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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
@@ -11,39 +11,36 @@
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#include "main.h"
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template<typename Dst, typename Lhs, typename Rhs>
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void check_scalar_multiple3(Dst &dst, const Lhs& A, const Rhs& B)
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{
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VERIFY_EVALUATION_COUNT( (dst.noalias() = A * B), 0);
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VERIFY_IS_APPROX( dst, (A.eval() * B.eval()).eval() );
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VERIFY_EVALUATION_COUNT( (dst.noalias() += A * B), 0);
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VERIFY_IS_APPROX( dst, 2*(A.eval() * B.eval()).eval() );
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VERIFY_EVALUATION_COUNT( (dst.noalias() -= A * B), 0);
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VERIFY_IS_APPROX( dst, (A.eval() * B.eval()).eval() );
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template <typename Dst, typename Lhs, typename Rhs>
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void check_scalar_multiple3(Dst& dst, const Lhs& A, const Rhs& B) {
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VERIFY_EVALUATION_COUNT((dst.noalias() = A * B), 0);
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VERIFY_IS_APPROX(dst, (A.eval() * B.eval()).eval());
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VERIFY_EVALUATION_COUNT((dst.noalias() += A * B), 0);
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VERIFY_IS_APPROX(dst, 2 * (A.eval() * B.eval()).eval());
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VERIFY_EVALUATION_COUNT((dst.noalias() -= A * B), 0);
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VERIFY_IS_APPROX(dst, (A.eval() * B.eval()).eval());
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}
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template<typename Dst, typename Lhs, typename Rhs, typename S2>
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void check_scalar_multiple2(Dst &dst, const Lhs& A, const Rhs& B, S2 s2)
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{
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CALL_SUBTEST( check_scalar_multiple3(dst, A, B) );
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CALL_SUBTEST( check_scalar_multiple3(dst, A, -B) );
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CALL_SUBTEST( check_scalar_multiple3(dst, A, s2*B) );
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CALL_SUBTEST( check_scalar_multiple3(dst, A, B*s2) );
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CALL_SUBTEST( check_scalar_multiple3(dst, A, (B*s2).conjugate()) );
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template <typename Dst, typename Lhs, typename Rhs, typename S2>
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void check_scalar_multiple2(Dst& dst, const Lhs& A, const Rhs& B, S2 s2) {
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CALL_SUBTEST(check_scalar_multiple3(dst, A, B));
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CALL_SUBTEST(check_scalar_multiple3(dst, A, -B));
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CALL_SUBTEST(check_scalar_multiple3(dst, A, s2 * B));
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CALL_SUBTEST(check_scalar_multiple3(dst, A, B * s2));
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CALL_SUBTEST(check_scalar_multiple3(dst, A, (B * s2).conjugate()));
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}
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template<typename Dst, typename Lhs, typename Rhs, typename S1, typename S2>
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void check_scalar_multiple1(Dst &dst, const Lhs& A, const Rhs& B, S1 s1, S2 s2)
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{
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CALL_SUBTEST( check_scalar_multiple2(dst, A, B, s2) );
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CALL_SUBTEST( check_scalar_multiple2(dst, -A, B, s2) );
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CALL_SUBTEST( check_scalar_multiple2(dst, s1*A, B, s2) );
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CALL_SUBTEST( check_scalar_multiple2(dst, A*s1, B, s2) );
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CALL_SUBTEST( check_scalar_multiple2(dst, (A*s1).conjugate(), B, s2) );
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template <typename Dst, typename Lhs, typename Rhs, typename S1, typename S2>
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void check_scalar_multiple1(Dst& dst, const Lhs& A, const Rhs& B, S1 s1, S2 s2) {
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CALL_SUBTEST(check_scalar_multiple2(dst, A, B, s2));
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CALL_SUBTEST(check_scalar_multiple2(dst, -A, B, s2));
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CALL_SUBTEST(check_scalar_multiple2(dst, s1 * A, B, s2));
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CALL_SUBTEST(check_scalar_multiple2(dst, A * s1, B, s2));
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CALL_SUBTEST(check_scalar_multiple2(dst, (A * s1).conjugate(), B, s2));
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}
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template<typename MatrixType> void product_notemporary(const MatrixType& m)
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{
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template <typename MatrixType>
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void product_notemporary(const MatrixType& m) {
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/* This test checks the number of temporaries created
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* during the evaluation of a complex expression */
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typedef typename MatrixType::Scalar Scalar;
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@@ -56,154 +53,167 @@ template<typename MatrixType> void product_notemporary(const MatrixType& m)
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Index rows = m.rows();
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Index cols = m.cols();
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ColMajorMatrixType m1 = MatrixType::Random(rows, cols),
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m2 = MatrixType::Random(rows, cols),
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m3(rows, cols);
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ColMajorMatrixType m1 = MatrixType::Random(rows, cols), m2 = MatrixType::Random(rows, cols), m3(rows, cols);
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RowVectorType rv1 = RowVectorType::Random(rows), rvres(rows);
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ColVectorType cv1 = ColVectorType::Random(cols), cvres(cols);
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RowMajorMatrixType rm3(rows, cols);
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Scalar s1 = internal::random<Scalar>(),
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s2 = internal::random<Scalar>(),
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s3 = internal::random<Scalar>();
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Scalar s1 = internal::random<Scalar>(), s2 = internal::random<Scalar>(), s3 = internal::random<Scalar>();
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Index c0 = internal::random<Index>(4,cols-8),
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c1 = internal::random<Index>(8,cols-c0),
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r0 = internal::random<Index>(4,cols-8),
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r1 = internal::random<Index>(8,rows-r0);
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Index c0 = internal::random<Index>(4, cols - 8), c1 = internal::random<Index>(8, cols - c0),
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r0 = internal::random<Index>(4, cols - 8), r1 = internal::random<Index>(8, rows - r0);
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VERIFY_EVALUATION_COUNT( m3 = (m1 * m2.adjoint()), 1);
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VERIFY_EVALUATION_COUNT( m3 = (m1 * m2.adjoint()).transpose(), 1);
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VERIFY_EVALUATION_COUNT( m3.noalias() = m1 * m2.adjoint(), 0);
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VERIFY_EVALUATION_COUNT(m3 = (m1 * m2.adjoint()), 1);
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VERIFY_EVALUATION_COUNT(m3 = (m1 * m2.adjoint()).transpose(), 1);
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VERIFY_EVALUATION_COUNT(m3.noalias() = m1 * m2.adjoint(), 0);
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VERIFY_EVALUATION_COUNT( m3 = s1 * (m1 * m2.transpose()), 1);
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// VERIFY_EVALUATION_COUNT( m3 = m3 + s1 * (m1 * m2.transpose()), 1);
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VERIFY_EVALUATION_COUNT( m3.noalias() = s1 * (m1 * m2.transpose()), 0);
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VERIFY_EVALUATION_COUNT(m3 = s1 * (m1 * m2.transpose()), 1);
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// VERIFY_EVALUATION_COUNT( m3 = m3 + s1 * (m1 * m2.transpose()), 1);
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VERIFY_EVALUATION_COUNT(m3.noalias() = s1 * (m1 * m2.transpose()), 0);
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VERIFY_EVALUATION_COUNT( m3 = m3 + (m1 * m2.adjoint()), 1);
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VERIFY_EVALUATION_COUNT( m3 = m3 - (m1 * m2.adjoint()), 1);
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VERIFY_EVALUATION_COUNT(m3 = m3 + (m1 * m2.adjoint()), 1);
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VERIFY_EVALUATION_COUNT(m3 = m3 - (m1 * m2.adjoint()), 1);
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VERIFY_EVALUATION_COUNT( m3 = m3 + (m1 * m2.adjoint()).transpose(), 1);
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VERIFY_EVALUATION_COUNT( m3.noalias() = m3 + m1 * m2.transpose(), 0);
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VERIFY_EVALUATION_COUNT( m3.noalias() += m3 + m1 * m2.transpose(), 0);
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VERIFY_EVALUATION_COUNT( m3.noalias() -= m3 + m1 * m2.transpose(), 0);
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VERIFY_EVALUATION_COUNT( m3.noalias() = m3 - m1 * m2.transpose(), 0);
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VERIFY_EVALUATION_COUNT( m3.noalias() += m3 - m1 * m2.transpose(), 0);
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VERIFY_EVALUATION_COUNT( m3.noalias() -= m3 - m1 * m2.transpose(), 0);
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VERIFY_EVALUATION_COUNT(m3 = m3 + (m1 * m2.adjoint()).transpose(), 1);
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VERIFY_EVALUATION_COUNT(m3.noalias() = m3 + m1 * m2.transpose(), 0);
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VERIFY_EVALUATION_COUNT(m3.noalias() += m3 + m1 * m2.transpose(), 0);
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VERIFY_EVALUATION_COUNT(m3.noalias() -= m3 + m1 * m2.transpose(), 0);
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VERIFY_EVALUATION_COUNT(m3.noalias() = m3 - m1 * m2.transpose(), 0);
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VERIFY_EVALUATION_COUNT(m3.noalias() += m3 - m1 * m2.transpose(), 0);
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VERIFY_EVALUATION_COUNT(m3.noalias() -= m3 - m1 * m2.transpose(), 0);
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VERIFY_EVALUATION_COUNT( m3.noalias() = s1 * m1 * s2 * m2.adjoint(), 0);
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VERIFY_EVALUATION_COUNT( m3.noalias() = s1 * m1 * s2 * (m1*s3+m2*s2).adjoint(), 1);
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VERIFY_EVALUATION_COUNT( m3.noalias() = (s1 * m1).adjoint() * s2 * m2, 0);
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VERIFY_EVALUATION_COUNT( m3.noalias() += s1 * (-m1*s3).adjoint() * (s2 * m2 * s3), 0);
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VERIFY_EVALUATION_COUNT( m3.noalias() -= s1 * (m1.transpose() * m2), 0);
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VERIFY_EVALUATION_COUNT(m3.noalias() = s1 * m1 * s2 * m2.adjoint(), 0);
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VERIFY_EVALUATION_COUNT(m3.noalias() = s1 * m1 * s2 * (m1 * s3 + m2 * s2).adjoint(), 1);
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VERIFY_EVALUATION_COUNT(m3.noalias() = (s1 * m1).adjoint() * s2 * m2, 0);
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VERIFY_EVALUATION_COUNT(m3.noalias() += s1 * (-m1 * s3).adjoint() * (s2 * m2 * s3), 0);
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VERIFY_EVALUATION_COUNT(m3.noalias() -= s1 * (m1.transpose() * m2), 0);
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VERIFY_EVALUATION_COUNT(( m3.block(r0,r0,r1,r1).noalias() += -m1.block(r0,c0,r1,c1) * (s2*m2.block(r0,c0,r1,c1)).adjoint() ), 0);
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VERIFY_EVALUATION_COUNT(( m3.block(r0,r0,r1,r1).noalias() -= s1 * m1.block(r0,c0,r1,c1) * m2.block(c0,r0,c1,r1) ), 0);
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VERIFY_EVALUATION_COUNT(
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(m3.block(r0, r0, r1, r1).noalias() += -m1.block(r0, c0, r1, c1) * (s2 * m2.block(r0, c0, r1, c1)).adjoint()), 0);
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VERIFY_EVALUATION_COUNT(
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(m3.block(r0, r0, r1, r1).noalias() -= s1 * m1.block(r0, c0, r1, c1) * m2.block(c0, r0, c1, r1)), 0);
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// NOTE this is because the Block expression is not handled yet by our expression analyser
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VERIFY_EVALUATION_COUNT(( m3.block(r0,r0,r1,r1).noalias() = s1 * m1.block(r0,c0,r1,c1) * (s1*m2).block(c0,r0,c1,r1) ), 1);
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VERIFY_EVALUATION_COUNT(
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(m3.block(r0, r0, r1, r1).noalias() = s1 * m1.block(r0, c0, r1, c1) * (s1 * m2).block(c0, r0, c1, r1)), 1);
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VERIFY_EVALUATION_COUNT( m3.noalias() -= (s1 * m1).template triangularView<Lower>() * m2, 0);
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VERIFY_EVALUATION_COUNT( rm3.noalias() = (s1 * m1.adjoint()).template triangularView<Upper>() * (m2+m2), 1);
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VERIFY_EVALUATION_COUNT( rm3.noalias() = (s1 * m1.adjoint()).template triangularView<UnitUpper>() * m2.adjoint(), 0);
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VERIFY_EVALUATION_COUNT(m3.noalias() -= (s1 * m1).template triangularView<Lower>() * m2, 0);
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VERIFY_EVALUATION_COUNT(rm3.noalias() = (s1 * m1.adjoint()).template triangularView<Upper>() * (m2 + m2), 1);
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VERIFY_EVALUATION_COUNT(rm3.noalias() = (s1 * m1.adjoint()).template triangularView<UnitUpper>() * m2.adjoint(), 0);
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VERIFY_EVALUATION_COUNT( m3.template triangularView<Upper>() = (m1 * m2.adjoint()), 0);
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VERIFY_EVALUATION_COUNT( m3.template triangularView<Upper>() -= (m1 * m2.adjoint()), 0);
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VERIFY_EVALUATION_COUNT(m3.template triangularView<Upper>() = (m1 * m2.adjoint()), 0);
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VERIFY_EVALUATION_COUNT(m3.template triangularView<Upper>() -= (m1 * m2.adjoint()), 0);
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// NOTE this is because the blas_traits require innerstride==1 to avoid a temporary, but that doesn't seem to be actually needed for the triangular products
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VERIFY_EVALUATION_COUNT( rm3.col(c0).noalias() = (s1 * m1.adjoint()).template triangularView<UnitUpper>() * (s2*m2.row(c0)).adjoint(), 1);
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// NOTE this is because the blas_traits require innerstride==1 to avoid a temporary, but that doesn't seem to be
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// actually needed for the triangular products
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VERIFY_EVALUATION_COUNT(
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rm3.col(c0).noalias() = (s1 * m1.adjoint()).template triangularView<UnitUpper>() * (s2 * m2.row(c0)).adjoint(),
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1);
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VERIFY_EVALUATION_COUNT( m1.template triangularView<Lower>().solveInPlace(m3), 0);
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VERIFY_EVALUATION_COUNT( m1.adjoint().template triangularView<Lower>().solveInPlace(m3.transpose()), 0);
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VERIFY_EVALUATION_COUNT(m1.template triangularView<Lower>().solveInPlace(m3), 0);
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VERIFY_EVALUATION_COUNT(m1.adjoint().template triangularView<Lower>().solveInPlace(m3.transpose()), 0);
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VERIFY_EVALUATION_COUNT( m3.noalias() -= (s1 * m1).adjoint().template selfadjointView<Lower>() * (-m2*s3).adjoint(), 0);
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VERIFY_EVALUATION_COUNT( m3.noalias() = s2 * m2.adjoint() * (s1 * m1.adjoint()).template selfadjointView<Upper>(), 0);
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VERIFY_EVALUATION_COUNT( rm3.noalias() = (s1 * m1.adjoint()).template selfadjointView<Lower>() * m2.adjoint(), 0);
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VERIFY_EVALUATION_COUNT(m3.noalias() -= (s1 * m1).adjoint().template selfadjointView<Lower>() * (-m2 * s3).adjoint(),
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0);
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VERIFY_EVALUATION_COUNT(m3.noalias() = s2 * m2.adjoint() * (s1 * m1.adjoint()).template selfadjointView<Upper>(), 0);
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VERIFY_EVALUATION_COUNT(rm3.noalias() = (s1 * m1.adjoint()).template selfadjointView<Lower>() * m2.adjoint(), 0);
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// NOTE this is because the blas_traits require innerstride==1 to avoid a temporary, but that doesn't seem to be actually needed for the triangular products
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VERIFY_EVALUATION_COUNT( m3.col(c0).noalias() = (s1 * m1).adjoint().template selfadjointView<Lower>() * (-m2.row(c0)*s3).adjoint(), 1);
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VERIFY_EVALUATION_COUNT( m3.col(c0).noalias() -= (s1 * m1).adjoint().template selfadjointView<Upper>() * (-m2.row(c0)*s3).adjoint(), 1);
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// NOTE this is because the blas_traits require innerstride==1 to avoid a temporary, but that doesn't seem to be
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// actually needed for the triangular products
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VERIFY_EVALUATION_COUNT(
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m3.col(c0).noalias() = (s1 * m1).adjoint().template selfadjointView<Lower>() * (-m2.row(c0) * s3).adjoint(), 1);
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VERIFY_EVALUATION_COUNT(
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m3.col(c0).noalias() -= (s1 * m1).adjoint().template selfadjointView<Upper>() * (-m2.row(c0) * s3).adjoint(), 1);
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VERIFY_EVALUATION_COUNT( m3.block(r0,c0,r1,c1).noalias() += m1.block(r0,r0,r1,r1).template selfadjointView<Upper>() * (s1*m2.block(r0,c0,r1,c1)), 0);
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VERIFY_EVALUATION_COUNT( m3.block(r0,c0,r1,c1).noalias() = m1.block(r0,r0,r1,r1).template selfadjointView<Upper>() * m2.block(r0,c0,r1,c1), 0);
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VERIFY_EVALUATION_COUNT(m3.block(r0, c0, r1, c1).noalias() +=
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m1.block(r0, r0, r1, r1).template selfadjointView<Upper>() * (s1 * m2.block(r0, c0, r1, c1)),
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0);
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VERIFY_EVALUATION_COUNT(m3.block(r0, c0, r1, c1).noalias() =
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m1.block(r0, r0, r1, r1).template selfadjointView<Upper>() * m2.block(r0, c0, r1, c1),
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0);
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VERIFY_EVALUATION_COUNT( m3.template selfadjointView<Lower>().rankUpdate(m2.adjoint()), 0);
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VERIFY_EVALUATION_COUNT(m3.template selfadjointView<Lower>().rankUpdate(m2.adjoint()), 0);
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// Here we will get 1 temporary for each resize operation of the lhs operator; resize(r1,c1) would lead to zero temporaries
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m3.resize(1,1);
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VERIFY_EVALUATION_COUNT( m3.noalias() = m1.block(r0,r0,r1,r1).template selfadjointView<Lower>() * m2.block(r0,c0,r1,c1), 1);
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m3.resize(1,1);
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VERIFY_EVALUATION_COUNT( m3.noalias() = m1.block(r0,r0,r1,r1).template triangularView<UnitUpper>() * m2.block(r0,c0,r1,c1), 1);
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// Here we will get 1 temporary for each resize operation of the lhs operator; resize(r1,c1) would lead to zero
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// temporaries
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m3.resize(1, 1);
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VERIFY_EVALUATION_COUNT(
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m3.noalias() = m1.block(r0, r0, r1, r1).template selfadjointView<Lower>() * m2.block(r0, c0, r1, c1), 1);
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m3.resize(1, 1);
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VERIFY_EVALUATION_COUNT(
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m3.noalias() = m1.block(r0, r0, r1, r1).template triangularView<UnitUpper>() * m2.block(r0, c0, r1, c1), 1);
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// Zero temporaries for lazy products ...
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m3.setRandom(rows,cols);
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VERIFY_EVALUATION_COUNT( Scalar tmp = 0; tmp += Scalar(RealScalar(1)) / (m3.transpose().lazyProduct(m3)).diagonal().sum(), 0 );
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VERIFY_EVALUATION_COUNT( m3.noalias() = m1.conjugate().lazyProduct(m2.conjugate()), 0);
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m3.setRandom(rows, cols);
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VERIFY_EVALUATION_COUNT(Scalar tmp = 0;
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tmp += Scalar(RealScalar(1)) / (m3.transpose().lazyProduct(m3)).diagonal().sum(), 0);
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VERIFY_EVALUATION_COUNT(m3.noalias() = m1.conjugate().lazyProduct(m2.conjugate()), 0);
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// ... and even no temporary for even deeply (>=2) nested products
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VERIFY_EVALUATION_COUNT( Scalar tmp = 0; tmp += Scalar(RealScalar(1)) / (m3.transpose() * m3).diagonal().sum(), 0 );
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VERIFY_EVALUATION_COUNT( Scalar tmp = 0; tmp += Scalar(RealScalar(1)) / (m3.transpose() * m3).diagonal().array().abs().sum(), 0 );
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VERIFY_EVALUATION_COUNT(Scalar tmp = 0; tmp += Scalar(RealScalar(1)) / (m3.transpose() * m3).diagonal().sum(), 0);
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VERIFY_EVALUATION_COUNT(Scalar tmp = 0;
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tmp += Scalar(RealScalar(1)) / (m3.transpose() * m3).diagonal().array().abs().sum(), 0);
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// Zero temporaries for ... CoeffBasedProductMode
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VERIFY_EVALUATION_COUNT( m3.col(0).template head<5>() * m3.col(0).transpose() + m3.col(0).template head<5>() * m3.col(0).transpose(), 0 );
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VERIFY_EVALUATION_COUNT(
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m3.col(0).template head<5>() * m3.col(0).transpose() + m3.col(0).template head<5>() * m3.col(0).transpose(), 0);
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// Check matrix * vectors
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VERIFY_EVALUATION_COUNT( cvres.noalias() = m1 * cv1, 0 );
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VERIFY_EVALUATION_COUNT( cvres.noalias() -= m1 * cv1, 0 );
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VERIFY_EVALUATION_COUNT( cvres.noalias() -= m1 * m2.col(0), 0 );
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VERIFY_EVALUATION_COUNT( cvres.noalias() -= m1 * rv1.adjoint(), 0 );
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VERIFY_EVALUATION_COUNT( cvres.noalias() -= m1 * m2.row(0).transpose(), 0 );
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VERIFY_EVALUATION_COUNT(cvres.noalias() = m1 * cv1, 0);
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VERIFY_EVALUATION_COUNT(cvres.noalias() -= m1 * cv1, 0);
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VERIFY_EVALUATION_COUNT(cvres.noalias() -= m1 * m2.col(0), 0);
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VERIFY_EVALUATION_COUNT(cvres.noalias() -= m1 * rv1.adjoint(), 0);
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VERIFY_EVALUATION_COUNT(cvres.noalias() -= m1 * m2.row(0).transpose(), 0);
|
||||
|
||||
VERIFY_EVALUATION_COUNT( cvres.noalias() = (m1+m1) * cv1, 0 );
|
||||
VERIFY_EVALUATION_COUNT( cvres.noalias() = (rm3+rm3) * cv1, 0 );
|
||||
VERIFY_EVALUATION_COUNT( cvres.noalias() = (m1+m1) * (m1*cv1), 1 );
|
||||
VERIFY_EVALUATION_COUNT( cvres.noalias() = (rm3+rm3) * (m1*cv1), 1 );
|
||||
VERIFY_EVALUATION_COUNT(cvres.noalias() = (m1 + m1) * cv1, 0);
|
||||
VERIFY_EVALUATION_COUNT(cvres.noalias() = (rm3 + rm3) * cv1, 0);
|
||||
VERIFY_EVALUATION_COUNT(cvres.noalias() = (m1 + m1) * (m1 * cv1), 1);
|
||||
VERIFY_EVALUATION_COUNT(cvres.noalias() = (rm3 + rm3) * (m1 * cv1), 1);
|
||||
|
||||
// Check outer products
|
||||
#ifdef EIGEN_ALLOCA
|
||||
bool temp_via_alloca = m3.rows()*sizeof(Scalar) <= EIGEN_STACK_ALLOCATION_LIMIT;
|
||||
#else
|
||||
// Check outer products
|
||||
#ifdef EIGEN_ALLOCA
|
||||
bool temp_via_alloca = m3.rows() * sizeof(Scalar) <= EIGEN_STACK_ALLOCATION_LIMIT;
|
||||
#else
|
||||
bool temp_via_alloca = false;
|
||||
#endif
|
||||
#endif
|
||||
m3 = cv1 * rv1;
|
||||
VERIFY_EVALUATION_COUNT( m3.noalias() = cv1 * rv1, 0 );
|
||||
VERIFY_EVALUATION_COUNT( m3.noalias() = (cv1+cv1) * (rv1+rv1), temp_via_alloca ? 0 : 1 );
|
||||
VERIFY_EVALUATION_COUNT( m3.noalias() = (m1*cv1) * (rv1), 1 );
|
||||
VERIFY_EVALUATION_COUNT( m3.noalias() += (m1*cv1) * (rv1), 1 );
|
||||
VERIFY_EVALUATION_COUNT(m3.noalias() = cv1 * rv1, 0);
|
||||
VERIFY_EVALUATION_COUNT(m3.noalias() = (cv1 + cv1) * (rv1 + rv1), temp_via_alloca ? 0 : 1);
|
||||
VERIFY_EVALUATION_COUNT(m3.noalias() = (m1 * cv1) * (rv1), 1);
|
||||
VERIFY_EVALUATION_COUNT(m3.noalias() += (m1 * cv1) * (rv1), 1);
|
||||
rm3 = cv1 * rv1;
|
||||
VERIFY_EVALUATION_COUNT( rm3.noalias() = cv1 * rv1, 0 );
|
||||
VERIFY_EVALUATION_COUNT( rm3.noalias() = (cv1+cv1) * (rv1+rv1), temp_via_alloca ? 0 : 1 );
|
||||
VERIFY_EVALUATION_COUNT( rm3.noalias() = (cv1) * (rv1 * m1), 1 );
|
||||
VERIFY_EVALUATION_COUNT( rm3.noalias() -= (cv1) * (rv1 * m1), 1 );
|
||||
VERIFY_EVALUATION_COUNT( rm3.noalias() = (m1*cv1) * (rv1 * m1), 2 );
|
||||
VERIFY_EVALUATION_COUNT( rm3.noalias() += (m1*cv1) * (rv1 * m1), 2 );
|
||||
VERIFY_EVALUATION_COUNT(rm3.noalias() = cv1 * rv1, 0);
|
||||
VERIFY_EVALUATION_COUNT(rm3.noalias() = (cv1 + cv1) * (rv1 + rv1), temp_via_alloca ? 0 : 1);
|
||||
VERIFY_EVALUATION_COUNT(rm3.noalias() = (cv1) * (rv1 * m1), 1);
|
||||
VERIFY_EVALUATION_COUNT(rm3.noalias() -= (cv1) * (rv1 * m1), 1);
|
||||
VERIFY_EVALUATION_COUNT(rm3.noalias() = (m1 * cv1) * (rv1 * m1), 2);
|
||||
VERIFY_EVALUATION_COUNT(rm3.noalias() += (m1 * cv1) * (rv1 * m1), 2);
|
||||
|
||||
// Check nested products
|
||||
VERIFY_EVALUATION_COUNT( cvres.noalias() = m1.adjoint() * m1 * cv1, 1 );
|
||||
VERIFY_EVALUATION_COUNT( rvres.noalias() = rv1 * (m1 * m2.adjoint()), 1 );
|
||||
VERIFY_EVALUATION_COUNT(cvres.noalias() = m1.adjoint() * m1 * cv1, 1);
|
||||
VERIFY_EVALUATION_COUNT(rvres.noalias() = rv1 * (m1 * m2.adjoint()), 1);
|
||||
|
||||
// exhaustively check all scalar multiple combinations:
|
||||
{
|
||||
// Generic path:
|
||||
check_scalar_multiple1(m3, m1, m2, s1, s2);
|
||||
// Force fall back to coeff-based:
|
||||
typename ColMajorMatrixType::BlockXpr m3_blck = m3.block(r0,r0,1,1);
|
||||
check_scalar_multiple1(m3_blck, m1.block(r0,c0,1,1), m2.block(c0,r0,1,1), s1, s2);
|
||||
typename ColMajorMatrixType::BlockXpr m3_blck = m3.block(r0, r0, 1, 1);
|
||||
check_scalar_multiple1(m3_blck, m1.block(r0, c0, 1, 1), m2.block(c0, r0, 1, 1), s1, s2);
|
||||
}
|
||||
}
|
||||
|
||||
EIGEN_DECLARE_TEST(product_notemporary)
|
||||
{
|
||||
EIGEN_DECLARE_TEST(product_notemporary) {
|
||||
int s;
|
||||
for(int i = 0; i < g_repeat; i++) {
|
||||
s = internal::random<int>(16,EIGEN_TEST_MAX_SIZE);
|
||||
CALL_SUBTEST_1( product_notemporary(MatrixXf(s, s)) );
|
||||
CALL_SUBTEST_2( product_notemporary(MatrixXd(s, s)) );
|
||||
for (int i = 0; i < g_repeat; i++) {
|
||||
s = internal::random<int>(16, EIGEN_TEST_MAX_SIZE);
|
||||
CALL_SUBTEST_1(product_notemporary(MatrixXf(s, s)));
|
||||
CALL_SUBTEST_2(product_notemporary(MatrixXd(s, s)));
|
||||
TEST_SET_BUT_UNUSED_VARIABLE(s)
|
||||
|
||||
s = internal::random<int>(16,EIGEN_TEST_MAX_SIZE/2);
|
||||
CALL_SUBTEST_3( product_notemporary(MatrixXcf(s,s)) );
|
||||
CALL_SUBTEST_4( product_notemporary(MatrixXcd(s,s)) );
|
||||
|
||||
s = internal::random<int>(16, EIGEN_TEST_MAX_SIZE / 2);
|
||||
CALL_SUBTEST_3(product_notemporary(MatrixXcf(s, s)));
|
||||
CALL_SUBTEST_4(product_notemporary(MatrixXcd(s, s)));
|
||||
TEST_SET_BUT_UNUSED_VARIABLE(s)
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user