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ADD: added other eigen lib

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This commit is contained in:
Henry Winkel
2022-12-21 16:19:04 +01:00
parent a570766dc6
commit 9e56c7f2c0
832 changed files with 36586 additions and 20006 deletions
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119
libs/eigen/test/sparse_basic.cpp
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@@ -28,8 +28,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
const Index rows = ref.rows();
const Index cols = ref.cols();
//const Index inner = ref.innerSize();
//const Index outer = ref.outerSize();
const Index inner = ref.innerSize();
const Index outer = ref.outerSize();
typedef typename SparseMatrixType::Scalar Scalar;
typedef typename SparseMatrixType::RealScalar RealScalar;
@@ -91,8 +91,12 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
for (Index k=0; k<nnz; ++k)
{
Index i = internal::random<Index>(0,rows-1);
if (m1.coeff(i,j)==Scalar(0))
m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
if (m1.coeff(i, j) == Scalar(0)) {
Scalar v = internal::random<Scalar>();
if (v == Scalar(0)) v = Scalar(1);
m1(i, j) = v;
m2.insert(i, j) = v;
}
}
}
@@ -116,13 +120,18 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
{
Index i = internal::random<Index>(0,rows-1);
Index j = internal::random<Index>(0,cols-1);
if ((m1.coeff(i,j)==Scalar(0)) && (internal::random<int>()%2))
m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
if ((m1.coeff(i, j) == Scalar(0)) && (internal::random<int>() % 2)) {
Scalar v = internal::random<Scalar>();
if (v == Scalar(0)) v = Scalar(1);
m1(i, j) = v;
m2.insert(i, j) = v;
}
else
{
Scalar v = internal::random<Scalar>();
m2.coeffRef(i,j) += v;
m1(i,j) += v;
if (v == Scalar(0)) v = Scalar(1);
m1(i, j) = v;
m2.coeffRef(i, j) = v;
}
}
VERIFY_IS_APPROX(m2,m1);
@@ -140,8 +149,12 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
{
Index i = internal::random<Index>(0,rows-1);
Index j = internal::random<Index>(0,cols-1);
if (m1.coeff(i,j)==Scalar(0))
m2.insert(i,j) = m1(i,j) = internal::random<Scalar>();
if (m1.coeff(i, j) == Scalar(0)) {
Scalar v = internal::random<Scalar>();
if (v == Scalar(0)) v = Scalar(1);
m1(i, j) = v;
m2.insert(i, j) = v;
}
if(mode==3)
m2.reserve(r);
}
@@ -150,6 +163,63 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
VERIFY_IS_APPROX(m2,m1);
}
// test sort
if (inner > 1) {
bool StorageOrdersMatch = DenseMatrix::IsRowMajor == SparseMatrixType::IsRowMajor;
DenseMatrix m1(rows, cols);
m1.setZero();
SparseMatrixType m2(rows, cols);
// generate random inner indices with no repeats
Vector<Index, Dynamic> innerIndices(inner);
innerIndices.setLinSpaced(inner, 0, inner - 1);
for (Index j = 0; j < outer; j++) {
std::random_shuffle(innerIndices.begin(), innerIndices.end());
Index nzj = internal::random<Index>(2, inner / 2);
for (Index k = 0; k < nzj; k++) {
Index i = innerIndices[k];
Scalar val = internal::random<Scalar>();
m1.coeffRefByOuterInner(StorageOrdersMatch ? j : i, StorageOrdersMatch ? i : j) = val;
m2.insertByOuterInner(j, i) = val;
}
}
VERIFY_IS_APPROX(m2, m1);
// sort wrt greater
m2.template sortInnerIndices<std::greater<>>();
// verify that all inner vectors are not sorted wrt less
VERIFY_IS_EQUAL(m2.template innerIndicesAreSorted<std::less<>>(), 0);
// verify that all inner vectors are sorted wrt greater
VERIFY_IS_EQUAL(m2.template innerIndicesAreSorted<std::greater<>>(), m2.outerSize());
// verify that sort does not change evaluation
VERIFY_IS_APPROX(m2, m1);
// sort wrt less
m2.template sortInnerIndices<std::less<>>();
// verify that all inner vectors are sorted wrt less
VERIFY_IS_EQUAL(m2.template innerIndicesAreSorted<std::less<>>(), m2.outerSize());
// verify that all inner vectors are not sorted wrt greater
VERIFY_IS_EQUAL(m2.template innerIndicesAreSorted<std::greater<>>(), 0);
// verify that sort does not change evaluation
VERIFY_IS_APPROX(m2, m1);
m2.makeCompressed();
// sort wrt greater
m2.template sortInnerIndices<std::greater<>>();
// verify that all inner vectors are not sorted wrt less
VERIFY_IS_EQUAL(m2.template innerIndicesAreSorted<std::less<>>(), 0);
// verify that all inner vectors are sorted wrt greater
VERIFY_IS_EQUAL(m2.template innerIndicesAreSorted<std::greater<>>(), m2.outerSize());
// verify that sort does not change evaluation
VERIFY_IS_APPROX(m2, m1);
// sort wrt less
m2.template sortInnerIndices<std::less<>>();
// verify that all inner vectors are sorted wrt less
VERIFY_IS_EQUAL(m2.template innerIndicesAreSorted<std::less<>>(), m2.outerSize());
// verify that all inner vectors are not sorted wrt greater
VERIFY_IS_EQUAL(m2.template innerIndicesAreSorted<std::greater<>>(), 0);
// verify that sort does not change evaluation
VERIFY_IS_APPROX(m2, m1);
}
// test basic computations
{
DenseMatrix refM1 = DenseMatrix::Zero(rows, cols);
@@ -413,10 +483,8 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
m.setFromTriplets(triplets.begin(), triplets.end(), std::multiplies<Scalar>());
VERIFY_IS_APPROX(m, refMat_prod);
#if (EIGEN_COMP_CXXVER >= 11)
m.setFromTriplets(triplets.begin(), triplets.end(), [] (Scalar,Scalar b) { return b; });
VERIFY_IS_APPROX(m, refMat_last);
#endif
}
// test Map
@@ -431,12 +499,6 @@ template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& re
VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3);
VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3);
}
{
MappedSparseMatrix<Scalar,SparseMatrixType::Options,StorageIndex> mapMat2(m2.rows(), m2.cols(), m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr());
MappedSparseMatrix<Scalar,SparseMatrixType::Options,StorageIndex> mapMat3(m3.rows(), m3.cols(), m3.nonZeros(), m3.outerIndexPtr(), m3.innerIndexPtr(), m3.valuePtr(), m3.innerNonZeroPtr());
VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3);
VERIFY_IS_APPROX(mapMat2+mapMat3, refMat2+refMat3);
}
Index i = internal::random<Index>(0,rows-1);
Index j = internal::random<Index>(0,cols-1);
@@ -687,7 +749,7 @@ void big_sparse_triplet(Index rows, Index cols, double density) {
typedef typename SparseMatrixType::Scalar Scalar;
typedef Triplet<Scalar,Index> TripletType;
std::vector<TripletType> triplets;
double nelements = density * rows*cols;
double nelements = density * static_cast<double>(rows*cols);
VERIFY(nelements>=0 && nelements < static_cast<double>(NumTraits<StorageIndex>::highest()));
Index ntriplets = Index(nelements);
triplets.reserve(ntriplets);
@@ -737,9 +799,12 @@ EIGEN_DECLARE_TEST(sparse_basic)
CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(8, 8)) ));
CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, ColMajor>(r, c)) ));
CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, RowMajor>(r, c)) ));
CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(r, c)) ));
CALL_SUBTEST_5(( sparse_basic(SparseMatrix<double,ColMajor,long int>(r, c)) ));
CALL_SUBTEST_5(( sparse_basic(SparseMatrix<double,RowMajor,long int>(r, c)) ));
CALL_SUBTEST_2(( sparse_basic(SparseMatrix<float, RowMajor>(r, c))));
CALL_SUBTEST_2(( sparse_basic(SparseMatrix<float, ColMajor>(r, c))));
CALL_SUBTEST_3(( sparse_basic(SparseMatrix<double, ColMajor>(r, c))));
CALL_SUBTEST_3(( sparse_basic(SparseMatrix<double, RowMajor>(r, c))));
CALL_SUBTEST_4(( sparse_basic(SparseMatrix<double, ColMajor,long int>(r, c)) ));
CALL_SUBTEST_4(( sparse_basic(SparseMatrix<double, RowMajor,long int>(r, c)) ));
r = Eigen::internal::random<int>(1,100);
c = Eigen::internal::random<int>(1,100);
@@ -747,14 +812,14 @@ EIGEN_DECLARE_TEST(sparse_basic)
r = c; // check square matrices in 25% of tries
}
CALL_SUBTEST_6(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(r), short(c))) ));
CALL_SUBTEST_6(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(r), short(c))) ));
CALL_SUBTEST_5(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(r), short(c))) ));
CALL_SUBTEST_5(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(r), short(c))) ));
}
// Regression test for bug 900: (manually insert higher values here, if you have enough RAM):
CALL_SUBTEST_3((big_sparse_triplet<SparseMatrix<float, RowMajor, int> >(10000, 10000, 0.125)));
CALL_SUBTEST_4((big_sparse_triplet<SparseMatrix<double, ColMajor, long int> >(10000, 10000, 0.125)));
CALL_SUBTEST_5(( big_sparse_triplet<SparseMatrix<float, RowMajor, int>>(10000, 10000, 0.125)));
CALL_SUBTEST_5(( big_sparse_triplet<SparseMatrix<double, ColMajor, long int>>(10000, 10000, 0.125)));
CALL_SUBTEST_7( bug1105<0>() );
CALL_SUBTEST_5(bug1105<0>());
}
#endif