1 // This file is part of Eigen, a lightweight C++ template library 2 // for linear algebra. 3 // 4 // Copyright (C) 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 T> bool isNotNaN(const T& x) 13 { 14 return x==x; 15 } 16 17 // workaround aggressive optimization in ICC 18 template<typename T> EIGEN_DONT_INLINE T sub(T a, T b) { return a - b; } 19 20 template<typename T> bool isFinite(const T& x) 21 { 22 return isNotNaN(sub(x,x)); 23 } 24 25 template<typename T> EIGEN_DONT_INLINE T copy(const T& x) 26 { 27 return x; 28 } 29 30 template<typename MatrixType> void stable_norm(const MatrixType& m) 31 { 32 /* this test covers the following files: 33 StableNorm.h 34 */ 35 using std::sqrt; 36 using std::abs; 37 typedef typename MatrixType::Index Index; 38 typedef typename MatrixType::Scalar Scalar; 39 typedef typename NumTraits<Scalar>::Real RealScalar; 40 41 // Check the basic machine-dependent constants. 42 { 43 int ibeta, it, iemin, iemax; 44 45 ibeta = std::numeric_limits<RealScalar>::radix; // base for floating-point numbers 46 it = std::numeric_limits<RealScalar>::digits; // number of base-beta digits in mantissa 47 iemin = std::numeric_limits<RealScalar>::min_exponent; // minimum exponent 48 iemax = std::numeric_limits<RealScalar>::max_exponent; // maximum exponent 49 50 VERIFY( (!(iemin > 1 - 2*it || 1+it>iemax || (it==2 && ibeta<5) || (it<=4 && ibeta <= 3 ) || it<2)) 51 && "the stable norm algorithm cannot be guaranteed on this computer"); 52 } 53 54 55 Index rows = m.rows(); 56 Index cols = m.cols(); 57 58 // get a non-zero random factor 59 Scalar factor = internal::random<Scalar>(); 60 while(numext::abs2(factor)<RealScalar(1e-4)) 61 factor = internal::random<Scalar>(); 62 Scalar big = factor * ((std::numeric_limits<RealScalar>::max)() * RealScalar(1e-4)); 63 64 factor = internal::random<Scalar>(); 65 while(numext::abs2(factor)<RealScalar(1e-4)) 66 factor = internal::random<Scalar>(); 67 Scalar small = factor * ((std::numeric_limits<RealScalar>::min)() * RealScalar(1e4)); 68 69 MatrixType vzero = MatrixType::Zero(rows, cols), 70 vrand = MatrixType::Random(rows, cols), 71 vbig(rows, cols), 72 vsmall(rows,cols); 73 74 vbig.fill(big); 75 vsmall.fill(small); 76 77 VERIFY_IS_MUCH_SMALLER_THAN(vzero.norm(), static_cast<RealScalar>(1)); 78 VERIFY_IS_APPROX(vrand.stableNorm(), vrand.norm()); 79 VERIFY_IS_APPROX(vrand.blueNorm(), vrand.norm()); 80 VERIFY_IS_APPROX(vrand.hypotNorm(), vrand.norm()); 81 82 RealScalar size = static_cast<RealScalar>(m.size()); 83 84 // test isFinite 85 VERIFY(!isFinite( std::numeric_limits<RealScalar>::infinity())); 86 VERIFY(!isFinite(sqrt(-abs(big)))); 87 88 // test overflow 89 VERIFY(isFinite(sqrt(size)*abs(big))); 90 VERIFY_IS_NOT_APPROX(sqrt(copy(vbig.squaredNorm())), abs(sqrt(size)*big)); // here the default norm must fail 91 VERIFY_IS_APPROX(vbig.stableNorm(), sqrt(size)*abs(big)); 92 VERIFY_IS_APPROX(vbig.blueNorm(), sqrt(size)*abs(big)); 93 VERIFY_IS_APPROX(vbig.hypotNorm(), sqrt(size)*abs(big)); 94 95 // test underflow 96 VERIFY(isFinite(sqrt(size)*abs(small))); 97 VERIFY_IS_NOT_APPROX(sqrt(copy(vsmall.squaredNorm())), abs(sqrt(size)*small)); // here the default norm must fail 98 VERIFY_IS_APPROX(vsmall.stableNorm(), sqrt(size)*abs(small)); 99 VERIFY_IS_APPROX(vsmall.blueNorm(), sqrt(size)*abs(small)); 100 VERIFY_IS_APPROX(vsmall.hypotNorm(), sqrt(size)*abs(small)); 101 102 // Test compilation of cwise() version 103 VERIFY_IS_APPROX(vrand.colwise().stableNorm(), vrand.colwise().norm()); 104 VERIFY_IS_APPROX(vrand.colwise().blueNorm(), vrand.colwise().norm()); 105 VERIFY_IS_APPROX(vrand.colwise().hypotNorm(), vrand.colwise().norm()); 106 VERIFY_IS_APPROX(vrand.rowwise().stableNorm(), vrand.rowwise().norm()); 107 VERIFY_IS_APPROX(vrand.rowwise().blueNorm(), vrand.rowwise().norm()); 108 VERIFY_IS_APPROX(vrand.rowwise().hypotNorm(), vrand.rowwise().norm()); 109 } 110 111 void test_stable_norm() 112 { 113 for(int i = 0; i < g_repeat; i++) { 114 CALL_SUBTEST_1( stable_norm(Matrix<float, 1, 1>()) ); 115 CALL_SUBTEST_2( stable_norm(Vector4d()) ); 116 CALL_SUBTEST_3( stable_norm(VectorXd(internal::random<int>(10,2000))) ); 117 CALL_SUBTEST_4( stable_norm(VectorXf(internal::random<int>(10,2000))) ); 118 CALL_SUBTEST_5( stable_norm(VectorXcd(internal::random<int>(10,2000))) ); 119 } 120 } 121
