1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2008 Benoit Jacob <[email protected]>
5 // Copyright (C) 2015 Gael Guennebaud <[email protected]>
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 #define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
13 // ^^ see bug 1449
14
15 #include "main.h"
16
matrixRedux(const MatrixType & m)17 template<typename MatrixType> void matrixRedux(const MatrixType& m)
18 {
19 typedef typename MatrixType::Scalar Scalar;
20 typedef typename MatrixType::RealScalar RealScalar;
21
22 Index rows = m.rows();
23 Index cols = m.cols();
24
25 MatrixType m1 = MatrixType::Random(rows, cols);
26
27 // The entries of m1 are uniformly distributed in [0,1], so m1.prod() is very small. This may lead to test
28 // failures if we underflow into denormals. Thus, we scale so that entries are close to 1.
29 MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + RealScalar(0.2) * m1;
30
31 Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> m2(rows,rows);
32 m2.setRandom();
33
34 VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1));
35 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
36 Scalar s(0), p(1), minc(numext::real(m1.coeff(0))), maxc(numext::real(m1.coeff(0)));
37 for(int j = 0; j < cols; j++)
38 for(int i = 0; i < rows; i++)
39 {
40 s += m1(i,j);
41 p *= m1_for_prod(i,j);
42 minc = (std::min)(numext::real(minc), numext::real(m1(i,j)));
43 maxc = (std::max)(numext::real(maxc), numext::real(m1(i,j)));
44 }
45 const Scalar mean = s/Scalar(RealScalar(rows*cols));
46
47 VERIFY_IS_APPROX(m1.sum(), s);
48 VERIFY_IS_APPROX(m1.mean(), mean);
49 VERIFY_IS_APPROX(m1_for_prod.prod(), p);
50 VERIFY_IS_APPROX(m1.real().minCoeff(), numext::real(minc));
51 VERIFY_IS_APPROX(m1.real().maxCoeff(), numext::real(maxc));
52
53 // test that partial reduction works if nested expressions is forced to evaluate early
54 VERIFY_IS_APPROX((m1.matrix() * m1.matrix().transpose()) .cwiseProduct(m2.matrix()).rowwise().sum().sum(),
55 (m1.matrix() * m1.matrix().transpose()).eval().cwiseProduct(m2.matrix()).rowwise().sum().sum());
56
57 // test slice vectorization assuming assign is ok
58 Index r0 = internal::random<Index>(0,rows-1);
59 Index c0 = internal::random<Index>(0,cols-1);
60 Index r1 = internal::random<Index>(r0+1,rows)-r0;
61 Index c1 = internal::random<Index>(c0+1,cols)-c0;
62 VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).sum(), m1.block(r0,c0,r1,c1).eval().sum());
63 VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).mean(), m1.block(r0,c0,r1,c1).eval().mean());
64 VERIFY_IS_APPROX(m1_for_prod.block(r0,c0,r1,c1).prod(), m1_for_prod.block(r0,c0,r1,c1).eval().prod());
65 VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().minCoeff(), m1.block(r0,c0,r1,c1).real().eval().minCoeff());
66 VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().maxCoeff(), m1.block(r0,c0,r1,c1).real().eval().maxCoeff());
67
68 // regression for bug 1090
69 const int R1 = MatrixType::RowsAtCompileTime>=2 ? MatrixType::RowsAtCompileTime/2 : 6;
70 const int C1 = MatrixType::ColsAtCompileTime>=2 ? MatrixType::ColsAtCompileTime/2 : 6;
71 if(R1<=rows-r0 && C1<=cols-c0)
72 {
73 VERIFY_IS_APPROX( (m1.template block<R1,C1>(r0,c0).sum()), m1.block(r0,c0,R1,C1).sum() );
74 }
75
76 // test empty objects
77 VERIFY_IS_APPROX(m1.block(r0,c0,0,0).sum(), Scalar(0));
78 VERIFY_IS_APPROX(m1.block(r0,c0,0,0).prod(), Scalar(1));
79
80 // test nesting complex expression
81 VERIFY_EVALUATION_COUNT( (m1.matrix()*m1.matrix().transpose()).sum(), (MatrixType::IsVectorAtCompileTime && MatrixType::SizeAtCompileTime!=1 ? 0 : 1) );
82 VERIFY_EVALUATION_COUNT( ((m1.matrix()*m1.matrix().transpose())+m2).sum(),(MatrixType::IsVectorAtCompileTime && MatrixType::SizeAtCompileTime!=1 ? 0 : 1));
83 }
84
vectorRedux(const VectorType & w)85 template<typename VectorType> void vectorRedux(const VectorType& w)
86 {
87 using std::abs;
88 typedef typename VectorType::Scalar Scalar;
89 typedef typename NumTraits<Scalar>::Real RealScalar;
90 Index size = w.size();
91
92 VectorType v = VectorType::Random(size);
93 VectorType v_for_prod = VectorType::Ones(size) + Scalar(0.2) * v; // see comment above declaration of m1_for_prod
94
95 for(int i = 1; i < size; i++)
96 {
97 Scalar s(0), p(1);
98 RealScalar minc(numext::real(v.coeff(0))), maxc(numext::real(v.coeff(0)));
99 for(int j = 0; j < i; j++)
100 {
101 s += v[j];
102 p *= v_for_prod[j];
103 minc = (std::min)(minc, numext::real(v[j]));
104 maxc = (std::max)(maxc, numext::real(v[j]));
105 }
106 VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.head(i).sum()), Scalar(1));
107 VERIFY_IS_APPROX(p, v_for_prod.head(i).prod());
108 VERIFY_IS_APPROX(minc, v.real().head(i).minCoeff());
109 VERIFY_IS_APPROX(maxc, v.real().head(i).maxCoeff());
110 }
111
112 for(int i = 0; i < size-1; i++)
113 {
114 Scalar s(0), p(1);
115 RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
116 for(int j = i; j < size; j++)
117 {
118 s += v[j];
119 p *= v_for_prod[j];
120 minc = (std::min)(minc, numext::real(v[j]));
121 maxc = (std::max)(maxc, numext::real(v[j]));
122 }
123 VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.tail(size-i).sum()), Scalar(1));
124 VERIFY_IS_APPROX(p, v_for_prod.tail(size-i).prod());
125 VERIFY_IS_APPROX(minc, v.real().tail(size-i).minCoeff());
126 VERIFY_IS_APPROX(maxc, v.real().tail(size-i).maxCoeff());
127 }
128
129 for(int i = 0; i < size/2; i++)
130 {
131 Scalar s(0), p(1);
132 RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
133 for(int j = i; j < size-i; j++)
134 {
135 s += v[j];
136 p *= v_for_prod[j];
137 minc = (std::min)(minc, numext::real(v[j]));
138 maxc = (std::max)(maxc, numext::real(v[j]));
139 }
140 VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.segment(i, size-2*i).sum()), Scalar(1));
141 VERIFY_IS_APPROX(p, v_for_prod.segment(i, size-2*i).prod());
142 VERIFY_IS_APPROX(minc, v.real().segment(i, size-2*i).minCoeff());
143 VERIFY_IS_APPROX(maxc, v.real().segment(i, size-2*i).maxCoeff());
144 }
145
146 // test empty objects
147 VERIFY_IS_APPROX(v.head(0).sum(), Scalar(0));
148 VERIFY_IS_APPROX(v.tail(0).prod(), Scalar(1));
149 VERIFY_RAISES_ASSERT(v.head(0).mean());
150 VERIFY_RAISES_ASSERT(v.head(0).minCoeff());
151 VERIFY_RAISES_ASSERT(v.head(0).maxCoeff());
152 }
153
EIGEN_DECLARE_TEST(redux)154 EIGEN_DECLARE_TEST(redux)
155 {
156 // the max size cannot be too large, otherwise reduxion operations obviously generate large errors.
157 int maxsize = (std::min)(100,EIGEN_TEST_MAX_SIZE);
158 TEST_SET_BUT_UNUSED_VARIABLE(maxsize);
159 for(int i = 0; i < g_repeat; i++) {
160 CALL_SUBTEST_1( matrixRedux(Matrix<float, 1, 1>()) );
161 CALL_SUBTEST_1( matrixRedux(Array<float, 1, 1>()) );
162 CALL_SUBTEST_2( matrixRedux(Matrix2f()) );
163 CALL_SUBTEST_2( matrixRedux(Array2f()) );
164 CALL_SUBTEST_2( matrixRedux(Array22f()) );
165 CALL_SUBTEST_3( matrixRedux(Matrix4d()) );
166 CALL_SUBTEST_3( matrixRedux(Array4d()) );
167 CALL_SUBTEST_3( matrixRedux(Array44d()) );
168 CALL_SUBTEST_4( matrixRedux(MatrixXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
169 CALL_SUBTEST_4( matrixRedux(ArrayXXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
170 CALL_SUBTEST_5( matrixRedux(MatrixXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
171 CALL_SUBTEST_5( matrixRedux(ArrayXXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
172 CALL_SUBTEST_6( matrixRedux(MatrixXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
173 CALL_SUBTEST_6( matrixRedux(ArrayXXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
174 }
175 for(int i = 0; i < g_repeat; i++) {
176 CALL_SUBTEST_7( vectorRedux(Vector4f()) );
177 CALL_SUBTEST_7( vectorRedux(Array4f()) );
178 CALL_SUBTEST_5( vectorRedux(VectorXd(internal::random<int>(1,maxsize))) );
179 CALL_SUBTEST_5( vectorRedux(ArrayXd(internal::random<int>(1,maxsize))) );
180 CALL_SUBTEST_8( vectorRedux(VectorXf(internal::random<int>(1,maxsize))) );
181 CALL_SUBTEST_8( vectorRedux(ArrayXf(internal::random<int>(1,maxsize))) );
182 }
183 }
184