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moved pruning code to SparseVector.h

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This commit is contained in:
Erik Schultheis 2021-11-15 22:16:01 +00:00 committed by Rasmus Munk Larsen
parent da79095923
commit 13954c4440
3 changed files with 54 additions and 19 deletions
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16
Eigen/src/SparseCore/CompressedStorage.h
View File

@ -225,22 +225,6 @@ class CompressedStorage
}
}
void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision())
{
Index k = 0;
Index n = size();
for (Index i=0; i<n; ++i)
{
if (!internal::isMuchSmallerThan(value(i), reference, epsilon))
{
value(k) = value(i);
index(k) = index(i);
++k;
}
}
resize(k,0);
}
protected:
inline void reallocate(Index size)

28
Eigen/src/SparseCore/SparseVector.h
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@ -209,9 +209,33 @@ class SparseVector
inline void finalize() {}
/** \copydoc SparseMatrix::prune(const Scalar&,const RealScalar&) */
void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision())
Index prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision()) {
return prune([&](const Scalar& val){ return !internal::isMuchSmallerThan(val, reference, epsilon); });
}
/**
* \brief Prunes the entries of the vector based on a `predicate`
* \tparam F Type of the predicate.
* \param keep_predicate The predicate that is used to test whether a value should be kept. A callable that
* gets passed om a `Scalar` value and returns a boolean. If the predicate returns true, the value is kept.
* \return The new number of structural non-zeros.
*/
template<class F>
Index prune(F&& keep_predicate)
{
m_data.prune(reference,epsilon);
Index k = 0;
Index n = m_data.size();
for (Index i = 0; i < n; ++i)
{
if (keep_predicate(m_data.value(i)))
{
m_data.value(k) = std::move(m_data.value(i));
m_data.index(k) = m_data.index(i);
++k;
}
}
m_data.resize(k);
return k;
}
/** Resizes the sparse vector to \a rows x \a cols

27
test/sparse_vector.cpp
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@ -144,6 +144,31 @@ template<typename Scalar,typename StorageIndex> void sparse_vector(int rows, int
}
}
void test_pruning() {
using SparseVectorType = SparseVector<double, 0, int>;
SparseVectorType vec;
auto init_vec = [&](){;
vec.resize(10);
vec.insert(3) = 0.1;
vec.insert(5) = 1.0;
vec.insert(8) = -0.1;
vec.insert(9) = -0.2;
};
init_vec();
VERIFY_IS_EQUAL(vec.nonZeros(), 4);
VERIFY_IS_EQUAL(vec.prune(0.1, 1.0), 2);
VERIFY_IS_EQUAL(vec.nonZeros(), 2);
VERIFY_IS_EQUAL(vec.coeff(5), 1.0);
VERIFY_IS_EQUAL(vec.coeff(9), -0.2);
init_vec();
VERIFY_IS_EQUAL(vec.prune([](double v) { return v >= 0; }), 2);
VERIFY_IS_EQUAL(vec.nonZeros(), 2);
VERIFY_IS_EQUAL(vec.coeff(3), 0.1);
VERIFY_IS_EQUAL(vec.coeff(5), 1.0);
}
EIGEN_DECLARE_TEST(sparse_vector)
{
@ -159,5 +184,7 @@ EIGEN_DECLARE_TEST(sparse_vector)
CALL_SUBTEST_1(( sparse_vector<double,long int>(r, c) ));
CALL_SUBTEST_1(( sparse_vector<double,short>(r, c) ));
}
CALL_SUBTEST_1(test_pruning());
}