209 lines
5.6 KiB
C++
209 lines
5.6 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) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
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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 <sstream>
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#ifdef EIGEN_TEST_MAX_SIZE
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#undef EIGEN_TEST_MAX_SIZE
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#endif
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#define EIGEN_TEST_MAX_SIZE 50
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#ifdef EIGEN_TEST_PART_1
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#include "cholesky.cpp"
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#endif
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#ifdef EIGEN_TEST_PART_2
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#include "lu.cpp"
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#endif
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#ifdef EIGEN_TEST_PART_3
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#include "qr.cpp"
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#endif
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#ifdef EIGEN_TEST_PART_4
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#include "qr_colpivoting.cpp"
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#endif
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#ifdef EIGEN_TEST_PART_5
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#include "qr_fullpivoting.cpp"
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#endif
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#ifdef EIGEN_TEST_PART_6
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#include "eigensolver_selfadjoint.cpp"
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#endif
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#ifdef EIGEN_TEST_PART_7
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#include "eigensolver_generic.cpp"
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#endif
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#ifdef EIGEN_TEST_PART_8
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#include "eigensolver_generalized_real.cpp"
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#endif
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#ifdef EIGEN_TEST_PART_9
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#include "jacobisvd.cpp"
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#endif
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#ifdef EIGEN_TEST_PART_10
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#include "bdcsvd.cpp"
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#endif
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#ifdef EIGEN_TEST_PART_11
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#include "simplicial_cholesky.cpp"
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#endif
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#include <Eigen/Dense>
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#undef min
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#undef max
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#undef isnan
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#undef isinf
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#undef isfinite
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#undef I
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#include <boost/serialization/nvp.hpp>
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#include <boost/multiprecision/cpp_dec_float.hpp>
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#include <boost/multiprecision/number.hpp>
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#include <boost/math/special_functions.hpp>
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#include <boost/math/complex.hpp>
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namespace mp = boost::multiprecision;
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typedef mp::number<mp::cpp_dec_float<100>, mp::et_on> Real;
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namespace Eigen {
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template<> struct NumTraits<Real> : GenericNumTraits<Real> {
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static inline Real dummy_precision() { return 1e-50; }
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};
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template<typename T1,typename T2,typename T3,typename T4,typename T5>
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struct NumTraits<boost::multiprecision::detail::expression<T1,T2,T3,T4,T5> > : NumTraits<Real> {};
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template<>
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Real test_precision<Real>() { return 1e-50; }
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// needed in C++93 mode where number does not support explicit cast.
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namespace internal {
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template<typename NewType>
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struct cast_impl<Real,NewType> {
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static inline NewType run(const Real& x) {
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return x.template convert_to<NewType>();
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}
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};
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template<>
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struct cast_impl<Real,std::complex<Real> > {
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static inline std::complex<Real> run(const Real& x) {
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return std::complex<Real>(x);
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}
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};
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}
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}
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namespace boost {
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namespace multiprecision {
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// to make ADL works as expected:
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using boost::math::isfinite;
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using boost::math::isnan;
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using boost::math::isinf;
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using boost::math::copysign;
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using boost::math::hypot;
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// The following is needed for std::complex<Real>:
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Real fabs(const Real& a) { return abs EIGEN_NOT_A_MACRO (a); }
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Real fmax(const Real& a, const Real& b) { using std::max; return max(a,b); }
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// some specialization for the unit tests:
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inline bool test_isMuchSmallerThan(const Real& a, const Real& b) {
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return internal::isMuchSmallerThan(a, b, test_precision<Real>());
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}
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inline bool test_isApprox(const Real& a, const Real& b) {
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return internal::isApprox(a, b, test_precision<Real>());
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}
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inline bool test_isApproxOrLessThan(const Real& a, const Real& b) {
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return internal::isApproxOrLessThan(a, b, test_precision<Real>());
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}
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Real get_test_precision(const Real&) {
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return test_precision<Real>();
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}
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Real test_relative_error(const Real &a, const Real &b) {
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using Eigen::numext::abs2;
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return sqrt(abs2<Real>(a-b)/Eigen::numext::mini<Real>(abs2(a),abs2(b)));
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}
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}
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}
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namespace Eigen {
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}
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EIGEN_DECLARE_TEST(boostmultiprec)
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{
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typedef Matrix<Real,Dynamic,Dynamic> Mat;
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typedef Matrix<std::complex<Real>,Dynamic,Dynamic> MatC;
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std::cout << "NumTraits<Real>::epsilon() = " << NumTraits<Real>::epsilon() << std::endl;
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std::cout << "NumTraits<Real>::dummy_precision() = " << NumTraits<Real>::dummy_precision() << std::endl;
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std::cout << "NumTraits<Real>::lowest() = " << NumTraits<Real>::lowest() << std::endl;
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std::cout << "NumTraits<Real>::highest() = " << NumTraits<Real>::highest() << std::endl;
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std::cout << "NumTraits<Real>::digits10() = " << NumTraits<Real>::digits10() << std::endl;
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// check stream output
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{
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Mat A(10,10);
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A.setRandom();
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std::stringstream ss;
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ss << A;
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}
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{
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MatC A(10,10);
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A.setRandom();
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std::stringstream ss;
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ss << A;
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}
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for(int i = 0; i < g_repeat; i++) {
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int s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
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CALL_SUBTEST_1( cholesky(Mat(s,s)) );
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CALL_SUBTEST_2( lu_non_invertible<Mat>() );
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CALL_SUBTEST_2( lu_invertible<Mat>() );
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CALL_SUBTEST_2( lu_non_invertible<MatC>() );
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CALL_SUBTEST_2( lu_invertible<MatC>() );
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CALL_SUBTEST_3( qr(Mat(internal::random<int>(1,EIGEN_TEST_MAX_SIZE),internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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CALL_SUBTEST_3( qr_invertible<Mat>() );
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CALL_SUBTEST_4( qr<Mat>() );
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CALL_SUBTEST_4( cod<Mat>() );
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CALL_SUBTEST_4( qr_invertible<Mat>() );
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CALL_SUBTEST_5( qr<Mat>() );
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CALL_SUBTEST_5( qr_invertible<Mat>() );
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CALL_SUBTEST_6( selfadjointeigensolver(Mat(s,s)) );
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CALL_SUBTEST_7( eigensolver(Mat(s,s)) );
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CALL_SUBTEST_8( generalized_eigensolver_real(Mat(s,s)) );
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TEST_SET_BUT_UNUSED_VARIABLE(s)
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}
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CALL_SUBTEST_9(( jacobisvd(Mat(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2))) ));
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CALL_SUBTEST_10(( bdcsvd(Mat(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2))) ));
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CALL_SUBTEST_11(( test_simplicial_cholesky_T<Real,int,ColMajor>() ));
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}
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