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
product_extra(const MatrixType & m)12 template<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
mat_mat_scalar_scalar_product()104 void 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
zero_sized_objects()113 void zero_sized_objects()
114 {
115 // Bug 127
116 //
117 // a product of the form lhs*rhs with
118 //
119 // lhs:
120 // rows = 1, cols = 4
121 // RowsAtCompileTime = 1, ColsAtCompileTime = -1
122 // MaxRowsAtCompileTime = 1, MaxColsAtCompileTime = 5
123 //
124 // rhs:
125 // rows = 4, cols = 0
126 // RowsAtCompileTime = -1, ColsAtCompileTime = -1
127 // MaxRowsAtCompileTime = 5, MaxColsAtCompileTime = 1
128 //
129 // was failing on a runtime assertion, because it had been mis-compiled as a dot product because Product.h was using the
130 // max-sizes to detect size 1 indicating vectors, and that didn't account for 0-sized object with max-size 1.
131
132 Matrix<float,1,Dynamic,RowMajor,1,5> a(1,4);
133 Matrix<float,Dynamic,Dynamic,ColMajor,5,1> b(4,0);
134 a*b;
135 }
136
unaligned_objects()137 void unaligned_objects()
138 {
139 // Regression test for the bug reported here:
140 // http://forum.kde.org/viewtopic.php?f=74&t=107541
141 // Recall the matrix*vector kernel avoid unaligned loads by loading two packets and then reassemble then.
142 // There was a mistake in the computation of the valid range for fully unaligned objects: in some rare cases,
143 // memory was read outside the allocated matrix memory. Though the values were not used, this might raise segfault.
144 for(int m=450;m<460;++m)
145 {
146 for(int n=8;n<12;++n)
147 {
148 MatrixXf M(m, n);
149 VectorXf v1(n), r1(500);
150 RowVectorXf v2(m), r2(16);
151
152 M.setRandom();
153 v1.setRandom();
154 v2.setRandom();
155 for(int o=0; o<4; ++o)
156 {
157 r1.segment(o,m).noalias() = M * v1;
158 VERIFY_IS_APPROX(r1.segment(o,m), M * MatrixXf(v1));
159 r2.segment(o,n).noalias() = v2 * M;
160 VERIFY_IS_APPROX(r2.segment(o,n), MatrixXf(v2) * M);
161 }
162 }
163 }
164 }
165
test_product_extra()166 void test_product_extra()
167 {
168 for(int i = 0; i < g_repeat; i++) {
169 CALL_SUBTEST_1( product_extra(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
170 CALL_SUBTEST_2( product_extra(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
171 CALL_SUBTEST_2( mat_mat_scalar_scalar_product() );
172 CALL_SUBTEST_3( product_extra(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
173 CALL_SUBTEST_4( product_extra(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
174 }
175 CALL_SUBTEST_5( zero_sized_objects() );
176 CALL_SUBTEST_6( unaligned_objects() );
177 }
178