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protect calls to min and max with parentheses to make Eigen compatible with default windows.h

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This commit is contained in:
Gael Guennebaud 2011-07-21 11:19:36 +02:00
parent f096553344
commit 49b6e9143e
67 changed files with 233 additions and 229 deletions
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7
Eigen/Core
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@ -175,13 +175,6 @@
#include <new>
#endif
// this needs to be done after all possible windows C header includes and before any Eigen source includes
// (system C++ includes are supposed to be able to deal with this already):
// windows.h defines min and max macros which would make Eigen fail to compile.
#if defined(min) || defined(max)
#error The preprocessor symbols 'min' or 'max' are defined. If you are compiling on Windows, do #define NOMINMAX to prevent windows.h from defining these symbols.
#endif
// defined in bits/termios.h
#undef B0

4
Eigen/src/Cholesky/LLT.h
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@ -233,7 +233,7 @@ template<> struct llt_inplace<Lower>
Index blockSize = size/8;
blockSize = (blockSize/16)*16;
blockSize = std::min(std::max(blockSize,Index(8)), Index(128));
blockSize = (std::min)((std::max)(blockSize,Index(8)), Index(128));
for (Index k=0; k<size; k+=blockSize)
{
@ -241,7 +241,7 @@ template<> struct llt_inplace<Lower>
// A00 | - | -
// lu = A10 | A11 | -
// A20 | A21 | A22
Index bs = std::min(blockSize, size-k);
Index bs = (std::min)(blockSize, size-k);
Index rs = size - k - bs;
Block<MatrixType,Dynamic,Dynamic> A11(m,k, k, bs,bs);
Block<MatrixType,Dynamic,Dynamic> A21(m,k+bs,k, rs,bs);

12
Eigen/src/Core/BandMatrix.h
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@ -87,7 +87,7 @@ class BandMatrixBase : public EigenBase<Derived>
if (i<=supers())
{
start = supers()-i;
len = std::min(rows(),std::max<Index>(0,coeffs().rows() - (supers()-i)));
len = (std::min)(rows(),std::max<Index>(0,coeffs().rows() - (supers()-i)));
}
else if (i>=rows()-subs())
len = std::max<Index>(0,coeffs().rows() - (i + 1 - rows() + subs()));
@ -96,11 +96,11 @@ class BandMatrixBase : public EigenBase<Derived>
/** \returns a vector expression of the main diagonal */
inline Block<CoefficientsType,1,SizeAtCompileTime> diagonal()
{ return Block<CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,std::min(rows(),cols())); }
{ return Block<CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
/** \returns a vector expression of the main diagonal (const version) */
inline const Block<const CoefficientsType,1,SizeAtCompileTime> diagonal() const
{ return Block<const CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,std::min(rows(),cols())); }
{ return Block<const CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
template<int Index> struct DiagonalIntReturnType {
enum {
@ -122,13 +122,13 @@ class BandMatrixBase : public EigenBase<Derived>
/** \returns a vector expression of the \a N -th sub or super diagonal */
template<int N> inline typename DiagonalIntReturnType<N>::Type diagonal()
{
return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, std::max(0,N), 1, diagonalLength(N));
return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
}
/** \returns a vector expression of the \a N -th sub or super diagonal */
template<int N> inline const typename DiagonalIntReturnType<N>::Type diagonal() const
{
return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, std::max(0,N), 1, diagonalLength(N));
return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
}
/** \returns a vector expression of the \a i -th sub or super diagonal */
@ -166,7 +166,7 @@ class BandMatrixBase : public EigenBase<Derived>
protected:
inline Index diagonalLength(Index i) const
{ return i<0 ? std::min(cols(),rows()+i) : std::min(rows(),cols()-i); }
{ return i<0 ? (std::min)(cols(),rows()+i) : (std::min)(rows(),cols()-i); }
};
/**

2
Eigen/src/Core/CwiseNullaryOp.h
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@ -742,7 +742,7 @@ struct setIdentity_impl<Derived, true>
static EIGEN_STRONG_INLINE Derived& run(Derived& m)
{
m.setZero();
const Index size = std::min(m.rows(), m.cols());
const Index size = (std::min)(m.rows(), m.cols());
for(Index i = 0; i < size; ++i) m.coeffRef(i,i) = typename Derived::Scalar(1);
return m;
}

2
Eigen/src/Core/Diagonal.h
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@ -87,7 +87,7 @@ template<typename MatrixType, int DiagIndex> class Diagonal
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
inline Index rows() const
{ return m_index.value()<0 ? std::min(m_matrix.cols(),m_matrix.rows()+m_index.value()) : std::min(m_matrix.rows(),m_matrix.cols()-m_index.value()); }
{ return m_index.value()<0 ? (std::min)(m_matrix.cols(),m_matrix.rows()+m_index.value()) : (std::min)(m_matrix.rows(),m_matrix.cols()-m_index.value()); }
inline Index cols() const { return 1; }

8
Eigen/src/Core/Functors.h
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@ -116,7 +116,7 @@ struct functor_traits<scalar_conj_product_op<LhsScalar,RhsScalar> > {
*/
template<typename Scalar> struct scalar_min_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_min_op)
EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { using std::min; return min(a, b); }
EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { using std::min; return (min)(a, b); }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pmin(a,b); }
@ -139,7 +139,7 @@ struct functor_traits<scalar_min_op<Scalar> > {
*/
template<typename Scalar> struct scalar_max_op {
EIGEN_EMPTY_STRUCT_CTOR(scalar_max_op)
EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { using std::max; return max(a, b); }
EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a, const Scalar& b) const { using std::max; return (max)(a, b); }
template<typename Packet>
EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
{ return internal::pmax(a,b); }
@ -167,8 +167,8 @@ template<typename Scalar> struct scalar_hypot_op {
{
using std::max;
using std::min;
Scalar p = max(_x, _y);
Scalar q = min(_x, _y);
Scalar p = (max)(_x, _y);
Scalar q = (min)(_x, _y);
Scalar qp = q/p;
return p * sqrt(Scalar(1) + qp*qp);
}

4
Eigen/src/Core/Fuzzy.h
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@ -37,7 +37,7 @@ struct isApprox_selector
using std::min;
const typename internal::nested<Derived,2>::type nested(x);
const typename internal::nested<OtherDerived,2>::type otherNested(y);
return (nested - otherNested).cwiseAbs2().sum() <= prec * prec * min(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
return (nested - otherNested).cwiseAbs2().sum() <= prec * prec * (min)(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
}
};
@ -94,7 +94,7 @@ struct isMuchSmallerThan_scalar_selector<Derived, true>
*
* \note The fuzzy compares are done multiplicatively. Two vectors \f$ v \f$ and \f$ w \f$
* are considered to be approximately equal within precision \f$ p \f$ if
* \f[ \Vert v - w \Vert \leqslant p\,\min(\Vert v\Vert, \Vert w\Vert). \f]
* \f[ \Vert v - w \Vert \leqslant p\,\(min)(\Vert v\Vert, \Vert w\Vert). \f]
* For matrices, the comparison is done using the Hilbert-Schmidt norm (aka Frobenius norm
* L2 norm).
*

4
Eigen/src/Core/GenericPacketMath.h
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@ -134,12 +134,12 @@ pdiv(const Packet& a,
/** \internal \returns the min of \a a and \a b (coeff-wise) */
template<typename Packet> inline Packet
pmin(const Packet& a,
const Packet& b) { using std::min; return min(a, b); }
const Packet& b) { using std::min; return (min)(a, b); }
/** \internal \returns the max of \a a and \a b (coeff-wise) */
template<typename Packet> inline Packet
pmax(const Packet& a,
const Packet& b) { using std::max; return max(a, b); }
const Packet& b) { using std::max; return (max)(a, b); }
/** \internal \returns the absolute value of \a a */
template<typename Packet> inline Packet

8
Eigen/src/Core/MathFunctions.h
View File

@ -378,8 +378,8 @@ struct hypot_impl
using std::min;
RealScalar _x = abs(x);
RealScalar _y = abs(y);
RealScalar p = max(_x, _y);
RealScalar q = min(_x, _y);
RealScalar p = (max)(_x, _y);
RealScalar q = (min)(_x, _y);
RealScalar qp = q/p;
return p * sqrt(RealScalar(1) + qp*qp);
}
@ -737,7 +737,7 @@ struct scalar_fuzzy_default_impl<Scalar, false, false>
static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec)
{
using std::min;
return abs(x - y) <= min(abs(x), abs(y)) * prec;
return abs(x - y) <= (min)(abs(x), abs(y)) * prec;
}
static inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, const RealScalar& prec)
{
@ -776,7 +776,7 @@ struct scalar_fuzzy_default_impl<Scalar, true, false>
static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec)
{
using std::min;
return abs2(x - y) <= min(abs2(x), abs2(y)) * prec * prec;
return abs2(x - y) <= (min)(abs2(x), abs2(y)) * prec * prec;
}
};

2
Eigen/src/Core/MatrixBase.h
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@ -111,7 +111,7 @@ template<typename Derived> class MatrixBase
/** \returns the size of the main diagonal, which is min(rows(),cols()).
* \sa rows(), cols(), SizeAtCompileTime. */
inline Index diagonalSize() const { return std::min(rows(),cols()); }
inline Index diagonalSize() const { return (std::min)(rows(),cols()); }
/** \brief The plain matrix type corresponding to this expression.
*

4
Eigen/src/Core/NumTraits.h
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@ -87,8 +87,8 @@ template<typename T> struct GenericNumTraits
// make sure to override this for floating-point types
return Real(0);
}
inline static T highest() { return std::numeric_limits<T>::max(); }
inline static T lowest() { return IsInteger ? std::numeric_limits<T>::min() : (-std::numeric_limits<T>::max()); }
inline static T highest() { return (std::numeric_limits<T>::max)(); }
inline static T lowest() { return IsInteger ? (std::numeric_limits<T>::min)() : (-(std::numeric_limits<T>::max)()); }
#ifdef EIGEN2_SUPPORT
enum {

8
Eigen/src/Core/PlainObjectBase.h
View File

@ -647,8 +647,8 @@ struct internal::conservative_resize_like_impl
{
// The storage order does not allow us to use reallocation.
typename Derived::PlainObject tmp(rows,cols);
const Index common_rows = std::min(rows, _this.rows());
const Index common_cols = std::min(cols, _this.cols());
const Index common_rows = (std::min)(rows, _this.rows());
const Index common_cols = (std::min)(cols, _this.cols());
tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols);
_this.derived().swap(tmp);
}
@ -681,8 +681,8 @@ struct internal::conservative_resize_like_impl
{
// The storage order does not allow us to use reallocation.
typename Derived::PlainObject tmp(other);
const Index common_rows = std::min(tmp.rows(), _this.rows());
const Index common_cols = std::min(tmp.cols(), _this.cols());
const Index common_rows = (std::min)(tmp.rows(), _this.rows());
const Index common_cols = (std::min)(tmp.cols(), _this.cols());
tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols);
_this.derived().swap(tmp);
}

10
Eigen/src/Core/StableNorm.h
View File

@ -69,7 +69,7 @@ MatrixBase<Derived>::stableNorm() const
if (bi>0)
internal::stable_norm_kernel(this->head(bi), ssq, scale, invScale);
for (; bi<n; bi+=blockSize)
internal::stable_norm_kernel(this->segment(bi,min(blockSize, n - bi)).template forceAlignedAccessIf<Alignment>(), ssq, scale, invScale);
internal::stable_norm_kernel(this->segment(bi,(min)(blockSize, n - bi)).template forceAlignedAccessIf<Alignment>(), ssq, scale, invScale);
return scale * internal::sqrt(ssq);
}
@ -103,12 +103,12 @@ MatrixBase<Derived>::blueNorm() const
// For portability, the PORT subprograms "ilmaeh" and "rlmach"
// are used. For any specific computer, each of the assignment
// statements can be replaced
nbig = std::numeric_limits<Index>::max(); // largest integer
nbig = (std::numeric_limits<Index>::max)(); // largest integer
ibeta = std::numeric_limits<RealScalar>::radix; // base for floating-point numbers
it = std::numeric_limits<RealScalar>::digits; // number of base-beta digits in mantissa
iemin = std::numeric_limits<RealScalar>::min_exponent; // minimum exponent
iemax = std::numeric_limits<RealScalar>::max_exponent; // maximum exponent
rbig = std::numeric_limits<RealScalar>::max(); // largest floating-point number
rbig = (std::numeric_limits<RealScalar>::max)(); // largest floating-point number
iexp = -((1-iemin)/2);
b1 = RealScalar(pow(RealScalar(ibeta),RealScalar(iexp))); // lower boundary of midrange
@ -167,8 +167,8 @@ MatrixBase<Derived>::blueNorm() const
}
else
return internal::sqrt(amed);
asml = min(abig, amed);
abig = max(abig, amed);
asml = (min)(abig, amed);
abig = (max)(abig, amed);
if(asml <= abig*relerr)
return abig;
else

16
Eigen/src/Core/TriangularMatrix.h
View File

@ -492,7 +492,7 @@ struct triangular_assignment_selector<Derived1, Derived2, Upper, Dynamic, ClearO
{
for(Index j = 0; j < dst.cols(); ++j)
{
Index maxi = std::min(j, dst.rows()-1);
Index maxi = (std::min)(j, dst.rows()-1);
for(Index i = 0; i <= maxi; ++i)
dst.copyCoeff(i, j, src);
if (ClearOpposite)
@ -512,7 +512,7 @@ struct triangular_assignment_selector<Derived1, Derived2, Lower, Dynamic, ClearO
{
for(Index i = j; i < dst.rows(); ++i)
dst.copyCoeff(i, j, src);
Index maxi = std::min(j, dst.rows());
Index maxi = (std::min)(j, dst.rows());
if (ClearOpposite)
for(Index i = 0; i < maxi; ++i)
dst.coeffRef(i, j) = static_cast<typename Derived1::Scalar>(0);
@ -528,7 +528,7 @@ struct triangular_assignment_selector<Derived1, Derived2, StrictlyUpper, Dynamic
{
for(Index j = 0; j < dst.cols(); ++j)
{
Index maxi = std::min(j, dst.rows());
Index maxi = (std::min)(j, dst.rows());
for(Index i = 0; i < maxi; ++i)
dst.copyCoeff(i, j, src);
if (ClearOpposite)
@ -548,7 +548,7 @@ struct triangular_assignment_selector<Derived1, Derived2, StrictlyLower, Dynamic
{
for(Index i = j+1; i < dst.rows(); ++i)
dst.copyCoeff(i, j, src);
Index maxi = std::min(j, dst.rows()-1);
Index maxi = (std::min)(j, dst.rows()-1);
if (ClearOpposite)
for(Index i = 0; i <= maxi; ++i)
dst.coeffRef(i, j) = static_cast<typename Derived1::Scalar>(0);
@ -564,7 +564,7 @@ struct triangular_assignment_selector<Derived1, Derived2, UnitUpper, Dynamic, Cl
{
for(Index j = 0; j < dst.cols(); ++j)
{
Index maxi = std::min(j, dst.rows());
Index maxi = (std::min)(j, dst.rows());
for(Index i = 0; i < maxi; ++i)
dst.copyCoeff(i, j, src);
if (ClearOpposite)
@ -584,7 +584,7 @@ struct triangular_assignment_selector<Derived1, Derived2, UnitLower, Dynamic, Cl
{
for(Index j = 0; j < dst.cols(); ++j)
{
Index maxi = std::min(j, dst.rows());
Index maxi = (std::min)(j, dst.rows());
for(Index i = maxi+1; i < dst.rows(); ++i)
dst.copyCoeff(i, j, src);
if (ClearOpposite)
@ -796,7 +796,7 @@ bool MatrixBase<Derived>::isUpperTriangular(RealScalar prec) const
RealScalar maxAbsOnUpperPart = static_cast<RealScalar>(-1);
for(Index j = 0; j < cols(); ++j)
{
Index maxi = std::min(j, rows()-1);
Index maxi = (std::min)(j, rows()-1);
for(Index i = 0; i <= maxi; ++i)
{
RealScalar absValue = internal::abs(coeff(i,j));
@ -828,7 +828,7 @@ bool MatrixBase<Derived>::isLowerTriangular(RealScalar prec) const
RealScalar threshold = maxAbsOnLowerPart * prec;
for(Index j = 1; j < cols(); ++j)
{
Index maxi = std::min(j, rows()-1);
Index maxi = (std::min)(j, rows()-1);
for(Index i = 0; i < maxi; ++i)
if(internal::abs(coeff(i, j)) > threshold) return false;
}

10
Eigen/src/Core/products/GeneralMatrixMatrix.h
View File

@ -78,7 +78,7 @@ static void run(Index rows, Index cols, Index depth,
typedef gebp_traits<LhsScalar,RhsScalar> Traits;
Index kc = blocking.kc(); // cache block size along the K direction
Index mc = std::min(rows,blocking.mc()); // cache block size along the M direction
Index mc = (std::min)(rows,blocking.mc()); // cache block size along the M direction
//Index nc = blocking.nc(); // cache block size along the N direction
gemm_pack_lhs<LhsScalar, Index, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs;
@ -103,7 +103,7 @@ static void run(Index rows, Index cols, Index depth,
// For each horizontal panel of the rhs, and corresponding vertical panel of the lhs...
for(Index k=0; k<depth; k+=kc)
{
const Index actual_kc = std::min(k+kc,depth)-k; // => rows of B', and cols of the A'
const Index actual_kc = (std::min)(k+kc,depth)-k; // => rows of B', and cols of the A'
// In order to reduce the chance that a thread has to wait for the other,
// let's start by packing A'.
@ -140,7 +140,7 @@ static void run(Index rows, Index cols, Index depth,
// Then keep going as usual with the remaining A'
for(Index i=mc; i<rows; i+=mc)
{
const Index actual_mc = std::min(i+mc,rows)-i;
const Index actual_mc = (std::min)(i+mc,rows)-i;
// pack A_i,k to A'
pack_lhs(blockA, &lhs(i,k), lhsStride, actual_kc, actual_mc);
@ -174,7 +174,7 @@ static void run(Index rows, Index cols, Index depth,
// (==GEMM_VAR1)
for(Index k2=0; k2<depth; k2+=kc)
{
const Index actual_kc = std::min(k2+kc,depth)-k2;
const Index actual_kc = (std::min)(k2+kc,depth)-k2;
// OK, here we have selected one horizontal panel of rhs and one vertical panel of lhs.
// => Pack rhs's panel into a sequential chunk of memory (L2 caching)
@ -187,7 +187,7 @@ static void run(Index rows, Index cols, Index depth,
// (==GEPP_VAR1)
for(Index i2=0; i2<rows; i2+=mc)
{
const Index actual_mc = std::min(i2+mc,rows)-i2;
const Index actual_mc = (std::min)(i2+mc,rows)-i2;
// We pack the lhs's block into a sequential chunk of memory (L1 caching)
// Note that this block will be read a very high number of times, which is equal to the number of

8
Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
View File

@ -96,14 +96,14 @@ struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,
for(Index k2=0; k2<depth; k2+=kc)
{
const Index actual_kc = std::min(k2+kc,depth)-k2;
const Index actual_kc = (std::min)(k2+kc,depth)-k2;
// note that the actual rhs is the transpose/adjoint of mat
pack_rhs(blockB, &rhs(k2,0), rhsStride, actual_kc, size);
for(Index i2=0; i2<size; i2+=mc)
{
const Index actual_mc = std::min(i2+mc,size)-i2;
const Index actual_mc = (std::min)(i2+mc,size)-i2;
pack_lhs(blockA, &lhs(i2, k2), lhsStride, actual_kc, actual_mc);
@ -112,7 +112,7 @@ struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,
// 2 - the actual_mc x actual_mc symmetric block => processed with a special kernel
// 3 - after the diagonal => processed with gebp or skipped
if (UpLo==Lower)
gebp(res+i2, resStride, blockA, blockB, actual_mc, actual_kc, std::min(size,i2), alpha,
gebp(res+i2, resStride, blockA, blockB, actual_mc, actual_kc, (std::min)(size,i2), alpha,
-1, -1, 0, 0, allocatedBlockB);
sybb(res+resStride*i2 + i2, resStride, blockA, blockB + actual_kc*i2, actual_mc, actual_kc, alpha, allocatedBlockB);
@ -120,7 +120,7 @@ struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,
if (UpLo==Upper)
{
Index j2 = i2+actual_mc;
gebp(res+resStride*j2+i2, resStride, blockA, blockB+actual_kc*j2, actual_mc, actual_kc, std::max(Index(0), size-j2), alpha,
gebp(res+resStride*j2+i2, resStride, blockA, blockB+actual_kc*j2, actual_mc, actual_kc, (std::max)(Index(0), size-j2), alpha,
-1, -1, 0, 0, allocatedBlockB);
}
}

4
Eigen/src/Core/products/GeneralMatrixVector.h
View File

@ -134,7 +134,7 @@ EIGEN_DONT_INLINE static void run(
}
else
{
skipColumns = std::min(skipColumns,cols);
skipColumns = (std::min)(skipColumns,cols);
// note that the skiped columns are processed later.
}
@ -386,7 +386,7 @@ EIGEN_DONT_INLINE static void run(
}
else
{
skipRows = std::min(skipRows,Index(rows));
skipRows = (std::min)(skipRows,Index(rows));
// note that the skiped columns are processed later.
}
eigen_internal_assert( alignmentPattern==NoneAligned

18
Eigen/src/Core/products/SelfadjointMatrixMatrix.h
View File

@ -114,7 +114,7 @@ struct symm_pack_rhs
}
// second part: diagonal block
for(Index j2=k2; j2<std::min(k2+rows,packet_cols); j2+=nr)
for(Index j2=k2; j2<(std::min)(k2+rows,packet_cols); j2+=nr)
{
// again we can split vertically in three different parts (transpose, symmetric, normal)
// transpose
@ -179,7 +179,7 @@ struct symm_pack_rhs
for(Index j2=packet_cols; j2<cols; ++j2)
{
// transpose
Index half = std::min(end_k,j2);
Index half = (std::min)(end_k,j2);
for(Index k=k2; k<half; k++)
{
blockB[count] = conj(rhs(j2,k));
@ -261,7 +261,7 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs
Index nc = cols; // cache block size along the N direction
computeProductBlockingSizes<Scalar,Scalar>(kc, mc, nc);
// kc must smaller than mc
kc = std::min(kc,mc);
kc = (std::min)(kc,mc);
std::size_t sizeW = kc*Traits::WorkSpaceFactor;
std::size_t sizeB = sizeW + kc*cols;
@ -276,7 +276,7 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs
for(Index k2=0; k2<size; k2+=kc)
{
const Index actual_kc = std::min(k2+kc,size)-k2;
const Index actual_kc = (std::min)(k2+kc,size)-k2;
// we have selected one row panel of rhs and one column panel of lhs
// pack rhs's panel into a sequential chunk of memory
@ -289,7 +289,7 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs
// 3 - the panel below the diagonal block => generic packed copy
for(Index i2=0; i2<k2; i2+=mc)
{
const Index actual_mc = std::min(i2+mc,k2)-i2;
const Index actual_mc = (std::min)(i2+mc,k2)-i2;
// transposed packed copy
pack_lhs_transposed(blockA, &lhs(k2, i2), lhsStride, actual_kc, actual_mc);
@ -297,7 +297,7 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs
}
// the block diagonal
{
const Index actual_mc = std::min(k2+kc,size)-k2;
const Index actual_mc = (std::min)(k2+kc,size)-k2;
// symmetric packed copy
pack_lhs(blockA, &lhs(k2,k2), lhsStride, actual_kc, actual_mc);
@ -306,7 +306,7 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs
for(Index i2=k2+kc; i2<size; i2+=mc)
{
const Index actual_mc = std::min(i2+mc,size)-i2;
const Index actual_mc = (std::min)(i2+mc,size)-i2;
gemm_pack_lhs<Scalar, Index, Traits::mr, Traits::LhsProgress, LhsStorageOrder,false>()
(blockA, &lhs(i2, k2), lhsStride, actual_kc, actual_mc);
@ -352,14 +352,14 @@ struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,false,ConjugateLh
for(Index k2=0; k2<size; k2+=kc)
{
const Index actual_kc = std::min(k2+kc,size)-k2;
const Index actual_kc = (std::min)(k2+kc,size)-k2;
pack_rhs(blockB, _rhs, rhsStride, actual_kc, cols, k2);
// => GEPP
for(Index i2=0; i2<rows; i2+=mc)
{
const Index actual_mc = std::min(i2+mc,rows)-i2;
const Index actual_mc = (std::min)(i2+mc,rows)-i2;
pack_lhs(blockA, &lhs(i2, k2), lhsStride, actual_kc, actual_mc);
gebp_kernel(res+i2, resStride, blockA, blockB, actual_mc, actual_kc, cols, alpha);

2
Eigen/src/Core/products/SelfadjointMatrixVector.h
View File

@ -70,7 +70,7 @@ static EIGEN_DONT_INLINE void product_selfadjoint_vector(
rhs[i] = *it;
}
Index bound = std::max(Index(0),size-8) & 0xfffffffe;
Index bound = (std::max)(Index(0),size-8) & 0xfffffffe;
if (FirstTriangular)
bound = size - bound;

16
Eigen/src/Core/products/TriangularMatrixMatrix.h
View File

@ -112,7 +112,7 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,true,
Scalar alpha)
{
// strip zeros
Index diagSize = std::min(_rows,_depth);
Index diagSize = (std::min)(_rows,_depth);
Index rows = IsLower ? _rows : diagSize;
Index depth = IsLower ? diagSize : _depth;
Index cols = _cols;
@ -145,7 +145,7 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,true,
IsLower ? k2>0 : k2<depth;
IsLower ? k2-=kc : k2+=kc)
{
Index actual_kc = std::min(IsLower ? k2 : depth-k2, kc);
Index actual_kc = (std::min)(IsLower ? k2 : depth-k2, kc);
Index actual_k2 = IsLower ? k2-actual_kc : k2;
// align blocks with the end of the triangular part for trapezoidal lhs
@ -203,10 +203,10 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,true,
// the part below (lower case) or above (upper case) the diagonal => GEPP
{
Index start = IsLower ? k2 : 0;
Index end = IsLower ? rows : std::min(actual_k2,rows);
Index end = IsLower ? rows : (std::min)(actual_k2,rows);
for(Index i2=start; i2<end; i2+=mc)
{
const Index actual_mc = std::min(i2+mc,end)-i2;
const Index actual_mc = (std::min)(i2+mc,end)-i2;
gemm_pack_lhs<Scalar, Index, Traits::mr,Traits::LhsProgress, LhsStorageOrder,false>()
(blockA, &lhs(i2, actual_k2), lhsStride, actual_kc, actual_mc);
@ -240,7 +240,7 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,false,
Scalar alpha)
{
// strip zeros
Index diagSize = std::min(_cols,_depth);
Index diagSize = (std::min)(_cols,_depth);
Index rows = _rows;
Index depth = IsLower ? _depth : diagSize;
Index cols = IsLower ? diagSize : _cols;
@ -275,7 +275,7 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,false,
IsLower ? k2<depth : k2>0;
IsLower ? k2+=kc : k2-=kc)
{
Index actual_kc = std::min(IsLower ? depth-k2 : k2, kc);
Index actual_kc = (std::min)(IsLower ? depth-k2 : k2, kc);
Index actual_k2 = IsLower ? k2 : k2-actual_kc;
// align blocks with the end of the triangular part for trapezoidal rhs
@ -286,7 +286,7 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,false,
}
// remaining size
Index rs = IsLower ? std::min(cols,actual_k2) : cols - k2;
Index rs = IsLower ? (std::min)(cols,actual_k2) : cols - k2;
// size of the triangular part
Index ts = (IsLower && actual_k2>=cols) ? 0 : actual_kc;
@ -327,7 +327,7 @@ struct product_triangular_matrix_matrix<Scalar,Index,Mode,false,
for (Index i2=0; i2<rows; i2+=mc)
{
const Index actual_mc = std::min(mc,rows-i2);
const Index actual_mc = (std::min)(mc,rows-i2);
pack_lhs(blockA, &lhs(i2, actual_k2), lhsStride, actual_kc, actual_mc);
// triangular kernel

4
Eigen/src/Core/products/TriangularMatrixVector.h
View File

@ -56,7 +56,7 @@ struct product_triangular_matrix_vector<Index,Mode,LhsScalar,ConjLhs,RhsScalar,C
for (Index pi=0; pi<cols; pi+=PanelWidth)
{
Index actualPanelWidth = std::min(PanelWidth, cols-pi);
Index actualPanelWidth = (std::min)(PanelWidth, cols-pi);
for (Index k=0; k<actualPanelWidth; ++k)
{
Index i = pi + k;
@ -107,7 +107,7 @@ struct product_triangular_matrix_vector<Index,Mode,LhsScalar,ConjLhs,RhsScalar,C
for (Index pi=0; pi<cols; pi+=PanelWidth)
{
Index actualPanelWidth = std::min(PanelWidth, cols-pi);
Index actualPanelWidth = (std::min)(PanelWidth, cols-pi);
for (Index k=0; k<actualPanelWidth; ++k)
{
Index i = pi + k;

8
Eigen/src/Core/products/TriangularSolverMatrix.h
View File

@ -85,7 +85,7 @@ struct triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageO
IsLower ? k2<size : k2>0;
IsLower ? k2+=kc : k2-=kc)
{
const Index actual_kc = std::min(IsLower ? size-k2 : k2, kc);
const Index actual_kc = (std::min)(IsLower ? size-k2 : k2, kc);
// We have selected and packed a big horizontal panel R1 of rhs. Let B be the packed copy of this panel,
// and R2 the remaining part of rhs. The corresponding vertical panel of lhs is split into
@ -164,7 +164,7 @@ struct triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageO
Index end = IsLower ? size : k2-kc;
for(Index i2=start; i2<end; i2+=mc)
{
const Index actual_mc = std::min(mc,end-i2);
const Index actual_mc = (std::min)(mc,end-i2);
if (actual_mc>0)
{
pack_lhs(blockA, &tri(i2, IsLower ? k2 : k2-kc), triStride, actual_kc, actual_mc);
@ -222,7 +222,7 @@ struct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorage
IsLower ? k2>0 : k2<size;
IsLower ? k2-=kc : k2+=kc)
{
const Index actual_kc = std::min(IsLower ? k2 : size-k2, kc);
const Index actual_kc = (std::min)(IsLower ? k2 : size-k2, kc);
Index actual_k2 = IsLower ? k2-actual_kc : k2 ;
Index startPanel = IsLower ? 0 : k2+actual_kc;
@ -251,7 +251,7 @@ struct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorage
for(Index i2=0; i2<rows; i2+=mc)
{
const Index actual_mc = std::min(mc,rows-i2);
const Index actual_mc = (std::min)(mc,rows-i2);
// triangular solver kernel
{

4
Eigen/src/Core/products/TriangularSolverVector.h
View File

@ -60,7 +60,7 @@ struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft, Mode, Con
IsLower ? pi<size : pi>0;
IsLower ? pi+=PanelWidth : pi-=PanelWidth)
{
Index actualPanelWidth = std::min(IsLower ? size - pi : pi, PanelWidth);
Index actualPanelWidth = (std::min)(IsLower ? size - pi : pi, PanelWidth);
Index r = IsLower ? pi : size - pi; // remaining size
if (r > 0)
@ -114,7 +114,7 @@ struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft, Mode, Con
IsLower ? pi<size : pi>0;
IsLower ? pi+=PanelWidth : pi-=PanelWidth)
{
Index actualPanelWidth = std::min(IsLower ? size - pi : pi, PanelWidth);
Index actualPanelWidth = (std::min)(IsLower ? size - pi : pi, PanelWidth);
Index startBlock = IsLower ? pi : pi-actualPanelWidth;
Index endBlock = IsLower ? pi + actualPanelWidth : 0;

2
Eigen/src/Core/util/Macros.h
View File

@ -399,7 +399,7 @@
#define EIGEN_MAKE_CWISE_BINARY_OP(METHOD,FUNCTOR) \
template<typename OtherDerived> \
EIGEN_STRONG_INLINE const CwiseBinaryOp<FUNCTOR<Scalar>, const Derived, const OtherDerived> \
METHOD(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
(METHOD)(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
{ \
return CwiseBinaryOp<FUNCTOR<Scalar>, const Derived, const OtherDerived>(derived(), other.derived()); \
}

6
Eigen/src/Core/util/Memory.h
View File

@ -156,7 +156,7 @@ inline void* generic_aligned_realloc(void* ptr, size_t size, size_t old_size)
if (ptr != 0)
{
std::memcpy(newptr, ptr, std::min(size,old_size));
std::memcpy(newptr, ptr, (std::min)(size,old_size));
aligned_free(ptr);
}
@ -663,7 +663,7 @@ public:
size_type max_size() const throw()
{
return std::numeric_limits<size_type>::max();
return (std::numeric_limits<size_type>::max)();
}
pointer allocate( size_type num, const void* hint = 0 )
@ -903,7 +903,7 @@ inline int queryTopLevelCacheSize()
{
int l1, l2(-1), l3(-1);
queryCacheSizes(l1,l2,l3);
return std::max(l2,l3);
return (std::max)(l2,l3);
}
} // end namespace internal

4
Eigen/src/Eigen2Support/Cwise.h
View File

@ -84,11 +84,11 @@ template<typename ExpressionType> class Cwise
template<typename OtherDerived>
const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_min_op)
min(const MatrixBase<OtherDerived> &other) const;
(min)(const MatrixBase<OtherDerived> &other) const;
template<typename OtherDerived>
const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_max_op)
max(const MatrixBase<OtherDerived> &other) const;
(max)(const MatrixBase<OtherDerived> &other) const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_abs_op) abs() const;
const EIGEN_CWISE_UNOP_RETURN_TYPE(internal::scalar_abs2_op) abs2() const;

4
Eigen/src/Eigen2Support/CwiseOperators.h
View File

@ -100,7 +100,7 @@ inline ExpressionType& Cwise<ExpressionType>::operator/=(const MatrixBase<OtherD
template<typename ExpressionType>
template<typename OtherDerived>
EIGEN_STRONG_INLINE const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_min_op)
Cwise<ExpressionType>::min(const MatrixBase<OtherDerived> &other) const
(Cwise<ExpressionType>::min)(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_min_op)(_expression(), other.derived());
}
@ -109,7 +109,7 @@ Cwise<ExpressionType>::min(const MatrixBase<OtherDerived> &other) const
template<typename ExpressionType>
template<typename OtherDerived>
EIGEN_STRONG_INLINE const EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_max_op)
Cwise<ExpressionType>::max(const MatrixBase<OtherDerived> &other) const
(Cwise<ExpressionType>::max)(const MatrixBase<OtherDerived> &other) const
{
return EIGEN_CWISE_BINOP_RETURN_TYPE(internal::scalar_max_op)(_expression(), other.derived());
}

30
Eigen/src/Eigen2Support/Geometry/AlignedBox.h
View File

@ -51,14 +51,14 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==
{ if (AmbientDimAtCompileTime!=Dynamic) setNull(); }
/** Constructs a null box with \a _dim the dimension of the ambient space. */
inline explicit AlignedBox(int _dim) : m_min(_dim), m_max(_dim)
inline explicit AlignedBox(int _dim) : m_(min)(_dim), m_(max)(_dim)
{ setNull(); }
/** Constructs a box with extremities \a _min and \a _max. */
inline AlignedBox(const VectorType& _min, const VectorType& _max) : m_min(_min), m_max(_max) {}
inline AlignedBox(const VectorType& _min, const VectorType& _max) : m_(min)(_min), m_(max)(_max) {}
/** Constructs a box containing a single point \a p. */
inline explicit AlignedBox(const VectorType& p) : m_min(p), m_max(p) {}
inline explicit AlignedBox(const VectorType& p) : m_(min)(p), m_(max)(p) {}
~AlignedBox() {}
@ -71,18 +71,18 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==
/** Makes \c *this a null/empty box. */
inline void setNull()
{
m_min.setConstant( std::numeric_limits<Scalar>::max());
m_max.setConstant(-std::numeric_limits<Scalar>::max());
m_min.setConstant( std::numeric_limits<Scalar>::(max)());
m_max.setConstant(-std::numeric_limits<Scalar>::(max)());
}
/** \returns the minimal corner */
inline const VectorType& min() const { return m_min; }
inline const VectorType& (min)() const { return m_min; }
/** \returns a non const reference to the minimal corner */
inline VectorType& min() { return m_min; }
inline VectorType& (min)() { return m_min; }
/** \returns the maximal corner */
inline const VectorType& max() const { return m_max; }
inline const VectorType& (max)() const { return m_max; }
/** \returns a non const reference to the maximal corner */
inline VectorType& max() { return m_max; }
inline VectorType& (max)() { return m_max; }
/** \returns true if the point \a p is inside the box \c *this. */
inline bool contains(const VectorType& p) const
@ -90,19 +90,19 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==
/** \returns true if the box \a b is entirely inside the box \c *this. */
inline bool contains(const AlignedBox& b) const
{ return (m_min.cwise()<=b.min()).all() && (b.max().cwise()<=m_max).all(); }
{ return (m_min.cwise()<=b.(min)()).all() && (b.(max)().cwise()<=m_max).all(); }
/** Extends \c *this such that it contains the point \a p and returns a reference to \c *this. */
inline AlignedBox& extend(const VectorType& p)
{ m_min = m_min.cwise().min(p); m_max = m_max.cwise().max(p); return *this; }
{ m_min = m_min.cwise().(min)(p); m_max = m_max.cwise().(max)(p); return *this; }
/** Extends \c *this such that it contains the box \a b and returns a reference to \c *this. */
inline AlignedBox& extend(const AlignedBox& b)
{ m_min = m_min.cwise().min(b.m_min); m_max = m_max.cwise().max(b.m_max); return *this; }
{ m_min = m_min.cwise().(min)(b.m_min); m_max = m_max.cwise().(max)(b.m_max); return *this; }
/** Clamps \c *this by the box \a b and returns a reference to \c *this. */
inline AlignedBox& clamp(const AlignedBox& b)
{ m_min = m_min.cwise().max(b.m_min); m_max = m_max.cwise().min(b.m_max); return *this; }
{ m_min = m_min.cwise().(max)(b.m_min); m_max = m_max.cwise().(min)(b.m_max); return *this; }
/** Translate \c *this by the vector \a t and returns a reference to \c *this. */
inline AlignedBox& translate(const VectorType& t)
@ -138,8 +138,8 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim==
template<typename OtherScalarType>
inline explicit AlignedBox(const AlignedBox<OtherScalarType,AmbientDimAtCompileTime>& other)
{
m_min = other.min().template cast<Scalar>();
m_max = other.max().template cast<Scalar>();
m_min = other.(min)().template cast<Scalar>();
m_max = other.(max)().template cast<Scalar>();
}
/** \returns \c true if \c *this is approximately equal to \a other, within the precision

18
Eigen/src/Eigen2Support/SVD.h
View File

@ -64,9 +64,9 @@ template<typename MatrixType> class SVD
SVD() {} // a user who relied on compiler-generated default compiler reported problems with MSVC in 2.0.7
SVD(const MatrixType& matrix)
: m_matU(matrix.rows(), std::min(matrix.rows(), matrix.cols())),
: m_matU(matrix.rows(), (std::min)(matrix.rows(), matrix.cols())),
m_matV(matrix.cols(),matrix.cols()),
m_sigma(std::min(matrix.rows(),matrix.cols()))
m_sigma((std::min)(matrix.rows(),matrix.cols()))
{
compute(matrix);
}
@ -108,13 +108,13 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
{
const int m = matrix.rows();
const int n = matrix.cols();
const int nu = std::min(m,n);
const int nu = (std::min)(m,n);
ei_assert(m>=n && "In Eigen 2.0, SVD only works for MxN matrices with M>=N. Sorry!");
ei_assert(m>1 && "In Eigen 2.0, SVD doesn't work on 1x1 matrices");
m_matU.resize(m, nu);
m_matU.setZero();
m_sigma.resize(std::min(m,n));
m_sigma.resize((std::min)(m,n));
m_matV.resize(n,n);
RowVector e(n);
@ -126,9 +126,9 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
// Reduce A to bidiagonal form, storing the diagonal elements
// in s and the super-diagonal elements in e.
int nct = std::min(m-1,n);
int nrt = std::max(0,std::min(n-2,m));
for (k = 0; k < std::max(nct,nrt); ++k)
int nct = (std::min)(m-1,n);
int nrt = (std::max)(0,(std::min)(n-2,m));
for (k = 0; k < (std::max)(nct,nrt); ++k)
{
if (k < nct)
{
@ -193,7 +193,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
// Set up the final bidiagonal matrix or order p.
int p = std::min(n,m+1);
int p = (std::min)(n,m+1);
if (nct < n)
m_sigma[nct] = matA(nct,nct);
if (m < p)
@ -380,7 +380,7 @@ void SVD<MatrixType>::compute(const MatrixType& matrix)
case 3:
{
// Calculate the shift.
Scalar scale = std::max(std::max(std::max(std::max(
Scalar scale = (std::max)((std::max)((std::max)((std::max)(
ei_abs(m_sigma[p-1]),ei_abs(m_sigma[p-2])),ei_abs(e[p-2])),
ei_abs(m_sigma[k])),ei_abs(e[k]));
Scalar sp = m_sigma[p-1]/scale;

4
Eigen/src/Eigenvalues/ComplexSchur.h
View File

@ -423,7 +423,7 @@ void ComplexSchur<MatrixType>::reduceToTriangularForm(bool computeU)
JacobiRotation<ComplexScalar> rot;
rot.makeGivens(m_matT.coeff(il,il) - shift, m_matT.coeff(il+1,il));
m_matT.rightCols(m_matT.cols()-il).applyOnTheLeft(il, il+1, rot.adjoint());
m_matT.topRows(std::min(il+2,iu)+1).applyOnTheRight(il, il+1, rot);
m_matT.topRows((std::min)(il+2,iu)+1).applyOnTheRight(il, il+1, rot);
if(computeU) m_matU.applyOnTheRight(il, il+1, rot);
for(Index i=il+1 ; i<iu ; i++)
@ -431,7 +431,7 @@ void ComplexSchur<MatrixType>::reduceToTriangularForm(bool computeU)
rot.makeGivens(m_matT.coeffRef(i,i-1), m_matT.coeffRef(i+1,i-1), &m_matT.coeffRef(i,i-1));
m_matT.coeffRef(i+1,i-1) = ComplexScalar(0);
m_matT.rightCols(m_matT.cols()-i).applyOnTheLeft(i, i+1, rot.adjoint());
m_matT.topRows(std::min(i+2,iu)+1).applyOnTheRight(i, i+1, rot);
m_matT.topRows((std::min)(i+2,iu)+1).applyOnTheRight(i, i+1, rot);
if(computeU) m_matU.applyOnTheRight(i, i+1, rot);
}
}

4
Eigen/src/Eigenvalues/EigenSolver.h
View File

@ -435,7 +435,7 @@ void EigenSolver<MatrixType>::doComputeEigenvectors()
Scalar norm = 0.0;
for (Index j = 0; j < size; ++j)
{
norm += m_matT.row(j).segment(std::max(j-1,Index(0)), size-std::max(j-1,Index(0))).cwiseAbs().sum();
norm += m_matT.row(j).segment((std::max)(j-1,Index(0)), size-(std::max)(j-1,Index(0))).cwiseAbs().sum();
}
// Backsubstitute to find vectors of upper triangular form
@ -564,7 +564,7 @@ void EigenSolver<MatrixType>::doComputeEigenvectors()
// Overflow control
using std::max;
Scalar t = max(internal::abs(m_matT.coeff(i,n-1)),internal::abs(m_matT.coeff(i,n)));
Scalar t = (max)(internal::abs(m_matT.coeff(i,n-1)),internal::abs(m_matT.coeff(i,n)));
if ((eps * t) * t > Scalar(1))
m_matT.block(i, n-1, size-i, 2) /= t;

4
Eigen/src/Eigenvalues/RealSchur.h
View File

@ -290,7 +290,7 @@ inline typename MatrixType::Scalar RealSchur<MatrixType>::computeNormOfT()
// + m_matT.bottomLeftCorner(size-1,size-1).diagonal().cwiseAbs().sum();
Scalar norm = 0.0;
for (Index j = 0; j < size; ++j)
norm += m_matT.row(j).segment(std::max(j-1,Index(0)), size-std::max(j-1,Index(0))).cwiseAbs().sum();
norm += m_matT.row(j).segment((std::max)(j-1,Index(0)), size-(std::max)(j-1,Index(0))).cwiseAbs().sum();
return norm;
}
@ -442,7 +442,7 @@ inline void RealSchur<MatrixType>::performFrancisQRStep(Index il, Index im, Inde
// These Householder transformations form the O(n^3) part of the algorithm
m_matT.block(k, k, 3, size-k).applyHouseholderOnTheLeft(ess, tau, workspace);
m_matT.block(0, k, std::min(iu,k+3) + 1, 3).applyHouseholderOnTheRight(ess, tau, workspace);
m_matT.block(0, k, (std::min)(iu,k+3) + 1, 3).applyHouseholderOnTheRight(ess, tau, workspace);
if (computeU)
m_matU.block(0, k, size, 3).applyHouseholderOnTheRight(ess, tau, workspace);
}

14
Eigen/src/Geometry/AlignedBox.h
View File

@ -111,13 +111,13 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
}
/** \returns the minimal corner */
inline const VectorType& min() const { return m_min; }
inline const VectorType& (min)() const { return m_min; }
/** \returns a non const reference to the minimal corner */
inline VectorType& min() { return m_min; }
inline VectorType& (min)() { return m_min; }
/** \returns the maximal corner */
inline const VectorType& max() const { return m_max; }
inline const VectorType& (max)() const { return m_max; }
/** \returns a non const reference to the maximal corner */
inline VectorType& max() { return m_max; }
inline VectorType& (max)() { return m_max; }
/** \returns the center of the box */
inline const CwiseUnaryOp<internal::scalar_quotient1_op<Scalar>,
@ -196,7 +196,7 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
/** \returns true if the box \a b is entirely inside the box \c *this. */
inline bool contains(const AlignedBox& b) const
{ return (m_min.array()<=b.min().array()).all() && (b.max().array()<=m_max.array()).all(); }
{ return (m_min.array()<=(b.min)().array()).all() && ((b.max)().array()<=m_max.array()).all(); }
/** Extends \c *this such that it contains the point \a p and returns a reference to \c *this. */
template<typename Derived>
@ -287,8 +287,8 @@ EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(_Scalar,_AmbientDim)
template<typename OtherScalarType>
inline explicit AlignedBox(const AlignedBox<OtherScalarType,AmbientDimAtCompileTime>& other)
{
m_min = other.min().template cast<Scalar>();
m_max = other.max().template cast<Scalar>();
m_min = (other.min)().template cast<Scalar>();
m_max = (other.max)().template cast<Scalar>();
}
/** \returns \c true if \c *this is approximately equal to \a other, within the precision

2
Eigen/src/Geometry/AngleAxis.h
View File

@ -182,7 +182,7 @@ AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const QuaternionBase<QuatDerived
}
else
{
m_angle = Scalar(2)*acos(min(max(Scalar(-1),q.w()),Scalar(1)));
m_angle = Scalar(2)*acos((min)((max)(Scalar(-1),q.w()),Scalar(1)));
m_axis = q.vec() / internal::sqrt(n2);
}
return *this;

4
Eigen/src/LU/FullPivLU.h
View File

@ -533,7 +533,7 @@ template<typename MatrixType>
MatrixType FullPivLU<MatrixType>::reconstructedMatrix() const
{
eigen_assert(m_isInitialized && "LU is not initialized.");
const Index smalldim = std::min(m_lu.rows(), m_lu.cols());
const Index smalldim = (std::min)(m_lu.rows(), m_lu.cols());
// LU
MatrixType res(m_lu.rows(),m_lu.cols());
// FIXME the .toDenseMatrix() should not be needed...
@ -695,7 +695,7 @@ struct solve_retval<FullPivLU<_MatrixType>, Rhs>
const Index rows = dec().rows(), cols = dec().cols(),
nonzero_pivots = dec().nonzeroPivots();
eigen_assert(rhs().rows() == rows);
const Index smalldim = std::min(rows, cols);
const Index smalldim = (std::min)(rows, cols);
if(nonzero_pivots == 0)
{

8
Eigen/src/LU/PartialPivLU.h
View File

@ -253,7 +253,7 @@ struct partial_lu_impl
{
const Index rows = lu.rows();
const Index cols = lu.cols();
const Index size = std::min(rows,cols);
const Index size = (std::min)(rows,cols);
nb_transpositions = 0;
int first_zero_pivot = -1;
for(Index k = 0; k < size; ++k)
@ -313,7 +313,7 @@ struct partial_lu_impl
MapLU lu1(lu_data,StorageOrder==RowMajor?rows:luStride,StorageOrder==RowMajor?luStride:cols);
MatrixType lu(lu1,0,0,rows,cols);
const Index size = std::min(rows,cols);
const Index size = (std::min)(rows,cols);
// if the matrix is too small, no blocking:
if(size<=16)
@ -327,14 +327,14 @@ struct partial_lu_impl
{
blockSize = size/8;
blockSize = (blockSize/16)*16;
blockSize = std::min(std::max(blockSize,Index(8)), maxBlockSize);
blockSize = (std::min)((std::max)(blockSize,Index(8)), maxBlockSize);
}
nb_transpositions = 0;
int first_zero_pivot = -1;
for(Index k = 0; k < size; k+=blockSize)
{
Index bs = std::min(size-k,blockSize); // actual size of the block
Index bs = (std::min)(size-k,blockSize); // actual size of the block
Index trows = rows - k - bs; // trailing rows
Index tsize = size - k - bs; // trailing size

4
Eigen/src/QR/ColPivHouseholderQR.h
View File

@ -93,7 +93,7 @@ template<typename _MatrixType> class ColPivHouseholderQR
*/
ColPivHouseholderQR(Index rows, Index cols)
: m_qr(rows, cols),
m_hCoeffs(std::min(rows,cols)),
m_hCoeffs((std::min)(rows,cols)),
m_colsPermutation(cols),
m_colsTranspositions(cols),
m_temp(cols),
@ -103,7 +103,7 @@ template<typename _MatrixType> class ColPivHouseholderQR
ColPivHouseholderQR(const MatrixType& matrix)
: m_qr(matrix.rows(), matrix.cols()),
m_hCoeffs(std::min(matrix.rows(),matrix.cols())),
m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
m_colsPermutation(matrix.cols()),
m_colsTranspositions(matrix.cols()),
m_temp(matrix.cols()),

14
Eigen/src/QR/FullPivHouseholderQR.h
View File

@ -93,21 +93,21 @@ template<typename _MatrixType> class FullPivHouseholderQR
*/
FullPivHouseholderQR(Index rows, Index cols)
: m_qr(rows, cols),
m_hCoeffs(std::min(rows,cols)),
m_hCoeffs((std::min)(rows,cols)),
m_rows_transpositions(rows),
m_cols_transpositions(cols),
m_cols_permutation(cols),
m_temp(std::min(rows,cols)),
m_temp((std::min)(rows,cols)),
m_isInitialized(false),
m_usePrescribedThreshold(false) {}
FullPivHouseholderQR(const MatrixType& matrix)
: m_qr(matrix.rows(), matrix.cols()),
m_hCoeffs(std::min(matrix.rows(), matrix.cols())),
m_hCoeffs((std::min)(matrix.rows(), matrix.cols())),
m_rows_transpositions(matrix.rows()),
m_cols_transpositions(matrix.cols()),
m_cols_permutation(matrix.cols()),
m_temp(std::min(matrix.rows(), matrix.cols())),
m_temp((std::min)(matrix.rows(), matrix.cols())),
m_isInitialized(false),
m_usePrescribedThreshold(false)
{
@ -379,7 +379,7 @@ FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(cons
{
Index rows = matrix.rows();
Index cols = matrix.cols();
Index size = std::min(rows,cols);
Index size = (std::min)(rows,cols);
m_qr = matrix;
m_hCoeffs.resize(size);
@ -493,7 +493,7 @@ struct solve_retval<FullPivHouseholderQR<_MatrixType>, Rhs>
RealScalar biggest_in_upper_part_of_c = c.topRows( dec().rank() ).cwiseAbs().maxCoeff();
RealScalar biggest_in_lower_part_of_c = c.bottomRows(rows-dec().rank()).cwiseAbs().maxCoeff();
// FIXME brain dead
const RealScalar m_precision = NumTraits<Scalar>::epsilon() * std::min(rows,cols);
const RealScalar m_precision = NumTraits<Scalar>::epsilon() * (std::min)(rows,cols);
// this internal:: prefix is needed by at least gcc 3.4 and ICC
if(!internal::isMuchSmallerThan(biggest_in_lower_part_of_c, biggest_in_upper_part_of_c, m_precision))
return;
@ -520,7 +520,7 @@ typename FullPivHouseholderQR<MatrixType>::MatrixQType FullPivHouseholderQR<Matr
// and v_k is the k-th Householder vector [1,m_qr(k+1,k), m_qr(k+2,k), ...]
Index rows = m_qr.rows();
Index cols = m_qr.cols();
Index size = std::min(rows,cols);
Index size = (std::min)(rows,cols);
MatrixQType res = MatrixQType::Identity(rows, rows);
Matrix<Scalar,1,MatrixType::RowsAtCompileTime> temp(rows);
for (Index k = size-1; k >= 0; k--)

16
Eigen/src/QR/HouseholderQR.h
View File

@ -88,13 +88,13 @@ template<typename _MatrixType> class HouseholderQR
*/
HouseholderQR(Index rows, Index cols)
: m_qr(rows, cols),
m_hCoeffs(std::min(rows,cols)),
m_hCoeffs((std::min)(rows,cols)),
m_temp(cols),
m_isInitialized(false) {}
HouseholderQR(const MatrixType& matrix)
: m_qr(matrix.rows(), matrix.cols()),
m_hCoeffs(std::min(matrix.rows(),matrix.cols())),
m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
m_temp(matrix.cols()),
m_isInitialized(false)
{
@ -210,7 +210,7 @@ void householder_qr_inplace_unblocked(MatrixQR& mat, HCoeffs& hCoeffs, typename
typedef typename MatrixQR::RealScalar RealScalar;
Index rows = mat.rows();
Index cols = mat.cols();
Index size = std::min(rows,cols);
Index size = (std::min)(rows,cols);
eigen_assert(hCoeffs.size() == size);
@ -250,7 +250,7 @@ void householder_qr_inplace_blocked(MatrixQR& mat, HCoeffs& hCoeffs,
Index rows = mat.rows();
Index cols = mat.cols();
Index size = std::min(rows, cols);
Index size = (std::min)(rows, cols);
typedef Matrix<Scalar,Dynamic,1,ColMajor,MatrixQR::MaxColsAtCompileTime,1> TempType;
TempType tempVector;
@ -260,12 +260,12 @@ void householder_qr_inplace_blocked(MatrixQR& mat, HCoeffs& hCoeffs,
tempData = tempVector.data();
}
Index blockSize = std::min(maxBlockSize,size);
Index blockSize = (std::min)(maxBlockSize,size);
int k = 0;
for (k = 0; k < size; k += blockSize)
{
Index bs = std::min(size-k,blockSize); // actual size of the block
Index bs = (std::min)(size-k,blockSize); // actual size of the block
Index tcols = cols - k - bs; // trailing columns
Index brows = rows-k; // rows of the block
@ -299,7 +299,7 @@ struct solve_retval<HouseholderQR<_MatrixType>, Rhs>
template<typename Dest> void evalTo(Dest& dst) const
{
const Index rows = dec().rows(), cols = dec().cols();
const Index rank = std::min(rows, cols);
const Index rank = (std::min)(rows, cols);
eigen_assert(rhs().rows() == rows);
typename Rhs::PlainObject c(rhs());
@ -327,7 +327,7 @@ HouseholderQR<MatrixType>& HouseholderQR<MatrixType>::compute(const MatrixType&
{
Index rows = matrix.rows();
Index cols = matrix.cols();
Index size = std::min(rows,cols);
Index size = (std::min)(rows,cols);
m_qr = matrix;
m_hCoeffs.resize(size);

8
Eigen/src/SVD/JacobiSVD.h
View File

@ -569,7 +569,7 @@ void JacobiSVD<MatrixType, QRPreconditioner>::allocate(Index rows, Index cols, u
"JacobiSVD: can't compute thin U or thin V with the FullPivHouseholderQR preconditioner. "
"Use the ColPivHouseholderQR preconditioner instead.");
}
m_diagSize = std::min(m_rows, m_cols);
m_diagSize = (std::min)(m_rows, m_cols);
m_singularValues.resize(m_diagSize);
m_matrixU.resize(m_rows, m_computeFullU ? m_rows
: m_computeThinU ? m_diagSize
@ -619,8 +619,8 @@ JacobiSVD<MatrixType, QRPreconditioner>::compute(const MatrixType& matrix, unsig
// notice that this comparison will evaluate to false if any NaN is involved, ensuring that NaN's don't
// keep us iterating forever.
using std::max;
if(max(internal::abs(m_workMatrix.coeff(p,q)),internal::abs(m_workMatrix.coeff(q,p)))
> max(internal::abs(m_workMatrix.coeff(p,p)),internal::abs(m_workMatrix.coeff(q,q)))*precision)
if((max)(internal::abs(m_workMatrix.coeff(p,q)),internal::abs(m_workMatrix.coeff(q,p)))
> (max)(internal::abs(m_workMatrix.coeff(p,p)),internal::abs(m_workMatrix.coeff(q,q)))*precision)
{
finished = false;
@ -689,7 +689,7 @@ struct solve_retval<JacobiSVD<_MatrixType, QRPreconditioner>, Rhs>
// A = U S V^*
// So A^{-1} = V S^{-1} U^*
Index diagSize = std::min(dec().rows(), dec().cols());
Index diagSize = (std::min)(dec().rows(), dec().cols());
typename JacobiSVDType::SingularValuesType invertedSingVals(diagSize);
Index nonzeroSingVals = dec().nonzeroSingularValues();

2
Eigen/src/Sparse/AmbiVector.h
View File

@ -97,7 +97,7 @@ class AmbiVector
void reallocateSparse()
{
Index copyElements = m_allocatedElements;
m_allocatedElements = std::min(Index(m_allocatedElements*1.5),m_size);
m_allocatedElements = (std::min)(Index(m_allocatedElements*1.5),m_size);
Index allocSize = m_allocatedElements * sizeof(ListEl);
allocSize = allocSize/sizeof(Scalar) + (allocSize%sizeof(Scalar)>0?1:0);
Scalar* newBuffer = new Scalar[allocSize];

2
Eigen/src/Sparse/CompressedStorage.h
View File

@ -216,7 +216,7 @@ class CompressedStorage
{
Scalar* newValues = new Scalar[size];
Index* newIndices = new Index[size];
size_t copySize = std::min(size, m_size);
size_t copySize = (std::min)(size, m_size);
// copy
memcpy(newValues, m_values, copySize * sizeof(Scalar));
memcpy(newIndices, m_indices, copySize * sizeof(Index));

2
Eigen/src/Sparse/DynamicSparseMatrix.h
View File

@ -141,7 +141,7 @@ class DynamicSparseMatrix
{
if (outerSize()>0)
{
Index reserveSizePerVector = std::max(reserveSize/outerSize(),Index(4));
Index reserveSizePerVector = (std::max)(reserveSize/outerSize(),Index(4));
for (Index j=0; j<outerSize(); ++j)
{
m_data[j].reserve(reserveSizePerVector);

2
Eigen/src/Sparse/SparseFuzzy.h
View File

@ -35,7 +35,7 @@
// const typename internal::nested<Derived,2>::type nested(derived());
// const typename internal::nested<OtherDerived,2>::type otherNested(other.derived());
// return (nested - otherNested).cwise().abs2().sum()
// <= prec * prec * std::min(nested.cwise().abs2().sum(), otherNested.cwise().abs2().sum());
// <= prec * prec * (std::min)(nested.cwise().abs2().sum(), otherNested.cwise().abs2().sum());
// }
#endif // EIGEN_SPARSE_FUZZY_H

2
Eigen/src/Sparse/SparseMatrix.h
View File

@ -257,7 +257,7 @@ class SparseMatrix
// furthermore we bound the realloc ratio to:
// 1) reduce multiple minor realloc when the matrix is almost filled
// 2) avoid to allocate too much memory when the matrix is almost empty
reallocRatio = std::min(std::max(reallocRatio,1.5f),8.f);
reallocRatio = (std::min)((std::max)(reallocRatio,1.5f),8.f);
}
}
m_data.resize(m_data.size()+1,reallocRatio);

4
Eigen/src/Sparse/SparseMatrixBase.h
View File

@ -223,7 +223,7 @@ template<typename Derived> class SparseMatrixBase : public EigenBase<Derived>
// thanks to shallow copies, we always eval to a tempary
Derived temp(other.rows(), other.cols());
temp.reserve(std::max(this->rows(),this->cols())*2);
temp.reserve((std::max)(this->rows(),this->cols())*2);
for (Index j=0; j<outerSize; ++j)
{
temp.startVec(j);
@ -253,7 +253,7 @@ template<typename Derived> class SparseMatrixBase : public EigenBase<Derived>
// eval without temporary
derived().resize(other.rows(), other.cols());
derived().setZero();
derived().reserve(std::max(this->rows(),this->cols())*2);
derived().reserve((std::max)(this->rows(),this->cols())*2);
for (Index j=0; j<outerSize; ++j)
{
derived().startVec(j);

6
Eigen/src/Sparse/SparseSelfAdjointView.h
View File

@ -383,7 +383,7 @@ void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixTyp
continue;
Index ip = perm ? perm[i] : i;
count[DstUpLo==Lower ? std::min(ip,jp) : std::max(ip,jp)]++;
count[DstUpLo==Lower ? (std::min)(ip,jp) : (std::max)(ip,jp)]++;
}
}
dest._outerIndexPtr()[0] = 0;
@ -403,8 +403,8 @@ void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixTyp
continue;
Index ip = perm? perm[i] : i;
Index k = count[DstUpLo==Lower ? std::min(ip,jp) : std::max(ip,jp)]++;
dest._innerIndexPtr()[k] = DstUpLo==Lower ? std::max(ip,jp) : std::min(ip,jp);
Index k = count[DstUpLo==Lower ? (std::min)(ip,jp) : (std::max)(ip,jp)]++;
dest._innerIndexPtr()[k] = DstUpLo==Lower ? (std::max)(ip,jp) : (std::min)(ip,jp);
if((DstUpLo==Lower && ip<jp) || (DstUpLo==Upper && ip>jp))
dest._valuePtr()[k] = conj(it.value());

6
Eigen/src/Sparse/SparseSparseProduct.h
View File

@ -45,7 +45,7 @@ static void sparse_product_impl2(const Lhs& lhs, const Rhs& rhs, ResultType& res
// estimate the number of non zero entries
float ratioLhs = float(lhs.nonZeros())/(float(lhs.rows())*float(lhs.cols()));
float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
float ratioRes = std::min(ratioLhs * avgNnzPerRhsColumn, 1.f);
float ratioRes = (std::min)(ratioLhs * avgNnzPerRhsColumn, 1.f);
// int t200 = rows/(log2(200)*1.39);
// int t = (rows*100)/139;
@ -131,7 +131,7 @@ static void sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
// estimate the number of non zero entries
float ratioLhs = float(lhs.nonZeros())/(float(lhs.rows())*float(lhs.cols()));
float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
float ratioRes = std::min(ratioLhs * avgNnzPerRhsColumn, 1.f);
float ratioRes = (std::min)(ratioLhs * avgNnzPerRhsColumn, 1.f);
// mimics a resizeByInnerOuter:
if(ResultType::IsRowMajor)
@ -143,7 +143,7 @@ static void sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
for (Index j=0; j<cols; ++j)
{
// let's do a more accurate determination of the nnz ratio for the current column j of res
//float ratioColRes = std::min(ratioLhs * rhs.innerNonZeros(j), 1.f);
//float ratioColRes = (std::min)(ratioLhs * rhs.innerNonZeros(j), 1.f);
// FIXME find a nice way to get the number of nonzeros of a sub matrix (here an inner vector)
float ratioColRes = ratioRes;
tempVector.init(ratioColRes);

4
test/adjoint.cpp
View File

@ -65,7 +65,7 @@ template<typename MatrixType> void adjoint(const MatrixType& m)
// check basic properties of dot, norm, norm2
typedef typename NumTraits<Scalar>::Real RealScalar;
RealScalar ref = NumTraits<Scalar>::IsInteger ? 0 : std::max((s1 * v1 + s2 * v2).norm(),v3.norm());
RealScalar ref = NumTraits<Scalar>::IsInteger ? 0 : (std::max)((s1 * v1 + s2 * v2).norm(),v3.norm());
VERIFY(test_isApproxWithRef((s1 * v1 + s2 * v2).dot(v3), internal::conj(s1) * v1.dot(v3) + internal::conj(s2) * v2.dot(v3), ref));
VERIFY(test_isApproxWithRef(v3.dot(s1 * v1 + s2 * v2), s1*v3.dot(v1)+s2*v3.dot(v2), ref));
VERIFY_IS_APPROX(internal::conj(v1.dot(v2)), v2.dot(v1));
@ -76,7 +76,7 @@ template<typename MatrixType> void adjoint(const MatrixType& m)
// check compatibility of dot and adjoint
ref = NumTraits<Scalar>::IsInteger ? 0 : std::max(std::max(v1.norm(),v2.norm()),std::max((square * v2).norm(),(square.adjoint() * v1).norm()));
ref = NumTraits<Scalar>::IsInteger ? 0 : (std::max)((std::max)(v1.norm(),v2.norm()),(std::max)((square * v2).norm(),(square.adjoint() * v1).norm()));
VERIFY(test_isApproxWithRef(v1.dot(square * v2), (square.adjoint() * v1).dot(v2), ref));
// like in testBasicStuff, test operator() to check const-qualification

2
test/bandmatrix.cpp
View File

@ -61,7 +61,7 @@ template<typename MatrixType> void bandmatrix(const MatrixType& _m)
m.col(i).setConstant(static_cast<RealScalar>(i+1));
dm1.col(i).setConstant(static_cast<RealScalar>(i+1));
}
Index d = std::min(rows,cols);
Index d = (std::min)(rows,cols);
Index a = std::max<Index>(0,cols-d-supers);
Index b = std::max<Index>(0,rows-d-subs);
if(a>0) dm1.block(0,d+supers,rows,a).setZero();

8
test/cwiseop.cpp
View File

@ -28,6 +28,14 @@
#include "main.h"
#include <functional>
#ifdef min
#undef min
#endif
#ifdef max
#undef max
#endif
using namespace std;
template<typename Scalar> struct AddIfNull {

5
test/main.h
View File

@ -23,6 +23,9 @@
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#define min(A,B) please_protect_your_min_with_parentheses
#define max(A,B) please_protect_your_max_with_parentheses
#include <cstdlib>
#include <cerrno>
#include <ctime>
@ -429,7 +432,7 @@ void createRandomPIMatrixOfRank(typename MatrixType::Index desired_rank, typenam
MatrixBType b = MatrixBType::Random(cols,cols);
// set the diagonal such that only desired_rank non-zero entries reamain
const Index diag_size = std::min(d.rows(),d.cols());
const Index diag_size = (std::min)(d.rows(),d.cols());
if(diag_size != desired_rank)
d.diagonal().segment(desired_rank, diag_size-desired_rank) = VectorType::Zero(diag_size-desired_rank);

2
unsupported/Eigen/MPRealSupport
View File

@ -148,7 +148,7 @@ int main()
inline bool isApprox(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& prec)
{
return mpfr::abs(a - b) <= mpfr::min(mpfr::abs(a), mpfr::abs(b)) * prec;
return mpfr::abs(a - b) <= (mpfr::min)(mpfr::abs(a), mpfr::abs(b)) * prec;
}
inline bool isApproxOrLessThan(const mpfr::mpreal& a, const mpfr::mpreal& b, const mpfr::mpreal& prec)

8
unsupported/Eigen/src/BVH/BVAlgorithms.h
View File

@ -178,7 +178,7 @@ typename Minimizer::Scalar minimize_helper(const BVH &tree, Minimizer &minimizer
todo.pop();
for(; oBegin != oEnd; ++oBegin) //go through child objects
minimum = std::min(minimum, minimizer.minimumOnObject(*oBegin));
minimum = (std::min)(minimum, minimizer.minimumOnObject(*oBegin));
for(; vBegin != vEnd; ++vBegin) { //go through child volumes
Scalar val = minimizer.minimumOnVolume(tree.getVolume(*vBegin));
@ -274,12 +274,12 @@ typename Minimizer::Scalar BVMinimize(const BVH1 &tree1, const BVH2 &tree2, Mini
for(; oBegin1 != oEnd1; ++oBegin1) { //go through child objects of first tree
for(oCur2 = oBegin2; oCur2 != oEnd2; ++oCur2) {//go through child objects of second tree
minimum = std::min(minimum, minimizer.minimumOnObjectObject(*oBegin1, *oCur2));
minimum = (std::min)(minimum, minimizer.minimumOnObjectObject(*oBegin1, *oCur2));
}
for(vCur2 = vBegin2; vCur2 != vEnd2; ++vCur2) { //go through child volumes of second tree
Helper2 helper(*oBegin1, minimizer);
minimum = std::min(minimum, internal::minimize_helper(tree2, helper, *vCur2, minimum));
minimum = (std::min)(minimum, internal::minimize_helper(tree2, helper, *vCur2, minimum));
}
}
@ -288,7 +288,7 @@ typename Minimizer::Scalar BVMinimize(const BVH1 &tree1, const BVH2 &tree2, Mini
for(oCur2 = oBegin2; oCur2 != oEnd2; ++oCur2) {//go through child objects of second tree
Helper1 helper(*oCur2, minimizer);
minimum = std::min(minimum, internal::minimize_helper(tree1, helper, *vBegin1, minimum));
minimum = (std::min)(minimum, internal::minimize_helper(tree1, helper, *vBegin1, minimum));
}
for(vCur2 = vBegin2; vCur2 != vEnd2; ++vCur2) { //go through child volumes of second tree

4
unsupported/Eigen/src/IterativeSolvers/ConstrainedConjGrad.h
View File

@ -172,7 +172,7 @@ void constrained_cg(const TMatrix& A, const CMatrix& C, VectorX& x,
if (iter.noiseLevel() > 0 && transition) std::cerr << "CCG: transition\n";
if (transition || iter.first()) gamma = 0.0;
else gamma = std::max(0.0, (rho - old_z.dot(z)) / rho_1);
else gamma = (std::max)(0.0, (rho - old_z.dot(z)) / rho_1);
p = z + gamma*p;
++iter;
@ -185,7 +185,7 @@ void constrained_cg(const TMatrix& A, const CMatrix& C, VectorX& x,
{
Scalar bb = C.row(i).dot(p) - f[i];
if (bb > 0.0)
lambda = std::min(lambda, (f.coeff(i)-C.row(i).dot(x)) / bb);
lambda = (std::min)(lambda, (f.coeff(i)-C.row(i).dot(x)) / bb);
}
}
x += lambda * p;

2
unsupported/Eigen/src/IterativeSolvers/IterationController.h
View File

@ -141,7 +141,7 @@ class IterationController
bool converged(double nr)
{
m_res = internal::abs(nr);
m_resminreach = std::min(m_resminreach, m_res);
m_resminreach = (std::min)(m_resminreach, m_res);
return converged();
}
template<typename VectorType> bool converged(const VectorType &v)

4
unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
View File

@ -127,10 +127,10 @@ bool MatrixFunctionAtomic<MatrixType>::taylorConverged(Index s, const MatrixType
for (Index r = 0; r < n; r++) {
RealScalar mx = 0;
for (Index i = 0; i < n; i++)
mx = std::max(mx, std::abs(m_f(m_Ashifted(i, i) + m_avgEival, static_cast<int>(s+r))));
mx = (std::max)(mx, std::abs(m_f(m_Ashifted(i, i) + m_avgEival, static_cast<int>(s+r))));
if (r != 0)
rfactorial *= RealScalar(r);
delta = std::max(delta, mx / rfactorial);
delta = (std::max)(delta, mx / rfactorial);
}
const RealScalar P_norm = P.cwiseAbs().rowwise().sum().maxCoeff();
if (m_mu * delta * P_norm < NumTraits<Scalar>::epsilon() * F_norm)

14
unsupported/Eigen/src/NonLinearOptimization/HybridNonLinearSolver.h
View File

@ -255,7 +255,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType &x)
/* on the first iteration, adjust the initial step bound. */
if (iter == 1)
delta = std::min(delta,pnorm);
delta = (std::min)(delta,pnorm);
/* evaluate the function at x + p and calculate its norm. */
if ( functor(wa2, wa4) < 0)
@ -289,7 +289,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType &x)
ncfail = 0;
++ncsuc;
if (ratio >= Scalar(.5) || ncsuc > 1)
delta = std::max(delta, pnorm / Scalar(.5));
delta = (std::max)(delta, pnorm / Scalar(.5));
if (internal::abs(ratio - 1.) <= Scalar(.1)) {
delta = pnorm / Scalar(.5);
}
@ -322,7 +322,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveOneStep(FVectorType &x)
/* tests for termination and stringent tolerances. */
if (nfev >= parameters.maxfev)
return HybridNonLinearSolverSpace::TooManyFunctionEvaluation;
if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
if (Scalar(.1) * (std::max)(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
return HybridNonLinearSolverSpace::TolTooSmall;
if (nslow2 == 5)
return HybridNonLinearSolverSpace::NotMakingProgressJacobian;
@ -449,7 +449,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
/* calculate the jacobian matrix. */
if (internal::fdjac1(functor, x, fvec, fjac, parameters.nb_of_subdiagonals, parameters.nb_of_superdiagonals, parameters.epsfcn) <0)
return HybridNonLinearSolverSpace::UserAsked;
nfev += std::min(parameters.nb_of_subdiagonals+parameters.nb_of_superdiagonals+ 1, n);
nfev += (std::min)(parameters.nb_of_subdiagonals+parameters.nb_of_superdiagonals+ 1, n);
wa2 = fjac.colwise().blueNorm();
@ -496,7 +496,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
/* on the first iteration, adjust the initial step bound. */
if (iter == 1)
delta = std::min(delta,pnorm);
delta = (std::min)(delta,pnorm);
/* evaluate the function at x + p and calculate its norm. */
if ( functor(wa2, wa4) < 0)
@ -530,7 +530,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
ncfail = 0;
++ncsuc;
if (ratio >= Scalar(.5) || ncsuc > 1)
delta = std::max(delta, pnorm / Scalar(.5));
delta = (std::max)(delta, pnorm / Scalar(.5));
if (internal::abs(ratio - 1.) <= Scalar(.1)) {
delta = pnorm / Scalar(.5);
}
@ -563,7 +563,7 @@ HybridNonLinearSolver<FunctorType,Scalar>::solveNumericalDiffOneStep(FVectorType
/* tests for termination and stringent tolerances. */
if (nfev >= parameters.maxfev)
return HybridNonLinearSolverSpace::TooManyFunctionEvaluation;
if (Scalar(.1) * std::max(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
if (Scalar(.1) * (std::max)(Scalar(.1) * delta, pnorm) <= NumTraits<Scalar>::epsilon() * xnorm)
return HybridNonLinearSolverSpace::TolTooSmall;
if (nslow2 == 5)
return HybridNonLinearSolverSpace::NotMakingProgressJacobian;

12
unsupported/Eigen/src/NonLinearOptimization/LevenbergMarquardt.h
View File

@ -263,7 +263,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x)
if (fnorm != 0.)
for (Index j = 0; j < n; ++j)
if (wa2[permutation.indices()[j]] != 0.)
gnorm = std::max(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
gnorm = (std::max)(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
/* test for convergence of the gradient norm. */
if (gnorm <= parameters.gtol)
@ -285,7 +285,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x)
/* on the first iteration, adjust the initial step bound. */
if (iter == 1)
delta = std::min(delta,pnorm);
delta = (std::min)(delta,pnorm);
/* evaluate the function at x + p and calculate its norm. */
if ( functor(wa2, wa4) < 0)
@ -321,7 +321,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOneStep(FVectorType &x)
if (Scalar(.1) * fnorm1 >= fnorm || temp < Scalar(.1))
temp = Scalar(.1);
/* Computing MIN */
delta = temp * std::min(delta, pnorm / Scalar(.1));
delta = temp * (std::min)(delta, pnorm / Scalar(.1));
par /= temp;
} else if (!(par != 0. && ratio < Scalar(.75))) {
delta = pnorm / Scalar(.5);
@ -510,7 +510,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
if (fnorm != 0.)
for (j = 0; j < n; ++j)
if (wa2[permutation.indices()[j]] != 0.)
gnorm = std::max(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
gnorm = (std::max)(gnorm, internal::abs( fjac.col(j).head(j+1).dot(qtf.head(j+1)/fnorm) / wa2[permutation.indices()[j]]));
/* test for convergence of the gradient norm. */
if (gnorm <= parameters.gtol)
@ -532,7 +532,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
/* on the first iteration, adjust the initial step bound. */
if (iter == 1)
delta = std::min(delta,pnorm);
delta = (std::min)(delta,pnorm);
/* evaluate the function at x + p and calculate its norm. */
if ( functor(wa2, wa4) < 0)
@ -568,7 +568,7 @@ LevenbergMarquardt<FunctorType,Scalar>::minimizeOptimumStorageOneStep(FVectorTyp
if (Scalar(.1) * fnorm1 >= fnorm || temp < Scalar(.1))
temp = Scalar(.1);
/* Computing MIN */
delta = temp * std::min(delta, pnorm / Scalar(.1));
delta = temp * (std::min)(delta, pnorm / Scalar(.1));
par /= temp;
} else if (!(par != 0. && ratio < Scalar(.75))) {
delta = pnorm / Scalar(.5);

2
unsupported/Eigen/src/NonLinearOptimization/dogleg.h
View File

@ -93,7 +93,7 @@ algo_end:
/* form appropriate convex combination of the gauss-newton */
/* direction and the scaled gradient direction. */
temp = (1.-alpha) * std::min(sgnorm,delta);
temp = (1.-alpha) * (std::min)(sgnorm,delta);
x = temp * wa1 + alpha * x;
}

4
unsupported/Eigen/src/NonLinearOptimization/fdjac1.h
View File

@ -26,7 +26,7 @@ DenseIndex fdjac1(
Matrix< Scalar, Dynamic, 1 > wa1(n);
Matrix< Scalar, Dynamic, 1 > wa2(n);
eps = sqrt(std::max(epsfcn,epsmch));
eps = sqrt((std::max)(epsfcn,epsmch));
msum = ml + mu + 1;
if (msum >= n) {
/* computation of dense approximate jacobian. */
@ -61,7 +61,7 @@ DenseIndex fdjac1(
if (h == 0.) h = eps;
fjac.col(j).setZero();
start = std::max<Index>(0,j-mu);
length = std::min(n-1, j+ml) - start + 1;
length = (std::min)(n-1, j+ml) - start + 1;
fjac.col(j).segment(start, length) = ( wa1.segment(start, length)-fvec.segment(start, length))/h;
}
}

28
unsupported/Eigen/src/NonLinearOptimization/lmpar.h
View File

@ -91,12 +91,12 @@ void lmpar(
gnorm = wa1.stableNorm();
paru = gnorm / delta;
if (paru == 0.)
paru = dwarf / std::min(delta,Scalar(0.1));
paru = dwarf / (std::min)(delta,Scalar(0.1));
/* if the input par lies outside of the interval (parl,paru), */
/* set par to the closer endpoint. */
par = std::max(par,parl);
par = std::min(par,paru);
par = (std::max)(par,parl);
par = (std::min)(par,paru);
if (par == 0.)
par = gnorm / dxnorm;
@ -106,7 +106,7 @@ void lmpar(
/* evaluate the function at the current value of par. */
if (par == 0.)
par = std::max(dwarf,Scalar(.001) * paru); /* Computing MAX */
par = (std::max)(dwarf,Scalar(.001) * paru); /* Computing MAX */
wa1 = sqrt(par)* diag;
Matrix< Scalar, Dynamic, 1 > sdiag(n);
@ -139,13 +139,13 @@ void lmpar(
/* depending on the sign of the function, update parl or paru. */
if (fp > 0.)
parl = std::max(parl,par);
parl = (std::max)(parl,par);
if (fp < 0.)
paru = std::min(paru,par);
paru = (std::min)(paru,par);
/* compute an improved estimate for par. */
/* Computing MAX */
par = std::max(parl,par+parc);
par = (std::max)(parl,par+parc);
/* end of an iteration. */
}
@ -227,12 +227,12 @@ void lmpar2(
gnorm = wa1.stableNorm();
paru = gnorm / delta;
if (paru == 0.)
paru = dwarf / std::min(delta,Scalar(0.1));
paru = dwarf / (std::min)(delta,Scalar(0.1));
/* if the input par lies outside of the interval (parl,paru), */
/* set par to the closer endpoint. */
par = std::max(par,parl);
par = std::min(par,paru);
par = (std::max)(par,parl);
par = (std::min)(par,paru);
if (par == 0.)
par = gnorm / dxnorm;
@ -243,7 +243,7 @@ void lmpar2(
/* evaluate the function at the current value of par. */
if (par == 0.)
par = std::max(dwarf,Scalar(.001) * paru); /* Computing MAX */
par = (std::max)(dwarf,Scalar(.001) * paru); /* Computing MAX */
wa1 = sqrt(par)* diag;
Matrix< Scalar, Dynamic, 1 > sdiag(n);
@ -275,12 +275,12 @@ void lmpar2(
/* depending on the sign of the function, update parl or paru. */
if (fp > 0.)
parl = std::max(parl,par);
parl = (std::max)(parl,par);
if (fp < 0.)
paru = std::min(paru,par);
paru = (std::min)(paru,par);
/* compute an improved estimate for par. */
par = std::max(parl,par+parc);
par = (std::max)(parl,par+parc);
}
if (iter == 0)
par = 0.;

2
unsupported/Eigen/src/NumericalDiff/NumericalDiff.h
View File

@ -80,7 +80,7 @@ public:
Scalar h;
int nfev=0;
const typename InputType::Index n = _x.size();
const Scalar eps = internal::sqrt((std::max(epsfcn,NumTraits<Scalar>::epsilon() )));
const Scalar eps = internal::sqrt(((std::max)(epsfcn,NumTraits<Scalar>::epsilon() )));
ValueType val1, val2;
InputType x = _x;
// TODO : we should do this only if the size is not already known

10
unsupported/Eigen/src/Skyline/SkylineStorage.h
View File

@ -221,11 +221,11 @@ protected:
Index* upperProfile = new Index[upperProfileSize];
Index* lowerProfile = new Index[lowerProfileSize];
Index copyDiagSize = std::min(diagSize, m_diagSize);
Index copyUpperSize = std::min(upperSize, m_upperSize);
Index copyLowerSize = std::min(lowerSize, m_lowerSize);
Index copyUpperProfileSize = std::min(upperProfileSize, m_upperProfileSize);
Index copyLowerProfileSize = std::min(lowerProfileSize, m_lowerProfileSize);
Index copyDiagSize = (std::min)(diagSize, m_diagSize);
Index copyUpperSize = (std::min)(upperSize, m_upperSize);
Index copyLowerSize = (std::min)(lowerSize, m_lowerSize);
Index copyUpperProfileSize = (std::min)(upperProfileSize, m_upperProfileSize);
Index copyLowerProfileSize = (std::min)(lowerProfileSize, m_lowerProfileSize);
// copy
memcpy(diag, m_diag, copyDiagSize * sizeof (Scalar));

4
unsupported/Eigen/src/SparseExtra/UmfPackSupport.h
View File

@ -295,10 +295,10 @@ void SparseLU<MatrixType,UmfPack>::extractData() const
umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar());
// allocate data
m_l.resize(rows,std::min(rows,cols));
m_l.resize(rows,(std::min)(rows,cols));
m_l.resizeNonZeros(lnz);
m_u.resize(std::min(rows,cols),cols);
m_u.resize((std::min)(rows,cols),cols);
m_u.resizeNonZeros(unz);
m_p.resize(rows);

8
unsupported/test/BVH.cpp
View File

@ -143,10 +143,10 @@ struct TreeTest
VectorType pt = VectorType::Random();
BallPointStuff<Dim> i1(pt), i2(pt);
double m1 = std::numeric_limits<double>::max(), m2 = m1;
double m1 = (std::numeric_limits<double>::max)(), m2 = m1;
for(int i = 0; i < (int)b.size(); ++i)
m1 = std::min(m1, i1.minimumOnObject(b[i]));
m1 = (std::min)(m1, i1.minimumOnObject(b[i]));
m2 = BVMinimize(tree, i2);
@ -194,11 +194,11 @@ struct TreeTest
BallPointStuff<Dim> i1, i2;
double m1 = std::numeric_limits<double>::max(), m2 = m1;
double m1 = (std::numeric_limits<double>::max)(), m2 = m1;
for(int i = 0; i < (int)b.size(); ++i)
for(int j = 0; j < (int)v.size(); ++j)
m1 = std::min(m1, i1.minimumOnObjectObject(b[i], v[j]));
m1 = (std::min)(m1, i1.minimumOnObjectObject(b[i], v[j]));
m2 = BVMinimize(tree, vTree, i2);

2
unsupported/test/FFTW.cpp
View File

@ -70,7 +70,7 @@ complex<long double> promote(long double x) { return complex<long double>( x);
{
long double totalpower=0;
long double difpower=0;
size_t n = min( buf1.size(),buf2.size() );
size_t n = (min)( buf1.size(),buf2.size() );
for (size_t k=0;k<n;++k) {
totalpower += (norm( buf1[k] ) + norm(buf2[k]) )/2.;
difpower += norm(buf1[k] - buf2[k]);