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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