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232 lines
10 KiB
C++
232 lines
10 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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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/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 = NumTraits<typename Derived1::RealScalar>::dummy_precision())
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{
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return !((m1-m2).cwiseAbs2().maxCoeff() < epsilon * epsilon
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* (std::max)(m1.cwiseAbs2().maxCoeff(), m2.cwiseAbs2().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 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::Flags&RowMajorBit?ColMajor:RowMajor> OtherMajorMatrixType;
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Index rows = m.rows();
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Index 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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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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ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols);
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OtherMajorMatrixType tm1 = m1;
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Scalar s1 = internal::random<Scalar>();
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Index r = internal::random<Index>(0, rows-1),
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c = internal::random<Index>(0, cols-1),
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c2 = internal::random<Index>(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 * (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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// 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>::IsInteger && (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.noalias() += m1 * m2.transpose();
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VERIFY_IS_APPROX(res, square + m1 * m2.transpose());
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if (!NumTraits<Scalar>::IsInteger && (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.noalias() += m1.transpose() * v1;
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VERIFY_IS_APPROX(vcres, vc2 + m1.transpose() * v1);
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// test optimized operator-= path
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res = square;
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res.noalias() -= m1 * m2.transpose();
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VERIFY_IS_APPROX(res, square - (m1 * m2.transpose()));
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if (!NumTraits<Scalar>::IsInteger && (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.noalias() -= m1.transpose() * v1;
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VERIFY_IS_APPROX(vcres, vc2 - m1.transpose() * v1);
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// test d ?= a+b*c rules
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res.noalias() = square + m1 * m2.transpose();
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VERIFY_IS_APPROX(res, square + m1 * m2.transpose());
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res.noalias() += square + m1 * m2.transpose();
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VERIFY_IS_APPROX(res, 2*(square + m1 * m2.transpose()));
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res.noalias() -= square + m1 * m2.transpose();
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VERIFY_IS_APPROX(res, square + m1 * m2.transpose());
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// test d ?= a-b*c rules
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res.noalias() = square - m1 * m2.transpose();
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VERIFY_IS_APPROX(res, square - m1 * m2.transpose());
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res.noalias() += square - m1 * m2.transpose();
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VERIFY_IS_APPROX(res, 2*(square - m1 * m2.transpose()));
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res.noalias() -= square - m1 * m2.transpose();
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VERIFY_IS_APPROX(res, square - m1 * m2.transpose());
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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.noalias() += m1.transpose() * m2;
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VERIFY_IS_APPROX(res2, square2 + m1.transpose() * m2);
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if (!NumTraits<Scalar>::IsInteger && (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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VERIFY_IS_APPROX(res.col(r).noalias() = square.adjoint() * square.col(r), (square.adjoint() * square.col(r)).eval());
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VERIFY_IS_APPROX(res.col(r).noalias() = square * square.col(r), (square * square.col(r)).eval());
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// vector at runtime (see bug 1166)
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{
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RowSquareMatrixType ref(square);
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ColSquareMatrixType ref2(square2);
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ref = res = square;
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VERIFY_IS_APPROX(res.block(0,0,1,rows).noalias() = m1.col(0).transpose() * square.transpose(), (ref.row(0) = m1.col(0).transpose() * square.transpose()));
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VERIFY_IS_APPROX(res.block(0,0,1,rows).noalias() = m1.block(0,0,rows,1).transpose() * square.transpose(), (ref.row(0) = m1.col(0).transpose() * square.transpose()));
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VERIFY_IS_APPROX(res.block(0,0,1,rows).noalias() = m1.col(0).transpose() * square, (ref.row(0) = m1.col(0).transpose() * square));
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VERIFY_IS_APPROX(res.block(0,0,1,rows).noalias() = m1.block(0,0,rows,1).transpose() * square, (ref.row(0) = m1.col(0).transpose() * square));
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ref2 = res2 = square2;
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VERIFY_IS_APPROX(res2.block(0,0,1,cols).noalias() = m1.row(0) * square2.transpose(), (ref2.row(0) = m1.row(0) * square2.transpose()));
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VERIFY_IS_APPROX(res2.block(0,0,1,cols).noalias() = m1.block(0,0,1,cols) * square2.transpose(), (ref2.row(0) = m1.row(0) * square2.transpose()));
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VERIFY_IS_APPROX(res2.block(0,0,1,cols).noalias() = m1.row(0) * square2, (ref2.row(0) = m1.row(0) * square2));
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VERIFY_IS_APPROX(res2.block(0,0,1,cols).noalias() = m1.block(0,0,1,cols) * square2, (ref2.row(0) = m1.row(0) * square2));
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}
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// vector.block() (see bug 1283)
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{
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RowVectorType w1(rows);
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VERIFY_IS_APPROX(square * v1.block(0,0,rows,1), square * v1);
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VERIFY_IS_APPROX(w1.noalias() = square * v1.block(0,0,rows,1), square * v1);
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VERIFY_IS_APPROX(w1.block(0,0,rows,1).noalias() = square * v1.block(0,0,rows,1), square * v1);
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Matrix<Scalar,1,MatrixType::ColsAtCompileTime> w2(cols);
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VERIFY_IS_APPROX(vc2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2);
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VERIFY_IS_APPROX(w2.noalias() = vc2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2);
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VERIFY_IS_APPROX(w2.block(0,0,1,cols).noalias() = vc2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2);
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vc2 = square2.block(0,0,1,cols).transpose();
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VERIFY_IS_APPROX(square2.block(0,0,1,cols) * square2, vc2.transpose() * square2);
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VERIFY_IS_APPROX(w2.noalias() = square2.block(0,0,1,cols) * square2, vc2.transpose() * square2);
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VERIFY_IS_APPROX(w2.block(0,0,1,cols).noalias() = square2.block(0,0,1,cols) * square2, vc2.transpose() * square2);
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vc2 = square2.block(0,0,cols,1);
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VERIFY_IS_APPROX(square2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2);
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VERIFY_IS_APPROX(w2.noalias() = square2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2);
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VERIFY_IS_APPROX(w2.block(0,0,1,cols).noalias() = square2.block(0,0,cols,1).transpose() * square2, vc2.transpose() * square2);
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}
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// inner product
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{
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Scalar x = square2.row(c) * square2.col(c2);
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VERIFY_IS_APPROX(x, square2.row(c).transpose().cwiseProduct(square2.col(c2)).sum());
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}
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// outer product
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{
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VERIFY_IS_APPROX(m1.col(c) * m1.row(r), m1.block(0,c,rows,1) * m1.block(r,0,1,cols));
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VERIFY_IS_APPROX(m1.row(r).transpose() * m1.col(c).transpose(), m1.block(r,0,1,cols).transpose() * m1.block(0,c,rows,1).transpose());
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VERIFY_IS_APPROX(m1.block(0,c,rows,1) * m1.row(r), m1.block(0,c,rows,1) * m1.block(r,0,1,cols));
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VERIFY_IS_APPROX(m1.col(c) * m1.block(r,0,1,cols), m1.block(0,c,rows,1) * m1.block(r,0,1,cols));
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VERIFY_IS_APPROX(m1.leftCols(1) * m1.row(r), m1.block(0,0,rows,1) * m1.block(r,0,1,cols));
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VERIFY_IS_APPROX(m1.col(c) * m1.topRows(1), m1.block(0,c,rows,1) * m1.block(0,0,1,cols));
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}
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// Aliasing
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{
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ColVectorType x(cols); x.setRandom();
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ColVectorType z(x);
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ColVectorType y(cols); y.setZero();
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ColSquareMatrixType A(cols,cols); A.setRandom();
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// CwiseBinaryOp
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VERIFY_IS_APPROX(x = y + A*x, A*z);
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x = z;
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// CwiseUnaryOp
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VERIFY_IS_APPROX(x = Scalar(1.)*(A*x), A*z);
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}
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// regression for blas_trais
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{
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VERIFY_IS_APPROX(square * (square*square).transpose(), square * square.transpose() * square.transpose());
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VERIFY_IS_APPROX(square * (-(square*square)), -square * square * square);
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VERIFY_IS_APPROX(square * (s1*(square*square)), s1 * square * square * square);
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VERIFY_IS_APPROX(square * (square*square).conjugate(), square * square.conjugate() * square.conjugate());
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}
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}
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