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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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2
Eigen/src/Sparse/AmbiVector.h
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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
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@@ -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);