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Revert "Clean up stableNorm"
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@ -46,29 +46,78 @@ inline void stable_norm_kernel(const ExpressionType& bl, Scalar& ssq, Scalar& sc
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ssq += (bl * invScale).squaredNorm();
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}
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template <typename VectorType, typename RealScalar>
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void stable_norm_impl_inner_step(const VectorType& vec, RealScalar& ssq, RealScalar& scale, RealScalar& invScale) {
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typedef typename VectorType::Scalar Scalar;
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const Index blockSize = 4096;
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typedef typename internal::nested_eval<VectorType, 2>::type VectorTypeCopy;
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typedef internal::remove_all_t<VectorTypeCopy> VectorTypeCopyClean;
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const VectorTypeCopy copy(vec);
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enum {
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CanAlign =
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((int(VectorTypeCopyClean::Flags) & DirectAccessBit) ||
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(int(internal::evaluator<VectorTypeCopyClean>::Alignment) > 0) // FIXME Alignment)>0 might not be enough
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) &&
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(blockSize * sizeof(Scalar) * 2 < EIGEN_STACK_ALLOCATION_LIMIT) &&
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(EIGEN_MAX_STATIC_ALIGN_BYTES >
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0) // if we cannot allocate on the stack, then let's not bother about this optimization
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};
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typedef std::conditional_t<
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CanAlign,
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Ref<const Matrix<Scalar, Dynamic, 1, 0, blockSize, 1>, internal::evaluator<VectorTypeCopyClean>::Alignment>,
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typename VectorTypeCopyClean::ConstSegmentReturnType>
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SegmentWrapper;
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Index n = vec.size();
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Index bi = internal::first_default_aligned(copy);
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if (bi > 0) internal::stable_norm_kernel(copy.head(bi), ssq, scale, invScale);
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for (; bi < n; bi += blockSize)
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internal::stable_norm_kernel(SegmentWrapper(copy.segment(bi, numext::mini(blockSize, n - bi))), ssq, scale,
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invScale);
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}
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template <typename VectorType>
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typename VectorType::RealScalar stable_norm_impl(const VectorType& vec,
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std::enable_if_t<VectorType::IsVectorAtCompileTime>* = 0) {
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using std::abs;
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using std::sqrt;
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Index n = vec.size();
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if (n == 1) return abs(vec.coeff(0));
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typedef typename VectorType::RealScalar RealScalar;
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RealScalar scale(0);
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RealScalar invScale(1);
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RealScalar ssq(0); // sum of squares
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stable_norm_impl_inner_step(vec, ssq, scale, invScale);
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return scale * sqrt(ssq);
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}
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template <typename MatrixType>
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typename MatrixType::RealScalar stable_norm_impl(const MatrixType& mat) {
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using numext::sqrt;
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typename MatrixType::RealScalar stable_norm_impl(const MatrixType& mat,
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std::enable_if_t<!MatrixType::IsVectorAtCompileTime>* = 0) {
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using std::sqrt;
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typedef typename MatrixType::RealScalar RealScalar;
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RealScalar scale(0);
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RealScalar invScale(1);
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RealScalar ssq(0); // sum of squares
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if (mat.size() == 0) {
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return RealScalar(0);
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}
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stable_norm_kernel(mat, ssq, scale, invScale);
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for (Index j = 0; j < mat.outerSize(); ++j) stable_norm_impl_inner_step(mat.innerVector(j), ssq, scale, invScale);
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return scale * sqrt(ssq);
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}
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template <typename Derived>
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inline typename NumTraits<typename traits<Derived>::Scalar>::Real blueNorm_impl(const EigenBase<Derived>& _vec) {
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typedef typename Derived::RealScalar RealScalar;
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using numext::abs;
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using numext::pow;
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using numext::sqrt;
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using std::abs;
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using std::pow;
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using std::sqrt;
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// This program calculates the machine-dependent constants
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// bl, b2, slm, s2m, relerr overfl
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@ -91,7 +140,7 @@ inline typename NumTraits<typename traits<Derived>::Scalar>::Real blueNorm_impl(
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RealScalar(pow(RealScalar(ibeta), RealScalar((2 - iemin) / 2))); // scaling factor for lower range
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static const RealScalar s2m =
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RealScalar(pow(RealScalar(ibeta), RealScalar(-((iemax + it) / 2)))); // scaling factor for upper range
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static const RealScalar eps = RealScalar(pow(double(ibeta), double(1 - it)));
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static const RealScalar eps = RealScalar(pow(double(ibeta), 1 - it));
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static const RealScalar relerr = sqrt(eps); // tolerance for neglecting asml
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const Derived& vec(_vec.derived());
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@ -225,7 +225,7 @@ void test_hypot() {
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while (numext::abs2(factor) < RealScalar(1e-4)) factor = internal::random<Scalar>();
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Scalar small = factor * ((std::numeric_limits<RealScalar>::min)() * RealScalar(1e4));
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Scalar one(1), zero(0), sqrt2(std::sqrt(Scalar(2))), nan(std::numeric_limits<RealScalar>::quiet_NaN());
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Scalar one(1), zero(0), sqrt2(std::sqrt(2)), nan(std::numeric_limits<RealScalar>::quiet_NaN());
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Scalar a = internal::random<Scalar>(-1, 1);
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Scalar b = internal::random<Scalar>(-1, 1);
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