[136] | 1 | // This file is part of Eigen, a lightweight C++ template library
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| 2 | // for linear algebra. Eigen itself is part of the KDE project.
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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 | #include "main.h"
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| 11 | #include <Eigen/Array>
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| 12 | #include <Eigen/QR>
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| 13 |
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| 14 | template<typename Derived1, typename Derived2>
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| 15 | bool areNotApprox(const MatrixBase<Derived1>& m1, const MatrixBase<Derived2>& m2, typename Derived1::RealScalar epsilon = precision<typename Derived1::RealScalar>())
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| 16 | {
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| 17 | return !((m1-m2).cwise().abs2().maxCoeff() < epsilon * epsilon
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| 18 | * std::max(m1.cwise().abs2().maxCoeff(), m2.cwise().abs2().maxCoeff()));
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| 19 | }
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| 20 |
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| 21 | template<typename MatrixType> void product(const MatrixType& m)
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| 22 | {
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| 23 | /* this test covers the following files:
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| 24 | Identity.h Product.h
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| 25 | */
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| 26 |
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| 27 | typedef typename MatrixType::Scalar Scalar;
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| 28 | typedef typename NumTraits<Scalar>::FloatingPoint FloatingPoint;
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| 29 | typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> RowVectorType;
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| 30 | typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> ColVectorType;
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| 31 | typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> RowSquareMatrixType;
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| 32 | typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> ColSquareMatrixType;
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| 33 | typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime,
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| 34 | MatrixType::Options^RowMajor> OtherMajorMatrixType;
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| 35 |
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| 36 | int rows = m.rows();
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| 37 | int cols = m.cols();
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| 38 |
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| 39 | // this test relies a lot on Random.h, and there's not much more that we can do
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| 40 | // to test it, hence I consider that we will have tested Random.h
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| 41 | MatrixType m1 = MatrixType::Random(rows, cols),
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| 42 | m2 = MatrixType::Random(rows, cols),
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| 43 | m3(rows, cols);
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| 44 | RowSquareMatrixType
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| 45 | identity = RowSquareMatrixType::Identity(rows, rows),
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| 46 | square = RowSquareMatrixType::Random(rows, rows),
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| 47 | res = RowSquareMatrixType::Random(rows, rows);
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| 48 | ColSquareMatrixType
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| 49 | square2 = ColSquareMatrixType::Random(cols, cols),
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| 50 | res2 = ColSquareMatrixType::Random(cols, cols);
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| 51 | RowVectorType v1 = RowVectorType::Random(rows);
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| 52 | ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols);
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| 53 | OtherMajorMatrixType tm1 = m1;
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| 54 |
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| 55 | Scalar s1 = ei_random<Scalar>();
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| 56 |
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| 57 | int r = ei_random<int>(0, rows-1),
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| 58 | c = ei_random<int>(0, cols-1);
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| 59 |
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| 60 | // begin testing Product.h: only associativity for now
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| 61 | // (we use Transpose.h but this doesn't count as a test for it)
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| 62 |
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| 63 | VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2));
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| 64 | m3 = m1;
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| 65 | m3 *= m1.transpose() * m2;
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| 66 | VERIFY_IS_APPROX(m3, m1 * (m1.transpose()*m2));
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| 67 | VERIFY_IS_APPROX(m3, m1.lazy() * (m1.transpose()*m2));
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| 68 |
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| 69 | // continue testing Product.h: distributivity
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| 70 | VERIFY_IS_APPROX(square*(m1 + m2), square*m1+square*m2);
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| 71 | VERIFY_IS_APPROX(square*(m1 - m2), square*m1-square*m2);
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| 72 |
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| 73 | // continue testing Product.h: compatibility with ScalarMultiple.h
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| 74 | VERIFY_IS_APPROX(s1*(square*m1), (s1*square)*m1);
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| 75 | VERIFY_IS_APPROX(s1*(square*m1), square*(m1*s1));
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| 76 |
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| 77 | // again, test operator() to check const-qualification
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| 78 | s1 += (square.lazy() * m1)(r,c);
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| 79 |
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| 80 | // test Product.h together with Identity.h
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| 81 | VERIFY_IS_APPROX(v1, identity*v1);
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| 82 | VERIFY_IS_APPROX(v1.transpose(), v1.transpose() * identity);
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| 83 | // again, test operator() to check const-qualification
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| 84 | VERIFY_IS_APPROX(MatrixType::Identity(rows, cols)(r,c), static_cast<Scalar>(r==c));
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| 85 |
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| 86 | if (rows!=cols)
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| 87 | VERIFY_RAISES_ASSERT(m3 = m1*m1);
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| 88 |
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| 89 | // test the previous tests were not screwed up because operator* returns 0
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| 90 | // (we use the more accurate default epsilon)
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| 91 | if (NumTraits<Scalar>::HasFloatingPoint && std::min(rows,cols)>1)
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| 92 | {
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| 93 | VERIFY(areNotApprox(m1.transpose()*m2,m2.transpose()*m1));
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| 94 | }
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| 95 |
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| 96 | // test optimized operator+= path
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| 97 | res = square;
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| 98 | res += (m1 * m2.transpose()).lazy();
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| 99 | VERIFY_IS_APPROX(res, square + m1 * m2.transpose());
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| 100 | if (NumTraits<Scalar>::HasFloatingPoint && std::min(rows,cols)>1)
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| 101 | {
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| 102 | VERIFY(areNotApprox(res,square + m2 * m1.transpose()));
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| 103 | }
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| 104 | vcres = vc2;
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| 105 | vcres += (m1.transpose() * v1).lazy();
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| 106 | VERIFY_IS_APPROX(vcres, vc2 + m1.transpose() * v1);
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| 107 | tm1 = m1;
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| 108 | VERIFY_IS_APPROX(tm1.transpose() * v1, m1.transpose() * v1);
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| 109 | VERIFY_IS_APPROX(v1.transpose() * tm1, v1.transpose() * m1);
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| 110 |
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| 111 | // test submatrix and matrix/vector product
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| 112 | for (int i=0; i<rows; ++i)
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| 113 | res.row(i) = m1.row(i) * m2.transpose();
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| 114 | VERIFY_IS_APPROX(res, m1 * m2.transpose());
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| 115 | // the other way round:
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| 116 | for (int i=0; i<rows; ++i)
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| 117 | res.col(i) = m1 * m2.transpose().col(i);
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| 118 | VERIFY_IS_APPROX(res, m1 * m2.transpose());
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| 119 |
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| 120 | res2 = square2;
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| 121 | res2 += (m1.transpose() * m2).lazy();
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| 122 | VERIFY_IS_APPROX(res2, square2 + m1.transpose() * m2);
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| 123 |
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| 124 | if (NumTraits<Scalar>::HasFloatingPoint && std::min(rows,cols)>1)
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| 125 | {
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| 126 | VERIFY(areNotApprox(res2,square2 + m2.transpose() * m1));
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| 127 | }
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| 128 | }
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| 129 |
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