253 lines
8.5 KiB
C++
253 lines
8.5 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) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
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// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
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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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#define TEST_ENABLE_TEMPORARY_TRACKING
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#define EIGEN_NO_STATIC_ASSERT
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#include "main.h"
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template<typename ArrayType> void vectorwiseop_array(const ArrayType& m)
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{
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typedef typename ArrayType::Index Index;
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typedef typename ArrayType::Scalar Scalar;
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typedef Array<Scalar, ArrayType::RowsAtCompileTime, 1> ColVectorType;
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typedef Array<Scalar, 1, ArrayType::ColsAtCompileTime> RowVectorType;
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Index rows = m.rows();
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Index cols = m.cols();
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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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ArrayType m1 = ArrayType::Random(rows, cols),
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m2(rows, cols),
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m3(rows, cols);
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ColVectorType colvec = ColVectorType::Random(rows);
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RowVectorType rowvec = RowVectorType::Random(cols);
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// test addition
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m2 = m1;
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m2.colwise() += colvec;
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VERIFY_IS_APPROX(m2, m1.colwise() + colvec);
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VERIFY_IS_APPROX(m2.col(c), m1.col(c) + colvec);
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VERIFY_RAISES_ASSERT(m2.colwise() += colvec.transpose());
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VERIFY_RAISES_ASSERT(m1.colwise() + colvec.transpose());
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m2 = m1;
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m2.rowwise() += rowvec;
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VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec);
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VERIFY_IS_APPROX(m2.row(r), m1.row(r) + rowvec);
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VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose());
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VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose());
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// test substraction
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m2 = m1;
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m2.colwise() -= colvec;
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VERIFY_IS_APPROX(m2, m1.colwise() - colvec);
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VERIFY_IS_APPROX(m2.col(c), m1.col(c) - colvec);
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VERIFY_RAISES_ASSERT(m2.colwise() -= colvec.transpose());
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VERIFY_RAISES_ASSERT(m1.colwise() - colvec.transpose());
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m2 = m1;
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m2.rowwise() -= rowvec;
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VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec);
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VERIFY_IS_APPROX(m2.row(r), m1.row(r) - rowvec);
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VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose());
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VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose());
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// test multiplication
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m2 = m1;
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m2.colwise() *= colvec;
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VERIFY_IS_APPROX(m2, m1.colwise() * colvec);
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VERIFY_IS_APPROX(m2.col(c), m1.col(c) * colvec);
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VERIFY_RAISES_ASSERT(m2.colwise() *= colvec.transpose());
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VERIFY_RAISES_ASSERT(m1.colwise() * colvec.transpose());
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m2 = m1;
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m2.rowwise() *= rowvec;
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VERIFY_IS_APPROX(m2, m1.rowwise() * rowvec);
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VERIFY_IS_APPROX(m2.row(r), m1.row(r) * rowvec);
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VERIFY_RAISES_ASSERT(m2.rowwise() *= rowvec.transpose());
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VERIFY_RAISES_ASSERT(m1.rowwise() * rowvec.transpose());
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// test quotient
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m2 = m1;
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m2.colwise() /= colvec;
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VERIFY_IS_APPROX(m2, m1.colwise() / colvec);
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VERIFY_IS_APPROX(m2.col(c), m1.col(c) / colvec);
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VERIFY_RAISES_ASSERT(m2.colwise() /= colvec.transpose());
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VERIFY_RAISES_ASSERT(m1.colwise() / colvec.transpose());
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m2 = m1;
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m2.rowwise() /= rowvec;
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VERIFY_IS_APPROX(m2, m1.rowwise() / rowvec);
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VERIFY_IS_APPROX(m2.row(r), m1.row(r) / rowvec);
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VERIFY_RAISES_ASSERT(m2.rowwise() /= rowvec.transpose());
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VERIFY_RAISES_ASSERT(m1.rowwise() / rowvec.transpose());
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m2 = m1;
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// yes, there might be an aliasing issue there but ".rowwise() /="
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// is supposed to evaluate " m2.colwise().sum()" into a temporary to avoid
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// evaluating the reduction multiple times
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if(ArrayType::RowsAtCompileTime>2 || ArrayType::RowsAtCompileTime==Dynamic)
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{
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m2.rowwise() /= m2.colwise().sum();
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VERIFY_IS_APPROX(m2, m1.rowwise() / m1.colwise().sum());
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}
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// all/any
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Array<bool,Dynamic,Dynamic> mb(rows,cols);
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mb = (m1.real()<=0.7).colwise().all();
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VERIFY( (mb.col(c) == (m1.real().col(c)<=0.7).all()).all() );
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mb = (m1.real()<=0.7).rowwise().all();
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VERIFY( (mb.row(r) == (m1.real().row(r)<=0.7).all()).all() );
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mb = (m1.real()>=0.7).colwise().any();
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VERIFY( (mb.col(c) == (m1.real().col(c)>=0.7).any()).all() );
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mb = (m1.real()>=0.7).rowwise().any();
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VERIFY( (mb.row(r) == (m1.real().row(r)>=0.7).any()).all() );
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}
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template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m)
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{
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typedef typename MatrixType::Index Index;
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typedef typename MatrixType::Scalar Scalar;
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typedef typename NumTraits<Scalar>::Real RealScalar;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVectorType;
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typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType;
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typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealColVectorType;
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typedef Matrix<RealScalar, 1, MatrixType::ColsAtCompileTime> RealRowVectorType;
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Index rows = m.rows();
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Index cols = m.cols();
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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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MatrixType m1 = MatrixType::Random(rows, cols),
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m2(rows, cols),
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m3(rows, cols);
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ColVectorType colvec = ColVectorType::Random(rows);
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RowVectorType rowvec = RowVectorType::Random(cols);
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RealColVectorType rcres;
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RealRowVectorType rrres;
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// test addition
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m2 = m1;
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m2.colwise() += colvec;
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VERIFY_IS_APPROX(m2, m1.colwise() + colvec);
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VERIFY_IS_APPROX(m2.col(c), m1.col(c) + colvec);
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if(rows>1)
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{
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VERIFY_RAISES_ASSERT(m2.colwise() += colvec.transpose());
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VERIFY_RAISES_ASSERT(m1.colwise() + colvec.transpose());
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}
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m2 = m1;
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m2.rowwise() += rowvec;
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VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec);
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VERIFY_IS_APPROX(m2.row(r), m1.row(r) + rowvec);
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if(cols>1)
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{
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VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose());
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VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose());
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}
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// test substraction
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m2 = m1;
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m2.colwise() -= colvec;
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VERIFY_IS_APPROX(m2, m1.colwise() - colvec);
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VERIFY_IS_APPROX(m2.col(c), m1.col(c) - colvec);
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if(rows>1)
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{
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VERIFY_RAISES_ASSERT(m2.colwise() -= colvec.transpose());
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VERIFY_RAISES_ASSERT(m1.colwise() - colvec.transpose());
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}
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m2 = m1;
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m2.rowwise() -= rowvec;
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VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec);
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VERIFY_IS_APPROX(m2.row(r), m1.row(r) - rowvec);
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if(cols>1)
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{
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VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose());
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VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose());
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}
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// test norm
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rrres = m1.colwise().norm();
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VERIFY_IS_APPROX(rrres(c), m1.col(c).norm());
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rcres = m1.rowwise().norm();
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VERIFY_IS_APPROX(rcres(r), m1.row(r).norm());
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VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum(), m1.colwise().template lpNorm<1>());
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VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().sum(), m1.rowwise().template lpNorm<1>());
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VERIFY_IS_APPROX(m1.cwiseAbs().colwise().maxCoeff(), m1.colwise().template lpNorm<Infinity>());
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VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().maxCoeff(), m1.rowwise().template lpNorm<Infinity>());
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// regression for bug 1158
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VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum().x(), m1.col(0).cwiseAbs().sum());
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// test normalized
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m2 = m1.colwise().normalized();
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VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized());
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m2 = m1.rowwise().normalized();
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VERIFY_IS_APPROX(m2.row(r), m1.row(r).normalized());
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// test normalize
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m2 = m1;
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m2.colwise().normalize();
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VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized());
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m2 = m1;
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m2.rowwise().normalize();
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VERIFY_IS_APPROX(m2.row(r), m1.row(r).normalized());
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// test with partial reduction of products
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Matrix<Scalar,MatrixType::RowsAtCompileTime,MatrixType::RowsAtCompileTime> m1m1 = m1 * m1.transpose();
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VERIFY_IS_APPROX( (m1 * m1.transpose()).colwise().sum(), m1m1.colwise().sum());
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Matrix<Scalar,1,MatrixType::RowsAtCompileTime> tmp(rows);
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VERIFY_EVALUATION_COUNT( tmp = (m1 * m1.transpose()).colwise().sum(), 1);
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m2 = m1.rowwise() - (m1.colwise().sum()/RealScalar(m1.rows())).eval();
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m1 = m1.rowwise() - (m1.colwise().sum()/RealScalar(m1.rows()));
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VERIFY_IS_APPROX( m1, m2 );
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VERIFY_EVALUATION_COUNT( m2 = (m1.rowwise() - m1.colwise().sum()/RealScalar(m1.rows())), (MatrixType::RowsAtCompileTime!=1 ? 1 : 0) );
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}
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void test_vectorwiseop()
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{
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CALL_SUBTEST_1( vectorwiseop_array(Array22cd()) );
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CALL_SUBTEST_2( vectorwiseop_array(Array<double, 3, 2>()) );
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CALL_SUBTEST_3( vectorwiseop_array(ArrayXXf(3, 4)) );
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CALL_SUBTEST_4( vectorwiseop_matrix(Matrix4cf()) );
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CALL_SUBTEST_5( vectorwiseop_matrix(Matrix<float,4,5>()) );
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CALL_SUBTEST_6( vectorwiseop_matrix(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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CALL_SUBTEST_7( vectorwiseop_matrix(VectorXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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CALL_SUBTEST_7( vectorwiseop_matrix(RowVectorXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
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}
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