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