1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library 2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra. Eigen itself is part of the KDE project. 3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@gmail.com> 5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla 7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed 8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "sparse.h" 11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar> void sparse_vector(int rows, int cols) 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath double densityMat = std::max(8./(rows*cols), 0.01); 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath double densityVec = std::max(8./float(rows), 0.1); 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar,Dynamic,1> DenseVector; 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef SparseVector<Scalar> SparseVectorType; 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef SparseMatrix<Scalar> SparseMatrixType; 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar eps = 1e-6; 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SparseMatrixType m1(rows,cols); 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SparseVectorType v1(rows), v2(rows), v3(rows); 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseMatrix refM1 = DenseMatrix::Zero(rows, cols); 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseVector refV1 = DenseVector::Random(rows), 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath refV2 = DenseVector::Random(rows), 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath refV3 = DenseVector::Random(rows); 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath std::vector<int> zerocoords, nonzerocoords; 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath initSparse<Scalar>(densityVec, refV1, v1, &zerocoords, &nonzerocoords); 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath initSparse<Scalar>(densityMat, refM1, m1); 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath initSparse<Scalar>(densityVec, refV2, v2); 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath initSparse<Scalar>(densityVec, refV3, v3); 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar s1 = ei_random<Scalar>(); 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test coeff and coeffRef 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (unsigned int i=0; i<zerocoords.size(); ++i) 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_MUCH_SMALLER_THAN( v1.coeff(zerocoords[i]), eps ); 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //VERIFY_RAISES_ASSERT( v1.coeffRef(zerocoords[i]) = 5 ); 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(int(nonzerocoords.size()) == v1.nonZeros()); 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int j=0; 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (typename SparseVectorType::InnerIterator it(v1); it; ++it,++j) 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(nonzerocoords[j]==it.index()); 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(it.value()==v1.coeff(it.index())); 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(it.value()==refV1.coeff(it.index())); 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1, refV1); 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath v1.coeffRef(nonzerocoords[0]) = Scalar(5); 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath refV1.coeffRef(nonzerocoords[0]) = Scalar(5); 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1, refV1); 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1+v2, refV1+refV2); 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1+v2+v3, refV1+refV2+refV3); 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1*s1-v2, refV1*s1-refV2); 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1*=s1, refV1*=s1); 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1/=s1, refV1/=s1); 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1+=v2, refV1+=refV2); 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1-=v2, refV1-=refV2); 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1.eigen2_dot(v2), refV1.eigen2_dot(refV2)); 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1.eigen2_dot(refV2), refV1.eigen2_dot(refV2)); 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_eigen2_sparse_vector() 77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < g_repeat; i++) { 79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( sparse_vector<double>(8, 8) ); 80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2( sparse_vector<std::complex<double> >(16, 16) ); 81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( sparse_vector<double>(299, 535) ); 82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 85