[136] | 1 | // This file is part of Eigen, a lightweight C++ template library
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| 2 | // for linear algebra.
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| 3 | //
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| 4 | // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
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| 5 | //
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| 6 | // This Source Code Form is subject to the terms of the Mozilla
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| 7 | // Public License v. 2.0. If a copy of the MPL was not distributed
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| 8 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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| 9 |
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| 10 | #include "main.h"
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| 11 |
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| 12 | template<typename MatrixType> void product_extra(const MatrixType& m)
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| 13 | {
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| 14 | typedef typename MatrixType::Index Index;
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| 15 | typedef typename MatrixType::Scalar Scalar;
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| 16 | typedef Matrix<Scalar, 1, Dynamic> RowVectorType;
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| 17 | typedef Matrix<Scalar, Dynamic, 1> ColVectorType;
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| 18 | typedef Matrix<Scalar, Dynamic, Dynamic,
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| 19 | MatrixType::Flags&RowMajorBit> OtherMajorMatrixType;
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| 20 |
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| 21 | Index rows = m.rows();
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| 22 | Index cols = m.cols();
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| 23 |
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| 24 | MatrixType m1 = MatrixType::Random(rows, cols),
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| 25 | m2 = MatrixType::Random(rows, cols),
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| 26 | m3(rows, cols),
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| 27 | mzero = MatrixType::Zero(rows, cols),
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| 28 | identity = MatrixType::Identity(rows, rows),
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| 29 | square = MatrixType::Random(rows, rows),
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| 30 | res = MatrixType::Random(rows, rows),
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| 31 | square2 = MatrixType::Random(cols, cols),
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| 32 | res2 = MatrixType::Random(cols, cols);
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| 33 | RowVectorType v1 = RowVectorType::Random(rows), vrres(rows);
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| 34 | ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols);
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| 35 | OtherMajorMatrixType tm1 = m1;
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| 36 |
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| 37 | Scalar s1 = internal::random<Scalar>(),
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| 38 | s2 = internal::random<Scalar>(),
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| 39 | s3 = internal::random<Scalar>();
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| 40 |
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| 41 | VERIFY_IS_APPROX(m3.noalias() = m1 * m2.adjoint(), m1 * m2.adjoint().eval());
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| 42 | VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * square.adjoint(), m1.adjoint().eval() * square.adjoint().eval());
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| 43 | VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * m2, m1.adjoint().eval() * m2);
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| 44 | VERIFY_IS_APPROX(m3.noalias() = (s1 * m1.adjoint()) * m2, (s1 * m1.adjoint()).eval() * m2);
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| 45 | VERIFY_IS_APPROX(m3.noalias() = ((s1 * m1).adjoint()) * m2, (numext::conj(s1) * m1.adjoint()).eval() * m2);
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| 46 | VERIFY_IS_APPROX(m3.noalias() = (- m1.adjoint() * s1) * (s3 * m2), (- m1.adjoint() * s1).eval() * (s3 * m2).eval());
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| 47 | VERIFY_IS_APPROX(m3.noalias() = (s2 * m1.adjoint() * s1) * m2, (s2 * m1.adjoint() * s1).eval() * m2);
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| 48 | VERIFY_IS_APPROX(m3.noalias() = (-m1*s2) * s1*m2.adjoint(), (-m1*s2).eval() * (s1*m2.adjoint()).eval());
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| 49 |
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| 50 | // a very tricky case where a scale factor has to be automatically conjugated:
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| 51 | VERIFY_IS_APPROX( m1.adjoint() * (s1*m2).conjugate(), (m1.adjoint()).eval() * ((s1*m2).conjugate()).eval());
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| 52 |
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| 53 |
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| 54 | // test all possible conjugate combinations for the four matrix-vector product cases:
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| 55 |
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| 56 | VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2),
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| 57 | (-m1.conjugate()*s2).eval() * (s1 * vc2).eval());
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| 58 | VERIFY_IS_APPROX((-m1 * s2) * (s1 * vc2.conjugate()),
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| 59 | (-m1*s2).eval() * (s1 * vc2.conjugate()).eval());
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| 60 | VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2.conjugate()),
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| 61 | (-m1.conjugate()*s2).eval() * (s1 * vc2.conjugate()).eval());
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| 62 |
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| 63 | VERIFY_IS_APPROX((s1 * vc2.transpose()) * (-m1.adjoint() * s2),
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| 64 | (s1 * vc2.transpose()).eval() * (-m1.adjoint()*s2).eval());
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| 65 | VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.transpose() * s2),
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| 66 | (s1 * vc2.adjoint()).eval() * (-m1.transpose()*s2).eval());
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| 67 | VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.adjoint() * s2),
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| 68 | (s1 * vc2.adjoint()).eval() * (-m1.adjoint()*s2).eval());
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| 69 |
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| 70 | VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.transpose()),
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| 71 | (-m1.adjoint()*s2).eval() * (s1 * v1.transpose()).eval());
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| 72 | VERIFY_IS_APPROX((-m1.transpose() * s2) * (s1 * v1.adjoint()),
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| 73 | (-m1.transpose()*s2).eval() * (s1 * v1.adjoint()).eval());
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| 74 | VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()),
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| 75 | (-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval());
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| 76 |
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| 77 | VERIFY_IS_APPROX((s1 * v1) * (-m1.conjugate() * s2),
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| 78 | (s1 * v1).eval() * (-m1.conjugate()*s2).eval());
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| 79 | VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1 * s2),
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| 80 | (s1 * v1.conjugate()).eval() * (-m1*s2).eval());
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| 81 | VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1.conjugate() * s2),
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| 82 | (s1 * v1.conjugate()).eval() * (-m1.conjugate()*s2).eval());
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| 83 |
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| 84 | VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()),
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| 85 | (-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval());
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| 86 |
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| 87 | // test the vector-matrix product with non aligned starts
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| 88 | Index i = internal::random<Index>(0,m1.rows()-2);
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| 89 | Index j = internal::random<Index>(0,m1.cols()-2);
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| 90 | Index r = internal::random<Index>(1,m1.rows()-i);
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| 91 | Index c = internal::random<Index>(1,m1.cols()-j);
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| 92 | Index i2 = internal::random<Index>(0,m1.rows()-1);
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| 93 | Index j2 = internal::random<Index>(0,m1.cols()-1);
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| 94 |
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| 95 | VERIFY_IS_APPROX(m1.col(j2).adjoint() * m1.block(0,j,m1.rows(),c), m1.col(j2).adjoint().eval() * m1.block(0,j,m1.rows(),c).eval());
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| 96 | VERIFY_IS_APPROX(m1.block(i,0,r,m1.cols()) * m1.row(i2).adjoint(), m1.block(i,0,r,m1.cols()).eval() * m1.row(i2).adjoint().eval());
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| 97 |
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| 98 | // regression test
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| 99 | MatrixType tmp = m1 * m1.adjoint() * s1;
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| 100 | VERIFY_IS_APPROX(tmp, m1 * m1.adjoint() * s1);
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| 101 | }
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| 102 |
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| 103 | // Regression test for bug reported at http://forum.kde.org/viewtopic.php?f=74&t=96947
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| 104 | void mat_mat_scalar_scalar_product()
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| 105 | {
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| 106 | Eigen::Matrix2Xd dNdxy(2, 3);
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| 107 | dNdxy << -0.5, 0.5, 0,
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| 108 | -0.3, 0, 0.3;
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| 109 | double det = 6.0, wt = 0.5;
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| 110 | VERIFY_IS_APPROX(dNdxy.transpose()*dNdxy*det*wt, det*wt*dNdxy.transpose()*dNdxy);
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| 111 | }
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| 112 |
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| 113 | template <typename MatrixType>
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| 114 | void zero_sized_objects(const MatrixType& m)
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| 115 | {
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| 116 | typedef typename MatrixType::Scalar Scalar;
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| 117 | const int PacketSize = internal::packet_traits<Scalar>::size;
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| 118 | const int PacketSize1 = PacketSize>1 ? PacketSize-1 : 1;
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| 119 | DenseIndex rows = m.rows();
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| 120 | DenseIndex cols = m.cols();
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| 121 |
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| 122 | {
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| 123 | MatrixType res, a(rows,0), b(0,cols);
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| 124 | VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(rows,cols) );
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| 125 | VERIFY_IS_APPROX( (res=a*a.transpose()), MatrixType::Zero(rows,rows) );
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| 126 | VERIFY_IS_APPROX( (res=b.transpose()*b), MatrixType::Zero(cols,cols) );
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| 127 | VERIFY_IS_APPROX( (res=b.transpose()*a.transpose()), MatrixType::Zero(cols,rows) );
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| 128 | }
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| 129 |
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| 130 | {
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| 131 | MatrixType res, a(rows,cols), b(cols,0);
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| 132 | res = a*b;
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| 133 | VERIFY(res.rows()==rows && res.cols()==0);
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| 134 | b.resize(0,rows);
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| 135 | res = b*a;
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| 136 | VERIFY(res.rows()==0 && res.cols()==cols);
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| 137 | }
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| 138 |
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| 139 | {
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| 140 | Matrix<Scalar,PacketSize,0> a;
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| 141 | Matrix<Scalar,0,1> b;
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| 142 | Matrix<Scalar,PacketSize,1> res;
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| 143 | VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize,1) );
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| 144 | VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize,1) );
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| 145 | }
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| 146 |
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| 147 | {
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| 148 | Matrix<Scalar,PacketSize1,0> a;
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| 149 | Matrix<Scalar,0,1> b;
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| 150 | Matrix<Scalar,PacketSize1,1> res;
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| 151 | VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize1,1) );
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| 152 | VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize1,1) );
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| 153 | }
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| 154 |
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| 155 | {
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| 156 | Matrix<Scalar,PacketSize,Dynamic> a(PacketSize,0);
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| 157 | Matrix<Scalar,Dynamic,1> b(0,1);
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| 158 | Matrix<Scalar,PacketSize,1> res;
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| 159 | VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize,1) );
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| 160 | VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize,1) );
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| 161 | }
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| 162 |
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| 163 | {
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| 164 | Matrix<Scalar,PacketSize1,Dynamic> a(PacketSize1,0);
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| 165 | Matrix<Scalar,Dynamic,1> b(0,1);
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| 166 | Matrix<Scalar,PacketSize1,1> res;
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| 167 | VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize1,1) );
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| 168 | VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize1,1) );
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| 169 | }
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| 170 | }
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| 171 |
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| 172 | void bug_127()
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| 173 | {
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| 174 | // Bug 127
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| 175 | //
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| 176 | // a product of the form lhs*rhs with
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| 177 | //
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| 178 | // lhs:
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| 179 | // rows = 1, cols = 4
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| 180 | // RowsAtCompileTime = 1, ColsAtCompileTime = -1
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| 181 | // MaxRowsAtCompileTime = 1, MaxColsAtCompileTime = 5
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| 182 | //
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| 183 | // rhs:
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| 184 | // rows = 4, cols = 0
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| 185 | // RowsAtCompileTime = -1, ColsAtCompileTime = -1
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| 186 | // MaxRowsAtCompileTime = 5, MaxColsAtCompileTime = 1
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| 187 | //
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| 188 | // was failing on a runtime assertion, because it had been mis-compiled as a dot product because Product.h was using the
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| 189 | // max-sizes to detect size 1 indicating vectors, and that didn't account for 0-sized object with max-size 1.
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| 190 |
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| 191 | Matrix<float,1,Dynamic,RowMajor,1,5> a(1,4);
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| 192 | Matrix<float,Dynamic,Dynamic,ColMajor,5,1> b(4,0);
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| 193 | a*b;
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| 194 | }
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| 195 |
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| 196 | void unaligned_objects()
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| 197 | {
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| 198 | // Regression test for the bug reported here:
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| 199 | // http://forum.kde.org/viewtopic.php?f=74&t=107541
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| 200 | // Recall the matrix*vector kernel avoid unaligned loads by loading two packets and then reassemble then.
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| 201 | // There was a mistake in the computation of the valid range for fully unaligned objects: in some rare cases,
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| 202 | // memory was read outside the allocated matrix memory. Though the values were not used, this might raise segfault.
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| 203 | for(int m=450;m<460;++m)
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| 204 | {
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| 205 | for(int n=8;n<12;++n)
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| 206 | {
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| 207 | MatrixXf M(m, n);
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| 208 | VectorXf v1(n), r1(500);
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| 209 | RowVectorXf v2(m), r2(16);
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| 210 |
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| 211 | M.setRandom();
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| 212 | v1.setRandom();
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| 213 | v2.setRandom();
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| 214 | for(int o=0; o<4; ++o)
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| 215 | {
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| 216 | r1.segment(o,m).noalias() = M * v1;
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| 217 | VERIFY_IS_APPROX(r1.segment(o,m), M * MatrixXf(v1));
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| 218 | r2.segment(o,n).noalias() = v2 * M;
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| 219 | VERIFY_IS_APPROX(r2.segment(o,n), MatrixXf(v2) * M);
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| 220 | }
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| 221 | }
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| 222 | }
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| 223 | }
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| 224 |
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| 225 | void test_product_extra()
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| 226 | {
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| 227 | for(int i = 0; i < g_repeat; i++) {
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| 228 | CALL_SUBTEST_1( product_extra(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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| 229 | CALL_SUBTEST_2( product_extra(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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| 230 | CALL_SUBTEST_2( mat_mat_scalar_scalar_product() );
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| 231 | CALL_SUBTEST_3( product_extra(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
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| 232 | CALL_SUBTEST_4( product_extra(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
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| 233 | CALL_SUBTEST_1( zero_sized_objects(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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| 234 | }
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| 235 | CALL_SUBTEST_5( bug_127() );
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| 236 | CALL_SUBTEST_6( unaligned_objects() );
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| 237 | }
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