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https://gitlab.com/libeigen/eigen.git
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c4c70669d1
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
240 lines
8.3 KiB
C++
240 lines
8.3 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra. Eigen itself is part of the KDE project.
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//
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// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@gmail.com>
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//
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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
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// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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#include "sparse.h"
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template<typename SetterType,typename DenseType, typename SparseType>
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bool test_random_setter(SparseType& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords)
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{
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{
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sm.setZero();
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SetterType w(sm);
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std::vector<Vector2i> remaining = nonzeroCoords;
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while(!remaining.empty())
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{
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int i = ei_random<int>(0,remaining.size()-1);
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w(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y());
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remaining[i] = remaining.back();
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remaining.pop_back();
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}
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}
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return sm.isApprox(ref);
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}
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template<typename Scalar> void sparse_basic(int rows, int cols)
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{
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double density = std::max(8./(rows*cols), 0.01);
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typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
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typedef Matrix<Scalar,Dynamic,1> DenseVector;
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Scalar eps = 1e-6;
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SparseMatrix<Scalar> m(rows, cols);
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DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
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DenseVector vec1 = DenseVector::Random(rows);
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std::vector<Vector2i> zeroCoords;
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std::vector<Vector2i> nonzeroCoords;
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initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
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if (zeroCoords.size()==0 || nonzeroCoords.size()==0)
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return;
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// test coeff and coeffRef
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for (int i=0; i<(int)zeroCoords.size(); ++i)
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{
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VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps );
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VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[0].x(),zeroCoords[0].y()) = 5 );
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}
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VERIFY_IS_APPROX(m, refMat);
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m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
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refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
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VERIFY_IS_APPROX(m, refMat);
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/*
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// test InnerIterators and Block expressions
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for (int t=0; t<10; ++t)
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{
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int j = ei_random<int>(0,cols-1);
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int i = ei_random<int>(0,rows-1);
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int w = ei_random<int>(1,cols-j-1);
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int h = ei_random<int>(1,rows-i-1);
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// VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w));
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for(int c=0; c<w; c++)
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{
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VERIFY_IS_APPROX(m.block(i,j,h,w).col(c), refMat.block(i,j,h,w).col(c));
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for(int r=0; r<h; r++)
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{
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// VERIFY_IS_APPROX(m.block(i,j,h,w).col(c).coeff(r), refMat.block(i,j,h,w).col(c).coeff(r));
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}
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}
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// for(int r=0; r<h; r++)
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// {
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// VERIFY_IS_APPROX(m.block(i,j,h,w).row(r), refMat.block(i,j,h,w).row(r));
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// for(int c=0; c<w; c++)
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// {
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// VERIFY_IS_APPROX(m.block(i,j,h,w).row(r).coeff(c), refMat.block(i,j,h,w).row(r).coeff(c));
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// }
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// }
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}
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for(int c=0; c<cols; c++)
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{
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VERIFY_IS_APPROX(m.col(c) + m.col(c), (m + m).col(c));
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VERIFY_IS_APPROX(m.col(c) + m.col(c), refMat.col(c) + refMat.col(c));
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}
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for(int r=0; r<rows; r++)
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{
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VERIFY_IS_APPROX(m.row(r) + m.row(r), (m + m).row(r));
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VERIFY_IS_APPROX(m.row(r) + m.row(r), refMat.row(r) + refMat.row(r));
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}
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*/
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// test SparseSetters
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// coherent setter
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// TODO extend the MatrixSetter
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// {
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// m.setZero();
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// VERIFY_IS_NOT_APPROX(m, refMat);
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// SparseSetter<SparseMatrix<Scalar>, FullyCoherentAccessPattern> w(m);
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// for (int i=0; i<nonzeroCoords.size(); ++i)
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// {
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// w->coeffRef(nonzeroCoords[i].x(),nonzeroCoords[i].y()) = refMat.coeff(nonzeroCoords[i].x(),nonzeroCoords[i].y());
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// }
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// }
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// VERIFY_IS_APPROX(m, refMat);
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// random setter
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// {
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// m.setZero();
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// VERIFY_IS_NOT_APPROX(m, refMat);
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// SparseSetter<SparseMatrix<Scalar>, RandomAccessPattern> w(m);
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// std::vector<Vector2i> remaining = nonzeroCoords;
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// while(!remaining.empty())
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// {
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// int i = ei_random<int>(0,remaining.size()-1);
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// w->coeffRef(remaining[i].x(),remaining[i].y()) = refMat.coeff(remaining[i].x(),remaining[i].y());
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// remaining[i] = remaining.back();
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// remaining.pop_back();
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// }
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// }
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// VERIFY_IS_APPROX(m, refMat);
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VERIFY(( test_random_setter<RandomSetter<SparseMatrix<Scalar>, StdMapTraits> >(m,refMat,nonzeroCoords) ));
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#ifdef _HASH_MAP
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VERIFY(( test_random_setter<RandomSetter<SparseMatrix<Scalar>, GnuHashMapTraits> >(m,refMat,nonzeroCoords) ));
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#endif
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#ifdef _DENSE_HASH_MAP_H_
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VERIFY(( test_random_setter<RandomSetter<SparseMatrix<Scalar>, GoogleDenseHashMapTraits> >(m,refMat,nonzeroCoords) ));
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#endif
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#ifdef _SPARSE_HASH_MAP_H_
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VERIFY(( test_random_setter<RandomSetter<SparseMatrix<Scalar>, GoogleSparseHashMapTraits> >(m,refMat,nonzeroCoords) ));
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#endif
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// test fillrand
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{
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DenseMatrix m1(rows,cols);
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m1.setZero();
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SparseMatrix<Scalar> m2(rows,cols);
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m2.startFill();
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for (int j=0; j<cols; ++j)
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{
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for (int k=0; k<rows/2; ++k)
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{
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int i = ei_random<int>(0,rows-1);
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if (m1.coeff(i,j)==Scalar(0))
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m2.fillrand(i,j) = m1(i,j) = ei_random<Scalar>();
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}
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}
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m2.endFill();
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std::cerr << m1 << "\n\n" << m2 << "\n";
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VERIFY_IS_APPROX(m2,m1);
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}
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// {
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// m.setZero();
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// VERIFY_IS_NOT_APPROX(m, refMat);
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// // RandomSetter<SparseMatrix<Scalar> > w(m);
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// RandomSetter<SparseMatrix<Scalar>, GoogleDenseHashMapTraits > w(m);
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// // RandomSetter<SparseMatrix<Scalar>, GnuHashMapTraits > w(m);
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// std::vector<Vector2i> remaining = nonzeroCoords;
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// while(!remaining.empty())
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// {
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// int i = ei_random<int>(0,remaining.size()-1);
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// w(remaining[i].x(),remaining[i].y()) = refMat.coeff(remaining[i].x(),remaining[i].y());
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// remaining[i] = remaining.back();
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// remaining.pop_back();
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// }
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// }
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// std::cerr << m.transpose() << "\n\n" << refMat.transpose() << "\n\n";
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// VERIFY_IS_APPROX(m, refMat);
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// test innerVector()
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{
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DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
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SparseMatrix<Scalar> m2(rows, rows);
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initSparse<Scalar>(density, refMat2, m2);
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int j0 = ei_random(0,rows-1);
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int j1 = ei_random(0,rows-1);
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// VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.col(j0));
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// VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.col(j0)+refMat2.col(j1));
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}
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// test transpose
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{
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DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
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SparseMatrix<Scalar> m2(rows, rows);
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initSparse<Scalar>(density, refMat2, m2);
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VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval());
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VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose());
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}
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// test matrix product
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{
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DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
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DenseMatrix refMat3 = DenseMatrix::Zero(rows, rows);
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DenseMatrix refMat4 = DenseMatrix::Zero(rows, rows);
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SparseMatrix<Scalar> m2(rows, rows);
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SparseMatrix<Scalar> m3(rows, rows);
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SparseMatrix<Scalar> m4(rows, rows);
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initSparse<Scalar>(density, refMat2, m2);
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initSparse<Scalar>(density, refMat3, m3);
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initSparse<Scalar>(density, refMat4, m4);
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VERIFY_IS_APPROX(m4=m2*m3, refMat4=refMat2*refMat3);
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VERIFY_IS_APPROX(m4=m2.transpose()*m3, refMat4=refMat2.transpose()*refMat3);
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VERIFY_IS_APPROX(m4=m2.transpose()*m3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose());
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VERIFY_IS_APPROX(m4=m2*m3.transpose(), refMat4=refMat2*refMat3.transpose());
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}
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}
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void test_sparse_basic()
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{
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST( sparse_basic<double>(8, 8) );
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CALL_SUBTEST( sparse_basic<std::complex<double> >(16, 16) );
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CALL_SUBTEST( sparse_basic<double>(33, 33) );
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}
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}
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