230 lines
9.5 KiB
C++
230 lines
9.5 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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#include "main.h"
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template <typename ArrayType>
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void vectorwiseop_array(const ArrayType& m) {
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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), c = internal::random<Index>(0, cols - 1);
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ArrayType m1 = ArrayType::Random(rows, cols), m2(rows, cols), 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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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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// test subtraction
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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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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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// 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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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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// 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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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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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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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>
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void vectorwiseop_matrix(const MatrixType& m) {
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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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typedef Matrix<Scalar, Dynamic, Dynamic> MatrixX;
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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), c = internal::random<Index>(0, cols - 1);
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MatrixType m1 = MatrixType::Random(rows, cols), m2(rows, cols), 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 broadcast assignment
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m2 = m1;
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m2.colwise() = colvec;
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for (Index j = 0; j < cols; ++j) VERIFY_IS_APPROX(m2.col(j), colvec);
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m2.rowwise() = rowvec;
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for (Index i = 0; i < rows; ++i) VERIFY_IS_APPROX(m2.row(i), rowvec);
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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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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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// test subtraction
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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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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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// ------ partial reductions ------
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#define TEST_PARTIAL_REDUX_BASIC(FUNC, ROW, COL, PREPROCESS) \
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{ \
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ROW = m1 PREPROCESS.colwise().FUNC; \
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for (Index k = 0; k < cols; ++k) VERIFY_IS_APPROX(ROW(k), m1.col(k) PREPROCESS.FUNC); \
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COL = m1 PREPROCESS.rowwise().FUNC; \
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for (Index k = 0; k < rows; ++k) VERIFY_IS_APPROX(COL(k), m1.row(k) PREPROCESS.FUNC); \
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}
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TEST_PARTIAL_REDUX_BASIC(sum(), rowvec, colvec, EIGEN_EMPTY);
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TEST_PARTIAL_REDUX_BASIC(prod(), rowvec, colvec, EIGEN_EMPTY);
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TEST_PARTIAL_REDUX_BASIC(mean(), rowvec, colvec, EIGEN_EMPTY);
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TEST_PARTIAL_REDUX_BASIC(minCoeff(), rrres, rcres, .real());
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TEST_PARTIAL_REDUX_BASIC(maxCoeff(), rrres, rcres, .real());
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TEST_PARTIAL_REDUX_BASIC(norm(), rrres, rcres, EIGEN_EMPTY);
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TEST_PARTIAL_REDUX_BASIC(squaredNorm(), rrres, rcres, EIGEN_EMPTY);
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TEST_PARTIAL_REDUX_BASIC(redux(internal::scalar_sum_op<Scalar, Scalar>()), rowvec, colvec, EIGEN_EMPTY);
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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())),
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(MatrixType::RowsAtCompileTime != 1 ? 1 : 0));
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// test empty expressions
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VERIFY_IS_APPROX(m1.matrix().middleCols(0, 0).rowwise().sum().eval(), MatrixX::Zero(rows, 1));
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VERIFY_IS_APPROX(m1.matrix().middleRows(0, 0).colwise().sum().eval(), MatrixX::Zero(1, cols));
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VERIFY_IS_APPROX(m1.matrix().middleCols(0, fix<0>).rowwise().sum().eval(), MatrixX::Zero(rows, 1));
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VERIFY_IS_APPROX(m1.matrix().middleRows(0, fix<0>).colwise().sum().eval(), MatrixX::Zero(1, cols));
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VERIFY_IS_APPROX(m1.matrix().middleCols(0, 0).rowwise().prod().eval(), MatrixX::Ones(rows, 1));
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VERIFY_IS_APPROX(m1.matrix().middleRows(0, 0).colwise().prod().eval(), MatrixX::Ones(1, cols));
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VERIFY_IS_APPROX(m1.matrix().middleCols(0, fix<0>).rowwise().prod().eval(), MatrixX::Ones(rows, 1));
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VERIFY_IS_APPROX(m1.matrix().middleRows(0, fix<0>).colwise().prod().eval(), MatrixX::Ones(1, cols));
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VERIFY_IS_APPROX(m1.matrix().middleCols(0, 0).rowwise().squaredNorm().eval(), MatrixX::Zero(rows, 1));
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VERIFY_IS_EQUAL(m1.real().middleRows(0, 0).rowwise().maxCoeff().eval().rows(), 0);
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VERIFY_IS_EQUAL(m1.real().middleCols(0, 0).colwise().maxCoeff().eval().cols(), 0);
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VERIFY_IS_EQUAL(m1.real().middleRows(0, fix<0>).rowwise().maxCoeff().eval().rows(), 0);
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VERIFY_IS_EQUAL(m1.real().middleCols(0, fix<0>).colwise().maxCoeff().eval().cols(), 0);
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}
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EIGEN_DECLARE_TEST(vectorwiseop) {
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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(Matrix4f()));
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CALL_SUBTEST_5(vectorwiseop_matrix(Vector4f()));
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CALL_SUBTEST_5(vectorwiseop_matrix(Matrix<float, 4, 5>()));
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CALL_SUBTEST_6(vectorwiseop_matrix(
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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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