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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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147
test/vectorwiseop.cpp
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@@ -12,20 +12,17 @@
#include "main.h"
template<typename ArrayType> void vectorwiseop_array(const ArrayType& m)
{
template <typename ArrayType>
void vectorwiseop_array(const ArrayType& m) {
typedef typename ArrayType::Scalar Scalar;
typedef Array<Scalar, ArrayType::RowsAtCompileTime, 1> ColVectorType;
typedef Array<Scalar, 1, ArrayType::ColsAtCompileTime> RowVectorType;
Index rows = m.rows();
Index cols = m.cols();
Index r = internal::random<Index>(0, rows-1),
c = internal::random<Index>(0, cols-1);
Index r = internal::random<Index>(0, rows - 1), c = internal::random<Index>(0, cols - 1);
ArrayType m1 = ArrayType::Random(rows, cols),
m2(rows, cols),
m3(rows, cols);
ArrayType m1 = ArrayType::Random(rows, cols), m2(rows, cols), m3(rows, cols);
ColVectorType colvec = ColVectorType::Random(rows);
RowVectorType rowvec = RowVectorType::Random(cols);
@@ -78,43 +75,39 @@ template<typename ArrayType> void vectorwiseop_array(const ArrayType& m)
// yes, there might be an aliasing issue there but ".rowwise() /="
// is supposed to evaluate " m2.colwise().sum()" into a temporary to avoid
// evaluating the reduction multiple times
if(ArrayType::RowsAtCompileTime>2 || ArrayType::RowsAtCompileTime==Dynamic)
{
if (ArrayType::RowsAtCompileTime > 2 || ArrayType::RowsAtCompileTime == Dynamic) {
m2.rowwise() /= m2.colwise().sum();
VERIFY_IS_APPROX(m2, m1.rowwise() / m1.colwise().sum());
}
// all/any
Array<bool,Dynamic,Dynamic> mb(rows,cols);
mb = (m1.real()<=0.7).colwise().all();
VERIFY( (mb.col(c) == (m1.real().col(c)<=0.7).all()).all() );
mb = (m1.real()<=0.7).rowwise().all();
VERIFY( (mb.row(r) == (m1.real().row(r)<=0.7).all()).all() );
Array<bool, Dynamic, Dynamic> mb(rows, cols);
mb = (m1.real() <= 0.7).colwise().all();
VERIFY((mb.col(c) == (m1.real().col(c) <= 0.7).all()).all());
mb = (m1.real() <= 0.7).rowwise().all();
VERIFY((mb.row(r) == (m1.real().row(r) <= 0.7).all()).all());
mb = (m1.real()>=0.7).colwise().any();
VERIFY( (mb.col(c) == (m1.real().col(c)>=0.7).any()).all() );
mb = (m1.real()>=0.7).rowwise().any();
VERIFY( (mb.row(r) == (m1.real().row(r)>=0.7).any()).all() );
mb = (m1.real() >= 0.7).colwise().any();
VERIFY((mb.col(c) == (m1.real().col(c) >= 0.7).any()).all());
mb = (m1.real() >= 0.7).rowwise().any();
VERIFY((mb.row(r) == (m1.real().row(r) >= 0.7).any()).all());
}
template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m)
{
template <typename MatrixType>
void vectorwiseop_matrix(const MatrixType& m) {
typedef typename MatrixType::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVectorType;
typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType;
typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealColVectorType;
typedef Matrix<RealScalar, 1, MatrixType::ColsAtCompileTime> RealRowVectorType;
typedef Matrix<Scalar,Dynamic,Dynamic> MatrixX;
typedef Matrix<Scalar, Dynamic, Dynamic> MatrixX;
Index rows = m.rows();
Index cols = m.cols();
Index r = internal::random<Index>(0, rows-1),
c = internal::random<Index>(0, cols-1);
Index r = internal::random<Index>(0, rows - 1), c = internal::random<Index>(0, cols - 1);
MatrixType m1 = MatrixType::Random(rows, cols),
m2(rows, cols),
m3(rows, cols);
MatrixType m1 = MatrixType::Random(rows, cols), m2(rows, cols), m3(rows, cols);
ColVectorType colvec = ColVectorType::Random(rows);
RowVectorType rowvec = RowVectorType::Random(cols);
@@ -124,11 +117,9 @@ template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m)
// test broadcast assignment
m2 = m1;
m2.colwise() = colvec;
for(Index j=0; j<cols; ++j)
VERIFY_IS_APPROX(m2.col(j), colvec);
for (Index j = 0; j < cols; ++j) VERIFY_IS_APPROX(m2.col(j), colvec);
m2.rowwise() = rowvec;
for(Index i=0; i<rows; ++i)
VERIFY_IS_APPROX(m2.row(i), rowvec);
for (Index i = 0; i < rows; ++i) VERIFY_IS_APPROX(m2.row(i), rowvec);
// test addition
m2 = m1;
@@ -141,7 +132,6 @@ template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m)
VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec);
VERIFY_IS_APPROX(m2.row(r), m1.row(r) + rowvec);
// test subtraction
m2 = m1;
m2.colwise() -= colvec;
@@ -153,24 +143,24 @@ template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m)
VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec);
VERIFY_IS_APPROX(m2.row(r), m1.row(r) - rowvec);
// ------ partial reductions ------
#define TEST_PARTIAL_REDUX_BASIC(FUNC,ROW,COL,PREPROCESS) { \
ROW = m1 PREPROCESS .colwise().FUNC ; \
for(Index k=0; k<cols; ++k) VERIFY_IS_APPROX(ROW(k), m1.col(k) PREPROCESS .FUNC ); \
COL = m1 PREPROCESS .rowwise().FUNC ; \
for(Index k=0; k<rows; ++k) VERIFY_IS_APPROX(COL(k), m1.row(k) PREPROCESS .FUNC ); \
#define TEST_PARTIAL_REDUX_BASIC(FUNC, ROW, COL, PREPROCESS) \
{ \
ROW = m1 PREPROCESS.colwise().FUNC; \
for (Index k = 0; k < cols; ++k) VERIFY_IS_APPROX(ROW(k), m1.col(k) PREPROCESS.FUNC); \
COL = m1 PREPROCESS.rowwise().FUNC; \
for (Index k = 0; k < rows; ++k) VERIFY_IS_APPROX(COL(k), m1.row(k) PREPROCESS.FUNC); \
}
TEST_PARTIAL_REDUX_BASIC(sum(), rowvec,colvec,EIGEN_EMPTY);
TEST_PARTIAL_REDUX_BASIC(prod(), rowvec,colvec,EIGEN_EMPTY);
TEST_PARTIAL_REDUX_BASIC(mean(), rowvec,colvec,EIGEN_EMPTY);
TEST_PARTIAL_REDUX_BASIC(minCoeff(), rrres, rcres, .real());
TEST_PARTIAL_REDUX_BASIC(maxCoeff(), rrres, rcres, .real());
TEST_PARTIAL_REDUX_BASIC(norm(), rrres, rcres, EIGEN_EMPTY);
TEST_PARTIAL_REDUX_BASIC(squaredNorm(),rrres, rcres, EIGEN_EMPTY);
TEST_PARTIAL_REDUX_BASIC(redux(internal::scalar_sum_op<Scalar,Scalar>()),rowvec,colvec,EIGEN_EMPTY);
TEST_PARTIAL_REDUX_BASIC(sum(), rowvec, colvec, EIGEN_EMPTY);
TEST_PARTIAL_REDUX_BASIC(prod(), rowvec, colvec, EIGEN_EMPTY);
TEST_PARTIAL_REDUX_BASIC(mean(), rowvec, colvec, EIGEN_EMPTY);
TEST_PARTIAL_REDUX_BASIC(minCoeff(), rrres, rcres, .real());
TEST_PARTIAL_REDUX_BASIC(maxCoeff(), rrres, rcres, .real());
TEST_PARTIAL_REDUX_BASIC(norm(), rrres, rcres, EIGEN_EMPTY);
TEST_PARTIAL_REDUX_BASIC(squaredNorm(), rrres, rcres, EIGEN_EMPTY);
TEST_PARTIAL_REDUX_BASIC(redux(internal::scalar_sum_op<Scalar, Scalar>()), rowvec, colvec, EIGEN_EMPTY);
VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum(), m1.colwise().template lpNorm<1>());
VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().sum(), m1.rowwise().template lpNorm<1>());
@@ -195,44 +185,45 @@ template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m)
VERIFY_IS_APPROX(m2.row(r), m1.row(r).normalized());
// test with partial reduction of products
Matrix<Scalar,MatrixType::RowsAtCompileTime,MatrixType::RowsAtCompileTime> m1m1 = m1 * m1.transpose();
VERIFY_IS_APPROX( (m1 * m1.transpose()).colwise().sum(), m1m1.colwise().sum());
Matrix<Scalar,1,MatrixType::RowsAtCompileTime> tmp(rows);
VERIFY_EVALUATION_COUNT( tmp = (m1 * m1.transpose()).colwise().sum(), 1);
Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> m1m1 = m1 * m1.transpose();
VERIFY_IS_APPROX((m1 * m1.transpose()).colwise().sum(), m1m1.colwise().sum());
Matrix<Scalar, 1, MatrixType::RowsAtCompileTime> tmp(rows);
VERIFY_EVALUATION_COUNT(tmp = (m1 * m1.transpose()).colwise().sum(), 1);
m2 = m1.rowwise() - (m1.colwise().sum()/RealScalar(m1.rows())).eval();
m1 = m1.rowwise() - (m1.colwise().sum()/RealScalar(m1.rows()));
VERIFY_IS_APPROX( m1, m2 );
VERIFY_EVALUATION_COUNT( m2 = (m1.rowwise() - m1.colwise().sum()/RealScalar(m1.rows())), (MatrixType::RowsAtCompileTime!=1 ? 1 : 0) );
m2 = m1.rowwise() - (m1.colwise().sum() / RealScalar(m1.rows())).eval();
m1 = m1.rowwise() - (m1.colwise().sum() / RealScalar(m1.rows()));
VERIFY_IS_APPROX(m1, m2);
VERIFY_EVALUATION_COUNT(m2 = (m1.rowwise() - m1.colwise().sum() / RealScalar(m1.rows())),
(MatrixType::RowsAtCompileTime != 1 ? 1 : 0));
// test empty expressions
VERIFY_IS_APPROX(m1.matrix().middleCols(0,0).rowwise().sum().eval(), MatrixX::Zero(rows,1));
VERIFY_IS_APPROX(m1.matrix().middleRows(0,0).colwise().sum().eval(), MatrixX::Zero(1,cols));
VERIFY_IS_APPROX(m1.matrix().middleCols(0,fix<0>).rowwise().sum().eval(), MatrixX::Zero(rows,1));
VERIFY_IS_APPROX(m1.matrix().middleRows(0,fix<0>).colwise().sum().eval(), MatrixX::Zero(1,cols));
VERIFY_IS_APPROX(m1.matrix().middleCols(0, 0).rowwise().sum().eval(), MatrixX::Zero(rows, 1));
VERIFY_IS_APPROX(m1.matrix().middleRows(0, 0).colwise().sum().eval(), MatrixX::Zero(1, cols));
VERIFY_IS_APPROX(m1.matrix().middleCols(0, fix<0>).rowwise().sum().eval(), MatrixX::Zero(rows, 1));
VERIFY_IS_APPROX(m1.matrix().middleRows(0, fix<0>).colwise().sum().eval(), MatrixX::Zero(1, cols));
VERIFY_IS_APPROX(m1.matrix().middleCols(0,0).rowwise().prod().eval(), MatrixX::Ones(rows,1));
VERIFY_IS_APPROX(m1.matrix().middleRows(0,0).colwise().prod().eval(), MatrixX::Ones(1,cols));
VERIFY_IS_APPROX(m1.matrix().middleCols(0,fix<0>).rowwise().prod().eval(), MatrixX::Ones(rows,1));
VERIFY_IS_APPROX(m1.matrix().middleRows(0,fix<0>).colwise().prod().eval(), MatrixX::Ones(1,cols));
VERIFY_IS_APPROX(m1.matrix().middleCols(0,0).rowwise().squaredNorm().eval(), MatrixX::Zero(rows,1));
VERIFY_IS_APPROX(m1.matrix().middleCols(0, 0).rowwise().prod().eval(), MatrixX::Ones(rows, 1));
VERIFY_IS_APPROX(m1.matrix().middleRows(0, 0).colwise().prod().eval(), MatrixX::Ones(1, cols));
VERIFY_IS_APPROX(m1.matrix().middleCols(0, fix<0>).rowwise().prod().eval(), MatrixX::Ones(rows, 1));
VERIFY_IS_APPROX(m1.matrix().middleRows(0, fix<0>).colwise().prod().eval(), MatrixX::Ones(1, cols));
VERIFY_IS_APPROX(m1.matrix().middleCols(0, 0).rowwise().squaredNorm().eval(), MatrixX::Zero(rows, 1));
VERIFY_IS_EQUAL(m1.real().middleRows(0,0).rowwise().maxCoeff().eval().rows(),0);
VERIFY_IS_EQUAL(m1.real().middleCols(0,0).colwise().maxCoeff().eval().cols(),0);
VERIFY_IS_EQUAL(m1.real().middleRows(0,fix<0>).rowwise().maxCoeff().eval().rows(),0);
VERIFY_IS_EQUAL(m1.real().middleCols(0,fix<0>).colwise().maxCoeff().eval().cols(),0);
VERIFY_IS_EQUAL(m1.real().middleRows(0, 0).rowwise().maxCoeff().eval().rows(), 0);
VERIFY_IS_EQUAL(m1.real().middleCols(0, 0).colwise().maxCoeff().eval().cols(), 0);
VERIFY_IS_EQUAL(m1.real().middleRows(0, fix<0>).rowwise().maxCoeff().eval().rows(), 0);
VERIFY_IS_EQUAL(m1.real().middleCols(0, fix<0>).colwise().maxCoeff().eval().cols(), 0);
}
EIGEN_DECLARE_TEST(vectorwiseop)
{
CALL_SUBTEST_1( vectorwiseop_array(Array22cd()) );
CALL_SUBTEST_2( vectorwiseop_array(Array<double, 3, 2>()) );
CALL_SUBTEST_3( vectorwiseop_array(ArrayXXf(3, 4)) );
CALL_SUBTEST_4( vectorwiseop_matrix(Matrix4cf()) );
CALL_SUBTEST_5( vectorwiseop_matrix(Matrix4f()) );
CALL_SUBTEST_5( vectorwiseop_matrix(Vector4f()) );
CALL_SUBTEST_5( vectorwiseop_matrix(Matrix<float,4,5>()) );
CALL_SUBTEST_6( vectorwiseop_matrix(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_7( vectorwiseop_matrix(VectorXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
CALL_SUBTEST_7( vectorwiseop_matrix(RowVectorXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
EIGEN_DECLARE_TEST(vectorwiseop) {
CALL_SUBTEST_1(vectorwiseop_array(Array22cd()));
CALL_SUBTEST_2(vectorwiseop_array(Array<double, 3, 2>()));
CALL_SUBTEST_3(vectorwiseop_array(ArrayXXf(3, 4)));
CALL_SUBTEST_4(vectorwiseop_matrix(Matrix4cf()));
CALL_SUBTEST_5(vectorwiseop_matrix(Matrix4f()));
CALL_SUBTEST_5(vectorwiseop_matrix(Vector4f()));
CALL_SUBTEST_5(vectorwiseop_matrix(Matrix<float, 4, 5>()));
CALL_SUBTEST_6(vectorwiseop_matrix(
MatrixXd(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
CALL_SUBTEST_7(vectorwiseop_matrix(VectorXd(internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
CALL_SUBTEST_7(vectorwiseop_matrix(RowVectorXd(internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
}