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Another big refactoring change:

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* add a new Eigen2Support module including Cwise, Flagged, and some other deprecated stuff
* add a few cwiseXxx functions
* adapt a few modules to use cwiseXxx instead of the .cwise() prefix
This commit is contained in:
Gael Guennebaud
2009-11-18 18:15:19 +01:00
parent 0529ecfe1b
commit e3d890bc5a
48 changed files with 634 additions and 408 deletions
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32
Eigen/src/LU/FullPivLU.h
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@@ -89,7 +89,7 @@ template<typename _MatrixType> class FullPivLU
* \returns a reference to *this
*/
FullPivLU& compute(const MatrixType& matrix);
/** \returns the LU decomposition matrix: the upper-triangular part is U, the
* unit-lower-triangular part is L (at least for square matrices; in the non-square
* case, special care is needed, see the documentation of class FullPivLU).
@@ -101,7 +101,7 @@ template<typename _MatrixType> class FullPivLU
ei_assert(m_isInitialized && "LU is not initialized.");
return m_lu;
}
/** \returns the number of nonzero pivots in the LU decomposition.
* Here nonzero is meant in the exact sense, not in a fuzzy sense.
* So that notion isn't really intrinsically interesting, but it is
@@ -114,12 +114,12 @@ template<typename _MatrixType> class FullPivLU
ei_assert(m_isInitialized && "LU is not initialized.");
return m_nonzero_pivots;
}
/** \returns the absolute value of the biggest pivot, i.e. the biggest
* diagonal coefficient of U.
*/
RealScalar maxPivot() const { return m_maxpivot; }
/** \returns a vector of integers, whose size is the number of rows of the matrix being decomposed,
* representing the P permutation i.e. the permutation of the rows. For its precise meaning,
* see the examples given in the documentation of class FullPivLU.
@@ -255,7 +255,7 @@ template<typename _MatrixType> class FullPivLU
m_usePrescribedThreshold = true;
m_prescribedThreshold = threshold;
}
/** Allows to come back to the default behavior, letting Eigen use its default formula for
* determining the threshold.
*
@@ -268,7 +268,7 @@ template<typename _MatrixType> class FullPivLU
{
m_usePrescribedThreshold = false;
}
/** Returns the threshold that will be used by certain methods such as rank().
*
* See the documentation of setThreshold(const RealScalar&).
@@ -281,7 +281,7 @@ template<typename _MatrixType> class FullPivLU
// 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.
@@ -297,7 +297,7 @@ template<typename _MatrixType> class FullPivLU
result += (ei_abs(m_lu.coeff(i,i)) > premultiplied_threshold);
return result;
}
/** \returns the dimension of the kernel of the matrix of which *this is the LU decomposition.
*
* \note This method has to determine which pivots should be considered nonzero.
@@ -365,7 +365,7 @@ template<typename _MatrixType> class FullPivLU
inline int rows() const { return m_lu.rows(); }
inline int cols() const { return m_lu.cols(); }
protected:
MatrixType m_lu;
IntColVectorType m_p;
@@ -416,7 +416,7 @@ FullPivLU<MatrixType>& FullPivLU<MatrixType>::compute(const MatrixType& matrix)
int row_of_biggest_in_corner, col_of_biggest_in_corner;
RealScalar biggest_in_corner;
biggest_in_corner = m_lu.corner(Eigen::BottomRight, rows-k, cols-k)
.cwise().abs()
.cwiseAbs()
.maxCoeff(&row_of_biggest_in_corner, &col_of_biggest_in_corner);
row_of_biggest_in_corner += k; // correct the values! since they were computed in the corner,
col_of_biggest_in_corner += k; // need to add k to them.
@@ -453,7 +453,7 @@ FullPivLU<MatrixType>& FullPivLU<MatrixType>::compute(const MatrixType& matrix)
// Now that the pivot is at the right location, we update the remaining
// bottom-right corner by Gaussian elimination.
if(k<rows-1)
m_lu.col(k).end(rows-k-1) /= m_lu.coeff(k,k);
if(k<size-1)
@@ -507,7 +507,7 @@ struct ei_kernel_retval<FullPivLU<_MatrixType> >
dst.setZero();
return;
}
/* Let us use the following lemma:
*
* Lemma: If the matrix A has the LU decomposition PAQ = LU,
@@ -575,7 +575,7 @@ struct ei_image_retval<FullPivLU<_MatrixType> >
: ei_image_retval_base<FullPivLU<_MatrixType> >
{
EIGEN_MAKE_IMAGE_HELPERS(FullPivLU<_MatrixType>)
enum { MaxSmallDimAtCompileTime = EIGEN_ENUM_MIN(
MatrixType::MaxColsAtCompileTime,
MatrixType::MaxRowsAtCompileTime)
@@ -591,7 +591,7 @@ struct ei_image_retval<FullPivLU<_MatrixType> >
dst.setZero();
return;
}
Matrix<int, Dynamic, 1, 0, MaxSmallDimAtCompileTime, 1> pivots(rank());
RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold();
int p = 0;
@@ -599,7 +599,7 @@ struct ei_image_retval<FullPivLU<_MatrixType> >
if(ei_abs(dec().matrixLU().coeff(i,i)) > premultiplied_threshold)
pivots.coeffRef(p++) = i;
ei_internal_assert(p == rank());
for(int i = 0; i < rank(); ++i)
dst.col(i) = originalMatrix().col(dec().permutationQ().coeff(pivots.coeff(i)));
}
@@ -612,7 +612,7 @@ struct ei_solve_retval<FullPivLU<_MatrixType>, Rhs>
: ei_solve_retval_base<FullPivLU<_MatrixType>, Rhs>
{
EIGEN_MAKE_SOLVE_HELPERS(FullPivLU<_MatrixType>,Rhs)
template<typename Dest> void evalTo(Dest& dst) const
{
/* The decomposition PAQ = LU can be rewritten as A = P^{-1} L U Q^{-1}.

6
Eigen/src/LU/Inverse.h
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@@ -148,7 +148,7 @@ struct ei_compute_inverse<MatrixType, ResultType, 3>
cofactors_col0.coeffRef(0) = matrix.minor(0,0).determinant();
cofactors_col0.coeffRef(1) = -matrix.minor(1,0).determinant();
cofactors_col0.coeffRef(2) = matrix.minor(2,0).determinant();
const Scalar det = (cofactors_col0.cwise()*matrix.col(0)).sum();
const Scalar det = (cofactors_col0.cwiseProduct(matrix.col(0))).sum();
const Scalar invdet = Scalar(1) / det;
ei_compute_inverse_size3_helper(matrix, invdet, cofactors_col0, result);
}
@@ -170,7 +170,7 @@ struct ei_compute_inverse_and_det_with_check<MatrixType, ResultType, 3>
cofactors_col0.coeffRef(0) = matrix.minor(0,0).determinant();
cofactors_col0.coeffRef(1) = -matrix.minor(1,0).determinant();
cofactors_col0.coeffRef(2) = matrix.minor(2,0).determinant();
determinant = (cofactors_col0.cwise()*matrix.col(0)).sum();
determinant = (cofactors_col0.cwiseProduct(matrix.col(0))).sum();
invertible = ei_abs(determinant) > absDeterminantThreshold;
if(!invertible) return;
const Scalar invdet = Scalar(1) / determinant;
@@ -256,7 +256,7 @@ struct ei_compute_inverse<MatrixType, ResultType, 4>
// now good_row0 and good_row1 are correctly set
good:
// do row permutations to move this 2x2 block to the top
matrix.row(0).swap(matrix.row(good_row0));
matrix.row(1).swap(matrix.row(good_row1));

6
Eigen/src/LU/PartialPivLU.h
View File

@@ -233,7 +233,7 @@ struct ei_partial_lu_impl
{
int row_of_biggest_in_col;
RealScalar biggest_in_corner
= lu.col(k).end(rows-k).cwise().abs().maxCoeff(&row_of_biggest_in_col);
= lu.col(k).end(rows-k).cwiseAbs().maxCoeff(&row_of_biggest_in_col);
row_of_biggest_in_col += k;
if(biggest_in_corner == 0) // the pivot is exactly zero: the matrix is singular
@@ -412,7 +412,7 @@ struct ei_solve_retval<PartialPivLU<_MatrixType>, Rhs>
: ei_solve_retval_base<PartialPivLU<_MatrixType>, Rhs>
{
EIGEN_MAKE_SOLVE_HELPERS(PartialPivLU<_MatrixType>,Rhs)
template<typename Dest> void evalTo(Dest& dst) const
{
/* The decomposition PA = LU can be rewritten as A = P^{-1} L U.
@@ -421,7 +421,7 @@ struct ei_solve_retval<PartialPivLU<_MatrixType>, Rhs>
* Step 2: replace c by the solution x to Lx = c.
* Step 3: replace c by the solution x to Ux = c.
*/
const int size = dec().matrixLU().rows();
ei_assert(rhs().rows() == size);