1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
|
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
template<typename MatrixType> void matrixVisitor(const MatrixType& p)
{
typedef typename MatrixType::Scalar Scalar;
Index rows = p.rows();
Index cols = p.cols();
// construct a random matrix where all coefficients are different
MatrixType m;
m = MatrixType::Random(rows, cols);
for(Index i = 0; i < m.size(); i++)
for(Index i2 = 0; i2 < i; i2++)
while(m(i) == m(i2)) // yes, ==
m(i) = internal::random<Scalar>();
Scalar minc = Scalar(1000), maxc = Scalar(-1000);
Index minrow=0,mincol=0,maxrow=0,maxcol=0;
for(Index j = 0; j < cols; j++)
for(Index i = 0; i < rows; i++)
{
if(m(i,j) < minc)
{
minc = m(i,j);
minrow = i;
mincol = j;
}
if(m(i,j) > maxc)
{
maxc = m(i,j);
maxrow = i;
maxcol = j;
}
}
Index eigen_minrow, eigen_mincol, eigen_maxrow, eigen_maxcol;
Scalar eigen_minc, eigen_maxc;
eigen_minc = m.minCoeff(&eigen_minrow,&eigen_mincol);
eigen_maxc = m.maxCoeff(&eigen_maxrow,&eigen_maxcol);
VERIFY(minrow == eigen_minrow);
VERIFY(maxrow == eigen_maxrow);
VERIFY(mincol == eigen_mincol);
VERIFY(maxcol == eigen_maxcol);
VERIFY_IS_APPROX(minc, eigen_minc);
VERIFY_IS_APPROX(maxc, eigen_maxc);
VERIFY_IS_APPROX(minc, m.minCoeff());
VERIFY_IS_APPROX(maxc, m.maxCoeff());
eigen_maxc = (m.adjoint()*m).maxCoeff(&eigen_maxrow,&eigen_maxcol);
Index maxrow2=0,maxcol2=0;
eigen_maxc = (m.adjoint()*m).eval().maxCoeff(&maxrow2,&maxcol2);
VERIFY(maxrow2 == eigen_maxrow);
VERIFY(maxcol2 == eigen_maxcol);
if (!NumTraits<Scalar>::IsInteger && m.size() > 2) {
// Test NaN propagation by replacing an element with NaN.
bool stop = false;
for (Index j = 0; j < cols && !stop; ++j) {
for (Index i = 0; i < rows && !stop; ++i) {
if (!(j == mincol && i == minrow) &&
!(j == maxcol && i == maxrow)) {
m(i,j) = NumTraits<Scalar>::quiet_NaN();
stop = true;
break;
}
}
}
eigen_minc = m.template minCoeff<PropagateNumbers>(&eigen_minrow, &eigen_mincol);
eigen_maxc = m.template maxCoeff<PropagateNumbers>(&eigen_maxrow, &eigen_maxcol);
VERIFY(minrow == eigen_minrow);
VERIFY(maxrow == eigen_maxrow);
VERIFY(mincol == eigen_mincol);
VERIFY(maxcol == eigen_maxcol);
VERIFY_IS_APPROX(minc, eigen_minc);
VERIFY_IS_APPROX(maxc, eigen_maxc);
VERIFY_IS_APPROX(minc, m.template minCoeff<PropagateNumbers>());
VERIFY_IS_APPROX(maxc, m.template maxCoeff<PropagateNumbers>());
eigen_minc = m.template minCoeff<PropagateNaN>(&eigen_minrow, &eigen_mincol);
eigen_maxc = m.template maxCoeff<PropagateNaN>(&eigen_maxrow, &eigen_maxcol);
VERIFY(minrow != eigen_minrow || mincol != eigen_mincol);
VERIFY(maxrow != eigen_maxrow || maxcol != eigen_maxcol);
VERIFY((numext::isnan)(eigen_minc));
VERIFY((numext::isnan)(eigen_maxc));
}
}
template<typename VectorType> void vectorVisitor(const VectorType& w)
{
typedef typename VectorType::Scalar Scalar;
Index size = w.size();
// construct a random vector where all coefficients are different
VectorType v;
v = VectorType::Random(size);
for(Index i = 0; i < size; i++)
for(Index i2 = 0; i2 < i; i2++)
while(v(i) == v(i2)) // yes, ==
v(i) = internal::random<Scalar>();
Scalar minc = v(0), maxc = v(0);
Index minidx=0, maxidx=0;
for(Index i = 0; i < size; i++)
{
if(v(i) < minc)
{
minc = v(i);
minidx = i;
}
if(v(i) > maxc)
{
maxc = v(i);
maxidx = i;
}
}
Index eigen_minidx, eigen_maxidx;
Scalar eigen_minc, eigen_maxc;
eigen_minc = v.minCoeff(&eigen_minidx);
eigen_maxc = v.maxCoeff(&eigen_maxidx);
VERIFY(minidx == eigen_minidx);
VERIFY(maxidx == eigen_maxidx);
VERIFY_IS_APPROX(minc, eigen_minc);
VERIFY_IS_APPROX(maxc, eigen_maxc);
VERIFY_IS_APPROX(minc, v.minCoeff());
VERIFY_IS_APPROX(maxc, v.maxCoeff());
Index idx0 = internal::random<Index>(0,size-1);
Index idx1 = eigen_minidx;
Index idx2 = eigen_maxidx;
VectorType v1(v), v2(v);
v1(idx0) = v1(idx1);
v2(idx0) = v2(idx2);
v1.minCoeff(&eigen_minidx);
v2.maxCoeff(&eigen_maxidx);
VERIFY(eigen_minidx == (std::min)(idx0,idx1));
VERIFY(eigen_maxidx == (std::min)(idx0,idx2));
if (!NumTraits<Scalar>::IsInteger && size > 2) {
// Test NaN propagation by replacing an element with NaN.
for (Index i = 0; i < size; ++i) {
if (i != minidx && i != maxidx) {
v(i) = NumTraits<Scalar>::quiet_NaN();
break;
}
}
eigen_minc = v.template minCoeff<PropagateNumbers>(&eigen_minidx);
eigen_maxc = v.template maxCoeff<PropagateNumbers>(&eigen_maxidx);
VERIFY(minidx == eigen_minidx);
VERIFY(maxidx == eigen_maxidx);
VERIFY_IS_APPROX(minc, eigen_minc);
VERIFY_IS_APPROX(maxc, eigen_maxc);
VERIFY_IS_APPROX(minc, v.template minCoeff<PropagateNumbers>());
VERIFY_IS_APPROX(maxc, v.template maxCoeff<PropagateNumbers>());
eigen_minc = v.template minCoeff<PropagateNaN>(&eigen_minidx);
eigen_maxc = v.template maxCoeff<PropagateNaN>(&eigen_maxidx);
VERIFY(minidx != eigen_minidx);
VERIFY(maxidx != eigen_maxidx);
VERIFY((numext::isnan)(eigen_minc));
VERIFY((numext::isnan)(eigen_maxc));
}
}
EIGEN_DECLARE_TEST(visitor)
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( matrixVisitor(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( matrixVisitor(Matrix2f()) );
CALL_SUBTEST_3( matrixVisitor(Matrix4d()) );
CALL_SUBTEST_4( matrixVisitor(MatrixXd(8, 12)) );
CALL_SUBTEST_5( matrixVisitor(Matrix<double,Dynamic,Dynamic,RowMajor>(20, 20)) );
CALL_SUBTEST_6( matrixVisitor(MatrixXi(8, 12)) );
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_7( vectorVisitor(Vector4f()) );
CALL_SUBTEST_7( vectorVisitor(Matrix<int,12,1>()) );
CALL_SUBTEST_8( vectorVisitor(VectorXd(10)) );
CALL_SUBTEST_9( vectorVisitor(RowVectorXd(10)) );
CALL_SUBTEST_10( vectorVisitor(VectorXf(33)) );
}
}
|
