1 | // This file is part of Eigen, a lightweight C++ template library
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2 | // for linear algebra. Eigen itself is part of the KDE project.
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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 | #include <Eigen/Array>
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12 | #include <Eigen/QR>
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13 |
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14 | template<typename Derived1, typename Derived2>
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15 | bool areNotApprox(const MatrixBase<Derived1>& m1, const MatrixBase<Derived2>& m2, typename Derived1::RealScalar epsilon = precision<typename Derived1::RealScalar>())
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16 | {
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17 | return !((m1-m2).cwise().abs2().maxCoeff() < epsilon * epsilon
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18 | * std::max(m1.cwise().abs2().maxCoeff(), m2.cwise().abs2().maxCoeff()));
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19 | }
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20 |
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21 | template<typename MatrixType> void product(const MatrixType& m)
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22 | {
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23 | /* this test covers the following files:
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24 | Identity.h Product.h
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25 | */
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26 |
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27 | typedef typename MatrixType::Scalar Scalar;
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28 | typedef typename NumTraits<Scalar>::FloatingPoint FloatingPoint;
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29 | typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> RowVectorType;
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30 | typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> ColVectorType;
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31 | typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> RowSquareMatrixType;
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32 | typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> ColSquareMatrixType;
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33 | typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime,
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34 | MatrixType::Options^RowMajor> OtherMajorMatrixType;
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35 |
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36 | int rows = m.rows();
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37 | int cols = m.cols();
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38 |
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39 | // this test relies a lot on Random.h, and there's not much more that we can do
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40 | // to test it, hence I consider that we will have tested Random.h
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41 | MatrixType m1 = MatrixType::Random(rows, cols),
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42 | m2 = MatrixType::Random(rows, cols),
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43 | m3(rows, cols);
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44 | RowSquareMatrixType
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45 | identity = RowSquareMatrixType::Identity(rows, rows),
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46 | square = RowSquareMatrixType::Random(rows, rows),
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47 | res = RowSquareMatrixType::Random(rows, rows);
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48 | ColSquareMatrixType
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49 | square2 = ColSquareMatrixType::Random(cols, cols),
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50 | res2 = ColSquareMatrixType::Random(cols, cols);
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51 | RowVectorType v1 = RowVectorType::Random(rows);
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52 | ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols);
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53 | OtherMajorMatrixType tm1 = m1;
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54 |
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55 | Scalar s1 = ei_random<Scalar>();
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56 |
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57 | int r = ei_random<int>(0, rows-1),
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58 | c = ei_random<int>(0, cols-1);
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59 |
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60 | // begin testing Product.h: only associativity for now
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61 | // (we use Transpose.h but this doesn't count as a test for it)
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62 |
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63 | VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2));
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64 | m3 = m1;
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65 | m3 *= m1.transpose() * m2;
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66 | VERIFY_IS_APPROX(m3, m1 * (m1.transpose()*m2));
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67 | VERIFY_IS_APPROX(m3, m1.lazy() * (m1.transpose()*m2));
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68 |
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69 | // continue testing Product.h: distributivity
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70 | VERIFY_IS_APPROX(square*(m1 + m2), square*m1+square*m2);
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71 | VERIFY_IS_APPROX(square*(m1 - m2), square*m1-square*m2);
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72 |
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73 | // continue testing Product.h: compatibility with ScalarMultiple.h
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74 | VERIFY_IS_APPROX(s1*(square*m1), (s1*square)*m1);
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75 | VERIFY_IS_APPROX(s1*(square*m1), square*(m1*s1));
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76 |
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77 | // again, test operator() to check const-qualification
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78 | s1 += (square.lazy() * m1)(r,c);
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79 |
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80 | // test Product.h together with Identity.h
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81 | VERIFY_IS_APPROX(v1, identity*v1);
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82 | VERIFY_IS_APPROX(v1.transpose(), v1.transpose() * identity);
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83 | // again, test operator() to check const-qualification
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84 | VERIFY_IS_APPROX(MatrixType::Identity(rows, cols)(r,c), static_cast<Scalar>(r==c));
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85 |
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86 | if (rows!=cols)
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87 | VERIFY_RAISES_ASSERT(m3 = m1*m1);
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88 |
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89 | // test the previous tests were not screwed up because operator* returns 0
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90 | // (we use the more accurate default epsilon)
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91 | if (NumTraits<Scalar>::HasFloatingPoint && std::min(rows,cols)>1)
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92 | {
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93 | VERIFY(areNotApprox(m1.transpose()*m2,m2.transpose()*m1));
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94 | }
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95 |
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96 | // test optimized operator+= path
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97 | res = square;
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98 | res += (m1 * m2.transpose()).lazy();
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99 | VERIFY_IS_APPROX(res, square + m1 * m2.transpose());
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100 | if (NumTraits<Scalar>::HasFloatingPoint && std::min(rows,cols)>1)
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101 | {
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102 | VERIFY(areNotApprox(res,square + m2 * m1.transpose()));
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103 | }
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104 | vcres = vc2;
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105 | vcres += (m1.transpose() * v1).lazy();
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106 | VERIFY_IS_APPROX(vcres, vc2 + m1.transpose() * v1);
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107 | tm1 = m1;
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108 | VERIFY_IS_APPROX(tm1.transpose() * v1, m1.transpose() * v1);
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109 | VERIFY_IS_APPROX(v1.transpose() * tm1, v1.transpose() * m1);
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110 |
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111 | // test submatrix and matrix/vector product
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112 | for (int i=0; i<rows; ++i)
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113 | res.row(i) = m1.row(i) * m2.transpose();
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114 | VERIFY_IS_APPROX(res, m1 * m2.transpose());
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115 | // the other way round:
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116 | for (int i=0; i<rows; ++i)
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117 | res.col(i) = m1 * m2.transpose().col(i);
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118 | VERIFY_IS_APPROX(res, m1 * m2.transpose());
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119 |
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120 | res2 = square2;
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121 | res2 += (m1.transpose() * m2).lazy();
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122 | VERIFY_IS_APPROX(res2, square2 + m1.transpose() * m2);
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123 |
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124 | if (NumTraits<Scalar>::HasFloatingPoint && std::min(rows,cols)>1)
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125 | {
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126 | VERIFY(areNotApprox(res2,square2 + m2.transpose() * m1));
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127 | }
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128 | }
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129 |
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