1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2008-2011 Gael Guennebaud <[email protected]>
5 // Copyright (C) 2008 Daniel Gomez Ferro <[email protected]>
6 // Copyright (C) 2013 Désiré Nuentsa-Wakam <[email protected]>
7 //
8 // This Source Code Form is subject to the terms of the Mozilla
9 // Public License v. 2.0. If a copy of the MPL was not distributed
10 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
11
12 #ifndef EIGEN_SPARSE_TEST_INCLUDED_FROM_SPARSE_EXTRA
13 static long g_realloc_count = 0;
14 #define EIGEN_SPARSE_COMPRESSED_STORAGE_REALLOCATE_PLUGIN g_realloc_count++;
15
16 static long g_dense_op_sparse_count = 0;
17 #define EIGEN_SPARSE_ASSIGNMENT_FROM_DENSE_OP_SPARSE_PLUGIN g_dense_op_sparse_count++;
18 #define EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_ADD_DENSE_PLUGIN g_dense_op_sparse_count+=10;
19 #define EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_SUB_DENSE_PLUGIN g_dense_op_sparse_count+=20;
20 #endif
21
22 #include "sparse.h"
23
sparse_basic(const SparseMatrixType & ref)24 template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& ref)
25 {
26 typedef typename SparseMatrixType::StorageIndex StorageIndex;
27 typedef Matrix<StorageIndex,2,1> Vector2;
28
29 const Index rows = ref.rows();
30 const Index cols = ref.cols();
31 //const Index inner = ref.innerSize();
32 //const Index outer = ref.outerSize();
33
34 typedef typename SparseMatrixType::Scalar Scalar;
35 typedef typename SparseMatrixType::RealScalar RealScalar;
36 enum { Flags = SparseMatrixType::Flags };
37
38 double density = (std::max)(8./(rows*cols), 0.01);
39 typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
40 typedef Matrix<Scalar,Dynamic,1> DenseVector;
41 Scalar eps = 1e-6;
42
43 Scalar s1 = internal::random<Scalar>();
44 {
45 SparseMatrixType m(rows, cols);
46 DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
47 DenseVector vec1 = DenseVector::Random(rows);
48
49 std::vector<Vector2> zeroCoords;
50 std::vector<Vector2> nonzeroCoords;
51 initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
52
53 // test coeff and coeffRef
54 for (std::size_t i=0; i<zeroCoords.size(); ++i)
55 {
56 VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps );
57 if(internal::is_same<SparseMatrixType,SparseMatrix<Scalar,Flags> >::value)
58 VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[i].x(),zeroCoords[i].y()) = 5 );
59 }
60 VERIFY_IS_APPROX(m, refMat);
61
62 if(!nonzeroCoords.empty()) {
63 m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
64 refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
65 }
66
67 VERIFY_IS_APPROX(m, refMat);
68
69 // test assertion
70 VERIFY_RAISES_ASSERT( m.coeffRef(-1,1) = 0 );
71 VERIFY_RAISES_ASSERT( m.coeffRef(0,m.cols()) = 0 );
72 }
73
74 // test insert (inner random)
75 {
76 DenseMatrix m1(rows,cols);
77 m1.setZero();
78 SparseMatrixType m2(rows,cols);
79 bool call_reserve = internal::random<int>()%2;
80 Index nnz = internal::random<int>(1,int(rows)/2);
81 if(call_reserve)
82 {
83 if(internal::random<int>()%2)
84 m2.reserve(VectorXi::Constant(m2.outerSize(), int(nnz)));
85 else
86 m2.reserve(m2.outerSize() * nnz);
87 }
88 g_realloc_count = 0;
89 for (Index j=0; j<cols; ++j)
90 {
91 for (Index k=0; k<nnz; ++k)
92 {
93 Index i = internal::random<Index>(0,rows-1);
94 if (m1.coeff(i,j)==Scalar(0))
95 m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
96 }
97 }
98
99 if(call_reserve && !SparseMatrixType::IsRowMajor)
100 {
101 VERIFY(g_realloc_count==0);
102 }
103
104 m2.finalize();
105 VERIFY_IS_APPROX(m2,m1);
106 }
107
108 // test insert (fully random)
109 {
110 DenseMatrix m1(rows,cols);
111 m1.setZero();
112 SparseMatrixType m2(rows,cols);
113 if(internal::random<int>()%2)
114 m2.reserve(VectorXi::Constant(m2.outerSize(), 2));
115 for (int k=0; k<rows*cols; ++k)
116 {
117 Index i = internal::random<Index>(0,rows-1);
118 Index j = internal::random<Index>(0,cols-1);
119 if ((m1.coeff(i,j)==Scalar(0)) && (internal::random<int>()%2))
120 m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
121 else
122 {
123 Scalar v = internal::random<Scalar>();
124 m2.coeffRef(i,j) += v;
125 m1(i,j) += v;
126 }
127 }
128 VERIFY_IS_APPROX(m2,m1);
129 }
130
131 // test insert (un-compressed)
132 for(int mode=0;mode<4;++mode)
133 {
134 DenseMatrix m1(rows,cols);
135 m1.setZero();
136 SparseMatrixType m2(rows,cols);
137 VectorXi r(VectorXi::Constant(m2.outerSize(), ((mode%2)==0) ? int(m2.innerSize()) : std::max<int>(1,int(m2.innerSize())/8)));
138 m2.reserve(r);
139 for (Index k=0; k<rows*cols; ++k)
140 {
141 Index i = internal::random<Index>(0,rows-1);
142 Index j = internal::random<Index>(0,cols-1);
143 if (m1.coeff(i,j)==Scalar(0))
144 m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
145 if(mode==3)
146 m2.reserve(r);
147 }
148 if(internal::random<int>()%2)
149 m2.makeCompressed();
150 VERIFY_IS_APPROX(m2,m1);
151 }
152
153 // test basic computations
154 {
155 DenseMatrix refM1 = DenseMatrix::Zero(rows, cols);
156 DenseMatrix refM2 = DenseMatrix::Zero(rows, cols);
157 DenseMatrix refM3 = DenseMatrix::Zero(rows, cols);
158 DenseMatrix refM4 = DenseMatrix::Zero(rows, cols);
159 SparseMatrixType m1(rows, cols);
160 SparseMatrixType m2(rows, cols);
161 SparseMatrixType m3(rows, cols);
162 SparseMatrixType m4(rows, cols);
163 initSparse<Scalar>(density, refM1, m1);
164 initSparse<Scalar>(density, refM2, m2);
165 initSparse<Scalar>(density, refM3, m3);
166 initSparse<Scalar>(density, refM4, m4);
167
168 if(internal::random<bool>())
169 m1.makeCompressed();
170
171 Index m1_nnz = m1.nonZeros();
172
173 VERIFY_IS_APPROX(m1*s1, refM1*s1);
174 VERIFY_IS_APPROX(m1+m2, refM1+refM2);
175 VERIFY_IS_APPROX(m1+m2+m3, refM1+refM2+refM3);
176 VERIFY_IS_APPROX(m3.cwiseProduct(m1+m2), refM3.cwiseProduct(refM1+refM2));
177 VERIFY_IS_APPROX(m1*s1-m2, refM1*s1-refM2);
178 VERIFY_IS_APPROX(m4=m1/s1, refM1/s1);
179 VERIFY_IS_EQUAL(m4.nonZeros(), m1_nnz);
180
181 if(SparseMatrixType::IsRowMajor)
182 VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.row(0)), refM1.row(0).dot(refM2.row(0)));
183 else
184 VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.col(0)), refM1.col(0).dot(refM2.col(0)));
185
186 DenseVector rv = DenseVector::Random(m1.cols());
187 DenseVector cv = DenseVector::Random(m1.rows());
188 Index r = internal::random<Index>(0,m1.rows()-2);
189 Index c = internal::random<Index>(0,m1.cols()-1);
190 VERIFY_IS_APPROX(( m1.template block<1,Dynamic>(r,0,1,m1.cols()).dot(rv)) , refM1.row(r).dot(rv));
191 VERIFY_IS_APPROX(m1.row(r).dot(rv), refM1.row(r).dot(rv));
192 VERIFY_IS_APPROX(m1.col(c).dot(cv), refM1.col(c).dot(cv));
193
194 VERIFY_IS_APPROX(m1.conjugate(), refM1.conjugate());
195 VERIFY_IS_APPROX(m1.real(), refM1.real());
196
197 refM4.setRandom();
198 // sparse cwise* dense
199 VERIFY_IS_APPROX(m3.cwiseProduct(refM4), refM3.cwiseProduct(refM4));
200 // dense cwise* sparse
201 VERIFY_IS_APPROX(refM4.cwiseProduct(m3), refM4.cwiseProduct(refM3));
202 // VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4);
203
204 // mixed sparse-dense
205 VERIFY_IS_APPROX(refM4 + m3, refM4 + refM3);
206 VERIFY_IS_APPROX(m3 + refM4, refM3 + refM4);
207 VERIFY_IS_APPROX(refM4 - m3, refM4 - refM3);
208 VERIFY_IS_APPROX(m3 - refM4, refM3 - refM4);
209 VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3);
210 VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3*RealScalar(0.5)).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3);
211 VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3.cwiseProduct(m3)).eval(), RealScalar(0.5)*refM4 + refM3.cwiseProduct(refM3));
212
213 VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3);
214 VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3*RealScalar(0.5)).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3);
215 VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (m3+m3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3));
216 VERIFY_IS_APPROX(((refM3+m3)+RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM3 + (refM3+refM3));
217 VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (refM3+m3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3));
218 VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (m3+refM3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3));
219
220
221 VERIFY_IS_APPROX(m1.sum(), refM1.sum());
222
223 m4 = m1; refM4 = m4;
224
225 VERIFY_IS_APPROX(m1*=s1, refM1*=s1);
226 VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
227 VERIFY_IS_APPROX(m1/=s1, refM1/=s1);
228 VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
229
230 VERIFY_IS_APPROX(m1+=m2, refM1+=refM2);
231 VERIFY_IS_APPROX(m1-=m2, refM1-=refM2);
232
233 refM3 = refM1;
234
235 VERIFY_IS_APPROX(refM1+=m2, refM3+=refM2);
236 VERIFY_IS_APPROX(refM1-=m2, refM3-=refM2);
237
238 g_dense_op_sparse_count=0; VERIFY_IS_APPROX(refM1 =m2+refM4, refM3 =refM2+refM4); VERIFY_IS_EQUAL(g_dense_op_sparse_count,10);
239 g_dense_op_sparse_count=0; VERIFY_IS_APPROX(refM1+=m2+refM4, refM3+=refM2+refM4); VERIFY_IS_EQUAL(g_dense_op_sparse_count,1);
240 g_dense_op_sparse_count=0; VERIFY_IS_APPROX(refM1-=m2+refM4, refM3-=refM2+refM4); VERIFY_IS_EQUAL(g_dense_op_sparse_count,1);
241 g_dense_op_sparse_count=0; VERIFY_IS_APPROX(refM1 =refM4+m2, refM3 =refM2+refM4); VERIFY_IS_EQUAL(g_dense_op_sparse_count,1);
242 g_dense_op_sparse_count=0; VERIFY_IS_APPROX(refM1+=refM4+m2, refM3+=refM2+refM4); VERIFY_IS_EQUAL(g_dense_op_sparse_count,1);
243 g_dense_op_sparse_count=0; VERIFY_IS_APPROX(refM1-=refM4+m2, refM3-=refM2+refM4); VERIFY_IS_EQUAL(g_dense_op_sparse_count,1);
244
245 g_dense_op_sparse_count=0; VERIFY_IS_APPROX(refM1 =m2-refM4, refM3 =refM2-refM4); VERIFY_IS_EQUAL(g_dense_op_sparse_count,20);
246 g_dense_op_sparse_count=0; VERIFY_IS_APPROX(refM1+=m2-refM4, refM3+=refM2-refM4); VERIFY_IS_EQUAL(g_dense_op_sparse_count,1);
247 g_dense_op_sparse_count=0; VERIFY_IS_APPROX(refM1-=m2-refM4, refM3-=refM2-refM4); VERIFY_IS_EQUAL(g_dense_op_sparse_count,1);
248 g_dense_op_sparse_count=0; VERIFY_IS_APPROX(refM1 =refM4-m2, refM3 =refM4-refM2); VERIFY_IS_EQUAL(g_dense_op_sparse_count,1);
249 g_dense_op_sparse_count=0; VERIFY_IS_APPROX(refM1+=refM4-m2, refM3+=refM4-refM2); VERIFY_IS_EQUAL(g_dense_op_sparse_count,1);
250 g_dense_op_sparse_count=0; VERIFY_IS_APPROX(refM1-=refM4-m2, refM3-=refM4-refM2); VERIFY_IS_EQUAL(g_dense_op_sparse_count,1);
251 refM3 = m3;
252
253 if (rows>=2 && cols>=2)
254 {
255 VERIFY_RAISES_ASSERT( m1 += m1.innerVector(0) );
256 VERIFY_RAISES_ASSERT( m1 -= m1.innerVector(0) );
257 VERIFY_RAISES_ASSERT( refM1 -= m1.innerVector(0) );
258 VERIFY_RAISES_ASSERT( refM1 += m1.innerVector(0) );
259 }
260 m1 = m4; refM1 = refM4;
261
262 // test aliasing
263 VERIFY_IS_APPROX((m1 = -m1), (refM1 = -refM1));
264 VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
265 m1 = m4; refM1 = refM4;
266 VERIFY_IS_APPROX((m1 = m1.transpose()), (refM1 = refM1.transpose().eval()));
267 VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
268 m1 = m4; refM1 = refM4;
269 VERIFY_IS_APPROX((m1 = -m1.transpose()), (refM1 = -refM1.transpose().eval()));
270 VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
271 m1 = m4; refM1 = refM4;
272 VERIFY_IS_APPROX((m1 += -m1), (refM1 += -refM1));
273 VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
274 m1 = m4; refM1 = refM4;
275
276 if(m1.isCompressed())
277 {
278 VERIFY_IS_APPROX(m1.coeffs().sum(), m1.sum());
279 m1.coeffs() += s1;
280 for(Index j = 0; j<m1.outerSize(); ++j)
281 for(typename SparseMatrixType::InnerIterator it(m1,j); it; ++it)
282 refM1(it.row(), it.col()) += s1;
283 VERIFY_IS_APPROX(m1, refM1);
284 }
285
286 // and/or
287 {
288 typedef SparseMatrix<bool, SparseMatrixType::Options, typename SparseMatrixType::StorageIndex> SpBool;
289 SpBool mb1 = m1.real().template cast<bool>();
290 SpBool mb2 = m2.real().template cast<bool>();
291 VERIFY_IS_EQUAL(mb1.template cast<int>().sum(), refM1.real().template cast<bool>().count());
292 VERIFY_IS_EQUAL((mb1 && mb2).template cast<int>().sum(), (refM1.real().template cast<bool>() && refM2.real().template cast<bool>()).count());
293 VERIFY_IS_EQUAL((mb1 || mb2).template cast<int>().sum(), (refM1.real().template cast<bool>() || refM2.real().template cast<bool>()).count());
294 SpBool mb3 = mb1 && mb2;
295 if(mb1.coeffs().all() && mb2.coeffs().all())
296 {
297 VERIFY_IS_EQUAL(mb3.nonZeros(), (refM1.real().template cast<bool>() && refM2.real().template cast<bool>()).count());
298 }
299 }
300 }
301
302 // test reverse iterators
303 {
304 DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
305 SparseMatrixType m2(rows, cols);
306 initSparse<Scalar>(density, refMat2, m2);
307 std::vector<Scalar> ref_value(m2.innerSize());
308 std::vector<Index> ref_index(m2.innerSize());
309 if(internal::random<bool>())
310 m2.makeCompressed();
311 for(Index j = 0; j<m2.outerSize(); ++j)
312 {
313 Index count_forward = 0;
314
315 for(typename SparseMatrixType::InnerIterator it(m2,j); it; ++it)
316 {
317 ref_value[ref_value.size()-1-count_forward] = it.value();
318 ref_index[ref_index.size()-1-count_forward] = it.index();
319 count_forward++;
320 }
321 Index count_reverse = 0;
322 for(typename SparseMatrixType::ReverseInnerIterator it(m2,j); it; --it)
323 {
324 VERIFY_IS_APPROX( std::abs(ref_value[ref_value.size()-count_forward+count_reverse])+1, std::abs(it.value())+1);
325 VERIFY_IS_EQUAL( ref_index[ref_index.size()-count_forward+count_reverse] , it.index());
326 count_reverse++;
327 }
328 VERIFY_IS_EQUAL(count_forward, count_reverse);
329 }
330 }
331
332 // test transpose
333 {
334 DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
335 SparseMatrixType m2(rows, cols);
336 initSparse<Scalar>(density, refMat2, m2);
337 VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval());
338 VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose());
339
340 VERIFY_IS_APPROX(SparseMatrixType(m2.adjoint()), refMat2.adjoint());
341
342 // check isApprox handles opposite storage order
343 typename Transpose<SparseMatrixType>::PlainObject m3(m2);
344 VERIFY(m2.isApprox(m3));
345 }
346
347 // test prune
348 {
349 SparseMatrixType m2(rows, cols);
350 DenseMatrix refM2(rows, cols);
351 refM2.setZero();
352 int countFalseNonZero = 0;
353 int countTrueNonZero = 0;
354 m2.reserve(VectorXi::Constant(m2.outerSize(), int(m2.innerSize())));
355 for (Index j=0; j<m2.cols(); ++j)
356 {
357 for (Index i=0; i<m2.rows(); ++i)
358 {
359 float x = internal::random<float>(0,1);
360 if (x<0.1f)
361 {
362 // do nothing
363 }
364 else if (x<0.5f)
365 {
366 countFalseNonZero++;
367 m2.insert(i,j) = Scalar(0);
368 }
369 else
370 {
371 countTrueNonZero++;
372 m2.insert(i,j) = Scalar(1);
373 refM2(i,j) = Scalar(1);
374 }
375 }
376 }
377 if(internal::random<bool>())
378 m2.makeCompressed();
379 VERIFY(countFalseNonZero+countTrueNonZero == m2.nonZeros());
380 if(countTrueNonZero>0)
381 VERIFY_IS_APPROX(m2, refM2);
382 m2.prune(Scalar(1));
383 VERIFY(countTrueNonZero==m2.nonZeros());
384 VERIFY_IS_APPROX(m2, refM2);
385 }
386
387 // test setFromTriplets
388 {
389 typedef Triplet<Scalar,StorageIndex> TripletType;
390 std::vector<TripletType> triplets;
391 Index ntriplets = rows*cols;
392 triplets.reserve(ntriplets);
393 DenseMatrix refMat_sum = DenseMatrix::Zero(rows,cols);
394 DenseMatrix refMat_prod = DenseMatrix::Zero(rows,cols);
395 DenseMatrix refMat_last = DenseMatrix::Zero(rows,cols);
396
397 for(Index i=0;i<ntriplets;++i)
398 {
399 StorageIndex r = internal::random<StorageIndex>(0,StorageIndex(rows-1));
400 StorageIndex c = internal::random<StorageIndex>(0,StorageIndex(cols-1));
401 Scalar v = internal::random<Scalar>();
402 triplets.push_back(TripletType(r,c,v));
403 refMat_sum(r,c) += v;
404 if(std::abs(refMat_prod(r,c))==0)
405 refMat_prod(r,c) = v;
406 else
407 refMat_prod(r,c) *= v;
408 refMat_last(r,c) = v;
409 }
410 SparseMatrixType m(rows,cols);
411 m.setFromTriplets(triplets.begin(), triplets.end());
412 VERIFY_IS_APPROX(m, refMat_sum);
413
414 m.setFromTriplets(triplets.begin(), triplets.end(), std::multiplies<Scalar>());
415 VERIFY_IS_APPROX(m, refMat_prod);
416 #if (EIGEN_COMP_CXXVER >= 11)
417 m.setFromTriplets(triplets.begin(), triplets.end(), [] (Scalar,Scalar b) { return b; });
418 VERIFY_IS_APPROX(m, refMat_last);
419 #endif
420 }
421
422 // test Map
423 {
424 DenseMatrix refMat2(rows, cols), refMat3(rows, cols);
425 SparseMatrixType m2(rows, cols), m3(rows, cols);
426 initSparse<Scalar>(density, refMat2, m2);
427 initSparse<Scalar>(density, refMat3, m3);
428 {
429 Map<SparseMatrixType> mapMat2(m2.rows(), m2.cols(), m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr());
430 Map<SparseMatrixType> mapMat3(m3.rows(), m3.cols(), m3.nonZeros(), m3.outerIndexPtr(), m3.innerIndexPtr(), m3.valuePtr(), m3.innerNonZeroPtr());
431 VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3);
432 VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3);
433 }
434 {
435 MappedSparseMatrix<Scalar,SparseMatrixType::Options,StorageIndex> mapMat2(m2.rows(), m2.cols(), m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr());
436 MappedSparseMatrix<Scalar,SparseMatrixType::Options,StorageIndex> mapMat3(m3.rows(), m3.cols(), m3.nonZeros(), m3.outerIndexPtr(), m3.innerIndexPtr(), m3.valuePtr(), m3.innerNonZeroPtr());
437 VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3);
438 VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3);
439 }
440
441 Index i = internal::random<Index>(0,rows-1);
442 Index j = internal::random<Index>(0,cols-1);
443 m2.coeffRef(i,j) = 123;
444 if(internal::random<bool>())
445 m2.makeCompressed();
446 Map<SparseMatrixType> mapMat2(rows, cols, m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr());
447 VERIFY_IS_EQUAL(m2.coeff(i,j),Scalar(123));
448 VERIFY_IS_EQUAL(mapMat2.coeff(i,j),Scalar(123));
449 mapMat2.coeffRef(i,j) = -123;
450 VERIFY_IS_EQUAL(m2.coeff(i,j),Scalar(-123));
451 }
452
453 // test triangularView
454 {
455 DenseMatrix refMat2(rows, cols), refMat3(rows, cols);
456 SparseMatrixType m2(rows, cols), m3(rows, cols);
457 initSparse<Scalar>(density, refMat2, m2);
458 refMat3 = refMat2.template triangularView<Lower>();
459 m3 = m2.template triangularView<Lower>();
460 VERIFY_IS_APPROX(m3, refMat3);
461
462 refMat3 = refMat2.template triangularView<Upper>();
463 m3 = m2.template triangularView<Upper>();
464 VERIFY_IS_APPROX(m3, refMat3);
465
466 {
467 refMat3 = refMat2.template triangularView<UnitUpper>();
468 m3 = m2.template triangularView<UnitUpper>();
469 VERIFY_IS_APPROX(m3, refMat3);
470
471 refMat3 = refMat2.template triangularView<UnitLower>();
472 m3 = m2.template triangularView<UnitLower>();
473 VERIFY_IS_APPROX(m3, refMat3);
474 }
475
476 refMat3 = refMat2.template triangularView<StrictlyUpper>();
477 m3 = m2.template triangularView<StrictlyUpper>();
478 VERIFY_IS_APPROX(m3, refMat3);
479
480 refMat3 = refMat2.template triangularView<StrictlyLower>();
481 m3 = m2.template triangularView<StrictlyLower>();
482 VERIFY_IS_APPROX(m3, refMat3);
483
484 // check sparse-triangular to dense
485 refMat3 = m2.template triangularView<StrictlyUpper>();
486 VERIFY_IS_APPROX(refMat3, DenseMatrix(refMat2.template triangularView<StrictlyUpper>()));
487 }
488
489 // test selfadjointView
490 if(!SparseMatrixType::IsRowMajor)
491 {
492 DenseMatrix refMat2(rows, rows), refMat3(rows, rows);
493 SparseMatrixType m2(rows, rows), m3(rows, rows);
494 initSparse<Scalar>(density, refMat2, m2);
495 refMat3 = refMat2.template selfadjointView<Lower>();
496 m3 = m2.template selfadjointView<Lower>();
497 VERIFY_IS_APPROX(m3, refMat3);
498
499 refMat3 += refMat2.template selfadjointView<Lower>();
500 m3 += m2.template selfadjointView<Lower>();
501 VERIFY_IS_APPROX(m3, refMat3);
502
503 refMat3 -= refMat2.template selfadjointView<Lower>();
504 m3 -= m2.template selfadjointView<Lower>();
505 VERIFY_IS_APPROX(m3, refMat3);
506
507 // selfadjointView only works for square matrices:
508 SparseMatrixType m4(rows, rows+1);
509 VERIFY_RAISES_ASSERT(m4.template selfadjointView<Lower>());
510 VERIFY_RAISES_ASSERT(m4.template selfadjointView<Upper>());
511 }
512
513 // test sparseView
514 {
515 DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
516 SparseMatrixType m2(rows, rows);
517 initSparse<Scalar>(density, refMat2, m2);
518 VERIFY_IS_APPROX(m2.eval(), refMat2.sparseView().eval());
519
520 // sparse view on expressions:
521 VERIFY_IS_APPROX((s1*m2).eval(), (s1*refMat2).sparseView().eval());
522 VERIFY_IS_APPROX((m2+m2).eval(), (refMat2+refMat2).sparseView().eval());
523 VERIFY_IS_APPROX((m2*m2).eval(), (refMat2.lazyProduct(refMat2)).sparseView().eval());
524 VERIFY_IS_APPROX((m2*m2).eval(), (refMat2*refMat2).sparseView().eval());
525 }
526
527 // test diagonal
528 {
529 DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
530 SparseMatrixType m2(rows, cols);
531 initSparse<Scalar>(density, refMat2, m2);
532 VERIFY_IS_APPROX(m2.diagonal(), refMat2.diagonal().eval());
533 DenseVector d = m2.diagonal();
534 VERIFY_IS_APPROX(d, refMat2.diagonal().eval());
535 d = m2.diagonal().array();
536 VERIFY_IS_APPROX(d, refMat2.diagonal().eval());
537 VERIFY_IS_APPROX(const_cast<const SparseMatrixType&>(m2).diagonal(), refMat2.diagonal().eval());
538
539 initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag);
540 m2.diagonal() += refMat2.diagonal();
541 refMat2.diagonal() += refMat2.diagonal();
542 VERIFY_IS_APPROX(m2, refMat2);
543 }
544
545 // test diagonal to sparse
546 {
547 DenseVector d = DenseVector::Random(rows);
548 DenseMatrix refMat2 = d.asDiagonal();
549 SparseMatrixType m2;
550 m2 = d.asDiagonal();
551 VERIFY_IS_APPROX(m2, refMat2);
552 SparseMatrixType m3(d.asDiagonal());
553 VERIFY_IS_APPROX(m3, refMat2);
554 refMat2 += d.asDiagonal();
555 m2 += d.asDiagonal();
556 VERIFY_IS_APPROX(m2, refMat2);
557 m2.setZero(); m2 += d.asDiagonal();
558 refMat2.setZero(); refMat2 += d.asDiagonal();
559 VERIFY_IS_APPROX(m2, refMat2);
560 m2.setZero(); m2 -= d.asDiagonal();
561 refMat2.setZero(); refMat2 -= d.asDiagonal();
562 VERIFY_IS_APPROX(m2, refMat2);
563
564 initSparse<Scalar>(density, refMat2, m2);
565 m2.makeCompressed();
566 m2 += d.asDiagonal();
567 refMat2 += d.asDiagonal();
568 VERIFY_IS_APPROX(m2, refMat2);
569
570 initSparse<Scalar>(density, refMat2, m2);
571 m2.makeCompressed();
572 VectorXi res(rows);
573 for(Index i=0; i<rows; ++i)
574 res(i) = internal::random<int>(0,3);
575 m2.reserve(res);
576 m2 -= d.asDiagonal();
577 refMat2 -= d.asDiagonal();
578 VERIFY_IS_APPROX(m2, refMat2);
579 }
580
581 // test conservative resize
582 {
583 std::vector< std::pair<StorageIndex,StorageIndex> > inc;
584 if(rows > 3 && cols > 2)
585 inc.push_back(std::pair<StorageIndex,StorageIndex>(-3,-2));
586 inc.push_back(std::pair<StorageIndex,StorageIndex>(0,0));
587 inc.push_back(std::pair<StorageIndex,StorageIndex>(3,2));
588 inc.push_back(std::pair<StorageIndex,StorageIndex>(3,0));
589 inc.push_back(std::pair<StorageIndex,StorageIndex>(0,3));
590 inc.push_back(std::pair<StorageIndex,StorageIndex>(0,-1));
591 inc.push_back(std::pair<StorageIndex,StorageIndex>(-1,0));
592 inc.push_back(std::pair<StorageIndex,StorageIndex>(-1,-1));
593
594 for(size_t i = 0; i< inc.size(); i++) {
595 StorageIndex incRows = inc[i].first;
596 StorageIndex incCols = inc[i].second;
597 SparseMatrixType m1(rows, cols);
598 DenseMatrix refMat1 = DenseMatrix::Zero(rows, cols);
599 initSparse<Scalar>(density, refMat1, m1);
600
601 SparseMatrixType m2 = m1;
602 m2.makeCompressed();
603
604 m1.conservativeResize(rows+incRows, cols+incCols);
605 m2.conservativeResize(rows+incRows, cols+incCols);
606 refMat1.conservativeResize(rows+incRows, cols+incCols);
607 if (incRows > 0) refMat1.bottomRows(incRows).setZero();
608 if (incCols > 0) refMat1.rightCols(incCols).setZero();
609
610 VERIFY_IS_APPROX(m1, refMat1);
611 VERIFY_IS_APPROX(m2, refMat1);
612
613 // Insert new values
614 if (incRows > 0)
615 m1.insert(m1.rows()-1, 0) = refMat1(refMat1.rows()-1, 0) = 1;
616 if (incCols > 0)
617 m1.insert(0, m1.cols()-1) = refMat1(0, refMat1.cols()-1) = 1;
618
619 VERIFY_IS_APPROX(m1, refMat1);
620
621
622 }
623 }
624
625 // test Identity matrix
626 {
627 DenseMatrix refMat1 = DenseMatrix::Identity(rows, rows);
628 SparseMatrixType m1(rows, rows);
629 m1.setIdentity();
630 VERIFY_IS_APPROX(m1, refMat1);
631 for(int k=0; k<rows*rows/4; ++k)
632 {
633 Index i = internal::random<Index>(0,rows-1);
634 Index j = internal::random<Index>(0,rows-1);
635 Scalar v = internal::random<Scalar>();
636 m1.coeffRef(i,j) = v;
637 refMat1.coeffRef(i,j) = v;
638 VERIFY_IS_APPROX(m1, refMat1);
639 if(internal::random<Index>(0,10)<2)
640 m1.makeCompressed();
641 }
642 m1.setIdentity();
643 refMat1.setIdentity();
644 VERIFY_IS_APPROX(m1, refMat1);
645 }
646
647 // test array/vector of InnerIterator
648 {
649 typedef typename SparseMatrixType::InnerIterator IteratorType;
650
651 DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
652 SparseMatrixType m2(rows, cols);
653 initSparse<Scalar>(density, refMat2, m2);
654 IteratorType static_array[2];
655 static_array[0] = IteratorType(m2,0);
656 static_array[1] = IteratorType(m2,m2.outerSize()-1);
657 VERIFY( static_array[0] || m2.innerVector(static_array[0].outer()).nonZeros() == 0 );
658 VERIFY( static_array[1] || m2.innerVector(static_array[1].outer()).nonZeros() == 0 );
659 if(static_array[0] && static_array[1])
660 {
661 ++(static_array[1]);
662 static_array[1] = IteratorType(m2,0);
663 VERIFY( static_array[1] );
664 VERIFY( static_array[1].index() == static_array[0].index() );
665 VERIFY( static_array[1].outer() == static_array[0].outer() );
666 VERIFY( static_array[1].value() == static_array[0].value() );
667 }
668
669 std::vector<IteratorType> iters(2);
670 iters[0] = IteratorType(m2,0);
671 iters[1] = IteratorType(m2,m2.outerSize()-1);
672 }
673
674 // test reserve with empty rows/columns
675 {
676 SparseMatrixType m1(0,cols);
677 m1.reserve(ArrayXi::Constant(m1.outerSize(),1));
678 SparseMatrixType m2(rows,0);
679 m2.reserve(ArrayXi::Constant(m2.outerSize(),1));
680 }
681 }
682
683
684 template<typename SparseMatrixType>
big_sparse_triplet(Index rows,Index cols,double density)685 void big_sparse_triplet(Index rows, Index cols, double density) {
686 typedef typename SparseMatrixType::StorageIndex StorageIndex;
687 typedef typename SparseMatrixType::Scalar Scalar;
688 typedef Triplet<Scalar,Index> TripletType;
689 std::vector<TripletType> triplets;
690 double nelements = density * rows*cols;
691 VERIFY(nelements>=0 && nelements < static_cast<double>(NumTraits<StorageIndex>::highest()));
692 Index ntriplets = Index(nelements);
693 triplets.reserve(ntriplets);
694 Scalar sum = Scalar(0);
695 for(Index i=0;i<ntriplets;++i)
696 {
697 Index r = internal::random<Index>(0,rows-1);
698 Index c = internal::random<Index>(0,cols-1);
699 // use positive values to prevent numerical cancellation errors in sum
700 Scalar v = numext::abs(internal::random<Scalar>());
701 triplets.push_back(TripletType(r,c,v));
702 sum += v;
703 }
704 SparseMatrixType m(rows,cols);
705 m.setFromTriplets(triplets.begin(), triplets.end());
706 VERIFY(m.nonZeros() <= ntriplets);
707 VERIFY_IS_APPROX(sum, m.sum());
708 }
709
710 template<int>
bug1105()711 void bug1105()
712 {
713 // Regression test for bug 1105
714 int n = Eigen::internal::random<int>(200,600);
715 SparseMatrix<std::complex<double>,0, long> mat(n, n);
716 std::complex<double> val;
717
718 for(int i=0; i<n; ++i)
719 {
720 mat.coeffRef(i, i%(n/10)) = val;
721 VERIFY(mat.data().allocatedSize()<20*n);
722 }
723 }
724
725 #ifndef EIGEN_SPARSE_TEST_INCLUDED_FROM_SPARSE_EXTRA
726
EIGEN_DECLARE_TEST(sparse_basic)727 EIGEN_DECLARE_TEST(sparse_basic)
728 {
729 g_dense_op_sparse_count = 0; // Suppresses compiler warning.
730 for(int i = 0; i < g_repeat; i++) {
731 int r = Eigen::internal::random<int>(1,200), c = Eigen::internal::random<int>(1,200);
732 if(Eigen::internal::random<int>(0,4) == 0) {
733 r = c; // check square matrices in 25% of tries
734 }
735 EIGEN_UNUSED_VARIABLE(r+c);
736 CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(1, 1)) ));
737 CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(8, 8)) ));
738 CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, ColMajor>(r, c)) ));
739 CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, RowMajor>(r, c)) ));
740 CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(r, c)) ));
741 CALL_SUBTEST_5(( sparse_basic(SparseMatrix<double,ColMajor,long int>(r, c)) ));
742 CALL_SUBTEST_5(( sparse_basic(SparseMatrix<double,RowMajor,long int>(r, c)) ));
743
744 r = Eigen::internal::random<int>(1,100);
745 c = Eigen::internal::random<int>(1,100);
746 if(Eigen::internal::random<int>(0,4) == 0) {
747 r = c; // check square matrices in 25% of tries
748 }
749
750 CALL_SUBTEST_6(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(r), short(c))) ));
751 CALL_SUBTEST_6(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(r), short(c))) ));
752 }
753
754 // Regression test for bug 900: (manually insert higher values here, if you have enough RAM):
755 CALL_SUBTEST_3((big_sparse_triplet<SparseMatrix<float, RowMajor, int> >(10000, 10000, 0.125)));
756 CALL_SUBTEST_4((big_sparse_triplet<SparseMatrix<double, ColMajor, long int> >(10000, 10000, 0.125)));
757
758 CALL_SUBTEST_7( bug1105<0>() );
759 }
760 #endif
761