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
matrixRedux(const MatrixType & m)12 template<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 // test empty objects
58 VERIFY_IS_APPROX(m1.block(r0,c0,0,0).sum(), Scalar(0));
59 VERIFY_IS_APPROX(m1.block(r0,c0,0,0).prod(), Scalar(1));
60 }
61
vectorRedux(const VectorType & w)62 template<typename VectorType> void vectorRedux(const VectorType& w)
63 {
64 using std::abs;
65 typedef typename VectorType::Index Index;
66 typedef typename VectorType::Scalar Scalar;
67 typedef typename NumTraits<Scalar>::Real RealScalar;
68 Index size = w.size();
69
70 VectorType v = VectorType::Random(size);
71 VectorType v_for_prod = VectorType::Ones(size) + Scalar(0.2) * v; // see comment above declaration of m1_for_prod
72
73 for(int i = 1; i < size; i++)
74 {
75 Scalar s(0), p(1);
76 RealScalar minc(numext::real(v.coeff(0))), maxc(numext::real(v.coeff(0)));
77 for(int j = 0; j < i; j++)
78 {
79 s += v[j];
80 p *= v_for_prod[j];
81 minc = (std::min)(minc, numext::real(v[j]));
82 maxc = (std::max)(maxc, numext::real(v[j]));
83 }
84 VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.head(i).sum()), Scalar(1));
85 VERIFY_IS_APPROX(p, v_for_prod.head(i).prod());
86 VERIFY_IS_APPROX(minc, v.real().head(i).minCoeff());
87 VERIFY_IS_APPROX(maxc, v.real().head(i).maxCoeff());
88 }
89
90 for(int i = 0; i < size-1; i++)
91 {
92 Scalar s(0), p(1);
93 RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
94 for(int j = i; j < size; j++)
95 {
96 s += v[j];
97 p *= v_for_prod[j];
98 minc = (std::min)(minc, numext::real(v[j]));
99 maxc = (std::max)(maxc, numext::real(v[j]));
100 }
101 VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.tail(size-i).sum()), Scalar(1));
102 VERIFY_IS_APPROX(p, v_for_prod.tail(size-i).prod());
103 VERIFY_IS_APPROX(minc, v.real().tail(size-i).minCoeff());
104 VERIFY_IS_APPROX(maxc, v.real().tail(size-i).maxCoeff());
105 }
106
107 for(int i = 0; i < size/2; i++)
108 {
109 Scalar s(0), p(1);
110 RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
111 for(int j = i; j < size-i; j++)
112 {
113 s += v[j];
114 p *= v_for_prod[j];
115 minc = (std::min)(minc, numext::real(v[j]));
116 maxc = (std::max)(maxc, numext::real(v[j]));
117 }
118 VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.segment(i, size-2*i).sum()), Scalar(1));
119 VERIFY_IS_APPROX(p, v_for_prod.segment(i, size-2*i).prod());
120 VERIFY_IS_APPROX(minc, v.real().segment(i, size-2*i).minCoeff());
121 VERIFY_IS_APPROX(maxc, v.real().segment(i, size-2*i).maxCoeff());
122 }
123
124 // test empty objects
125 VERIFY_IS_APPROX(v.head(0).sum(), Scalar(0));
126 VERIFY_IS_APPROX(v.tail(0).prod(), Scalar(1));
127 VERIFY_RAISES_ASSERT(v.head(0).mean());
128 VERIFY_RAISES_ASSERT(v.head(0).minCoeff());
129 VERIFY_RAISES_ASSERT(v.head(0).maxCoeff());
130 }
131
test_redux()132 void test_redux()
133 {
134 // the max size cannot be too large, otherwise reduxion operations obviously generate large errors.
135 int maxsize = (std::min)(100,EIGEN_TEST_MAX_SIZE);
136 TEST_SET_BUT_UNUSED_VARIABLE(maxsize);
137 for(int i = 0; i < g_repeat; i++) {
138 CALL_SUBTEST_1( matrixRedux(Matrix<float, 1, 1>()) );
139 CALL_SUBTEST_1( matrixRedux(Array<float, 1, 1>()) );
140 CALL_SUBTEST_2( matrixRedux(Matrix2f()) );
141 CALL_SUBTEST_2( matrixRedux(Array2f()) );
142 CALL_SUBTEST_3( matrixRedux(Matrix4d()) );
143 CALL_SUBTEST_3( matrixRedux(Array4d()) );
144 CALL_SUBTEST_4( matrixRedux(MatrixXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
145 CALL_SUBTEST_4( matrixRedux(ArrayXXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
146 CALL_SUBTEST_5( matrixRedux(MatrixXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
147 CALL_SUBTEST_5( matrixRedux(ArrayXXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
148 CALL_SUBTEST_6( matrixRedux(MatrixXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
149 CALL_SUBTEST_6( matrixRedux(ArrayXXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
150 }
151 for(int i = 0; i < g_repeat; i++) {
152 CALL_SUBTEST_7( vectorRedux(Vector4f()) );
153 CALL_SUBTEST_7( vectorRedux(Array4f()) );
154 CALL_SUBTEST_5( vectorRedux(VectorXd(internal::random<int>(1,maxsize))) );
155 CALL_SUBTEST_5( vectorRedux(ArrayXd(internal::random<int>(1,maxsize))) );
156 CALL_SUBTEST_8( vectorRedux(VectorXf(internal::random<int>(1,maxsize))) );
157 CALL_SUBTEST_8( vectorRedux(ArrayXf(internal::random<int>(1,maxsize))) );
158 }
159 }
160