1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is triangularView of Eigen, a lightweight C++ template library 2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra. 3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr> 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> void bandmatrix(const MatrixType& _m) 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Index Index; 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Scalar Scalar; 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename NumTraits<Scalar>::Real RealScalar; 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrixType; 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index rows = _m.rows(); 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index cols = _m.cols(); 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index supers = _m.supers(); 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index subs = _m.subs(); 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType m(rows,cols,supers,subs); 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath DenseMatrixType dm1(rows,cols); 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath dm1.setZero(); 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m.diagonal().setConstant(123); 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath dm1.diagonal().setConstant(123); 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (int i=1; i<=m.supers();++i) 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m.diagonal(i).setConstant(static_cast<RealScalar>(i)); 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath dm1.diagonal(i).setConstant(static_cast<RealScalar>(i)); 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (int i=1; i<=m.subs();++i) 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m.diagonal(-i).setConstant(-static_cast<RealScalar>(i)); 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath dm1.diagonal(-i).setConstant(-static_cast<RealScalar>(i)); 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //std::cerr << m.m_data << "\n\n" << m.toDense() << "\n\n" << dm1 << "\n\n\n\n"; 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(dm1,m.toDenseMatrix()); 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for (int i=0; i<cols; ++i) 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m.col(i).setConstant(static_cast<RealScalar>(i+1)); 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath dm1.col(i).setConstant(static_cast<RealScalar>(i+1)); 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index d = (std::min)(rows,cols); 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index a = std::max<Index>(0,cols-d-supers); 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index b = std::max<Index>(0,rows-d-subs); 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(a>0) dm1.block(0,d+supers,rows,a).setZero(); 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath dm1.block(0,supers+1,cols-supers-1-a,cols-supers-1-a).template triangularView<Upper>().setZero(); 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath dm1.block(subs+1,0,rows-subs-1-b,rows-subs-1-b).template triangularView<Lower>().setZero(); 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(b>0) dm1.block(d+subs,0,b,cols).setZero(); 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //std::cerr << m.m_data << "\n\n" << m.toDense() << "\n\n" << dm1 << "\n\n"; 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(dm1,m.toDenseMatrix()); 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathusing Eigen::internal::BandMatrix; 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_bandmatrix() 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef BandMatrix<float>::Index Index; 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < 10*g_repeat ; i++) { 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index rows = internal::random<Index>(1,10); 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index cols = internal::random<Index>(1,10); 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index sups = internal::random<Index>(0,cols-1); 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Index subs = internal::random<Index>(0,rows-1); 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST(bandmatrix(BandMatrix<float>(rows,cols,sups,subs)) ); 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 75