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eigen/Eigen/src/Sparse/SparseMatrixBase.h

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various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_SPARSEMATRIXBASE_H
#define EIGEN_SPARSEMATRIXBASE_H
Big rewrite in the Sparse module: SparseMatrixBase no longer inherits MatrixBase. That means a lot of features which were available for sparse matrices via the dense (and super slow) implemention are no longer available. All features which make sense for sparse matrices (aka can be implemented efficiently) will be implemented soon, but don't expect to see an API as rich as for the dense path. Other changes: * no block(), row(), col() anymore. * instead use .innerVector() to get a col or row vector of a matrix. * .segment(), start(), end() will be back soon, not sure for block() * faster cwise product
2009-01-14 14:24:10 +00:00
template<typename Derived> class SparseMatrixBase
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
{
public:
Big rewrite in the Sparse module: SparseMatrixBase no longer inherits MatrixBase. That means a lot of features which were available for sparse matrices via the dense (and super slow) implemention are no longer available. All features which make sense for sparse matrices (aka can be implemented efficiently) will be implemented soon, but don't expect to see an API as rich as for the dense path. Other changes: * no block(), row(), col() anymore. * instead use .innerVector() to get a col or row vector of a matrix. * .segment(), start(), end() will be back soon, not sure for block() * faster cwise product
2009-01-14 14:24:10 +00:00
typedef typename ei_traits<Derived>::Scalar Scalar;
// typedef typename Derived::InnerIterator InnerIterator;
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
enum {
Big rewrite in the Sparse module: SparseMatrixBase no longer inherits MatrixBase. That means a lot of features which were available for sparse matrices via the dense (and super slow) implemention are no longer available. All features which make sense for sparse matrices (aka can be implemented efficiently) will be implemented soon, but don't expect to see an API as rich as for the dense path. Other changes: * no block(), row(), col() anymore. * instead use .innerVector() to get a col or row vector of a matrix. * .segment(), start(), end() will be back soon, not sure for block() * faster cwise product
2009-01-14 14:24:10 +00:00
RowsAtCompileTime = ei_traits<Derived>::RowsAtCompileTime,
/**< The number of rows at compile-time. This is just a copy of the value provided
* by the \a Derived type. If a value is not known at compile-time,
* it is set to the \a Dynamic constant.
* \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
ColsAtCompileTime = ei_traits<Derived>::ColsAtCompileTime,
/**< The number of columns at compile-time. This is just a copy of the value provided
* by the \a Derived type. If a value is not known at compile-time,
* it is set to the \a Dynamic constant.
* \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
SizeAtCompileTime = (ei_size_at_compile_time<ei_traits<Derived>::RowsAtCompileTime,
ei_traits<Derived>::ColsAtCompileTime>::ret),
/**< This is equal to the number of coefficients, i.e. the number of
* rows times the number of columns, or to \a Dynamic if this is not
* known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
MaxRowsAtCompileTime = RowsAtCompileTime,
MaxColsAtCompileTime = ColsAtCompileTime,
MaxSizeAtCompileTime = (ei_size_at_compile_time<MaxRowsAtCompileTime,
MaxColsAtCompileTime>::ret),
IsVectorAtCompileTime = RowsAtCompileTime == 1 || ColsAtCompileTime == 1,
/**< This is set to true if either the number of rows or the number of
* columns is known at compile-time to be equal to 1. Indeed, in that case,
* we are dealing with a column-vector (if there is only one column) or with
* a row-vector (if there is only one row). */
Flags = ei_traits<Derived>::Flags,
/**< This stores expression \ref flags flags which may or may not be inherited by new expressions
* constructed from this one. See the \ref flags "list of flags".
*/
CoeffReadCost = ei_traits<Derived>::CoeffReadCost,
/**< This is a rough measure of how expensive it is to read one coefficient from
* this expression.
*/
IsRowMajor = Flags&RowMajorBit ? 1 : 0
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
};
Big rewrite in the Sparse module: SparseMatrixBase no longer inherits MatrixBase. That means a lot of features which were available for sparse matrices via the dense (and super slow) implemention are no longer available. All features which make sense for sparse matrices (aka can be implemented efficiently) will be implemented soon, but don't expect to see an API as rich as for the dense path. Other changes: * no block(), row(), col() anymore. * instead use .innerVector() to get a col or row vector of a matrix. * .segment(), start(), end() will be back soon, not sure for block() * faster cwise product
2009-01-14 14:24:10 +00:00
/** \internal the return type of MatrixBase::conjugate() */
typedef typename ei_meta_if<NumTraits<Scalar>::IsComplex,
const SparseCwiseUnaryOp<ei_scalar_conjugate_op<Scalar>, Derived>,
const Derived&
>::ret ConjugateReturnType;
/** \internal the return type of MatrixBase::real() */
typedef CwiseUnaryOp<ei_scalar_real_op<Scalar>, Derived> RealReturnType;
/** \internal the return type of MatrixBase::imag() */
typedef CwiseUnaryOp<ei_scalar_imag_op<Scalar>, Derived> ImagReturnType;
/** \internal the return type of MatrixBase::adjoint() */
typedef Eigen::Transpose<NestByValue<typename ei_cleantype<ConjugateReturnType>::type> >
AdjointReturnType;
#ifndef EIGEN_PARSED_BY_DOXYGEN
/** This is the "real scalar" type; if the \a Scalar type is already real numbers
* (e.g. int, float or double) then \a RealScalar is just the same as \a Scalar. If
* \a Scalar is \a std::complex<T> then RealScalar is \a T.
*
* \sa class NumTraits
*/
typedef typename NumTraits<Scalar>::Real RealScalar;
/** type of the equivalent square matrix */
typedef Matrix<Scalar,EIGEN_ENUM_MAX(RowsAtCompileTime,ColsAtCompileTime),
EIGEN_ENUM_MAX(RowsAtCompileTime,ColsAtCompileTime)> SquareMatrixType;
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
inline Derived& derived() { return *static_cast<Derived*>(this); }
inline Derived& const_cast_derived() const
{ return *static_cast<Derived*>(const_cast<SparseMatrixBase*>(this)); }
Big rewrite in the Sparse module: SparseMatrixBase no longer inherits MatrixBase. That means a lot of features which were available for sparse matrices via the dense (and super slow) implemention are no longer available. All features which make sense for sparse matrices (aka can be implemented efficiently) will be implemented soon, but don't expect to see an API as rich as for the dense path. Other changes: * no block(), row(), col() anymore. * instead use .innerVector() to get a col or row vector of a matrix. * .segment(), start(), end() will be back soon, not sure for block() * faster cwise product
2009-01-14 14:24:10 +00:00
#endif // not EIGEN_PARSED_BY_DOXYGEN
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
Big rewrite in the Sparse module: SparseMatrixBase no longer inherits MatrixBase. That means a lot of features which were available for sparse matrices via the dense (and super slow) implemention are no longer available. All features which make sense for sparse matrices (aka can be implemented efficiently) will be implemented soon, but don't expect to see an API as rich as for the dense path. Other changes: * no block(), row(), col() anymore. * instead use .innerVector() to get a col or row vector of a matrix. * .segment(), start(), end() will be back soon, not sure for block() * faster cwise product
2009-01-14 14:24:10 +00:00
/** \returns the number of rows. \sa cols(), RowsAtCompileTime */
inline int rows() const { return derived().rows(); }
/** \returns the number of columns. \sa rows(), ColsAtCompileTime*/
inline int cols() const { return derived().cols(); }
/** \returns the number of coefficients, which is \a rows()*cols().
* \sa rows(), cols(), SizeAtCompileTime. */
inline int size() const { return rows() * cols(); }
/** \returns the number of nonzero coefficients which is in practice the number
* of stored coefficients. */
inline int nonZeros() const { return derived.nonZeros(); }
/** \returns true if either the number of rows or the number of columns is equal to 1.
* In other words, this function returns
* \code rows()==1 || cols()==1 \endcode
* \sa rows(), cols(), IsVectorAtCompileTime. */
inline bool isVector() const { return rows()==1 || cols()==1; }
/** \returns the size of the storage major dimension,
* i.e., the number of columns for a columns major matrix, and the number of rows otherwise */
int outerSize() const { return (int(Flags)&RowMajorBit) ? this->rows() : this->cols(); }
/** \returns the size of the inner dimension according to the storage order,
* i.e., the number of rows for a columns major matrix, and the number of cols otherwise */
int innerSize() const { return (int(Flags)&RowMajorBit) ? this->cols() : this->rows(); }
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
bool isRValue() const { return m_isRValue; }
* added an in-place version of inverseProduct which might be twice faster fot small fixed size matrix * added a sparse triangular solver (sparse version of inverseProduct) * various other improvements in the Sparse module
2008-06-29 21:29:12 +00:00
Derived& markAsRValue() { m_isRValue = true; return derived(); }
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
Big rewrite in the Sparse module: SparseMatrixBase no longer inherits MatrixBase. That means a lot of features which were available for sparse matrices via the dense (and super slow) implemention are no longer available. All features which make sense for sparse matrices (aka can be implemented efficiently) will be implemented soon, but don't expect to see an API as rich as for the dense path. Other changes: * no block(), row(), col() anymore. * instead use .innerVector() to get a col or row vector of a matrix. * .segment(), start(), end() will be back soon, not sure for block() * faster cwise product
2009-01-14 14:24:10 +00:00
SparseMatrixBase() : m_isRValue(false) { /* TODO check flags */ }
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
inline Derived& operator=(const Derived& other)
{
Sparse module: * several fixes (transpose, matrix product, etc...) * Added a basic cholesky factorization * Added a low level hybrid dense/sparse vector class to help writing code involving intensive read/write in a fixed vector. It is currently used to implement the matrix product itself as well as in the Cholesky factorization.
2008-10-04 14:23:00 +00:00
// std::cout << "Derived& operator=(const Derived& other)\n";
Big rewrite in the Sparse module: SparseMatrixBase no longer inherits MatrixBase. That means a lot of features which were available for sparse matrices via the dense (and super slow) implemention are no longer available. All features which make sense for sparse matrices (aka can be implemented efficiently) will be implemented soon, but don't expect to see an API as rich as for the dense path. Other changes: * no block(), row(), col() anymore. * instead use .innerVector() to get a col or row vector of a matrix. * .segment(), start(), end() will be back soon, not sure for block() * faster cwise product
2009-01-14 14:24:10 +00:00
// if (other.isRValue())
// derived().swap(other.const_cast_derived());
// else
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
this->operator=<Derived>(other);
return derived();
}
Big rewrite in the Sparse module: SparseMatrixBase no longer inherits MatrixBase. That means a lot of features which were available for sparse matrices via the dense (and super slow) implemention are no longer available. All features which make sense for sparse matrices (aka can be implemented efficiently) will be implemented soon, but don't expect to see an API as rich as for the dense path. Other changes: * no block(), row(), col() anymore. * instead use .innerVector() to get a col or row vector of a matrix. * .segment(), start(), end() will be back soon, not sure for block() * faster cwise product
2009-01-14 14:24:10 +00:00
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
template<typename OtherDerived>
Big rewrite in the Sparse module: SparseMatrixBase no longer inherits MatrixBase. That means a lot of features which were available for sparse matrices via the dense (and super slow) implemention are no longer available. All features which make sense for sparse matrices (aka can be implemented efficiently) will be implemented soon, but don't expect to see an API as rich as for the dense path. Other changes: * no block(), row(), col() anymore. * instead use .innerVector() to get a col or row vector of a matrix. * .segment(), start(), end() will be back soon, not sure for block() * faster cwise product
2009-01-14 14:24:10 +00:00
inline void assignGeneric(const OtherDerived& other)
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
{
Sparse module: * several fixes (transpose, matrix product, etc...) * Added a basic cholesky factorization * Added a low level hybrid dense/sparse vector class to help writing code involving intensive read/write in a fixed vector. It is currently used to implement the matrix product itself as well as in the Cholesky factorization.
2008-10-04 14:23:00 +00:00
// std::cout << "Derived& operator=(const MatrixBase<OtherDerived>& other)\n";
Sparse module: add a more flexible SparseMatrix::fillrand() function which allows to fill a matrix with random inner coordinates (makes sense only when a very few coeffs are inserted per col/row)
2008-12-11 18:26:24 +00:00
//const bool transpose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
ei_assert((!((Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit))) && "the transpose operation is supposed to be handled in SparseMatrix::operator=");
* added innerSize / outerSize functions to MatrixBase * added complete implementation of sparse matrix product (with a little glue in Eigen/Core) * added an exhaustive bench of sparse products including GMM++ and MTL4 => Eigen outperforms in all transposed/density configurations !
2008-06-28 23:07:14 +00:00
const int outerSize = other.outerSize();
sparse module: - remove some useless stuff => let's focus on a single sparse matrix format - finalize the new RandomSetter
2008-10-21 13:35:04 +00:00
//typedef typename ei_meta_if<transpose, LinkedVectorMatrix<Scalar,Flags&RowMajorBit>, Derived>::ret TempType;
// thanks to shallow copies, we always eval to a tempary
Derived temp(other.rows(), other.cols());
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
temp.startFill(std::max(this->rows(),this->cols())*2);
for (int j=0; j<outerSize; ++j)
{
for (typename OtherDerived::InnerIterator it(other.derived(), j); it; ++it)
{
Scalar v = it.value();
if (v!=Scalar(0))
fix a number of compiler errors and warnings with gcc 4.3. There was a missing #include<algorithm> so I'm not sure how it compiled at all for you :)
2008-08-22 01:19:53 +00:00
{
* added innerSize / outerSize functions to MatrixBase * added complete implementation of sparse matrix product (with a little glue in Eigen/Core) * added an exhaustive bench of sparse products including GMM++ and MTL4 => Eigen outperforms in all transposed/density configurations !
2008-06-28 23:07:14 +00:00
if (OtherDerived::Flags & RowMajorBit) temp.fill(j,it.index()) = v;
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
else temp.fill(it.index(),j) = v;
fix a number of compiler errors and warnings with gcc 4.3. There was a missing #include<algorithm> so I'm not sure how it compiled at all for you :)
2008-08-22 01:19:53 +00:00
}
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
}
}
temp.endFill();
* added innerSize / outerSize functions to MatrixBase * added complete implementation of sparse matrix product (with a little glue in Eigen/Core) * added an exhaustive bench of sparse products including GMM++ and MTL4 => Eigen outperforms in all transposed/density configurations !
2008-06-28 23:07:14 +00:00
* added an in-place version of inverseProduct which might be twice faster fot small fixed size matrix * added a sparse triangular solver (sparse version of inverseProduct) * various other improvements in the Sparse module
2008-06-29 21:29:12 +00:00
derived() = temp.markAsRValue();
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
}
Big rewrite in the Sparse module: SparseMatrixBase no longer inherits MatrixBase. That means a lot of features which were available for sparse matrices via the dense (and super slow) implemention are no longer available. All features which make sense for sparse matrices (aka can be implemented efficiently) will be implemented soon, but don't expect to see an API as rich as for the dense path. Other changes: * no block(), row(), col() anymore. * instead use .innerVector() to get a col or row vector of a matrix. * .segment(), start(), end() will be back soon, not sure for block() * faster cwise product
2009-01-14 14:24:10 +00:00
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
template<typename OtherDerived>
inline Derived& operator=(const SparseMatrixBase<OtherDerived>& other)
{
Sparse module: * several fixes (transpose, matrix product, etc...) * Added a basic cholesky factorization * Added a low level hybrid dense/sparse vector class to help writing code involving intensive read/write in a fixed vector. It is currently used to implement the matrix product itself as well as in the Cholesky factorization.
2008-10-04 14:23:00 +00:00
// std::cout << typeid(OtherDerived).name() << "\n";
// std::cout << Flags << " " << OtherDerived::Flags << "\n";
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
const bool transpose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
* added innerSize / outerSize functions to MatrixBase * added complete implementation of sparse matrix product (with a little glue in Eigen/Core) * added an exhaustive bench of sparse products including GMM++ and MTL4 => Eigen outperforms in all transposed/density configurations !
2008-06-28 23:07:14 +00:00
// std::cout << "eval transpose = " << transpose << "\n";
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
const int outerSize = (int(OtherDerived::Flags) & RowMajorBit) ? other.rows() : other.cols();
if ((!transpose) && other.isRValue())
{
// eval without temporary
derived().resize(other.rows(), other.cols());
derived().startFill(std::max(this->rows(),this->cols())*2);
for (int j=0; j<outerSize; ++j)
{
for (typename OtherDerived::InnerIterator it(other.derived(), j); it; ++it)
{
Scalar v = it.value();
if (v!=Scalar(0))
fix a number of compiler errors and warnings with gcc 4.3. There was a missing #include<algorithm> so I'm not sure how it compiled at all for you :)
2008-08-22 01:19:53 +00:00
{
Big rewrite in the Sparse module: SparseMatrixBase no longer inherits MatrixBase. That means a lot of features which were available for sparse matrices via the dense (and super slow) implemention are no longer available. All features which make sense for sparse matrices (aka can be implemented efficiently) will be implemented soon, but don't expect to see an API as rich as for the dense path. Other changes: * no block(), row(), col() anymore. * instead use .innerVector() to get a col or row vector of a matrix. * .segment(), start(), end() will be back soon, not sure for block() * faster cwise product
2009-01-14 14:24:10 +00:00
if (IsRowMajor) derived().fill(j,it.index()) = v;
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
else derived().fill(it.index(),j) = v;
fix a number of compiler errors and warnings with gcc 4.3. There was a missing #include<algorithm> so I'm not sure how it compiled at all for you :)
2008-08-22 01:19:53 +00:00
}
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
}
}
derived().endFill();
}
else
Big rewrite in the Sparse module: SparseMatrixBase no longer inherits MatrixBase. That means a lot of features which were available for sparse matrices via the dense (and super slow) implemention are no longer available. All features which make sense for sparse matrices (aka can be implemented efficiently) will be implemented soon, but don't expect to see an API as rich as for the dense path. Other changes: * no block(), row(), col() anymore. * instead use .innerVector() to get a col or row vector of a matrix. * .segment(), start(), end() will be back soon, not sure for block() * faster cwise product
2009-01-14 14:24:10 +00:00
{
assignGeneric(other.derived());
}
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
return derived();
}
* added innerSize / outerSize functions to MatrixBase * added complete implementation of sparse matrix product (with a little glue in Eigen/Core) * added an exhaustive bench of sparse products including GMM++ and MTL4 => Eigen outperforms in all transposed/density configurations !
2008-06-28 23:07:14 +00:00
template<typename Lhs, typename Rhs>
Add support for sparse * dense and dense * sparse matrix/vector products
2009-01-14 17:41:55 +00:00
inline Derived& operator=(const SparseProduct<Lhs,Rhs,SparseTimeSparseProduct>& product);
* added innerSize / outerSize functions to MatrixBase * added complete implementation of sparse matrix product (with a little glue in Eigen/Core) * added an exhaustive bench of sparse products including GMM++ and MTL4 => Eigen outperforms in all transposed/density configurations !
2008-06-28 23:07:14 +00:00
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
friend std::ostream & operator << (std::ostream & s, const SparseMatrixBase& m)
{
if (Flags&RowMajorBit)
{
* added innerSize / outerSize functions to MatrixBase * added complete implementation of sparse matrix product (with a little glue in Eigen/Core) * added an exhaustive bench of sparse products including GMM++ and MTL4 => Eigen outperforms in all transposed/density configurations !
2008-06-28 23:07:14 +00:00
for (int row=0; row<m.outerSize(); ++row)
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
{
int col = 0;
for (typename Derived::InnerIterator it(m.derived(), row); it; ++it)
{
for ( ; col<it.index(); ++col)
s << "0 ";
extend sparse unit test and more bugfix, major todo: finilize the SparseSetter
2008-08-21 18:40:56 +00:00
s << it.value() << " ";
* added an in-place version of inverseProduct which might be twice faster fot small fixed size matrix * added a sparse triangular solver (sparse version of inverseProduct) * various other improvements in the Sparse module
2008-06-29 21:29:12 +00:00
++col;
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
}
for ( ; col<m.cols(); ++col)
s << "0 ";
s << std::endl;
}
}
else
{
Extended sparse unit-test: nested blocks and InnerIterators. Block specialization for sparse matrices. InnerIterators for Blocks and fixes in CoreIterators.
2008-09-02 15:28:49 +00:00
if (m.cols() == 1) {
int row = 0;
for (typename Derived::InnerIterator it(m.derived(), 0); it; ++it)
{
for ( ; row<it.index(); ++row)
s << "0" << std::endl;
s << it.value() << std::endl;
++row;
}
for ( ; row<m.rows(); ++row)
s << "0" << std::endl;
}
else
{
Several improvements in sparse module: * add a LDL^T factorization with solver using code from T. Davis's LDL library (LPGL2.1+) * various bug fixes in trianfular solver, matrix product, etc. * improve cmake files for the supported libraries * split the sparse unit test * etc.
2008-11-05 13:47:55 +00:00
SparseMatrix<Scalar, RowMajorBit> trans = m.derived();
Extended sparse unit-test: nested blocks and InnerIterators. Block specialization for sparse matrices. InnerIterators for Blocks and fixes in CoreIterators.
2008-09-02 15:28:49 +00:00
s << trans;
}
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
}
return s;
}
Big rewrite in the Sparse module: SparseMatrixBase no longer inherits MatrixBase. That means a lot of features which were available for sparse matrices via the dense (and super slow) implemention are no longer available. All features which make sense for sparse matrices (aka can be implemented efficiently) will be implemented soon, but don't expect to see an API as rich as for the dense path. Other changes: * no block(), row(), col() anymore. * instead use .innerVector() to get a col or row vector of a matrix. * .segment(), start(), end() will be back soon, not sure for block() * faster cwise product
2009-01-14 14:24:10 +00:00
const SparseCwiseUnaryOp<ei_scalar_opposite_op<typename ei_traits<Derived>::Scalar>,Derived> operator-() const;
template<typename OtherDerived>
const SparseCwiseBinaryOp<ei_scalar_sum_op<typename ei_traits<Derived>::Scalar>, Derived, OtherDerived>
operator+(const SparseMatrixBase<OtherDerived> &other) const;
template<typename OtherDerived>
const SparseCwiseBinaryOp<ei_scalar_difference_op<typename ei_traits<Derived>::Scalar>, Derived, OtherDerived>
operator-(const SparseMatrixBase<OtherDerived> &other) const;
template<typename OtherDerived>
Derived& operator+=(const SparseMatrixBase<OtherDerived>& other);
template<typename OtherDerived>
Derived& operator-=(const SparseMatrixBase<OtherDerived>& other);
// template<typename Lhs,typename Rhs>
// Derived& operator+=(const Flagged<Product<Lhs,Rhs,CacheFriendlyProduct>, 0, EvalBeforeNestingBit | EvalBeforeAssigningBit>& other);
Derived& operator*=(const Scalar& other);
Derived& operator/=(const Scalar& other);
const SparseCwiseUnaryOp<ei_scalar_multiple_op<typename ei_traits<Derived>::Scalar>, Derived>
operator*(const Scalar& scalar) const;
const SparseCwiseUnaryOp<ei_scalar_quotient1_op<typename ei_traits<Derived>::Scalar>, Derived>
operator/(const Scalar& scalar) const;
inline friend const SparseCwiseUnaryOp<ei_scalar_multiple_op<typename ei_traits<Derived>::Scalar>, Derived>
operator*(const Scalar& scalar, const SparseMatrixBase& matrix)
{ return matrix*scalar; }
template<typename OtherDerived>
const typename SparseProductReturnType<Derived,OtherDerived>::Type
operator*(const SparseMatrixBase<OtherDerived> &other) const;
Add support for sparse * dense and dense * sparse matrix/vector products
2009-01-14 17:41:55 +00:00
// dense * sparse (return a dense object)
template<typename OtherDerived> friend
const typename SparseProductReturnType<OtherDerived,Derived>::Type
operator*(const MatrixBase<OtherDerived>& lhs, const Derived& rhs)
{ return typename SparseProductReturnType<OtherDerived,Derived>::Type(lhs.derived(),rhs); }
template<typename OtherDerived>
const typename SparseProductReturnType<Derived,OtherDerived>::Type
operator*(const MatrixBase<OtherDerived> &other) const;
Big rewrite in the Sparse module: SparseMatrixBase no longer inherits MatrixBase. That means a lot of features which were available for sparse matrices via the dense (and super slow) implemention are no longer available. All features which make sense for sparse matrices (aka can be implemented efficiently) will be implemented soon, but don't expect to see an API as rich as for the dense path. Other changes: * no block(), row(), col() anymore. * instead use .innerVector() to get a col or row vector of a matrix. * .segment(), start(), end() will be back soon, not sure for block() * faster cwise product
2009-01-14 14:24:10 +00:00
template<typename OtherDerived>
Derived& operator*=(const SparseMatrixBase<OtherDerived>& other);
template<typename OtherDerived>
typename ei_plain_matrix_type_column_major<OtherDerived>::type
solveTriangular(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived>
void solveTriangularInPlace(MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived> Scalar dot(const MatrixBase<OtherDerived>& other) const;
template<typename OtherDerived> Scalar dot(const SparseMatrixBase<OtherDerived>& other) const;
RealScalar squaredNorm() const;
RealScalar norm() const;
// const PlainMatrixType normalized() const;
// void normalize();
SparseTranspose<Derived> transpose() { return derived(); }
const SparseTranspose<Derived> transpose() const { return derived(); }
// void transposeInPlace();
// const AdjointReturnType adjoint() const;
SparseInnerVector<Derived> innerVector(int outer);
const SparseInnerVector<Derived> innerVector(int outer) const;
// RowXpr row(int i);
// const RowXpr row(int i) const;
// ColXpr col(int i);
// const ColXpr col(int i) const;
// typename BlockReturnType<Derived>::Type block(int startRow, int startCol, int blockRows, int blockCols);
// const typename BlockReturnType<Derived>::Type
// block(int startRow, int startCol, int blockRows, int blockCols) const;
//
// typename BlockReturnType<Derived>::SubVectorType segment(int start, int size);
// const typename BlockReturnType<Derived>::SubVectorType segment(int start, int size) const;
//
// typename BlockReturnType<Derived,Dynamic>::SubVectorType start(int size);
// const typename BlockReturnType<Derived,Dynamic>::SubVectorType start(int size) const;
//
// typename BlockReturnType<Derived,Dynamic>::SubVectorType end(int size);
// const typename BlockReturnType<Derived,Dynamic>::SubVectorType end(int size) const;
//
// typename BlockReturnType<Derived>::Type corner(CornerType type, int cRows, int cCols);
// const typename BlockReturnType<Derived>::Type corner(CornerType type, int cRows, int cCols) const;
//
// template<int BlockRows, int BlockCols>
// typename BlockReturnType<Derived, BlockRows, BlockCols>::Type block(int startRow, int startCol);
// template<int BlockRows, int BlockCols>
// const typename BlockReturnType<Derived, BlockRows, BlockCols>::Type block(int startRow, int startCol) const;
// template<int CRows, int CCols>
// typename BlockReturnType<Derived, CRows, CCols>::Type corner(CornerType type);
// template<int CRows, int CCols>
// const typename BlockReturnType<Derived, CRows, CCols>::Type corner(CornerType type) const;
// template<int Size> typename BlockReturnType<Derived,Size>::SubVectorType start(void);
// template<int Size> const typename BlockReturnType<Derived,Size>::SubVectorType start() const;
// template<int Size> typename BlockReturnType<Derived,Size>::SubVectorType end();
// template<int Size> const typename BlockReturnType<Derived,Size>::SubVectorType end() const;
// template<int Size> typename BlockReturnType<Derived,Size>::SubVectorType segment(int start);
// template<int Size> const typename BlockReturnType<Derived,Size>::SubVectorType segment(int start) const;
// DiagonalCoeffs<Derived> diagonal();
// const DiagonalCoeffs<Derived> diagonal() const;
// template<unsigned int Mode> Part<Derived, Mode> part();
// template<unsigned int Mode> const Part<Derived, Mode> part() const;
// static const ConstantReturnType Constant(int rows, int cols, const Scalar& value);
// static const ConstantReturnType Constant(int size, const Scalar& value);
// static const ConstantReturnType Constant(const Scalar& value);
// template<typename CustomNullaryOp>
// static const CwiseNullaryOp<CustomNullaryOp, Derived> NullaryExpr(int rows, int cols, const CustomNullaryOp& func);
// template<typename CustomNullaryOp>
// static const CwiseNullaryOp<CustomNullaryOp, Derived> NullaryExpr(int size, const CustomNullaryOp& func);
// template<typename CustomNullaryOp>
// static const CwiseNullaryOp<CustomNullaryOp, Derived> NullaryExpr(const CustomNullaryOp& func);
// static const ConstantReturnType Zero(int rows, int cols);
// static const ConstantReturnType Zero(int size);
// static const ConstantReturnType Zero();
// static const ConstantReturnType Ones(int rows, int cols);
// static const ConstantReturnType Ones(int size);
// static const ConstantReturnType Ones();
// static const IdentityReturnType Identity();
// static const IdentityReturnType Identity(int rows, int cols);
// static const BasisReturnType Unit(int size, int i);
// static const BasisReturnType Unit(int i);
// static const BasisReturnType UnitX();
// static const BasisReturnType UnitY();
// static const BasisReturnType UnitZ();
// static const BasisReturnType UnitW();
// const DiagonalMatrix<Derived> asDiagonal() const;
// Derived& setConstant(const Scalar& value);
// Derived& setZero();
// Derived& setOnes();
// Derived& setRandom();
// Derived& setIdentity();
Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> toDense() const
{
Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> res(rows(),cols());
res.setZero();
for (int j=0; j<outerSize(); ++j)
{
for (typename Derived::InnerIterator i(derived(),j); i; ++i)
if(IsRowMajor)
res.coeffRef(j,i.index()) = i.value();
else
res.coeffRef(i.index(),j) = i.value();
}
return res;
}
template<typename OtherDerived>
bool isApprox(const SparseMatrixBase<OtherDerived>& other,
RealScalar prec = precision<Scalar>()) const
{ return toDense().isApprox(other.toDense(),prec); }
template<typename OtherDerived>
bool isApprox(const MatrixBase<OtherDerived>& other,
RealScalar prec = precision<Scalar>()) const
{ return toDense().isApprox(other,prec); }
// bool isMuchSmallerThan(const RealScalar& other,
// RealScalar prec = precision<Scalar>()) const;
// template<typename OtherDerived>
// bool isMuchSmallerThan(const MatrixBase<OtherDerived>& other,
// RealScalar prec = 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 isUpperTriangular(RealScalar prec = precision<Scalar>()) const;
// bool isLowerTriangular(RealScalar prec = precision<Scalar>()) const;
// template<typename OtherDerived>
// bool isOrthogonal(const MatrixBase<OtherDerived>& other,
// RealScalar prec = precision<Scalar>()) const;
// bool isUnitary(RealScalar prec = precision<Scalar>()) const;
// template<typename OtherDerived>
// inline bool operator==(const MatrixBase<OtherDerived>& other) const
// { return (cwise() == other).all(); }
// template<typename OtherDerived>
// inline bool operator!=(const MatrixBase<OtherDerived>& other) const
// { return (cwise() != other).any(); }
template<typename NewType>
const SparseCwiseUnaryOp<ei_scalar_cast_op<typename ei_traits<Derived>::Scalar, NewType>, Derived> cast() const;
/** \returns the matrix or vector obtained by evaluating this expression.
*
* Notice that in the case of a plain matrix or vector (not an expression) this function just returns
* a const reference, in order to avoid a useless copy.
*/
EIGEN_STRONG_INLINE const typename ei_eval<Derived>::type eval() const
{ return typename ei_eval<Derived>::type(derived()); }
// template<typename OtherDerived>
// void swap(const MatrixBase<OtherDerived>& other);
template<unsigned int Added>
const SparseFlagged<Derived, Added, 0> marked() const;
// const Flagged<Derived, 0, EvalBeforeNestingBit | EvalBeforeAssigningBit> lazy() const;
/** \returns number of elements to skip to pass from one row (resp. column) to another
* for a row-major (resp. column-major) matrix.
* Combined with coeffRef() and the \ref flags flags, it allows a direct access to the data
* of the underlying matrix.
*/
// inline int stride(void) const { return derived().stride(); }
// inline const NestByValue<Derived> nestByValue() const;
ConjugateReturnType conjugate() const;
const RealReturnType real() const;
const ImagReturnType imag() const;
template<typename CustomUnaryOp>
const SparseCwiseUnaryOp<CustomUnaryOp, Derived> unaryExpr(const CustomUnaryOp& func = CustomUnaryOp()) const;
// template<typename CustomBinaryOp, typename OtherDerived>
// const CwiseBinaryOp<CustomBinaryOp, Derived, OtherDerived>
// binaryExpr(const MatrixBase<OtherDerived> &other, const CustomBinaryOp& func = CustomBinaryOp()) const;
Scalar sum() const;
// Scalar trace() const;
// typename ei_traits<Derived>::Scalar minCoeff() const;
// typename ei_traits<Derived>::Scalar maxCoeff() const;
// typename ei_traits<Derived>::Scalar minCoeff(int* row, int* col = 0) const;
// typename ei_traits<Derived>::Scalar maxCoeff(int* row, int* col = 0) const;
// template<typename BinaryOp>
// typename ei_result_of<BinaryOp(typename ei_traits<Derived>::Scalar)>::type
// redux(const BinaryOp& func) const;
// template<typename Visitor>
// void visit(Visitor& func) const;
const SparseCwise<Derived> cwise() const;
SparseCwise<Derived> cwise();
// inline const WithFormat<Derived> format(const IOFormat& fmt) const;
/////////// Array module ///////////
/*
bool all(void) const;
bool any(void) const;
const PartialRedux<Derived,Horizontal> rowwise() const;
const PartialRedux<Derived,Vertical> colwise() const;
static const CwiseNullaryOp<ei_scalar_random_op<Scalar>,Derived> Random(int rows, int cols);
static const CwiseNullaryOp<ei_scalar_random_op<Scalar>,Derived> Random(int size);
static const CwiseNullaryOp<ei_scalar_random_op<Scalar>,Derived> Random();
template<typename ThenDerived,typename ElseDerived>
const Select<Derived,ThenDerived,ElseDerived>
select(const MatrixBase<ThenDerived>& thenMatrix,
const MatrixBase<ElseDerived>& elseMatrix) const;
template<typename ThenDerived>
inline const Select<Derived,ThenDerived, NestByValue<typename ThenDerived::ConstantReturnType> >
select(const MatrixBase<ThenDerived>& thenMatrix, typename ThenDerived::Scalar elseScalar) const;
template<typename ElseDerived>
inline const Select<Derived, NestByValue<typename ElseDerived::ConstantReturnType>, ElseDerived >
select(typename ElseDerived::Scalar thenScalar, const MatrixBase<ElseDerived>& elseMatrix) const;
template<int p> RealScalar lpNorm() const;
*/
Sparse module: * extend unit tests * add support for generic sum reduction and dot product * optimize the cwise()* : this is a special case of CwiseBinaryOp where we only have to process the coeffs which are not null for *both* matrices. Perhaps there exist some other binary operations like that ?
2009-01-07 17:01:57 +00:00
// template<typename OtherDerived>
// Scalar dot(const MatrixBase<OtherDerived>& other) const
// {
// EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
// EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
// EIGEN_STATIC_ASSERT((ei_is_same_type<Scalar, typename OtherDerived::Scalar>::ret),
// YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
//
// ei_assert(derived().size() == other.size());
// // short version, but the assembly looks more complicated because
// // of the CwiseBinaryOp iterator complexity
// // return res = (derived().cwise() * other.derived().conjugate()).sum();
//
// // optimized, generic version
// typename Derived::InnerIterator i(derived(),0);
// typename OtherDerived::InnerIterator j(other.derived(),0);
// Scalar res = 0;
// while (i && j)
// {
// if (i.index()==j.index())
// {
// // std::cerr << i.value() << " * " << j.value() << "\n";
// res += i.value() * ei_conj(j.value());
// ++i; ++j;
// }
// else if (i.index()<j.index())
// ++i;
// else
// ++j;
// }
// return res;
// }
//
// Scalar sum() const
// {
// Scalar res = 0;
// for (typename Derived::InnerIterator iter(*this,0); iter; ++iter)
// {
// res += iter.value();
// }
// return res;
// }
various work on the Sparse module: * added some glue to Eigen/Core (SparseBit, ei_eval, Matrix) * add two new sparse matrix types: HashMatrix: based on std::map (for random writes) LinkedVectorMatrix: array of linked vectors (for outer coherent writes, e.g. to transpose a matrix) * add a SparseSetter class to easily set/update any kind of matrices, e.g.: { SparseSetter<MatrixType,RandomAccessPattern> wrapper(mymatrix); for (...) wrapper->coeffRef(rand(),rand()) = rand(); } * automatic shallow copy for RValue * and a lot of mess ! plus: * remove the remaining ArrayBit related stuff * don't use alloca in product for very large memory allocation
2008-06-26 23:22:26 +00:00
protected:
bool m_isRValue;
};
#endif // EIGEN_SPARSEMATRIXBASE_H
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