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82f0ce2726
This provide several advantages: - more flexibility in designing unit tests - unit tests can be glued to speed up compilation - unit tests are compiled with same predefined macros, which is a requirement for zapcc
66 lines
2.3 KiB
C++
66 lines
2.3 KiB
C++
#include "main.h"
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#include <Eigen/MPRealSupport>
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#include <Eigen/LU>
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#include <Eigen/Eigenvalues>
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#include <sstream>
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using namespace mpfr;
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using namespace Eigen;
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EIGEN_DECLARE_TEST(mpreal_support)
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{
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// set precision to 256 bits (double has only 53 bits)
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mpreal::set_default_prec(256);
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typedef Matrix<mpreal,Eigen::Dynamic,Eigen::Dynamic> MatrixXmp;
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typedef Matrix<std::complex<mpreal>,Eigen::Dynamic,Eigen::Dynamic> MatrixXcmp;
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std::cerr << "epsilon = " << NumTraits<mpreal>::epsilon() << "\n";
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std::cerr << "dummy_precision = " << NumTraits<mpreal>::dummy_precision() << "\n";
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std::cerr << "highest = " << NumTraits<mpreal>::highest() << "\n";
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std::cerr << "lowest = " << NumTraits<mpreal>::lowest() << "\n";
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std::cerr << "digits10 = " << NumTraits<mpreal>::digits10() << "\n";
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for(int i = 0; i < g_repeat; i++) {
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int s = Eigen::internal::random<int>(1,100);
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MatrixXmp A = MatrixXmp::Random(s,s);
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MatrixXmp B = MatrixXmp::Random(s,s);
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MatrixXmp S = A.adjoint() * A;
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MatrixXmp X;
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MatrixXcmp Ac = MatrixXcmp::Random(s,s);
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MatrixXcmp Bc = MatrixXcmp::Random(s,s);
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MatrixXcmp Sc = Ac.adjoint() * Ac;
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MatrixXcmp Xc;
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// Basic stuffs
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VERIFY_IS_APPROX(A.real(), A);
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VERIFY(Eigen::internal::isApprox(A.array().abs2().sum(), A.squaredNorm()));
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VERIFY_IS_APPROX(A.array().exp(), exp(A.array()));
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VERIFY_IS_APPROX(A.array().abs2().sqrt(), A.array().abs());
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VERIFY_IS_APPROX(A.array().sin(), sin(A.array()));
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VERIFY_IS_APPROX(A.array().cos(), cos(A.array()));
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// Cholesky
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X = S.selfadjointView<Lower>().llt().solve(B);
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VERIFY_IS_APPROX((S.selfadjointView<Lower>()*X).eval(),B);
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Xc = Sc.selfadjointView<Lower>().llt().solve(Bc);
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VERIFY_IS_APPROX((Sc.selfadjointView<Lower>()*Xc).eval(),Bc);
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// partial LU
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X = A.lu().solve(B);
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VERIFY_IS_APPROX((A*X).eval(),B);
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// symmetric eigenvalues
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SelfAdjointEigenSolver<MatrixXmp> eig(S);
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VERIFY_IS_EQUAL(eig.info(), Success);
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VERIFY( (S.selfadjointView<Lower>() * eig.eigenvectors()).isApprox(eig.eigenvectors() * eig.eigenvalues().asDiagonal(), NumTraits<mpreal>::dummy_precision()*1e3) );
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}
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{
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MatrixXmp A(8,3); A.setRandom();
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// test output (interesting things happen in this code)
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std::stringstream stream;
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stream << A;
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}
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}
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