1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library 2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra. Eigen itself is part of the KDE project. 3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// 4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> 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 Kamath// check minor separately in order to avoid the possible creation of a zero-sized 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// array. Comes from a compilation error with gcc-3.4 or gcc-4 with -ansi -pedantic. 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Another solution would be to declare the array like this: T m_data[Size==0?1:Size]; in ei_matrix_storage 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// but this is probably not bad to raise such an error at compile time... 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar, int _Rows, int _Cols> struct CheckMinor 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar, _Rows, _Cols> MatrixType; 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CheckMinor(MatrixType& m1, int r1, int c1) 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int rows = m1.rows(); 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int cols = m1.cols(); 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Matrix<Scalar, Dynamic, Dynamic> mi = m1.minor(0,0).eval(); 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(mi, m1.block(1,1,rows-1,cols-1)); 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath mi = m1.minor(r1,c1); 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(mi.transpose(), m1.transpose().minor(c1,r1)); 28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //check operator(), both constant and non-constant, on minor() 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m1.minor(r1,c1)(0,0) = m1.minor(0,0)(0,0); 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar> struct CheckMinor<Scalar,1,1> 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar, 1, 1> MatrixType; 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CheckMinor(MatrixType&, int, int) {} 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}; 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> void submatrices(const MatrixType& m) 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /* this test covers the following files: 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Row.h Column.h Block.h Minor.h DiagonalCoeffs.h 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Scalar Scalar; 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::RealScalar RealScalar; 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType; 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int rows = m.rows(); 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int cols = m.cols(); 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType m1 = MatrixType::Random(rows, cols), 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2 = MatrixType::Random(rows, cols), 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3(rows, cols), 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath mzero = MatrixType::Zero(rows, cols), 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ones = MatrixType::Ones(rows, cols), 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath identity = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ::Identity(rows, rows), 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath square = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath ::Random(rows, rows); 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VectorType v1 = VectorType::Random(rows), 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath v2 = VectorType::Random(rows), 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath v3 = VectorType::Random(rows), 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath vzero = VectorType::Zero(rows); 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar s1 = ei_random<Scalar>(); 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int r1 = ei_random<int>(0,rows-1); 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int r2 = ei_random<int>(r1,rows-1); 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int c1 = ei_random<int>(0,cols-1); 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int c2 = ei_random<int>(c1,cols-1); 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //check row() and col() 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.col(c1).transpose(), m1.transpose().row(c1)); 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(square.row(r1).eigen2_dot(m1.col(c1)), (square.lazy() * m1.conjugate())(r1,c1)); 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //check operator(), both constant and non-constant, on row() and col() 76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m1.row(r1) += s1 * m1.row(r2); 77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m1.col(c1) += s1 * m1.col(c2); 78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //check block() 80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Matrix<Scalar,Dynamic,Dynamic> b1(1,1); b1(0,0) = m1(r1,c1); 81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath RowVectorType br1(m1.block(r1,0,1,cols)); 82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VectorType bc1(m1.block(0,c1,rows,1)); 83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(b1, m1.block(r1,c1,1,1)); 84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.row(r1), br1); 85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.col(c1), bc1); 86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //check operator(), both constant and non-constant, on block() 87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m1.block(r1,c1,r2-r1+1,c2-c1+1) = s1 * m2.block(0, 0, r2-r1+1,c2-c1+1); 88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m1.block(r1,c1,r2-r1+1,c2-c1+1)(r2-r1,c2-c1) = m2.block(0, 0, r2-r1+1,c2-c1+1)(0,0); 89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //check minor() 91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CheckMinor<Scalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime> checkminor(m1,r1,c1); 92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath //check diagonal() 94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.diagonal(), m1.transpose().diagonal()); 95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.diagonal() = 2 * m1.diagonal(); 96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2.diagonal()[0] *= 3; 97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m2.diagonal()[0], static_cast<Scalar>(6) * m1.diagonal()[0]); 98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath enum { 100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath BlockRows = EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::RowsAtCompileTime,2), 101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath BlockCols = EIGEN_SIZE_MIN_PREFER_FIXED(MatrixType::ColsAtCompileTime,5) 102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath }; 103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if (rows>=5 && cols>=8) 104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test fixed block() as lvalue 106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m1.template block<BlockRows,BlockCols>(1,1) *= s1; 107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test operator() on fixed block() both as constant and non-constant 108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m1.template block<BlockRows,BlockCols>(1,1)(0, 3) = m1.template block<2,5>(1,1)(1,2); 109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // check that fixed block() and block() agree 110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Matrix<Scalar,Dynamic,Dynamic> b = m1.template block<BlockRows,BlockCols>(3,3); 111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(b, m1.block(3,3,BlockRows,BlockCols)); 112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if (rows>2) 115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // test sub vectors 117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1.template start<2>(), v1.block(0,0,2,1)); 118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1.template start<2>(), v1.start(2)); 119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1.template start<2>(), v1.segment(0,2)); 120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1.template start<2>(), v1.template segment<2>(0)); 121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int i = rows-2; 122c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1.template end<2>(), v1.block(i,0,2,1)); 123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1.template end<2>(), v1.end(2)); 124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1.template end<2>(), v1.segment(i,2)); 125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1.template end<2>(), v1.template segment<2>(i)); 126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath i = ei_random(0,rows-2); 127c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(v1.segment(i,2), v1.template segment<2>(i)); 128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 130c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // stress some basic stuffs with block matrices 131c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(ei_real(ones.col(c1).sum()) == RealScalar(rows)); 132c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(ei_real(ones.row(r1).sum()) == RealScalar(cols)); 133c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 134c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(ei_real(ones.col(c1).eigen2_dot(ones.col(c2))) == RealScalar(rows)); 135c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY(ei_real(ones.row(r1).eigen2_dot(ones.row(r2))) == RealScalar(cols)); 136c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 137c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 138c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_eigen2_submatrices() 139c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 140c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < g_repeat; i++) { 141c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( submatrices(Matrix<float, 1, 1>()) ); 142c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2( submatrices(Matrix4d()) ); 143c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( submatrices(MatrixXcf(3, 3)) ); 144c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_4( submatrices(MatrixXi(8, 12)) ); 145c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_5( submatrices(MatrixXcd(20, 20)) ); 146c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_6( submatrices(MatrixXf(20, 20)) ); 147c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 148c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 149