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272 lines
8.8 KiB
C++
272 lines
8.8 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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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 SparseMatrixType> void sparse_basic(const SparseMatrixType& ref)
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{
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typedef typename SparseMatrixType::Index Index;
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const Index rows = ref.rows();
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const Index cols = ref.cols();
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typedef typename SparseMatrixType::Scalar Scalar;
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enum { Flags = SparseMatrixType::Flags };
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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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SparseMatrixType 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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Scalar s1 = internal::random<Scalar>();
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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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if(internal::is_same<SparseMatrixType,SparseMatrix<Scalar,Flags> >::value)
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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 = internal::random<int>(0,cols-1);
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int i = internal::random<int>(0,rows-1);
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int w = internal::random<int>(1,cols-j-1);
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int h = internal::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 insert (inner random)
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{
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DenseMatrix m1(rows,cols);
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m1.setZero();
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SparseMatrixType m2(rows,cols);
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m2.reserve(10);
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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 = internal::random<int>(0,rows-1);
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if (m1.coeff(i,j)==Scalar(0))
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m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
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}
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}
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m2.finalize();
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VERIFY_IS_APPROX(m2,m1);
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}
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// test insert (fully random)
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{
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DenseMatrix m1(rows,cols);
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m1.setZero();
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SparseMatrixType m2(rows,cols);
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m2.reserve(10);
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for (int k=0; k<rows*cols; ++k)
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{
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int i = internal::random<int>(0,rows-1);
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int j = internal::random<int>(0,cols-1);
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if (m1.coeff(i,j)==Scalar(0))
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m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
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}
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m2.finalize();
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VERIFY_IS_APPROX(m2,m1);
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}
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// test basic computations
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{
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DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
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DenseMatrix refM2 = DenseMatrix::Zero(rows, rows);
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DenseMatrix refM3 = DenseMatrix::Zero(rows, rows);
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DenseMatrix refM4 = DenseMatrix::Zero(rows, rows);
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SparseMatrixType m1(rows, rows);
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SparseMatrixType m2(rows, rows);
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SparseMatrixType m3(rows, rows);
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SparseMatrixType m4(rows, rows);
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initSparse<Scalar>(density, refM1, m1);
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initSparse<Scalar>(density, refM2, m2);
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initSparse<Scalar>(density, refM3, m3);
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initSparse<Scalar>(density, refM4, m4);
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VERIFY_IS_APPROX(m1+m2, refM1+refM2);
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VERIFY_IS_APPROX(m1+m2+m3, refM1+refM2+refM3);
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VERIFY_IS_APPROX(m3.cwiseProduct(m1+m2), refM3.cwiseProduct(refM1+refM2));
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VERIFY_IS_APPROX(m1*s1-m2, refM1*s1-refM2);
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VERIFY_IS_APPROX(m1*=s1, refM1*=s1);
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VERIFY_IS_APPROX(m1/=s1, refM1/=s1);
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VERIFY_IS_APPROX(m1+=m2, refM1+=refM2);
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VERIFY_IS_APPROX(m1-=m2, refM1-=refM2);
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VERIFY_IS_APPROX(m1.col(0).dot(refM2.row(0)), refM1.col(0).dot(refM2.row(0)));
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refM4.setRandom();
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// sparse cwise* dense
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VERIFY_IS_APPROX(m3.cwiseProduct(refM4), refM3.cwiseProduct(refM4));
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// VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4);
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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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SparseMatrixType 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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VERIFY_IS_APPROX(SparseMatrixType(m2.adjoint()), refMat2.adjoint());
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}
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// test innerVector()
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{
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DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
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SparseMatrixType m2(rows, rows);
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initSparse<Scalar>(density, refMat2, m2);
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int j0 = internal::random(0,rows-1);
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int j1 = internal::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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//m2.innerVector(j0) = 2*m2.innerVector(j1);
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//refMat2.col(j0) = 2*refMat2.col(j1);
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//VERIFY_IS_APPROX(m2, refMat2);
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}
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// test innerVectors()
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{
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DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
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SparseMatrixType m2(rows, rows);
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initSparse<Scalar>(density, refMat2, m2);
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int j0 = internal::random(0,rows-2);
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int j1 = internal::random(0,rows-2);
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int n0 = internal::random<int>(1,rows-std::max(j0,j1));
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VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(0,j0,rows,n0));
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VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0),
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refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));
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//m2.innerVectors(j0,n0) = m2.innerVectors(j0,n0) + m2.innerVectors(j1,n0);
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//refMat2.block(0,j0,rows,n0) = refMat2.block(0,j0,rows,n0) + refMat2.block(0,j1,rows,n0);
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}
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// test prune
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{
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SparseMatrixType m2(rows, rows);
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DenseMatrix refM2(rows, rows);
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refM2.setZero();
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int countFalseNonZero = 0;
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int countTrueNonZero = 0;
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for (int j=0; j<m2.outerSize(); ++j)
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{
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m2.startVec(j);
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for (int i=0; i<m2.innerSize(); ++i)
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{
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float x = internal::random<float>(0,1);
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if (x<0.1)
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{
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// do nothing
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}
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else if (x<0.5)
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{
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countFalseNonZero++;
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m2.insertBackByOuterInner(j,i) = Scalar(0);
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}
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else
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{
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countTrueNonZero++;
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m2.insertBackByOuterInner(j,i) = refM2(i,j) = Scalar(1);
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}
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}
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}
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m2.finalize();
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VERIFY(countFalseNonZero+countTrueNonZero == m2.nonZeros());
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VERIFY_IS_APPROX(m2, refM2);
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m2.prune(Scalar(1));
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VERIFY(countTrueNonZero==m2.nonZeros());
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VERIFY_IS_APPROX(m2, refM2);
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}
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// test sparseView
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{
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DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
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SparseMatrixType m2(rows, rows);
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VERIFY_IS_APPROX(m2.eval(), refMat2.sparseView().eval());
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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_1( sparse_basic(SparseMatrix<double>(8, 8)) );
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CALL_SUBTEST_2( sparse_basic(SparseMatrix<std::complex<double> >(16, 16)) );
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CALL_SUBTEST_1( sparse_basic(SparseMatrix<double>(33, 33)) );
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CALL_SUBTEST_3( sparse_basic(DynamicSparseMatrix<double>(8, 8)) );
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}
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}
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