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add sparse * permutation products with assiciated unit tests

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Gael Guennebaud 2012-03-29 11:29:43 +02:00
parent 8ff882aa4c
commit c172abdcc7
3 changed files with 216 additions and 0 deletions
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1
Eigen/SparseCore
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@ -44,6 +44,7 @@ struct Sparse {};
#include "src/SparseCore/SparseCwiseUnaryOp.h"
#include "src/SparseCore/SparseCwiseBinaryOp.h"
#include "src/SparseCore/SparseDot.h"
#include "src/SparseCore/SparsePermutation.h"
#include "src/SparseCore/SparseAssign.h"
#include "src/SparseCore/SparseRedux.h"
#include "src/SparseCore/SparseFuzzy.h"

160
Eigen/src/SparseCore/SparsePermutation.h Normal file
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@ -0,0 +1,160 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.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_SPARSE_PERMUTATION_H
#define EIGEN_SPARSE_PERMUTATION_H
// This file implements sparse * permutation products
namespace internal {
template<typename PermutationType, typename MatrixType, int Side, bool Transposed>
struct traits<permut_sparsematrix_product_retval<PermutationType, MatrixType, Side, Transposed> >
{
typedef typename remove_all<typename MatrixType::Nested>::type MatrixTypeNestedCleaned;
typedef typename MatrixTypeNestedCleaned::Scalar Scalar;
typedef typename MatrixTypeNestedCleaned::Index Index;
enum {
SrcStorageOrder = MatrixTypeNestedCleaned::Flags&RowMajorBit ? RowMajor : ColMajor,
MoveOuter = SrcStorageOrder==RowMajor ? Side==OnTheLeft : Side==OnTheRight
};
typedef typename internal::conditional<MoveOuter,
SparseMatrix<Scalar,SrcStorageOrder,Index>,
SparseMatrix<Scalar,int(SrcStorageOrder)==RowMajor?ColMajor:RowMajor,Index> >::type ReturnType;
};
template<typename PermutationType, typename MatrixType, int Side, bool Transposed>
struct permut_sparsematrix_product_retval
: public ReturnByValue<permut_sparsematrix_product_retval<PermutationType, MatrixType, Side, Transposed> >
{
typedef typename remove_all<typename MatrixType::Nested>::type MatrixTypeNestedCleaned;
typedef typename MatrixTypeNestedCleaned::Scalar Scalar;
typedef typename MatrixTypeNestedCleaned::Index Index;
enum {
SrcStorageOrder = MatrixTypeNestedCleaned::Flags&RowMajorBit ? RowMajor : ColMajor,
MoveOuter = SrcStorageOrder==RowMajor ? Side==OnTheLeft : Side==OnTheRight
};
permut_sparsematrix_product_retval(const PermutationType& perm, const MatrixType& matrix)
: m_permutation(perm), m_matrix(matrix)
{}
inline int rows() const { return m_matrix.rows(); }
inline int cols() const { return m_matrix.cols(); }
template<typename Dest> inline void evalTo(Dest& dst) const
{
if(MoveOuter)
{
SparseMatrix<Scalar,SrcStorageOrder,Index> tmp(m_matrix.rows(), m_matrix.cols());
VectorXi sizes(m_matrix.outerSize());
for(Index j=0; j<m_matrix.outerSize(); ++j)
{
Index jp = m_permutation.indices().coeff(j);
sizes[((Side==OnTheLeft) ^ Transposed) ? jp : j] = m_matrix.innerVector(((Side==OnTheRight) ^ Transposed) ? jp : j).size();
}
tmp.reserve(sizes);
for(Index j=0; j<m_matrix.outerSize(); ++j)
{
Index jp = m_permutation.indices().coeff(j);
Index jsrc = ((Side==OnTheRight) ^ Transposed) ? jp : j;
Index jdst = ((Side==OnTheLeft) ^ Transposed) ? jp : j;
for(typename MatrixTypeNestedCleaned::InnerIterator it(m_matrix,jsrc); it; ++it)
tmp.insertByOuterInner(jdst,it.index()) = it.value();
}
dst = tmp;
}
else
{
SparseMatrix<Scalar,int(SrcStorageOrder)==RowMajor?ColMajor:RowMajor,Index> tmp(m_matrix.rows(), m_matrix.cols());
VectorXi sizes(tmp.outerSize());
sizes.setZero();
PermutationMatrix<Dynamic,Dynamic,Index> perm;
if((Side==OnTheLeft) ^ Transposed)
perm = m_permutation;
else
perm = m_permutation.transpose();
for(Index j=0; j<m_matrix.outerSize(); ++j)
for(typename MatrixTypeNestedCleaned::InnerIterator it(m_matrix,j); it; ++it)
sizes[perm.indices().coeff(it.index())]++;
tmp.reserve(sizes);
for(Index j=0; j<m_matrix.outerSize(); ++j)
for(typename MatrixTypeNestedCleaned::InnerIterator it(m_matrix,j); it; ++it)
tmp.insertByOuterInner(perm.indices().coeff(it.index()),j) = it.value();
dst = tmp;
}
}
protected:
const PermutationType& m_permutation;
typename MatrixType::Nested m_matrix;
};
}
/** \returns the matrix with the permutation applied to the columns
*/
template<typename SparseDerived, typename PermDerived>
inline const internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheRight, false>
operator*(const SparseMatrixBase<SparseDerived>& matrix, const PermutationBase<PermDerived>& perm)
{
return internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheRight, false>(perm, matrix.derived());
}
/** \returns the matrix with the permutation applied to the rows
*/
template<typename SparseDerived, typename PermDerived>
inline const internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheLeft, false>
operator*( const PermutationBase<PermDerived>& perm, const SparseMatrixBase<SparseDerived>& matrix)
{
return internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheLeft, false>(perm, matrix.derived());
}
/** \returns the matrix with the inverse permutation applied to the columns.
*/
template<typename SparseDerived, typename PermDerived>
inline const internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheRight, true>
operator*(const SparseMatrixBase<SparseDerived>& matrix, const Transpose<PermutationBase<PermDerived> >& tperm)
{
return internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheRight, true>(tperm.nestedPermutation(), matrix.derived());
}
/** \returns the matrix with the inverse permutation applied to the rows.
*/
template<typename SparseDerived, typename PermDerived>
inline const internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheLeft, true>
operator*(const Transpose<PermutationBase<PermDerived> >& tperm, const SparseMatrixBase<SparseDerived>& matrix)
{
return internal::permut_sparsematrix_product_retval<PermutationBase<PermDerived>, SparseDerived, OnTheLeft, true>(tperm.nestedPermutation(), matrix.derived());
}
#endif // EIGEN_SPARSE_SELFADJOINTVIEW_H

55
test/sparse_permutations.cpp
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@ -59,6 +59,26 @@ template<int OtherStorage, typename SparseMatrixType> void sparse_permutations(c
randomPermutationVector(pi, cols);
p.indices() = pi;
res = mat*p;
res_d = mat_d*p;
VERIFY(res.isApprox(res_d) && "mat*p");
res = p*mat;
res_d = p*mat_d;
VERIFY(res.isApprox(res_d) && "p*mat");
res = mat*p.inverse();
res_d = mat*p.inverse();
VERIFY(res.isApprox(res_d) && "mat*inv(p)");
res = p.inverse()*mat;
res_d = p.inverse()*mat_d;
VERIFY(res.isApprox(res_d) && "inv(p)*mat");
res = mat.twistedBy(p);
res_d = (p * mat_d) * p.inverse();
VERIFY(res.isApprox(res_d) && "p*mat*inv(p)");
res = mat.template selfadjointView<Upper>().twistedBy(p_null);
res_d = up_sym_d;
@ -76,6 +96,41 @@ template<int OtherStorage, typename SparseMatrixType> void sparse_permutations(c
res = lo.template selfadjointView<Lower>().twistedBy(p_null);
res_d = lo_sym_d;
VERIFY(res.isApprox(res_d) && "lower selfadjoint full");
res = mat.template selfadjointView<Upper>();
res_d = up_sym_d;
VERIFY(res.isApprox(res_d) && "full selfadjoint upper to full");
res = mat.template selfadjointView<Lower>();
res_d = lo_sym_d;
VERIFY(res.isApprox(res_d) && "full selfadjoint lower to full");
res = up.template selfadjointView<Upper>();
res_d = up_sym_d;
VERIFY(res.isApprox(res_d) && "upper selfadjoint to full");
res = lo.template selfadjointView<Lower>();
res_d = lo_sym_d;
VERIFY(res.isApprox(res_d) && "lower selfadjoint full");
res.template selfadjointView<Upper>() = mat.template selfadjointView<Upper>();
res_d = up_sym_d.template triangularView<Upper>();
VERIFY(res.isApprox(res_d) && "full selfadjoint upper to upper");
res.template selfadjointView<Lower>() = mat.template selfadjointView<Upper>();
res_d = up_sym_d.template triangularView<Lower>();
VERIFY(res.isApprox(res_d) && "full selfadjoint upper to lower");
res.template selfadjointView<Upper>() = mat.template selfadjointView<Lower>();
res_d = lo_sym_d.template triangularView<Upper>();
VERIFY(res.isApprox(res_d) && "full selfadjoint lower to upper");
res.template selfadjointView<Lower>() = mat.template selfadjointView<Lower>();
res_d = lo_sym_d.template triangularView<Lower>();
VERIFY(res.isApprox(res_d) && "full selfadjoint lower to lower");
res.template selfadjointView<Upper>() = mat.template selfadjointView<Upper>().twistedBy(p);