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84 lines
3.3 KiB
C++
84 lines
3.3 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
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// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
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//
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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
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// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU Lesser General Public
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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// this hack is needed to make this file compiles with -pedantic (gcc)
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#ifdef __GNUC__
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#define throw(X)
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#endif
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// discard stack allocation as that too bypasses malloc
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#define EIGEN_STACK_ALLOCATION_LIMIT 0
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// any heap allocation will raise an assert
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#define EIGEN_NO_MALLOC
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#include "main.h"
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template<typename MatrixType> void nomalloc(const MatrixType& m)
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{
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/* this test check no dynamic memory allocation are issued with fixed-size matrices
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*/
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typedef typename MatrixType::Scalar Scalar;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
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int rows = m.rows();
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int cols = m.cols();
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MatrixType m1 = MatrixType::Random(rows, cols),
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m2 = MatrixType::Random(rows, cols),
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m3(rows, cols),
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mzero = MatrixType::Zero(rows, cols),
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identity = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
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::Identity(rows, rows),
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square = Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime>
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::Random(rows, rows);
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VectorType v1 = VectorType::Random(rows),
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v2 = VectorType::Random(rows),
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vzero = VectorType::Zero(rows);
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Scalar s1 = ei_random<Scalar>();
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int r = ei_random<int>(0, rows-1),
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c = ei_random<int>(0, cols-1);
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VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2);
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VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c)));
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VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0,0,rows,cols)), (m1.array()*m1.array()).matrix());
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if (MatrixType::RowsAtCompileTime<EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD) {
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// If the matrices are too large, we have better to use the optimized GEMM
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// routines which allocates temporaries. However, on some platforms
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// these temporaries are allocated on the stack using alloca.
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VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2));
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}
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}
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void test_nomalloc()
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{
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// check that our operator new is indeed called:
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VERIFY_RAISES_ASSERT(MatrixXd dummy = MatrixXd::Random(3,3));
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CALL_SUBTEST(nomalloc(Matrix<float, 1, 1>()) );
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CALL_SUBTEST(nomalloc(Matrix4d()) );
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CALL_SUBTEST(nomalloc(Matrix<float,32,32>()) );
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}
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