1 // This file is part of Eigen, a lightweight C++ template library 2 // for linear algebra. 3 // 4 // Copyright (C) 2008 Benoit Jacob <jacob.benoit.1 (at) gmail.com> 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 matrixRedux(const MatrixType& m) 13 { 14 typedef typename MatrixType::Index Index; 15 typedef typename MatrixType::Scalar Scalar; 16 typedef typename MatrixType::RealScalar RealScalar; 17 18 Index rows = m.rows(); 19 Index cols = m.cols(); 20 21 MatrixType m1 = MatrixType::Random(rows, cols); 22 23 // The entries of m1 are uniformly distributed in [0,1], so m1.prod() is very small. This may lead to test 24 // failures if we underflow into denormals. Thus, we scale so that entires are close to 1. 25 MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + Scalar(0.2) * m1; 26 27 VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1)); 28 VERIFY_IS_APPROX(MatrixType::Ones(rows, cols).sum(), Scalar(float(rows*cols))); // the float() here to shut up excessive MSVC warning about int->complex conversion being lossy 29 Scalar s(0), p(1), minc(internal::real(m1.coeff(0))), maxc(internal::real(m1.coeff(0))); 30 for(int j = 0; j < cols; j++) 31 for(int i = 0; i < rows; i++) 32 { 33 s += m1(i,j); 34 p *= m1_for_prod(i,j); 35 minc = (std::min)(internal::real(minc), internal::real(m1(i,j))); 36 maxc = (std::max)(internal::real(maxc), internal::real(m1(i,j))); 37 } 38 const Scalar mean = s/Scalar(RealScalar(rows*cols)); 39 40 VERIFY_IS_APPROX(m1.sum(), s); 41 VERIFY_IS_APPROX(m1.mean(), mean); 42 VERIFY_IS_APPROX(m1_for_prod.prod(), p); 43 VERIFY_IS_APPROX(m1.real().minCoeff(), internal::real(minc)); 44 VERIFY_IS_APPROX(m1.real().maxCoeff(), internal::real(maxc)); 45 46 // test slice vectorization assuming assign is ok 47 Index r0 = internal::random<Index>(0,rows-1); 48 Index c0 = internal::random<Index>(0,cols-1); 49 Index r1 = internal::random<Index>(r0+1,rows)-r0; 50 Index c1 = internal::random<Index>(c0+1,cols)-c0; 51 VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).sum(), m1.block(r0,c0,r1,c1).eval().sum()); 52 VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).mean(), m1.block(r0,c0,r1,c1).eval().mean()); 53 VERIFY_IS_APPROX(m1_for_prod.block(r0,c0,r1,c1).prod(), m1_for_prod.block(r0,c0,r1,c1).eval().prod()); 54 VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().minCoeff(), m1.block(r0,c0,r1,c1).real().eval().minCoeff()); 55 VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().maxCoeff(), m1.block(r0,c0,r1,c1).real().eval().maxCoeff()); 56 57 // test empty objects 58 VERIFY_IS_APPROX(m1.block(r0,c0,0,0).sum(), Scalar(0)); 59 VERIFY_IS_APPROX(m1.block(r0,c0,0,0).prod(), Scalar(1)); 60 } 61 62 template<typename VectorType> void vectorRedux(const VectorType& w) 63 { 64 typedef typename VectorType::Index Index; 65 typedef typename VectorType::Scalar Scalar; 66 typedef typename NumTraits<Scalar>::Real RealScalar; 67 Index size = w.size(); 68 69 VectorType v = VectorType::Random(size); 70 VectorType v_for_prod = VectorType::Ones(size) + Scalar(0.2) * v; // see comment above declaration of m1_for_prod 71 72 for(int i = 1; i < size; i++) 73 { 74 Scalar s(0), p(1); 75 RealScalar minc(internal::real(v.coeff(0))), maxc(internal::real(v.coeff(0))); 76 for(int j = 0; j < i; j++) 77 { 78 s += v[j]; 79 p *= v_for_prod[j]; 80 minc = (std::min)(minc, internal::real(v[j])); 81 maxc = (std::max)(maxc, internal::real(v[j])); 82 } 83 VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.head(i).sum()), Scalar(1)); 84 VERIFY_IS_APPROX(p, v_for_prod.head(i).prod()); 85 VERIFY_IS_APPROX(minc, v.real().head(i).minCoeff()); 86 VERIFY_IS_APPROX(maxc, v.real().head(i).maxCoeff()); 87 } 88 89 for(int i = 0; i < size-1; i++) 90 { 91 Scalar s(0), p(1); 92 RealScalar minc(internal::real(v.coeff(i))), maxc(internal::real(v.coeff(i))); 93 for(int j = i; j < size; j++) 94 { 95 s += v[j]; 96 p *= v_for_prod[j]; 97 minc = (std::min)(minc, internal::real(v[j])); 98 maxc = (std::max)(maxc, internal::real(v[j])); 99 } 100 VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.tail(size-i).sum()), Scalar(1)); 101 VERIFY_IS_APPROX(p, v_for_prod.tail(size-i).prod()); 102 VERIFY_IS_APPROX(minc, v.real().tail(size-i).minCoeff()); 103 VERIFY_IS_APPROX(maxc, v.real().tail(size-i).maxCoeff()); 104 } 105 106 for(int i = 0; i < size/2; i++) 107 { 108 Scalar s(0), p(1); 109 RealScalar minc(internal::real(v.coeff(i))), maxc(internal::real(v.coeff(i))); 110 for(int j = i; j < size-i; j++) 111 { 112 s += v[j]; 113 p *= v_for_prod[j]; 114 minc = (std::min)(minc, internal::real(v[j])); 115 maxc = (std::max)(maxc, internal::real(v[j])); 116 } 117 VERIFY_IS_MUCH_SMALLER_THAN(internal::abs(s - v.segment(i, size-2*i).sum()), Scalar(1)); 118 VERIFY_IS_APPROX(p, v_for_prod.segment(i, size-2*i).prod()); 119 VERIFY_IS_APPROX(minc, v.real().segment(i, size-2*i).minCoeff()); 120 VERIFY_IS_APPROX(maxc, v.real().segment(i, size-2*i).maxCoeff()); 121 } 122 123 // test empty objects 124 VERIFY_IS_APPROX(v.head(0).sum(), Scalar(0)); 125 VERIFY_IS_APPROX(v.tail(0).prod(), Scalar(1)); 126 VERIFY_RAISES_ASSERT(v.head(0).mean()); 127 VERIFY_RAISES_ASSERT(v.head(0).minCoeff()); 128 VERIFY_RAISES_ASSERT(v.head(0).maxCoeff()); 129 } 130 131 void test_redux() 132 { 133 // the max size cannot be too large, otherwise reduxion operations obviously generate large errors. 134 int maxsize = (std::min)(100,EIGEN_TEST_MAX_SIZE); 135 EIGEN_UNUSED_VARIABLE(maxsize); 136 for(int i = 0; i < g_repeat; i++) { 137 CALL_SUBTEST_1( matrixRedux(Matrix<float, 1, 1>()) ); 138 CALL_SUBTEST_1( matrixRedux(Array<float, 1, 1>()) ); 139 CALL_SUBTEST_2( matrixRedux(Matrix2f()) ); 140 CALL_SUBTEST_2( matrixRedux(Array2f()) ); 141 CALL_SUBTEST_3( matrixRedux(Matrix4d()) ); 142 CALL_SUBTEST_3( matrixRedux(Array4d()) ); 143 CALL_SUBTEST_4( matrixRedux(MatrixXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); 144 CALL_SUBTEST_4( matrixRedux(ArrayXXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); 145 CALL_SUBTEST_5( matrixRedux(MatrixXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); 146 CALL_SUBTEST_5( matrixRedux(ArrayXXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); 147 CALL_SUBTEST_6( matrixRedux(MatrixXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); 148 CALL_SUBTEST_6( matrixRedux(ArrayXXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) ); 149 } 150 for(int i = 0; i < g_repeat; i++) { 151 CALL_SUBTEST_7( vectorRedux(Vector4f()) ); 152 CALL_SUBTEST_7( vectorRedux(Array4f()) ); 153 CALL_SUBTEST_5( vectorRedux(VectorXd(internal::random<int>(1,maxsize))) ); 154 CALL_SUBTEST_5( vectorRedux(ArrayXd(internal::random<int>(1,maxsize))) ); 155 CALL_SUBTEST_8( vectorRedux(VectorXf(internal::random<int>(1,maxsize))) ); 156 CALL_SUBTEST_8( vectorRedux(ArrayXf(internal::random<int>(1,maxsize))) ); 157 } 158 } 159
