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 SetterType,typename DenseType, typename Scalar, int Options> 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathbool test_random_setter(SparseMatrix<Scalar,Options>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords) 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef SparseMatrix<Scalar,Options> SparseType; 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath sm.setZero(); 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SetterType w(sm); 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath std::vector<Vector2i> remaining = nonzeroCoords; 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath while(!remaining.empty()) 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int i = ei_random<int>(0,remaining.size()-1); 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath w(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y()); 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath remaining[i] = remaining.back(); 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath remaining.pop_back(); 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return sm.isApprox(ref); 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename SetterType,typename DenseType, typename T> 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathbool test_random_setter(DynamicSparseMatrix<T>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords) 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath sm.setZero(); 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath std::vector<Vector2i> remaining = nonzeroCoords; 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath while(!remaining.empty()) 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int i = ei_random<int>(0,remaining.size()-1); 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath sm.coeffRef(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y()); 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath remaining[i] = remaining.back(); 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath remaining.pop_back(); 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return sm.isApprox(ref); 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& ref) 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const int rows = ref.rows(); 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const int cols = ref.cols(); 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename SparseMatrixType::Scalar Scalar; 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { Flags = SparseMatrixType::Flags }; 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath double density = std::max(8./(rows*cols), 0.01); 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix; 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar,Dynamic,1> DenseVector; 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar eps = 1e-6; 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SparseMatrixType m(rows, cols); 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseMatrix refMat = DenseMatrix::Zero(rows, cols); 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseVector vec1 = DenseVector::Random(rows); 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar s1 = ei_random<Scalar>(); 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath std::vector<Vector2i> zeroCoords; 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath std::vector<Vector2i> nonzeroCoords; 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords); 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if (zeroCoords.size()==0 || nonzeroCoords.size()==0) 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return; 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test coeff and coeffRef 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (int i=0; i<(int)zeroCoords.size(); ++i) 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps ); 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(ei_is_same_type<SparseMatrixType,SparseMatrix<Scalar,Flags> >::ret) 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[0].x(),zeroCoords[0].y()) = 5 ); 76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m, refMat); 78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5); 80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5); 81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m, refMat); 83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /* 84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test InnerIterators and Block expressions 85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (int t=0; t<10; ++t) 86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int j = ei_random<int>(0,cols-1); 88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int i = ei_random<int>(0,rows-1); 89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int w = ei_random<int>(1,cols-j-1); 90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int h = ei_random<int>(1,rows-i-1); 91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w)); 93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int c=0; c<w; c++) 94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m.block(i,j,h,w).col(c), refMat.block(i,j,h,w).col(c)); 96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int r=0; r<h; r++) 97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// VERIFY_IS_APPROX(m.block(i,j,h,w).col(c).coeff(r), refMat.block(i,j,h,w).col(c).coeff(r)); 99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for(int r=0; r<h; r++) 102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// { 103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// VERIFY_IS_APPROX(m.block(i,j,h,w).row(r), refMat.block(i,j,h,w).row(r)); 104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for(int c=0; c<w; c++) 105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// { 106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// VERIFY_IS_APPROX(m.block(i,j,h,w).row(r).coeff(c), refMat.block(i,j,h,w).row(r).coeff(c)); 107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// } 108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// } 109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int c=0; c<cols; c++) 112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m.col(c) + m.col(c), (m + m).col(c)); 114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m.col(c) + m.col(c), refMat.col(c) + refMat.col(c)); 115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int r=0; r<rows; r++) 118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m.row(r) + m.row(r), (m + m).row(r)); 120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m.row(r) + m.row(r), refMat.row(r) + refMat.row(r)); 121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 122c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test SparseSetters 125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // coherent setter 126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // TODO extend the MatrixSetter 127c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// { 128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// m.setZero(); 129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// VERIFY_IS_NOT_APPROX(m, refMat); 130c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// SparseSetter<SparseMatrixType, FullyCoherentAccessPattern> w(m); 131c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for (int i=0; i<nonzeroCoords.size(); ++i) 132c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// { 133c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// w->coeffRef(nonzeroCoords[i].x(),nonzeroCoords[i].y()) = refMat.coeff(nonzeroCoords[i].x(),nonzeroCoords[i].y()); 134c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// } 135c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// } 136c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// VERIFY_IS_APPROX(m, refMat); 137c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 138c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // random setter 139c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// { 140c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// m.setZero(); 141c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// VERIFY_IS_NOT_APPROX(m, refMat); 142c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// SparseSetter<SparseMatrixType, RandomAccessPattern> w(m); 143c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// std::vector<Vector2i> remaining = nonzeroCoords; 144c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// while(!remaining.empty()) 145c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// { 146c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// int i = ei_random<int>(0,remaining.size()-1); 147c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// w->coeffRef(remaining[i].x(),remaining[i].y()) = refMat.coeff(remaining[i].x(),remaining[i].y()); 148c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// remaining[i] = remaining.back(); 149c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// remaining.pop_back(); 150c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// } 151c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// } 152c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// VERIFY_IS_APPROX(m, refMat); 153c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 154c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdMapTraits> >(m,refMat,nonzeroCoords) )); 155c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #ifdef EIGEN_UNORDERED_MAP_SUPPORT 156c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdUnorderedMapTraits> >(m,refMat,nonzeroCoords) )); 157c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #endif 158c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #ifdef _DENSE_HASH_MAP_H_ 159c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleDenseHashMapTraits> >(m,refMat,nonzeroCoords) )); 160c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #endif 161c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #ifdef _SPARSE_HASH_MAP_H_ 162c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleSparseHashMapTraits> >(m,refMat,nonzeroCoords) )); 163c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath #endif 164c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 165c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test fillrand 166c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 167c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseMatrix m1(rows,cols); 168c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m1.setZero(); 169c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SparseMatrixType m2(rows,cols); 170c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.startFill(); 171c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (int j=0; j<cols; ++j) 172c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 173c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (int k=0; k<rows/2; ++k) 174c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 175c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int i = ei_random<int>(0,rows-1); 176c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if (m1.coeff(i,j)==Scalar(0)) 177c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.fillrand(i,j) = m1(i,j) = ei_random<Scalar>(); 178c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 179c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 180c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.endFill(); 181c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2,m1); 182c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 183c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 184c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test RandomSetter 185c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /*{ 186c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SparseMatrixType m1(rows,cols), m2(rows,cols); 187c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseMatrix refM1 = DenseMatrix::Zero(rows, rows); 188c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath initSparse<Scalar>(density, refM1, m1); 189c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 190c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Eigen::RandomSetter<SparseMatrixType > setter(m2); 191c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (int j=0; j<m1.outerSize(); ++j) 192c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (typename SparseMatrixType::InnerIterator i(m1,j); i; ++i) 193c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath setter(i.index(), j) = i.value(); 194c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 195c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1, m2); 196c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }*/ 197c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// std::cerr << m.transpose() << "\n\n" << refMat.transpose() << "\n\n"; 198c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// VERIFY_IS_APPROX(m, refMat); 199c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 200c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test basic computations 201c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 202c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseMatrix refM1 = DenseMatrix::Zero(rows, rows); 203c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseMatrix refM2 = DenseMatrix::Zero(rows, rows); 204c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseMatrix refM3 = DenseMatrix::Zero(rows, rows); 205c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseMatrix refM4 = DenseMatrix::Zero(rows, rows); 206c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SparseMatrixType m1(rows, rows); 207c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SparseMatrixType m2(rows, rows); 208c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SparseMatrixType m3(rows, rows); 209c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SparseMatrixType m4(rows, rows); 210c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath initSparse<Scalar>(density, refM1, m1); 211c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath initSparse<Scalar>(density, refM2, m2); 212c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath initSparse<Scalar>(density, refM3, m3); 213c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath initSparse<Scalar>(density, refM4, m4); 214c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 215c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1+m2, refM1+refM2); 216c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1+m2+m3, refM1+refM2+refM3); 217c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3.cwise()*(m1+m2), refM3.cwise()*(refM1+refM2)); 218c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1*s1-m2, refM1*s1-refM2); 219c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 220c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1*=s1, refM1*=s1); 221c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1/=s1, refM1/=s1); 222c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 223c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1+=m2, refM1+=refM2); 224c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1-=m2, refM1-=refM2); 225c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 226c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.col(0).eigen2_dot(refM2.row(0)), refM1.col(0).eigen2_dot(refM2.row(0))); 227c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 228c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath refM4.setRandom(); 229c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // sparse cwise* dense 230c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3.cwise()*refM4, refM3.cwise()*refM4); 231c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4); 232c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 233c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 234c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test innerVector() 235c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 236c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows); 237c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SparseMatrixType m2(rows, rows); 238c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath initSparse<Scalar>(density, refMat2, m2); 239c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int j0 = ei_random(0,rows-1); 240c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int j1 = ei_random(0,rows-1); 241c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.col(j0)); 242c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.col(j0)+refMat2.col(j1)); 243c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //m2.innerVector(j0) = 2*m2.innerVector(j1); 244c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //refMat2.col(j0) = 2*refMat2.col(j1); 245c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //VERIFY_IS_APPROX(m2, refMat2); 246c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 247c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 248c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test innerVectors() 249c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 250c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows); 251c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SparseMatrixType m2(rows, rows); 252c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath initSparse<Scalar>(density, refMat2, m2); 253c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int j0 = ei_random(0,rows-2); 254c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int j1 = ei_random(0,rows-2); 255c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int n0 = ei_random<int>(1,rows-std::max(j0,j1)); 256c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(0,j0,rows,n0)); 257c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0), 258c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0)); 259c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //m2.innerVectors(j0,n0) = m2.innerVectors(j0,n0) + m2.innerVectors(j1,n0); 260c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //refMat2.block(0,j0,rows,n0) = refMat2.block(0,j0,rows,n0) + refMat2.block(0,j1,rows,n0); 261c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 262c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 263c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test transpose 264c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 265c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows); 266c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SparseMatrixType m2(rows, rows); 267c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath initSparse<Scalar>(density, refMat2, m2); 268c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval()); 269c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose()); 270c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 271c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 272c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test prune 273c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 274c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath SparseMatrixType m2(rows, rows); 275c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseMatrix refM2(rows, rows); 276c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath refM2.setZero(); 277c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int countFalseNonZero = 0; 278c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int countTrueNonZero = 0; 279c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.startFill(); 280c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (int j=0; j<m2.outerSize(); ++j) 281c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (int i=0; i<m2.innerSize(); ++i) 282c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 283c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath float x = ei_random<float>(0,1); 284c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if (x<0.1) 285c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 286c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // do nothing 287c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 288c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath else if (x<0.5) 289c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 290c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath countFalseNonZero++; 291c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.fill(i,j) = Scalar(0); 292c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 293c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath else 294c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 295c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath countTrueNonZero++; 296c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.fill(i,j) = refM2(i,j) = Scalar(1); 297c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 298c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 299c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.endFill(); 300c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(countFalseNonZero+countTrueNonZero == m2.nonZeros()); 301c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2, refM2); 302c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.prune(1); 303c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(countTrueNonZero==m2.nonZeros()); 304c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2, refM2); 305c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 306c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 307c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 308c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_eigen2_sparse_basic() 309c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 310c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < g_repeat; i++) { 311c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( sparse_basic(SparseMatrix<double>(8, 8)) ); 312c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2( sparse_basic(SparseMatrix<std::complex<double> >(16, 16)) ); 313c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( sparse_basic(SparseMatrix<double>(33, 33)) ); 314c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 315c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( sparse_basic(DynamicSparseMatrix<double>(8, 8)) ); 316c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 317c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 318