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the Index types change.

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As discussed on the list (too long to explain here).
This commit is contained in:
Benoit Jacob
2010-05-30 16:00:58 -04:00
parent faa3ff3be6
commit aaaade4b3d
158 changed files with 3137 additions and 2878 deletions
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51
Eigen/src/QR/ColPivHouseholderQR.h
View File

@@ -56,10 +56,11 @@ template<typename _MatrixType> class ColPivHouseholderQR
};
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, Options, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixQType;
typedef typename ei_plain_diag_type<MatrixType>::type HCoeffsType;
typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime> PermutationType;
typedef typename ei_plain_row_type<MatrixType, int>::type IntRowVectorType;
typedef typename ei_plain_row_type<MatrixType, Index>::type IntRowVectorType;
typedef typename ei_plain_row_type<MatrixType>::type RowVectorType;
typedef typename ei_plain_row_type<MatrixType, RealScalar>::type RealRowVectorType;
typedef typename HouseholderSequence<MatrixType,HCoeffsType>::ConjugateReturnType HouseholderSequenceType;
@@ -85,7 +86,7 @@ template<typename _MatrixType> class ColPivHouseholderQR
* according to the specified problem \a size.
* \sa ColPivHouseholderQR()
*/
ColPivHouseholderQR(int rows, int cols)
ColPivHouseholderQR(Index rows, Index cols)
: m_qr(rows, cols),
m_hCoeffs(std::min(rows,cols)),
m_colsPermutation(cols),
@@ -186,12 +187,12 @@ template<typename _MatrixType> class ColPivHouseholderQR
* For that, it uses the threshold value that you can control by calling
* setThreshold(const RealScalar&).
*/
inline int rank() const
inline Index rank() const
{
ei_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
RealScalar premultiplied_threshold = ei_abs(m_maxpivot) * threshold();
int result = 0;
for(int i = 0; i < m_nonzero_pivots; ++i)
Index result = 0;
for(Index i = 0; i < m_nonzero_pivots; ++i)
result += (ei_abs(m_qr.coeff(i,i)) > premultiplied_threshold);
return result;
}
@@ -202,7 +203,7 @@ template<typename _MatrixType> class ColPivHouseholderQR
* For that, it uses the threshold value that you can control by calling
* setThreshold(const RealScalar&).
*/
inline int dimensionOfKernel() const
inline Index dimensionOfKernel() const
{
ei_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
return cols() - rank();
@@ -260,8 +261,8 @@ template<typename _MatrixType> class ColPivHouseholderQR
(*this, MatrixType::Identity(m_qr.rows(), m_qr.cols()));
}
inline int rows() const { return m_qr.rows(); }
inline int cols() const { return m_qr.cols(); }
inline Index rows() const { return m_qr.rows(); }
inline Index cols() const { return m_qr.cols(); }
const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
/** Allows to prescribe a threshold to be used by certain methods, such as rank(),
@@ -320,7 +321,7 @@ template<typename _MatrixType> class ColPivHouseholderQR
*
* \sa rank()
*/
inline int nonzeroPivots() const
inline Index nonzeroPivots() const
{
ei_assert(m_isInitialized && "LU is not initialized.");
return m_nonzero_pivots;
@@ -340,8 +341,8 @@ template<typename _MatrixType> class ColPivHouseholderQR
RealRowVectorType m_colSqNorms;
bool m_isInitialized, m_usePrescribedThreshold;
RealScalar m_prescribedThreshold, m_maxpivot;
int m_nonzero_pivots;
int m_det_pq;
Index m_nonzero_pivots;
Index m_det_pq;
};
#ifndef EIGEN_HIDE_HEAVY_CODE
@@ -365,9 +366,9 @@ typename MatrixType::RealScalar ColPivHouseholderQR<MatrixType>::logAbsDetermina
template<typename MatrixType>
ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const MatrixType& matrix)
{
int rows = matrix.rows();
int cols = matrix.cols();
int size = matrix.diagonalSize();
Index rows = matrix.rows();
Index cols = matrix.cols();
Index size = matrix.diagonalSize();
m_qr = matrix;
m_hCoeffs.resize(size);
@@ -375,10 +376,10 @@ ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const
m_temp.resize(cols);
m_colsTranspositions.resize(matrix.cols());
int number_of_transpositions = 0;
Index number_of_transpositions = 0;
m_colSqNorms.resize(cols);
for(int k = 0; k < cols; ++k)
for(Index k = 0; k < cols; ++k)
m_colSqNorms.coeffRef(k) = m_qr.col(k).squaredNorm();
RealScalar threshold_helper = m_colSqNorms.maxCoeff() * ei_abs2(NumTraits<Scalar>::epsilon()) / rows;
@@ -386,10 +387,10 @@ ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const
m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case)
m_maxpivot = RealScalar(0);
for(int k = 0; k < size; ++k)
for(Index k = 0; k < size; ++k)
{
// first, we look up in our table m_colSqNorms which column has the biggest squared norm
int biggest_col_index;
Index biggest_col_index;
RealScalar biggest_col_sq_norm = m_colSqNorms.tail(cols-k).maxCoeff(&biggest_col_index);
biggest_col_index += k;
@@ -444,7 +445,7 @@ ColPivHouseholderQR<MatrixType>& ColPivHouseholderQR<MatrixType>::compute(const
}
m_colsPermutation.setIdentity(cols);
for(int k = 0; k < m_nonzero_pivots; ++k)
for(Index k = 0; k < m_nonzero_pivots; ++k)
m_colsPermutation.applyTranspositionOnTheRight(k, m_colsTranspositions.coeff(k));
m_det_pq = (number_of_transpositions%2) ? -1 : 1;
@@ -461,12 +462,10 @@ struct ei_solve_retval<ColPivHouseholderQR<_MatrixType>, Rhs>
template<typename Dest> void evalTo(Dest& dst) const
{
#ifndef EIGEN_NO_DEBUG
const int rows = dec().rows();
ei_assert(rhs().rows() == rows);
#endif
ei_assert(rhs().rows() == dec().rows());
const int cols = dec().cols(),
nonzero_pivots = dec().nonzeroPivots();
nonzero_pivots = dec().nonzeroPivots();
if(nonzero_pivots == 0)
{
@@ -498,8 +497,8 @@ struct ei_solve_retval<ColPivHouseholderQR<_MatrixType>, Rhs>
.template triangularView<Upper>()
* c.topRows(nonzero_pivots);
for(int i = 0; i < nonzero_pivots; ++i) dst.row(dec().colsPermutation().indices().coeff(i)) = c.row(i);
for(int i = nonzero_pivots; i < cols; ++i) dst.row(dec().colsPermutation().indices().coeff(i)).setZero();
for(Index i = 0; i < nonzero_pivots; ++i) dst.row(dec().colsPermutation().indices().coeff(i)) = c.row(i);
for(Index i = nonzero_pivots; i < cols; ++i) dst.row(dec().colsPermutation().indices().coeff(i)).setZero();
}
};

53
Eigen/src/QR/FullPivHouseholderQR.h
View File

@@ -56,11 +56,12 @@ template<typename _MatrixType> class FullPivHouseholderQR
};
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, Options, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixQType;
typedef typename ei_plain_diag_type<MatrixType>::type HCoeffsType;
typedef Matrix<int, 1, ColsAtCompileTime, RowMajor, 1, MaxColsAtCompileTime> IntRowVectorType;
typedef Matrix<Index, 1, ColsAtCompileTime, RowMajor, 1, MaxColsAtCompileTime> IntRowVectorType;
typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime> PermutationType;
typedef typename ei_plain_col_type<MatrixType, int>::type IntColVectorType;
typedef typename ei_plain_col_type<MatrixType, Index>::type IntColVectorType;
typedef typename ei_plain_row_type<MatrixType>::type RowVectorType;
typedef typename ei_plain_col_type<MatrixType>::type ColVectorType;
@@ -84,7 +85,7 @@ template<typename _MatrixType> class FullPivHouseholderQR
* according to the specified problem \a size.
* \sa FullPivHouseholderQR()
*/
FullPivHouseholderQR(int rows, int cols)
FullPivHouseholderQR(Index rows, Index cols)
: m_qr(rows, cols),
m_hCoeffs(std::min(rows,cols)),
m_rows_transpositions(rows),
@@ -188,7 +189,7 @@ template<typename _MatrixType> class FullPivHouseholderQR
* \note This is computed at the time of the construction of the QR decomposition. This
* method does not perform any further computation.
*/
inline int rank() const
inline Index rank() const
{
ei_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
return m_rank;
@@ -199,7 +200,7 @@ template<typename _MatrixType> class FullPivHouseholderQR
* \note Since the rank is computed at the time of the construction of the QR decomposition, this
* method almost does not perform any further computation.
*/
inline int dimensionOfKernel() const
inline Index dimensionOfKernel() const
{
ei_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
return m_qr.cols() - m_rank;
@@ -253,8 +254,8 @@ template<typename _MatrixType> class FullPivHouseholderQR
(*this, MatrixType::Identity(m_qr.rows(), m_qr.cols()));
}
inline int rows() const { return m_qr.rows(); }
inline int cols() const { return m_qr.cols(); }
inline Index rows() const { return m_qr.rows(); }
inline Index cols() const { return m_qr.cols(); }
const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
protected:
@@ -266,8 +267,8 @@ template<typename _MatrixType> class FullPivHouseholderQR
RowVectorType m_temp;
bool m_isInitialized;
RealScalar m_precision;
int m_rank;
int m_det_pq;
Index m_rank;
Index m_det_pq;
};
#ifndef EIGEN_HIDE_HEAVY_CODE
@@ -291,9 +292,9 @@ typename MatrixType::RealScalar FullPivHouseholderQR<MatrixType>::logAbsDetermin
template<typename MatrixType>
FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(const MatrixType& matrix)
{
int rows = matrix.rows();
int cols = matrix.cols();
int size = std::min(rows,cols);
Index rows = matrix.rows();
Index cols = matrix.cols();
Index size = std::min(rows,cols);
m_rank = size;
m_qr = matrix;
@@ -305,13 +306,13 @@ FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(cons
m_rows_transpositions.resize(matrix.rows());
m_cols_transpositions.resize(matrix.cols());
int number_of_transpositions = 0;
Index number_of_transpositions = 0;
RealScalar biggest(0);
for (int k = 0; k < size; ++k)
for (Index k = 0; k < size; ++k)
{
int row_of_biggest_in_corner, col_of_biggest_in_corner;
Index row_of_biggest_in_corner, col_of_biggest_in_corner;
RealScalar biggest_in_corner;
biggest_in_corner = m_qr.bottomRightCorner(rows-k, cols-k)
@@ -325,7 +326,7 @@ FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(cons
if(ei_isMuchSmallerThan(biggest_in_corner, biggest, m_precision))
{
m_rank = k;
for(int i = k; i < size; i++)
for(Index i = k; i < size; i++)
{
m_rows_transpositions.coeffRef(i) = i;
m_cols_transpositions.coeffRef(i) = i;
@@ -354,7 +355,7 @@ FullPivHouseholderQR<MatrixType>& FullPivHouseholderQR<MatrixType>::compute(cons
}
m_cols_permutation.setIdentity(cols);
for(int k = 0; k < size; ++k)
for(Index k = 0; k < size; ++k)
m_cols_permutation.applyTranspositionOnTheRight(k, m_cols_transpositions.coeff(k));
m_det_pq = (number_of_transpositions%2) ? -1 : 1;
@@ -371,7 +372,7 @@ struct ei_solve_retval<FullPivHouseholderQR<_MatrixType>, Rhs>
template<typename Dest> void evalTo(Dest& dst) const
{
const int rows = dec().rows(), cols = dec().cols();
const Index rows = dec().rows(), cols = dec().cols();
ei_assert(rhs().rows() == rows);
// FIXME introduce nonzeroPivots() and use it here. and more generally,
@@ -385,9 +386,9 @@ struct ei_solve_retval<FullPivHouseholderQR<_MatrixType>, Rhs>
typename Rhs::PlainObject c(rhs());
Matrix<Scalar,1,Rhs::ColsAtCompileTime> temp(rhs().cols());
for (int k = 0; k < dec().rank(); ++k)
for (Index k = 0; k < dec().rank(); ++k)
{
int remainingSize = rows-k;
Index remainingSize = rows-k;
c.row(k).swap(c.row(dec().rowsTranspositions().coeff(k)));
c.bottomRightCorner(remainingSize, rhs().cols())
.applyHouseholderOnTheLeft(dec().matrixQR().col(k).tail(remainingSize-1),
@@ -409,8 +410,8 @@ struct ei_solve_retval<FullPivHouseholderQR<_MatrixType>, Rhs>
.template triangularView<Upper>()
.solveInPlace(c.topRows(dec().rank()));
for(int i = 0; i < dec().rank(); ++i) dst.row(dec().colsPermutation().indices().coeff(i)) = c.row(i);
for(int i = dec().rank(); i < cols; ++i) dst.row(dec().colsPermutation().indices().coeff(i)).setZero();
for(Index i = 0; i < dec().rank(); ++i) dst.row(dec().colsPermutation().indices().coeff(i)) = c.row(i);
for(Index i = dec().rank(); i < cols; ++i) dst.row(dec().colsPermutation().indices().coeff(i)).setZero();
}
};
@@ -422,12 +423,12 @@ typename FullPivHouseholderQR<MatrixType>::MatrixQType FullPivHouseholderQR<Matr
// compute the product H'_0 H'_1 ... H'_n-1,
// where H_k is the k-th Householder transformation I - h_k v_k v_k'
// and v_k is the k-th Householder vector [1,m_qr(k+1,k), m_qr(k+2,k), ...]
int rows = m_qr.rows();
int cols = m_qr.cols();
int size = std::min(rows,cols);
Index rows = m_qr.rows();
Index cols = m_qr.cols();
Index size = std::min(rows,cols);
MatrixQType res = MatrixQType::Identity(rows, rows);
Matrix<Scalar,1,MatrixType::RowsAtCompileTime> temp(rows);
for (int k = size-1; k >= 0; k--)
for (Index k = size-1; k >= 0; k--)
{
res.block(k, k, rows-k, rows-k)
.applyHouseholderOnTheLeft(m_qr.col(k).tail(rows-k-1), ei_conj(m_hCoeffs.coeff(k)), &temp.coeffRef(k));

23
Eigen/src/QR/HouseholderQR.h
View File

@@ -60,6 +60,7 @@ template<typename _MatrixType> class HouseholderQR
};
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef typename MatrixType::Index Index;
typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, ei_traits<MatrixType>::Flags&RowMajorBit ? RowMajor : ColMajor, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixQType;
typedef typename ei_plain_diag_type<MatrixType>::type HCoeffsType;
typedef typename ei_plain_row_type<MatrixType>::type RowVectorType;
@@ -79,7 +80,7 @@ template<typename _MatrixType> class HouseholderQR
* according to the specified problem \a size.
* \sa HouseholderQR()
*/
HouseholderQR(int rows, int cols)
HouseholderQR(Index rows, Index cols)
: m_qr(rows, cols),
m_hCoeffs(std::min(rows,cols)),
m_temp(cols),
@@ -165,8 +166,8 @@ template<typename _MatrixType> class HouseholderQR
*/
typename MatrixType::RealScalar logAbsDeterminant() const;
inline int rows() const { return m_qr.rows(); }
inline int cols() const { return m_qr.cols(); }
inline Index rows() const { return m_qr.rows(); }
inline Index cols() const { return m_qr.cols(); }
const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
protected:
@@ -197,19 +198,19 @@ typename MatrixType::RealScalar HouseholderQR<MatrixType>::logAbsDeterminant() c
template<typename MatrixType>
HouseholderQR<MatrixType>& HouseholderQR<MatrixType>::compute(const MatrixType& matrix)
{
int rows = matrix.rows();
int cols = matrix.cols();
int size = std::min(rows,cols);
Index rows = matrix.rows();
Index cols = matrix.cols();
Index size = std::min(rows,cols);
m_qr = matrix;
m_hCoeffs.resize(size);
m_temp.resize(cols);
for(int k = 0; k < size; ++k)
for(Index k = 0; k < size; ++k)
{
int remainingRows = rows - k;
int remainingCols = cols - k - 1;
Index remainingRows = rows - k;
Index remainingCols = cols - k - 1;
RealScalar beta;
m_qr.col(k).tail(remainingRows).makeHouseholderInPlace(m_hCoeffs.coeffRef(k), beta);
@@ -231,8 +232,8 @@ struct ei_solve_retval<HouseholderQR<_MatrixType>, Rhs>
template<typename Dest> void evalTo(Dest& dst) const
{
const int rows = dec().rows(), cols = dec().cols();
const int rank = std::min(rows, cols);
const Index rows = dec().rows(), cols = dec().cols();
const Index rank = std::min(rows, cols);
ei_assert(rhs().rows() == rows);
typename Rhs::PlainObject c(rhs());