155 lines
6.0 KiB
C++
155 lines
6.0 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
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// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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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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// SPDX-License-Identifier: MPL-2.0
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#include "main.h"
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#include <Eigen/SVD>
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template <typename MatrixType, typename JacobiScalar>
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void jacobi(const MatrixType& m = MatrixType()) {
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Index rows = m.rows();
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Index cols = m.cols();
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enum { RowsAtCompileTime = MatrixType::RowsAtCompileTime, ColsAtCompileTime = MatrixType::ColsAtCompileTime };
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typedef Matrix<JacobiScalar, 2, 1> JacobiVector;
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const MatrixType a(MatrixType::Random(rows, cols));
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JacobiVector v = JacobiVector::Random().normalized();
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JacobiScalar c = v.x(), s = v.y();
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JacobiRotation<JacobiScalar> rot(c, s);
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{
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Index p = internal::random<Index>(0, rows - 1);
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Index q;
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do {
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q = internal::random<Index>(0, rows - 1);
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} while (q == p);
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MatrixType b = a;
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b.applyOnTheLeft(p, q, rot);
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VERIFY_IS_APPROX(b.row(p), c * a.row(p) + numext::conj(s) * a.row(q));
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VERIFY_IS_APPROX(b.row(q), -s * a.row(p) + numext::conj(c) * a.row(q));
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}
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{
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Index p = internal::random<Index>(0, cols - 1);
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Index q;
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do {
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q = internal::random<Index>(0, cols - 1);
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} while (q == p);
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MatrixType b = a;
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b.applyOnTheRight(p, q, rot);
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VERIFY_IS_APPROX(b.col(p), c * a.col(p) - s * a.col(q));
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VERIFY_IS_APPROX(b.col(q), numext::conj(s) * a.col(p) + numext::conj(c) * a.col(q));
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}
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}
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// Verify that JacobiRotation::makeGivens(p, q, &r) produces a rotation that
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// zeros out q, even when (p, q) straddle the over-/underflow thresholds
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// where the direct formula r = p * sqrt(1 + (q/p)^2) would over- or
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// underflow. Eigen's convention is r >= 0 with sign carried in c.
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template <typename Scalar>
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void verify_makeGivens(const Scalar& p, const Scalar& q) {
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using std::abs;
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Scalar r;
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JacobiRotation<Scalar> rot;
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rot.makeGivens(p, q, &r);
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// Eigen's J^T * [p; q] = [r; 0] with J = [c s; -s c], so:
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// c*p - s*q = r, s*p + c*q = 0.
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Scalar rotated0 = rot.c() * p - rot.s() * q;
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Scalar rotated1 = rot.s() * p + rot.c() * q;
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// The check itself performs two rounded products and an addition/subtraction, sometimes at the safe-scaling
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// overflow threshold. Keep the tolerance relative to r, but leave enough room for compiler-specific contraction and
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// reassociation in the verification expression.
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Scalar tol = NumTraits<Scalar>::epsilon() * (abs(r) + (std::numeric_limits<Scalar>::min)()) * Scalar(64);
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VERIFY(abs(rotated0 - r) <= tol);
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VERIFY(abs(rotated1) <= tol);
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VERIFY(r >= Scalar(0));
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VERIFY_IS_APPROX(numext::abs2(rot.c()) + numext::abs2(rot.s()), Scalar(1));
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}
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template <typename Scalar>
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void jacobi_makegivens_safe_scaling() {
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using std::sqrt;
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const Scalar safmin = (std::numeric_limits<Scalar>::min)();
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const Scalar safmax = Scalar(1) / safmin;
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const Scalar rtmin = sqrt(safmin);
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const Scalar rtmax = sqrt(safmax / Scalar(2));
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const Scalar one(1);
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const Scalar two(2);
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const Scalar half(0.5);
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// Safe-range cases (regression — must keep existing fast path working).
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verify_makeGivens<Scalar>(Scalar(3), Scalar(4));
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verify_makeGivens<Scalar>(Scalar(-3), Scalar(4));
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verify_makeGivens<Scalar>(Scalar(3), Scalar(-4));
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verify_makeGivens<Scalar>(Scalar(-3), Scalar(-4));
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// Both inputs near overflow: direct formula r = p * sqrt(1+(q/p)^2) would
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// overflow because sqrt(1+1) > 1. Prescaling avoids this.
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verify_makeGivens<Scalar>(rtmax * two, rtmax);
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verify_makeGivens<Scalar>(-rtmax * two, rtmax);
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verify_makeGivens<Scalar>(rtmax, rtmax);
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verify_makeGivens<Scalar>(rtmax * Scalar(1.5), rtmax * Scalar(1.5));
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// Both inputs near underflow / subnormal: direct (q/p)^2 underflows to 0.
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verify_makeGivens<Scalar>(rtmin * half, rtmin * half);
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verify_makeGivens<Scalar>(safmin, safmin);
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verify_makeGivens<Scalar>(-safmin, safmin);
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// Mixed: one near overflow, one normal.
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verify_makeGivens<Scalar>(rtmax * Scalar(1.5), one);
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verify_makeGivens<Scalar>(one, rtmax * Scalar(1.5));
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verify_makeGivens<Scalar>(-rtmax * Scalar(1.5), one);
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// Mixed: one near underflow, one normal.
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verify_makeGivens<Scalar>(safmin, one);
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verify_makeGivens<Scalar>(one, safmin);
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// Mixed: subnormal and near-overflow simultaneously.
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verify_makeGivens<Scalar>(safmin, rtmax);
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verify_makeGivens<Scalar>(rtmax, safmin);
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}
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EIGEN_DECLARE_TEST(jacobi) {
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for (int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST_7((jacobi_makegivens_safe_scaling<float>()));
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CALL_SUBTEST_7((jacobi_makegivens_safe_scaling<double>()));
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CALL_SUBTEST_1((jacobi<Matrix3f, float>()));
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CALL_SUBTEST_2((jacobi<Matrix4d, double>()));
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CALL_SUBTEST_3((jacobi<Matrix4cf, float>()));
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CALL_SUBTEST_3((jacobi<Matrix4cf, std::complex<float> >()));
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CALL_SUBTEST_1((jacobi<Matrix<float, 3, 3, RowMajor>, float>()));
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CALL_SUBTEST_2((jacobi<Matrix<double, 4, 4, RowMajor>, double>()));
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CALL_SUBTEST_3((jacobi<Matrix<std::complex<float>, 4, 4, RowMajor>, float>()));
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CALL_SUBTEST_3((jacobi<Matrix<std::complex<float>, 4, 4, RowMajor>, std::complex<float> >()));
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int r = internal::random<int>(2, internal::random<int>(1, EIGEN_TEST_MAX_SIZE) / 2),
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c = internal::random<int>(2, internal::random<int>(1, EIGEN_TEST_MAX_SIZE) / 2);
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CALL_SUBTEST_4((jacobi<MatrixXf, float>(MatrixXf(r, c))));
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CALL_SUBTEST_5((jacobi<MatrixXcd, double>(MatrixXcd(r, c))));
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CALL_SUBTEST_5((jacobi<MatrixXcd, std::complex<double> >(MatrixXcd(r, c))));
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// complex<float> is really important to test as it is the only way to cover conjugation issues in certain unaligned
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// paths
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CALL_SUBTEST_6((jacobi<MatrixXcf, float>(MatrixXcf(r, c))));
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CALL_SUBTEST_6((jacobi<MatrixXcf, std::complex<float> >(MatrixXcf(r, c))));
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TEST_SET_BUT_UNUSED_VARIABLE(r);
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TEST_SET_BUT_UNUSED_VARIABLE(c);
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}
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}
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