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							84 lines
						
					
					
						
							3.1 KiB
						
					
					
				| // This file is part of Eigen, a lightweight C++ template library | |
| // for linear algebra. Eigen itself is part of the KDE project. | |
| // | |
| // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> | |
| // | |
| // This Source Code Form is subject to the terms of the Mozilla | |
| // Public License v. 2.0. If a copy of the MPL was not distributed | |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. | |
|  | |
| #include "main.h" | |
|  | |
| template<typename MatrixType> void linearStructure(const MatrixType& m) | |
| { | |
|   /* this test covers the following files: | |
|      Sum.h Difference.h Opposite.h ScalarMultiple.h | |
|   */ | |
| 
 | |
|   typedef typename MatrixType::Scalar Scalar; | |
|   typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType; | |
| 
 | |
|   int rows = m.rows(); | |
|   int cols = m.cols(); | |
| 
 | |
|   // this test relies a lot on Random.h, and there's not much more that we can do | |
|   // to test it, hence I consider that we will have tested Random.h | |
|   MatrixType m1 = MatrixType::Random(rows, cols), | |
|              m2 = MatrixType::Random(rows, cols), | |
|              m3(rows, cols), | |
|              mzero = MatrixType::Zero(rows, cols); | |
| 
 | |
|   Scalar s1 = ei_random<Scalar>(); | |
|   while (ei_abs(s1)<1e-3) s1 = ei_random<Scalar>(); | |
| 
 | |
|   int r = ei_random<int>(0, rows-1), | |
|       c = ei_random<int>(0, cols-1); | |
| 
 | |
|   VERIFY_IS_APPROX(-(-m1),                  m1); | |
|   VERIFY_IS_APPROX(m1+m1,                   2*m1); | |
|   VERIFY_IS_APPROX(m1+m2-m1,                m2); | |
|   VERIFY_IS_APPROX(-m2+m1+m2,               m1); | |
|   VERIFY_IS_APPROX(m1*s1,                   s1*m1); | |
|   VERIFY_IS_APPROX((m1+m2)*s1,              s1*m1+s1*m2); | |
|   VERIFY_IS_APPROX((-m1+m2)*s1,             -s1*m1+s1*m2); | |
|   m3 = m2; m3 += m1; | |
|   VERIFY_IS_APPROX(m3,                      m1+m2); | |
|   m3 = m2; m3 -= m1; | |
|   VERIFY_IS_APPROX(m3,                      m2-m1); | |
|   m3 = m2; m3 *= s1; | |
|   VERIFY_IS_APPROX(m3,                      s1*m2); | |
|   if(NumTraits<Scalar>::HasFloatingPoint) | |
|   { | |
|     m3 = m2; m3 /= s1; | |
|     VERIFY_IS_APPROX(m3,                    m2/s1); | |
|   } | |
| 
 | |
|   // again, test operator() to check const-qualification | |
|   VERIFY_IS_APPROX((-m1)(r,c), -(m1(r,c))); | |
|   VERIFY_IS_APPROX((m1-m2)(r,c), (m1(r,c))-(m2(r,c))); | |
|   VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c))); | |
|   VERIFY_IS_APPROX((s1*m1)(r,c), s1*(m1(r,c))); | |
|   VERIFY_IS_APPROX((m1*s1)(r,c), (m1(r,c))*s1); | |
|   if(NumTraits<Scalar>::HasFloatingPoint) | |
|     VERIFY_IS_APPROX((m1/s1)(r,c), (m1(r,c))/s1); | |
| 
 | |
|   // use .block to disable vectorization and compare to the vectorized version | |
|   VERIFY_IS_APPROX(m1+m1.block(0,0,rows,cols), m1+m1); | |
|   VERIFY_IS_APPROX(m1.cwise() * m1.block(0,0,rows,cols), m1.cwise() * m1); | |
|   VERIFY_IS_APPROX(m1 - m1.block(0,0,rows,cols), m1 - m1); | |
|   VERIFY_IS_APPROX(m1.block(0,0,rows,cols) * s1, m1 * s1); | |
| } | |
| 
 | |
| void test_eigen2_linearstructure() | |
| { | |
|   for(int i = 0; i < g_repeat; i++) { | |
|     CALL_SUBTEST_1( linearStructure(Matrix<float, 1, 1>()) ); | |
|     CALL_SUBTEST_2( linearStructure(Matrix2f()) ); | |
|     CALL_SUBTEST_3( linearStructure(Vector3d()) ); | |
|     CALL_SUBTEST_4( linearStructure(Matrix4d()) ); | |
|     CALL_SUBTEST_5( linearStructure(MatrixXcf(3, 3)) ); | |
|     CALL_SUBTEST_6( linearStructure(MatrixXf(8, 12)) ); | |
|     CALL_SUBTEST_7( linearStructure(MatrixXi(8, 12)) ); | |
|     CALL_SUBTEST_8( linearStructure(MatrixXcd(20, 20)) ); | |
|   } | |
| }
 |