[136] | 1 | // This file is part of Eigen, a lightweight C++ template library
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| 2 | // for linear algebra. Eigen itself is part of the KDE project.
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| 3 | //
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| 4 | // Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@gmail.com>
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| 5 | //
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| 6 | // This Source Code Form is subject to the terms of the Mozilla
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| 7 | // Public License v. 2.0. If a copy of the MPL was not distributed
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| 8 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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| 9 |
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| 10 | #include "sparse.h"
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| 11 |
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| 12 | template<typename SetterType,typename DenseType, typename Scalar, int Options>
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| 13 | bool test_random_setter(SparseMatrix<Scalar,Options>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords)
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| 14 | {
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| 15 | typedef SparseMatrix<Scalar,Options> SparseType;
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| 16 | {
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| 17 | sm.setZero();
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| 18 | SetterType w(sm);
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| 19 | std::vector<Vector2i> remaining = nonzeroCoords;
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| 20 | while(!remaining.empty())
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| 21 | {
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| 22 | int i = ei_random<int>(0,remaining.size()-1);
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| 23 | w(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y());
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| 24 | remaining[i] = remaining.back();
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| 25 | remaining.pop_back();
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| 26 | }
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| 27 | }
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| 28 | return sm.isApprox(ref);
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| 29 | }
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| 30 |
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| 31 | template<typename SetterType,typename DenseType, typename T>
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| 32 | bool test_random_setter(DynamicSparseMatrix<T>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords)
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| 33 | {
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| 34 | sm.setZero();
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| 35 | std::vector<Vector2i> remaining = nonzeroCoords;
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| 36 | while(!remaining.empty())
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| 37 | {
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| 38 | int i = ei_random<int>(0,remaining.size()-1);
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| 39 | sm.coeffRef(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y());
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| 40 | remaining[i] = remaining.back();
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| 41 | remaining.pop_back();
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| 42 | }
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| 43 | return sm.isApprox(ref);
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| 44 | }
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| 45 |
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| 46 | template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& ref)
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| 47 | {
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| 48 | const int rows = ref.rows();
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| 49 | const int cols = ref.cols();
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| 50 | typedef typename SparseMatrixType::Scalar Scalar;
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| 51 | enum { Flags = SparseMatrixType::Flags };
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| 52 |
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| 53 | double density = std::max(8./(rows*cols), 0.01);
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| 54 | typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
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| 55 | typedef Matrix<Scalar,Dynamic,1> DenseVector;
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| 56 | Scalar eps = 1e-6;
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| 57 |
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| 58 | SparseMatrixType m(rows, cols);
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| 59 | DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
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| 60 | DenseVector vec1 = DenseVector::Random(rows);
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| 61 | Scalar s1 = ei_random<Scalar>();
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| 62 |
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| 63 | std::vector<Vector2i> zeroCoords;
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| 64 | std::vector<Vector2i> nonzeroCoords;
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| 65 | initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
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| 66 |
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| 67 | if (zeroCoords.size()==0 || nonzeroCoords.size()==0)
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| 68 | return;
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| 69 |
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| 70 | // test coeff and coeffRef
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| 71 | for (int i=0; i<(int)zeroCoords.size(); ++i)
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| 72 | {
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| 73 | VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps );
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| 74 | if(ei_is_same_type<SparseMatrixType,SparseMatrix<Scalar,Flags> >::ret)
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| 75 | VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[0].x(),zeroCoords[0].y()) = 5 );
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| 76 | }
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| 77 | VERIFY_IS_APPROX(m, refMat);
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| 78 |
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| 79 | m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
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| 80 | refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
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| 81 |
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| 82 | VERIFY_IS_APPROX(m, refMat);
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| 83 | /*
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| 84 | // test InnerIterators and Block expressions
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| 85 | for (int t=0; t<10; ++t)
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| 86 | {
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| 87 | int j = ei_random<int>(0,cols-1);
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| 88 | int i = ei_random<int>(0,rows-1);
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| 89 | int w = ei_random<int>(1,cols-j-1);
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| 90 | int h = ei_random<int>(1,rows-i-1);
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| 91 |
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| 92 | // VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w));
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| 93 | for(int c=0; c<w; c++)
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| 94 | {
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| 95 | VERIFY_IS_APPROX(m.block(i,j,h,w).col(c), refMat.block(i,j,h,w).col(c));
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| 96 | for(int r=0; r<h; r++)
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| 97 | {
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| 98 | // 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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| 99 | }
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| 100 | }
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| 101 | // for(int r=0; r<h; r++)
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| 102 | // {
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| 103 | // VERIFY_IS_APPROX(m.block(i,j,h,w).row(r), refMat.block(i,j,h,w).row(r));
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| 104 | // for(int c=0; c<w; c++)
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| 105 | // {
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| 106 | // 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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| 107 | // }
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| 108 | // }
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| 109 | }
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| 110 |
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| 111 | for(int c=0; c<cols; c++)
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| 112 | {
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| 113 | VERIFY_IS_APPROX(m.col(c) + m.col(c), (m + m).col(c));
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| 114 | VERIFY_IS_APPROX(m.col(c) + m.col(c), refMat.col(c) + refMat.col(c));
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| 115 | }
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| 116 |
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| 117 | for(int r=0; r<rows; r++)
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| 118 | {
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| 119 | VERIFY_IS_APPROX(m.row(r) + m.row(r), (m + m).row(r));
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| 120 | VERIFY_IS_APPROX(m.row(r) + m.row(r), refMat.row(r) + refMat.row(r));
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| 121 | }
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| 122 | */
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| 123 |
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| 124 | // test SparseSetters
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| 125 | // coherent setter
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| 126 | // TODO extend the MatrixSetter
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| 127 | // {
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| 128 | // m.setZero();
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| 129 | // VERIFY_IS_NOT_APPROX(m, refMat);
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| 130 | // SparseSetter<SparseMatrixType, FullyCoherentAccessPattern> w(m);
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| 131 | // for (int i=0; i<nonzeroCoords.size(); ++i)
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| 132 | // {
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| 133 | // w->coeffRef(nonzeroCoords[i].x(),nonzeroCoords[i].y()) = refMat.coeff(nonzeroCoords[i].x(),nonzeroCoords[i].y());
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| 134 | // }
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| 135 | // }
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| 136 | // VERIFY_IS_APPROX(m, refMat);
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| 137 |
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| 138 | // random setter
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| 139 | // {
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| 140 | // m.setZero();
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| 141 | // VERIFY_IS_NOT_APPROX(m, refMat);
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| 142 | // SparseSetter<SparseMatrixType, RandomAccessPattern> w(m);
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| 143 | // std::vector<Vector2i> remaining = nonzeroCoords;
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| 144 | // while(!remaining.empty())
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| 145 | // {
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| 146 | // int i = ei_random<int>(0,remaining.size()-1);
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| 147 | // w->coeffRef(remaining[i].x(),remaining[i].y()) = refMat.coeff(remaining[i].x(),remaining[i].y());
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| 148 | // remaining[i] = remaining.back();
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| 149 | // remaining.pop_back();
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| 150 | // }
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| 151 | // }
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| 152 | // VERIFY_IS_APPROX(m, refMat);
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| 153 |
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| 154 | VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdMapTraits> >(m,refMat,nonzeroCoords) ));
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| 155 | #ifdef EIGEN_UNORDERED_MAP_SUPPORT
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| 156 | VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdUnorderedMapTraits> >(m,refMat,nonzeroCoords) ));
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| 157 | #endif
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| 158 | #ifdef _DENSE_HASH_MAP_H_
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| 159 | VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleDenseHashMapTraits> >(m,refMat,nonzeroCoords) ));
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| 160 | #endif
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| 161 | #ifdef _SPARSE_HASH_MAP_H_
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| 162 | VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleSparseHashMapTraits> >(m,refMat,nonzeroCoords) ));
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| 163 | #endif
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| 164 |
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| 165 | // test fillrand
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| 166 | {
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| 167 | DenseMatrix m1(rows,cols);
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| 168 | m1.setZero();
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| 169 | SparseMatrixType m2(rows,cols);
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| 170 | m2.startFill();
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| 171 | for (int j=0; j<cols; ++j)
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| 172 | {
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| 173 | for (int k=0; k<rows/2; ++k)
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| 174 | {
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| 175 | int i = ei_random<int>(0,rows-1);
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| 176 | if (m1.coeff(i,j)==Scalar(0))
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| 177 | m2.fillrand(i,j) = m1(i,j) = ei_random<Scalar>();
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| 178 | }
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| 179 | }
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| 180 | m2.endFill();
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| 181 | VERIFY_IS_APPROX(m2,m1);
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| 182 | }
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| 183 |
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| 184 | // test RandomSetter
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| 185 | /*{
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| 186 | SparseMatrixType m1(rows,cols), m2(rows,cols);
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| 187 | DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
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| 188 | initSparse<Scalar>(density, refM1, m1);
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| 189 | {
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| 190 | Eigen::RandomSetter<SparseMatrixType > setter(m2);
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| 191 | for (int j=0; j<m1.outerSize(); ++j)
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| 192 | for (typename SparseMatrixType::InnerIterator i(m1,j); i; ++i)
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| 193 | setter(i.index(), j) = i.value();
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| 194 | }
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| 195 | VERIFY_IS_APPROX(m1, m2);
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| 196 | }*/
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| 197 | // std::cerr << m.transpose() << "\n\n" << refMat.transpose() << "\n\n";
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| 198 | // VERIFY_IS_APPROX(m, refMat);
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| 199 |
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| 200 | // test basic computations
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| 201 | {
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| 202 | DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
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| 203 | DenseMatrix refM2 = DenseMatrix::Zero(rows, rows);
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| 204 | DenseMatrix refM3 = DenseMatrix::Zero(rows, rows);
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| 205 | DenseMatrix refM4 = DenseMatrix::Zero(rows, rows);
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| 206 | SparseMatrixType m1(rows, rows);
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| 207 | SparseMatrixType m2(rows, rows);
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| 208 | SparseMatrixType m3(rows, rows);
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| 209 | SparseMatrixType m4(rows, rows);
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| 210 | initSparse<Scalar>(density, refM1, m1);
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| 211 | initSparse<Scalar>(density, refM2, m2);
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| 212 | initSparse<Scalar>(density, refM3, m3);
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| 213 | initSparse<Scalar>(density, refM4, m4);
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| 214 |
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| 215 | VERIFY_IS_APPROX(m1+m2, refM1+refM2);
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| 216 | VERIFY_IS_APPROX(m1+m2+m3, refM1+refM2+refM3);
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| 217 | VERIFY_IS_APPROX(m3.cwise()*(m1+m2), refM3.cwise()*(refM1+refM2));
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| 218 | VERIFY_IS_APPROX(m1*s1-m2, refM1*s1-refM2);
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| 219 |
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| 220 | VERIFY_IS_APPROX(m1*=s1, refM1*=s1);
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| 221 | VERIFY_IS_APPROX(m1/=s1, refM1/=s1);
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| 222 |
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| 223 | VERIFY_IS_APPROX(m1+=m2, refM1+=refM2);
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| 224 | VERIFY_IS_APPROX(m1-=m2, refM1-=refM2);
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| 225 |
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| 226 | VERIFY_IS_APPROX(m1.col(0).eigen2_dot(refM2.row(0)), refM1.col(0).eigen2_dot(refM2.row(0)));
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| 227 |
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| 228 | refM4.setRandom();
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| 229 | // sparse cwise* dense
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| 230 | VERIFY_IS_APPROX(m3.cwise()*refM4, refM3.cwise()*refM4);
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| 231 | // VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4);
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| 232 | }
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| 233 |
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| 234 | // test innerVector()
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| 235 | {
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| 236 | DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
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| 237 | SparseMatrixType m2(rows, rows);
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| 238 | initSparse<Scalar>(density, refMat2, m2);
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| 239 | int j0 = ei_random(0,rows-1);
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| 240 | int j1 = ei_random(0,rows-1);
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| 241 | VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.col(j0));
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| 242 | VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.col(j0)+refMat2.col(j1));
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| 243 | //m2.innerVector(j0) = 2*m2.innerVector(j1);
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| 244 | //refMat2.col(j0) = 2*refMat2.col(j1);
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| 245 | //VERIFY_IS_APPROX(m2, refMat2);
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| 246 | }
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| 247 |
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| 248 | // test innerVectors()
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| 249 | {
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| 250 | DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
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| 251 | SparseMatrixType m2(rows, rows);
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| 252 | initSparse<Scalar>(density, refMat2, m2);
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| 253 | int j0 = ei_random(0,rows-2);
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| 254 | int j1 = ei_random(0,rows-2);
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| 255 | int n0 = ei_random<int>(1,rows-std::max(j0,j1));
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| 256 | VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(0,j0,rows,n0));
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| 257 | VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0),
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| 258 | refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));
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| 259 | //m2.innerVectors(j0,n0) = m2.innerVectors(j0,n0) + m2.innerVectors(j1,n0);
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| 260 | //refMat2.block(0,j0,rows,n0) = refMat2.block(0,j0,rows,n0) + refMat2.block(0,j1,rows,n0);
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| 261 | }
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| 262 |
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| 263 | // test transpose
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| 264 | {
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| 265 | DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
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| 266 | SparseMatrixType m2(rows, rows);
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| 267 | initSparse<Scalar>(density, refMat2, m2);
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| 268 | VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval());
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| 269 | VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose());
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| 270 | }
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| 271 |
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| 272 | // test prune
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| 273 | {
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| 274 | SparseMatrixType m2(rows, rows);
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| 275 | DenseMatrix refM2(rows, rows);
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| 276 | refM2.setZero();
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| 277 | int countFalseNonZero = 0;
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| 278 | int countTrueNonZero = 0;
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| 279 | m2.startFill();
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| 280 | for (int j=0; j<m2.outerSize(); ++j)
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| 281 | for (int i=0; i<m2.innerSize(); ++i)
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| 282 | {
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| 283 | float x = ei_random<float>(0,1);
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| 284 | if (x<0.1)
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| 285 | {
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| 286 | // do nothing
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| 287 | }
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| 288 | else if (x<0.5)
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| 289 | {
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| 290 | countFalseNonZero++;
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| 291 | m2.fill(i,j) = Scalar(0);
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| 292 | }
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| 293 | else
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| 294 | {
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| 295 | countTrueNonZero++;
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| 296 | m2.fill(i,j) = refM2(i,j) = Scalar(1);
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| 297 | }
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| 298 | }
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| 299 | m2.endFill();
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| 300 | VERIFY(countFalseNonZero+countTrueNonZero == m2.nonZeros());
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| 301 | VERIFY_IS_APPROX(m2, refM2);
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| 302 | m2.prune(1);
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| 303 | VERIFY(countTrueNonZero==m2.nonZeros());
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| 304 | VERIFY_IS_APPROX(m2, refM2);
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| 305 | }
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| 306 | }
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| 307 |
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| 308 | void test_eigen2_sparse_basic()
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| 309 | {
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| 310 | for(int i = 0; i < g_repeat; i++) {
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| 311 | CALL_SUBTEST_1( sparse_basic(SparseMatrix<double>(8, 8)) );
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| 312 | CALL_SUBTEST_2( sparse_basic(SparseMatrix<std::complex<double> >(16, 16)) );
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| 313 | CALL_SUBTEST_1( sparse_basic(SparseMatrix<double>(33, 33)) );
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| 314 |
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| 315 | CALL_SUBTEST_3( sparse_basic(DynamicSparseMatrix<double>(8, 8)) );
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| 316 | }
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| 317 | }
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