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174 lines
7.0 KiB
C++
174 lines
7.0 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_product(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 s1 = internal::random<Scalar>();
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Scalar s2 = internal::random<Scalar>();
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// test matrix-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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DenseMatrix refMat5 = DenseMatrix::Random(rows, rows);
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DenseMatrix dm4 = DenseMatrix::Zero(rows, rows);
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DenseVector dv1 = DenseVector::Random(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, refMat2, m2);
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initSparse<Scalar>(density, refMat3, m3);
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initSparse<Scalar>(density, refMat4, m4);
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int c = internal::random<int>(0,rows-1);
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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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VERIFY_IS_APPROX(m4 = m2*m3/s1, refMat4 = refMat2*refMat3/s1);
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VERIFY_IS_APPROX(m4 = m2*m3*s1, refMat4 = refMat2*refMat3*s1);
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VERIFY_IS_APPROX(m4 = s2*m2*m3*s1, refMat4 = s2*refMat2*refMat3*s1);
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// sparse * dense
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VERIFY_IS_APPROX(dm4=m2*refMat3, refMat4=refMat2*refMat3);
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VERIFY_IS_APPROX(dm4=m2*refMat3.transpose(), refMat4=refMat2*refMat3.transpose());
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VERIFY_IS_APPROX(dm4=m2.transpose()*refMat3, refMat4=refMat2.transpose()*refMat3);
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VERIFY_IS_APPROX(dm4=m2.transpose()*refMat3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose());
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VERIFY_IS_APPROX(dm4=m2*(refMat3+refMat3), refMat4=refMat2*(refMat3+refMat3));
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VERIFY_IS_APPROX(dm4=m2.transpose()*(refMat3+refMat5)*0.5, refMat4=refMat2.transpose()*(refMat3+refMat5)*0.5);
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// dense * sparse
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VERIFY_IS_APPROX(dm4=refMat2*m3, refMat4=refMat2*refMat3);
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VERIFY_IS_APPROX(dm4=refMat2*m3.transpose(), refMat4=refMat2*refMat3.transpose());
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VERIFY_IS_APPROX(dm4=refMat2.transpose()*m3, refMat4=refMat2.transpose()*refMat3);
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VERIFY_IS_APPROX(dm4=refMat2.transpose()*m3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose());
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// sparse * dense and dense * sparse outer product
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VERIFY_IS_APPROX(m4=m2.col(c)*dv1.transpose(), refMat4=refMat2.col(c)*dv1.transpose());
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VERIFY_IS_APPROX(m4=dv1*m2.col(c).transpose(), refMat4=dv1*refMat2.col(c).transpose());
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VERIFY_IS_APPROX(m3=m3*m3, refMat3=refMat3*refMat3);
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}
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// test matrix - diagonal product
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{
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DenseMatrix refM2 = DenseMatrix::Zero(rows, rows);
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DenseMatrix refM3 = DenseMatrix::Zero(rows, rows);
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DiagonalMatrix<Scalar,Dynamic> d1(DenseVector::Random(rows));
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SparseMatrixType m2(rows, rows);
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SparseMatrixType m3(rows, rows);
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initSparse<Scalar>(density, refM2, m2);
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initSparse<Scalar>(density, refM3, m3);
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VERIFY_IS_APPROX(m3=m2*d1, refM3=refM2*d1);
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VERIFY_IS_APPROX(m3=m2.transpose()*d1, refM3=refM2.transpose()*d1);
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VERIFY_IS_APPROX(m3=d1*m2, refM3=d1*refM2);
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VERIFY_IS_APPROX(m3=d1*m2.transpose(), refM3=d1 * refM2.transpose());
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}
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// test self adjoint products
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{
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DenseMatrix b = DenseMatrix::Random(rows, rows);
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DenseMatrix x = DenseMatrix::Random(rows, rows);
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DenseMatrix refX = DenseMatrix::Random(rows, rows);
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DenseMatrix refUp = DenseMatrix::Zero(rows, rows);
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DenseMatrix refLo = DenseMatrix::Zero(rows, rows);
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DenseMatrix refS = DenseMatrix::Zero(rows, rows);
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SparseMatrixType mUp(rows, rows);
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SparseMatrixType mLo(rows, rows);
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SparseMatrixType mS(rows, rows);
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do {
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initSparse<Scalar>(density, refUp, mUp, ForceRealDiag|/*ForceNonZeroDiag|*/MakeUpperTriangular);
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} while (refUp.isZero());
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refLo = refUp.transpose().conjugate();
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mLo = mUp.transpose().conjugate();
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refS = refUp + refLo;
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refS.diagonal() *= 0.5;
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mS = mUp + mLo;
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for (int k=0; k<mS.outerSize(); ++k)
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for (typename SparseMatrixType::InnerIterator it(mS,k); it; ++it)
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if (it.index() == k)
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it.valueRef() *= 0.5;
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VERIFY_IS_APPROX(refS.adjoint(), refS);
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VERIFY_IS_APPROX(mS.transpose().conjugate(), mS);
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VERIFY_IS_APPROX(mS, refS);
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VERIFY_IS_APPROX(x=mS*b, refX=refS*b);
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VERIFY_IS_APPROX(x=mUp.template selfadjointView<Upper>()*b, refX=refS*b);
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VERIFY_IS_APPROX(x=mLo.template selfadjointView<Lower>()*b, refX=refS*b);
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VERIFY_IS_APPROX(x=mS.template selfadjointView<Upper|Lower>()*b, refX=refS*b);
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}
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}
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// New test for Bug in SparseTimeDenseProduct
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template<typename SparseMatrixType, typename DenseMatrixType> void sparse_product_regression_test()
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{
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// This code does not compile with afflicted versions of the bug
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SparseMatrixType sm1(3,2);
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DenseMatrixType m2(2,2);
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sm1.setZero();
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m2.setZero();
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DenseMatrixType m3 = sm1*m2;
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// This code produces a segfault with afflicted versions of another SparseTimeDenseProduct
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// bug
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SparseMatrixType sm2(20000,2);
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sm2.setZero();
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DenseMatrixType m4(sm2*m2);
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VERIFY_IS_APPROX( m4(0,0), 0.0 );
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}
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void test_sparse_product()
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{
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST_1( sparse_product(SparseMatrix<double>(8, 8)) );
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CALL_SUBTEST_2( sparse_product(SparseMatrix<std::complex<double> >(16, 16)) );
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CALL_SUBTEST_1( sparse_product(SparseMatrix<double>(33, 33)) );
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CALL_SUBTEST_3( sparse_product(DynamicSparseMatrix<double>(8, 8)) );
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CALL_SUBTEST_4( (sparse_product_regression_test<SparseMatrix<double,RowMajor>, Matrix<double, Dynamic, Dynamic, RowMajor> >()) );
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}
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}
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