product_extra.cpp revision c981c48f5bc9aefeffc0bcb0cc3934c2fae179dd 
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 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
105void 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
114void 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
138void 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