1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
5 // Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
6 //
7 // This Source Code Form is subject to the terms of the Mozilla
8 // Public License v. 2.0. If a copy of the MPL was not distributed
9 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
10
11 #include "svd_common.h"
12
13 template<typename MatrixType, int QRPreconditioner>
jacobisvd_check_full(const MatrixType & m,const JacobiSVD<MatrixType,QRPreconditioner> & svd)14 void jacobisvd_check_full(const MatrixType& m, const JacobiSVD<MatrixType, QRPreconditioner>& svd)
15 {
16 svd_check_full<MatrixType, JacobiSVD<MatrixType, QRPreconditioner > >(m, svd);
17 }
18
19 template<typename MatrixType, int QRPreconditioner>
jacobisvd_compare_to_full(const MatrixType & m,unsigned int computationOptions,const JacobiSVD<MatrixType,QRPreconditioner> & referenceSvd)20 void jacobisvd_compare_to_full(const MatrixType& m,
21 unsigned int computationOptions,
22 const JacobiSVD<MatrixType, QRPreconditioner>& referenceSvd)
23 {
24 svd_compare_to_full<MatrixType, JacobiSVD<MatrixType, QRPreconditioner> >(m, computationOptions, referenceSvd);
25 }
26
27
28 template<typename MatrixType, int QRPreconditioner>
jacobisvd_solve(const MatrixType & m,unsigned int computationOptions)29 void jacobisvd_solve(const MatrixType& m, unsigned int computationOptions)
30 {
31 svd_solve< MatrixType, JacobiSVD< MatrixType, QRPreconditioner > >(m, computationOptions);
32 }
33
34
35
36 template<typename MatrixType, int QRPreconditioner>
jacobisvd_test_all_computation_options(const MatrixType & m)37 void jacobisvd_test_all_computation_options(const MatrixType& m)
38 {
39
40 if (QRPreconditioner == NoQRPreconditioner && m.rows() != m.cols())
41 return;
42
43 JacobiSVD< MatrixType, QRPreconditioner > fullSvd(m, ComputeFullU|ComputeFullV);
44 svd_test_computation_options_1< MatrixType, JacobiSVD< MatrixType, QRPreconditioner > >(m, fullSvd);
45
46 if(QRPreconditioner == FullPivHouseholderQRPreconditioner)
47 return;
48 svd_test_computation_options_2< MatrixType, JacobiSVD< MatrixType, QRPreconditioner > >(m, fullSvd);
49
50 }
51
52 template<typename MatrixType>
jacobisvd(const MatrixType & a=MatrixType (),bool pickrandom=true)53 void jacobisvd(const MatrixType& a = MatrixType(), bool pickrandom = true)
54 {
55 MatrixType m = pickrandom ? MatrixType::Random(a.rows(), a.cols()) : a;
56
57 jacobisvd_test_all_computation_options<MatrixType, FullPivHouseholderQRPreconditioner>(m);
58 jacobisvd_test_all_computation_options<MatrixType, ColPivHouseholderQRPreconditioner>(m);
59 jacobisvd_test_all_computation_options<MatrixType, HouseholderQRPreconditioner>(m);
60 jacobisvd_test_all_computation_options<MatrixType, NoQRPreconditioner>(m);
61 }
62
63
64 template<typename MatrixType>
jacobisvd_verify_assert(const MatrixType & m)65 void jacobisvd_verify_assert(const MatrixType& m)
66 {
67
68 svd_verify_assert<MatrixType, JacobiSVD< MatrixType > >(m);
69
70 typedef typename MatrixType::Index Index;
71 Index rows = m.rows();
72 Index cols = m.cols();
73
74 enum {
75 RowsAtCompileTime = MatrixType::RowsAtCompileTime,
76 ColsAtCompileTime = MatrixType::ColsAtCompileTime
77 };
78
79 MatrixType a = MatrixType::Zero(rows, cols);
80 a.setZero();
81
82 if (ColsAtCompileTime == Dynamic)
83 {
84 JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner> svd_fullqr;
85 VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeFullU|ComputeThinV))
86 VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeThinU|ComputeThinV))
87 VERIFY_RAISES_ASSERT(svd_fullqr.compute(a, ComputeThinU|ComputeFullV))
88 }
89 }
90
91 template<typename MatrixType>
jacobisvd_method()92 void jacobisvd_method()
93 {
94 enum { Size = MatrixType::RowsAtCompileTime };
95 typedef typename MatrixType::RealScalar RealScalar;
96 typedef Matrix<RealScalar, Size, 1> RealVecType;
97 MatrixType m = MatrixType::Identity();
98 VERIFY_IS_APPROX(m.jacobiSvd().singularValues(), RealVecType::Ones());
99 VERIFY_RAISES_ASSERT(m.jacobiSvd().matrixU());
100 VERIFY_RAISES_ASSERT(m.jacobiSvd().matrixV());
101 VERIFY_IS_APPROX(m.jacobiSvd(ComputeFullU|ComputeFullV).solve(m), m);
102 }
103
104
105
106 template<typename MatrixType>
jacobisvd_inf_nan()107 void jacobisvd_inf_nan()
108 {
109 svd_inf_nan<MatrixType, JacobiSVD< MatrixType > >();
110 }
111
112
113 // Regression test for bug 286: JacobiSVD loops indefinitely with some
114 // matrices containing denormal numbers.
jacobisvd_bug286()115 void jacobisvd_bug286()
116 {
117 #if defined __INTEL_COMPILER
118 // shut up warning #239: floating point underflow
119 #pragma warning push
120 #pragma warning disable 239
121 #endif
122 Matrix2d M;
123 M << -7.90884e-313, -4.94e-324,
124 0, 5.60844e-313;
125 #if defined __INTEL_COMPILER
126 #pragma warning pop
127 #endif
128 JacobiSVD<Matrix2d> svd;
129 svd.compute(M); // just check we don't loop indefinitely
130 }
131
132
jacobisvd_preallocate()133 void jacobisvd_preallocate()
134 {
135 svd_preallocate< JacobiSVD <MatrixXf> >();
136 }
137
test_jacobisvd()138 void test_jacobisvd()
139 {
140 CALL_SUBTEST_11(( jacobisvd<Matrix<double,Dynamic,Dynamic> >
141 (Matrix<double,Dynamic,Dynamic>(16, 6)) ));
142
143 CALL_SUBTEST_3(( jacobisvd_verify_assert(Matrix3f()) ));
144 CALL_SUBTEST_4(( jacobisvd_verify_assert(Matrix4d()) ));
145 CALL_SUBTEST_7(( jacobisvd_verify_assert(MatrixXf(10,12)) ));
146 CALL_SUBTEST_8(( jacobisvd_verify_assert(MatrixXcd(7,5)) ));
147
148 for(int i = 0; i < g_repeat; i++) {
149 Matrix2cd m;
150 m << 0, 1,
151 0, 1;
152 CALL_SUBTEST_1(( jacobisvd(m, false) ));
153 m << 1, 0,
154 1, 0;
155 CALL_SUBTEST_1(( jacobisvd(m, false) ));
156
157 Matrix2d n;
158 n << 0, 0,
159 0, 0;
160 CALL_SUBTEST_2(( jacobisvd(n, false) ));
161 n << 0, 0,
162 0, 1;
163 CALL_SUBTEST_2(( jacobisvd(n, false) ));
164
165 CALL_SUBTEST_3(( jacobisvd<Matrix3f>() ));
166 CALL_SUBTEST_4(( jacobisvd<Matrix4d>() ));
167 CALL_SUBTEST_5(( jacobisvd<Matrix<float,3,5> >() ));
168 CALL_SUBTEST_6(( jacobisvd<Matrix<double,Dynamic,2> >(Matrix<double,Dynamic,2>(10,2)) ));
169
170 int r = internal::random<int>(1, 30),
171 c = internal::random<int>(1, 30);
172 CALL_SUBTEST_7(( jacobisvd<MatrixXf>(MatrixXf(r,c)) ));
173 CALL_SUBTEST_8(( jacobisvd<MatrixXcd>(MatrixXcd(r,c)) ));
174 (void) r;
175 (void) c;
176
177 // Test on inf/nan matrix
178 CALL_SUBTEST_7( jacobisvd_inf_nan<MatrixXf>() );
179 }
180
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))) ));
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))) ));
183
184
185 // test matrixbase method
186 CALL_SUBTEST_1(( jacobisvd_method<Matrix2cd>() ));
187 CALL_SUBTEST_3(( jacobisvd_method<Matrix3f>() ));
188
189
190 // Test problem size constructors
191 CALL_SUBTEST_7( JacobiSVD<MatrixXf>(10,10) );
192
193 // Check that preallocation avoids subsequent mallocs
194 CALL_SUBTEST_9( jacobisvd_preallocate() );
195
196 // Regression check for bug 286
197 CALL_SUBTEST_2( jacobisvd_bug286() );
198 }
199