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132 lines
5.2 KiB
132 lines
5.2 KiB
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra. Eigen itself is part of the KDE project.
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//
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// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
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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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#include "main.h"
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#include <Eigen/Array>
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#include <Eigen/QR>
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template<typename Derived1, typename Derived2>
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bool areNotApprox(const MatrixBase<Derived1>& m1, const MatrixBase<Derived2>& m2, typename Derived1::RealScalar epsilon = precision<typename Derived1::RealScalar>())
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{
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return !((m1-m2).cwise().abs2().maxCoeff() < epsilon * epsilon
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* std::max(m1.cwise().abs2().maxCoeff(), m2.cwise().abs2().maxCoeff()));
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}
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template<typename MatrixType> void product(const MatrixType& m)
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{
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/* this test covers the following files:
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Identity.h Product.h
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*/
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typedef typename MatrixType::Scalar Scalar;
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typedef typename NumTraits<Scalar>::FloatingPoint FloatingPoint;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> RowVectorType;
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typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> ColVectorType;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> RowSquareMatrixType;
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typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> ColSquareMatrixType;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime,
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MatrixType::Options^RowMajor> OtherMajorMatrixType;
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int rows = m.rows();
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int cols = m.cols();
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// this test relies a lot on Random.h, and there's not much more that we can do
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// to test it, hence I consider that we will have tested Random.h
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MatrixType m1 = MatrixType::Random(rows, cols),
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m2 = MatrixType::Random(rows, cols),
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m3(rows, cols),
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mzero = MatrixType::Zero(rows, cols);
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RowSquareMatrixType
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identity = RowSquareMatrixType::Identity(rows, rows),
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square = RowSquareMatrixType::Random(rows, rows),
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res = RowSquareMatrixType::Random(rows, rows);
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ColSquareMatrixType
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square2 = ColSquareMatrixType::Random(cols, cols),
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res2 = ColSquareMatrixType::Random(cols, cols);
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RowVectorType v1 = RowVectorType::Random(rows),
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v2 = RowVectorType::Random(rows),
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vzero = RowVectorType::Zero(rows);
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ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols);
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OtherMajorMatrixType tm1 = m1;
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Scalar s1 = ei_random<Scalar>();
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int r = ei_random<int>(0, rows-1),
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c = ei_random<int>(0, cols-1);
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// begin testing Product.h: only associativity for now
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// (we use Transpose.h but this doesn't count as a test for it)
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VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2));
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m3 = m1;
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m3 *= m1.transpose() * m2;
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VERIFY_IS_APPROX(m3, m1 * (m1.transpose()*m2));
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VERIFY_IS_APPROX(m3, m1.lazy() * (m1.transpose()*m2));
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// continue testing Product.h: distributivity
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VERIFY_IS_APPROX(square*(m1 + m2), square*m1+square*m2);
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VERIFY_IS_APPROX(square*(m1 - m2), square*m1-square*m2);
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// continue testing Product.h: compatibility with ScalarMultiple.h
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VERIFY_IS_APPROX(s1*(square*m1), (s1*square)*m1);
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VERIFY_IS_APPROX(s1*(square*m1), square*(m1*s1));
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// again, test operator() to check const-qualification
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s1 += (square.lazy() * m1)(r,c);
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// test Product.h together with Identity.h
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VERIFY_IS_APPROX(v1, identity*v1);
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VERIFY_IS_APPROX(v1.transpose(), v1.transpose() * identity);
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// again, test operator() to check const-qualification
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VERIFY_IS_APPROX(MatrixType::Identity(rows, cols)(r,c), static_cast<Scalar>(r==c));
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if (rows!=cols)
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VERIFY_RAISES_ASSERT(m3 = m1*m1);
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// test the previous tests were not screwed up because operator* returns 0
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// (we use the more accurate default epsilon)
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if (NumTraits<Scalar>::HasFloatingPoint && std::min(rows,cols)>1)
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{
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VERIFY(areNotApprox(m1.transpose()*m2,m2.transpose()*m1));
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}
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// test optimized operator+= path
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res = square;
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res += (m1 * m2.transpose()).lazy();
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VERIFY_IS_APPROX(res, square + m1 * m2.transpose());
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if (NumTraits<Scalar>::HasFloatingPoint && std::min(rows,cols)>1)
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{
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VERIFY(areNotApprox(res,square + m2 * m1.transpose()));
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}
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vcres = vc2;
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vcres += (m1.transpose() * v1).lazy();
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VERIFY_IS_APPROX(vcres, vc2 + m1.transpose() * v1);
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tm1 = m1;
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VERIFY_IS_APPROX(tm1.transpose() * v1, m1.transpose() * v1);
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VERIFY_IS_APPROX(v1.transpose() * tm1, v1.transpose() * m1);
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// test submatrix and matrix/vector product
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for (int i=0; i<rows; ++i)
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res.row(i) = m1.row(i) * m2.transpose();
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VERIFY_IS_APPROX(res, m1 * m2.transpose());
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// the other way round:
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for (int i=0; i<rows; ++i)
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res.col(i) = m1 * m2.transpose().col(i);
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VERIFY_IS_APPROX(res, m1 * m2.transpose());
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res2 = square2;
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res2 += (m1.transpose() * m2).lazy();
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VERIFY_IS_APPROX(res2, square2 + m1.transpose() * m2);
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if (NumTraits<Scalar>::HasFloatingPoint && std::min(rows,cols)>1)
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{
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VERIFY(areNotApprox(res2,square2 + m2.transpose() * m1));
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}
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}
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