[136] | 1 | // This file is part of Eigen, a lightweight C++ template library
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| 2 | // for linear algebra.
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| 3 | //
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| 4 | // Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
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| 5 | //
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| 6 | // This Source Code Form is subject to the terms of the Mozilla
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| 7 | // Public License v. 2.0. If a copy of the MPL was not distributed
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| 8 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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| 9 |
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| 10 | static int nb_temporaries;
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| 11 |
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| 12 | inline void on_temporary_creation(int size) {
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| 13 | // here's a great place to set a breakpoint when debugging failures in this test!
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| 14 | if(size!=0) nb_temporaries++;
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| 15 | }
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| 16 |
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| 17 |
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| 18 | #define EIGEN_DENSE_STORAGE_CTOR_PLUGIN { on_temporary_creation(size); }
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| 19 |
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| 20 | #include "main.h"
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| 21 |
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| 22 | #define VERIFY_EVALUATION_COUNT(XPR,N) {\
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| 23 | nb_temporaries = 0; \
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| 24 | XPR; \
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| 25 | if(nb_temporaries!=N) std::cerr << "nb_temporaries == " << nb_temporaries << "\n"; \
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| 26 | VERIFY( (#XPR) && nb_temporaries==N ); \
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| 27 | }
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| 28 |
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| 29 | template<typename MatrixType> void product_notemporary(const MatrixType& m)
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| 30 | {
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| 31 | /* This test checks the number of temporaries created
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| 32 | * during the evaluation of a complex expression */
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| 33 | typedef typename MatrixType::Index Index;
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| 34 | typedef typename MatrixType::Scalar Scalar;
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| 35 | typedef typename MatrixType::RealScalar RealScalar;
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| 36 | typedef Matrix<Scalar, 1, Dynamic> RowVectorType;
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| 37 | typedef Matrix<Scalar, Dynamic, 1> ColVectorType;
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| 38 | typedef Matrix<Scalar, Dynamic, Dynamic, ColMajor> ColMajorMatrixType;
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| 39 | typedef Matrix<Scalar, Dynamic, Dynamic, RowMajor> RowMajorMatrixType;
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| 40 |
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| 41 | Index rows = m.rows();
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| 42 | Index cols = m.cols();
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| 43 |
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| 44 | ColMajorMatrixType m1 = MatrixType::Random(rows, cols),
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| 45 | m2 = MatrixType::Random(rows, cols),
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| 46 | m3(rows, cols);
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| 47 | RowVectorType rv1 = RowVectorType::Random(rows), rvres(rows);
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| 48 | ColVectorType cv1 = ColVectorType::Random(cols), cvres(cols);
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| 49 | RowMajorMatrixType rm3(rows, cols);
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| 50 |
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| 51 | Scalar s1 = internal::random<Scalar>(),
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| 52 | s2 = internal::random<Scalar>(),
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| 53 | s3 = internal::random<Scalar>();
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| 54 |
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| 55 | Index c0 = internal::random<Index>(4,cols-8),
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| 56 | c1 = internal::random<Index>(8,cols-c0),
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| 57 | r0 = internal::random<Index>(4,cols-8),
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| 58 | r1 = internal::random<Index>(8,rows-r0);
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| 59 |
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| 60 | VERIFY_EVALUATION_COUNT( m3 = (m1 * m2.adjoint()), 1);
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| 61 | VERIFY_EVALUATION_COUNT( m3 = (m1 * m2.adjoint()).transpose(), 1);
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| 62 | VERIFY_EVALUATION_COUNT( m3.noalias() = m1 * m2.adjoint(), 0);
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| 63 |
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| 64 | VERIFY_EVALUATION_COUNT( m3 = s1 * (m1 * m2.transpose()), 1);
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| 65 | VERIFY_EVALUATION_COUNT( m3 = m3 + s1 * (m1 * m2.transpose()), 1);
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| 66 | VERIFY_EVALUATION_COUNT( m3.noalias() = s1 * (m1 * m2.transpose()), 0);
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| 67 |
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| 68 | VERIFY_EVALUATION_COUNT( m3 = m3 + (m1 * m2.adjoint()), 1);
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| 69 | VERIFY_EVALUATION_COUNT( m3 = m3 + (m1 * m2.adjoint()).transpose(), 1);
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| 70 | VERIFY_EVALUATION_COUNT( m3.noalias() = m3 + m1 * m2.transpose(), 1); // 0 in 3.3
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| 71 | VERIFY_EVALUATION_COUNT( m3.noalias() += m3 + m1 * m2.transpose(), 1); // 0 in 3.3
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| 72 | VERIFY_EVALUATION_COUNT( m3.noalias() -= m3 + m1 * m2.transpose(), 1); // 0 in 3.3
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| 73 |
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| 74 | VERIFY_EVALUATION_COUNT( m3.noalias() = s1 * m1 * s2 * m2.adjoint(), 0);
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| 75 | VERIFY_EVALUATION_COUNT( m3.noalias() = s1 * m1 * s2 * (m1*s3+m2*s2).adjoint(), 1);
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| 76 | VERIFY_EVALUATION_COUNT( m3.noalias() = (s1 * m1).adjoint() * s2 * m2, 0);
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| 77 | VERIFY_EVALUATION_COUNT( m3.noalias() += s1 * (-m1*s3).adjoint() * (s2 * m2 * s3), 0);
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| 78 | VERIFY_EVALUATION_COUNT( m3.noalias() -= s1 * (m1.transpose() * m2), 0);
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| 79 |
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| 80 | VERIFY_EVALUATION_COUNT(( m3.block(r0,r0,r1,r1).noalias() += -m1.block(r0,c0,r1,c1) * (s2*m2.block(r0,c0,r1,c1)).adjoint() ), 0);
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| 81 | VERIFY_EVALUATION_COUNT(( m3.block(r0,r0,r1,r1).noalias() -= s1 * m1.block(r0,c0,r1,c1) * m2.block(c0,r0,c1,r1) ), 0);
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| 82 |
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| 83 | // NOTE this is because the Block expression is not handled yet by our expression analyser
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| 84 | VERIFY_EVALUATION_COUNT(( m3.block(r0,r0,r1,r1).noalias() = s1 * m1.block(r0,c0,r1,c1) * (s1*m2).block(c0,r0,c1,r1) ), 1);
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| 85 |
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| 86 | VERIFY_EVALUATION_COUNT( m3.noalias() -= (s1 * m1).template triangularView<Lower>() * m2, 0);
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| 87 | VERIFY_EVALUATION_COUNT( rm3.noalias() = (s1 * m1.adjoint()).template triangularView<Upper>() * (m2+m2), 1);
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| 88 | VERIFY_EVALUATION_COUNT( rm3.noalias() = (s1 * m1.adjoint()).template triangularView<UnitUpper>() * m2.adjoint(), 0);
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| 89 |
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| 90 | VERIFY_EVALUATION_COUNT( m3.template triangularView<Upper>() = (m1 * m2.adjoint()), 0);
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| 91 | VERIFY_EVALUATION_COUNT( m3.template triangularView<Upper>() -= (m1 * m2.adjoint()), 0);
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| 92 |
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| 93 | // NOTE this is because the blas_traits require innerstride==1 to avoid a temporary, but that doesn't seem to be actually needed for the triangular products
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| 94 | VERIFY_EVALUATION_COUNT( rm3.col(c0).noalias() = (s1 * m1.adjoint()).template triangularView<UnitUpper>() * (s2*m2.row(c0)).adjoint(), 1);
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| 95 |
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| 96 | VERIFY_EVALUATION_COUNT( m1.template triangularView<Lower>().solveInPlace(m3), 0);
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| 97 | VERIFY_EVALUATION_COUNT( m1.adjoint().template triangularView<Lower>().solveInPlace(m3.transpose()), 0);
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| 98 |
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| 99 | VERIFY_EVALUATION_COUNT( m3.noalias() -= (s1 * m1).adjoint().template selfadjointView<Lower>() * (-m2*s3).adjoint(), 0);
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| 100 | VERIFY_EVALUATION_COUNT( m3.noalias() = s2 * m2.adjoint() * (s1 * m1.adjoint()).template selfadjointView<Upper>(), 0);
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| 101 | VERIFY_EVALUATION_COUNT( rm3.noalias() = (s1 * m1.adjoint()).template selfadjointView<Lower>() * m2.adjoint(), 0);
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| 102 |
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| 103 | // NOTE this is because the blas_traits require innerstride==1 to avoid a temporary, but that doesn't seem to be actually needed for the triangular products
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| 104 | VERIFY_EVALUATION_COUNT( m3.col(c0).noalias() = (s1 * m1).adjoint().template selfadjointView<Lower>() * (-m2.row(c0)*s3).adjoint(), 1);
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| 105 | VERIFY_EVALUATION_COUNT( m3.col(c0).noalias() -= (s1 * m1).adjoint().template selfadjointView<Upper>() * (-m2.row(c0)*s3).adjoint(), 1);
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| 106 |
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| 107 | VERIFY_EVALUATION_COUNT( m3.block(r0,c0,r1,c1).noalias() += m1.block(r0,r0,r1,r1).template selfadjointView<Upper>() * (s1*m2.block(r0,c0,r1,c1)), 0);
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| 108 | VERIFY_EVALUATION_COUNT( m3.block(r0,c0,r1,c1).noalias() = m1.block(r0,r0,r1,r1).template selfadjointView<Upper>() * m2.block(r0,c0,r1,c1), 0);
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| 109 |
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| 110 | VERIFY_EVALUATION_COUNT( m3.template selfadjointView<Lower>().rankUpdate(m2.adjoint()), 0);
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| 111 |
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| 112 | // Here we will get 1 temporary for each resize operation of the lhs operator; resize(r1,c1) would lead to zero temporaries
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| 113 | m3.resize(1,1);
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| 114 | VERIFY_EVALUATION_COUNT( m3.noalias() = m1.block(r0,r0,r1,r1).template selfadjointView<Lower>() * m2.block(r0,c0,r1,c1), 1);
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| 115 | m3.resize(1,1);
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| 116 | VERIFY_EVALUATION_COUNT( m3.noalias() = m1.block(r0,r0,r1,r1).template triangularView<UnitUpper>() * m2.block(r0,c0,r1,c1), 1);
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| 117 |
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| 118 | // Zero temporaries for lazy products ...
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| 119 | VERIFY_EVALUATION_COUNT( Scalar tmp = 0; tmp += Scalar(RealScalar(1)) / (m3.transpose().lazyProduct(m3)).diagonal().sum(), 0 );
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| 120 |
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| 121 | // ... and even no temporary for even deeply (>=2) nested products
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| 122 | VERIFY_EVALUATION_COUNT( Scalar tmp = 0; tmp += Scalar(RealScalar(1)) / (m3.transpose() * m3).diagonal().sum(), 0 );
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| 123 | VERIFY_EVALUATION_COUNT( Scalar tmp = 0; tmp += Scalar(RealScalar(1)) / (m3.transpose() * m3).diagonal().array().abs().sum(), 0 );
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| 124 |
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| 125 | // Zero temporaries for ... CoeffBasedProductMode
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| 126 | // - does not work with GCC because of the <..>, we'ld need variadic macros ...
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| 127 | //VERIFY_EVALUATION_COUNT( m3.col(0).head<5>() * m3.col(0).transpose() + m3.col(0).head<5>() * m3.col(0).transpose(), 0 );
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| 128 |
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| 129 | // Check matrix * vectors
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| 130 | VERIFY_EVALUATION_COUNT( cvres.noalias() = m1 * cv1, 0 );
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| 131 | VERIFY_EVALUATION_COUNT( cvres.noalias() -= m1 * cv1, 0 );
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| 132 | VERIFY_EVALUATION_COUNT( cvres.noalias() -= m1 * m2.col(0), 0 );
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| 133 | VERIFY_EVALUATION_COUNT( cvres.noalias() -= m1 * rv1.adjoint(), 0 );
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| 134 | VERIFY_EVALUATION_COUNT( cvres.noalias() -= m1 * m2.row(0).transpose(), 0 );
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| 135 | }
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| 136 |
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| 137 | void test_product_notemporary()
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| 138 | {
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| 139 | int s;
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| 140 | for(int i = 0; i < g_repeat; i++) {
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| 141 | s = internal::random<int>(16,EIGEN_TEST_MAX_SIZE);
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| 142 | CALL_SUBTEST_1( product_notemporary(MatrixXf(s, s)) );
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| 143 | s = internal::random<int>(16,EIGEN_TEST_MAX_SIZE);
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| 144 | CALL_SUBTEST_2( product_notemporary(MatrixXd(s, s)) );
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| 145 | s = internal::random<int>(16,EIGEN_TEST_MAX_SIZE/2);
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| 146 | CALL_SUBTEST_3( product_notemporary(MatrixXcf(s,s)) );
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| 147 | s = internal::random<int>(16,EIGEN_TEST_MAX_SIZE/2);
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| 148 | CALL_SUBTEST_4( product_notemporary(MatrixXcd(s,s)) );
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| 149 | }
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| 150 | }
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