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Diffstat (limited to 'test/product.h')
| -rw-r--r-- | test/product.h | 143 |
1 files changed, 143 insertions, 0 deletions
diff --git a/test/product.h b/test/product.h new file mode 100644 index 000000000..4aa9fd56d --- /dev/null +++ b/test/product.h @@ -0,0 +1,143 @@ +// This file is part of Eigen, a lightweight C++ template library +// for linear algebra. +// +// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com> +// +// This Source Code Form is subject to the terms of the Mozilla +// Public License v. 2.0. If a copy of the MPL was not distributed +// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. + +#include "main.h" +#include <Eigen/QR> + +template<typename Derived1, typename Derived2> +bool areNotApprox(const MatrixBase<Derived1>& m1, const MatrixBase<Derived2>& m2, typename Derived1::RealScalar epsilon = NumTraits<typename Derived1::RealScalar>::dummy_precision()) +{ + return !((m1-m2).cwiseAbs2().maxCoeff() < epsilon * epsilon + * (std::max)(m1.cwiseAbs2().maxCoeff(), m2.cwiseAbs2().maxCoeff())); +} + +template<typename MatrixType> void product(const MatrixType& m) +{ + /* this test covers the following files: + Identity.h Product.h + */ + typedef typename MatrixType::Index Index; + typedef typename MatrixType::Scalar Scalar; + typedef typename NumTraits<Scalar>::NonInteger NonInteger; + typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> RowVectorType; + typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> ColVectorType; + typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> RowSquareMatrixType; + typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> ColSquareMatrixType; + typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime, + MatrixType::Flags&RowMajorBit?ColMajor:RowMajor> OtherMajorMatrixType; + + Index rows = m.rows(); + Index cols = m.cols(); + + // this test relies a lot on Random.h, and there's not much more that we can do + // to test it, hence I consider that we will have tested Random.h + MatrixType m1 = MatrixType::Random(rows, cols), + m2 = MatrixType::Random(rows, cols), + m3(rows, cols); + RowSquareMatrixType + identity = RowSquareMatrixType::Identity(rows, rows), + square = RowSquareMatrixType::Random(rows, rows), + res = RowSquareMatrixType::Random(rows, rows); + ColSquareMatrixType + square2 = ColSquareMatrixType::Random(cols, cols), + res2 = ColSquareMatrixType::Random(cols, cols); + RowVectorType v1 = RowVectorType::Random(rows); + ColVectorType vc2 = ColVectorType::Random(cols), vcres(cols); + OtherMajorMatrixType tm1 = m1; + + Scalar s1 = internal::random<Scalar>(); + + Index r = internal::random<Index>(0, rows-1), + c = internal::random<Index>(0, cols-1), + c2 = internal::random<Index>(0, cols-1); + + // begin testing Product.h: only associativity for now + // (we use Transpose.h but this doesn't count as a test for it) + VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2)); + m3 = m1; + m3 *= m1.transpose() * m2; + VERIFY_IS_APPROX(m3, m1 * (m1.transpose()*m2)); + VERIFY_IS_APPROX(m3, m1 * (m1.transpose()*m2)); + + // continue testing Product.h: distributivity + VERIFY_IS_APPROX(square*(m1 + m2), square*m1+square*m2); + VERIFY_IS_APPROX(square*(m1 - m2), square*m1-square*m2); + + // continue testing Product.h: compatibility with ScalarMultiple.h + VERIFY_IS_APPROX(s1*(square*m1), (s1*square)*m1); + VERIFY_IS_APPROX(s1*(square*m1), square*(m1*s1)); + + // test Product.h together with Identity.h + VERIFY_IS_APPROX(v1, identity*v1); + VERIFY_IS_APPROX(v1.transpose(), v1.transpose() * identity); + // again, test operator() to check const-qualification + VERIFY_IS_APPROX(MatrixType::Identity(rows, cols)(r,c), static_cast<Scalar>(r==c)); + + if (rows!=cols) + VERIFY_RAISES_ASSERT(m3 = m1*m1); + + // test the previous tests were not screwed up because operator* returns 0 + // (we use the more accurate default epsilon) + if (!NumTraits<Scalar>::IsInteger && (std::min)(rows,cols)>1) + { + VERIFY(areNotApprox(m1.transpose()*m2,m2.transpose()*m1)); + } + + // test optimized operator+= path + res = square; + res.noalias() += m1 * m2.transpose(); + VERIFY_IS_APPROX(res, square + m1 * m2.transpose()); + if (!NumTraits<Scalar>::IsInteger && (std::min)(rows,cols)>1) + { + VERIFY(areNotApprox(res,square + m2 * m1.transpose())); + } + vcres = vc2; + vcres.noalias() += m1.transpose() * v1; + VERIFY_IS_APPROX(vcres, vc2 + m1.transpose() * v1); + + // test optimized operator-= path + res = square; + res.noalias() -= m1 * m2.transpose(); + VERIFY_IS_APPROX(res, square - (m1 * m2.transpose())); + if (!NumTraits<Scalar>::IsInteger && (std::min)(rows,cols)>1) + { + VERIFY(areNotApprox(res,square - m2 * m1.transpose())); + } + vcres = vc2; + vcres.noalias() -= m1.transpose() * v1; + VERIFY_IS_APPROX(vcres, vc2 - m1.transpose() * v1); + + tm1 = m1; + VERIFY_IS_APPROX(tm1.transpose() * v1, m1.transpose() * v1); + VERIFY_IS_APPROX(v1.transpose() * tm1, v1.transpose() * m1); + + // test submatrix and matrix/vector product + for (int i=0; i<rows; ++i) + res.row(i) = m1.row(i) * m2.transpose(); + VERIFY_IS_APPROX(res, m1 * m2.transpose()); + // the other way round: + for (int i=0; i<rows; ++i) + res.col(i) = m1 * m2.transpose().col(i); + VERIFY_IS_APPROX(res, m1 * m2.transpose()); + + res2 = square2; + res2.noalias() += m1.transpose() * m2; + VERIFY_IS_APPROX(res2, square2 + m1.transpose() * m2); + if (!NumTraits<Scalar>::IsInteger && (std::min)(rows,cols)>1) + { + VERIFY(areNotApprox(res2,square2 + m2.transpose() * m1)); + } + + VERIFY_IS_APPROX(res.col(r).noalias() = square.adjoint() * square.col(r), (square.adjoint() * square.col(r)).eval()); + VERIFY_IS_APPROX(res.col(r).noalias() = square * square.col(r), (square * square.col(r)).eval()); + + // inner product + Scalar x = square2.row(c) * square2.col(c2); + VERIFY_IS_APPROX(x, square2.row(c).transpose().cwiseProduct(square2.col(c2)).sum()); +} |