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>()));
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 
comparisons(const MatrixType & m)73 template<typename MatrixType> void comparisons(const MatrixType& m)
74 {
75   using std::abs;
76   typedef typename MatrixType::Index Index;
77   typedef typename MatrixType::Scalar Scalar;
78   typedef typename NumTraits<Scalar>::Real RealScalar;
79 
80   Index rows = m.rows();
81   Index cols = m.cols();
82 
83   Index r = internal::random<Index>(0, rows-1),
84         c = internal::random<Index>(0, cols-1);
85 
86   MatrixType m1 = MatrixType::Random(rows, cols),
87              m2 = MatrixType::Random(rows, cols),
88              m3(rows, cols);
89 
90   VERIFY(((m1.array() + Scalar(1)) > m1.array()).all());
91   VERIFY(((m1.array() - Scalar(1)) < m1.array()).all());
92   if (rows*cols>1)
93   {
94     m3 = m1;
95     m3(r,c) += 1;
96     VERIFY(! (m1.array() < m3.array()).all() );
97     VERIFY(! (m1.array() > m3.array()).all() );
98   }
99 
100   // comparisons to scalar
101   VERIFY( (m1.array() != (m1(r,c)+1) ).any() );
102   VERIFY( (m1.array() > (m1(r,c)-1) ).any() );
103   VERIFY( (m1.array() < (m1(r,c)+1) ).any() );
104   VERIFY( (m1.array() == m1(r,c) ).any() );
105 
106   // test Select
107   VERIFY_IS_APPROX( (m1.array()<m2.array()).select(m1,m2), m1.cwiseMin(m2) );
108   VERIFY_IS_APPROX( (m1.array()>m2.array()).select(m1,m2), m1.cwiseMax(m2) );
109   Scalar mid = (m1.cwiseAbs().minCoeff() + m1.cwiseAbs().maxCoeff())/Scalar(2);
110   for (int j=0; j<cols; ++j)
111   for (int i=0; i<rows; ++i)
112     m3(i,j) = abs(m1(i,j))<mid ? 0 : m1(i,j);
113   VERIFY_IS_APPROX( (m1.array().abs()<MatrixType::Constant(rows,cols,mid).array())
114                         .select(MatrixType::Zero(rows,cols),m1), m3);
115   // shorter versions:
116   VERIFY_IS_APPROX( (m1.array().abs()<MatrixType::Constant(rows,cols,mid).array())
117                         .select(0,m1), m3);
118   VERIFY_IS_APPROX( (m1.array().abs()>=MatrixType::Constant(rows,cols,mid).array())
119                         .select(m1,0), m3);
120   // even shorter version:
121   VERIFY_IS_APPROX( (m1.array().abs()<mid).select(0,m1), m3);
122 
123   // count
124   VERIFY(((m1.array().abs()+1)>RealScalar(0.1)).count() == rows*cols);
125 
126   typedef Matrix<typename MatrixType::Index, Dynamic, 1> VectorOfIndices;
127 
128   // TODO allows colwise/rowwise for array
129   VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().colwise().count(), VectorOfIndices::Constant(cols,rows).transpose());
130   VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().rowwise().count(), VectorOfIndices::Constant(rows, cols));
131 }
132 
lpNorm(const VectorType & v)133 template<typename VectorType> void lpNorm(const VectorType& v)
134 {
135   using std::sqrt;
136   VectorType u = VectorType::Random(v.size());
137 
138   VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), u.cwiseAbs().maxCoeff());
139   VERIFY_IS_APPROX(u.template lpNorm<1>(), u.cwiseAbs().sum());
140   VERIFY_IS_APPROX(u.template lpNorm<2>(), sqrt(u.array().abs().square().sum()));
141   VERIFY_IS_APPROX(numext::pow(u.template lpNorm<5>(), typename VectorType::RealScalar(5)), u.array().abs().pow(5).sum());
142 }
143 
cwise_min_max(const MatrixType & m)144 template<typename MatrixType> void cwise_min_max(const MatrixType& m)
145 {
146   typedef typename MatrixType::Index Index;
147   typedef typename MatrixType::Scalar Scalar;
148 
149   Index rows = m.rows();
150   Index cols = m.cols();
151 
152   MatrixType m1 = MatrixType::Random(rows, cols);
153 
154   // min/max with array
155   Scalar maxM1 = m1.maxCoeff();
156   Scalar minM1 = m1.minCoeff();
157 
158   VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1), m1.cwiseMin(MatrixType::Constant(rows,cols, minM1)));
159   VERIFY_IS_APPROX(m1, m1.cwiseMin(MatrixType::Constant(rows,cols, maxM1)));
160 
161   VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1), m1.cwiseMax(MatrixType::Constant(rows,cols, maxM1)));
162   VERIFY_IS_APPROX(m1, m1.cwiseMax(MatrixType::Constant(rows,cols, minM1)));
163 
164   // min/max with scalar input
165   VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1), m1.cwiseMin( minM1));
166   VERIFY_IS_APPROX(m1, m1.cwiseMin(maxM1));
167   VERIFY_IS_APPROX(-m1, (-m1).cwiseMin(-minM1));
168   VERIFY_IS_APPROX(-m1.array(), ((-m1).array().min)( -minM1));
169 
170   VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1), m1.cwiseMax( maxM1));
171   VERIFY_IS_APPROX(m1, m1.cwiseMax(minM1));
172   VERIFY_IS_APPROX(-m1, (-m1).cwiseMax(-maxM1));
173   VERIFY_IS_APPROX(-m1.array(), ((-m1).array().max)(-maxM1));
174 
175   VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1).array(), (m1.array().min)( minM1));
176   VERIFY_IS_APPROX(m1.array(), (m1.array().min)( maxM1));
177 
178   VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1).array(), (m1.array().max)( maxM1));
179   VERIFY_IS_APPROX(m1.array(), (m1.array().max)( minM1));
180 
181 }
182 
resize(const MatrixTraits & t)183 template<typename MatrixTraits> void resize(const MatrixTraits& t)
184 {
185   typedef typename MatrixTraits::Index Index;
186   typedef typename MatrixTraits::Scalar Scalar;
187   typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType;
188   typedef Array<Scalar,Dynamic,Dynamic> Array2DType;
189   typedef Matrix<Scalar,Dynamic,1> VectorType;
190   typedef Array<Scalar,Dynamic,1> Array1DType;
191 
192   Index rows = t.rows(), cols = t.cols();
193 
194   MatrixType m(rows,cols);
195   VectorType v(rows);
196   Array2DType a2(rows,cols);
197   Array1DType a1(rows);
198 
199   m.array().resize(rows+1,cols+1);
200   VERIFY(m.rows()==rows+1 && m.cols()==cols+1);
201   a2.matrix().resize(rows+1,cols+1);
202   VERIFY(a2.rows()==rows+1 && a2.cols()==cols+1);
203   v.array().resize(cols);
204   VERIFY(v.size()==cols);
205   a1.matrix().resize(cols);
206   VERIFY(a1.size()==cols);
207 }
208 
regression_bug_654()209 void regression_bug_654()
210 {
211   ArrayXf a = RowVectorXf(3);
212   VectorXf v = Array<float,1,Dynamic>(3);
213 }
214 
test_array_for_matrix()215 void test_array_for_matrix()
216 {
217   for(int i = 0; i < g_repeat; i++) {
218     CALL_SUBTEST_1( array_for_matrix(Matrix<float, 1, 1>()) );
219     CALL_SUBTEST_2( array_for_matrix(Matrix2f()) );
220     CALL_SUBTEST_3( array_for_matrix(Matrix4d()) );
221     CALL_SUBTEST_4( array_for_matrix(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
222     CALL_SUBTEST_5( array_for_matrix(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
223     CALL_SUBTEST_6( array_for_matrix(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
224   }
225   for(int i = 0; i < g_repeat; i++) {
226     CALL_SUBTEST_1( comparisons(Matrix<float, 1, 1>()) );
227     CALL_SUBTEST_2( comparisons(Matrix2f()) );
228     CALL_SUBTEST_3( comparisons(Matrix4d()) );
229     CALL_SUBTEST_5( comparisons(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
230     CALL_SUBTEST_6( comparisons(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
231   }
232   for(int i = 0; i < g_repeat; i++) {
233     CALL_SUBTEST_1( cwise_min_max(Matrix<float, 1, 1>()) );
234     CALL_SUBTEST_2( cwise_min_max(Matrix2f()) );
235     CALL_SUBTEST_3( cwise_min_max(Matrix4d()) );
236     CALL_SUBTEST_5( cwise_min_max(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
237     CALL_SUBTEST_6( cwise_min_max(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
238   }
239   for(int i = 0; i < g_repeat; i++) {
240     CALL_SUBTEST_1( lpNorm(Matrix<float, 1, 1>()) );
241     CALL_SUBTEST_2( lpNorm(Vector2f()) );
242     CALL_SUBTEST_7( lpNorm(Vector3d()) );
243     CALL_SUBTEST_8( lpNorm(Vector4f()) );
244     CALL_SUBTEST_5( lpNorm(VectorXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
245     CALL_SUBTEST_4( lpNorm(VectorXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
246   }
247   for(int i = 0; i < g_repeat; i++) {
248     CALL_SUBTEST_4( resize(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
249     CALL_SUBTEST_5( resize(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
250     CALL_SUBTEST_6( resize(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
251   }
252   CALL_SUBTEST_6( regression_bug_654() );
253 }
254