[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 matrixVisitor(const MatrixType& p)
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| 13 | {
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| 14 | typedef typename MatrixType::Scalar Scalar;
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| 15 | typedef typename MatrixType::Index Index;
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| 16 |
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| 17 | Index rows = p.rows();
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| 18 | Index cols = p.cols();
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| 19 |
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| 20 | // construct a random matrix where all coefficients are different
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| 21 | MatrixType m;
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| 22 | m = MatrixType::Random(rows, cols);
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| 23 | for(Index i = 0; i < m.size(); i++)
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| 24 | for(Index i2 = 0; i2 < i; i2++)
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| 25 | while(m(i) == m(i2)) // yes, ==
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| 26 | m(i) = internal::random<Scalar>();
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| 27 |
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| 28 | Scalar minc = Scalar(1000), maxc = Scalar(-1000);
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| 29 | Index minrow=0,mincol=0,maxrow=0,maxcol=0;
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| 30 | for(Index j = 0; j < cols; j++)
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| 31 | for(Index i = 0; i < rows; i++)
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| 32 | {
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| 33 | if(m(i,j) < minc)
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| 34 | {
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| 35 | minc = m(i,j);
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| 36 | minrow = i;
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| 37 | mincol = j;
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| 38 | }
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| 39 | if(m(i,j) > maxc)
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| 40 | {
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| 41 | maxc = m(i,j);
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| 42 | maxrow = i;
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| 43 | maxcol = j;
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| 44 | }
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| 45 | }
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| 46 | Index eigen_minrow, eigen_mincol, eigen_maxrow, eigen_maxcol;
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| 47 | Scalar eigen_minc, eigen_maxc;
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| 48 | eigen_minc = m.minCoeff(&eigen_minrow,&eigen_mincol);
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| 49 | eigen_maxc = m.maxCoeff(&eigen_maxrow,&eigen_maxcol);
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| 50 | VERIFY(minrow == eigen_minrow);
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| 51 | VERIFY(maxrow == eigen_maxrow);
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| 52 | VERIFY(mincol == eigen_mincol);
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| 53 | VERIFY(maxcol == eigen_maxcol);
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| 54 | VERIFY_IS_APPROX(minc, eigen_minc);
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| 55 | VERIFY_IS_APPROX(maxc, eigen_maxc);
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| 56 | VERIFY_IS_APPROX(minc, m.minCoeff());
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| 57 | VERIFY_IS_APPROX(maxc, m.maxCoeff());
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| 58 |
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| 59 | eigen_maxc = (m.adjoint()*m).maxCoeff(&eigen_maxrow,&eigen_maxcol);
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| 60 | eigen_maxc = (m.adjoint()*m).eval().maxCoeff(&maxrow,&maxcol);
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| 61 | VERIFY(maxrow == eigen_maxrow);
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| 62 | VERIFY(maxcol == eigen_maxcol);
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| 63 | }
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| 64 |
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| 65 | template<typename VectorType> void vectorVisitor(const VectorType& w)
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| 66 | {
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| 67 | typedef typename VectorType::Scalar Scalar;
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| 68 | typedef typename VectorType::Index Index;
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| 69 |
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| 70 | Index size = w.size();
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| 71 |
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| 72 | // construct a random vector where all coefficients are different
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| 73 | VectorType v;
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| 74 | v = VectorType::Random(size);
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| 75 | for(Index i = 0; i < size; i++)
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| 76 | for(Index i2 = 0; i2 < i; i2++)
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| 77 | while(v(i) == v(i2)) // yes, ==
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| 78 | v(i) = internal::random<Scalar>();
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| 79 |
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| 80 | Scalar minc = v(0), maxc = v(0);
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| 81 | Index minidx=0, maxidx=0;
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| 82 | for(Index i = 0; i < size; i++)
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| 83 | {
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| 84 | if(v(i) < minc)
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| 85 | {
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| 86 | minc = v(i);
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| 87 | minidx = i;
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| 88 | }
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| 89 | if(v(i) > maxc)
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| 90 | {
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| 91 | maxc = v(i);
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| 92 | maxidx = i;
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| 93 | }
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| 94 | }
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| 95 | Index eigen_minidx, eigen_maxidx;
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| 96 | Scalar eigen_minc, eigen_maxc;
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| 97 | eigen_minc = v.minCoeff(&eigen_minidx);
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| 98 | eigen_maxc = v.maxCoeff(&eigen_maxidx);
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| 99 | VERIFY(minidx == eigen_minidx);
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| 100 | VERIFY(maxidx == eigen_maxidx);
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| 101 | VERIFY_IS_APPROX(minc, eigen_minc);
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| 102 | VERIFY_IS_APPROX(maxc, eigen_maxc);
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| 103 | VERIFY_IS_APPROX(minc, v.minCoeff());
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| 104 | VERIFY_IS_APPROX(maxc, v.maxCoeff());
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| 105 |
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| 106 | Index idx0 = internal::random<Index>(0,size-1);
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| 107 | Index idx1 = eigen_minidx;
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| 108 | Index idx2 = eigen_maxidx;
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| 109 | VectorType v1(v), v2(v);
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| 110 | v1(idx0) = v1(idx1);
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| 111 | v2(idx0) = v2(idx2);
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| 112 | v1.minCoeff(&eigen_minidx);
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| 113 | v2.maxCoeff(&eigen_maxidx);
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| 114 | VERIFY(eigen_minidx == (std::min)(idx0,idx1));
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| 115 | VERIFY(eigen_maxidx == (std::min)(idx0,idx2));
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| 116 | }
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| 117 |
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| 118 | void test_visitor()
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| 119 | {
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| 120 | for(int i = 0; i < g_repeat; i++) {
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| 121 | CALL_SUBTEST_1( matrixVisitor(Matrix<float, 1, 1>()) );
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| 122 | CALL_SUBTEST_2( matrixVisitor(Matrix2f()) );
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| 123 | CALL_SUBTEST_3( matrixVisitor(Matrix4d()) );
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| 124 | CALL_SUBTEST_4( matrixVisitor(MatrixXd(8, 12)) );
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| 125 | CALL_SUBTEST_5( matrixVisitor(Matrix<double,Dynamic,Dynamic,RowMajor>(20, 20)) );
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| 126 | CALL_SUBTEST_6( matrixVisitor(MatrixXi(8, 12)) );
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| 127 | }
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| 128 | for(int i = 0; i < g_repeat; i++) {
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| 129 | CALL_SUBTEST_7( vectorVisitor(Vector4f()) );
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| 130 | CALL_SUBTEST_7( vectorVisitor(Matrix<int,12,1>()) );
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| 131 | CALL_SUBTEST_8( vectorVisitor(VectorXd(10)) );
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| 132 | CALL_SUBTEST_9( vectorVisitor(RowVectorXd(10)) );
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| 133 | CALL_SUBTEST_10( vectorVisitor(VectorXf(33)) );
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| 134 | }
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| 135 | }
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