array_for_matrix.cpp (12883B)
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 12 template<typename MatrixType> void array_for_matrix(const MatrixType& m) 13 { 14 typedef typename MatrixType::Scalar Scalar; 15 typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVectorType; 16 typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType; 17 18 Index rows = m.rows(); 19 Index cols = m.cols(); 20 21 MatrixType m1 = MatrixType::Random(rows, cols), 22 m2 = MatrixType::Random(rows, cols), 23 m3(rows, cols); 24 25 ColVectorType cv1 = ColVectorType::Random(rows); 26 RowVectorType rv1 = RowVectorType::Random(cols); 27 28 Scalar s1 = internal::random<Scalar>(), 29 s2 = internal::random<Scalar>(); 30 31 // scalar addition 32 VERIFY_IS_APPROX(m1.array() + s1, s1 + m1.array()); 33 VERIFY_IS_APPROX((m1.array() + s1).matrix(), MatrixType::Constant(rows,cols,s1) + m1); 34 VERIFY_IS_APPROX(((m1*Scalar(2)).array() - s2).matrix(), (m1+m1) - MatrixType::Constant(rows,cols,s2) ); 35 m3 = m1; 36 m3.array() += s2; 37 VERIFY_IS_APPROX(m3, (m1.array() + s2).matrix()); 38 m3 = m1; 39 m3.array() -= s1; 40 VERIFY_IS_APPROX(m3, (m1.array() - s1).matrix()); 41 42 // reductions 43 VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum().sum() - m1.sum(), m1.squaredNorm()); 44 VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum().sum() - m1.sum(), m1.squaredNorm()); 45 VERIFY_IS_MUCH_SMALLER_THAN(m1.colwise().sum() + m2.colwise().sum() - (m1+m2).colwise().sum(), (m1+m2).squaredNorm()); 46 VERIFY_IS_MUCH_SMALLER_THAN(m1.rowwise().sum() - m2.rowwise().sum() - (m1-m2).rowwise().sum(), (m1-m2).squaredNorm()); 47 VERIFY_IS_APPROX(m1.colwise().sum(), m1.colwise().redux(internal::scalar_sum_op<Scalar,Scalar>())); 48 49 // vector-wise ops 50 m3 = m1; 51 VERIFY_IS_APPROX(m3.colwise() += cv1, m1.colwise() + cv1); 52 m3 = m1; 53 VERIFY_IS_APPROX(m3.colwise() -= cv1, m1.colwise() - cv1); 54 m3 = m1; 55 VERIFY_IS_APPROX(m3.rowwise() += rv1, m1.rowwise() + rv1); 56 m3 = m1; 57 VERIFY_IS_APPROX(m3.rowwise() -= rv1, m1.rowwise() - rv1); 58 59 // empty objects 60 VERIFY_IS_APPROX((m1.template block<0,Dynamic>(0,0,0,cols).colwise().sum()), RowVectorType::Zero(cols)); 61 VERIFY_IS_APPROX((m1.template block<Dynamic,0>(0,0,rows,0).rowwise().sum()), ColVectorType::Zero(rows)); 62 VERIFY_IS_APPROX((m1.template block<0,Dynamic>(0,0,0,cols).colwise().prod()), RowVectorType::Ones(cols)); 63 VERIFY_IS_APPROX((m1.template block<Dynamic,0>(0,0,rows,0).rowwise().prod()), ColVectorType::Ones(rows)); 64 65 VERIFY_IS_APPROX(m1.block(0,0,0,cols).colwise().sum(), RowVectorType::Zero(cols)); 66 VERIFY_IS_APPROX(m1.block(0,0,rows,0).rowwise().sum(), ColVectorType::Zero(rows)); 67 VERIFY_IS_APPROX(m1.block(0,0,0,cols).colwise().prod(), RowVectorType::Ones(cols)); 68 VERIFY_IS_APPROX(m1.block(0,0,rows,0).rowwise().prod(), ColVectorType::Ones(rows)); 69 70 // verify the const accessors exist 71 const Scalar& ref_m1 = m.matrix().array().coeffRef(0); 72 const Scalar& ref_m2 = m.matrix().array().coeffRef(0,0); 73 const Scalar& ref_a1 = m.array().matrix().coeffRef(0); 74 const Scalar& ref_a2 = m.array().matrix().coeffRef(0,0); 75 VERIFY(&ref_a1 == &ref_m1); 76 VERIFY(&ref_a2 == &ref_m2); 77 78 // Check write accessors: 79 m1.array().coeffRef(0,0) = 1; 80 VERIFY_IS_APPROX(m1(0,0),Scalar(1)); 81 m1.array()(0,0) = 2; 82 VERIFY_IS_APPROX(m1(0,0),Scalar(2)); 83 m1.array().matrix().coeffRef(0,0) = 3; 84 VERIFY_IS_APPROX(m1(0,0),Scalar(3)); 85 m1.array().matrix()(0,0) = 4; 86 VERIFY_IS_APPROX(m1(0,0),Scalar(4)); 87 } 88 89 template<typename MatrixType> void comparisons(const MatrixType& m) 90 { 91 using std::abs; 92 typedef typename MatrixType::Scalar Scalar; 93 typedef typename NumTraits<Scalar>::Real RealScalar; 94 95 Index rows = m.rows(); 96 Index cols = m.cols(); 97 98 Index r = internal::random<Index>(0, rows-1), 99 c = internal::random<Index>(0, cols-1); 100 101 MatrixType m1 = MatrixType::Random(rows, cols), 102 m2 = MatrixType::Random(rows, cols), 103 m3(rows, cols); 104 105 VERIFY(((m1.array() + Scalar(1)) > m1.array()).all()); 106 VERIFY(((m1.array() - Scalar(1)) < m1.array()).all()); 107 if (rows*cols>1) 108 { 109 m3 = m1; 110 m3(r,c) += 1; 111 VERIFY(! (m1.array() < m3.array()).all() ); 112 VERIFY(! (m1.array() > m3.array()).all() ); 113 } 114 115 // comparisons to scalar 116 VERIFY( (m1.array() != (m1(r,c)+1) ).any() ); 117 VERIFY( (m1.array() > (m1(r,c)-1) ).any() ); 118 VERIFY( (m1.array() < (m1(r,c)+1) ).any() ); 119 VERIFY( (m1.array() == m1(r,c) ).any() ); 120 VERIFY( m1.cwiseEqual(m1(r,c)).any() ); 121 122 // test Select 123 VERIFY_IS_APPROX( (m1.array()<m2.array()).select(m1,m2), m1.cwiseMin(m2) ); 124 VERIFY_IS_APPROX( (m1.array()>m2.array()).select(m1,m2), m1.cwiseMax(m2) ); 125 Scalar mid = (m1.cwiseAbs().minCoeff() + m1.cwiseAbs().maxCoeff())/Scalar(2); 126 for (int j=0; j<cols; ++j) 127 for (int i=0; i<rows; ++i) 128 m3(i,j) = abs(m1(i,j))<mid ? 0 : m1(i,j); 129 VERIFY_IS_APPROX( (m1.array().abs()<MatrixType::Constant(rows,cols,mid).array()) 130 .select(MatrixType::Zero(rows,cols),m1), m3); 131 // shorter versions: 132 VERIFY_IS_APPROX( (m1.array().abs()<MatrixType::Constant(rows,cols,mid).array()) 133 .select(0,m1), m3); 134 VERIFY_IS_APPROX( (m1.array().abs()>=MatrixType::Constant(rows,cols,mid).array()) 135 .select(m1,0), m3); 136 // even shorter version: 137 VERIFY_IS_APPROX( (m1.array().abs()<mid).select(0,m1), m3); 138 139 // count 140 VERIFY(((m1.array().abs()+1)>RealScalar(0.1)).count() == rows*cols); 141 142 // and/or 143 VERIFY( ((m1.array()<RealScalar(0)).matrix() && (m1.array()>RealScalar(0)).matrix()).count() == 0); 144 VERIFY( ((m1.array()<RealScalar(0)).matrix() || (m1.array()>=RealScalar(0)).matrix()).count() == rows*cols); 145 RealScalar a = m1.cwiseAbs().mean(); 146 VERIFY( ((m1.array()<-a).matrix() || (m1.array()>a).matrix()).count() == (m1.cwiseAbs().array()>a).count()); 147 148 typedef Matrix<Index, Dynamic, 1> VectorOfIndices; 149 150 // TODO allows colwise/rowwise for array 151 VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().colwise().count(), VectorOfIndices::Constant(cols,rows).transpose()); 152 VERIFY_IS_APPROX(((m1.array().abs()+1)>RealScalar(0.1)).matrix().rowwise().count(), VectorOfIndices::Constant(rows, cols)); 153 } 154 155 template<typename VectorType> void lpNorm(const VectorType& v) 156 { 157 using std::sqrt; 158 typedef typename VectorType::RealScalar RealScalar; 159 VectorType u = VectorType::Random(v.size()); 160 161 if(v.size()==0) 162 { 163 VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), RealScalar(0)); 164 VERIFY_IS_APPROX(u.template lpNorm<1>(), RealScalar(0)); 165 VERIFY_IS_APPROX(u.template lpNorm<2>(), RealScalar(0)); 166 VERIFY_IS_APPROX(u.template lpNorm<5>(), RealScalar(0)); 167 } 168 else 169 { 170 VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), u.cwiseAbs().maxCoeff()); 171 } 172 173 VERIFY_IS_APPROX(u.template lpNorm<1>(), u.cwiseAbs().sum()); 174 VERIFY_IS_APPROX(u.template lpNorm<2>(), sqrt(u.array().abs().square().sum())); 175 VERIFY_IS_APPROX(numext::pow(u.template lpNorm<5>(), typename VectorType::RealScalar(5)), u.array().abs().pow(5).sum()); 176 } 177 178 template<typename MatrixType> void cwise_min_max(const MatrixType& m) 179 { 180 typedef typename MatrixType::Scalar Scalar; 181 182 Index rows = m.rows(); 183 Index cols = m.cols(); 184 185 MatrixType m1 = MatrixType::Random(rows, cols); 186 187 // min/max with array 188 Scalar maxM1 = m1.maxCoeff(); 189 Scalar minM1 = m1.minCoeff(); 190 191 VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1), m1.cwiseMin(MatrixType::Constant(rows,cols, minM1))); 192 VERIFY_IS_APPROX(m1, m1.cwiseMin(MatrixType::Constant(rows,cols, maxM1))); 193 194 VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1), m1.cwiseMax(MatrixType::Constant(rows,cols, maxM1))); 195 VERIFY_IS_APPROX(m1, m1.cwiseMax(MatrixType::Constant(rows,cols, minM1))); 196 197 // min/max with scalar input 198 VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1), m1.cwiseMin( minM1)); 199 VERIFY_IS_APPROX(m1, m1.cwiseMin(maxM1)); 200 VERIFY_IS_APPROX(-m1, (-m1).cwiseMin(-minM1)); 201 VERIFY_IS_APPROX(-m1.array(), ((-m1).array().min)( -minM1)); 202 203 VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1), m1.cwiseMax( maxM1)); 204 VERIFY_IS_APPROX(m1, m1.cwiseMax(minM1)); 205 VERIFY_IS_APPROX(-m1, (-m1).cwiseMax(-maxM1)); 206 VERIFY_IS_APPROX(-m1.array(), ((-m1).array().max)(-maxM1)); 207 208 VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, minM1).array(), (m1.array().min)( minM1)); 209 VERIFY_IS_APPROX(m1.array(), (m1.array().min)( maxM1)); 210 211 VERIFY_IS_APPROX(MatrixType::Constant(rows,cols, maxM1).array(), (m1.array().max)( maxM1)); 212 VERIFY_IS_APPROX(m1.array(), (m1.array().max)( minM1)); 213 214 } 215 216 template<typename MatrixTraits> void resize(const MatrixTraits& t) 217 { 218 typedef typename MatrixTraits::Scalar Scalar; 219 typedef Matrix<Scalar,Dynamic,Dynamic> MatrixType; 220 typedef Array<Scalar,Dynamic,Dynamic> Array2DType; 221 typedef Matrix<Scalar,Dynamic,1> VectorType; 222 typedef Array<Scalar,Dynamic,1> Array1DType; 223 224 Index rows = t.rows(), cols = t.cols(); 225 226 MatrixType m(rows,cols); 227 VectorType v(rows); 228 Array2DType a2(rows,cols); 229 Array1DType a1(rows); 230 231 m.array().resize(rows+1,cols+1); 232 VERIFY(m.rows()==rows+1 && m.cols()==cols+1); 233 a2.matrix().resize(rows+1,cols+1); 234 VERIFY(a2.rows()==rows+1 && a2.cols()==cols+1); 235 v.array().resize(cols); 236 VERIFY(v.size()==cols); 237 a1.matrix().resize(cols); 238 VERIFY(a1.size()==cols); 239 } 240 241 template<int> 242 void regression_bug_654() 243 { 244 ArrayXf a = RowVectorXf(3); 245 VectorXf v = Array<float,1,Dynamic>(3); 246 } 247 248 // Check propagation of LvalueBit through Array/Matrix-Wrapper 249 template<int> 250 void regrrssion_bug_1410() 251 { 252 const Matrix4i M; 253 const Array4i A; 254 ArrayWrapper<const Matrix4i> MA = M.array(); 255 MA.row(0); 256 MatrixWrapper<const Array4i> AM = A.matrix(); 257 AM.row(0); 258 259 VERIFY((internal::traits<ArrayWrapper<const Matrix4i> >::Flags&LvalueBit)==0); 260 VERIFY((internal::traits<MatrixWrapper<const Array4i> >::Flags&LvalueBit)==0); 261 262 VERIFY((internal::traits<ArrayWrapper<Matrix4i> >::Flags&LvalueBit)==LvalueBit); 263 VERIFY((internal::traits<MatrixWrapper<Array4i> >::Flags&LvalueBit)==LvalueBit); 264 } 265 266 EIGEN_DECLARE_TEST(array_for_matrix) 267 { 268 for(int i = 0; i < g_repeat; i++) { 269 CALL_SUBTEST_1( array_for_matrix(Matrix<float, 1, 1>()) ); 270 CALL_SUBTEST_2( array_for_matrix(Matrix2f()) ); 271 CALL_SUBTEST_3( array_for_matrix(Matrix4d()) ); 272 CALL_SUBTEST_4( array_for_matrix(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 273 CALL_SUBTEST_5( array_for_matrix(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 274 CALL_SUBTEST_6( array_for_matrix(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 275 } 276 for(int i = 0; i < g_repeat; i++) { 277 CALL_SUBTEST_1( comparisons(Matrix<float, 1, 1>()) ); 278 CALL_SUBTEST_2( comparisons(Matrix2f()) ); 279 CALL_SUBTEST_3( comparisons(Matrix4d()) ); 280 CALL_SUBTEST_5( comparisons(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 281 CALL_SUBTEST_6( comparisons(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 282 } 283 for(int i = 0; i < g_repeat; i++) { 284 CALL_SUBTEST_1( cwise_min_max(Matrix<float, 1, 1>()) ); 285 CALL_SUBTEST_2( cwise_min_max(Matrix2f()) ); 286 CALL_SUBTEST_3( cwise_min_max(Matrix4d()) ); 287 CALL_SUBTEST_5( cwise_min_max(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 288 CALL_SUBTEST_6( cwise_min_max(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 289 } 290 for(int i = 0; i < g_repeat; i++) { 291 CALL_SUBTEST_1( lpNorm(Matrix<float, 1, 1>()) ); 292 CALL_SUBTEST_2( lpNorm(Vector2f()) ); 293 CALL_SUBTEST_7( lpNorm(Vector3d()) ); 294 CALL_SUBTEST_8( lpNorm(Vector4f()) ); 295 CALL_SUBTEST_5( lpNorm(VectorXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 296 CALL_SUBTEST_4( lpNorm(VectorXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 297 } 298 CALL_SUBTEST_5( lpNorm(VectorXf(0)) ); 299 CALL_SUBTEST_4( lpNorm(VectorXcf(0)) ); 300 for(int i = 0; i < g_repeat; i++) { 301 CALL_SUBTEST_4( resize(MatrixXcf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 302 CALL_SUBTEST_5( resize(MatrixXf(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 303 CALL_SUBTEST_6( resize(MatrixXi(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) ); 304 } 305 CALL_SUBTEST_6( regression_bug_654<0>() ); 306 CALL_SUBTEST_6( regrrssion_bug_1410<0>() ); 307 }