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) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
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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 array_for_matrix(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, MatrixType::RowsAtCompileTime, 1> ColVectorType;
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17 | typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType;
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18 |
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19 | Index rows = m.rows();
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20 | Index cols = m.cols();
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21 |
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22 | MatrixType m1 = MatrixType::Random(rows, cols),
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23 | m2 = MatrixType::Random(rows, cols),
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24 | m3(rows, cols);
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25 |
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26 | ColVectorType cv1 = ColVectorType::Random(rows);
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27 | RowVectorType rv1 = RowVectorType::Random(cols);
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28 |
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29 | Scalar s1 = internal::random<Scalar>(),
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30 | s2 = internal::random<Scalar>();
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31 |
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32 | // scalar addition
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33 | VERIFY_IS_APPROX(m1.array() + s1, s1 + m1.array());
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34 | VERIFY_IS_APPROX((m1.array() + s1).matrix(), MatrixType::Constant(rows,cols,s1) + m1);
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35 | VERIFY_IS_APPROX(((m1*Scalar(2)).array() - s2).matrix(), (m1+m1) - MatrixType::Constant(rows,cols,s2) );
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36 | m3 = m1;
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37 | m3.array() += s2;
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38 | VERIFY_IS_APPROX(m3, (m1.array() + s2).matrix());
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39 | m3 = m1;
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40 | m3.array() -= s1;
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41 | VERIFY_IS_APPROX(m3, (m1.array() - s1).matrix());
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42 |
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43 | // reductions
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44 | VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum().sum() - m1.sum(), m1.squaredNorm());
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45 | VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum().sum() - m1.sum(), m1.squaredNorm());
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46 | VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum() + m2.colwise().sum() - (m1+m2).colwise().sum(), (m1+m2).squaredNorm());
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47 | VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum() - m2.rowwise().sum() - (m1-m2).rowwise().sum(), (m1-m2).squaredNorm());
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48 | VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar>()));
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49 |
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50 | // vector-wise ops
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51 | m3 = m1;
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52 | VERIFY_IS_APPROX(m3.colwise() += cv1, m1.colwise() + cv1);
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53 | m3 = m1;
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54 | VERIFY_IS_APPROX(m3.colwise() -= cv1, m1.colwise() - cv1);
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55 | m3 = m1;
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56 | VERIFY_IS_APPROX(m3.rowwise() += rv1, m1.rowwise() + rv1);
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57 | m3 = m1;
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58 | VERIFY_IS_APPROX(m3.rowwise() -= rv1, m1.rowwise() - rv1);
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59 |
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60 | // empty objects
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61 | VERIFY_IS_APPROX(m1.block(0,0,0,cols).colwise().sum(), RowVectorType::Zero(cols));
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62 | VERIFY_IS_APPROX(m1.block(0,0,rows,0).rowwise().prod(), ColVectorType::Ones(rows));
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63 |
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64 | // verify the const accessors exist
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65 | const Scalar& ref_m1 = m.matrix().array().coeffRef(0);
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66 | const Scalar& ref_m2 = m.matrix().array().coeffRef(0,0);
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67 | const Scalar& ref_a1 = m.array().matrix().coeffRef(0);
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68 | const Scalar& ref_a2 = m.array().matrix().coeffRef(0,0);
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69 | VERIFY(&ref_a1 == &ref_m1);
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70 | VERIFY(&ref_a2 == &ref_m2);
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71 | }
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72 |
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73 | template<typename MatrixType> void comparisons(const MatrixType& m)
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74 | {
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75 | using std::abs;
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76 | typedef typename MatrixType::Index Index;
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77 | typedef typename MatrixType::Scalar Scalar;
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78 | typedef typename NumTraits<Scalar>::Real RealScalar;
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79 |
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80 | Index rows = m.rows();
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81 | Index cols = m.cols();
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82 |
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83 | Index r = internal::random<Index>(0, rows-1),
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84 | c = internal::random<Index>(0, cols-1);
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85 |
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86 | MatrixType m1 = MatrixType::Random(rows, cols),
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87 | m2 = MatrixType::Random(rows, cols),
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88 | m3(rows, cols);
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89 |
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90 | VERIFY(((m1.array() + Scalar(1)) > m1.array()).all());
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91 | VERIFY(((m1.array() - Scalar(1)) < m1.array()).all());
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92 | if (rows*cols>1)
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93 | {
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94 | m3 = m1;
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95 | m3(r,c) += 1;
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96 | VERIFY(! (m1.array() < m3.array()).all() );
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97 | VERIFY(! (m1.array() > m3.array()).all() );
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98 | }
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99 |
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100 | // comparisons to scalar
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101 | VERIFY( (m1.array() != (m1(r,c)+1) ).any() );
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102 | VERIFY( (m1.array() > (m1(r,c)-1) ).any() );
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103 | VERIFY( (m1.array() < (m1(r,c)+1) ).any() );
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104 | VERIFY( (m1.array() == m1(r,c) ).any() );
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105 | VERIFY( m1.cwiseEqual(m1(r,c)).any() );
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106 |
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107 | // test Select
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108 | VERIFY_IS_APPROX( (m1.array()<m2.array()).select(m1,m2), m1.cwiseMin(m2) );
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109 | VERIFY_IS_APPROX( (m1.array()>m2.array()).select(m1,m2), m1.cwiseMax(m2) );
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110 | Scalar mid = (m1.cwiseAbs().minCoeff() + m1.cwiseAbs().maxCoeff())/Scalar(2);
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111 | for (int j=0; j<cols; ++j)
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112 | for (int i=0; i<rows; ++i)
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113 | m3(i,j) = abs(m1(i,j))<mid ? 0 : m1(i,j);
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114 | VERIFY_IS_APPROX( (m1.array().abs()<MatrixType::Constant(rows,cols,mid).array())
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115 | .select(MatrixType::Zero(rows,cols),m1), m3);
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116 | // shorter versions:
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117 | VERIFY_IS_APPROX( (m1.array().abs()<MatrixType::Constant(rows,cols,mid).array())
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118 | .select(0,m1), m3);
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119 | VERIFY_IS_APPROX( (m1.array().abs()>=MatrixType::Constant(rows,cols,mid).array())
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120 | .select(m1,0), m3);
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121 | // even shorter version:
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122 | VERIFY_IS_APPROX( (m1.array().abs()<mid).select(0,m1), m3);
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123 |
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124 | // count
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125 | VERIFY(((m1.array().abs()+1)>RealScalar(0.1)).count() == rows*cols);
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126 |
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127 | typedef Matrix<typename MatrixType::Index, Dynamic, 1> VectorOfIndices;
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128 |
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129 | // TODO allows colwise/rowwise for array
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130 | VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().colwise().count(), VectorOfIndices::Constant(cols,rows).transpose());
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131 | VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().rowwise().count(), VectorOfIndices::Constant(rows, cols));
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132 | }
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133 |
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134 | template<typename VectorType> void lpNorm(const VectorType& v)
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135 | {
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136 | using std::sqrt;
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137 | VectorType u = VectorType::Random(v.size());
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138 |
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139 | VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), u.cwiseAbs().maxCoeff());
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140 | VERIFY_IS_APPROX(u.template lpNorm<1>(), u.cwiseAbs().sum());
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141 | VERIFY_IS_APPROX(u.template lpNorm<2>(), sqrt(u.array().abs().square().sum()));
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142 | VERIFY_IS_APPROX(numext::pow(u.template lpNorm<5>(), typename VectorType::RealScalar(5)), u.array().abs().pow(5).sum());
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143 | }
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144 |
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145 | template<typename MatrixType> void cwise_min_max(const MatrixType& m)
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146 | {
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147 | typedef typename MatrixType::Index Index;
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148 | typedef typename MatrixType::Scalar Scalar;
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149 |
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150 | Index rows = m.rows();
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151 | Index cols = m.cols();
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152 |
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153 | MatrixType m1 = MatrixType::Random(rows, cols);
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154 |
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155 | // min/max with array
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156 | Scalar maxM1 = m1.maxCoeff();
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157 | Scalar minM1 = m1.minCoeff();
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158 |
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159 | VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1), m1.cwiseMin(MatrixType::Constant(rows,cols, minM1)));
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160 | VERIFY_IS_APPROX(m1, m1.cwiseMin(MatrixType::Constant(rows,cols, maxM1)));
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161 |
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162 | VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1), m1.cwiseMax(MatrixType::Constant(rows,cols, maxM1)));
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163 | VERIFY_IS_APPROX(m1, m1.cwiseMax(MatrixType::Constant(rows,cols, minM1)));
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164 |
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165 | // min/max with scalar input
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166 | VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1), m1.cwiseMin( minM1));
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167 | VERIFY_IS_APPROX(m1, m1.cwiseMin(maxM1));
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168 | VERIFY_IS_APPROX(-m1, (-m1).cwiseMin(-minM1));
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169 | VERIFY_IS_APPROX(-m1.array(), ((-m1).array().min)( -minM1));
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170 |
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171 | VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1), m1.cwiseMax( maxM1));
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172 | VERIFY_IS_APPROX(m1, m1.cwiseMax(minM1));
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173 | VERIFY_IS_APPROX(-m1, (-m1).cwiseMax(-maxM1));
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174 | VERIFY_IS_APPROX(-m1.array(), ((-m1).array().max)(-maxM1));
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175 |
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176 | VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1).array(), (m1.array().min)( minM1));
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177 | VERIFY_IS_APPROX(m1.array(), (m1.array().min)( maxM1));
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178 |
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179 | VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1).array(), (m1.array().max)( maxM1));
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180 | VERIFY_IS_APPROX(m1.array(), (m1.array().max)( minM1));
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181 |
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182 | }
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183 |
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184 | template<typename MatrixTraits> void resize(const MatrixTraits& t)
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185 | {
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186 | typedef typename MatrixTraits::Index Index;
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187 | typedef typename MatrixTraits::Scalar Scalar;
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188 | typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType;
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189 | typedef Array<Scalar,Dynamic,Dynamic> Array2DType;
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190 | typedef Matrix<Scalar,Dynamic,1> VectorType;
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191 | typedef Array<Scalar,Dynamic,1> Array1DType;
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192 |
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193 | Index rows = t.rows(), cols = t.cols();
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194 |
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195 | MatrixType m(rows,cols);
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196 | VectorType v(rows);
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197 | Array2DType a2(rows,cols);
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198 | Array1DType a1(rows);
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199 |
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200 | m.array().resize(rows+1,cols+1);
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201 | VERIFY(m.rows()==rows+1 && m.cols()==cols+1);
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202 | a2.matrix().resize(rows+1,cols+1);
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203 | VERIFY(a2.rows()==rows+1 && a2.cols()==cols+1);
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204 | v.array().resize(cols);
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205 | VERIFY(v.size()==cols);
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206 | a1.matrix().resize(cols);
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207 | VERIFY(a1.size()==cols);
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208 | }
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209 |
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210 | void regression_bug_654()
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211 | {
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212 | ArrayXf a = RowVectorXf(3);
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213 | VectorXf v = Array<float,1,Dynamic>(3);
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214 | }
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215 |
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216 | void test_array_for_matrix()
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217 | {
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218 | for(int i = 0; i < g_repeat; i++) {
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219 | CALL_SUBTEST_1( array_for_matrix(Matrix<float, 1, 1>()) );
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220 | CALL_SUBTEST_2( array_for_matrix(Matrix2f()) );
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221 | CALL_SUBTEST_3( array_for_matrix(Matrix4d()) );
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222 | CALL_SUBTEST_4( array_for_matrix(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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223 | CALL_SUBTEST_5( array_for_matrix(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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224 | CALL_SUBTEST_6( array_for_matrix(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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225 | }
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226 | for(int i = 0; i < g_repeat; i++) {
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227 | CALL_SUBTEST_1( comparisons(Matrix<float, 1, 1>()) );
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228 | CALL_SUBTEST_2( comparisons(Matrix2f()) );
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229 | CALL_SUBTEST_3( comparisons(Matrix4d()) );
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230 | CALL_SUBTEST_5( comparisons(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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231 | CALL_SUBTEST_6( comparisons(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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232 | }
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233 | for(int i = 0; i < g_repeat; i++) {
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234 | CALL_SUBTEST_1( cwise_min_max(Matrix<float, 1, 1>()) );
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235 | CALL_SUBTEST_2( cwise_min_max(Matrix2f()) );
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236 | CALL_SUBTEST_3( cwise_min_max(Matrix4d()) );
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237 | CALL_SUBTEST_5( cwise_min_max(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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238 | CALL_SUBTEST_6( cwise_min_max(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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239 | }
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240 | for(int i = 0; i < g_repeat; i++) {
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241 | CALL_SUBTEST_1( lpNorm(Matrix<float, 1, 1>()) );
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242 | CALL_SUBTEST_2( lpNorm(Vector2f()) );
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243 | CALL_SUBTEST_7( lpNorm(Vector3d()) );
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244 | CALL_SUBTEST_8( lpNorm(Vector4f()) );
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245 | CALL_SUBTEST_5( lpNorm(VectorXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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246 | CALL_SUBTEST_4( lpNorm(VectorXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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247 | }
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248 | for(int i = 0; i < g_repeat; i++) {
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249 | CALL_SUBTEST_4( resize(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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250 | CALL_SUBTEST_5( resize(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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251 | CALL_SUBTEST_6( resize(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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252 | }
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253 | CALL_SUBTEST_6( regression_bug_654() );
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254 | }
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