1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2011 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_NO_STATIC_ASSERT
13
14 #include "main.h"
15
vectorwiseop_array(const ArrayType & m)16 template<typename ArrayType> void vectorwiseop_array(const ArrayType& m)
17 {
18 typedef typename ArrayType::Scalar Scalar;
19 typedef Array<Scalar, ArrayType::RowsAtCompileTime, 1> ColVectorType;
20 typedef Array<Scalar, 1, ArrayType::ColsAtCompileTime> RowVectorType;
21
22 Index rows = m.rows();
23 Index cols = m.cols();
24 Index r = internal::random<Index>(0, rows-1),
25 c = internal::random<Index>(0, cols-1);
26
27 ArrayType m1 = ArrayType::Random(rows, cols),
28 m2(rows, cols),
29 m3(rows, cols);
30
31 ColVectorType colvec = ColVectorType::Random(rows);
32 RowVectorType rowvec = RowVectorType::Random(cols);
33
34 // test addition
35
36 m2 = m1;
37 m2.colwise() += colvec;
38 VERIFY_IS_APPROX(m2, m1.colwise() + colvec);
39 VERIFY_IS_APPROX(m2.col(c), m1.col(c) + colvec);
40
41 VERIFY_RAISES_ASSERT(m2.colwise() += colvec.transpose());
42 VERIFY_RAISES_ASSERT(m1.colwise() + colvec.transpose());
43
44 m2 = m1;
45 m2.rowwise() += rowvec;
46 VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec);
47 VERIFY_IS_APPROX(m2.row(r), m1.row(r) + rowvec);
48
49 VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose());
50 VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose());
51
52 // test substraction
53
54 m2 = m1;
55 m2.colwise() -= colvec;
56 VERIFY_IS_APPROX(m2, m1.colwise() - colvec);
57 VERIFY_IS_APPROX(m2.col(c), m1.col(c) - colvec);
58
59 VERIFY_RAISES_ASSERT(m2.colwise() -= colvec.transpose());
60 VERIFY_RAISES_ASSERT(m1.colwise() - colvec.transpose());
61
62 m2 = m1;
63 m2.rowwise() -= rowvec;
64 VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec);
65 VERIFY_IS_APPROX(m2.row(r), m1.row(r) - rowvec);
66
67 VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose());
68 VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose());
69
70 // test multiplication
71
72 m2 = m1;
73 m2.colwise() *= colvec;
74 VERIFY_IS_APPROX(m2, m1.colwise() * colvec);
75 VERIFY_IS_APPROX(m2.col(c), m1.col(c) * colvec);
76
77 VERIFY_RAISES_ASSERT(m2.colwise() *= colvec.transpose());
78 VERIFY_RAISES_ASSERT(m1.colwise() * colvec.transpose());
79
80 m2 = m1;
81 m2.rowwise() *= rowvec;
82 VERIFY_IS_APPROX(m2, m1.rowwise() * rowvec);
83 VERIFY_IS_APPROX(m2.row(r), m1.row(r) * rowvec);
84
85 VERIFY_RAISES_ASSERT(m2.rowwise() *= rowvec.transpose());
86 VERIFY_RAISES_ASSERT(m1.rowwise() * rowvec.transpose());
87
88 // test quotient
89
90 m2 = m1;
91 m2.colwise() /= colvec;
92 VERIFY_IS_APPROX(m2, m1.colwise() / colvec);
93 VERIFY_IS_APPROX(m2.col(c), m1.col(c) / colvec);
94
95 VERIFY_RAISES_ASSERT(m2.colwise() /= colvec.transpose());
96 VERIFY_RAISES_ASSERT(m1.colwise() / colvec.transpose());
97
98 m2 = m1;
99 m2.rowwise() /= rowvec;
100 VERIFY_IS_APPROX(m2, m1.rowwise() / rowvec);
101 VERIFY_IS_APPROX(m2.row(r), m1.row(r) / rowvec);
102
103 VERIFY_RAISES_ASSERT(m2.rowwise() /= rowvec.transpose());
104 VERIFY_RAISES_ASSERT(m1.rowwise() / rowvec.transpose());
105
106 m2 = m1;
107 // yes, there might be an aliasing issue there but ".rowwise() /="
108 // is supposed to evaluate " m2.colwise().sum()" into a temporary to avoid
109 // evaluating the reduction multiple times
110 if(ArrayType::RowsAtCompileTime>2 || ArrayType::RowsAtCompileTime==Dynamic)
111 {
112 m2.rowwise() /= m2.colwise().sum();
113 VERIFY_IS_APPROX(m2, m1.rowwise() / m1.colwise().sum());
114 }
115
116 // all/any
117 Array<bool,Dynamic,Dynamic> mb(rows,cols);
118 mb = (m1.real()<=0.7).colwise().all();
119 VERIFY( (mb.col(c) == (m1.real().col(c)<=0.7).all()).all() );
120 mb = (m1.real()<=0.7).rowwise().all();
121 VERIFY( (mb.row(r) == (m1.real().row(r)<=0.7).all()).all() );
122
123 mb = (m1.real()>=0.7).colwise().any();
124 VERIFY( (mb.col(c) == (m1.real().col(c)>=0.7).any()).all() );
125 mb = (m1.real()>=0.7).rowwise().any();
126 VERIFY( (mb.row(r) == (m1.real().row(r)>=0.7).any()).all() );
127 }
128
vectorwiseop_matrix(const MatrixType & m)129 template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m)
130 {
131 typedef typename MatrixType::Scalar Scalar;
132 typedef typename NumTraits<Scalar>::Real RealScalar;
133 typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVectorType;
134 typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType;
135 typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealColVectorType;
136 typedef Matrix<RealScalar, 1, MatrixType::ColsAtCompileTime> RealRowVectorType;
137 typedef Matrix<Scalar,Dynamic,Dynamic> MatrixX;
138
139 Index rows = m.rows();
140 Index cols = m.cols();
141 Index r = internal::random<Index>(0, rows-1),
142 c = internal::random<Index>(0, cols-1);
143
144 MatrixType m1 = MatrixType::Random(rows, cols),
145 m2(rows, cols),
146 m3(rows, cols);
147
148 ColVectorType colvec = ColVectorType::Random(rows);
149 RowVectorType rowvec = RowVectorType::Random(cols);
150 RealColVectorType rcres;
151 RealRowVectorType rrres;
152
153 // test broadcast assignment
154 m2 = m1;
155 m2.colwise() = colvec;
156 for(Index j=0; j<cols; ++j)
157 VERIFY_IS_APPROX(m2.col(j), colvec);
158 m2.rowwise() = rowvec;
159 for(Index i=0; i<rows; ++i)
160 VERIFY_IS_APPROX(m2.row(i), rowvec);
161 if(rows>1)
162 VERIFY_RAISES_ASSERT(m2.colwise() = colvec.transpose());
163 if(cols>1)
164 VERIFY_RAISES_ASSERT(m2.rowwise() = rowvec.transpose());
165
166 // test addition
167
168 m2 = m1;
169 m2.colwise() += colvec;
170 VERIFY_IS_APPROX(m2, m1.colwise() + colvec);
171 VERIFY_IS_APPROX(m2.col(c), m1.col(c) + colvec);
172
173 if(rows>1)
174 {
175 VERIFY_RAISES_ASSERT(m2.colwise() += colvec.transpose());
176 VERIFY_RAISES_ASSERT(m1.colwise() + colvec.transpose());
177 }
178
179 m2 = m1;
180 m2.rowwise() += rowvec;
181 VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec);
182 VERIFY_IS_APPROX(m2.row(r), m1.row(r) + rowvec);
183
184 if(cols>1)
185 {
186 VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose());
187 VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose());
188 }
189
190 // test substraction
191
192 m2 = m1;
193 m2.colwise() -= colvec;
194 VERIFY_IS_APPROX(m2, m1.colwise() - colvec);
195 VERIFY_IS_APPROX(m2.col(c), m1.col(c) - colvec);
196
197 if(rows>1)
198 {
199 VERIFY_RAISES_ASSERT(m2.colwise() -= colvec.transpose());
200 VERIFY_RAISES_ASSERT(m1.colwise() - colvec.transpose());
201 }
202
203 m2 = m1;
204 m2.rowwise() -= rowvec;
205 VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec);
206 VERIFY_IS_APPROX(m2.row(r), m1.row(r) - rowvec);
207
208 if(cols>1)
209 {
210 VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose());
211 VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose());
212 }
213
214 // ------ partial reductions ------
215
216 #define TEST_PARTIAL_REDUX_BASIC(FUNC,ROW,COL,PREPROCESS) { \
217 ROW = m1 PREPROCESS .colwise().FUNC ; \
218 for(Index k=0; k<cols; ++k) VERIFY_IS_APPROX(ROW(k), m1.col(k) PREPROCESS .FUNC ); \
219 COL = m1 PREPROCESS .rowwise().FUNC ; \
220 for(Index k=0; k<rows; ++k) VERIFY_IS_APPROX(COL(k), m1.row(k) PREPROCESS .FUNC ); \
221 }
222
223 TEST_PARTIAL_REDUX_BASIC(sum(), rowvec,colvec,EIGEN_EMPTY);
224 TEST_PARTIAL_REDUX_BASIC(prod(), rowvec,colvec,EIGEN_EMPTY);
225 TEST_PARTIAL_REDUX_BASIC(mean(), rowvec,colvec,EIGEN_EMPTY);
226 TEST_PARTIAL_REDUX_BASIC(minCoeff(), rrres, rcres, .real());
227 TEST_PARTIAL_REDUX_BASIC(maxCoeff(), rrres, rcres, .real());
228 TEST_PARTIAL_REDUX_BASIC(norm(), rrres, rcres, EIGEN_EMPTY);
229 TEST_PARTIAL_REDUX_BASIC(squaredNorm(),rrres, rcres, EIGEN_EMPTY);
230 TEST_PARTIAL_REDUX_BASIC(redux(internal::scalar_sum_op<Scalar,Scalar>()),rowvec,colvec,EIGEN_EMPTY);
231
232 VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum(), m1.colwise().template lpNorm<1>());
233 VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().sum(), m1.rowwise().template lpNorm<1>());
234 VERIFY_IS_APPROX(m1.cwiseAbs().colwise().maxCoeff(), m1.colwise().template lpNorm<Infinity>());
235 VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().maxCoeff(), m1.rowwise().template lpNorm<Infinity>());
236
237 // regression for bug 1158
238 VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum().x(), m1.col(0).cwiseAbs().sum());
239
240 // test normalized
241 m2 = m1.colwise().normalized();
242 VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized());
243 m2 = m1.rowwise().normalized();
244 VERIFY_IS_APPROX(m2.row(r), m1.row(r).normalized());
245
246 // test normalize
247 m2 = m1;
248 m2.colwise().normalize();
249 VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized());
250 m2 = m1;
251 m2.rowwise().normalize();
252 VERIFY_IS_APPROX(m2.row(r), m1.row(r).normalized());
253
254 // test with partial reduction of products
255 Matrix<Scalar,MatrixType::RowsAtCompileTime,MatrixType::RowsAtCompileTime> m1m1 = m1 * m1.transpose();
256 VERIFY_IS_APPROX( (m1 * m1.transpose()).colwise().sum(), m1m1.colwise().sum());
257 Matrix<Scalar,1,MatrixType::RowsAtCompileTime> tmp(rows);
258 VERIFY_EVALUATION_COUNT( tmp = (m1 * m1.transpose()).colwise().sum(), 1);
259
260 m2 = m1.rowwise() - (m1.colwise().sum()/RealScalar(m1.rows())).eval();
261 m1 = m1.rowwise() - (m1.colwise().sum()/RealScalar(m1.rows()));
262 VERIFY_IS_APPROX( m1, m2 );
263 VERIFY_EVALUATION_COUNT( m2 = (m1.rowwise() - m1.colwise().sum()/RealScalar(m1.rows())), (MatrixType::RowsAtCompileTime!=1 ? 1 : 0) );
264
265 // test empty expressions
266 VERIFY_IS_APPROX(m1.matrix().middleCols(0,0).rowwise().sum().eval(), MatrixX::Zero(rows,1));
267 VERIFY_IS_APPROX(m1.matrix().middleRows(0,0).colwise().sum().eval(), MatrixX::Zero(1,cols));
268 VERIFY_IS_APPROX(m1.matrix().middleCols(0,fix<0>).rowwise().sum().eval(), MatrixX::Zero(rows,1));
269 VERIFY_IS_APPROX(m1.matrix().middleRows(0,fix<0>).colwise().sum().eval(), MatrixX::Zero(1,cols));
270
271 VERIFY_IS_APPROX(m1.matrix().middleCols(0,0).rowwise().prod().eval(), MatrixX::Ones(rows,1));
272 VERIFY_IS_APPROX(m1.matrix().middleRows(0,0).colwise().prod().eval(), MatrixX::Ones(1,cols));
273 VERIFY_IS_APPROX(m1.matrix().middleCols(0,fix<0>).rowwise().prod().eval(), MatrixX::Ones(rows,1));
274 VERIFY_IS_APPROX(m1.matrix().middleRows(0,fix<0>).colwise().prod().eval(), MatrixX::Ones(1,cols));
275
276 VERIFY_IS_APPROX(m1.matrix().middleCols(0,0).rowwise().squaredNorm().eval(), MatrixX::Zero(rows,1));
277
278 VERIFY_RAISES_ASSERT(m1.real().middleCols(0,0).rowwise().minCoeff().eval());
279 VERIFY_RAISES_ASSERT(m1.real().middleRows(0,0).colwise().maxCoeff().eval());
280 VERIFY_IS_EQUAL(m1.real().middleRows(0,0).rowwise().maxCoeff().eval().rows(),0);
281 VERIFY_IS_EQUAL(m1.real().middleCols(0,0).colwise().maxCoeff().eval().cols(),0);
282 VERIFY_IS_EQUAL(m1.real().middleRows(0,fix<0>).rowwise().maxCoeff().eval().rows(),0);
283 VERIFY_IS_EQUAL(m1.real().middleCols(0,fix<0>).colwise().maxCoeff().eval().cols(),0);
284 }
285
EIGEN_DECLARE_TEST(vectorwiseop)286 EIGEN_DECLARE_TEST(vectorwiseop)
287 {
288 CALL_SUBTEST_1( vectorwiseop_array(Array22cd()) );
289 CALL_SUBTEST_2( vectorwiseop_array(Array<double, 3, 2>()) );
290 CALL_SUBTEST_3( vectorwiseop_array(ArrayXXf(3, 4)) );
291 CALL_SUBTEST_4( vectorwiseop_matrix(Matrix4cf()) );
292 CALL_SUBTEST_5( vectorwiseop_matrix(Matrix4f()) );
293 CALL_SUBTEST_5( vectorwiseop_matrix(Vector4f()) );
294 CALL_SUBTEST_5( vectorwiseop_matrix(Matrix<float,4,5>()) );
295 CALL_SUBTEST_6( vectorwiseop_matrix(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
296 CALL_SUBTEST_7( vectorwiseop_matrix(VectorXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
297 CALL_SUBTEST_7( vectorwiseop_matrix(RowVectorXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
298 }
299