1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library
2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra.
3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2009 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#define EIGEN_NO_STATIC_ASSERT
11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "main.h"
12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename T>
14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct other_matrix_type
15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef int type;
17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename _Scalar, int _Rows, int _Cols, int _Options, int _MaxRows, int _MaxCols>
20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathstruct other_matrix_type<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols> >
21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<_Scalar, _Rows, _Cols, _Options^RowMajor, _MaxRows, _MaxCols> type;
23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath};
24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> void swap(const MatrixType& m)
26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename other_matrix_type<MatrixType>::type OtherMatrixType;
28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename MatrixType::Scalar Scalar;
29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  ei_assert((!ei_is_same_type<MatrixType,OtherMatrixType>::ret));
31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  int rows = m.rows();
32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  int cols = m.cols();
33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // construct 3 matrix guaranteed to be distinct
35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  MatrixType m1 = MatrixType::Random(rows,cols);
36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  MatrixType m2 = MatrixType::Random(rows,cols) + Scalar(100) * MatrixType::Identity(rows,cols);
37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  OtherMatrixType m3 = OtherMatrixType::Random(rows,cols) + Scalar(200) * OtherMatrixType::Identity(rows,cols);
38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  MatrixType m1_copy = m1;
40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  MatrixType m2_copy = m2;
41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  OtherMatrixType m3_copy = m3;
42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // test swapping 2 matrices of same type
44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m1.swap(m2);
45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m1,m2_copy);
46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m2,m1_copy);
47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m1 = m1_copy;
48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m2 = m2_copy;
49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // test swapping 2 matrices of different types
51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m1.swap(m3);
52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m1,m3_copy);
53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m3,m1_copy);
54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m1 = m1_copy;
55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m3 = m3_copy;
56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // test swapping matrix with expression
58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m1.swap(m2.block(0,0,rows,cols));
59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m1,m2_copy);
60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m2,m1_copy);
61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m1 = m1_copy;
62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m2 = m2_copy;
63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // test swapping two expressions of different types
65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m1.transpose().swap(m3.transpose());
66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m1,m3_copy);
67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m3,m1_copy);
68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m1 = m1_copy;
69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m3 = m3_copy;
70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // test assertion on mismatching size -- matrix case
72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_RAISES_ASSERT(m1.swap(m1.row(0)));
73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // test assertion on mismatching size -- xpr case
74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_RAISES_ASSERT(m1.row(0).swap(m1));
75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_eigen2_swap()
78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  CALL_SUBTEST_1( swap(Matrix3f()) ); // fixed size, no vectorization
80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  CALL_SUBTEST_1( swap(Matrix4d()) ); // fixed size, possible vectorization
81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  CALL_SUBTEST_1( swap(MatrixXd(3,3)) ); // dyn size, no vectorization
82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  CALL_SUBTEST_1( swap(MatrixXf(30,30)) ); // dyn size, possible vectorization
83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
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