mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-21 07:19:46 +08:00
255 lines
9.8 KiB
C++
255 lines
9.8 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
|
|
// for linear algebra.
|
|
//
|
|
// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
|
|
//
|
|
// This Source Code Form is subject to the terms of the Mozilla
|
|
// Public License v. 2.0. If a copy of the MPL was not distributed
|
|
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
|
|
|
|
#include "sparse.h"
|
|
|
|
template<typename SparseMatrixType> void sparse_block(const SparseMatrixType& ref)
|
|
{
|
|
const Index rows = ref.rows();
|
|
const Index cols = ref.cols();
|
|
const Index inner = ref.innerSize();
|
|
const Index outer = ref.outerSize();
|
|
|
|
typedef typename SparseMatrixType::Scalar Scalar;
|
|
|
|
double density = (std::max)(8./(rows*cols), 0.01);
|
|
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
|
|
typedef Matrix<Scalar,Dynamic,1> DenseVector;
|
|
typedef Matrix<Scalar,1,Dynamic> RowDenseVector;
|
|
|
|
Scalar s1 = internal::random<Scalar>();
|
|
{
|
|
SparseMatrixType m(rows, cols);
|
|
DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
|
|
initSparse<Scalar>(density, refMat, m);
|
|
|
|
VERIFY_IS_APPROX(m, refMat);
|
|
|
|
// test InnerIterators and Block expressions
|
|
for (int t=0; t<10; ++t)
|
|
{
|
|
Index j = internal::random<Index>(0,cols-2);
|
|
Index i = internal::random<Index>(0,rows-2);
|
|
Index w = internal::random<Index>(1,cols-j);
|
|
Index h = internal::random<Index>(1,rows-i);
|
|
|
|
VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w));
|
|
for(Index c=0; c<w; c++)
|
|
{
|
|
VERIFY_IS_APPROX(m.block(i,j,h,w).col(c), refMat.block(i,j,h,w).col(c));
|
|
for(Index r=0; r<h; r++)
|
|
{
|
|
VERIFY_IS_APPROX(m.block(i,j,h,w).col(c).coeff(r), refMat.block(i,j,h,w).col(c).coeff(r));
|
|
VERIFY_IS_APPROX(m.block(i,j,h,w).coeff(r,c), refMat.block(i,j,h,w).coeff(r,c));
|
|
}
|
|
}
|
|
for(Index r=0; r<h; r++)
|
|
{
|
|
VERIFY_IS_APPROX(m.block(i,j,h,w).row(r), refMat.block(i,j,h,w).row(r));
|
|
for(Index c=0; c<w; c++)
|
|
{
|
|
VERIFY_IS_APPROX(m.block(i,j,h,w).row(r).coeff(c), refMat.block(i,j,h,w).row(r).coeff(c));
|
|
VERIFY_IS_APPROX(m.block(i,j,h,w).coeff(r,c), refMat.block(i,j,h,w).coeff(r,c));
|
|
}
|
|
}
|
|
|
|
VERIFY_IS_APPROX(m.middleCols(j,w), refMat.middleCols(j,w));
|
|
VERIFY_IS_APPROX(m.middleRows(i,h), refMat.middleRows(i,h));
|
|
for(Index r=0; r<h; r++)
|
|
{
|
|
VERIFY_IS_APPROX(m.middleCols(j,w).row(r), refMat.middleCols(j,w).row(r));
|
|
VERIFY_IS_APPROX(m.middleRows(i,h).row(r), refMat.middleRows(i,h).row(r));
|
|
for(Index c=0; c<w; c++)
|
|
{
|
|
VERIFY_IS_APPROX(m.col(c).coeff(r), refMat.col(c).coeff(r));
|
|
VERIFY_IS_APPROX(m.row(r).coeff(c), refMat.row(r).coeff(c));
|
|
|
|
VERIFY_IS_APPROX(m.middleCols(j,w).coeff(r,c), refMat.middleCols(j,w).coeff(r,c));
|
|
VERIFY_IS_APPROX(m.middleRows(i,h).coeff(r,c), refMat.middleRows(i,h).coeff(r,c));
|
|
if(m.middleCols(j,w).coeff(r,c) != Scalar(0))
|
|
{
|
|
VERIFY_IS_APPROX(m.middleCols(j,w).coeffRef(r,c), refMat.middleCols(j,w).coeff(r,c));
|
|
}
|
|
if(m.middleRows(i,h).coeff(r,c) != Scalar(0))
|
|
{
|
|
VERIFY_IS_APPROX(m.middleRows(i,h).coeff(r,c), refMat.middleRows(i,h).coeff(r,c));
|
|
}
|
|
}
|
|
}
|
|
for(Index c=0; c<w; c++)
|
|
{
|
|
VERIFY_IS_APPROX(m.middleCols(j,w).col(c), refMat.middleCols(j,w).col(c));
|
|
VERIFY_IS_APPROX(m.middleRows(i,h).col(c), refMat.middleRows(i,h).col(c));
|
|
}
|
|
}
|
|
|
|
for(Index c=0; c<cols; c++)
|
|
{
|
|
VERIFY_IS_APPROX(m.col(c) + m.col(c), (m + m).col(c));
|
|
VERIFY_IS_APPROX(m.col(c) + m.col(c), refMat.col(c) + refMat.col(c));
|
|
}
|
|
|
|
for(Index r=0; r<rows; r++)
|
|
{
|
|
VERIFY_IS_APPROX(m.row(r) + m.row(r), (m + m).row(r));
|
|
VERIFY_IS_APPROX(m.row(r) + m.row(r), refMat.row(r) + refMat.row(r));
|
|
}
|
|
}
|
|
|
|
// test innerVector()
|
|
{
|
|
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
|
|
SparseMatrixType m2(rows, cols);
|
|
initSparse<Scalar>(density, refMat2, m2);
|
|
Index j0 = internal::random<Index>(0,outer-1);
|
|
Index j1 = internal::random<Index>(0,outer-1);
|
|
if(SparseMatrixType::IsRowMajor)
|
|
VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.row(j0));
|
|
else
|
|
VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.col(j0));
|
|
|
|
if(SparseMatrixType::IsRowMajor)
|
|
VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.row(j0)+refMat2.row(j1));
|
|
else
|
|
VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.col(j0)+refMat2.col(j1));
|
|
|
|
SparseMatrixType m3(rows,cols);
|
|
m3.reserve(VectorXi::Constant(outer,int(inner/2)));
|
|
for(Index j=0; j<outer; ++j)
|
|
for(Index k=0; k<(std::min)(j,inner); ++k)
|
|
m3.insertByOuterInner(j,k) = k+1;
|
|
for(Index j=0; j<(std::min)(outer, inner); ++j)
|
|
{
|
|
VERIFY(j==numext::real(m3.innerVector(j).nonZeros()));
|
|
if(j>0)
|
|
VERIFY(j==numext::real(m3.innerVector(j).lastCoeff()));
|
|
}
|
|
m3.makeCompressed();
|
|
for(Index j=0; j<(std::min)(outer, inner); ++j)
|
|
{
|
|
VERIFY(j==numext::real(m3.innerVector(j).nonZeros()));
|
|
if(j>0)
|
|
VERIFY(j==numext::real(m3.innerVector(j).lastCoeff()));
|
|
}
|
|
|
|
VERIFY(m3.innerVector(j0).nonZeros() == m3.transpose().innerVector(j0).nonZeros());
|
|
|
|
// m2.innerVector(j0) = 2*m2.innerVector(j1);
|
|
// refMat2.col(j0) = 2*refMat2.col(j1);
|
|
// VERIFY_IS_APPROX(m2, refMat2);
|
|
}
|
|
|
|
// test innerVectors()
|
|
{
|
|
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
|
|
SparseMatrixType m2(rows, cols);
|
|
initSparse<Scalar>(density, refMat2, m2);
|
|
if(internal::random<float>(0,1)>0.5f) m2.makeCompressed();
|
|
Index j0 = internal::random<Index>(0,outer-2);
|
|
Index j1 = internal::random<Index>(0,outer-2);
|
|
Index n0 = internal::random<Index>(1,outer-(std::max)(j0,j1));
|
|
if(SparseMatrixType::IsRowMajor)
|
|
VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(j0,0,n0,cols));
|
|
else
|
|
VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(0,j0,rows,n0));
|
|
if(SparseMatrixType::IsRowMajor)
|
|
VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0),
|
|
refMat2.middleRows(j0,n0)+refMat2.middleRows(j1,n0));
|
|
else
|
|
VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0),
|
|
refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));
|
|
|
|
VERIFY_IS_APPROX(m2, refMat2);
|
|
|
|
VERIFY(m2.innerVectors(j0,n0).nonZeros() == m2.transpose().innerVectors(j0,n0).nonZeros());
|
|
|
|
m2.innerVectors(j0,n0) = m2.innerVectors(j0,n0) + m2.innerVectors(j1,n0);
|
|
if(SparseMatrixType::IsRowMajor)
|
|
refMat2.middleRows(j0,n0) = (refMat2.middleRows(j0,n0) + refMat2.middleRows(j1,n0)).eval();
|
|
else
|
|
refMat2.middleCols(j0,n0) = (refMat2.middleCols(j0,n0) + refMat2.middleCols(j1,n0)).eval();
|
|
|
|
VERIFY_IS_APPROX(m2, refMat2);
|
|
}
|
|
|
|
// test generic blocks
|
|
{
|
|
DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
|
|
SparseMatrixType m2(rows, cols);
|
|
initSparse<Scalar>(density, refMat2, m2);
|
|
Index j0 = internal::random<Index>(0,outer-2);
|
|
Index j1 = internal::random<Index>(0,outer-2);
|
|
Index n0 = internal::random<Index>(1,outer-(std::max)(j0,j1));
|
|
if(SparseMatrixType::IsRowMajor)
|
|
VERIFY_IS_APPROX(m2.block(j0,0,n0,cols), refMat2.block(j0,0,n0,cols));
|
|
else
|
|
VERIFY_IS_APPROX(m2.block(0,j0,rows,n0), refMat2.block(0,j0,rows,n0));
|
|
|
|
if(SparseMatrixType::IsRowMajor)
|
|
VERIFY_IS_APPROX(m2.block(j0,0,n0,cols)+m2.block(j1,0,n0,cols),
|
|
refMat2.block(j0,0,n0,cols)+refMat2.block(j1,0,n0,cols));
|
|
else
|
|
VERIFY_IS_APPROX(m2.block(0,j0,rows,n0)+m2.block(0,j1,rows,n0),
|
|
refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));
|
|
|
|
Index i = internal::random<Index>(0,m2.outerSize()-1);
|
|
if(SparseMatrixType::IsRowMajor) {
|
|
m2.innerVector(i) = m2.innerVector(i) * s1;
|
|
refMat2.row(i) = refMat2.row(i) * s1;
|
|
VERIFY_IS_APPROX(m2,refMat2);
|
|
} else {
|
|
m2.innerVector(i) = m2.innerVector(i) * s1;
|
|
refMat2.col(i) = refMat2.col(i) * s1;
|
|
VERIFY_IS_APPROX(m2,refMat2);
|
|
}
|
|
|
|
Index r0 = internal::random<Index>(0,rows-2);
|
|
Index c0 = internal::random<Index>(0,cols-2);
|
|
Index r1 = internal::random<Index>(1,rows-r0);
|
|
Index c1 = internal::random<Index>(1,cols-c0);
|
|
|
|
VERIFY_IS_APPROX(DenseVector(m2.col(c0)), refMat2.col(c0));
|
|
VERIFY_IS_APPROX(m2.col(c0), refMat2.col(c0));
|
|
|
|
VERIFY_IS_APPROX(RowDenseVector(m2.row(r0)), refMat2.row(r0));
|
|
VERIFY_IS_APPROX(m2.row(r0), refMat2.row(r0));
|
|
|
|
VERIFY_IS_APPROX(m2.block(r0,c0,r1,c1), refMat2.block(r0,c0,r1,c1));
|
|
VERIFY_IS_APPROX((2*m2).block(r0,c0,r1,c1), (2*refMat2).block(r0,c0,r1,c1));
|
|
}
|
|
}
|
|
|
|
void test_sparse_block()
|
|
{
|
|
for(int i = 0; i < g_repeat; i++) {
|
|
int r = Eigen::internal::random<int>(1,200), c = Eigen::internal::random<int>(1,200);
|
|
if(Eigen::internal::random<int>(0,4) == 0) {
|
|
r = c; // check square matrices in 25% of tries
|
|
}
|
|
EIGEN_UNUSED_VARIABLE(r+c);
|
|
CALL_SUBTEST_1(( sparse_block(SparseMatrix<double>(1, 1)) ));
|
|
CALL_SUBTEST_1(( sparse_block(SparseMatrix<double>(8, 8)) ));
|
|
CALL_SUBTEST_1(( sparse_block(SparseMatrix<double>(r, c)) ));
|
|
CALL_SUBTEST_2(( sparse_block(SparseMatrix<std::complex<double>, ColMajor>(r, c)) ));
|
|
CALL_SUBTEST_2(( sparse_block(SparseMatrix<std::complex<double>, RowMajor>(r, c)) ));
|
|
|
|
CALL_SUBTEST_3(( sparse_block(SparseMatrix<double,ColMajor,long int>(r, c)) ));
|
|
CALL_SUBTEST_3(( sparse_block(SparseMatrix<double,RowMajor,long int>(r, c)) ));
|
|
|
|
r = Eigen::internal::random<int>(1,100);
|
|
c = Eigen::internal::random<int>(1,100);
|
|
if(Eigen::internal::random<int>(0,4) == 0) {
|
|
r = c; // check square matrices in 25% of tries
|
|
}
|
|
|
|
CALL_SUBTEST_4(( sparse_block(SparseMatrix<double,ColMajor,short int>(short(r), short(c))) ));
|
|
CALL_SUBTEST_4(( sparse_block(SparseMatrix<double,RowMajor,short int>(short(r), short(c))) ));
|
|
}
|
|
}
|