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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
152
test/svd_fill.h
152
test/svd_fill.h
@@ -7,115 +7,103 @@
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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template<typename T>
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Array<T,4,1> four_denorms();
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template <typename T>
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Array<T, 4, 1> four_denorms();
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template<>
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Array4f four_denorms() { return Array4f(5.60844e-39f, -5.60844e-39f, 4.94e-44f, -4.94e-44f); }
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template<>
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Array4d four_denorms() { return Array4d(5.60844e-313, -5.60844e-313, 4.94e-324, -4.94e-324); }
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template<typename T>
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Array<T,4,1> four_denorms() { return four_denorms<double>().cast<T>(); }
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template <>
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Array4f four_denorms() {
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return Array4f(5.60844e-39f, -5.60844e-39f, 4.94e-44f, -4.94e-44f);
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}
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template <>
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Array4d four_denorms() {
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return Array4d(5.60844e-313, -5.60844e-313, 4.94e-324, -4.94e-324);
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}
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template <typename T>
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Array<T, 4, 1> four_denorms() {
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return four_denorms<double>().cast<T>();
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}
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template<typename MatrixType>
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void svd_fill_random(MatrixType &m, int Option = 0)
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{
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template <typename MatrixType>
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void svd_fill_random(MatrixType &m, int Option = 0) {
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using std::pow;
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typedef typename MatrixType::Scalar Scalar;
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typedef typename MatrixType::RealScalar RealScalar;
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Index diagSize = (std::min)(m.rows(), m.cols());
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RealScalar s = std::numeric_limits<RealScalar>::max_exponent10/4;
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s = internal::random<RealScalar>(1,s);
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Matrix<RealScalar,Dynamic,1> d = Matrix<RealScalar,Dynamic,1>::Random(diagSize);
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for(Index k=0; k<diagSize; ++k)
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d(k) = d(k)*pow(RealScalar(10),internal::random<RealScalar>(-s,s));
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RealScalar s = std::numeric_limits<RealScalar>::max_exponent10 / 4;
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s = internal::random<RealScalar>(1, s);
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Matrix<RealScalar, Dynamic, 1> d = Matrix<RealScalar, Dynamic, 1>::Random(diagSize);
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for (Index k = 0; k < diagSize; ++k) d(k) = d(k) * pow(RealScalar(10), internal::random<RealScalar>(-s, s));
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bool dup = internal::random<int>(0, 10) < 3;
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bool unit_uv =
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internal::random<int>(0, 10) < (dup ? 7 : 3); // if we duplicate some diagonal entries, then increase the chance
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// to preserve them using unitary U and V factors
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bool dup = internal::random<int>(0,10) < 3;
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bool unit_uv = internal::random<int>(0,10) < (dup?7:3); // if we duplicate some diagonal entries, then increase the chance to preserve them using unitary U and V factors
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// duplicate some singular values
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if(dup)
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{
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Index n = internal::random<Index>(0,d.size()-1);
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for(Index i=0; i<n; ++i)
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d(internal::random<Index>(0,d.size()-1)) = d(internal::random<Index>(0,d.size()-1));
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if (dup) {
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Index n = internal::random<Index>(0, d.size() - 1);
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for (Index i = 0; i < n; ++i)
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d(internal::random<Index>(0, d.size() - 1)) = d(internal::random<Index>(0, d.size() - 1));
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}
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Matrix<Scalar,Dynamic,Dynamic> U(m.rows(),diagSize);
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Matrix<Scalar,Dynamic,Dynamic> VT(diagSize,m.cols());
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if(unit_uv)
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{
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Matrix<Scalar, Dynamic, Dynamic> U(m.rows(), diagSize);
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Matrix<Scalar, Dynamic, Dynamic> VT(diagSize, m.cols());
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if (unit_uv) {
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// in very rare cases let's try with a pure diagonal matrix
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if(internal::random<int>(0,10) < 1)
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{
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if (internal::random<int>(0, 10) < 1) {
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U.setIdentity();
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VT.setIdentity();
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} else {
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createRandomPIMatrixOfRank(diagSize, U.rows(), U.cols(), U);
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createRandomPIMatrixOfRank(diagSize, VT.rows(), VT.cols(), VT);
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}
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else
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{
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createRandomPIMatrixOfRank(diagSize,U.rows(), U.cols(), U);
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createRandomPIMatrixOfRank(diagSize,VT.rows(), VT.cols(), VT);
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}
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}
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else
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{
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} else {
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U.setRandom();
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VT.setRandom();
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}
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Matrix<Scalar,Dynamic,1> samples(9);
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samples << Scalar(0), four_denorms<RealScalar>(),
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-RealScalar(1)/NumTraits<RealScalar>::highest(),
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RealScalar(1)/NumTraits<RealScalar>::highest(),
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(std::numeric_limits<RealScalar>::min)(),
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pow((std::numeric_limits<RealScalar>::min)(), RealScalar(0.8));
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if(Option==Symmetric)
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{
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Matrix<Scalar, Dynamic, 1> samples(9);
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samples << Scalar(0), four_denorms<RealScalar>(), -RealScalar(1) / NumTraits<RealScalar>::highest(),
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RealScalar(1) / NumTraits<RealScalar>::highest(), (std::numeric_limits<RealScalar>::min)(),
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pow((std::numeric_limits<RealScalar>::min)(), RealScalar(0.8));
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if (Option == Symmetric) {
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m = U * d.asDiagonal() * U.transpose();
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// randomly nullify some rows/columns
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{
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Index count = internal::random<Index>(-diagSize,diagSize);
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for(Index k=0; k<count; ++k)
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{
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Index i = internal::random<Index>(0,diagSize-1);
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Index count = internal::random<Index>(-diagSize, diagSize);
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for (Index k = 0; k < count; ++k) {
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Index i = internal::random<Index>(0, diagSize - 1);
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m.row(i).setZero();
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m.col(i).setZero();
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}
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if(count<0)
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// (partly) cancel some coeffs
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if(!(dup && unit_uv))
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{
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Index n = internal::random<Index>(0,m.size()-1);
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for(Index k=0; k<n; ++k)
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{
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Index i = internal::random<Index>(0,m.rows()-1);
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Index j = internal::random<Index>(0,m.cols()-1);
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m(j,i) = m(i,j) = samples(internal::random<Index>(0,samples.size()-1));
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if(NumTraits<Scalar>::IsComplex)
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*(&numext::real_ref(m(j,i))+1) = *(&numext::real_ref(m(i,j))+1) = samples.real()(internal::random<Index>(0,samples.size()-1));
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if (count < 0)
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// (partly) cancel some coeffs
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if (!(dup && unit_uv)) {
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Index n = internal::random<Index>(0, m.size() - 1);
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for (Index k = 0; k < n; ++k) {
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Index i = internal::random<Index>(0, m.rows() - 1);
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Index j = internal::random<Index>(0, m.cols() - 1);
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m(j, i) = m(i, j) = samples(internal::random<Index>(0, samples.size() - 1));
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if (NumTraits<Scalar>::IsComplex)
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*(&numext::real_ref(m(j, i)) + 1) = *(&numext::real_ref(m(i, j)) + 1) =
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samples.real()(internal::random<Index>(0, samples.size() - 1));
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}
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}
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}
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}
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}
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else
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{
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} else {
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m = U * d.asDiagonal() * VT;
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// (partly) cancel some coeffs
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if(!(dup && unit_uv))
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{
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Index n = internal::random<Index>(0,m.size()-1);
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for(Index k=0; k<n; ++k)
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{
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Index i = internal::random<Index>(0,m.rows()-1);
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Index j = internal::random<Index>(0,m.cols()-1);
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m(i,j) = samples(internal::random<Index>(0,samples.size()-1));
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if(NumTraits<Scalar>::IsComplex)
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*(&numext::real_ref(m(i,j))+1) = samples.real()(internal::random<Index>(0,samples.size()-1));
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if (!(dup && unit_uv)) {
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Index n = internal::random<Index>(0, m.size() - 1);
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for (Index k = 0; k < n; ++k) {
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Index i = internal::random<Index>(0, m.rows() - 1);
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Index j = internal::random<Index>(0, m.cols() - 1);
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m(i, j) = samples(internal::random<Index>(0, samples.size() - 1));
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if (NumTraits<Scalar>::IsComplex)
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*(&numext::real_ref(m(i, j)) + 1) = samples.real()(internal::random<Index>(0, samples.size() - 1));
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}
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}
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}
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}
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