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 (at) 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::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 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 } 132 133 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 } 143 144 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 } 182 183 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 } 208 209 void regression_bug_654() 210 { 211 ArrayXf a = RowVectorXf(3); 212 VectorXf v = Array<float,1,Dynamic>(3); 213 } 214 215 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
