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 typename NumTraits<Scalar>::NonInteger NonInteger;
17 typedef Matrix<Scalar, 1, Dynamic> RowVectorType;
18 typedef Matrix<Scalar, Dynamic, 1> ColVectorType;
19 typedef Matrix<Scalar, Dynamic, Dynamic,
20 MatrixType::Flags&RowMajorBit> OtherMajorMatrixType;
21
22 Index rows = m.rows();
23 Index cols = m.cols();
24
25 MatrixType m1 = MatrixType::Random(rows, cols),
26 m2 = MatrixType::Random(rows, cols),
27 m3(rows, cols),
28 mzero = MatrixType::Zero(rows, cols),
29 identity = MatrixType::Identity(rows, rows),
30 square = MatrixType::Random(rows, rows),
31 res = MatrixType::Random(rows, rows),
32 square2 = MatrixType::Random(cols, cols),
33 res2 = MatrixType::Random(cols, cols);
34 RowVectorType v1 = RowVectorType::Random(rows), vrres(rows);
35 ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols);
36 OtherMajorMatrixType tm1 = m1;
37
38 Scalar s1 = internal::random<Scalar>(),
39 s2 = internal::random<Scalar>(),
40 s3 = internal::random<Scalar>();
41
42 VERIFY_IS_APPROX(m3.noalias() = m1 * m2.adjoint(), m1 * m2.adjoint().eval());
43 VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * square.adjoint(), m1.adjoint().eval() * square.adjoint().eval());
44 VERIFY_IS_APPROX(m3.noalias() = m1.adjoint() * m2, m1.adjoint().eval() * m2);
45 VERIFY_IS_APPROX(m3.noalias() = (s1 * m1.adjoint()) * m2, (s1 * m1.adjoint()).eval() * m2);
46 VERIFY_IS_APPROX(m3.noalias() = ((s1 * m1).adjoint()) * m2, (internal::conj(s1) * m1.adjoint()).eval() * m2);
47 VERIFY_IS_APPROX(m3.noalias() = (- m1.adjoint() * s1) * (s3 * m2), (- m1.adjoint() * s1).eval() * (s3 * m2).eval());
48 VERIFY_IS_APPROX(m3.noalias() = (s2 * m1.adjoint() * s1) * m2, (s2 * m1.adjoint() * s1).eval() * m2);
49 VERIFY_IS_APPROX(m3.noalias() = (-m1*s2) * s1*m2.adjoint(), (-m1*s2).eval() * (s1*m2.adjoint()).eval());
50
51 // a very tricky case where a scale factor has to be automatically conjugated:
52 VERIFY_IS_APPROX( m1.adjoint() * (s1*m2).conjugate(), (m1.adjoint()).eval() * ((s1*m2).conjugate()).eval());
53
54
55 // test all possible conjugate combinations for the four matrix-vector product cases:
56
57 VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2),
58 (-m1.conjugate()*s2).eval() * (s1 * vc2).eval());
59 VERIFY_IS_APPROX((-m1 * s2) * (s1 * vc2.conjugate()),
60 (-m1*s2).eval() * (s1 * vc2.conjugate()).eval());
61 VERIFY_IS_APPROX((-m1.conjugate() * s2) * (s1 * vc2.conjugate()),
62 (-m1.conjugate()*s2).eval() * (s1 * vc2.conjugate()).eval());
63
64 VERIFY_IS_APPROX((s1 * vc2.transpose()) * (-m1.adjoint() * s2),
65 (s1 * vc2.transpose()).eval() * (-m1.adjoint()*s2).eval());
66 VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.transpose() * s2),
67 (s1 * vc2.adjoint()).eval() * (-m1.transpose()*s2).eval());
68 VERIFY_IS_APPROX((s1 * vc2.adjoint()) * (-m1.adjoint() * s2),
69 (s1 * vc2.adjoint()).eval() * (-m1.adjoint()*s2).eval());
70
71 VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.transpose()),
72 (-m1.adjoint()*s2).eval() * (s1 * v1.transpose()).eval());
73 VERIFY_IS_APPROX((-m1.transpose() * s2) * (s1 * v1.adjoint()),
74 (-m1.transpose()*s2).eval() * (s1 * v1.adjoint()).eval());
75 VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()),
76 (-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval());
77
78 VERIFY_IS_APPROX((s1 * v1) * (-m1.conjugate() * s2),
79 (s1 * v1).eval() * (-m1.conjugate()*s2).eval());
80 VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1 * s2),
81 (s1 * v1.conjugate()).eval() * (-m1*s2).eval());
82 VERIFY_IS_APPROX((s1 * v1.conjugate()) * (-m1.conjugate() * s2),
83 (s1 * v1.conjugate()).eval() * (-m1.conjugate()*s2).eval());
84
85 VERIFY_IS_APPROX((-m1.adjoint() * s2) * (s1 * v1.adjoint()),
86 (-m1.adjoint()*s2).eval() * (s1 * v1.adjoint()).eval());
87
88 // test the vector-matrix product with non aligned starts
89 Index i = internal::random<Index>(0,m1.rows()-2);
90 Index j = internal::random<Index>(0,m1.cols()-2);
91 Index r = internal::random<Index>(1,m1.rows()-i);
92 Index c = internal::random<Index>(1,m1.cols()-j);
93 Index i2 = internal::random<Index>(0,m1.rows()-1);
94 Index j2 = internal::random<Index>(0,m1.cols()-1);
95
96 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());
97 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());
98
99 // regression test
100 MatrixType tmp = m1 * m1.adjoint() * s1;
101 VERIFY_IS_APPROX(tmp, m1 * m1.adjoint() * s1);
102 }
103
104 // Regression test for bug reported at http://forum.kde.org/viewtopic.php?f=74&t=96947
mat_mat_scalar_scalar_product()105 void mat_mat_scalar_scalar_product()
106 {
107 Eigen::Matrix2Xd dNdxy(2, 3);
108 dNdxy << -0.5, 0.5, 0,
109 -0.3, 0, 0.3;
110 double det = 6.0, wt = 0.5;
111 VERIFY_IS_APPROX(dNdxy.transpose()*dNdxy*det*wt, det*wt*dNdxy.transpose()*dNdxy);
112 }
113
zero_sized_objects()114 void zero_sized_objects()
115 {
116 // Bug 127
117 //
118 // a product of the form lhs*rhs with
119 //
120 // lhs:
121 // rows = 1, cols = 4
122 // RowsAtCompileTime = 1, ColsAtCompileTime = -1
123 // MaxRowsAtCompileTime = 1, MaxColsAtCompileTime = 5
124 //
125 // rhs:
126 // rows = 4, cols = 0
127 // RowsAtCompileTime = -1, ColsAtCompileTime = -1
128 // MaxRowsAtCompileTime = 5, MaxColsAtCompileTime = 1
129 //
130 // was failing on a runtime assertion, because it had been mis-compiled as a dot product because Product.h was using the
131 // max-sizes to detect size 1 indicating vectors, and that didn't account for 0-sized object with max-size 1.
132
133 Matrix<float,1,Dynamic,RowMajor,1,5> a(1,4);
134 Matrix<float,Dynamic,Dynamic,ColMajor,5,1> b(4,0);
135 a*b;
136 }
137
test_product_extra()138 void test_product_extra()
139 {
140 for(int i = 0; i < g_repeat; i++) {
141 CALL_SUBTEST_1( product_extra(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
142 CALL_SUBTEST_2( product_extra(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
143 CALL_SUBTEST_2( mat_mat_scalar_scalar_product() );
144 CALL_SUBTEST_3( product_extra(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
145 CALL_SUBTEST_4( product_extra(MatrixXcd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2), internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2))) );
146 CALL_SUBTEST_5( zero_sized_objects() );
147 }
148 }
149