1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library 2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra. 3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2010-2011 Jitse Niesen <jitse@maths.leeds.ac.uk> 5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla 7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed 8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. 9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "main.h" 11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathbool equalsIdentity(const MatrixType& A) 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Index Index; 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Scalar Scalar; 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar zero = static_cast<Scalar>(0); 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath bool offDiagOK = true; 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (Index i = 0; i < A.rows(); ++i) { 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (Index j = i+1; j < A.cols(); ++j) { 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath offDiagOK = offDiagOK && (A(i,j) == zero); 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (Index i = 0; i < A.rows(); ++i) { 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (Index j = 0; j < (std::min)(i, A.cols()); ++j) { 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath offDiagOK = offDiagOK && (A(i,j) == zero); 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath bool diagOK = (A.diagonal().array() == 1).all(); 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath return offDiagOK && diagOK; 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename VectorType> 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid testVectorType(const VectorType& base) 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::traits<VectorType>::Index Index; 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename internal::traits<VectorType>::Scalar Scalar; 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Index size = base.size(); 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar high = internal::random<Scalar>(-500,500); 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar low = (size == 1 ? high : internal::random<Scalar>(-500,500)); 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if (low>high) std::swap(low,high); 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Scalar step = ((size == 1) ? 1 : (high-low)/(size-1)); 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // check whether the result yields what we expect it to do 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VectorType m(base); 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m.setLinSpaced(size,low,high); 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VectorType n(size); 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (int i=0; i<size; ++i) 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath n(i) = low+i*step; 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m,n); 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // random access version 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m = VectorType::LinSpaced(size,low,high); 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m,n); 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // Assignment of a RowVectorXd to a MatrixXd (regression test for bug #79). 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY( (MatrixXd(RowVectorXd::LinSpaced(3, 0, 1)) - RowVector3d(0, 0.5, 1)).norm() < std::numeric_limits<Scalar>::epsilon() ); 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // These guys sometimes fail! This is not good. Any ideas how to fix them!? 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //VERIFY( m(m.size()-1) == high ); 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //VERIFY( m(0) == low ); 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // sequential access version 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m = VectorType::LinSpaced(Sequential,size,low,high); 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m,n); 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // These guys sometimes fail! This is not good. Any ideas how to fix them!? 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //VERIFY( m(m.size()-1) == high ); 76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //VERIFY( m(0) == low ); 77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // check whether everything works with row and col major vectors 79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Matrix<Scalar,Dynamic,1> row_vector(size); 80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Matrix<Scalar,1,Dynamic> col_vector(size); 81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath row_vector.setLinSpaced(size,low,high); 82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath col_vector.setLinSpaced(size,low,high); 83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY( row_vector.isApprox(col_vector.transpose(), NumTraits<Scalar>::epsilon())); 84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Matrix<Scalar,Dynamic,1> size_changer(size+50); 86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath size_changer.setLinSpaced(size,low,high); 87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY( size_changer.size() == size ); 88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar,1,1> ScalarMatrix; 90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ScalarMatrix scalar; 91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath scalar.setLinSpaced(1,low,high); 92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX( scalar, ScalarMatrix::Constant(high) ); 93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX( ScalarMatrix::LinSpaced(1,low,high), ScalarMatrix::Constant(high) ); 947faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez 957faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez // regression test for bug 526 (linear vectorized transversal) 967faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez if (size > 1) { 977faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez m.tail(size-1).setLinSpaced(low, high); 987faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez VERIFY_IS_APPROX(m(size-1), high); 997faaa9f3f0df9d23790277834d426c3d992ac3baCarlos Hernandez } 100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> 103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid testMatrixType(const MatrixType& m) 104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Index Index; 106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Index rows = m.rows(); 107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath const Index cols = m.cols(); 108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType A; 110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath A.setIdentity(rows, cols); 111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(equalsIdentity(A)); 112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(equalsIdentity(MatrixType::Identity(rows, cols))); 113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_nullary() 116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( testMatrixType(Matrix2d()) ); 118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2( testMatrixType(MatrixXcf(internal::random<int>(1,300),internal::random<int>(1,300))) ); 119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( testMatrixType(MatrixXf(internal::random<int>(1,300),internal::random<int>(1,300))) ); 120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < g_repeat; i++) { 122c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_4( testVectorType(VectorXd(internal::random<int>(1,300))) ); 123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_5( testVectorType(Vector4d()) ); // regression test for bug 232 124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_6( testVectorType(Vector3d()) ); 125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_7( testVectorType(VectorXf(internal::random<int>(1,300))) ); 126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_8( testVectorType(Vector3f()) ); 127c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_8( testVectorType(Matrix<float,1,1>()) ); 128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 130