diff --git a/test/householder.cpp b/test/householder.cpp index 8031025e5..e77fa7ad0 100644 --- a/test/householder.cpp +++ b/test/householder.cpp @@ -48,7 +48,7 @@ template void householder(const MatrixType& m) typedef Matrix VBlockMatrixType; typedef Matrix TMatrixType; - Matrix _tmp(std::max(rows,cols)); + Matrix _tmp((std::max)(rows,cols)); Scalar* tmp = &_tmp.coeffRef(0,0); Scalar beta; diff --git a/test/jacobisvd.cpp b/test/jacobisvd.cpp index 907b290af..25c4ad0c8 100644 --- a/test/jacobisvd.cpp +++ b/test/jacobisvd.cpp @@ -66,7 +66,7 @@ void jacobisvd_compare_to_full(const MatrixType& m, typedef typename MatrixType::Index Index; Index rows = m.rows(); Index cols = m.cols(); - Index diagSize = std::min(rows, cols); + Index diagSize = (std::min)(rows, cols); JacobiSVD svd(m, computationOptions); diff --git a/test/lu.cpp b/test/lu.cpp index eac7c1ee6..552364d29 100644 --- a/test/lu.cpp +++ b/test/lu.cpp @@ -64,7 +64,7 @@ template void lu_non_invertible() typedef Matrix RMatrixType; - Index rank = internal::random(1, std::min(rows, cols)-1); + Index rank = internal::random(1, (std::min)(rows, cols)-1); // The image of the zero matrix should consist of a single (zero) column vector VERIFY((MatrixType::Zero(rows,cols).fullPivLu().image(MatrixType::Zero(rows,cols)).cols() == 1)); @@ -84,8 +84,8 @@ template void lu_non_invertible() MatrixType u(rows,cols); u = lu.matrixLU().template triangularView(); RMatrixType l = RMatrixType::Identity(rows,rows); - l.block(0,0,rows,std::min(rows,cols)).template triangularView() - = lu.matrixLU().block(0,0,rows,std::min(rows,cols)); + l.block(0,0,rows,(std::min)(rows,cols)).template triangularView() + = lu.matrixLU().block(0,0,rows,(std::min)(rows,cols)); VERIFY_IS_APPROX(lu.permutationP() * m1 * lu.permutationQ(), l*u); diff --git a/test/nullary.cpp b/test/nullary.cpp index 0bde253df..0df15c081 100644 --- a/test/nullary.cpp +++ b/test/nullary.cpp @@ -38,7 +38,7 @@ bool equalsIdentity(const MatrixType& A) } } for (Index i = 0; i < A.rows(); ++i) { - for (Index j = 0; j < std::min(i, A.cols()); ++j) { + for (Index j = 0; j < (std::min)(i, A.cols()); ++j) { offDiagOK = offDiagOK && (A(i,j) == zero); } } diff --git a/test/packetmath.cpp b/test/packetmath.cpp index a7a0cd132..279f080b0 100644 --- a/test/packetmath.cpp +++ b/test/packetmath.cpp @@ -128,7 +128,7 @@ template void packetmath() { data1[i] = internal::random()/RealScalar(PacketSize); data2[i] = internal::random()/RealScalar(PacketSize); - refvalue = std::max(refvalue,internal::abs(data1[i])); + refvalue = (std::max)(refvalue,internal::abs(data1[i])); } internal::pstore(data2, internal::pload(data1)); @@ -264,16 +264,16 @@ template void packetmath_real() ref[0] = data1[0]; for (int i=0; i(data1))) && "internal::predux_min"); - CHECK_CWISE2(std::min, internal::pmin); - CHECK_CWISE2(std::max, internal::pmax); + CHECK_CWISE2((std::min), internal::pmin); + CHECK_CWISE2((std::max), internal::pmax); CHECK_CWISE1(internal::abs, internal::pabs); ref[0] = data1[0]; for (int i=0; i(data1))) && "internal::predux_max"); for (int i=0; i void inverse_general_4x4(int repeat) MatrixType inv = m.inverse(); double error = double( (m*inv-MatrixType::Identity()).norm() * absdet / NumTraits::epsilon() ); error_sum += error; - error_max = std::max(error_max, error); + error_max = (std::max)(error_max, error); } std::cerr << "inverse_general_4x4, Scalar = " << type_name() << std::endl; double error_avg = error_sum / repeat; diff --git a/test/product.h b/test/product.h index 101766b18..40ae4d51b 100644 --- a/test/product.h +++ b/test/product.h @@ -29,7 +29,7 @@ template bool areNotApprox(const MatrixBase& m1, const MatrixBase& m2, typename Derived1::RealScalar epsilon = NumTraits::dummy_precision()) { return !((m1-m2).cwiseAbs2().maxCoeff() < epsilon * epsilon - * std::max(m1.cwiseAbs2().maxCoeff(), m2.cwiseAbs2().maxCoeff())); + * (std::max)(m1.cwiseAbs2().maxCoeff(), m2.cwiseAbs2().maxCoeff())); } template void product(const MatrixType& m) @@ -102,7 +102,7 @@ template void product(const MatrixType& m) // test the previous tests were not screwed up because operator* returns 0 // (we use the more accurate default epsilon) - if (!NumTraits::IsInteger && std::min(rows,cols)>1) + if (!NumTraits::IsInteger && (std::min)(rows,cols)>1) { VERIFY(areNotApprox(m1.transpose()*m2,m2.transpose()*m1)); } @@ -111,7 +111,7 @@ template void product(const MatrixType& m) res = square; res.noalias() += m1 * m2.transpose(); VERIFY_IS_APPROX(res, square + m1 * m2.transpose()); - if (!NumTraits::IsInteger && std::min(rows,cols)>1) + if (!NumTraits::IsInteger && (std::min)(rows,cols)>1) { VERIFY(areNotApprox(res,square + m2 * m1.transpose())); } @@ -123,7 +123,7 @@ template void product(const MatrixType& m) res = square; res.noalias() -= m1 * m2.transpose(); VERIFY_IS_APPROX(res, square - (m1 * m2.transpose())); - if (!NumTraits::IsInteger && std::min(rows,cols)>1) + if (!NumTraits::IsInteger && (std::min)(rows,cols)>1) { VERIFY(areNotApprox(res,square - m2 * m1.transpose())); } @@ -147,7 +147,7 @@ template void product(const MatrixType& m) res2 = square2; res2.noalias() += m1.transpose() * m2; VERIFY_IS_APPROX(res2, square2 + m1.transpose() * m2); - if (!NumTraits::IsInteger && std::min(rows,cols)>1) + if (!NumTraits::IsInteger && (std::min)(rows,cols)>1) { VERIFY(areNotApprox(res2,square2 + m2.transpose() * m1)); } diff --git a/test/qr_colpivoting.cpp b/test/qr_colpivoting.cpp index ddfb1bad5..3cf651fa7 100644 --- a/test/qr_colpivoting.cpp +++ b/test/qr_colpivoting.cpp @@ -31,7 +31,7 @@ template void qr() typedef typename MatrixType::Index Index; Index rows = internal::random(2,200), cols = internal::random(2,200), cols2 = internal::random(2,200); - Index rank = internal::random(1, std::min(rows, cols)-1); + Index rank = internal::random(1, (std::min)(rows, cols)-1); typedef typename MatrixType::Scalar Scalar; typedef typename MatrixType::RealScalar RealScalar; @@ -64,7 +64,7 @@ template void qr_fixedsize() { enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime }; typedef typename MatrixType::Scalar Scalar; - int rank = internal::random(1, std::min(int(Rows), int(Cols))-1); + int rank = internal::random(1, (std::min)(int(Rows), int(Cols))-1); Matrix m1; createRandomPIMatrixOfRank(rank,Rows,Cols,m1); ColPivHouseholderQR > qr(m1); diff --git a/test/qr_fullpivoting.cpp b/test/qr_fullpivoting.cpp index 175c293b3..d281b26a8 100644 --- a/test/qr_fullpivoting.cpp +++ b/test/qr_fullpivoting.cpp @@ -31,7 +31,7 @@ template void qr() typedef typename MatrixType::Index Index; Index rows = internal::random(20,200), cols = internal::random(20,200), cols2 = internal::random(20,200); - Index rank = internal::random(1, std::min(rows, cols)-1); + Index rank = internal::random(1, (std::min)(rows, cols)-1); typedef typename MatrixType::Scalar Scalar; typedef Matrix MatrixQType; diff --git a/test/redux.cpp b/test/redux.cpp index 57b4603c5..a8bcf3b51 100644 --- a/test/redux.cpp +++ b/test/redux.cpp @@ -43,8 +43,8 @@ template void matrixRedux(const MatrixType& m) { s += m1(i,j); p *= m1(i,j); - minc = std::min(internal::real(minc), internal::real(m1(i,j))); - maxc = std::max(internal::real(maxc), internal::real(m1(i,j))); + minc = (std::min)(internal::real(minc), internal::real(m1(i,j))); + maxc = (std::max)(internal::real(maxc), internal::real(m1(i,j))); } const Scalar mean = s/Scalar(RealScalar(rows*cols)); @@ -86,8 +86,8 @@ template void vectorRedux(const VectorType& w) { s += v[j]; p *= v[j]; - minc = std::min(minc, internal::real(v[j])); - maxc = std::max(maxc, internal::real(v[j])); + minc = (std::min)(minc, internal::real(v[j])); + maxc = (std::max)(maxc, internal::real(v[j])); } VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.head(i).sum()), Scalar(1)); VERIFY_IS_APPROX(p, v.head(i).prod()); @@ -103,8 +103,8 @@ template void vectorRedux(const VectorType& w) { s += v[j]; p *= v[j]; - minc = std::min(minc, internal::real(v[j])); - maxc = std::max(maxc, internal::real(v[j])); + minc = (std::min)(minc, internal::real(v[j])); + maxc = (std::max)(maxc, internal::real(v[j])); } VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.tail(size-i).sum()), Scalar(1)); VERIFY_IS_APPROX(p, v.tail(size-i).prod()); @@ -120,8 +120,8 @@ template void vectorRedux(const VectorType& w) { s += v[j]; p *= v[j]; - minc = std::min(minc, internal::real(v[j])); - maxc = std::max(maxc, internal::real(v[j])); + minc = (std::min)(minc, internal::real(v[j])); + maxc = (std::max)(maxc, internal::real(v[j])); } VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.segment(i, size-2*i).sum()), Scalar(1)); VERIFY_IS_APPROX(p, v.segment(i, size-2*i).prod()); diff --git a/test/sparse_basic.cpp b/test/sparse_basic.cpp index 9d79ca740..6f54d2ebc 100644 --- a/test/sparse_basic.cpp +++ b/test/sparse_basic.cpp @@ -33,7 +33,7 @@ template void sparse_basic(const SparseMatrixType& re typedef typename SparseMatrixType::Scalar Scalar; enum { Flags = SparseMatrixType::Flags }; - double density = std::max(8./(rows*cols), 0.01); + double density = (std::max)(8./(rows*cols), 0.01); typedef Matrix DenseMatrix; typedef Matrix DenseVector; Scalar eps = 1e-6; @@ -206,7 +206,7 @@ template void sparse_basic(const SparseMatrixType& re initSparse(density, refMat2, m2); int j0 = internal::random(0,rows-2); int j1 = internal::random(0,rows-2); - int n0 = internal::random(1,rows-std::max(j0,j1)); + int n0 = internal::random(1,rows-(std::max)(j0,j1)); VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(0,j0,rows,n0)); VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0), refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0)); diff --git a/test/sparse_product.cpp b/test/sparse_product.cpp index 90ec3781e..a53ab3f1b 100644 --- a/test/sparse_product.cpp +++ b/test/sparse_product.cpp @@ -32,7 +32,7 @@ template void sparse_product(const SparseMatrixType& typedef typename SparseMatrixType::Scalar Scalar; enum { Flags = SparseMatrixType::Flags }; - double density = std::max(8./(rows*cols), 0.01); + double density = (std::max)(8./(rows*cols), 0.01); typedef Matrix DenseMatrix; typedef Matrix DenseVector; diff --git a/test/sparse_solvers.cpp b/test/sparse_solvers.cpp index aba61e6c0..12a1cb9b6 100644 --- a/test/sparse_solvers.cpp +++ b/test/sparse_solvers.cpp @@ -47,7 +47,7 @@ initSPD(double density, template void sparse_solvers(int rows, int cols) { - double density = std::max(8./(rows*cols), 0.01); + double density = (std::max)(8./(rows*cols), 0.01); typedef Matrix DenseMatrix; typedef Matrix DenseVector; // Scalar eps = 1e-6; diff --git a/test/sparse_vector.cpp b/test/sparse_vector.cpp index be85740c0..e0c281c83 100644 --- a/test/sparse_vector.cpp +++ b/test/sparse_vector.cpp @@ -26,8 +26,8 @@ template void sparse_vector(int rows, int cols) { - double densityMat = std::max(8./(rows*cols), 0.01); - double densityVec = std::max(8./float(rows), 0.1); + double densityMat = (std::max)(8./(rows*cols), 0.01); + double densityVec = (std::max)(8./float(rows), 0.1); typedef Matrix DenseMatrix; typedef Matrix DenseVector; typedef SparseVector SparseVectorType; diff --git a/test/stable_norm.cpp b/test/stable_norm.cpp index 5bf249577..206a274d6 100644 --- a/test/stable_norm.cpp +++ b/test/stable_norm.cpp @@ -68,8 +68,8 @@ template void stable_norm(const MatrixType& m) Index rows = m.rows(); Index cols = m.cols(); - Scalar big = internal::random() * (std::numeric_limits::max() * RealScalar(1e-4)); - Scalar small = internal::random() * (std::numeric_limits::min() * RealScalar(1e4)); + Scalar big = internal::random() * ((std::numeric_limits::max)() * RealScalar(1e-4)); + Scalar small = internal::random() * ((std::numeric_limits::min)() * RealScalar(1e4)); MatrixType vzero = MatrixType::Zero(rows, cols), vrand = MatrixType::Random(rows, cols), diff --git a/unsupported/Eigen/FFT b/unsupported/Eigen/FFT index 0b2823058..c56bd63d6 100644 --- a/unsupported/Eigen/FFT +++ b/unsupported/Eigen/FFT @@ -331,7 +331,7 @@ class FFT // if the vector is strided, then we need to copy it to a packed temporary Matrix tmp; if ( resize_input ) { - size_t ncopy = std::min(src.size(),src.size() + resize_input); + size_t ncopy = (std::min)(src.size(),src.size() + resize_input); tmp.setZero(src.size() + resize_input); if ( realfft && HasFlag(HalfSpectrum) ) { // pad at the Nyquist bin diff --git a/unsupported/Eigen/src/BVH/BVAlgorithms.h b/unsupported/Eigen/src/BVH/BVAlgorithms.h index e6fdf4737..d65a97740 100644 --- a/unsupported/Eigen/src/BVH/BVAlgorithms.h +++ b/unsupported/Eigen/src/BVH/BVAlgorithms.h @@ -231,7 +231,7 @@ private: template typename Minimizer::Scalar BVMinimize(const BVH &tree, Minimizer &minimizer) { - return internal::minimize_helper(tree, minimizer, tree.getRootIndex(), std::numeric_limits::max()); + return internal::minimize_helper(tree, minimizer, tree.getRootIndex(), (std::numeric_limits::max)()); } /** Given two BVH's, runs the query on their cartesian product encapsulated by \a minimizer. @@ -264,7 +264,7 @@ typename Minimizer::Scalar BVMinimize(const BVH1 &tree1, const BVH2 &tree2, Mini ObjIter2 oBegin2 = ObjIter2(), oEnd2 = ObjIter2(), oCur2 = ObjIter2(); std::priority_queue, std::greater > todo; //smallest is at the top - Scalar minimum = std::numeric_limits::max(); + Scalar minimum = (std::numeric_limits::max)(); todo.push(std::make_pair(Scalar(), std::make_pair(tree1.getRootIndex(), tree2.getRootIndex()))); while(!todo.empty()) { diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h b/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h index cedb1d551..50c0ca84e 100644 --- a/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h +++ b/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h @@ -259,7 +259,7 @@ void MatrixExponential::computeUV(float) pade5(m_M); } else { const float maxnorm = 3.925724783138660f; - m_squarings = max(0, (int)ceil(log2(m_l1norm / maxnorm))); + m_squarings = (max)(0, (int)ceil(log2(m_l1norm / maxnorm))); MatrixType A = m_M / pow(Scalar(2), Scalar(static_cast(m_squarings))); pade7(A); } @@ -281,7 +281,7 @@ void MatrixExponential::computeUV(double) pade9(m_M); } else { const double maxnorm = 5.371920351148152; - m_squarings = max(0, (int)ceil(log2(m_l1norm / maxnorm))); + m_squarings = (max)(0, (int)ceil(log2(m_l1norm / maxnorm))); MatrixType A = m_M / pow(Scalar(2), Scalar(m_squarings)); pade13(A); } diff --git a/unsupported/test/BVH.cpp b/unsupported/test/BVH.cpp index d773afb77..e77e84b6d 100644 --- a/unsupported/test/BVH.cpp +++ b/unsupported/test/BVH.cpp @@ -90,13 +90,13 @@ struct BallPointStuff //this class provides functions to be both an intersector } double minimumOnVolume(const BoxType &r) { ++calls; return r.squaredExteriorDistance(p); } - double minimumOnObject(const BallType &b) { ++calls; return std::max(0., (b.center - p).squaredNorm() - SQR(b.radius)); } + double minimumOnObject(const BallType &b) { ++calls; return (std::max)(0., (b.center - p).squaredNorm() - SQR(b.radius)); } double minimumOnVolumeVolume(const BoxType &r1, const BoxType &r2) { ++calls; return r1.squaredExteriorDistance(r2); } - double minimumOnVolumeObject(const BoxType &r, const BallType &b) { ++calls; return SQR(std::max(0., r.exteriorDistance(b.center) - b.radius)); } - double minimumOnObjectVolume(const BallType &b, const BoxType &r) { ++calls; return SQR(std::max(0., r.exteriorDistance(b.center) - b.radius)); } - double minimumOnObjectObject(const BallType &b1, const BallType &b2){ ++calls; return SQR(std::max(0., (b1.center - b2.center).norm() - b1.radius - b2.radius)); } + double minimumOnVolumeObject(const BoxType &r, const BallType &b) { ++calls; return SQR((std::max)(0., r.exteriorDistance(b.center) - b.radius)); } + double minimumOnObjectVolume(const BallType &b, const BoxType &r) { ++calls; return SQR((std::max)(0., r.exteriorDistance(b.center) - b.radius)); } + double minimumOnObjectObject(const BallType &b1, const BallType &b2){ ++calls; return SQR((std::max)(0., (b1.center - b2.center).norm() - b1.radius - b2.radius)); } double minimumOnVolumeObject(const BoxType &r, const VectorType &v) { ++calls; return r.squaredExteriorDistance(v); } - double minimumOnObjectObject(const BallType &b, const VectorType &v){ ++calls; return SQR(std::max(0., (b.center - v).norm() - b.radius)); } + double minimumOnObjectObject(const BallType &b, const VectorType &v){ ++calls; return SQR((std::max)(0., (b.center - v).norm() - b.radius)); } VectorType p; int calls; diff --git a/unsupported/test/matrix_exponential.cpp b/unsupported/test/matrix_exponential.cpp index 5ea438c2a..996b42a7f 100644 --- a/unsupported/test/matrix_exponential.cpp +++ b/unsupported/test/matrix_exponential.cpp @@ -36,7 +36,7 @@ double binom(int n, int k) template double relerr(const MatrixBase& A, const MatrixBase& B) { - return std::sqrt((A - B).cwiseAbs2().sum() / std::min(A.cwiseAbs2().sum(), B.cwiseAbs2().sum())); + return std::sqrt((A - B).cwiseAbs2().sum() / (std::min)(A.cwiseAbs2().sum(), B.cwiseAbs2().sum())); } template diff --git a/unsupported/test/sparse_extra.cpp b/unsupported/test/sparse_extra.cpp index a004995f6..b1fd481e8 100644 --- a/unsupported/test/sparse_extra.cpp +++ b/unsupported/test/sparse_extra.cpp @@ -67,7 +67,7 @@ template void sparse_extra(const SparseMatrixType& re typedef typename SparseMatrixType::Scalar Scalar; enum { Flags = SparseMatrixType::Flags }; - double density = std::max(8./(rows*cols), 0.01); + double density = (std::max)(8./(rows*cols), 0.01); typedef Matrix DenseMatrix; typedef Matrix DenseVector; Scalar eps = 1e-6; diff --git a/unsupported/test/sparse_ldlt.cpp b/unsupported/test/sparse_ldlt.cpp index 4ceda3188..03a26bcd2 100644 --- a/unsupported/test/sparse_ldlt.cpp +++ b/unsupported/test/sparse_ldlt.cpp @@ -33,7 +33,7 @@ template void sparse_ldlt(int rows, int cols) { static bool odd = true; odd = !odd; - double density = std::max(8./(rows*cols), 0.01); + double density = (std::max)(8./(rows*cols), 0.01); typedef Matrix DenseMatrix; typedef Matrix DenseVector; diff --git a/unsupported/test/sparse_llt.cpp b/unsupported/test/sparse_llt.cpp index df198cd52..5f8a7ce36 100644 --- a/unsupported/test/sparse_llt.cpp +++ b/unsupported/test/sparse_llt.cpp @@ -31,7 +31,7 @@ template void sparse_llt(int rows, int cols) { - double density = std::max(8./(rows*cols), 0.01); + double density = (std::max)(8./(rows*cols), 0.01); typedef Matrix DenseMatrix; typedef Matrix DenseVector; diff --git a/unsupported/test/sparse_lu.cpp b/unsupported/test/sparse_lu.cpp index 188d291cc..d58e85a0a 100644 --- a/unsupported/test/sparse_lu.cpp +++ b/unsupported/test/sparse_lu.cpp @@ -35,7 +35,7 @@ template void sparse_lu(int rows, int cols) { - double density = std::max(8./(rows*cols), 0.01); + double density = (std::max)(8./(rows*cols), 0.01); typedef Matrix DenseMatrix; typedef Matrix DenseVector;