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 Kamathtemplate<typename MatrixType> void linearStructure(const MatrixType& m) 13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath /* this test covers the following files: 15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Sum.h Difference.h Opposite.h ScalarMultiple.h 16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath */ 17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef typename MatrixType::Scalar Scalar; 19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; 20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int rows = m.rows(); 22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int cols = m.cols(); 23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // this test relies a lot on Random.h, and there's not much more that we can do 25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // to test it, hence I consider that we will have tested Random.h 26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath MatrixType m1 = MatrixType::Random(rows, cols), 27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m2 = MatrixType::Random(rows, cols), 28615d816d068b4d0f5e8df601930b5f160bf7eda1Tim Murray m3(rows, cols); 29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath Scalar s1 = ei_random<Scalar>(); 31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath while (ei_abs(s1)<1e-3) s1 = ei_random<Scalar>(); 32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath int r = ei_random<int>(0, rows-1), 34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath c = ei_random<int>(0, cols-1); 35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(-(-m1), m1); 37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1+m1, 2*m1); 38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1+m2-m1, m2); 39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(-m2+m1+m2, m1); 40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1*s1, s1*m1); 41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2); 42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((-m1+m2)*s1, -s1*m1+s1*m2); 43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m2; m3 += m1; 44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3, m1+m2); 45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m2; m3 -= m1; 46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3, m2-m1); 47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m2; m3 *= s1; 48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3, s1*m2); 49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(NumTraits<Scalar>::HasFloatingPoint) 50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath { 51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath m3 = m2; m3 /= s1; 52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m3, m2/s1); 53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // again, test operator() to check const-qualification 56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((-m1)(r,c), -(m1(r,c))); 57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((m1-m2)(r,c), (m1(r,c))-(m2(r,c))); 58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c))); 59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((s1*m1)(r,c), s1*(m1(r,c))); 60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((m1*s1)(r,c), (m1(r,c))*s1); 61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath if(NumTraits<Scalar>::HasFloatingPoint) 62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX((m1/s1)(r,c), (m1(r,c))/s1); 63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath // use .block to disable vectorization and compare to the vectorized version 65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1+m1.block(0,0,rows,cols), m1+m1); 66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.cwise() * m1.block(0,0,rows,cols), m1.cwise() * m1); 67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1 - m1.block(0,0,rows,cols), m1 - m1); 68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath VERIFY_IS_APPROX(m1.block(0,0,rows,cols) * s1, m1 * s1); 69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath 71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_eigen2_linearstructure() 72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{ 73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath for(int i = 0; i < g_repeat; i++) { 74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_1( linearStructure(Matrix<float, 1, 1>()) ); 75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_2( linearStructure(Matrix2f()) ); 76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_3( linearStructure(Vector3d()) ); 77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_4( linearStructure(Matrix4d()) ); 78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_5( linearStructure(MatrixXcf(3, 3)) ); 79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_6( linearStructure(MatrixXf(8, 12)) ); 80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_7( linearStructure(MatrixXi(8, 12)) ); 81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath CALL_SUBTEST_8( linearStructure(MatrixXcd(20, 20)) ); 82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath } 83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath} 84