source: pacpussensors/trunk/Vislab/lib3dv/eigen/test/product_extra.cpp@ 136

Last change on this file since 136 was 136, checked in by ldecherf, 7 years ago

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1// This file is part of Eigen, a lightweight C++ template library
2// for linear algebra.
3//
4// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
5//
6// This Source Code Form is subject to the terms of the Mozilla
7// Public License v. 2.0. If a copy of the MPL was not distributed
8// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9
10#include "main.h"
11
12template<typename MatrixType> void product_extra(const MatrixType& m)
13{
14 typedef typename MatrixType::Index Index;
15 typedef typename MatrixType::Scalar Scalar;
16 typedef Matrix<Scalar, 1, Dynamic> RowVectorType;
17 typedef Matrix<Scalar, Dynamic, 1> ColVectorType;
18 typedef Matrix<Scalar, Dynamic, Dynamic,
19 MatrixType::Flags&RowMajorBit> OtherMajorMatrixType;
20
21 Index rows = m.rows();
22 Index cols = m.cols();
23
24 MatrixType m1 = MatrixType::Random(rows, cols),
25 m2 = MatrixType::Random(rows, cols),
26 m3(rows, cols),
27 mzero = MatrixType::Zero(rows, cols),
28 identity = MatrixType::Identity(rows, rows),
29 square = MatrixType::Random(rows, rows),
30 res = MatrixType::Random(rows, rows),
31 square2 = MatrixType::Random(cols, cols),
32 res2 = MatrixType::Random(cols, cols);
33 RowVectorType v1 = RowVectorType::Random(rows), vrres(rows);
34 ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols);
35 OtherMajorMatrixType tm1 = m1;
36
37 Scalar s1 = internal::random<Scalar>(),
38 s2 = internal::random<Scalar>(),
39 s3 = internal::random<Scalar>();
40
41 VERIFY_IS_APPROX(m3.noalias() = m1 * m2.adjoint(), m1 * m2.adjoint().eval());
42 VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * square.adjoint(), m1.adjoint().eval() * square.adjoint().eval());
43 VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * m2, m1.adjoint().eval() * m2);
44 VERIFY_IS_APPROX(m3.noalias() = (s1 * m1.adjoint()) * m2, (s1 * m1.adjoint()).eval() * m2);
45 VERIFY_IS_APPROX(m3.noalias() = ((s1 * m1).adjoint()) * m2, (numext::conj(s1) * m1.adjoint()).eval() * m2);
46 VERIFY_IS_APPROX(m3.noalias() = (- m1.adjoint() * s1) * (s3 * m2), (- m1.adjoint() * s1).eval() * (s3 * m2).eval());
47 VERIFY_IS_APPROX(m3.noalias() = (s2 * m1.adjoint() * s1) * m2, (s2 * m1.adjoint() * s1).eval() * m2);
48 VERIFY_IS_APPROX(m3.noalias() = (-m1*s2) * s1*m2.adjoint(), (-m1*s2).eval() * (s1*m2.adjoint()).eval());
49
50 // a very tricky case where a scale factor has to be automatically conjugated:
51 VERIFY_IS_APPROX( m1.adjoint() * (s1*m2).conjugate(), (m1.adjoint()).eval() * ((s1*m2).conjugate()).eval());
52
53
54 // test all possible conjugate combinations for the four matrix-vector product cases:
55
56 VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2),
57 (-m1.conjugate()*s2).eval() * (s1 * vc2).eval());
58 VERIFY_IS_APPROX((-m1 * s2) * (s1 * vc2.conjugate()),
59 (-m1*s2).eval() * (s1 * vc2.conjugate()).eval());
60 VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2.conjugate()),
61 (-m1.conjugate()*s2).eval() * (s1 * vc2.conjugate()).eval());
62
63 VERIFY_IS_APPROX((s1 * vc2.transpose()) * (-m1.adjoint() * s2),
64 (s1 * vc2.transpose()).eval() * (-m1.adjoint()*s2).eval());
65 VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.transpose() * s2),
66 (s1 * vc2.adjoint()).eval() * (-m1.transpose()*s2).eval());
67 VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.adjoint() * s2),
68 (s1 * vc2.adjoint()).eval() * (-m1.adjoint()*s2).eval());
69
70 VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.transpose()),
71 (-m1.adjoint()*s2).eval() * (s1 * v1.transpose()).eval());
72 VERIFY_IS_APPROX((-m1.transpose() * s2) * (s1 * v1.adjoint()),
73 (-m1.transpose()*s2).eval() * (s1 * v1.adjoint()).eval());
74 VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()),
75 (-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval());
76
77 VERIFY_IS_APPROX((s1 * v1) * (-m1.conjugate() * s2),
78 (s1 * v1).eval() * (-m1.conjugate()*s2).eval());
79 VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1 * s2),
80 (s1 * v1.conjugate()).eval() * (-m1*s2).eval());
81 VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1.conjugate() * s2),
82 (s1 * v1.conjugate()).eval() * (-m1.conjugate()*s2).eval());
83
84 VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()),
85 (-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval());
86
87 // test the vector-matrix product with non aligned starts
88 Index i = internal::random<Index>(0,m1.rows()-2);
89 Index j = internal::random<Index>(0,m1.cols()-2);
90 Index r = internal::random<Index>(1,m1.rows()-i);
91 Index c = internal::random<Index>(1,m1.cols()-j);
92 Index i2 = internal::random<Index>(0,m1.rows()-1);
93 Index j2 = internal::random<Index>(0,m1.cols()-1);
94
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());
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());
97
98 // regression test
99 MatrixType tmp = m1 * m1.adjoint() * s1;
100 VERIFY_IS_APPROX(tmp, m1 * m1.adjoint() * s1);
101}
102
103// Regression test for bug reported at http://forum.kde.org/viewtopic.php?f=74&t=96947
104void mat_mat_scalar_scalar_product()
105{
106 Eigen::Matrix2Xd dNdxy(2, 3);
107 dNdxy << -0.5, 0.5, 0,
108 -0.3, 0, 0.3;
109 double det = 6.0, wt = 0.5;
110 VERIFY_IS_APPROX(dNdxy.transpose()*dNdxy*det*wt, det*wt*dNdxy.transpose()*dNdxy);
111}
112
113template <typename MatrixType>
114void zero_sized_objects(const MatrixType& m)
115{
116 typedef typename MatrixType::Scalar Scalar;
117 const int PacketSize = internal::packet_traits<Scalar>::size;
118 const int PacketSize1 = PacketSize>1 ? PacketSize-1 : 1;
119 DenseIndex rows = m.rows();
120 DenseIndex cols = m.cols();
121
122 {
123 MatrixType res, a(rows,0), b(0,cols);
124 VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(rows,cols) );
125 VERIFY_IS_APPROX( (res=a*a.transpose()), MatrixType::Zero(rows,rows) );
126 VERIFY_IS_APPROX( (res=b.transpose()*b), MatrixType::Zero(cols,cols) );
127 VERIFY_IS_APPROX( (res=b.transpose()*a.transpose()), MatrixType::Zero(cols,rows) );
128 }
129
130 {
131 MatrixType res, a(rows,cols), b(cols,0);
132 res = a*b;
133 VERIFY(res.rows()==rows && res.cols()==0);
134 b.resize(0,rows);
135 res = b*a;
136 VERIFY(res.rows()==0 && res.cols()==cols);
137 }
138
139 {
140 Matrix<Scalar,PacketSize,0> a;
141 Matrix<Scalar,0,1> b;
142 Matrix<Scalar,PacketSize,1> res;
143 VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize,1) );
144 VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize,1) );
145 }
146
147 {
148 Matrix<Scalar,PacketSize1,0> a;
149 Matrix<Scalar,0,1> b;
150 Matrix<Scalar,PacketSize1,1> res;
151 VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize1,1) );
152 VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize1,1) );
153 }
154
155 {
156 Matrix<Scalar,PacketSize,Dynamic> a(PacketSize,0);
157 Matrix<Scalar,Dynamic,1> b(0,1);
158 Matrix<Scalar,PacketSize,1> res;
159 VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize,1) );
160 VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize,1) );
161 }
162
163 {
164 Matrix<Scalar,PacketSize1,Dynamic> a(PacketSize1,0);
165 Matrix<Scalar,Dynamic,1> b(0,1);
166 Matrix<Scalar,PacketSize1,1> res;
167 VERIFY_IS_APPROX( (res=a*b), MatrixType::Zero(PacketSize1,1) );
168 VERIFY_IS_APPROX( (res=a.lazyProduct(b)), MatrixType::Zero(PacketSize1,1) );
169 }
170}
171
172void bug_127()
173{
174 // Bug 127
175 //
176 // a product of the form lhs*rhs with
177 //
178 // lhs:
179 // rows = 1, cols = 4
180 // RowsAtCompileTime = 1, ColsAtCompileTime = -1
181 // MaxRowsAtCompileTime = 1, MaxColsAtCompileTime = 5
182 //
183 // rhs:
184 // rows = 4, cols = 0
185 // RowsAtCompileTime = -1, ColsAtCompileTime = -1
186 // MaxRowsAtCompileTime = 5, MaxColsAtCompileTime = 1
187 //
188 // was failing on a runtime assertion, because it had been mis-compiled as a dot product because Product.h was using the
189 // max-sizes to detect size 1 indicating vectors, and that didn't account for 0-sized object with max-size 1.
190
191 Matrix<float,1,Dynamic,RowMajor,1,5> a(1,4);
192 Matrix<float,Dynamic,Dynamic,ColMajor,5,1> b(4,0);
193 a*b;
194}
195
196void unaligned_objects()
197{
198 // Regression test for the bug reported here:
199 // http://forum.kde.org/viewtopic.php?f=74&t=107541
200 // Recall the matrix*vector kernel avoid unaligned loads by loading two packets and then reassemble then.
201 // There was a mistake in the computation of the valid range for fully unaligned objects: in some rare cases,
202 // memory was read outside the allocated matrix memory. Though the values were not used, this might raise segfault.
203 for(int m=450;m<460;++m)
204 {
205 for(int n=8;n<12;++n)
206 {
207 MatrixXf M(m, n);
208 VectorXf v1(n), r1(500);
209 RowVectorXf v2(m), r2(16);
210
211 M.setRandom();
212 v1.setRandom();
213 v2.setRandom();
214 for(int o=0; o<4; ++o)
215 {
216 r1.segment(o,m).noalias() = M * v1;
217 VERIFY_IS_APPROX(r1.segment(o,m), M * MatrixXf(v1));
218 r2.segment(o,n).noalias() = v2 * M;
219 VERIFY_IS_APPROX(r2.segment(o,n), MatrixXf(v2) * M);
220 }
221 }
222 }
223}
224
225void test_product_extra()
226{
227 for(int i = 0; i < g_repeat; i++) {
228 CALL_SUBTEST_1( product_extra(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
229 CALL_SUBTEST_2( product_extra(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
230 CALL_SUBTEST_2( mat_mat_scalar_scalar_product() );
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))) );
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))) );
233 CALL_SUBTEST_1( zero_sized_objects(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
234 }
235 CALL_SUBTEST_5( bug_127() );
236 CALL_SUBTEST_6( unaligned_objects() );
237}
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