Squashed 'third_party/eigen/' changes from 61d72f6..cf794d3
Change-Id: I9b814151b01f49af6337a8605d0c42a3a1ed4c72
git-subtree-dir: third_party/eigen
git-subtree-split: cf794d3b741a6278df169e58461f8529f43bce5d
diff --git a/test/sparse_basic.cpp b/test/sparse_basic.cpp
index ce41d71..d0ef722 100644
--- a/test/sparse_basic.cpp
+++ b/test/sparse_basic.cpp
@@ -9,22 +9,28 @@
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+static long g_realloc_count = 0;
+#define EIGEN_SPARSE_COMPRESSED_STORAGE_REALLOCATE_PLUGIN g_realloc_count++;
+
#include "sparse.h"
template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& ref)
{
- typedef typename SparseMatrixType::Index Index;
- typedef Matrix<Index,2,1> Vector2;
+ typedef typename SparseMatrixType::StorageIndex StorageIndex;
+ typedef Matrix<StorageIndex,2,1> Vector2;
const Index rows = ref.rows();
const Index cols = ref.cols();
+ //const Index inner = ref.innerSize();
+ //const Index outer = ref.outerSize();
+
typedef typename SparseMatrixType::Scalar Scalar;
+ typedef typename SparseMatrixType::RealScalar RealScalar;
enum { Flags = SparseMatrixType::Flags };
double density = (std::max)(8./(rows*cols), 0.01);
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
- typedef Matrix<Scalar,1,Dynamic> RowDenseVector;
Scalar eps = 1e-6;
Scalar s1 = internal::random<Scalar>();
@@ -37,94 +43,22 @@
std::vector<Vector2> nonzeroCoords;
initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
- if (zeroCoords.size()==0 || nonzeroCoords.size()==0)
- return;
-
// test coeff and coeffRef
- for (int i=0; i<(int)zeroCoords.size(); ++i)
+ for (std::size_t i=0; i<zeroCoords.size(); ++i)
{
VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps );
if(internal::is_same<SparseMatrixType,SparseMatrix<Scalar,Flags> >::value)
- VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[0].x(),zeroCoords[0].y()) = 5 );
+ VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[i].x(),zeroCoords[i].y()) = 5 );
}
VERIFY_IS_APPROX(m, refMat);
- m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
- refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
+ if(!nonzeroCoords.empty()) {
+ m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
+ refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
+ }
VERIFY_IS_APPROX(m, refMat);
-
- // test InnerIterators and Block expressions
- for (int t=0; t<10; ++t)
- {
- int j = internal::random<int>(0,cols-1);
- int i = internal::random<int>(0,rows-1);
- int w = internal::random<int>(1,cols-j-1);
- int h = internal::random<int>(1,rows-i-1);
- VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w));
- for(int c=0; c<w; c++)
- {
- VERIFY_IS_APPROX(m.block(i,j,h,w).col(c), refMat.block(i,j,h,w).col(c));
- for(int r=0; r<h; r++)
- {
- VERIFY_IS_APPROX(m.block(i,j,h,w).col(c).coeff(r), refMat.block(i,j,h,w).col(c).coeff(r));
- VERIFY_IS_APPROX(m.block(i,j,h,w).coeff(r,c), refMat.block(i,j,h,w).coeff(r,c));
- }
- }
- for(int r=0; r<h; r++)
- {
- VERIFY_IS_APPROX(m.block(i,j,h,w).row(r), refMat.block(i,j,h,w).row(r));
- for(int c=0; c<w; c++)
- {
- VERIFY_IS_APPROX(m.block(i,j,h,w).row(r).coeff(c), refMat.block(i,j,h,w).row(r).coeff(c));
- VERIFY_IS_APPROX(m.block(i,j,h,w).coeff(r,c), refMat.block(i,j,h,w).coeff(r,c));
- }
- }
-
- VERIFY_IS_APPROX(m.middleCols(j,w), refMat.middleCols(j,w));
- VERIFY_IS_APPROX(m.middleRows(i,h), refMat.middleRows(i,h));
- for(int r=0; r<h; r++)
- {
- VERIFY_IS_APPROX(m.middleCols(j,w).row(r), refMat.middleCols(j,w).row(r));
- VERIFY_IS_APPROX(m.middleRows(i,h).row(r), refMat.middleRows(i,h).row(r));
- for(int c=0; c<w; c++)
- {
- VERIFY_IS_APPROX(m.col(c).coeff(r), refMat.col(c).coeff(r));
- VERIFY_IS_APPROX(m.row(r).coeff(c), refMat.row(r).coeff(c));
-
- VERIFY_IS_APPROX(m.middleCols(j,w).coeff(r,c), refMat.middleCols(j,w).coeff(r,c));
- VERIFY_IS_APPROX(m.middleRows(i,h).coeff(r,c), refMat.middleRows(i,h).coeff(r,c));
- if(m.middleCols(j,w).coeff(r,c) != Scalar(0))
- {
- VERIFY_IS_APPROX(m.middleCols(j,w).coeffRef(r,c), refMat.middleCols(j,w).coeff(r,c));
- }
- if(m.middleRows(i,h).coeff(r,c) != Scalar(0))
- {
- VERIFY_IS_APPROX(m.middleRows(i,h).coeff(r,c), refMat.middleRows(i,h).coeff(r,c));
- }
- }
- }
- for(int c=0; c<w; c++)
- {
- VERIFY_IS_APPROX(m.middleCols(j,w).col(c), refMat.middleCols(j,w).col(c));
- VERIFY_IS_APPROX(m.middleRows(i,h).col(c), refMat.middleRows(i,h).col(c));
- }
- }
-
- for(int c=0; c<cols; c++)
- {
- VERIFY_IS_APPROX(m.col(c) + m.col(c), (m + m).col(c));
- VERIFY_IS_APPROX(m.col(c) + m.col(c), refMat.col(c) + refMat.col(c));
- }
-
- for(int r=0; r<rows; r++)
- {
- VERIFY_IS_APPROX(m.row(r) + m.row(r), (m + m).row(r));
- VERIFY_IS_APPROX(m.row(r) + m.row(r), refMat.row(r) + refMat.row(r));
- }
-
-
// test assertion
VERIFY_RAISES_ASSERT( m.coeffRef(-1,1) = 0 );
VERIFY_RAISES_ASSERT( m.coeffRef(0,m.cols()) = 0 );
@@ -135,17 +69,31 @@
DenseMatrix m1(rows,cols);
m1.setZero();
SparseMatrixType m2(rows,cols);
- if(internal::random<int>()%2)
- m2.reserve(VectorXi::Constant(m2.outerSize(), 2));
+ bool call_reserve = internal::random<int>()%2;
+ Index nnz = internal::random<int>(1,int(rows)/2);
+ if(call_reserve)
+ {
+ if(internal::random<int>()%2)
+ m2.reserve(VectorXi::Constant(m2.outerSize(), int(nnz)));
+ else
+ m2.reserve(m2.outerSize() * nnz);
+ }
+ g_realloc_count = 0;
for (Index j=0; j<cols; ++j)
{
- for (Index k=0; k<rows/2; ++k)
+ 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(call_reserve && !SparseMatrixType::IsRowMajor)
+ {
+ VERIFY(g_realloc_count==0);
+ }
+
m2.finalize();
VERIFY_IS_APPROX(m2,m1);
}
@@ -179,9 +127,9 @@
DenseMatrix m1(rows,cols);
m1.setZero();
SparseMatrixType m2(rows,cols);
- VectorXi r(VectorXi::Constant(m2.outerSize(), ((mode%2)==0) ? m2.innerSize() : std::max<int>(1,m2.innerSize()/8)));
+ VectorXi r(VectorXi::Constant(m2.outerSize(), ((mode%2)==0) ? int(m2.innerSize()) : std::max<int>(1,int(m2.innerSize())/8)));
m2.reserve(r);
- for (int k=0; k<rows*cols; ++k)
+ for (Index k=0; k<rows*cols; ++k)
{
Index i = internal::random<Index>(0,rows-1);
Index j = internal::random<Index>(0,cols-1);
@@ -195,110 +143,46 @@
VERIFY_IS_APPROX(m2,m1);
}
- // test innerVector()
- {
- DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
- SparseMatrixType m2(rows, rows);
- initSparse<Scalar>(density, refMat2, m2);
- Index j0 = internal::random<Index>(0,rows-1);
- Index j1 = internal::random<Index>(0,rows-1);
- if(SparseMatrixType::IsRowMajor)
- VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.row(j0));
- else
- VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.col(j0));
-
- if(SparseMatrixType::IsRowMajor)
- VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.row(j0)+refMat2.row(j1));
- else
- VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.col(j0)+refMat2.col(j1));
-
- SparseMatrixType m3(rows,rows);
- m3.reserve(VectorXi::Constant(rows,rows/2));
- for(Index j=0; j<rows; ++j)
- for(Index k=0; k<j; ++k)
- m3.insertByOuterInner(j,k) = k+1;
- for(Index j=0; j<rows; ++j)
- {
- VERIFY(j==numext::real(m3.innerVector(j).nonZeros()));
- if(j>0)
- VERIFY(j==numext::real(m3.innerVector(j).lastCoeff()));
- }
- m3.makeCompressed();
- for(Index j=0; j<rows; ++j)
- {
- VERIFY(j==numext::real(m3.innerVector(j).nonZeros()));
- if(j>0)
- VERIFY(j==numext::real(m3.innerVector(j).lastCoeff()));
- }
-
- //m2.innerVector(j0) = 2*m2.innerVector(j1);
- //refMat2.col(j0) = 2*refMat2.col(j1);
- //VERIFY_IS_APPROX(m2, refMat2);
- }
-
- // test innerVectors()
- {
- DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
- SparseMatrixType m2(rows, rows);
- initSparse<Scalar>(density, refMat2, m2);
- if(internal::random<float>(0,1)>0.5) m2.makeCompressed();
-
- Index j0 = internal::random<Index>(0,rows-2);
- Index j1 = internal::random<Index>(0,rows-2);
- Index n0 = internal::random<Index>(1,rows-(std::max)(j0,j1));
- if(SparseMatrixType::IsRowMajor)
- VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(j0,0,n0,cols));
- else
- VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(0,j0,rows,n0));
- if(SparseMatrixType::IsRowMajor)
- VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0),
- refMat2.middleRows(j0,n0)+refMat2.middleRows(j1,n0));
- else
- VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0),
- refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));
-
- VERIFY_IS_APPROX(m2, refMat2);
-
- m2.innerVectors(j0,n0) = m2.innerVectors(j0,n0) + m2.innerVectors(j1,n0);
- if(SparseMatrixType::IsRowMajor)
- refMat2.middleRows(j0,n0) = (refMat2.middleRows(j0,n0) + refMat2.middleRows(j1,n0)).eval();
- else
- refMat2.middleCols(j0,n0) = (refMat2.middleCols(j0,n0) + refMat2.middleCols(j1,n0)).eval();
-
- VERIFY_IS_APPROX(m2, refMat2);
-
- }
-
// test basic computations
{
- DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
- DenseMatrix refM2 = DenseMatrix::Zero(rows, rows);
- DenseMatrix refM3 = DenseMatrix::Zero(rows, rows);
- DenseMatrix refM4 = DenseMatrix::Zero(rows, rows);
- SparseMatrixType m1(rows, rows);
- SparseMatrixType m2(rows, rows);
- SparseMatrixType m3(rows, rows);
- SparseMatrixType m4(rows, rows);
+ DenseMatrix refM1 = DenseMatrix::Zero(rows, cols);
+ DenseMatrix refM2 = DenseMatrix::Zero(rows, cols);
+ DenseMatrix refM3 = DenseMatrix::Zero(rows, cols);
+ DenseMatrix refM4 = DenseMatrix::Zero(rows, cols);
+ SparseMatrixType m1(rows, cols);
+ SparseMatrixType m2(rows, cols);
+ SparseMatrixType m3(rows, cols);
+ SparseMatrixType m4(rows, cols);
initSparse<Scalar>(density, refM1, m1);
initSparse<Scalar>(density, refM2, m2);
initSparse<Scalar>(density, refM3, m3);
initSparse<Scalar>(density, refM4, m4);
+ if(internal::random<bool>())
+ m1.makeCompressed();
+
+ Index m1_nnz = m1.nonZeros();
+
+ VERIFY_IS_APPROX(m1*s1, refM1*s1);
VERIFY_IS_APPROX(m1+m2, refM1+refM2);
VERIFY_IS_APPROX(m1+m2+m3, refM1+refM2+refM3);
VERIFY_IS_APPROX(m3.cwiseProduct(m1+m2), refM3.cwiseProduct(refM1+refM2));
VERIFY_IS_APPROX(m1*s1-m2, refM1*s1-refM2);
-
- VERIFY_IS_APPROX(m1*=s1, refM1*=s1);
- VERIFY_IS_APPROX(m1/=s1, refM1/=s1);
-
- VERIFY_IS_APPROX(m1+=m2, refM1+=refM2);
- VERIFY_IS_APPROX(m1-=m2, refM1-=refM2);
+ VERIFY_IS_APPROX(m4=m1/s1, refM1/s1);
+ VERIFY_IS_EQUAL(m4.nonZeros(), m1_nnz);
if(SparseMatrixType::IsRowMajor)
VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.row(0)), refM1.row(0).dot(refM2.row(0)));
else
- VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.row(0)), refM1.col(0).dot(refM2.row(0)));
+ VERIFY_IS_APPROX(m1.innerVector(0).dot(refM2.col(0)), refM1.col(0).dot(refM2.col(0)));
+
+ DenseVector rv = DenseVector::Random(m1.cols());
+ DenseVector cv = DenseVector::Random(m1.rows());
+ Index r = internal::random<Index>(0,m1.rows()-2);
+ Index c = internal::random<Index>(0,m1.cols()-1);
+ VERIFY_IS_APPROX(( m1.template block<1,Dynamic>(r,0,1,m1.cols()).dot(rv)) , refM1.row(r).dot(rv));
+ VERIFY_IS_APPROX(m1.row(r).dot(rv), refM1.row(r).dot(rv));
+ VERIFY_IS_APPROX(m1.col(c).dot(cv), refM1.col(c).dot(cv));
VERIFY_IS_APPROX(m1.conjugate(), refM1.conjugate());
VERIFY_IS_APPROX(m1.real(), refM1.real());
@@ -306,105 +190,167 @@
refM4.setRandom();
// sparse cwise* dense
VERIFY_IS_APPROX(m3.cwiseProduct(refM4), refM3.cwiseProduct(refM4));
+ // dense cwise* sparse
+ VERIFY_IS_APPROX(refM4.cwiseProduct(m3), refM4.cwiseProduct(refM3));
// VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4);
+ VERIFY_IS_APPROX(refM4 + m3, refM4 + refM3);
+ VERIFY_IS_APPROX(m3 + refM4, refM3 + refM4);
+ VERIFY_IS_APPROX(refM4 - m3, refM4 - refM3);
+ VERIFY_IS_APPROX(m3 - refM4, refM3 - refM4);
+ VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3);
+ VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3*RealScalar(0.5)).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3);
+ VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3.cwiseProduct(m3)).eval(), RealScalar(0.5)*refM4 + refM3.cwiseProduct(refM3));
+
+ VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3);
+ VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + m3*RealScalar(0.5)).eval(), RealScalar(0.5)*refM4 + RealScalar(0.5)*refM3);
+ VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (m3+m3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3));
+ VERIFY_IS_APPROX(((refM3+m3)+RealScalar(0.5)*m3).eval(), RealScalar(0.5)*refM3 + (refM3+refM3));
+ VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (refM3+m3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3));
+ VERIFY_IS_APPROX((RealScalar(0.5)*refM4 + (m3+refM3)).eval(), RealScalar(0.5)*refM4 + (refM3+refM3));
+
+
+ VERIFY_IS_APPROX(m1.sum(), refM1.sum());
+
+ m4 = m1; refM4 = m4;
+
+ VERIFY_IS_APPROX(m1*=s1, refM1*=s1);
+ VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
+ VERIFY_IS_APPROX(m1/=s1, refM1/=s1);
+ VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
+
+ VERIFY_IS_APPROX(m1+=m2, refM1+=refM2);
+ VERIFY_IS_APPROX(m1-=m2, refM1-=refM2);
+
+ if (rows>=2 && cols>=2)
+ {
+ VERIFY_RAISES_ASSERT( m1 += m1.innerVector(0) );
+ VERIFY_RAISES_ASSERT( m1 -= m1.innerVector(0) );
+ VERIFY_RAISES_ASSERT( refM1 -= m1.innerVector(0) );
+ VERIFY_RAISES_ASSERT( refM1 += m1.innerVector(0) );
+ }
+ m1 = m4; refM1 = refM4;
+
// test aliasing
VERIFY_IS_APPROX((m1 = -m1), (refM1 = -refM1));
+ VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
+ m1 = m4; refM1 = refM4;
VERIFY_IS_APPROX((m1 = m1.transpose()), (refM1 = refM1.transpose().eval()));
+ VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
+ m1 = m4; refM1 = refM4;
VERIFY_IS_APPROX((m1 = -m1.transpose()), (refM1 = -refM1.transpose().eval()));
+ VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
+ m1 = m4; refM1 = refM4;
VERIFY_IS_APPROX((m1 += -m1), (refM1 += -refM1));
+ VERIFY_IS_EQUAL(m1.nonZeros(), m1_nnz);
+ m1 = m4; refM1 = refM4;
+
+ if(m1.isCompressed())
+ {
+ VERIFY_IS_APPROX(m1.coeffs().sum(), m1.sum());
+ m1.coeffs() += s1;
+ for(Index j = 0; j<m1.outerSize(); ++j)
+ for(typename SparseMatrixType::InnerIterator it(m1,j); it; ++it)
+ refM1(it.row(), it.col()) += s1;
+ VERIFY_IS_APPROX(m1, refM1);
+ }
+
+ // and/or
+ {
+ typedef SparseMatrix<bool, SparseMatrixType::Options, typename SparseMatrixType::StorageIndex> SpBool;
+ SpBool mb1 = m1.real().template cast<bool>();
+ SpBool mb2 = m2.real().template cast<bool>();
+ VERIFY_IS_EQUAL(mb1.template cast<int>().sum(), refM1.real().template cast<bool>().count());
+ VERIFY_IS_EQUAL((mb1 && mb2).template cast<int>().sum(), (refM1.real().template cast<bool>() && refM2.real().template cast<bool>()).count());
+ VERIFY_IS_EQUAL((mb1 || mb2).template cast<int>().sum(), (refM1.real().template cast<bool>() || refM2.real().template cast<bool>()).count());
+ SpBool mb3 = mb1 && mb2;
+ if(mb1.coeffs().all() && mb2.coeffs().all())
+ {
+ VERIFY_IS_EQUAL(mb3.nonZeros(), (refM1.real().template cast<bool>() && refM2.real().template cast<bool>()).count());
+ }
+ }
+ }
+
+ // test reverse iterators
+ {
+ DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
+ SparseMatrixType m2(rows, cols);
+ initSparse<Scalar>(density, refMat2, m2);
+ std::vector<Scalar> ref_value(m2.innerSize());
+ std::vector<Index> ref_index(m2.innerSize());
+ if(internal::random<bool>())
+ m2.makeCompressed();
+ for(Index j = 0; j<m2.outerSize(); ++j)
+ {
+ Index count_forward = 0;
+
+ for(typename SparseMatrixType::InnerIterator it(m2,j); it; ++it)
+ {
+ ref_value[ref_value.size()-1-count_forward] = it.value();
+ ref_index[ref_index.size()-1-count_forward] = it.index();
+ count_forward++;
+ }
+ Index count_reverse = 0;
+ for(typename SparseMatrixType::ReverseInnerIterator it(m2,j); it; --it)
+ {
+ VERIFY_IS_APPROX( std::abs(ref_value[ref_value.size()-count_forward+count_reverse])+1, std::abs(it.value())+1);
+ VERIFY_IS_EQUAL( ref_index[ref_index.size()-count_forward+count_reverse] , it.index());
+ count_reverse++;
+ }
+ VERIFY_IS_EQUAL(count_forward, count_reverse);
+ }
}
// test transpose
{
- DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
- SparseMatrixType m2(rows, rows);
+ DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
+ SparseMatrixType m2(rows, cols);
initSparse<Scalar>(density, refMat2, m2);
VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval());
VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose());
VERIFY_IS_APPROX(SparseMatrixType(m2.adjoint()), refMat2.adjoint());
- }
-
-
-
- // test generic blocks
- {
- DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
- SparseMatrixType m2(rows, rows);
- initSparse<Scalar>(density, refMat2, m2);
- Index j0 = internal::random<Index>(0,rows-2);
- Index j1 = internal::random<Index>(0,rows-2);
- Index n0 = internal::random<Index>(1,rows-(std::max)(j0,j1));
- if(SparseMatrixType::IsRowMajor)
- VERIFY_IS_APPROX(m2.block(j0,0,n0,cols), refMat2.block(j0,0,n0,cols));
- else
- VERIFY_IS_APPROX(m2.block(0,j0,rows,n0), refMat2.block(0,j0,rows,n0));
- if(SparseMatrixType::IsRowMajor)
- VERIFY_IS_APPROX(m2.block(j0,0,n0,cols)+m2.block(j1,0,n0,cols),
- refMat2.block(j0,0,n0,cols)+refMat2.block(j1,0,n0,cols));
- else
- VERIFY_IS_APPROX(m2.block(0,j0,rows,n0)+m2.block(0,j1,rows,n0),
- refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));
-
- Index i = internal::random<Index>(0,m2.outerSize()-1);
- if(SparseMatrixType::IsRowMajor) {
- m2.innerVector(i) = m2.innerVector(i) * s1;
- refMat2.row(i) = refMat2.row(i) * s1;
- VERIFY_IS_APPROX(m2,refMat2);
- } else {
- m2.innerVector(i) = m2.innerVector(i) * s1;
- refMat2.col(i) = refMat2.col(i) * s1;
- VERIFY_IS_APPROX(m2,refMat2);
- }
-
- VERIFY_IS_APPROX(DenseVector(m2.col(j0)), refMat2.col(j0));
- VERIFY_IS_APPROX(m2.col(j0), refMat2.col(j0));
-
- VERIFY_IS_APPROX(RowDenseVector(m2.row(j0)), refMat2.row(j0));
- VERIFY_IS_APPROX(m2.row(j0), refMat2.row(j0));
-
- VERIFY_IS_APPROX(m2.block(j0,j1,n0,n0), refMat2.block(j0,j1,n0,n0));
- VERIFY_IS_APPROX((2*m2).block(j0,j1,n0,n0), (2*refMat2).block(j0,j1,n0,n0));
+ // check isApprox handles opposite storage order
+ typename Transpose<SparseMatrixType>::PlainObject m3(m2);
+ VERIFY(m2.isApprox(m3));
}
// test prune
{
- SparseMatrixType m2(rows, rows);
- DenseMatrix refM2(rows, rows);
+ SparseMatrixType m2(rows, cols);
+ DenseMatrix refM2(rows, cols);
refM2.setZero();
int countFalseNonZero = 0;
int countTrueNonZero = 0;
- for (Index j=0; j<m2.outerSize(); ++j)
+ m2.reserve(VectorXi::Constant(m2.outerSize(), int(m2.innerSize())));
+ for (Index j=0; j<m2.cols(); ++j)
{
- m2.startVec(j);
- for (Index i=0; i<m2.innerSize(); ++i)
+ for (Index i=0; i<m2.rows(); ++i)
{
float x = internal::random<float>(0,1);
- if (x<0.1)
+ if (x<0.1f)
{
// do nothing
}
- else if (x<0.5)
+ else if (x<0.5f)
{
countFalseNonZero++;
- m2.insertBackByOuterInner(j,i) = Scalar(0);
+ m2.insert(i,j) = Scalar(0);
}
else
{
countTrueNonZero++;
- m2.insertBackByOuterInner(j,i) = Scalar(1);
- if(SparseMatrixType::IsRowMajor)
- refM2(j,i) = Scalar(1);
- else
- refM2(i,j) = Scalar(1);
+ m2.insert(i,j) = Scalar(1);
+ refM2(i,j) = Scalar(1);
}
}
}
- m2.finalize();
+ if(internal::random<bool>())
+ m2.makeCompressed();
VERIFY(countFalseNonZero+countTrueNonZero == m2.nonZeros());
- VERIFY_IS_APPROX(m2, refM2);
+ if(countTrueNonZero>0)
+ VERIFY_IS_APPROX(m2, refM2);
m2.prune(Scalar(1));
VERIFY(countTrueNonZero==m2.nonZeros());
VERIFY_IS_APPROX(m2, refM2);
@@ -412,29 +358,74 @@
// test setFromTriplets
{
- typedef Triplet<Scalar,Index> TripletType;
+ typedef Triplet<Scalar,StorageIndex> TripletType;
std::vector<TripletType> triplets;
- int ntriplets = rows*cols;
+ Index ntriplets = rows*cols;
triplets.reserve(ntriplets);
- DenseMatrix refMat(rows,cols);
- refMat.setZero();
- for(int i=0;i<ntriplets;++i)
+ DenseMatrix refMat_sum = DenseMatrix::Zero(rows,cols);
+ DenseMatrix refMat_prod = DenseMatrix::Zero(rows,cols);
+ DenseMatrix refMat_last = DenseMatrix::Zero(rows,cols);
+
+ for(Index i=0;i<ntriplets;++i)
{
- Index r = internal::random<Index>(0,rows-1);
- Index c = internal::random<Index>(0,cols-1);
+ StorageIndex r = internal::random<StorageIndex>(0,StorageIndex(rows-1));
+ StorageIndex c = internal::random<StorageIndex>(0,StorageIndex(cols-1));
Scalar v = internal::random<Scalar>();
triplets.push_back(TripletType(r,c,v));
- refMat(r,c) += v;
+ refMat_sum(r,c) += v;
+ if(std::abs(refMat_prod(r,c))==0)
+ refMat_prod(r,c) = v;
+ else
+ refMat_prod(r,c) *= v;
+ refMat_last(r,c) = v;
}
SparseMatrixType m(rows,cols);
m.setFromTriplets(triplets.begin(), triplets.end());
- VERIFY_IS_APPROX(m, refMat);
+ VERIFY_IS_APPROX(m, refMat_sum);
+
+ m.setFromTriplets(triplets.begin(), triplets.end(), std::multiplies<Scalar>());
+ VERIFY_IS_APPROX(m, refMat_prod);
+#if (defined(__cplusplus) && __cplusplus >= 201103L)
+ m.setFromTriplets(triplets.begin(), triplets.end(), [] (Scalar,Scalar b) { return b; });
+ VERIFY_IS_APPROX(m, refMat_last);
+#endif
+ }
+
+ // test Map
+ {
+ DenseMatrix refMat2(rows, cols), refMat3(rows, cols);
+ SparseMatrixType m2(rows, cols), m3(rows, cols);
+ initSparse<Scalar>(density, refMat2, m2);
+ initSparse<Scalar>(density, refMat3, m3);
+ {
+ Map<SparseMatrixType> mapMat2(m2.rows(), m2.cols(), m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr());
+ Map<SparseMatrixType> 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);
+ }
+ {
+ 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);
+ m2.coeffRef(i,j) = 123;
+ if(internal::random<bool>())
+ m2.makeCompressed();
+ Map<SparseMatrixType> mapMat2(rows, cols, m2.nonZeros(), m2.outerIndexPtr(), m2.innerIndexPtr(), m2.valuePtr(), m2.innerNonZeroPtr());
+ VERIFY_IS_EQUAL(m2.coeff(i,j),Scalar(123));
+ VERIFY_IS_EQUAL(mapMat2.coeff(i,j),Scalar(123));
+ mapMat2.coeffRef(i,j) = -123;
+ VERIFY_IS_EQUAL(m2.coeff(i,j),Scalar(-123));
}
// test triangularView
{
- DenseMatrix refMat2(rows, rows), refMat3(rows, rows);
- SparseMatrixType m2(rows, rows), m3(rows, rows);
+ DenseMatrix refMat2(rows, cols), refMat3(rows, cols);
+ SparseMatrixType m2(rows, cols), m3(rows, cols);
initSparse<Scalar>(density, refMat2, m2);
refMat3 = refMat2.template triangularView<Lower>();
m3 = m2.template triangularView<Lower>();
@@ -444,13 +435,15 @@
m3 = m2.template triangularView<Upper>();
VERIFY_IS_APPROX(m3, refMat3);
- refMat3 = refMat2.template triangularView<UnitUpper>();
- m3 = m2.template triangularView<UnitUpper>();
- VERIFY_IS_APPROX(m3, refMat3);
+ {
+ refMat3 = refMat2.template triangularView<UnitUpper>();
+ m3 = m2.template triangularView<UnitUpper>();
+ VERIFY_IS_APPROX(m3, refMat3);
- refMat3 = refMat2.template triangularView<UnitLower>();
- m3 = m2.template triangularView<UnitLower>();
- VERIFY_IS_APPROX(m3, refMat3);
+ refMat3 = refMat2.template triangularView<UnitLower>();
+ m3 = m2.template triangularView<UnitLower>();
+ VERIFY_IS_APPROX(m3, refMat3);
+ }
refMat3 = refMat2.template triangularView<StrictlyUpper>();
m3 = m2.template triangularView<StrictlyUpper>();
@@ -459,6 +452,10 @@
refMat3 = refMat2.template triangularView<StrictlyLower>();
m3 = m2.template triangularView<StrictlyLower>();
VERIFY_IS_APPROX(m3, refMat3);
+
+ // check sparse-triangular to dense
+ refMat3 = m2.template triangularView<StrictlyUpper>();
+ VERIFY_IS_APPROX(refMat3, DenseMatrix(refMat2.template triangularView<StrictlyUpper>()));
}
// test selfadjointView
@@ -470,6 +467,19 @@
refMat3 = refMat2.template selfadjointView<Lower>();
m3 = m2.template selfadjointView<Lower>();
VERIFY_IS_APPROX(m3, refMat3);
+
+ refMat3 += refMat2.template selfadjointView<Lower>();
+ m3 += m2.template selfadjointView<Lower>();
+ VERIFY_IS_APPROX(m3, refMat3);
+
+ refMat3 -= refMat2.template selfadjointView<Lower>();
+ m3 -= m2.template selfadjointView<Lower>();
+ VERIFY_IS_APPROX(m3, refMat3);
+
+ // selfadjointView only works for square matrices:
+ SparseMatrixType m4(rows, rows+1);
+ VERIFY_RAISES_ASSERT(m4.template selfadjointView<Lower>());
+ VERIFY_RAISES_ASSERT(m4.template selfadjointView<Upper>());
}
// test sparseView
@@ -478,28 +488,59 @@
SparseMatrixType m2(rows, rows);
initSparse<Scalar>(density, refMat2, m2);
VERIFY_IS_APPROX(m2.eval(), refMat2.sparseView().eval());
+
+ // sparse view on expressions:
+ VERIFY_IS_APPROX((s1*m2).eval(), (s1*refMat2).sparseView().eval());
+ VERIFY_IS_APPROX((m2+m2).eval(), (refMat2+refMat2).sparseView().eval());
+ VERIFY_IS_APPROX((m2*m2).eval(), (refMat2.lazyProduct(refMat2)).sparseView().eval());
+ VERIFY_IS_APPROX((m2*m2).eval(), (refMat2*refMat2).sparseView().eval());
}
// test diagonal
{
- DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
- SparseMatrixType m2(rows, rows);
+ DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
+ SparseMatrixType m2(rows, cols);
initSparse<Scalar>(density, refMat2, m2);
VERIFY_IS_APPROX(m2.diagonal(), refMat2.diagonal().eval());
+ DenseVector d = m2.diagonal();
+ VERIFY_IS_APPROX(d, refMat2.diagonal().eval());
+ d = m2.diagonal().array();
+ VERIFY_IS_APPROX(d, refMat2.diagonal().eval());
+ VERIFY_IS_APPROX(const_cast<const SparseMatrixType&>(m2).diagonal(), refMat2.diagonal().eval());
+
+ initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag);
+ m2.diagonal() += refMat2.diagonal();
+ refMat2.diagonal() += refMat2.diagonal();
+ VERIFY_IS_APPROX(m2, refMat2);
+ }
+
+ // test diagonal to sparse
+ {
+ DenseVector d = DenseVector::Random(rows);
+ DenseMatrix refMat2 = d.asDiagonal();
+ SparseMatrixType m2(rows, rows);
+ m2 = d.asDiagonal();
+ VERIFY_IS_APPROX(m2, refMat2);
+ SparseMatrixType m3(d.asDiagonal());
+ VERIFY_IS_APPROX(m3, refMat2);
+ refMat2 += d.asDiagonal();
+ m2 += d.asDiagonal();
+ VERIFY_IS_APPROX(m2, refMat2);
}
// test conservative resize
{
- std::vector< std::pair<Index,Index> > inc;
- inc.push_back(std::pair<Index,Index>(-3,-2));
- inc.push_back(std::pair<Index,Index>(0,0));
- inc.push_back(std::pair<Index,Index>(3,2));
- inc.push_back(std::pair<Index,Index>(3,0));
- inc.push_back(std::pair<Index,Index>(0,3));
+ std::vector< std::pair<StorageIndex,StorageIndex> > inc;
+ if(rows > 3 && cols > 2)
+ inc.push_back(std::pair<StorageIndex,StorageIndex>(-3,-2));
+ inc.push_back(std::pair<StorageIndex,StorageIndex>(0,0));
+ inc.push_back(std::pair<StorageIndex,StorageIndex>(3,2));
+ inc.push_back(std::pair<StorageIndex,StorageIndex>(3,0));
+ inc.push_back(std::pair<StorageIndex,StorageIndex>(0,3));
for(size_t i = 0; i< inc.size(); i++) {
- Index incRows = inc[i].first;
- Index incCols = inc[i].second;
+ StorageIndex incRows = inc[i].first;
+ StorageIndex incCols = inc[i].second;
SparseMatrixType m1(rows, cols);
DenseMatrix refMat1 = DenseMatrix::Zero(rows, cols);
initSparse<Scalar>(density, refMat1, m1);
@@ -529,22 +570,120 @@
SparseMatrixType m1(rows, rows);
m1.setIdentity();
VERIFY_IS_APPROX(m1, refMat1);
+ for(int k=0; k<rows*rows/4; ++k)
+ {
+ Index i = internal::random<Index>(0,rows-1);
+ Index j = internal::random<Index>(0,rows-1);
+ Scalar v = internal::random<Scalar>();
+ m1.coeffRef(i,j) = v;
+ refMat1.coeffRef(i,j) = v;
+ VERIFY_IS_APPROX(m1, refMat1);
+ if(internal::random<Index>(0,10)<2)
+ m1.makeCompressed();
+ }
+ m1.setIdentity();
+ refMat1.setIdentity();
+ VERIFY_IS_APPROX(m1, refMat1);
+ }
+
+ // test array/vector of InnerIterator
+ {
+ typedef typename SparseMatrixType::InnerIterator IteratorType;
+
+ DenseMatrix refMat2 = DenseMatrix::Zero(rows, cols);
+ SparseMatrixType m2(rows, cols);
+ initSparse<Scalar>(density, refMat2, m2);
+ IteratorType static_array[2];
+ static_array[0] = IteratorType(m2,0);
+ static_array[1] = IteratorType(m2,m2.outerSize()-1);
+ VERIFY( static_array[0] || m2.innerVector(static_array[0].outer()).nonZeros() == 0 );
+ VERIFY( static_array[1] || m2.innerVector(static_array[1].outer()).nonZeros() == 0 );
+ if(static_array[0] && static_array[1])
+ {
+ ++(static_array[1]);
+ static_array[1] = IteratorType(m2,0);
+ VERIFY( static_array[1] );
+ VERIFY( static_array[1].index() == static_array[0].index() );
+ VERIFY( static_array[1].outer() == static_array[0].outer() );
+ VERIFY( static_array[1].value() == static_array[0].value() );
+ }
+
+ std::vector<IteratorType> iters(2);
+ iters[0] = IteratorType(m2,0);
+ iters[1] = IteratorType(m2,m2.outerSize()-1);
}
}
+
+template<typename SparseMatrixType>
+void big_sparse_triplet(Index rows, Index cols, double density) {
+ typedef typename SparseMatrixType::StorageIndex StorageIndex;
+ typedef typename SparseMatrixType::Scalar Scalar;
+ typedef Triplet<Scalar,Index> TripletType;
+ std::vector<TripletType> triplets;
+ double nelements = density * rows*cols;
+ VERIFY(nelements>=0 && nelements < NumTraits<StorageIndex>::highest());
+ Index ntriplets = Index(nelements);
+ triplets.reserve(ntriplets);
+ Scalar sum = Scalar(0);
+ for(Index i=0;i<ntriplets;++i)
+ {
+ Index r = internal::random<Index>(0,rows-1);
+ Index c = internal::random<Index>(0,cols-1);
+ // use positive values to prevent numerical cancellation errors in sum
+ Scalar v = numext::abs(internal::random<Scalar>());
+ triplets.push_back(TripletType(r,c,v));
+ sum += v;
+ }
+ SparseMatrixType m(rows,cols);
+ m.setFromTriplets(triplets.begin(), triplets.end());
+ VERIFY(m.nonZeros() <= ntriplets);
+ VERIFY_IS_APPROX(sum, m.sum());
+}
+
+
void test_sparse_basic()
{
for(int i = 0; i < g_repeat; i++) {
- int s = Eigen::internal::random<int>(1,50);
- EIGEN_UNUSED_VARIABLE(s);
+ int r = Eigen::internal::random<int>(1,200), c = Eigen::internal::random<int>(1,200);
+ if(Eigen::internal::random<int>(0,4) == 0) {
+ r = c; // check square matrices in 25% of tries
+ }
+ EIGEN_UNUSED_VARIABLE(r+c);
+ CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(1, 1)) ));
CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(8, 8)) ));
- CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, ColMajor>(s, s)) ));
- CALL_SUBTEST_2(( sparse_basic(SparseMatrix<std::complex<double>, RowMajor>(s, s)) ));
- CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double>(s, s)) ));
- CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,ColMajor,long int>(s, s)) ));
- CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,long int>(s, s)) ));
+ 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_1(( sparse_basic(SparseMatrix<double,ColMajor,short int>(short(s), short(s))) ));
- CALL_SUBTEST_1(( sparse_basic(SparseMatrix<double,RowMajor,short int>(short(s), short(s))) ));
+ r = Eigen::internal::random<int>(1,100);
+ c = Eigen::internal::random<int>(1,100);
+ if(Eigen::internal::random<int>(0,4) == 0) {
+ 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))) ));
}
+
+ // 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)));
+
+ // Regression test for bug 1105
+#ifdef EIGEN_TEST_PART_7
+ {
+ int n = Eigen::internal::random<int>(200,600);
+ SparseMatrix<std::complex<double>,0, long> mat(n, n);
+ std::complex<double> val;
+
+ for(int i=0; i<n; ++i)
+ {
+ mat.coeffRef(i, i%(n/10)) = val;
+ VERIFY(mat.data().allocatedSize()<20*n);
+ }
+ }
+#endif
}