1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library 2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra. 3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2008-2010 Gael Guennebaud <g.gael@free.fr> 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 11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// import basic and product tests for deprectaed DynamicSparseMatrix 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define EIGEN_NO_DEPRECATED_WARNING 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "sparse_basic.cpp" 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "sparse_product.cpp" 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/SparseExtra> 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename SetterType,typename DenseType, typename Scalar, int Options> 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathbool test_random_setter(SparseMatrix<Scalar,Options>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords) 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath sm.setZero(); 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SetterType w(sm); 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath std::vector<Vector2i> remaining = nonzeroCoords; 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath while(!remaining.empty()) 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int i = internal::random<int>(0,static_cast<int>(remaining.size())-1); 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath w(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y()); 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath remaining[i] = remaining.back(); 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath remaining.pop_back(); 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return sm.isApprox(ref); 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename SetterType,typename DenseType, typename T> 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathbool test_random_setter(DynamicSparseMatrix<T>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords) 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath sm.setZero(); 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath std::vector<Vector2i> remaining = nonzeroCoords; 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath while(!remaining.empty()) 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int i = internal::random<int>(0,static_cast<int>(remaining.size())-1); 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath sm.coeffRef(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y()); 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath remaining[i] = remaining.back(); 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath remaining.pop_back(); 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return sm.isApprox(ref); 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename SparseMatrixType> void sparse_extra(const SparseMatrixType& ref) 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename SparseMatrixType::Index Index; 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Index rows = ref.rows(); 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Index cols = ref.cols(); 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename SparseMatrixType::Scalar Scalar; 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { Flags = SparseMatrixType::Flags }; 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath double density = (std::max)(8./(rows*cols), 0.01); 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar,Dynamic,1> DenseVector; 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar eps = 1e-6; 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SparseMatrixType m(rows, cols); 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseMatrix refMat = DenseMatrix::Zero(rows, cols); 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseVector vec1 = DenseVector::Random(rows); 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath std::vector<Vector2i> zeroCoords; 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath std::vector<Vector2i> nonzeroCoords; 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords); 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if (zeroCoords.size()==0 || nonzeroCoords.size()==0) 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return; 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test coeff and coeffRef 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (int i=0; i<(int)zeroCoords.size(); ++i) 76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps ); 78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(internal::is_same<SparseMatrixType,SparseMatrix<Scalar,Flags> >::value) 79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[0].x(),zeroCoords[0].y()) = 5 ); 80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m, refMat); 82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5); 84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5); 85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m, refMat); 87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // random setter 89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// { 90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// m.setZero(); 91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// VERIFY_IS_NOT_APPROX(m, refMat); 92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// SparseSetter<SparseMatrixType, RandomAccessPattern> w(m); 93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// std::vector<Vector2i> remaining = nonzeroCoords; 94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// while(!remaining.empty()) 95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// { 96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// int i = internal::random<int>(0,remaining.size()-1); 97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// w->coeffRef(remaining[i].x(),remaining[i].y()) = refMat.coeff(remaining[i].x(),remaining[i].y()); 98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// remaining[i] = remaining.back(); 99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// remaining.pop_back(); 100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// } 101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// } 102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// VERIFY_IS_APPROX(m, refMat); 103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdMapTraits> >(m,refMat,nonzeroCoords) )); 105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #ifdef EIGEN_UNORDERED_MAP_SUPPORT 106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdUnorderedMapTraits> >(m,refMat,nonzeroCoords) )); 107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #endif 108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #ifdef _DENSE_HASH_MAP_H_ 109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleDenseHashMapTraits> >(m,refMat,nonzeroCoords) )); 110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #endif 111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #ifdef _SPARSE_HASH_MAP_H_ 112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleSparseHashMapTraits> >(m,refMat,nonzeroCoords) )); 113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #endif 114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test RandomSetter 117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /*{ 118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SparseMatrixType m1(rows,cols), m2(rows,cols); 119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseMatrix refM1 = DenseMatrix::Zero(rows, rows); 120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath initSparse<Scalar>(density, refM1, m1); 121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 122c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::RandomSetter<SparseMatrixType > setter(m2); 123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (int j=0; j<m1.outerSize(); ++j) 124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (typename SparseMatrixType::InnerIterator i(m1,j); i; ++i) 125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath setter(i.index(), j) = i.value(); 126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 127c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1, m2); 128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }*/ 129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 130c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 131c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 132c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 133c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_sparse_extra() 134c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 135c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < g_repeat; i++) { 136c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int s = Eigen::internal::random<int>(1,50); 137c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( sparse_extra(SparseMatrix<double>(8, 8)) ); 138c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2( sparse_extra(SparseMatrix<std::complex<double> >(s, s)) ); 139c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( sparse_extra(SparseMatrix<double>(s, s)) ); 140c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 141c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( sparse_extra(DynamicSparseMatrix<double>(s, s)) ); 142c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// CALL_SUBTEST_3(( sparse_basic(DynamicSparseMatrix<double>(s, s)) )); 143c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// CALL_SUBTEST_3(( sparse_basic(DynamicSparseMatrix<double,ColMajor,long int>(s, s)) )); 144c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 145c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( (sparse_product<DynamicSparseMatrix<float, ColMajor> >()) ); 146c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( (sparse_product<DynamicSparseMatrix<float, RowMajor> >()) ); 147c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 148c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 149