 +120,8 @@ template<typename MatrixType> void matrixVisitor(const MatrixType& p)
}
}
template<typename VectorType> void vectorVisitor(const VectorType& w)
{
template <typename VectorType>
void vectorVisitor(const VectorType& w) {
typedef typename VectorType::Scalar Scalar;
Index size = w.size();
@@ -133,22 +129,19 @@ template<typename VectorType> void vectorVisitor(const VectorType& w)
// construct a random vector where all coefficients are different
VectorType v;
v = VectorType::Random(size);
for(Index i = 0; i < size; i++)
for(Index i2 = 0; i2 < i; i2++)
while(v(i) == v(i2)) // yes, ==
for (Index i = 0; i < size; i++)
for (Index i2 = 0; i2 < i; i2++)
while (v(i) == v(i2)) // yes, ==
v(i) = internal::random<Scalar>();
Scalar minc = v(0), maxc = v(0);
Index minidx=0, maxidx=0;
for(Index i = 0; i < size; i++)
{
if(v(i) < minc)
{
Index minidx = 0, maxidx = 0;
for (Index i = 0; i < size; i++) {
if (v(i) < minc) {
minc = v(i);
minidx = i;
}
if(v(i) > maxc)
{
if (v(i) > maxc) {
maxc = v(i);
maxidx = i;
}
@@ -163,8 +156,8 @@ template<typename VectorType> void vectorVisitor(const VectorType& w)
VERIFY_IS_APPROX(maxc, eigen_maxc);
VERIFY_IS_APPROX(minc, v.minCoeff());
VERIFY_IS_APPROX(maxc, v.maxCoeff());
Index idx0 = internal::random<Index>(0,size-1);
Index idx0 = internal::random<Index>(0, size - 1);
Index idx1 = eigen_minidx;
Index idx2 = eigen_maxidx;
VectorType v1(v), v2(v);
@@ -172,8 +165,8 @@ template<typename VectorType> void vectorVisitor(const VectorType& w)
v2(idx0) = v2(idx2);
v1.minCoeff(&eigen_minidx);
v2.maxCoeff(&eigen_maxidx);
VERIFY(eigen_minidx == (std::min)(idx0,idx1));
VERIFY(eigen_maxidx == (std::min)(idx0,idx2));
VERIFY(eigen_minidx == (std::min)(idx0, idx1));
VERIFY(eigen_maxidx == (std::min)(idx0, idx2));
if (!NumTraits<Scalar>::IsInteger && size > 2) {
// Test NaN propagation by replacing an element with NaN.
@@ -236,8 +229,8 @@ struct TrackedVisitor {
namespace Eigen {
namespace internal {
template<typename T, bool Vectorizable>
struct functor_traits<TrackedVisitor<T, Vectorizable> > {
template <typename T, bool Vectorizable>
struct functor_traits<TrackedVisitor<T, Vectorizable>> {
enum { PacketAccess = Vectorizable, LinearAccess = false, Cost = 1 };
};
@@ -271,7 +264,6 @@ void checkOptimalTraversal_impl(const DenseBase<Derived>& mat) {
}
void checkOptimalTraversal() {
using Scalar = float;
constexpr int PacketSize = Eigen::internal::packet_traits<Scalar>::size;
// use sizes that mix vector and scalar ops
@@ -286,7 +278,7 @@ void checkOptimalTraversal() {
using DynamicRowMajor = Matrix<Scalar, Dynamic, Dynamic, RowMajor>;
// Scalar-only visitors
checkOptimalTraversal_impl<UnrollColMajor, false>(UnrollColMajor(Rows,Cols));
checkOptimalTraversal_impl<UnrollColMajor, false>(UnrollColMajor(Rows, Cols));
checkOptimalTraversal_impl<UnrollRowMajor, false>(UnrollRowMajor(Rows, Cols));
checkOptimalTraversal_impl<DynamicColMajor, false>(DynamicColMajor(rows, cols));
checkOptimalTraversal_impl<DynamicRowMajor, false>(DynamicRowMajor(rows, cols));
@@ -298,22 +290,21 @@ void checkOptimalTraversal() {
checkOptimalTraversal_impl<DynamicRowMajor, true>(DynamicRowMajor(rows, cols));
}
EIGEN_DECLARE_TEST(visitor)
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( matrixVisitor(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( matrixVisitor(Matrix2f()) );
CALL_SUBTEST_3( matrixVisitor(Matrix4d()) );
CALL_SUBTEST_4( matrixVisitor(MatrixXd(8, 12)) );
CALL_SUBTEST_5( matrixVisitor(Matrix<double,Dynamic,Dynamic,RowMajor>(20, 20)) );
CALL_SUBTEST_6( matrixVisitor(MatrixXi(8, 12)) );
EIGEN_DECLARE_TEST(visitor) {
for (int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(matrixVisitor(Matrix<float, 1, 1>()));
CALL_SUBTEST_2(matrixVisitor(Matrix2f()));
CALL_SUBTEST_3(matrixVisitor(Matrix4d()));
CALL_SUBTEST_4(matrixVisitor(MatrixXd(8, 12)));
CALL_SUBTEST_5(matrixVisitor(Matrix<double, Dynamic, Dynamic, RowMajor>(20, 20)));
CALL_SUBTEST_6(matrixVisitor(MatrixXi(8, 12)));
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_7( vectorVisitor(Vector4f()) );
CALL_SUBTEST_7( vectorVisitor(Matrix<int,12,1>()) );
CALL_SUBTEST_8( vectorVisitor(VectorXd(10)) );
CALL_SUBTEST_9( vectorVisitor(RowVectorXd(10)) );
CALL_SUBTEST_10( vectorVisitor(VectorXf(33)) );
for (int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_7(vectorVisitor(Vector4f()));
CALL_SUBTEST_7(vectorVisitor(Matrix<int, 12, 1>()));
CALL_SUBTEST_8(vectorVisitor(VectorXd(10)));
CALL_SUBTEST_9(vectorVisitor(RowVectorXd(10)));
CALL_SUBTEST_10(vectorVisitor(VectorXf(33)));
}
CALL_SUBTEST_11(checkOptimalTraversal());
}