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 // Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
6 //
7 // This Source Code Form is subject to the terms of the Mozilla
8 // Public License v. 2.0. If a copy of the MPL was not distributed
9 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
10
11 #define TEST_ENABLE_TEMPORARY_TRACKING
12
13 #include "main.h"
14
matrixRedux(const MatrixType & m)15 template<typename MatrixType> void matrixRedux(const MatrixType& m)
16 {
17 typedef typename MatrixType::Index Index;
18 typedef typename MatrixType::Scalar Scalar;
19 typedef typename MatrixType::RealScalar RealScalar;
20
21 Index rows = m.rows();
22 Index cols = m.cols();
23
24 MatrixType m1 = MatrixType::Random(rows, cols);
25
26 // The entries of m1 are uniformly distributed in [0,1], so m1.prod() is very small. This may lead to test
27 // failures if we underflow into denormals. Thus, we scale so that entries are close to 1.
28 MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + RealScalar(0.2) * m1;
29
30 VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1));
31 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
32 Scalar s(0), p(1), minc(numext::real(m1.coeff(0))), maxc(numext::real(m1.coeff(0)));
33 for(int j = 0; j < cols; j++)
34 for(int i = 0; i < rows; i++)
35 {
36 s += m1(i,j);
37 p *= m1_for_prod(i,j);
38 minc = (std::min)(numext::real(minc), numext::real(m1(i,j)));
39 maxc = (std::max)(numext::real(maxc), numext::real(m1(i,j)));
40 }
41 const Scalar mean = s/Scalar(RealScalar(rows*cols));
42
43 VERIFY_IS_APPROX(m1.sum(), s);
44 VERIFY_IS_APPROX(m1.mean(), mean);
45 VERIFY_IS_APPROX(m1_for_prod.prod(), p);
46 VERIFY_IS_APPROX(m1.real().minCoeff(), numext::real(minc));
47 VERIFY_IS_APPROX(m1.real().maxCoeff(), numext::real(maxc));
48
49 // test slice vectorization assuming assign is ok
50 Index r0 = internal::random<Index>(0,rows-1);
51 Index c0 = internal::random<Index>(0,cols-1);
52 Index r1 = internal::random<Index>(r0+1,rows)-r0;
53 Index c1 = internal::random<Index>(c0+1,cols)-c0;
54 VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).sum(), m1.block(r0,c0,r1,c1).eval().sum());
55 VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).mean(), m1.block(r0,c0,r1,c1).eval().mean());
56 VERIFY_IS_APPROX(m1_for_prod.block(r0,c0,r1,c1).prod(), m1_for_prod.block(r0,c0,r1,c1).eval().prod());
57 VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().minCoeff(), m1.block(r0,c0,r1,c1).real().eval().minCoeff());
58 VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().maxCoeff(), m1.block(r0,c0,r1,c1).real().eval().maxCoeff());
59
60 // regression for bug 1090
61 const int R1 = MatrixType::RowsAtCompileTime>=2 ? MatrixType::RowsAtCompileTime/2 : 6;
62 const int C1 = MatrixType::ColsAtCompileTime>=2 ? MatrixType::ColsAtCompileTime/2 : 6;
63 if(R1<=rows-r0 && C1<=cols-c0)
64 {
65 VERIFY_IS_APPROX( (m1.template block<R1,C1>(r0,c0).sum()), m1.block(r0,c0,R1,C1).sum() );
66 }
67
68 // test empty objects
69 VERIFY_IS_APPROX(m1.block(r0,c0,0,0).sum(), Scalar(0));
70 VERIFY_IS_APPROX(m1.block(r0,c0,0,0).prod(), Scalar(1));
71
72 // test nesting complex expression
73 VERIFY_EVALUATION_COUNT( (m1.matrix()*m1.matrix().transpose()).sum(), (MatrixType::IsVectorAtCompileTime && MatrixType::SizeAtCompileTime!=1 ? 0 : 1) );
74 Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> m2(rows,rows);
75 m2.setRandom();
76 VERIFY_EVALUATION_COUNT( ((m1.matrix()*m1.matrix().transpose())+m2).sum(),(MatrixType::IsVectorAtCompileTime && MatrixType::SizeAtCompileTime!=1 ? 0 : 1));
77 }
78
vectorRedux(const VectorType & w)79 template<typename VectorType> void vectorRedux(const VectorType& w)
80 {
81 using std::abs;
82 typedef typename VectorType::Index Index;
83 typedef typename VectorType::Scalar Scalar;
84 typedef typename NumTraits<Scalar>::Real RealScalar;
85 Index size = w.size();
86
87 VectorType v = VectorType::Random(size);
88 VectorType v_for_prod = VectorType::Ones(size) + Scalar(0.2) * v; // see comment above declaration of m1_for_prod
89
90 for(int i = 1; i < size; i++)
91 {
92 Scalar s(0), p(1);
93 RealScalar minc(numext::real(v.coeff(0))), maxc(numext::real(v.coeff(0)));
94 for(int j = 0; j < i; 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.head(i).sum()), Scalar(1));
102 VERIFY_IS_APPROX(p, v_for_prod.head(i).prod());
103 VERIFY_IS_APPROX(minc, v.real().head(i).minCoeff());
104 VERIFY_IS_APPROX(maxc, v.real().head(i).maxCoeff());
105 }
106
107 for(int i = 0; i < size-1; 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; 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.tail(size-i).sum()), Scalar(1));
119 VERIFY_IS_APPROX(p, v_for_prod.tail(size-i).prod());
120 VERIFY_IS_APPROX(minc, v.real().tail(size-i).minCoeff());
121 VERIFY_IS_APPROX(maxc, v.real().tail(size-i).maxCoeff());
122 }
123
124 for(int i = 0; i < size/2; i++)
125 {
126 Scalar s(0), p(1);
127 RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
128 for(int j = i; j < size-i; j++)
129 {
130 s += v[j];
131 p *= v_for_prod[j];
132 minc = (std::min)(minc, numext::real(v[j]));
133 maxc = (std::max)(maxc, numext::real(v[j]));
134 }
135 VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.segment(i, size-2*i).sum()), Scalar(1));
136 VERIFY_IS_APPROX(p, v_for_prod.segment(i, size-2*i).prod());
137 VERIFY_IS_APPROX(minc, v.real().segment(i, size-2*i).minCoeff());
138 VERIFY_IS_APPROX(maxc, v.real().segment(i, size-2*i).maxCoeff());
139 }
140
141 // test empty objects
142 VERIFY_IS_APPROX(v.head(0).sum(), Scalar(0));
143 VERIFY_IS_APPROX(v.tail(0).prod(), Scalar(1));
144 VERIFY_RAISES_ASSERT(v.head(0).mean());
145 VERIFY_RAISES_ASSERT(v.head(0).minCoeff());
146 VERIFY_RAISES_ASSERT(v.head(0).maxCoeff());
147 }
148
test_redux()149 void test_redux()
150 {
151 // the max size cannot be too large, otherwise reduxion operations obviously generate large errors.
152 int maxsize = (std::min)(100,EIGEN_TEST_MAX_SIZE);
153 TEST_SET_BUT_UNUSED_VARIABLE(maxsize);
154 for(int i = 0; i < g_repeat; i++) {
155 CALL_SUBTEST_1( matrixRedux(Matrix<float, 1, 1>()) );
156 CALL_SUBTEST_1( matrixRedux(Array<float, 1, 1>()) );
157 CALL_SUBTEST_2( matrixRedux(Matrix2f()) );
158 CALL_SUBTEST_2( matrixRedux(Array2f()) );
159 CALL_SUBTEST_2( matrixRedux(Array22f()) );
160 CALL_SUBTEST_3( matrixRedux(Matrix4d()) );
161 CALL_SUBTEST_3( matrixRedux(Array4d()) );
162 CALL_SUBTEST_3( matrixRedux(Array44d()) );
163 CALL_SUBTEST_4( matrixRedux(MatrixXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
164 CALL_SUBTEST_4( matrixRedux(ArrayXXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
165 CALL_SUBTEST_5( matrixRedux(MatrixXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
166 CALL_SUBTEST_5( matrixRedux(ArrayXXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
167 CALL_SUBTEST_6( matrixRedux(MatrixXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
168 CALL_SUBTEST_6( matrixRedux(ArrayXXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
169 }
170 for(int i = 0; i < g_repeat; i++) {
171 CALL_SUBTEST_7( vectorRedux(Vector4f()) );
172 CALL_SUBTEST_7( vectorRedux(Array4f()) );
173 CALL_SUBTEST_5( vectorRedux(VectorXd(internal::random<int>(1,maxsize))) );
174 CALL_SUBTEST_5( vectorRedux(ArrayXd(internal::random<int>(1,maxsize))) );
175 CALL_SUBTEST_8( vectorRedux(VectorXf(internal::random<int>(1,maxsize))) );
176 CALL_SUBTEST_8( vectorRedux(ArrayXf(internal::random<int>(1,maxsize))) );
177 }
178 }
179