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 Gael Guennebaud <gael.guennebaud@inria.fr>
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5 | // Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
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6 | //
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7 | // This Source Code Form is subject to the terms of the Mozilla
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8 | // Public License v. 2.0. If a copy of the MPL was not distributed
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9 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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10 |
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11 | #include "svd_common.h"
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12 |
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13 | template<typename MatrixType, int QRPreconditioner>
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14 | void jacobisvd_check_full(const MatrixType& m, const JacobiSVD<MatrixType, QRPreconditioner>& svd)
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15 | {
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16 | svd_check_full<MatrixType, JacobiSVD<MatrixType, QRPreconditioner > >(m, svd);
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17 | }
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18 |
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19 | template<typename MatrixType, int QRPreconditioner>
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20 | void jacobisvd_compare_to_full(const MatrixType& m,
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21 | unsigned int computationOptions,
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22 | const JacobiSVD<MatrixType, QRPreconditioner>& referenceSvd)
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23 | {
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24 | svd_compare_to_full<MatrixType, JacobiSVD<MatrixType, QRPreconditioner> >(m, computationOptions, referenceSvd);
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25 | }
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26 |
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27 |
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28 | template<typename MatrixType, int QRPreconditioner>
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29 | void jacobisvd_solve(const MatrixType& m, unsigned int computationOptions)
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30 | {
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31 | svd_solve< MatrixType, JacobiSVD< MatrixType, QRPreconditioner > >(m, computationOptions);
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32 | }
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33 |
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34 |
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35 |
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36 | template<typename MatrixType, int QRPreconditioner>
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37 | void jacobisvd_test_all_computation_options(const MatrixType& m)
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38 | {
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39 |
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40 | if (QRPreconditioner == NoQRPreconditioner && m.rows() != m.cols())
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41 | return;
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42 |
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43 | JacobiSVD< MatrixType, QRPreconditioner > fullSvd(m, ComputeFullU|ComputeFullV);
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44 | svd_test_computation_options_1< MatrixType, JacobiSVD< MatrixType, QRPreconditioner > >(m, fullSvd);
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45 |
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46 | if(QRPreconditioner == FullPivHouseholderQRPreconditioner)
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47 | return;
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48 | svd_test_computation_options_2< MatrixType, JacobiSVD< MatrixType, QRPreconditioner > >(m, fullSvd);
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49 |
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50 | }
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51 |
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52 | template<typename MatrixType>
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53 | void jacobisvd(const MatrixType& a = MatrixType(), bool pickrandom = true)
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54 | {
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55 | MatrixType m = pickrandom ? MatrixType::Random(a.rows(), a.cols()) : a;
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56 |
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57 | jacobisvd_test_all_computation_options<MatrixType, FullPivHouseholderQRPreconditioner>(m);
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58 | jacobisvd_test_all_computation_options<MatrixType, ColPivHouseholderQRPreconditioner>(m);
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59 | jacobisvd_test_all_computation_options<MatrixType, HouseholderQRPreconditioner>(m);
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60 | jacobisvd_test_all_computation_options<MatrixType, NoQRPreconditioner>(m);
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61 | }
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62 |
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63 |
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64 | template<typename MatrixType>
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65 | void jacobisvd_verify_assert(const MatrixType& m)
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66 | {
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67 |
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68 | svd_verify_assert<MatrixType, JacobiSVD< MatrixType > >(m);
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69 |
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70 | typedef typename MatrixType::Index Index;
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71 | Index rows = m.rows();
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72 | Index cols = m.cols();
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73 |
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74 | enum {
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75 | RowsAtCompileTime = MatrixType::RowsAtCompileTime,
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76 | ColsAtCompileTime = MatrixType::ColsAtCompileTime
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77 | };
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78 |
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79 | MatrixType a = MatrixType::Zero(rows, cols);
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80 | a.setZero();
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81 |
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82 | if (ColsAtCompileTime == Dynamic)
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83 | {
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84 | JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner> svd_fullqr;
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85 | VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeFullU|ComputeThinV))
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86 | VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeThinU|ComputeThinV))
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87 | VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeThinU|ComputeFullV))
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88 | }
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89 | }
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90 |
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91 | template<typename MatrixType>
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92 | void jacobisvd_method()
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93 | {
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94 | enum { Size = MatrixType::RowsAtCompileTime };
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95 | typedef typename MatrixType::RealScalar RealScalar;
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96 | typedef Matrix<RealScalar, Size, 1> RealVecType;
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97 | MatrixType m = MatrixType::Identity();
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98 | VERIFY_IS_APPROX(m.jacobiSvd().singularValues(), RealVecType::Ones());
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99 | VERIFY_RAISES_ASSERT(m.jacobiSvd().matrixU());
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100 | VERIFY_RAISES_ASSERT(m.jacobiSvd().matrixV());
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101 | VERIFY_IS_APPROX(m.jacobiSvd(ComputeFullU|ComputeFullV).solve(m), m);
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102 | }
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103 |
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104 |
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105 |
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106 | template<typename MatrixType>
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107 | void jacobisvd_inf_nan()
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108 | {
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109 | svd_inf_nan<MatrixType, JacobiSVD< MatrixType > >();
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110 | }
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111 |
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112 |
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113 | // Regression test for bug 286: JacobiSVD loops indefinitely with some
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114 | // matrices containing denormal numbers.
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115 | void jacobisvd_bug286()
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116 | {
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117 | #if defined __INTEL_COMPILER
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118 | // shut up warning #239: floating point underflow
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119 | #pragma warning push
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120 | #pragma warning disable 239
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121 | #endif
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122 | Matrix2d M;
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123 | M << -7.90884e-313, -4.94e-324,
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124 | 0, 5.60844e-313;
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125 | #if defined __INTEL_COMPILER
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126 | #pragma warning pop
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127 | #endif
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128 | JacobiSVD<Matrix2d> svd;
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129 | svd.compute(M); // just check we don't loop indefinitely
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130 | }
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131 |
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132 |
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133 | void jacobisvd_preallocate()
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134 | {
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135 | svd_preallocate< JacobiSVD <MatrixXf> >();
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136 | }
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137 |
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138 | void test_jacobisvd()
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139 | {
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140 | CALL_SUBTEST_11(( jacobisvd<Matrix<double,Dynamic,Dynamic> >
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141 | (Matrix<double,Dynamic,Dynamic>(16, 6)) ));
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142 |
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143 | CALL_SUBTEST_3(( jacobisvd_verify_assert(Matrix3f()) ));
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144 | CALL_SUBTEST_4(( jacobisvd_verify_assert(Matrix4d()) ));
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145 | CALL_SUBTEST_7(( jacobisvd_verify_assert(MatrixXf(10,12)) ));
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146 | CALL_SUBTEST_8(( jacobisvd_verify_assert(MatrixXcd(7,5)) ));
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147 |
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148 | for(int i = 0; i < g_repeat; i++) {
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149 | Matrix2cd m;
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150 | m << 0, 1,
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151 | 0, 1;
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152 | CALL_SUBTEST_1(( jacobisvd(m, false) ));
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153 | m << 1, 0,
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154 | 1, 0;
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155 | CALL_SUBTEST_1(( jacobisvd(m, false) ));
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156 |
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157 | Matrix2d n;
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158 | n << 0, 0,
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159 | 0, 0;
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160 | CALL_SUBTEST_2(( jacobisvd(n, false) ));
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161 | n << 0, 0,
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162 | 0, 1;
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163 | CALL_SUBTEST_2(( jacobisvd(n, false) ));
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164 |
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165 | CALL_SUBTEST_3(( jacobisvd<Matrix3f>() ));
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166 | CALL_SUBTEST_4(( jacobisvd<Matrix4d>() ));
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167 | CALL_SUBTEST_5(( jacobisvd<Matrix<float,3,5> >() ));
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168 | CALL_SUBTEST_6(( jacobisvd<Matrix<double,Dynamic,2> >(Matrix<double,Dynamic,2>(10,2)) ));
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169 |
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170 | int r = internal::random<int>(1, 30),
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171 | c = internal::random<int>(1, 30);
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172 | CALL_SUBTEST_7(( jacobisvd<MatrixXf>(MatrixXf(r,c)) ));
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173 | CALL_SUBTEST_8(( jacobisvd<MatrixXcd>(MatrixXcd(r,c)) ));
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174 | (void) r;
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175 | (void) c;
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176 |
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177 | // Test on inf/nan matrix
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178 | CALL_SUBTEST_7( jacobisvd_inf_nan<MatrixXf>() );
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179 | }
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180 |
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181 | CALL_SUBTEST_7(( jacobisvd<MatrixXf>(MatrixXf(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/2))) ));
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182 | CALL_SUBTEST_8(( jacobisvd<MatrixXcd>(MatrixXcd(internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/3), internal::random<int>(EIGEN_TEST_MAX_SIZE/4, EIGEN_TEST_MAX_SIZE/3))) ));
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183 |
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184 |
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185 | // test matrixbase method
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186 | CALL_SUBTEST_1(( jacobisvd_method<Matrix2cd>() ));
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187 | CALL_SUBTEST_3(( jacobisvd_method<Matrix3f>() ));
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188 |
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189 |
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190 | // Test problem size constructors
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191 | CALL_SUBTEST_7( JacobiSVD<MatrixXf>(10,10) );
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192 |
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193 | // Check that preallocation avoids subsequent mallocs
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194 | CALL_SUBTEST_9( jacobisvd_preallocate() );
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195 |
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196 | // Regression check for bug 286
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197 | CALL_SUBTEST_2( jacobisvd_bug286() );
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198 | }
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