1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
5 //
6 // This Source Code Form is subject to the terms of the Mozilla
7 // Public License v. 2.0. If a copy of the MPL was not distributed
8 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9
10 #include "sparse.h"
11
sparse_vector(int rows,int cols)12 template<typename Scalar,typename StorageIndex> void sparse_vector(int rows, int cols)
13 {
14 double densityMat = (std::max)(8./(rows*cols), 0.01);
15 double densityVec = (std::max)(8./(rows), 0.1);
16 typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
17 typedef Matrix<Scalar,Dynamic,1> DenseVector;
18 typedef SparseVector<Scalar,0,StorageIndex> SparseVectorType;
19 typedef SparseMatrix<Scalar,0,StorageIndex> SparseMatrixType;
20 Scalar eps = 1e-6;
21
22 SparseMatrixType m1(rows,rows);
23 SparseVectorType v1(rows), v2(rows), v3(rows);
24 DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
25 DenseVector refV1 = DenseVector::Random(rows),
26 refV2 = DenseVector::Random(rows),
27 refV3 = DenseVector::Random(rows);
28
29 std::vector<int> zerocoords, nonzerocoords;
30 initSparse<Scalar>(densityVec, refV1, v1, &zerocoords, &nonzerocoords);
31 initSparse<Scalar>(densityMat, refM1, m1);
32
33 initSparse<Scalar>(densityVec, refV2, v2);
34 initSparse<Scalar>(densityVec, refV3, v3);
35
36 Scalar s1 = internal::random<Scalar>();
37
38 // test coeff and coeffRef
39 for (unsigned int i=0; i<zerocoords.size(); ++i)
40 {
41 VERIFY_IS_MUCH_SMALLER_THAN( v1.coeff(zerocoords[i]), eps );
42 //VERIFY_RAISES_ASSERT( v1.coeffRef(zerocoords[i]) = 5 );
43 }
44 {
45 VERIFY(int(nonzerocoords.size()) == v1.nonZeros());
46 int j=0;
47 for (typename SparseVectorType::InnerIterator it(v1); it; ++it,++j)
48 {
49 VERIFY(nonzerocoords[j]==it.index());
50 VERIFY(it.value()==v1.coeff(it.index()));
51 VERIFY(it.value()==refV1.coeff(it.index()));
52 }
53 }
54 VERIFY_IS_APPROX(v1, refV1);
55
56 // test coeffRef with reallocation
57 {
58 SparseVectorType v4(rows);
59 DenseVector v5 = DenseVector::Zero(rows);
60 for(int k=0; k<rows; ++k)
61 {
62 int i = internal::random<int>(0,rows-1);
63 Scalar v = internal::random<Scalar>();
64 v4.coeffRef(i) += v;
65 v5.coeffRef(i) += v;
66 }
67 VERIFY_IS_APPROX(v4,v5);
68 }
69
70 v1.coeffRef(nonzerocoords[0]) = Scalar(5);
71 refV1.coeffRef(nonzerocoords[0]) = Scalar(5);
72 VERIFY_IS_APPROX(v1, refV1);
73
74 VERIFY_IS_APPROX(v1+v2, refV1+refV2);
75 VERIFY_IS_APPROX(v1+v2+v3, refV1+refV2+refV3);
76
77 VERIFY_IS_APPROX(v1*s1-v2, refV1*s1-refV2);
78
79 VERIFY_IS_APPROX(v1*=s1, refV1*=s1);
80 VERIFY_IS_APPROX(v1/=s1, refV1/=s1);
81
82 VERIFY_IS_APPROX(v1+=v2, refV1+=refV2);
83 VERIFY_IS_APPROX(v1-=v2, refV1-=refV2);
84
85 VERIFY_IS_APPROX(v1.dot(v2), refV1.dot(refV2));
86 VERIFY_IS_APPROX(v1.dot(refV2), refV1.dot(refV2));
87
88 VERIFY_IS_APPROX(m1*v2, refM1*refV2);
89 VERIFY_IS_APPROX(v1.dot(m1*v2), refV1.dot(refM1*refV2));
90 {
91 int i = internal::random<int>(0,rows-1);
92 VERIFY_IS_APPROX(v1.dot(m1.col(i)), refV1.dot(refM1.col(i)));
93 }
94
95
96 VERIFY_IS_APPROX(v1.squaredNorm(), refV1.squaredNorm());
97
98 VERIFY_IS_APPROX(v1.blueNorm(), refV1.blueNorm());
99
100 // test aliasing
101 VERIFY_IS_APPROX((v1 = -v1), (refV1 = -refV1));
102 VERIFY_IS_APPROX((v1 = v1.transpose()), (refV1 = refV1.transpose().eval()));
103 VERIFY_IS_APPROX((v1 += -v1), (refV1 += -refV1));
104
105 // sparse matrix to sparse vector
106 SparseMatrixType mv1;
107 VERIFY_IS_APPROX((mv1=v1),v1);
108 VERIFY_IS_APPROX(mv1,(v1=mv1));
109 VERIFY_IS_APPROX(mv1,(v1=mv1.transpose()));
110
111 // check copy to dense vector with transpose
112 refV3.resize(0);
113 VERIFY_IS_APPROX(refV3 = v1.transpose(),v1.toDense());
114 VERIFY_IS_APPROX(DenseVector(v1),v1.toDense());
115
116 // test conservative resize
117 {
118 std::vector<StorageIndex> inc;
119 if(rows > 3)
120 inc.push_back(-3);
121 inc.push_back(0);
122 inc.push_back(3);
123 inc.push_back(1);
124 inc.push_back(10);
125
126 for(std::size_t i = 0; i< inc.size(); i++) {
127 StorageIndex incRows = inc[i];
128 SparseVectorType vec1(rows);
129 DenseVector refVec1 = DenseVector::Zero(rows);
130 initSparse<Scalar>(densityVec, refVec1, vec1);
131
132 vec1.conservativeResize(rows+incRows);
133 refVec1.conservativeResize(rows+incRows);
134 if (incRows > 0) refVec1.tail(incRows).setZero();
135
136 VERIFY_IS_APPROX(vec1, refVec1);
137
138 // Insert new values
139 if (incRows > 0)
140 vec1.insert(vec1.rows()-1) = refVec1(refVec1.rows()-1) = 1;
141
142 VERIFY_IS_APPROX(vec1, refVec1);
143 }
144 }
145
146 }
147
test_sparse_vector()148 void test_sparse_vector()
149 {
150 for(int i = 0; i < g_repeat; i++) {
151 int r = Eigen::internal::random<int>(1,500), c = Eigen::internal::random<int>(1,500);
152 if(Eigen::internal::random<int>(0,4) == 0) {
153 r = c; // check square matrices in 25% of tries
154 }
155 EIGEN_UNUSED_VARIABLE(r+c);
156
157 CALL_SUBTEST_1(( sparse_vector<double,int>(8, 8) ));
158 CALL_SUBTEST_2(( sparse_vector<std::complex<double>, int>(r, c) ));
159 CALL_SUBTEST_1(( sparse_vector<double,long int>(r, c) ));
160 CALL_SUBTEST_1(( sparse_vector<double,short>(r, c) ));
161 }
162 }
163
164