You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
249 lines
8.5 KiB
249 lines
8.5 KiB
// This file is part of Eigen, a lightweight C++ template library
|
|
// for linear algebra.
|
|
//
|
|
// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
|
|
// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
|
|
//
|
|
// 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/.
|
|
|
|
#define TEST_ENABLE_TEMPORARY_TRACKING
|
|
#define EIGEN_NO_STATIC_ASSERT
|
|
|
|
#include "main.h"
|
|
|
|
template<typename ArrayType> void vectorwiseop_array(const ArrayType& m)
|
|
{
|
|
typedef typename ArrayType::Index Index;
|
|
typedef typename ArrayType::Scalar Scalar;
|
|
typedef Array<Scalar, ArrayType::RowsAtCompileTime, 1> ColVectorType;
|
|
typedef Array<Scalar, 1, ArrayType::ColsAtCompileTime> RowVectorType;
|
|
|
|
Index rows = m.rows();
|
|
Index cols = m.cols();
|
|
Index r = internal::random<Index>(0, rows-1),
|
|
c = internal::random<Index>(0, cols-1);
|
|
|
|
ArrayType m1 = ArrayType::Random(rows, cols),
|
|
m2(rows, cols),
|
|
m3(rows, cols);
|
|
|
|
ColVectorType colvec = ColVectorType::Random(rows);
|
|
RowVectorType rowvec = RowVectorType::Random(cols);
|
|
|
|
// test addition
|
|
|
|
m2 = m1;
|
|
m2.colwise() += colvec;
|
|
VERIFY_IS_APPROX(m2, m1.colwise() + colvec);
|
|
VERIFY_IS_APPROX(m2.col(c), m1.col(c) + colvec);
|
|
|
|
VERIFY_RAISES_ASSERT(m2.colwise() += colvec.transpose());
|
|
VERIFY_RAISES_ASSERT(m1.colwise() + colvec.transpose());
|
|
|
|
m2 = m1;
|
|
m2.rowwise() += rowvec;
|
|
VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec);
|
|
VERIFY_IS_APPROX(m2.row(r), m1.row(r) + rowvec);
|
|
|
|
VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose());
|
|
VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose());
|
|
|
|
// test substraction
|
|
|
|
m2 = m1;
|
|
m2.colwise() -= colvec;
|
|
VERIFY_IS_APPROX(m2, m1.colwise() - colvec);
|
|
VERIFY_IS_APPROX(m2.col(c), m1.col(c) - colvec);
|
|
|
|
VERIFY_RAISES_ASSERT(m2.colwise() -= colvec.transpose());
|
|
VERIFY_RAISES_ASSERT(m1.colwise() - colvec.transpose());
|
|
|
|
m2 = m1;
|
|
m2.rowwise() -= rowvec;
|
|
VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec);
|
|
VERIFY_IS_APPROX(m2.row(r), m1.row(r) - rowvec);
|
|
|
|
VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose());
|
|
VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose());
|
|
|
|
// test multiplication
|
|
|
|
m2 = m1;
|
|
m2.colwise() *= colvec;
|
|
VERIFY_IS_APPROX(m2, m1.colwise() * colvec);
|
|
VERIFY_IS_APPROX(m2.col(c), m1.col(c) * colvec);
|
|
|
|
VERIFY_RAISES_ASSERT(m2.colwise() *= colvec.transpose());
|
|
VERIFY_RAISES_ASSERT(m1.colwise() * colvec.transpose());
|
|
|
|
m2 = m1;
|
|
m2.rowwise() *= rowvec;
|
|
VERIFY_IS_APPROX(m2, m1.rowwise() * rowvec);
|
|
VERIFY_IS_APPROX(m2.row(r), m1.row(r) * rowvec);
|
|
|
|
VERIFY_RAISES_ASSERT(m2.rowwise() *= rowvec.transpose());
|
|
VERIFY_RAISES_ASSERT(m1.rowwise() * rowvec.transpose());
|
|
|
|
// test quotient
|
|
|
|
m2 = m1;
|
|
m2.colwise() /= colvec;
|
|
VERIFY_IS_APPROX(m2, m1.colwise() / colvec);
|
|
VERIFY_IS_APPROX(m2.col(c), m1.col(c) / colvec);
|
|
|
|
VERIFY_RAISES_ASSERT(m2.colwise() /= colvec.transpose());
|
|
VERIFY_RAISES_ASSERT(m1.colwise() / colvec.transpose());
|
|
|
|
m2 = m1;
|
|
m2.rowwise() /= rowvec;
|
|
VERIFY_IS_APPROX(m2, m1.rowwise() / rowvec);
|
|
VERIFY_IS_APPROX(m2.row(r), m1.row(r) / rowvec);
|
|
|
|
VERIFY_RAISES_ASSERT(m2.rowwise() /= rowvec.transpose());
|
|
VERIFY_RAISES_ASSERT(m1.rowwise() / rowvec.transpose());
|
|
|
|
m2 = m1;
|
|
// yes, there might be an aliasing issue there but ".rowwise() /="
|
|
// is supposed to evaluate " m2.colwise().sum()" into a temporary to avoid
|
|
// evaluating the reduction multiple times
|
|
if(ArrayType::RowsAtCompileTime>2 || ArrayType::RowsAtCompileTime==Dynamic)
|
|
{
|
|
m2.rowwise() /= m2.colwise().sum();
|
|
VERIFY_IS_APPROX(m2, m1.rowwise() / m1.colwise().sum());
|
|
}
|
|
|
|
// all/any
|
|
Array<bool,Dynamic,Dynamic> mb(rows,cols);
|
|
mb = (m1.real()<=0.7).colwise().all();
|
|
VERIFY( (mb.col(c) == (m1.real().col(c)<=0.7).all()).all() );
|
|
mb = (m1.real()<=0.7).rowwise().all();
|
|
VERIFY( (mb.row(r) == (m1.real().row(r)<=0.7).all()).all() );
|
|
|
|
mb = (m1.real()>=0.7).colwise().any();
|
|
VERIFY( (mb.col(c) == (m1.real().col(c)>=0.7).any()).all() );
|
|
mb = (m1.real()>=0.7).rowwise().any();
|
|
VERIFY( (mb.row(r) == (m1.real().row(r)>=0.7).any()).all() );
|
|
}
|
|
|
|
template<typename MatrixType> void vectorwiseop_matrix(const MatrixType& m)
|
|
{
|
|
typedef typename MatrixType::Index Index;
|
|
typedef typename MatrixType::Scalar Scalar;
|
|
typedef typename NumTraits<Scalar>::Real RealScalar;
|
|
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVectorType;
|
|
typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType;
|
|
typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealColVectorType;
|
|
typedef Matrix<RealScalar, 1, MatrixType::ColsAtCompileTime> RealRowVectorType;
|
|
|
|
Index rows = m.rows();
|
|
Index cols = m.cols();
|
|
Index r = internal::random<Index>(0, rows-1),
|
|
c = internal::random<Index>(0, cols-1);
|
|
|
|
MatrixType m1 = MatrixType::Random(rows, cols),
|
|
m2(rows, cols),
|
|
m3(rows, cols);
|
|
|
|
ColVectorType colvec = ColVectorType::Random(rows);
|
|
RowVectorType rowvec = RowVectorType::Random(cols);
|
|
RealColVectorType rcres;
|
|
RealRowVectorType rrres;
|
|
|
|
// test addition
|
|
|
|
m2 = m1;
|
|
m2.colwise() += colvec;
|
|
VERIFY_IS_APPROX(m2, m1.colwise() + colvec);
|
|
VERIFY_IS_APPROX(m2.col(c), m1.col(c) + colvec);
|
|
|
|
if(rows>1)
|
|
{
|
|
VERIFY_RAISES_ASSERT(m2.colwise() += colvec.transpose());
|
|
VERIFY_RAISES_ASSERT(m1.colwise() + colvec.transpose());
|
|
}
|
|
|
|
m2 = m1;
|
|
m2.rowwise() += rowvec;
|
|
VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec);
|
|
VERIFY_IS_APPROX(m2.row(r), m1.row(r) + rowvec);
|
|
|
|
if(cols>1)
|
|
{
|
|
VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose());
|
|
VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose());
|
|
}
|
|
|
|
// test substraction
|
|
|
|
m2 = m1;
|
|
m2.colwise() -= colvec;
|
|
VERIFY_IS_APPROX(m2, m1.colwise() - colvec);
|
|
VERIFY_IS_APPROX(m2.col(c), m1.col(c) - colvec);
|
|
|
|
if(rows>1)
|
|
{
|
|
VERIFY_RAISES_ASSERT(m2.colwise() -= colvec.transpose());
|
|
VERIFY_RAISES_ASSERT(m1.colwise() - colvec.transpose());
|
|
}
|
|
|
|
m2 = m1;
|
|
m2.rowwise() -= rowvec;
|
|
VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec);
|
|
VERIFY_IS_APPROX(m2.row(r), m1.row(r) - rowvec);
|
|
|
|
if(cols>1)
|
|
{
|
|
VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose());
|
|
VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose());
|
|
}
|
|
|
|
// test norm
|
|
rrres = m1.colwise().norm();
|
|
VERIFY_IS_APPROX(rrres(c), m1.col(c).norm());
|
|
rcres = m1.rowwise().norm();
|
|
VERIFY_IS_APPROX(rcres(r), m1.row(r).norm());
|
|
|
|
VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum(), m1.colwise().template lpNorm<1>());
|
|
VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().sum(), m1.rowwise().template lpNorm<1>());
|
|
VERIFY_IS_APPROX(m1.cwiseAbs().colwise().maxCoeff(), m1.colwise().template lpNorm<Infinity>());
|
|
VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().maxCoeff(), m1.rowwise().template lpNorm<Infinity>());
|
|
|
|
// test normalized
|
|
m2 = m1.colwise().normalized();
|
|
VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized());
|
|
m2 = m1.rowwise().normalized();
|
|
VERIFY_IS_APPROX(m2.row(r), m1.row(r).normalized());
|
|
|
|
// test normalize
|
|
m2 = m1;
|
|
m2.colwise().normalize();
|
|
VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized());
|
|
m2 = m1;
|
|
m2.rowwise().normalize();
|
|
VERIFY_IS_APPROX(m2.row(r), m1.row(r).normalized());
|
|
|
|
// test with partial reduction of products
|
|
Matrix<Scalar,MatrixType::RowsAtCompileTime,MatrixType::RowsAtCompileTime> m1m1 = m1 * m1.transpose();
|
|
VERIFY_IS_APPROX( (m1 * m1.transpose()).colwise().sum(), m1m1.colwise().sum());
|
|
Matrix<Scalar,1,MatrixType::RowsAtCompileTime> tmp(rows);
|
|
VERIFY_EVALUATION_COUNT( tmp = (m1 * m1.transpose()).colwise().sum(), (MatrixType::RowsAtCompileTime==Dynamic ? 1 : 0));
|
|
|
|
m2 = m1.rowwise() - (m1.colwise().sum()/m1.rows()).eval();
|
|
m1 = m1.rowwise() - (m1.colwise().sum()/m1.rows());
|
|
VERIFY_IS_APPROX( m1, m2 );
|
|
VERIFY_EVALUATION_COUNT( m2 = (m1.rowwise() - m1.colwise().sum()/m1.rows()), (MatrixType::RowsAtCompileTime==Dynamic && MatrixType::ColsAtCompileTime!=1 ? 1 : 0) );
|
|
}
|
|
|
|
void test_vectorwiseop()
|
|
{
|
|
CALL_SUBTEST_1( vectorwiseop_array(Array22cd()) );
|
|
CALL_SUBTEST_2( vectorwiseop_array(Array<double, 3, 2>()) );
|
|
CALL_SUBTEST_3( vectorwiseop_array(ArrayXXf(3, 4)) );
|
|
CALL_SUBTEST_4( vectorwiseop_matrix(Matrix4cf()) );
|
|
CALL_SUBTEST_5( vectorwiseop_matrix(Matrix<float,4,5>()) );
|
|
CALL_SUBTEST_6( vectorwiseop_matrix(MatrixXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE), internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
|
|
CALL_SUBTEST_7( vectorwiseop_matrix(VectorXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
|
|
CALL_SUBTEST_7( vectorwiseop_matrix(RowVectorXd(internal::random<int>(1,EIGEN_TEST_MAX_SIZE))) );
|
|
}
|