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Add SparseVector::conservativeResize() method.

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This commit is contained in:
Gael Guennebaud 2016-01-25 11:55:39 +01:00
parent e3a15a03a4
commit 869b4443ac
2 changed files with 64 additions and 8 deletions
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18
Eigen/src/SparseCore/SparseVector.h
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@ -222,6 +222,24 @@ class SparseVector
m_data.clear();
}
/** Resizes the sparse vector to \a newSize, while leaving old values untouched.
*
* If the size of the vector is decreased, then the storage of the out-of bounds coefficients is kept and reserved.
* Call .data().squeeze() to free extra memory.
*
* \sa reserve(), setZero()
*/
void conservativeResize(Index newSize)
{
if (newSize < m_size)
{
Index i = 0;
while (i<m_data.size() && m_data.index(i)<newSize) ++i;
m_data.resize(i);
}
m_size = newSize;
}
void resizeNonZeros(Index size) { m_data.resize(size); }
inline SparseVector() : m_size(0) { check_template_parameters(); resize(0); }

54
test/sparse_vector.cpp
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@ -9,14 +9,14 @@
#include "sparse.h"
template<typename Scalar,typename Index> void sparse_vector(int rows, int cols)
template<typename Scalar,typename StorageIndex> void sparse_vector(int rows, int cols)
{
double densityMat = (std::max)(8./(rows*cols), 0.01);
double densityVec = (std::max)(8./float(rows), 0.1);
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
typedef SparseVector<Scalar,0,Index> SparseVectorType;
typedef SparseMatrix<Scalar,0,Index> SparseMatrixType;
typedef SparseVector<Scalar,0,StorageIndex> SparseVectorType;
typedef SparseMatrix<Scalar,0,StorageIndex> SparseMatrixType;
Scalar eps = 1e-6;
SparseMatrixType m1(rows,rows);
@ -87,8 +87,10 @@ template<typename Scalar,typename Index> void sparse_vector(int rows, int cols)
VERIFY_IS_APPROX(m1*v2, refM1*refV2);
VERIFY_IS_APPROX(v1.dot(m1*v2), refV1.dot(refM1*refV2));
int i = internal::random<int>(0,rows-1);
VERIFY_IS_APPROX(v1.dot(m1.col(i)), refV1.dot(refM1.col(i)));
{
int i = internal::random<int>(0,rows-1);
VERIFY_IS_APPROX(v1.dot(m1.col(i)), refV1.dot(refM1.col(i)));
}
VERIFY_IS_APPROX(v1.squaredNorm(), refV1.squaredNorm());
@ -111,15 +113,51 @@ template<typename Scalar,typename Index> void sparse_vector(int rows, int cols)
VERIFY_IS_APPROX(refV3 = v1.transpose(),v1.toDense());
VERIFY_IS_APPROX(DenseVector(v1),v1.toDense());
// test conservative resize
{
std::vector<StorageIndex> inc;
if(rows > 3)
inc.push_back(-3);
inc.push_back(0);
inc.push_back(3);
inc.push_back(1);
inc.push_back(10);
for(std::size_t i = 0; i< inc.size(); i++) {
StorageIndex incRows = inc[i];
SparseVectorType vec1(rows);
DenseVector refVec1 = DenseVector::Zero(rows);
initSparse<Scalar>(densityVec, refVec1, vec1);
vec1.conservativeResize(rows+incRows);
refVec1.conservativeResize(rows+incRows);
if (incRows > 0) refVec1.tail(incRows).setZero();
VERIFY_IS_APPROX(vec1, refVec1);
// Insert new values
if (incRows > 0)
vec1.insert(vec1.rows()-1) = refVec1(refVec1.rows()-1) = 1;
VERIFY_IS_APPROX(vec1, refVec1);
}
}
}
void test_sparse_vector()
{
for(int i = 0; i < g_repeat; i++) {
int r = Eigen::internal::random<int>(1,500), c = Eigen::internal::random<int>(1,500);
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_vector<double,int>(8, 8) ));
CALL_SUBTEST_2(( sparse_vector<std::complex<double>, int>(16, 16) ));
CALL_SUBTEST_1(( sparse_vector<double,long int>(299, 535) ));
CALL_SUBTEST_1(( sparse_vector<double,short>(299, 535) ));
CALL_SUBTEST_2(( sparse_vector<std::complex<double>, int>(r, c) ));
CALL_SUBTEST_1(( sparse_vector<double,long int>(r, c) ));
CALL_SUBTEST_1(( sparse_vector<double,short>(r, c) ));
}
}