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https://gitlab.com/libeigen/eigen.git
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Clang-format tests, examples, libraries, benchmarks, etc.
This commit is contained in:
Antonio Sánchez
committed by
Rasmus Munk Larsen
Rasmus Munk Larsen
parent
3252ecc7a4
commit
46e9cdb7fe
@@ -9,23 +9,21 @@
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#include "sparse.h"
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template<typename Scalar,typename StorageIndex> void sparse_vector(int rows, int cols)
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{
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double densityMat = (std::max)(8./(rows*cols), 0.01);
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double densityVec = (std::max)(8./(rows), 0.1);
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typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
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typedef Matrix<Scalar,Dynamic,1> DenseVector;
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typedef Matrix<DenseIndex,Dynamic,1> DenseIndexVector;
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typedef SparseVector<Scalar,0,StorageIndex> SparseVectorType;
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typedef SparseMatrix<Scalar,0,StorageIndex> SparseMatrixType;
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template <typename Scalar, typename StorageIndex>
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void sparse_vector(int rows, int cols) {
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double densityMat = (std::max)(8. / (rows * cols), 0.01);
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double densityVec = (std::max)(8. / (rows), 0.1);
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typedef Matrix<Scalar, Dynamic, Dynamic> DenseMatrix;
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typedef Matrix<Scalar, Dynamic, 1> DenseVector;
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typedef Matrix<DenseIndex, Dynamic, 1> DenseIndexVector;
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typedef SparseVector<Scalar, 0, StorageIndex> SparseVectorType;
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typedef SparseMatrix<Scalar, 0, StorageIndex> SparseMatrixType;
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Scalar eps = 1e-6;
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SparseMatrixType m1(rows,rows);
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SparseMatrixType m1(rows, rows);
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SparseVectorType v1(rows), v2(rows), v3(rows);
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DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
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DenseVector refV1 = DenseVector::Random(rows),
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refV2 = DenseVector::Random(rows),
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refV3 = DenseVector::Random(rows);
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DenseVector refV1 = DenseVector::Random(rows), refV2 = DenseVector::Random(rows), refV3 = DenseVector::Random(rows);
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std::vector<int> zerocoords, nonzerocoords;
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initSparse<Scalar>(densityVec, refV1, v1, &zerocoords, &nonzerocoords);
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@@ -37,116 +35,109 @@ template<typename Scalar,typename StorageIndex> void sparse_vector(int rows, int
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Scalar s1 = internal::random<Scalar>();
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// test coeff and coeffRef
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for (unsigned int i=0; i<zerocoords.size(); ++i)
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{
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VERIFY_IS_MUCH_SMALLER_THAN( v1.coeff(zerocoords[i]), eps );
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//VERIFY_RAISES_ASSERT( v1.coeffRef(zerocoords[i]) = 5 );
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for (unsigned int i = 0; i < zerocoords.size(); ++i) {
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VERIFY_IS_MUCH_SMALLER_THAN(v1.coeff(zerocoords[i]), eps);
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// VERIFY_RAISES_ASSERT( v1.coeffRef(zerocoords[i]) = 5 );
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}
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{
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VERIFY(int(nonzerocoords.size()) == v1.nonZeros());
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int j=0;
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for (typename SparseVectorType::InnerIterator it(v1); it; ++it,++j)
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{
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VERIFY(nonzerocoords[j]==it.index());
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int j = 0;
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for (typename SparseVectorType::InnerIterator it(v1); it; ++it, ++j) {
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VERIFY(nonzerocoords[j] == it.index());
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VERIFY_IS_EQUAL(it.value(), v1.coeff(it.index()));
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VERIFY_IS_EQUAL(it.value(), refV1.coeff(it.index()));
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}
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}
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VERIFY_IS_APPROX(v1, refV1);
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// test coeffRef with reallocation
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{
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SparseVectorType v4(rows);
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DenseVector v5 = DenseVector::Zero(rows);
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for(int k=0; k<rows; ++k)
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{
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int i = internal::random<int>(0,rows-1);
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for (int k = 0; k < rows; ++k) {
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int i = internal::random<int>(0, rows - 1);
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Scalar v = internal::random<Scalar>();
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v4.coeffRef(i) += v;
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v5.coeffRef(i) += v;
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}
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VERIFY_IS_APPROX(v4,v5);
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VERIFY_IS_APPROX(v4, v5);
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}
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v1.coeffRef(nonzerocoords[0]) = Scalar(5);
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refV1.coeffRef(nonzerocoords[0]) = Scalar(5);
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VERIFY_IS_APPROX(v1, refV1);
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VERIFY_IS_APPROX(v1+v2, refV1+refV2);
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VERIFY_IS_APPROX(v1+v2+v3, refV1+refV2+refV3);
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VERIFY_IS_APPROX(v1 + v2, refV1 + refV2);
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VERIFY_IS_APPROX(v1 + v2 + v3, refV1 + refV2 + refV3);
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VERIFY_IS_APPROX(v1*s1-v2, refV1*s1-refV2);
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VERIFY_IS_APPROX(v1 * s1 - v2, refV1 * s1 - refV2);
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VERIFY_IS_APPROX(v1*=s1, refV1*=s1);
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VERIFY_IS_APPROX(v1/=s1, refV1/=s1);
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VERIFY_IS_APPROX(v1 *= s1, refV1 *= s1);
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VERIFY_IS_APPROX(v1 /= s1, refV1 /= s1);
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VERIFY_IS_APPROX(v1+=v2, refV1+=refV2);
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VERIFY_IS_APPROX(v1-=v2, refV1-=refV2);
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VERIFY_IS_APPROX(v1 += v2, refV1 += refV2);
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VERIFY_IS_APPROX(v1 -= v2, refV1 -= refV2);
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VERIFY_IS_APPROX(v1.dot(v2), refV1.dot(refV2));
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VERIFY_IS_APPROX(v1.dot(refV2), refV1.dot(refV2));
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VERIFY_IS_APPROX(m1*v2, refM1*refV2);
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VERIFY_IS_APPROX(v1.dot(m1*v2), refV1.dot(refM1*refV2));
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VERIFY_IS_APPROX(m1 * v2, refM1 * refV2);
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VERIFY_IS_APPROX(v1.dot(m1 * v2), refV1.dot(refM1 * refV2));
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{
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int i = internal::random<int>(0,rows-1);
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int i = internal::random<int>(0, rows - 1);
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VERIFY_IS_APPROX(v1.dot(m1.col(i)), refV1.dot(refM1.col(i)));
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}
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VERIFY_IS_APPROX(v1.squaredNorm(), refV1.squaredNorm());
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VERIFY_IS_APPROX(v1.blueNorm(), refV1.blueNorm());
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// test aliasing
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VERIFY_IS_APPROX((v1 = -v1), (refV1 = -refV1));
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VERIFY_IS_APPROX((v1 = v1.transpose()), (refV1 = refV1.transpose().eval()));
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VERIFY_IS_APPROX((v1 += -v1), (refV1 += -refV1));
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// sparse matrix to sparse vector
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SparseMatrixType mv1;
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VERIFY_IS_APPROX((mv1=v1),v1);
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VERIFY_IS_APPROX(mv1,(v1=mv1));
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VERIFY_IS_APPROX(mv1,(v1=mv1.transpose()));
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VERIFY_IS_APPROX((mv1 = v1), v1);
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VERIFY_IS_APPROX(mv1, (v1 = mv1));
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VERIFY_IS_APPROX(mv1, (v1 = mv1.transpose()));
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// check copy to dense vector with transpose
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refV3.resize(0);
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VERIFY_IS_APPROX(refV3 = v1.transpose(),v1.toDense());
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VERIFY_IS_APPROX(DenseVector(v1),v1.toDense());
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VERIFY_IS_APPROX(refV3 = v1.transpose(), v1.toDense());
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VERIFY_IS_APPROX(DenseVector(v1), v1.toDense());
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// test conservative resize
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{
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std::vector<StorageIndex> inc;
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if(rows > 3)
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inc.push_back(-3);
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if (rows > 3) inc.push_back(-3);
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inc.push_back(0);
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inc.push_back(3);
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inc.push_back(1);
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inc.push_back(10);
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for(std::size_t i = 0; i< inc.size(); i++) {
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for (std::size_t i = 0; i < inc.size(); i++) {
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StorageIndex incRows = inc[i];
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SparseVectorType vec1(rows);
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DenseVector refVec1 = DenseVector::Zero(rows);
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initSparse<Scalar>(densityVec, refVec1, vec1);
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vec1.conservativeResize(rows+incRows);
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refVec1.conservativeResize(rows+incRows);
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vec1.conservativeResize(rows + incRows);
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refVec1.conservativeResize(rows + incRows);
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if (incRows > 0) refVec1.tail(incRows).setZero();
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VERIFY_IS_APPROX(vec1, refVec1);
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// Insert new values
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if (incRows > 0)
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vec1.insert(vec1.rows()-1) = refVec1(refVec1.rows()-1) = 1;
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if (incRows > 0) vec1.insert(vec1.rows() - 1) = refVec1(refVec1.rows() - 1) = 1;
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VERIFY_IS_APPROX(vec1, refVec1);
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}
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}
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// test sort
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if(rows > 1)
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{
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if (rows > 1) {
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SparseVectorType vec1(rows);
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DenseVector refVec1 = DenseVector::Zero(rows);
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DenseIndexVector innerIndices(rows);
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@@ -155,12 +146,11 @@ template<typename Scalar,typename StorageIndex> void sparse_vector(int rows, int
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std::mt19937 g(rd());
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std::shuffle(innerIndices.begin(), innerIndices.end(), g);
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Index nz = internal::random<Index>(2, rows / 2);
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for (Index k = 0; k < nz; k++)
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{
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Index i = innerIndices[k];
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Scalar val = internal::random<Scalar>();
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refVec1.coeffRef(i) = val;
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vec1.insert(i) = val;
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for (Index k = 0; k < nz; k++) {
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Index i = innerIndices[k];
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Scalar val = internal::random<Scalar>();
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refVec1.coeffRef(i) = val;
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vec1.insert(i) = val;
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}
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vec1.template sortInnerIndices<std::greater<>>();
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@@ -172,49 +162,47 @@ template<typename Scalar,typename StorageIndex> void sparse_vector(int rows, int
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VERIFY_IS_EQUAL(vec1.template innerIndicesAreSorted<std::greater<>>(), 0);
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VERIFY_IS_EQUAL(vec1.template innerIndicesAreSorted<std::less<>>(), 1);
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}
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}
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void test_pruning() {
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using SparseVectorType = SparseVector<double, 0, int>;
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using SparseVectorType = SparseVector<double, 0, int>;
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SparseVectorType vec;
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auto init_vec = [&](){;
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vec.resize(10);
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vec.insert(3) = 0.1;
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vec.insert(5) = 1.0;
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vec.insert(8) = -0.1;
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vec.insert(9) = -0.2;
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};
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init_vec();
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SparseVectorType vec;
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auto init_vec = [&]() {
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;
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vec.resize(10);
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vec.insert(3) = 0.1;
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vec.insert(5) = 1.0;
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vec.insert(8) = -0.1;
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vec.insert(9) = -0.2;
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};
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init_vec();
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VERIFY_IS_EQUAL(vec.nonZeros(), 4);
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VERIFY_IS_EQUAL(vec.prune(0.1, 1.0), 2);
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VERIFY_IS_EQUAL(vec.nonZeros(), 2);
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VERIFY_IS_EQUAL(vec.coeff(5), 1.0);
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VERIFY_IS_EQUAL(vec.coeff(9), -0.2);
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VERIFY_IS_EQUAL(vec.nonZeros(), 4);
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VERIFY_IS_EQUAL(vec.prune(0.1, 1.0), 2);
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VERIFY_IS_EQUAL(vec.nonZeros(), 2);
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VERIFY_IS_EQUAL(vec.coeff(5), 1.0);
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VERIFY_IS_EQUAL(vec.coeff(9), -0.2);
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init_vec();
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VERIFY_IS_EQUAL(vec.prune([](double v) { return v >= 0; }), 2);
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VERIFY_IS_EQUAL(vec.nonZeros(), 2);
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VERIFY_IS_EQUAL(vec.coeff(3), 0.1);
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VERIFY_IS_EQUAL(vec.coeff(5), 1.0);
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init_vec();
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VERIFY_IS_EQUAL(vec.prune([](double v) { return v >= 0; }), 2);
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VERIFY_IS_EQUAL(vec.nonZeros(), 2);
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VERIFY_IS_EQUAL(vec.coeff(3), 0.1);
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VERIFY_IS_EQUAL(vec.coeff(5), 1.0);
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}
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EIGEN_DECLARE_TEST(sparse_vector)
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{
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for(int i = 0; i < g_repeat; i++) {
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int r = Eigen::internal::random<int>(1,500), c = Eigen::internal::random<int>(1,500);
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if(Eigen::internal::random<int>(0,4) == 0) {
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r = c; // check square matrices in 25% of tries
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EIGEN_DECLARE_TEST(sparse_vector) {
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for (int i = 0; i < g_repeat; i++) {
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int r = Eigen::internal::random<int>(1, 500), c = Eigen::internal::random<int>(1, 500);
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if (Eigen::internal::random<int>(0, 4) == 0) {
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r = c; // check square matrices in 25% of tries
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}
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EIGEN_UNUSED_VARIABLE(r+c);
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EIGEN_UNUSED_VARIABLE(r + c);
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CALL_SUBTEST_1(( sparse_vector<double,int>(8, 8) ));
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CALL_SUBTEST_2(( sparse_vector<std::complex<double>, int>(r, c) ));
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CALL_SUBTEST_1(( sparse_vector<double,long int>(r, c) ));
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CALL_SUBTEST_1(( sparse_vector<double,short>(r, c) ));
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CALL_SUBTEST_1((sparse_vector<double, int>(8, 8)));
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CALL_SUBTEST_2((sparse_vector<std::complex<double>, int>(r, c)));
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CALL_SUBTEST_1((sparse_vector<double, long int>(r, c)));
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CALL_SUBTEST_1((sparse_vector<double, short>(r, c)));
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}
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CALL_SUBTEST_1(test_pruning());
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}
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