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199 lines
6.3 KiB
C++
199 lines
6.3 KiB
C++
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// 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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#include "svd_common.h"
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template<typename MatrixType, int QRPreconditioner>
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void jacobisvd_check_full(const MatrixType& m, const JacobiSVD<MatrixType, QRPreconditioner>& svd)
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{
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svd_check_full<MatrixType, JacobiSVD<MatrixType, QRPreconditioner > >(m, svd);
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}
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template<typename MatrixType, int QRPreconditioner>
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void jacobisvd_compare_to_full(const MatrixType& m,
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unsigned int computationOptions,
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const JacobiSVD<MatrixType, QRPreconditioner>& referenceSvd)
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{
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svd_compare_to_full<MatrixType, JacobiSVD<MatrixType, QRPreconditioner> >(m, computationOptions, referenceSvd);
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}
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template<typename MatrixType, int QRPreconditioner>
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void jacobisvd_solve(const MatrixType& m, unsigned int computationOptions)
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{
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svd_solve< MatrixType, JacobiSVD< MatrixType, QRPreconditioner > >(m, computationOptions);
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}
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template<typename MatrixType, int QRPreconditioner>
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void jacobisvd_test_all_computation_options(const MatrixType& m)
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{
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if (QRPreconditioner == NoQRPreconditioner && m.rows() != m.cols())
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return;
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JacobiSVD< MatrixType, QRPreconditioner > fullSvd(m, ComputeFullU|ComputeFullV);
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svd_test_computation_options_1< MatrixType, JacobiSVD< MatrixType, QRPreconditioner > >(m, fullSvd);
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if(QRPreconditioner == FullPivHouseholderQRPreconditioner)
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return;
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svd_test_computation_options_2< MatrixType, JacobiSVD< MatrixType, QRPreconditioner > >(m, fullSvd);
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}
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template<typename MatrixType>
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void jacobisvd(const MatrixType& a = MatrixType(), bool pickrandom = true)
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{
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MatrixType m = pickrandom ? MatrixType::Random(a.rows(), a.cols()) : a;
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jacobisvd_test_all_computation_options<MatrixType, FullPivHouseholderQRPreconditioner>(m);
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jacobisvd_test_all_computation_options<MatrixType, ColPivHouseholderQRPreconditioner>(m);
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jacobisvd_test_all_computation_options<MatrixType, HouseholderQRPreconditioner>(m);
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jacobisvd_test_all_computation_options<MatrixType, NoQRPreconditioner>(m);
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}
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template<typename MatrixType>
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void jacobisvd_verify_assert(const MatrixType& m)
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{
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svd_verify_assert<MatrixType, JacobiSVD< MatrixType > >(m);
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typedef typename MatrixType::Index Index;
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Index rows = m.rows();
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Index cols = m.cols();
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enum {
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RowsAtCompileTime = MatrixType::RowsAtCompileTime,
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ColsAtCompileTime = MatrixType::ColsAtCompileTime
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};
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MatrixType a = MatrixType::Zero(rows, cols);
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a.setZero();
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if (ColsAtCompileTime == Dynamic)
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{
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JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner> svd_fullqr;
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VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeFullU|ComputeThinV))
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VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeThinU|ComputeThinV))
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VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeThinU|ComputeFullV))
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}
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}
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template<typename MatrixType>
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void jacobisvd_method()
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{
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enum { Size = MatrixType::RowsAtCompileTime };
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typedef typename MatrixType::RealScalar RealScalar;
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typedef Matrix<RealScalar, Size, 1> RealVecType;
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MatrixType m = MatrixType::Identity();
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VERIFY_IS_APPROX(m.jacobiSvd().singularValues(), RealVecType::Ones());
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VERIFY_RAISES_ASSERT(m.jacobiSvd().matrixU());
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VERIFY_RAISES_ASSERT(m.jacobiSvd().matrixV());
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VERIFY_IS_APPROX(m.jacobiSvd(ComputeFullU|ComputeFullV).solve(m), m);
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}
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template<typename MatrixType>
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void jacobisvd_inf_nan()
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{
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svd_inf_nan<MatrixType, JacobiSVD< MatrixType > >();
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}
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// Regression test for bug 286: JacobiSVD loops indefinitely with some
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// matrices containing denormal numbers.
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void jacobisvd_bug286()
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{
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#if defined __INTEL_COMPILER
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// shut up warning #239: floating point underflow
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#pragma warning push
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#pragma warning disable 239
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#endif
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Matrix2d M;
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M << -7.90884e-313, -4.94e-324,
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0, 5.60844e-313;
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#if defined __INTEL_COMPILER
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#pragma warning pop
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#endif
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JacobiSVD<Matrix2d> svd;
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svd.compute(M); // just check we don't loop indefinitely
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}
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void jacobisvd_preallocate()
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{
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svd_preallocate< JacobiSVD <MatrixXf> >();
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}
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void test_jacobisvd()
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{
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CALL_SUBTEST_11(( jacobisvd<Matrix<double,Dynamic,Dynamic> >
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(Matrix<double,Dynamic,Dynamic>(16, 6)) ));
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CALL_SUBTEST_3(( jacobisvd_verify_assert(Matrix3f()) ));
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CALL_SUBTEST_4(( jacobisvd_verify_assert(Matrix4d()) ));
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CALL_SUBTEST_7(( jacobisvd_verify_assert(MatrixXf(10,12)) ));
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CALL_SUBTEST_8(( jacobisvd_verify_assert(MatrixXcd(7,5)) ));
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for(int i = 0; i < g_repeat; i++) {
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Matrix2cd m;
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m << 0, 1,
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0, 1;
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CALL_SUBTEST_1(( jacobisvd(m, false) ));
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m << 1, 0,
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1, 0;
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CALL_SUBTEST_1(( jacobisvd(m, false) ));
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Matrix2d n;
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n << 0, 0,
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0, 0;
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CALL_SUBTEST_2(( jacobisvd(n, false) ));
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n << 0, 0,
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0, 1;
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CALL_SUBTEST_2(( jacobisvd(n, false) ));
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CALL_SUBTEST_3(( jacobisvd<Matrix3f>() ));
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CALL_SUBTEST_4(( jacobisvd<Matrix4d>() ));
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CALL_SUBTEST_5(( jacobisvd<Matrix<float,3,5> >() ));
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CALL_SUBTEST_6(( jacobisvd<Matrix<double,Dynamic,2> >(Matrix<double,Dynamic,2>(10,2)) ));
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int r = internal::random<int>(1, 30),
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c = internal::random<int>(1, 30);
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CALL_SUBTEST_7(( jacobisvd<MatrixXf>(MatrixXf(r,c)) ));
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CALL_SUBTEST_8(( jacobisvd<MatrixXcd>(MatrixXcd(r,c)) ));
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(void) r;
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(void) c;
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// Test on inf/nan matrix
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CALL_SUBTEST_7( jacobisvd_inf_nan<MatrixXf>() );
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}
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CALL_SUBTEST_7(( jacobisvd<MatrixXf>(MatrixXf(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2))) ));
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CALL_SUBTEST_8(( jacobisvd<MatrixXcd>(MatrixXcd(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/3), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/3))) ));
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// test matrixbase method
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CALL_SUBTEST_1(( jacobisvd_method<Matrix2cd>() ));
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CALL_SUBTEST_3(( jacobisvd_method<Matrix3f>() ));
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// Test problem size constructors
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CALL_SUBTEST_7( JacobiSVD<MatrixXf>(10,10) );
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// Check that preallocation avoids subsequent mallocs
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CALL_SUBTEST_9( jacobisvd_preallocate() );
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// Regression check for bug 286
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CALL_SUBTEST_2( jacobisvd_bug286() );
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}
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