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