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https://gitlab.com/libeigen/eigen.git
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76faf4a965
improve mixing type support in operations between arrays and scalars: - 2 * ArrayXcf is now optimized in the sense that the integer 2 is properly promoted to a float instead of a complex<float> (fix a regression) - 2.1 * ArrayXi is now forbiden (previously, 2.1 was converted to 2) - This mechanism should be applicable to any custom scalar type, assuming NumTraits<T>::Literal is properly defined (it defaults to T)
108 lines
4.3 KiB
C++
108 lines
4.3 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) 2010 Hauke Heibel <hauke.heibel@gmail.com>
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// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#define TEST_ENABLE_TEMPORARY_TRACKING
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#include "main.h"
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template <int N, typename XprType>
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void use_n_times(const XprType &xpr)
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{
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typename internal::nested_eval<XprType,N>::type mat(xpr);
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typename XprType::PlainObject res(mat.rows(), mat.cols());
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nb_temporaries--; // remove res
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res.setZero();
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for(int i=0; i<N; ++i)
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res += mat;
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}
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template <int N, typename ReferenceType, typename XprType>
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bool verify_eval_type(const XprType &, const ReferenceType&)
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{
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typedef typename internal::nested_eval<XprType,N>::type EvalType;
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return internal::is_same<typename internal::remove_all<EvalType>::type, typename internal::remove_all<ReferenceType>::type>::value;
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}
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template <typename MatrixType> void run_nesting_ops_1(const MatrixType& _m)
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{
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typename internal::nested_eval<MatrixType,2>::type m(_m);
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// Make really sure that we are in debug mode!
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VERIFY_RAISES_ASSERT(eigen_assert(false));
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// The only intention of these tests is to ensure that this code does
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// not trigger any asserts or segmentation faults... more to come.
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VERIFY_IS_APPROX( (m.transpose() * m).diagonal().sum(), (m.transpose() * m).diagonal().sum() );
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VERIFY_IS_APPROX( (m.transpose() * m).diagonal().array().abs().sum(), (m.transpose() * m).diagonal().array().abs().sum() );
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VERIFY_IS_APPROX( (m.transpose() * m).array().abs().sum(), (m.transpose() * m).array().abs().sum() );
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}
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template <typename MatrixType> void run_nesting_ops_2(const MatrixType& _m)
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{
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typedef typename MatrixType::Scalar Scalar;
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Index rows = _m.rows();
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Index cols = _m.cols();
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MatrixType m1 = MatrixType::Random(rows,cols);
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Matrix<Scalar,MatrixType::RowsAtCompileTime,MatrixType::ColsAtCompileTime,ColMajor> m2;
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if((MatrixType::SizeAtCompileTime==Dynamic))
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{
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VERIFY_EVALUATION_COUNT( use_n_times<1>(m1 + m1*m1), 1 );
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VERIFY_EVALUATION_COUNT( use_n_times<10>(m1 + m1*m1), 1 );
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VERIFY_EVALUATION_COUNT( use_n_times<1>(m1.template triangularView<Lower>().solve(m1.col(0))), 1 );
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VERIFY_EVALUATION_COUNT( use_n_times<10>(m1.template triangularView<Lower>().solve(m1.col(0))), 1 );
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VERIFY_EVALUATION_COUNT( use_n_times<1>(Scalar(2)*m1.template triangularView<Lower>().solve(m1.col(0))), 2 ); // FIXME could be one by applying the scaling in-place on the solve result
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VERIFY_EVALUATION_COUNT( use_n_times<1>(m1.col(0)+m1.template triangularView<Lower>().solve(m1.col(0))), 2 ); // FIXME could be one by adding m1.col() inplace
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VERIFY_EVALUATION_COUNT( use_n_times<10>(m1.col(0)+m1.template triangularView<Lower>().solve(m1.col(0))), 2 );
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}
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{
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VERIFY( verify_eval_type<10>(m1, m1) );
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if(!NumTraits<Scalar>::IsComplex)
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{
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VERIFY( verify_eval_type<3>(2*m1, 2*m1) );
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VERIFY( verify_eval_type<4>(2*m1, m1) );
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}
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else
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{
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VERIFY( verify_eval_type<2>(2*m1, 2*m1) );
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VERIFY( verify_eval_type<3>(2*m1, m1) );
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}
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VERIFY( verify_eval_type<2>(m1+m1, m1+m1) );
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VERIFY( verify_eval_type<3>(m1+m1, m1) );
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VERIFY( verify_eval_type<1>(m1*m1.transpose(), m2) );
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VERIFY( verify_eval_type<1>(m1*(m1+m1).transpose(), m2) );
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VERIFY( verify_eval_type<2>(m1*m1.transpose(), m2) );
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VERIFY( verify_eval_type<1>(m1+m1*m1, m1) );
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VERIFY( verify_eval_type<1>(m1.template triangularView<Lower>().solve(m1), m1) );
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VERIFY( verify_eval_type<1>(m1+m1.template triangularView<Lower>().solve(m1), m1) );
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}
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}
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void test_nesting_ops()
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{
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CALL_SUBTEST_1(run_nesting_ops_1(MatrixXf::Random(25,25)));
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CALL_SUBTEST_2(run_nesting_ops_1(MatrixXcd::Random(25,25)));
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CALL_SUBTEST_3(run_nesting_ops_1(Matrix4f::Random()));
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CALL_SUBTEST_4(run_nesting_ops_1(Matrix2d::Random()));
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Index s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
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CALL_SUBTEST_1( run_nesting_ops_2(MatrixXf(s,s)) );
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CALL_SUBTEST_2( run_nesting_ops_2(MatrixXcd(s,s)) );
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CALL_SUBTEST_3( run_nesting_ops_2(Matrix4f()) );
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CALL_SUBTEST_4( run_nesting_ops_2(Matrix2d()) );
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TEST_SET_BUT_UNUSED_VARIABLE(s)
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}
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