[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) 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 | #include "main.h"
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| 11 |
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| 12 | template<typename MatrixType> void matrixRedux(const MatrixType& m)
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| 13 | {
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| 14 | typedef typename MatrixType::Index Index;
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| 15 | typedef typename MatrixType::Scalar Scalar;
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| 16 | typedef typename MatrixType::RealScalar RealScalar;
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| 17 |
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| 18 | Index rows = m.rows();
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| 19 | Index cols = m.cols();
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| 20 |
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| 21 | MatrixType m1 = MatrixType::Random(rows, cols);
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| 22 |
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| 23 | // The entries of m1 are uniformly distributed in [0,1], so m1.prod() is very small. This may lead to test
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| 24 | // failures if we underflow into denormals. Thus, we scale so that entires are close to 1.
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| 25 | MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + RealScalar(0.2) * m1;
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| 26 |
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| 27 | VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1));
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| 28 | VERIFY_IS_APPROX(MatrixType::Ones(rows, cols).sum(), Scalar(float(rows*cols))); // the float() here to shut up excessive MSVC warning about int->complex conversion being lossy
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| 29 | Scalar s(0), p(1), minc(numext::real(m1.coeff(0))), maxc(numext::real(m1.coeff(0)));
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| 30 | for(int j = 0; j < cols; j++)
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| 31 | for(int i = 0; i < rows; i++)
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| 32 | {
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| 33 | s += m1(i,j);
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| 34 | p *= m1_for_prod(i,j);
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| 35 | minc = (std::min)(numext::real(minc), numext::real(m1(i,j)));
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| 36 | maxc = (std::max)(numext::real(maxc), numext::real(m1(i,j)));
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| 37 | }
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| 38 | const Scalar mean = s/Scalar(RealScalar(rows*cols));
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| 39 |
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| 40 | VERIFY_IS_APPROX(m1.sum(), s);
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| 41 | VERIFY_IS_APPROX(m1.mean(), mean);
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| 42 | VERIFY_IS_APPROX(m1_for_prod.prod(), p);
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| 43 | VERIFY_IS_APPROX(m1.real().minCoeff(), numext::real(minc));
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| 44 | VERIFY_IS_APPROX(m1.real().maxCoeff(), numext::real(maxc));
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| 45 |
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| 46 | // test slice vectorization assuming assign is ok
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| 47 | Index r0 = internal::random<Index>(0,rows-1);
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| 48 | Index c0 = internal::random<Index>(0,cols-1);
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| 49 | Index r1 = internal::random<Index>(r0+1,rows)-r0;
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| 50 | Index c1 = internal::random<Index>(c0+1,cols)-c0;
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| 51 | VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).sum(), m1.block(r0,c0,r1,c1).eval().sum());
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| 52 | VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).mean(), m1.block(r0,c0,r1,c1).eval().mean());
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| 53 | VERIFY_IS_APPROX(m1_for_prod.block(r0,c0,r1,c1).prod(), m1_for_prod.block(r0,c0,r1,c1).eval().prod());
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| 54 | VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().minCoeff(), m1.block(r0,c0,r1,c1).real().eval().minCoeff());
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| 55 | VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().maxCoeff(), m1.block(r0,c0,r1,c1).real().eval().maxCoeff());
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| 56 |
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| 57 | // regression for bug 1090
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| 58 | const int R1 = MatrixType::RowsAtCompileTime>=2 ? MatrixType::RowsAtCompileTime/2 : 6;
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| 59 | const int C1 = MatrixType::ColsAtCompileTime>=2 ? MatrixType::ColsAtCompileTime/2 : 6;
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| 60 | if(R1<=rows-r0 && C1<=cols-c0)
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| 61 | {
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| 62 | VERIFY_IS_APPROX( (m1.template block<R1,C1>(r0,c0).sum()), m1.block(r0,c0,R1,C1).sum() );
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| 63 | }
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| 64 |
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| 65 | // test empty objects
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| 66 | VERIFY_IS_APPROX(m1.block(r0,c0,0,0).sum(), Scalar(0));
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| 67 | VERIFY_IS_APPROX(m1.block(r0,c0,0,0).prod(), Scalar(1));
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| 68 | }
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| 69 |
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| 70 | template<typename VectorType> void vectorRedux(const VectorType& w)
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| 71 | {
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| 72 | using std::abs;
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| 73 | typedef typename VectorType::Index Index;
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| 74 | typedef typename VectorType::Scalar Scalar;
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| 75 | typedef typename NumTraits<Scalar>::Real RealScalar;
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| 76 | Index size = w.size();
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| 77 |
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| 78 | VectorType v = VectorType::Random(size);
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| 79 | VectorType v_for_prod = VectorType::Ones(size) + Scalar(0.2) * v; // see comment above declaration of m1_for_prod
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| 80 |
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| 81 | for(int i = 1; i < size; i++)
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| 82 | {
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| 83 | Scalar s(0), p(1);
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| 84 | RealScalar minc(numext::real(v.coeff(0))), maxc(numext::real(v.coeff(0)));
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| 85 | for(int j = 0; j < i; j++)
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| 86 | {
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| 87 | s += v[j];
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| 88 | p *= v_for_prod[j];
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| 89 | minc = (std::min)(minc, numext::real(v[j]));
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| 90 | maxc = (std::max)(maxc, numext::real(v[j]));
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| 91 | }
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| 92 | VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.head(i).sum()), Scalar(1));
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| 93 | VERIFY_IS_APPROX(p, v_for_prod.head(i).prod());
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| 94 | VERIFY_IS_APPROX(minc, v.real().head(i).minCoeff());
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| 95 | VERIFY_IS_APPROX(maxc, v.real().head(i).maxCoeff());
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| 96 | }
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| 97 |
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| 98 | for(int i = 0; i < size-1; i++)
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| 99 | {
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| 100 | Scalar s(0), p(1);
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| 101 | RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
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| 102 | for(int j = i; j < size; j++)
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| 103 | {
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| 104 | s += v[j];
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| 105 | p *= v_for_prod[j];
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| 106 | minc = (std::min)(minc, numext::real(v[j]));
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| 107 | maxc = (std::max)(maxc, numext::real(v[j]));
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| 108 | }
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| 109 | VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.tail(size-i).sum()), Scalar(1));
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| 110 | VERIFY_IS_APPROX(p, v_for_prod.tail(size-i).prod());
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| 111 | VERIFY_IS_APPROX(minc, v.real().tail(size-i).minCoeff());
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| 112 | VERIFY_IS_APPROX(maxc, v.real().tail(size-i).maxCoeff());
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| 113 | }
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| 114 |
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| 115 | for(int i = 0; i < size/2; i++)
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| 116 | {
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| 117 | Scalar s(0), p(1);
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| 118 | RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
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| 119 | for(int j = i; j < size-i; j++)
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| 120 | {
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| 121 | s += v[j];
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| 122 | p *= v_for_prod[j];
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| 123 | minc = (std::min)(minc, numext::real(v[j]));
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| 124 | maxc = (std::max)(maxc, numext::real(v[j]));
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| 125 | }
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| 126 | VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.segment(i, size-2*i).sum()), Scalar(1));
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| 127 | VERIFY_IS_APPROX(p, v_for_prod.segment(i, size-2*i).prod());
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| 128 | VERIFY_IS_APPROX(minc, v.real().segment(i, size-2*i).minCoeff());
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| 129 | VERIFY_IS_APPROX(maxc, v.real().segment(i, size-2*i).maxCoeff());
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| 130 | }
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| 131 |
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| 132 | // test empty objects
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| 133 | VERIFY_IS_APPROX(v.head(0).sum(), Scalar(0));
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| 134 | VERIFY_IS_APPROX(v.tail(0).prod(), Scalar(1));
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| 135 | VERIFY_RAISES_ASSERT(v.head(0).mean());
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| 136 | VERIFY_RAISES_ASSERT(v.head(0).minCoeff());
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| 137 | VERIFY_RAISES_ASSERT(v.head(0).maxCoeff());
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| 138 | }
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| 139 |
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| 140 | void test_redux()
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| 141 | {
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| 142 | // the max size cannot be too large, otherwise reduxion operations obviously generate large errors.
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| 143 | int maxsize = (std::min)(100,EIGEN_TEST_MAX_SIZE);
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| 144 | TEST_SET_BUT_UNUSED_VARIABLE(maxsize);
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| 145 | for(int i = 0; i < g_repeat; i++) {
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| 146 | CALL_SUBTEST_1( matrixRedux(Matrix<float, 1, 1>()) );
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| 147 | CALL_SUBTEST_1( matrixRedux(Array<float, 1, 1>()) );
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| 148 | CALL_SUBTEST_2( matrixRedux(Matrix2f()) );
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| 149 | CALL_SUBTEST_2( matrixRedux(Array2f()) );
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| 150 | CALL_SUBTEST_3( matrixRedux(Matrix4d()) );
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| 151 | CALL_SUBTEST_3( matrixRedux(Array4d()) );
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| 152 | CALL_SUBTEST_4( matrixRedux(MatrixXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
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| 153 | CALL_SUBTEST_4( matrixRedux(ArrayXXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
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| 154 | CALL_SUBTEST_5( matrixRedux(MatrixXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
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| 155 | CALL_SUBTEST_5( matrixRedux(ArrayXXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
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| 156 | CALL_SUBTEST_6( matrixRedux(MatrixXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
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| 157 | CALL_SUBTEST_6( matrixRedux(ArrayXXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
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| 158 | }
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| 159 | for(int i = 0; i < g_repeat; i++) {
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| 160 | CALL_SUBTEST_7( vectorRedux(Vector4f()) );
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| 161 | CALL_SUBTEST_7( vectorRedux(Array4f()) );
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| 162 | CALL_SUBTEST_5( vectorRedux(VectorXd(internal::random<int>(1,maxsize))) );
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| 163 | CALL_SUBTEST_5( vectorRedux(ArrayXd(internal::random<int>(1,maxsize))) );
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| 164 | CALL_SUBTEST_8( vectorRedux(VectorXf(internal::random<int>(1,maxsize))) );
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| 165 | CALL_SUBTEST_8( vectorRedux(ArrayXf(internal::random<int>(1,maxsize))) );
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| 166 | }
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| 167 | }
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