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precision ---> dummy_precision

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This commit is contained in:
Benoit Jacob
2009-11-26 22:05:02 -05:00
parent 5923bcb1b9
commit 4b1aca2288
27 changed files with 87 additions and 87 deletions
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2
Eigen/src/Sparse/AmbiVector.h
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@@ -300,7 +300,7 @@ class AmbiVector<_Scalar>::Iterator
* In practice, all coefficients having a magnitude smaller than \a epsilon
* are skipped.
*/
Iterator(const AmbiVector& vec, RealScalar epsilon = RealScalar(0.1)*precision<RealScalar>())
Iterator(const AmbiVector& vec, RealScalar epsilon = RealScalar(0.1)*dummy_precision<RealScalar>())
: m_vector(vec)
{
m_epsilon = epsilon;

2
Eigen/src/Sparse/CompressedStorage.h
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@@ -185,7 +185,7 @@ class CompressedStorage
return m_values[id];
}
void prune(Scalar reference, RealScalar epsilon = precision<RealScalar>())
void prune(Scalar reference, RealScalar epsilon = dummy_precision<RealScalar>())
{
size_t k = 0;
size_t n = size();

2
Eigen/src/Sparse/DynamicSparseMatrix.h
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@@ -208,7 +208,7 @@ class DynamicSparseMatrix
inline void finalize() {}
void prune(Scalar reference, RealScalar epsilon = precision<RealScalar>())
void prune(Scalar reference, RealScalar epsilon = dummy_precision<RealScalar>())
{
for (int j=0; j<outerSize(); ++j)
m_data[j].prune(reference,epsilon);

4
Eigen/src/Sparse/SparseLDLT.h
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@@ -94,7 +94,7 @@ class SparseLDLT
: m_flags(flags), m_status(0)
{
ei_assert((MatrixType::Flags&RowMajorBit)==0);
m_precision = RealScalar(0.1) * Eigen::precision<RealScalar>();
m_precision = RealScalar(0.1) * Eigen::dummy_precision<RealScalar>();
}
/** Creates a LDLT object and compute the respective factorization of \a matrix using
@@ -103,7 +103,7 @@ class SparseLDLT
: m_matrix(matrix.rows(), matrix.cols()), m_flags(flags), m_status(0)
{
ei_assert((MatrixType::Flags&RowMajorBit)==0);
m_precision = RealScalar(0.1) * Eigen::precision<RealScalar>();
m_precision = RealScalar(0.1) * Eigen::dummy_precision<RealScalar>();
compute(matrix);
}

4
Eigen/src/Sparse/SparseLLT.h
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@@ -54,7 +54,7 @@ class SparseLLT
SparseLLT(int flags = 0)
: m_flags(flags), m_status(0)
{
m_precision = RealScalar(0.1) * Eigen::precision<RealScalar>();
m_precision = RealScalar(0.1) * Eigen::dummy_precision<RealScalar>();
}
/** Creates a LLT object and compute the respective factorization of \a matrix using
@@ -62,7 +62,7 @@ class SparseLLT
SparseLLT(const MatrixType& matrix, int flags = 0)
: m_matrix(matrix.rows(), matrix.cols()), m_flags(flags), m_status(0)
{
m_precision = RealScalar(0.1) * Eigen::precision<RealScalar>();
m_precision = RealScalar(0.1) * Eigen::dummy_precision<RealScalar>();
compute(matrix);
}

4
Eigen/src/Sparse/SparseLU.h
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@@ -59,7 +59,7 @@ class SparseLU
SparseLU(int flags = 0)
: m_flags(flags), m_status(0)
{
m_precision = RealScalar(0.1) * Eigen::precision<RealScalar>();
m_precision = RealScalar(0.1) * Eigen::dummy_precision<RealScalar>();
}
/** Creates a LU object and compute the respective factorization of \a matrix using
@@ -67,7 +67,7 @@ class SparseLU
SparseLU(const MatrixType& matrix, int flags = 0)
: /*m_matrix(matrix.rows(), matrix.cols()),*/ m_flags(flags), m_status(0)
{
m_precision = RealScalar(0.1) * Eigen::precision<RealScalar>();
m_precision = RealScalar(0.1) * Eigen::dummy_precision<RealScalar>();
compute(matrix);
}

2
Eigen/src/Sparse/SparseMatrix.h
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@@ -341,7 +341,7 @@ class SparseMatrix
}
}
void prune(Scalar reference, RealScalar epsilon = precision<RealScalar>())
void prune(Scalar reference, RealScalar epsilon = dummy_precision<RealScalar>())
{
int k = 0;
for (int j=0; j<m_outerSize; ++j)

26
Eigen/src/Sparse/SparseMatrixBase.h
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@@ -467,32 +467,32 @@ template<typename Derived> class SparseMatrixBase : public AnyMatrixBase<Derived
template<typename OtherDerived>
bool isApprox(const SparseMatrixBase<OtherDerived>& other,
RealScalar prec = precision<Scalar>()) const
RealScalar prec = dummy_precision<Scalar>()) const
{ return toDense().isApprox(other.toDense(),prec); }
template<typename OtherDerived>
bool isApprox(const MatrixBase<OtherDerived>& other,
RealScalar prec = precision<Scalar>()) const
RealScalar prec = dummy_precision<Scalar>()) const
{ return toDense().isApprox(other,prec); }
// bool isMuchSmallerThan(const RealScalar& other,
// RealScalar prec = precision<Scalar>()) const;
// RealScalar prec = dummy_precision<Scalar>()) const;
// template<typename OtherDerived>
// bool isMuchSmallerThan(const MatrixBase<OtherDerived>& other,
// RealScalar prec = precision<Scalar>()) const;
// RealScalar prec = dummy_precision<Scalar>()) const;
// bool isApproxToConstant(const Scalar& value, RealScalar prec = precision<Scalar>()) const;
// bool isZero(RealScalar prec = precision<Scalar>()) const;
// bool isOnes(RealScalar prec = precision<Scalar>()) const;
// bool isIdentity(RealScalar prec = precision<Scalar>()) const;
// bool isDiagonal(RealScalar prec = precision<Scalar>()) const;
// bool isApproxToConstant(const Scalar& value, RealScalar prec = dummy_precision<Scalar>()) const;
// bool isZero(RealScalar prec = dummy_precision<Scalar>()) const;
// bool isOnes(RealScalar prec = dummy_precision<Scalar>()) const;
// bool isIdentity(RealScalar prec = dummy_precision<Scalar>()) const;
// bool isDiagonal(RealScalar prec = dummy_precision<Scalar>()) const;
// bool isUpperTriangular(RealScalar prec = precision<Scalar>()) const;
// bool isLowerTriangular(RealScalar prec = precision<Scalar>()) const;
// bool isUpperTriangular(RealScalar prec = dummy_precision<Scalar>()) const;
// bool isLowerTriangular(RealScalar prec = dummy_precision<Scalar>()) const;
// template<typename OtherDerived>
// bool isOrthogonal(const MatrixBase<OtherDerived>& other,
// RealScalar prec = precision<Scalar>()) const;
// bool isUnitary(RealScalar prec = precision<Scalar>()) const;
// RealScalar prec = dummy_precision<Scalar>()) const;
// bool isUnitary(RealScalar prec = dummy_precision<Scalar>()) const;
// template<typename OtherDerived>
// inline bool operator==(const MatrixBase<OtherDerived>& other) const

2
Eigen/src/Sparse/SparseVector.h
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@@ -201,7 +201,7 @@ class SparseVector
EIGEN_DEPRECATED void endFill() {}
inline void finalize() {}
void prune(Scalar reference, RealScalar epsilon = precision<RealScalar>())
void prune(Scalar reference, RealScalar epsilon = dummy_precision<RealScalar>())
{
m_data.prune(reference,epsilon);
}