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) 2008 Gael Guennebaud <g.gael@free.fr>
5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla
8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed
9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// this hack is needed to make this file compiles with -pedantic (gcc)
12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#ifdef __GNUC__
13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define throw(X)
14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#endif
15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// discard stack allocation as that too bypasses malloc
16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define EIGEN_STACK_ALLOCATION_LIMIT 0
17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// any heap allocation will raise an assert
18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define EIGEN_NO_MALLOC
19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "main.h"
21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename MatrixType> void nomalloc(const MatrixType& m)
23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  /* this test check no dynamic memory allocation are issued with fixed-size matrices
25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  */
26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename MatrixType::Scalar Scalar;
28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  int rows = m.rows();
31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  int cols = m.cols();
32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  MatrixType m1 = MatrixType::Random(rows, cols),
34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath             m2 = MatrixType::Random(rows, cols),
35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath             m3(rows, cols),
36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath             mzero = MatrixType::Zero(rows, cols),
37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath             identity = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                              ::Identity(rows, rows),
39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath             square = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath                              ::Random(rows, rows);
41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VectorType v1 = VectorType::Random(rows),
42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath             v2 = VectorType::Random(rows),
43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath             vzero = VectorType::Zero(rows);
44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Scalar s1 = ei_random<Scalar>();
46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  int r = ei_random<int>(0, rows-1),
48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      c = ei_random<int>(0, cols-1);
49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX((m1+m2)*s1,              s1*m1+s1*m2);
51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c)));
52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m1.cwise() * m1.block(0,0,rows,cols), m1.cwise() * m1);
53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX((m1*m1.transpose())*m2,  m1*(m1.transpose()*m2));
54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_eigen2_nomalloc()
57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // check that our operator new is indeed called:
59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_RAISES_ASSERT(MatrixXd dummy = MatrixXd::Random(3,3));
60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  CALL_SUBTEST_1( nomalloc(Matrix<float, 1, 1>()) );
61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  CALL_SUBTEST_2( nomalloc(Matrix4d()) );
62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  CALL_SUBTEST_3( nomalloc(Matrix<float,32,32>()) );
63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
64