// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2006-2008 Benoit Jacob // // This Source Code Form is subject to the terms of the Mozilla // Public License v. 2.0. If a copy of the MPL was not distributed // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. #include "main.h" template struct adjoint_specific; template<> struct adjoint_specific { template static void run(const Vec& v1, const Vec& v2, Vec& v3, const Mat& square, Scalar s1, Scalar s2) { VERIFY(test_isApproxWithRef((s1 * v1 + s2 * v2).dot(v3), numext::conj(s1) * v1.dot(v3) + numext::conj(s2) * v2.dot(v3), 0)); VERIFY(test_isApproxWithRef(v3.dot(s1 * v1 + s2 * v2), s1*v3.dot(v1)+s2*v3.dot(v2), 0)); // check compatibility of dot and adjoint VERIFY(test_isApproxWithRef(v1.dot(square * v2), (square.adjoint() * v1).dot(v2), 0)); } }; template<> struct adjoint_specific { template static void run(const Vec& v1, const Vec& v2, Vec& v3, const Mat& square, Scalar s1, Scalar s2) { typedef typename NumTraits::Real RealScalar; using std::abs; RealScalar ref = NumTraits::IsInteger ? RealScalar(0) : (std::max)((s1 * v1 + s2 * v2).norm(),v3.norm()); VERIFY(test_isApproxWithRef((s1 * v1 + s2 * v2).dot(v3), numext::conj(s1) * v1.dot(v3) + numext::conj(s2) * v2.dot(v3), ref)); VERIFY(test_isApproxWithRef(v3.dot(s1 * v1 + s2 * v2), s1*v3.dot(v1)+s2*v3.dot(v2), ref)); VERIFY_IS_APPROX(v1.squaredNorm(), v1.norm() * v1.norm()); // check normalized() and normalize() VERIFY_IS_APPROX(v1, v1.norm() * v1.normalized()); v3 = v1; v3.normalize(); VERIFY_IS_APPROX(v1, v1.norm() * v3); VERIFY_IS_APPROX(v3, v1.normalized()); VERIFY_IS_APPROX(v3.norm(), RealScalar(1)); // check null inputs VERIFY_IS_APPROX((v1*0).normalized(), (v1*0)); #if (!EIGEN_ARCH_i386) || defined(EIGEN_VECTORIZE) RealScalar very_small = (std::numeric_limits::min)(); VERIFY( (v1*very_small).norm() == 0 ); VERIFY_IS_APPROX((v1*very_small).normalized(), (v1*very_small)); v3 = v1*very_small; v3.normalize(); VERIFY_IS_APPROX(v3, (v1*very_small)); #endif // check compatibility of dot and adjoint ref = NumTraits::IsInteger ? 0 : (std::max)((std::max)(v1.norm(),v2.norm()),(std::max)((square * v2).norm(),(square.adjoint() * v1).norm())); VERIFY(internal::isMuchSmallerThan(abs(v1.dot(square * v2) - (square.adjoint() * v1).dot(v2)), ref, test_precision())); // check that Random().normalized() works: tricky as the random xpr must be evaluated by // normalized() in order to produce a consistent result. VERIFY_IS_APPROX(Vec::Random(v1.size()).normalized().norm(), RealScalar(1)); } }; template void adjoint(const MatrixType& m) { /* this test covers the following files: Transpose.h Conjugate.h Dot.h */ using std::abs; typedef typename MatrixType::Scalar Scalar; typedef typename NumTraits::Real RealScalar; typedef Matrix VectorType; typedef Matrix SquareMatrixType; const Index PacketSize = internal::packet_traits::size; Index rows = m.rows(); Index cols = m.cols(); MatrixType m1 = MatrixType::Random(rows, cols), m2 = MatrixType::Random(rows, cols), m3(rows, cols), square = SquareMatrixType::Random(rows, rows); VectorType v1 = VectorType::Random(rows), v2 = VectorType::Random(rows), v3 = VectorType::Random(rows), vzero = VectorType::Zero(rows); Scalar s1 = internal::random(), s2 = internal::random(); // check basic compatibility of adjoint, transpose, conjugate VERIFY_IS_APPROX(m1.transpose().conjugate().adjoint(), m1); VERIFY_IS_APPROX(m1.adjoint().conjugate().transpose(), m1); // check multiplicative behavior VERIFY_IS_APPROX((m1.adjoint() * m2).adjoint(), m2.adjoint() * m1); VERIFY_IS_APPROX((s1 * m1).adjoint(), numext::conj(s1) * m1.adjoint()); // check basic properties of dot, squaredNorm VERIFY_IS_APPROX(numext::conj(v1.dot(v2)), v2.dot(v1)); VERIFY_IS_APPROX(numext::real(v1.dot(v1)), v1.squaredNorm()); adjoint_specific::IsInteger>::run(v1, v2, v3, square, s1, s2); VERIFY_IS_MUCH_SMALLER_THAN(abs(vzero.dot(v1)), static_cast(1)); // like in testBasicStuff, test operator() to check const-qualification Index r = internal::random(0, rows-1), c = internal::random(0, cols-1); VERIFY_IS_APPROX(m1.conjugate()(r,c), numext::conj(m1(r,c))); VERIFY_IS_APPROX(m1.adjoint()(c,r), numext::conj(m1(r,c))); // check inplace transpose m3 = m1; m3.transposeInPlace(); VERIFY_IS_APPROX(m3,m1.transpose()); m3.transposeInPlace(); VERIFY_IS_APPROX(m3,m1); if(PacketSize(0,m3.rows()-PacketSize); Index j = internal::random(0,m3.cols()-PacketSize); m3.template block(i,j).transposeInPlace(); VERIFY_IS_APPROX( (m3.template block(i,j)), (m1.template block(i,j).transpose()) ); m3.template block(i,j).transposeInPlace(); VERIFY_IS_APPROX(m3,m1); } // check inplace adjoint m3 = m1; m3.adjointInPlace(); VERIFY_IS_APPROX(m3,m1.adjoint()); m3.transposeInPlace(); VERIFY_IS_APPROX(m3,m1.conjugate()); // check mixed dot product typedef Matrix RealVectorType; RealVectorType rv1 = RealVectorType::Random(rows); VERIFY_IS_APPROX(v1.dot(rv1.template cast()), v1.dot(rv1)); VERIFY_IS_APPROX(rv1.template cast().dot(v1), rv1.dot(v1)); VERIFY( is_same_type(m1,m1.template conjugateIf()) ); VERIFY( is_same_type(m1.conjugate(),m1.template conjugateIf()) ); } template void adjoint_extra() { MatrixXcf a(10,10), b(10,10); VERIFY_RAISES_ASSERT(a = a.transpose()); VERIFY_RAISES_ASSERT(a = a.transpose() + b); VERIFY_RAISES_ASSERT(a = b + a.transpose()); VERIFY_RAISES_ASSERT(a = a.conjugate().transpose()); VERIFY_RAISES_ASSERT(a = a.adjoint()); VERIFY_RAISES_ASSERT(a = a.adjoint() + b); VERIFY_RAISES_ASSERT(a = b + a.adjoint()); // no assertion should be triggered for these cases: a.transpose() = a.transpose(); a.transpose() += a.transpose(); a.transpose() += a.transpose() + b; a.transpose() = a.adjoint(); a.transpose() += a.adjoint(); a.transpose() += a.adjoint() + b; // regression tests for check_for_aliasing MatrixXd c(10,10); c = 1.0 * MatrixXd::Ones(10,10) + c; c = MatrixXd::Ones(10,10) * 1.0 + c; c = c + MatrixXd::Ones(10,10) .cwiseProduct( MatrixXd::Zero(10,10) ); c = MatrixXd::Ones(10,10) * MatrixXd::Zero(10,10); // regression for bug 1646 for (int j = 0; j < 10; ++j) { c.col(j).head(j) = c.row(j).head(j); } for (int j = 0; j < 10; ++j) { c.col(j) = c.row(j); } a.conservativeResize(1,1); a = a.transpose(); a.conservativeResize(0,0); a = a.transpose(); } EIGEN_DECLARE_TEST(adjoint) { for(int i = 0; i < g_repeat; i++) { CALL_SUBTEST_1( adjoint(Matrix()) ); CALL_SUBTEST_2( adjoint(Matrix3d()) ); CALL_SUBTEST_3( adjoint(Matrix4f()) ); CALL_SUBTEST_4( adjoint(MatrixXcf(internal::random(1,EIGEN_TEST_MAX_SIZE/2), internal::random(1,EIGEN_TEST_MAX_SIZE/2))) ); CALL_SUBTEST_5( adjoint(MatrixXi(internal::random(1,EIGEN_TEST_MAX_SIZE), internal::random(1,EIGEN_TEST_MAX_SIZE))) ); CALL_SUBTEST_6( adjoint(MatrixXf(internal::random(1,EIGEN_TEST_MAX_SIZE), internal::random(1,EIGEN_TEST_MAX_SIZE))) ); // Complement for 128 bits vectorization: CALL_SUBTEST_8( adjoint(Matrix2d()) ); CALL_SUBTEST_9( adjoint(Matrix()) ); // 256 bits vectorization: CALL_SUBTEST_10( adjoint(Matrix()) ); CALL_SUBTEST_11( adjoint(Matrix()) ); CALL_SUBTEST_12( adjoint(Matrix()) ); } // test a large static matrix only once CALL_SUBTEST_7( adjoint(Matrix()) ); CALL_SUBTEST_13( adjoint_extra<0>() ); }