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* useThreshold -> setThreshold

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* remove defaultThreshold()
This commit is contained in:
Benoit Jacob 2009-10-18 14:20:14 -04:00
parent 0255f28279
commit d71c7f42d3
1 changed files with 22 additions and 35 deletions
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57
Eigen/src/LU/LU.h
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@ -168,7 +168,7 @@ template<typename MatrixType> class LU
*
* \note This method has to determine which pivots should be considered nonzero.
* For that, it uses the threshold value that you can control by calling
* useThreshold(const RealScalar&).
* setThreshold(const RealScalar&).
*
* Example: \include LU_kernel.cpp
* Output: \verbinclude LU_kernel.out
@ -188,7 +188,7 @@ template<typename MatrixType> class LU
*
* \note This method has to determine which pivots should be considered nonzero.
* For that, it uses the threshold value that you can control by calling
* useThreshold(const RealScalar&).
* setThreshold(const RealScalar&).
*
* Example: \include LU_image.cpp
* Output: \verbinclude LU_image.out
@ -250,67 +250,54 @@ template<typename MatrixType> class LU
* LU decomposition itself.
*
* When it needs to get the threshold value, Eigen calls threshold(). By default, this calls
* defaultThreshold(). Once you have called the present method useThreshold(const RealScalar&),
* defaultThreshold(). Once you have called the present method setThreshold(const RealScalar&),
* your value is used instead.
*
* \param threshold The new value to use as the threshold.
*
* A pivot will be considered nonzero if its absolute value is strictly greater than
* \f$ \vert pivot \vert \leqslant precision \times \vert maxpivot \vert \f$
* \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
* where maxpivot is the biggest pivot.
*
* If you want to come back to the default behavior, call useThreshold(Default_t)
* If you want to come back to the default behavior, call setThreshold(Default_t)
*/
LU& useThreshold(const RealScalar& threshold)
LU& setThreshold(const RealScalar& threshold)
{
m_usePrescribedThreshold = true;
m_prescribedThreshold = threshold;
}
/** Allows to come back to the default behavior, to use the return value of defaultThreshold().
/** Allows to come back to the default behavior, letting Eigen use its default formula for
* determining the threshold.
*
* You should pass the special object Eigen::Default as parameter here.
* \code lu.useThreshold(Eigen::Default); \endcode
* \code lu.setThreshold(Eigen::Default); \endcode
*
* See the documentation of useThreshold(const RealScalar&).
* See the documentation of setThreshold(const RealScalar&).
*/
LU& useThreshold(Default_t)
LU& setThreshold(Default_t)
{
m_usePrescribedThreshold = false;
}
/** Returns the threshold that will be used by default by certain methods such as rank(),
* unless the user overrides it by calling useThreshold(const RealScalar&).
*
* See the documentation of useThreshold(const RealScalar&).
*
* Notice that this method returns a value that depends on the size of the matrix being decomposed.
* Namely, it is the product of the diagonal size times the machine epsilon.
*
* \sa threshold()
*/
RealScalar defaultThreshold() const
{
// this formula comes from experimenting (see "LU precision tuning" thread on the list)
// and turns out to be identical to Higham's formula used already in LDLt.
ei_assert(m_originalMatrix != 0 && "LU is not initialized.");
return epsilon<Scalar>() * m_lu.diagonalSize();
}
/** Returns the threshold that will be used by certain methods such as rank().
*
* See the documentation of useThreshold(const RealScalar&).
* See the documentation of setThreshold(const RealScalar&).
*/
RealScalar threshold() const
{
return m_usePrescribedThreshold ? m_prescribedThreshold : defaultThreshold();
ei_assert(m_originalMatrix != 0 || m_usePrescribedThreshold);
return m_usePrescribedThreshold ? m_prescribedThreshold
// this formula comes from experimenting (see "LU precision tuning" thread on the list)
// and turns out to be identical to Higham's formula used already in LDLt.
: epsilon<Scalar>() * m_lu.diagonalSize();
}
/** \returns the rank of the matrix of which *this is the LU decomposition.
*
* \note This method has to determine which pivots should be considered nonzero.
* For that, it uses the threshold value that you can control by calling
* useThreshold(const RealScalar&).
* setThreshold(const RealScalar&).
*/
inline int rank() const
{
@ -326,7 +313,7 @@ template<typename MatrixType> class LU
*
* \note This method has to determine which pivots should be considered nonzero.
* For that, it uses the threshold value that you can control by calling
* useThreshold(const RealScalar&).
* setThreshold(const RealScalar&).
*/
inline int dimensionOfKernel() const
{
@ -339,7 +326,7 @@ template<typename MatrixType> class LU
*
* \note This method has to determine which pivots should be considered nonzero.
* For that, it uses the threshold value that you can control by calling
* useThreshold(const RealScalar&).
* setThreshold(const RealScalar&).
*/
inline bool isInjective() const
{
@ -352,7 +339,7 @@ template<typename MatrixType> class LU
*
* \note This method has to determine which pivots should be considered nonzero.
* For that, it uses the threshold value that you can control by calling
* useThreshold(const RealScalar&).
* setThreshold(const RealScalar&).
*/
inline bool isSurjective() const
{
@ -364,7 +351,7 @@ template<typename MatrixType> class LU
*
* \note This method has to determine which pivots should be considered nonzero.
* For that, it uses the threshold value that you can control by calling
* useThreshold(const RealScalar&).
* setThreshold(const RealScalar&).
*/
inline bool isInvertible() const
{