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// 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::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::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;
typedef Matrix<Scalar,Dynamic,Dynamic> MatrixX;
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());
}
// ------ partial reductions ------
#define TEST_PARTIAL_REDUX_BASIC(FUNC,ROW,COL,PREPROCESS) { \
ROW = m1 PREPROCESS .colwise().FUNC ; \
for(Index k=0; k<cols; ++k) VERIFY_IS_APPROX(ROW(k), m1.col(k) PREPROCESS .FUNC ); \
COL = m1 PREPROCESS .rowwise().FUNC ; \
for(Index k=0; k<rows; ++k) VERIFY_IS_APPROX(COL(k), m1.row(k) PREPROCESS .FUNC ); \
}
TEST_PARTIAL_REDUX_BASIC(sum(), rowvec,colvec,EIGEN_EMPTY);
TEST_PARTIAL_REDUX_BASIC(prod(), rowvec,colvec,EIGEN_EMPTY);
TEST_PARTIAL_REDUX_BASIC(mean(), rowvec,colvec,EIGEN_EMPTY);
TEST_PARTIAL_REDUX_BASIC(minCoeff(), rrres, rcres, .real());
TEST_PARTIAL_REDUX_BASIC(maxCoeff(), rrres, rcres, .real());
TEST_PARTIAL_REDUX_BASIC(norm(), rrres, rcres, EIGEN_EMPTY);
TEST_PARTIAL_REDUX_BASIC(squaredNorm(),rrres, rcres, EIGEN_EMPTY);
TEST_PARTIAL_REDUX_BASIC(redux(internal::scalar_sum_op<Scalar,Scalar>()),rowvec,colvec,EIGEN_EMPTY);
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>());
// regression for bug 1158
VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum().x(), m1.col(0).cwiseAbs().sum());
// 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(), 1);
m2 = m1.rowwise() - (m1.colwise().sum()/RealScalar(m1.rows())).eval();
m1 = m1.rowwise() - (m1.colwise().sum()/RealScalar(m1.rows()));
VERIFY_IS_APPROX( m1, m2 );
VERIFY_EVALUATION_COUNT( m2 = (m1.rowwise() - m1.colwise().sum()/RealScalar(m1.rows())), (MatrixType::RowsAtCompileTime!=1 ? 1 : 0) );
// test empty expressions
VERIFY_IS_APPROX(m1.matrix().middleCols(0,0).rowwise().sum().eval(), MatrixX::Zero(rows,1));
VERIFY_IS_APPROX(m1.matrix().middleRows(0,0).colwise().sum().eval(), MatrixX::Zero(1,cols));
VERIFY_IS_APPROX(m1.matrix().middleCols(0,fix<0>).rowwise().sum().eval(), MatrixX::Zero(rows,1));
VERIFY_IS_APPROX(m1.matrix().middleRows(0,fix<0>).colwise().sum().eval(), MatrixX::Zero(1,cols));
VERIFY_IS_APPROX(m1.matrix().middleCols(0,0).rowwise().prod().eval(), MatrixX::Ones(rows,1));
VERIFY_IS_APPROX(m1.matrix().middleRows(0,0).colwise().prod().eval(), MatrixX::Ones(1,cols));
VERIFY_IS_APPROX(m1.matrix().middleCols(0,fix<0>).rowwise().prod().eval(), MatrixX::Ones(rows,1));
VERIFY_IS_APPROX(m1.matrix().middleRows(0,fix<0>).colwise().prod().eval(), MatrixX::Ones(1,cols));
VERIFY_IS_APPROX(m1.matrix().middleCols(0,0).rowwise().squaredNorm().eval(), MatrixX::Zero(rows,1));
VERIFY_RAISES_ASSERT(m1.real().middleCols(0,0).rowwise().minCoeff().eval());
VERIFY_RAISES_ASSERT(m1.real().middleRows(0,0).colwise().maxCoeff().eval());
VERIFY_IS_EQUAL(m1.real().middleRows(0,0).rowwise().maxCoeff().eval().rows(),0);
VERIFY_IS_EQUAL(m1.real().middleCols(0,0).colwise().maxCoeff().eval().cols(),0);
VERIFY_IS_EQUAL(m1.real().middleRows(0,fix<0>).rowwise().maxCoeff().eval().rows(),0);
VERIFY_IS_EQUAL(m1.real().middleCols(0,fix<0>).colwise().maxCoeff().eval().cols(),0);
}
EIGEN_DECLARE_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(Matrix4f()) );
CALL_SUBTEST_5( vectorwiseop_matrix(Vector4f()) );
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))) );
}
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