test/vectorwiseop.cpp - manifest_repos/eigen - Git at Google

 // 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>());

   // 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) );
 }

 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))) );
 }
	// 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>());

	// 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) );
	}

	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))) );
	}