source: pacpussensors/trunk/Vislab/lib3dv-1.2.0/lib3dv/eigen/test/redux.cpp

Last change on this file was 136, checked in by ldecherf, 8 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) 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 matrixRedux(const MatrixType& m)
13{
14 typedef typename MatrixType::Index Index;
15 typedef typename MatrixType::Scalar Scalar;
16 typedef typename MatrixType::RealScalar RealScalar;
17
18 Index rows = m.rows();
19 Index cols = m.cols();
20
21 MatrixType m1 = MatrixType::Random(rows, cols);
22
23 // The entries of m1 are uniformly distributed in [0,1], so m1.prod() is very small. This may lead to test
24 // failures if we underflow into denormals. Thus, we scale so that entires are close to 1.
25 MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + RealScalar(0.2) * m1;
26
27 VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1));
28 VERIFY_IS_APPROX(MatrixType::Ones(rows, cols).sum(), Scalar(float(rows*cols))); // the float() here to shut up excessive MSVC warning about int->complex conversion being lossy
29 Scalar s(0), p(1), minc(numext::real(m1.coeff(0))), maxc(numext::real(m1.coeff(0)));
30 for(int j = 0; j < cols; j++)
31 for(int i = 0; i < rows; i++)
32 {
33 s += m1(i,j);
34 p *= m1_for_prod(i,j);
35 minc = (std::min)(numext::real(minc), numext::real(m1(i,j)));
36 maxc = (std::max)(numext::real(maxc), numext::real(m1(i,j)));
37 }
38 const Scalar mean = s/Scalar(RealScalar(rows*cols));
39
40 VERIFY_IS_APPROX(m1.sum(), s);
41 VERIFY_IS_APPROX(m1.mean(), mean);
42 VERIFY_IS_APPROX(m1_for_prod.prod(), p);
43 VERIFY_IS_APPROX(m1.real().minCoeff(), numext::real(minc));
44 VERIFY_IS_APPROX(m1.real().maxCoeff(), numext::real(maxc));
45
46 // test slice vectorization assuming assign is ok
47 Index r0 = internal::random<Index>(0,rows-1);
48 Index c0 = internal::random<Index>(0,cols-1);
49 Index r1 = internal::random<Index>(r0+1,rows)-r0;
50 Index c1 = internal::random<Index>(c0+1,cols)-c0;
51 VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).sum(), m1.block(r0,c0,r1,c1).eval().sum());
52 VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).mean(), m1.block(r0,c0,r1,c1).eval().mean());
53 VERIFY_IS_APPROX(m1_for_prod.block(r0,c0,r1,c1).prod(), m1_for_prod.block(r0,c0,r1,c1).eval().prod());
54 VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().minCoeff(), m1.block(r0,c0,r1,c1).real().eval().minCoeff());
55 VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().maxCoeff(), m1.block(r0,c0,r1,c1).real().eval().maxCoeff());
56
57 // regression for bug 1090
58 const int R1 = MatrixType::RowsAtCompileTime>=2 ? MatrixType::RowsAtCompileTime/2 : 6;
59 const int C1 = MatrixType::ColsAtCompileTime>=2 ? MatrixType::ColsAtCompileTime/2 : 6;
60 if(R1<=rows-r0 && C1<=cols-c0)
61 {
62 VERIFY_IS_APPROX( (m1.template block<R1,C1>(r0,c0).sum()), m1.block(r0,c0,R1,C1).sum() );
63 }
64
65 // test empty objects
66 VERIFY_IS_APPROX(m1.block(r0,c0,0,0).sum(), Scalar(0));
67 VERIFY_IS_APPROX(m1.block(r0,c0,0,0).prod(), Scalar(1));
68}
69
70template<typename VectorType> void vectorRedux(const VectorType& w)
71{
72 using std::abs;
73 typedef typename VectorType::Index Index;
74 typedef typename VectorType::Scalar Scalar;
75 typedef typename NumTraits<Scalar>::Real RealScalar;
76 Index size = w.size();
77
78 VectorType v = VectorType::Random(size);
79 VectorType v_for_prod = VectorType::Ones(size) + Scalar(0.2) * v; // see comment above declaration of m1_for_prod
80
81 for(int i = 1; i < size; i++)
82 {
83 Scalar s(0), p(1);
84 RealScalar minc(numext::real(v.coeff(0))), maxc(numext::real(v.coeff(0)));
85 for(int j = 0; j < i; j++)
86 {
87 s += v[j];
88 p *= v_for_prod[j];
89 minc = (std::min)(minc, numext::real(v[j]));
90 maxc = (std::max)(maxc, numext::real(v[j]));
91 }
92 VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.head(i).sum()), Scalar(1));
93 VERIFY_IS_APPROX(p, v_for_prod.head(i).prod());
94 VERIFY_IS_APPROX(minc, v.real().head(i).minCoeff());
95 VERIFY_IS_APPROX(maxc, v.real().head(i).maxCoeff());
96 }
97
98 for(int i = 0; i < size-1; i++)
99 {
100 Scalar s(0), p(1);
101 RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
102 for(int j = i; j < size; j++)
103 {
104 s += v[j];
105 p *= v_for_prod[j];
106 minc = (std::min)(minc, numext::real(v[j]));
107 maxc = (std::max)(maxc, numext::real(v[j]));
108 }
109 VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.tail(size-i).sum()), Scalar(1));
110 VERIFY_IS_APPROX(p, v_for_prod.tail(size-i).prod());
111 VERIFY_IS_APPROX(minc, v.real().tail(size-i).minCoeff());
112 VERIFY_IS_APPROX(maxc, v.real().tail(size-i).maxCoeff());
113 }
114
115 for(int i = 0; i < size/2; i++)
116 {
117 Scalar s(0), p(1);
118 RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
119 for(int j = i; j < size-i; j++)
120 {
121 s += v[j];
122 p *= v_for_prod[j];
123 minc = (std::min)(minc, numext::real(v[j]));
124 maxc = (std::max)(maxc, numext::real(v[j]));
125 }
126 VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.segment(i, size-2*i).sum()), Scalar(1));
127 VERIFY_IS_APPROX(p, v_for_prod.segment(i, size-2*i).prod());
128 VERIFY_IS_APPROX(minc, v.real().segment(i, size-2*i).minCoeff());
129 VERIFY_IS_APPROX(maxc, v.real().segment(i, size-2*i).maxCoeff());
130 }
131
132 // test empty objects
133 VERIFY_IS_APPROX(v.head(0).sum(), Scalar(0));
134 VERIFY_IS_APPROX(v.tail(0).prod(), Scalar(1));
135 VERIFY_RAISES_ASSERT(v.head(0).mean());
136 VERIFY_RAISES_ASSERT(v.head(0).minCoeff());
137 VERIFY_RAISES_ASSERT(v.head(0).maxCoeff());
138}
139
140void test_redux()
141{
142 // the max size cannot be too large, otherwise reduxion operations obviously generate large errors.
143 int maxsize = (std::min)(100,EIGEN_TEST_MAX_SIZE);
144 TEST_SET_BUT_UNUSED_VARIABLE(maxsize);
145 for(int i = 0; i < g_repeat; i++) {
146 CALL_SUBTEST_1( matrixRedux(Matrix<float, 1, 1>()) );
147 CALL_SUBTEST_1( matrixRedux(Array<float, 1, 1>()) );
148 CALL_SUBTEST_2( matrixRedux(Matrix2f()) );
149 CALL_SUBTEST_2( matrixRedux(Array2f()) );
150 CALL_SUBTEST_3( matrixRedux(Matrix4d()) );
151 CALL_SUBTEST_3( matrixRedux(Array4d()) );
152 CALL_SUBTEST_4( matrixRedux(MatrixXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
153 CALL_SUBTEST_4( matrixRedux(ArrayXXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
154 CALL_SUBTEST_5( matrixRedux(MatrixXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
155 CALL_SUBTEST_5( matrixRedux(ArrayXXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
156 CALL_SUBTEST_6( matrixRedux(MatrixXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
157 CALL_SUBTEST_6( matrixRedux(ArrayXXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
158 }
159 for(int i = 0; i < g_repeat; i++) {
160 CALL_SUBTEST_7( vectorRedux(Vector4f()) );
161 CALL_SUBTEST_7( vectorRedux(Array4f()) );
162 CALL_SUBTEST_5( vectorRedux(VectorXd(internal::random<int>(1,maxsize))) );
163 CALL_SUBTEST_5( vectorRedux(ArrayXd(internal::random<int>(1,maxsize))) );
164 CALL_SUBTEST_8( vectorRedux(VectorXf(internal::random<int>(1,maxsize))) );
165 CALL_SUBTEST_8( vectorRedux(ArrayXf(internal::random<int>(1,maxsize))) );
166 }
167}
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