1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
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
array_for_matrix(const MatrixType & m)12 template<typename MatrixType> void array_for_matrix(const MatrixType& m)
13 {
14 typedef typename MatrixType::Index Index;
15 typedef typename MatrixType::Scalar Scalar;
16 typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVectorType;
17 typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType;
18
19 Index rows = m.rows();
20 Index cols = m.cols();
21
22 MatrixType m1 = MatrixType::Random(rows, cols),
23 m2 = MatrixType::Random(rows, cols),
24 m3(rows, cols);
25
26 ColVectorType cv1 = ColVectorType::Random(rows);
27 RowVectorType rv1 = RowVectorType::Random(cols);
28
29 Scalar s1 = internal::random<Scalar>(),
30 s2 = internal::random<Scalar>();
31
32 // scalar addition
33 VERIFY_IS_APPROX(m1.array() + s1, s1 + m1.array());
34 VERIFY_IS_APPROX((m1.array() + s1).matrix(), MatrixType::Constant(rows,cols,s1) + m1);
35 VERIFY_IS_APPROX(((m1*Scalar(2)).array() - s2).matrix(), (m1+m1) - MatrixType::Constant(rows,cols,s2) );
36 m3 = m1;
37 m3.array() += s2;
38 VERIFY_IS_APPROX(m3, (m1.array() + s2).matrix());
39 m3 = m1;
40 m3.array() -= s1;
41 VERIFY_IS_APPROX(m3, (m1.array() - s1).matrix());
42
43 // reductions
44 VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum().sum() - m1.sum(), m1.squaredNorm());
45 VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum().sum() - m1.sum(), m1.squaredNorm());
46 VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum() + m2.colwise().sum() - (m1+m2).colwise().sum(), (m1+m2).squaredNorm());
47 VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum() - m2.rowwise().sum() - (m1-m2).rowwise().sum(), (m1-m2).squaredNorm());
48 VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar,Scalar>()));
49
50 // vector-wise ops
51 m3 = m1;
52 VERIFY_IS_APPROX(m3.colwise() += cv1, m1.colwise() + cv1);
53 m3 = m1;
54 VERIFY_IS_APPROX(m3.colwise() -= cv1, m1.colwise() - cv1);
55 m3 = m1;
56 VERIFY_IS_APPROX(m3.rowwise() += rv1, m1.rowwise() + rv1);
57 m3 = m1;
58 VERIFY_IS_APPROX(m3.rowwise() -= rv1, m1.rowwise() - rv1);
59
60 // empty objects
61 VERIFY_IS_APPROX(m1.block(0,0,0,cols).colwise().sum(), RowVectorType::Zero(cols));
62 VERIFY_IS_APPROX(m1.block(0,0,rows,0).rowwise().prod(), ColVectorType::Ones(rows));
63
64 // verify the const accessors exist
65 const Scalar& ref_m1 = m.matrix().array().coeffRef(0);
66 const Scalar& ref_m2 = m.matrix().array().coeffRef(0,0);
67 const Scalar& ref_a1 = m.array().matrix().coeffRef(0);
68 const Scalar& ref_a2 = m.array().matrix().coeffRef(0,0);
69 VERIFY(&ref_a1 == &ref_m1);
70 VERIFY(&ref_a2 == &ref_m2);
71
72 // Check write accessors:
73 m1.array().coeffRef(0,0) = 1;
74 VERIFY_IS_APPROX(m1(0,0),Scalar(1));
75 m1.array()(0,0) = 2;
76 VERIFY_IS_APPROX(m1(0,0),Scalar(2));
77 m1.array().matrix().coeffRef(0,0) = 3;
78 VERIFY_IS_APPROX(m1(0,0),Scalar(3));
79 m1.array().matrix()(0,0) = 4;
80 VERIFY_IS_APPROX(m1(0,0),Scalar(4));
81 }
82
comparisons(const MatrixType & m)83 template<typename MatrixType> void comparisons(const MatrixType& m)
84 {
85 using std::abs;
86 typedef typename MatrixType::Index Index;
87 typedef typename MatrixType::Scalar Scalar;
88 typedef typename NumTraits<Scalar>::Real RealScalar;
89
90 Index rows = m.rows();
91 Index cols = m.cols();
92
93 Index r = internal::random<Index>(0, rows-1),
94 c = internal::random<Index>(0, cols-1);
95
96 MatrixType m1 = MatrixType::Random(rows, cols),
97 m2 = MatrixType::Random(rows, cols),
98 m3(rows, cols);
99
100 VERIFY(((m1.array() + Scalar(1)) > m1.array()).all());
101 VERIFY(((m1.array() - Scalar(1)) < m1.array()).all());
102 if (rows*cols>1)
103 {
104 m3 = m1;
105 m3(r,c) += 1;
106 VERIFY(! (m1.array() < m3.array()).all() );
107 VERIFY(! (m1.array() > m3.array()).all() );
108 }
109
110 // comparisons to scalar
111 VERIFY( (m1.array() != (m1(r,c)+1) ).any() );
112 VERIFY( (m1.array() > (m1(r,c)-1) ).any() );
113 VERIFY( (m1.array() < (m1(r,c)+1) ).any() );
114 VERIFY( (m1.array() == m1(r,c) ).any() );
115 VERIFY( m1.cwiseEqual(m1(r,c)).any() );
116
117 // test Select
118 VERIFY_IS_APPROX( (m1.array()<m2.array()).select(m1,m2), m1.cwiseMin(m2) );
119 VERIFY_IS_APPROX( (m1.array()>m2.array()).select(m1,m2), m1.cwiseMax(m2) );
120 Scalar mid = (m1.cwiseAbs().minCoeff() + m1.cwiseAbs().maxCoeff())/Scalar(2);
121 for (int j=0; j<cols; ++j)
122 for (int i=0; i<rows; ++i)
123 m3(i,j) = abs(m1(i,j))<mid ? 0 : m1(i,j);
124 VERIFY_IS_APPROX( (m1.array().abs()<MatrixType::Constant(rows,cols,mid).array())
125 .select(MatrixType::Zero(rows,cols),m1), m3);
126 // shorter versions:
127 VERIFY_IS_APPROX( (m1.array().abs()<MatrixType::Constant(rows,cols,mid).array())
128 .select(0,m1), m3);
129 VERIFY_IS_APPROX( (m1.array().abs()>=MatrixType::Constant(rows,cols,mid).array())
130 .select(m1,0), m3);
131 // even shorter version:
132 VERIFY_IS_APPROX( (m1.array().abs()<mid).select(0,m1), m3);
133
134 // count
135 VERIFY(((m1.array().abs()+1)>RealScalar(0.1)).count() == rows*cols);
136
137 // and/or
138 VERIFY( ((m1.array()<RealScalar(0)).matrix() && (m1.array()>RealScalar(0)).matrix()).count() == 0);
139 VERIFY( ((m1.array()<RealScalar(0)).matrix() || (m1.array()>=RealScalar(0)).matrix()).count() == rows*cols);
140 RealScalar a = m1.cwiseAbs().mean();
141 VERIFY( ((m1.array()<-a).matrix() || (m1.array()>a).matrix()).count() == (m1.cwiseAbs().array()>a).count());
142
143 typedef Matrix<typename MatrixType::Index, Dynamic, 1> VectorOfIndices;
144
145 // TODO allows colwise/rowwise for array
146 VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().colwise().count(), VectorOfIndices::Constant(cols,rows).transpose());
147 VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().rowwise().count(), VectorOfIndices::Constant(rows, cols));
148 }
149
lpNorm(const VectorType & v)150 template<typename VectorType> void lpNorm(const VectorType& v)
151 {
152 using std::sqrt;
153 typedef typename VectorType::RealScalar RealScalar;
154 VectorType u = VectorType::Random(v.size());
155
156 if(v.size()==0)
157 {
158 VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), RealScalar(0));
159 VERIFY_IS_APPROX(u.template lpNorm<1>(), RealScalar(0));
160 VERIFY_IS_APPROX(u.template lpNorm<2>(), RealScalar(0));
161 VERIFY_IS_APPROX(u.template lpNorm<5>(), RealScalar(0));
162 }
163 else
164 {
165 VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), u.cwiseAbs().maxCoeff());
166 }
167
168 VERIFY_IS_APPROX(u.template lpNorm<1>(), u.cwiseAbs().sum());
169 VERIFY_IS_APPROX(u.template lpNorm<2>(), sqrt(u.array().abs().square().sum()));
170 VERIFY_IS_APPROX(numext::pow(u.template lpNorm<5>(), typename VectorType::RealScalar(5)), u.array().abs().pow(5).sum());
171 }
172
cwise_min_max(const MatrixType & m)173 template<typename MatrixType> void cwise_min_max(const MatrixType& m)
174 {
175 typedef typename MatrixType::Index Index;
176 typedef typename MatrixType::Scalar Scalar;
177
178 Index rows = m.rows();
179 Index cols = m.cols();
180
181 MatrixType m1 = MatrixType::Random(rows, cols);
182
183 // min/max with array
184 Scalar maxM1 = m1.maxCoeff();
185 Scalar minM1 = m1.minCoeff();
186
187 VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1), m1.cwiseMin(MatrixType::Constant(rows,cols, minM1)));
188 VERIFY_IS_APPROX(m1, m1.cwiseMin(MatrixType::Constant(rows,cols, maxM1)));
189
190 VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1), m1.cwiseMax(MatrixType::Constant(rows,cols, maxM1)));
191 VERIFY_IS_APPROX(m1, m1.cwiseMax(MatrixType::Constant(rows,cols, minM1)));
192
193 // min/max with scalar input
194 VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1), m1.cwiseMin( minM1));
195 VERIFY_IS_APPROX(m1, m1.cwiseMin(maxM1));
196 VERIFY_IS_APPROX(-m1, (-m1).cwiseMin(-minM1));
197 VERIFY_IS_APPROX(-m1.array(), ((-m1).array().min)( -minM1));
198
199 VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1), m1.cwiseMax( maxM1));
200 VERIFY_IS_APPROX(m1, m1.cwiseMax(minM1));
201 VERIFY_IS_APPROX(-m1, (-m1).cwiseMax(-maxM1));
202 VERIFY_IS_APPROX(-m1.array(), ((-m1).array().max)(-maxM1));
203
204 VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1).array(), (m1.array().min)( minM1));
205 VERIFY_IS_APPROX(m1.array(), (m1.array().min)( maxM1));
206
207 VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1).array(), (m1.array().max)( maxM1));
208 VERIFY_IS_APPROX(m1.array(), (m1.array().max)( minM1));
209
210 }
211
resize(const MatrixTraits & t)212 template<typename MatrixTraits> void resize(const MatrixTraits& t)
213 {
214 typedef typename MatrixTraits::Index Index;
215 typedef typename MatrixTraits::Scalar Scalar;
216 typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType;
217 typedef Array<Scalar,Dynamic,Dynamic> Array2DType;
218 typedef Matrix<Scalar,Dynamic,1> VectorType;
219 typedef Array<Scalar,Dynamic,1> Array1DType;
220
221 Index rows = t.rows(), cols = t.cols();
222
223 MatrixType m(rows,cols);
224 VectorType v(rows);
225 Array2DType a2(rows,cols);
226 Array1DType a1(rows);
227
228 m.array().resize(rows+1,cols+1);
229 VERIFY(m.rows()==rows+1 && m.cols()==cols+1);
230 a2.matrix().resize(rows+1,cols+1);
231 VERIFY(a2.rows()==rows+1 && a2.cols()==cols+1);
232 v.array().resize(cols);
233 VERIFY(v.size()==cols);
234 a1.matrix().resize(cols);
235 VERIFY(a1.size()==cols);
236 }
237
238 template<int>
regression_bug_654()239 void regression_bug_654()
240 {
241 ArrayXf a = RowVectorXf(3);
242 VectorXf v = Array<float,1,Dynamic>(3);
243 }
244
245 // Check propagation of LvalueBit through Array/Matrix-Wrapper
246 template<int>
regrrssion_bug_1410()247 void regrrssion_bug_1410()
248 {
249 const Matrix4i M;
250 const Array4i A;
251 ArrayWrapper<const Matrix4i> MA = M.array();
252 MA.row(0);
253 MatrixWrapper<const Array4i> AM = A.matrix();
254 AM.row(0);
255
256 VERIFY((internal::traits<ArrayWrapper<const Matrix4i> >::Flags&LvalueBit)==0);
257 VERIFY((internal::traits<MatrixWrapper<const Array4i> >::Flags&LvalueBit)==0);
258
259 VERIFY((internal::traits<ArrayWrapper<Matrix4i> >::Flags&LvalueBit)==LvalueBit);
260 VERIFY((internal::traits<MatrixWrapper<Array4i> >::Flags&LvalueBit)==LvalueBit);
261 }
262
test_array_for_matrix()263 void test_array_for_matrix()
264 {
265 for(int i = 0; i < g_repeat; i++) {
266 CALL_SUBTEST_1( array_for_matrix(Matrix<float, 1, 1>()) );
267 CALL_SUBTEST_2( array_for_matrix(Matrix2f()) );
268 CALL_SUBTEST_3( array_for_matrix(Matrix4d()) );
269 CALL_SUBTEST_4( array_for_matrix(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
270 CALL_SUBTEST_5( array_for_matrix(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
271 CALL_SUBTEST_6( array_for_matrix(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
272 }
273 for(int i = 0; i < g_repeat; i++) {
274 CALL_SUBTEST_1( comparisons(Matrix<float, 1, 1>()) );
275 CALL_SUBTEST_2( comparisons(Matrix2f()) );
276 CALL_SUBTEST_3( comparisons(Matrix4d()) );
277 CALL_SUBTEST_5( comparisons(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
278 CALL_SUBTEST_6( comparisons(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
279 }
280 for(int i = 0; i < g_repeat; i++) {
281 CALL_SUBTEST_1( cwise_min_max(Matrix<float, 1, 1>()) );
282 CALL_SUBTEST_2( cwise_min_max(Matrix2f()) );
283 CALL_SUBTEST_3( cwise_min_max(Matrix4d()) );
284 CALL_SUBTEST_5( cwise_min_max(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
285 CALL_SUBTEST_6( cwise_min_max(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
286 }
287 for(int i = 0; i < g_repeat; i++) {
288 CALL_SUBTEST_1( lpNorm(Matrix<float, 1, 1>()) );
289 CALL_SUBTEST_2( lpNorm(Vector2f()) );
290 CALL_SUBTEST_7( lpNorm(Vector3d()) );
291 CALL_SUBTEST_8( lpNorm(Vector4f()) );
292 CALL_SUBTEST_5( lpNorm(VectorXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
293 CALL_SUBTEST_4( lpNorm(VectorXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
294 }
295 CALL_SUBTEST_5( lpNorm(VectorXf(0)) );
296 CALL_SUBTEST_4( lpNorm(VectorXcf(0)) );
297 for(int i = 0; i < g_repeat; i++) {
298 CALL_SUBTEST_4( resize(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
299 CALL_SUBTEST_5( resize(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
300 CALL_SUBTEST_6( resize(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
301 }
302 CALL_SUBTEST_6( regression_bug_654<0>() );
303 CALL_SUBTEST_6( regrrssion_bug_1410<0>() );
304 }
305