test/redux.cpp - nest-cam/v366/eigen - Git at Google

 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2008 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_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
 // ^^ see bug 1449

 #include "main.h"

 template<typename MatrixType> void matrixRedux(const MatrixType& m)
 {
   typedef typename MatrixType::Scalar Scalar;
   typedef typename MatrixType::RealScalar RealScalar;

   Index rows = m.rows();
   Index cols = m.cols();

   MatrixType m1 = MatrixType::Random(rows, cols);

   // The entries of m1 are uniformly distributed in [0,1], so m1.prod() is very small. This may lead to test
   // failures if we underflow into denormals. Thus, we scale so that entries are close to 1.
   MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + RealScalar(0.2) * m1;

   Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> m2(rows,rows);
   m2.setRandom();

   VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1));
   VERIFY_IS_APPROX(MatrixType::Ones(rows, cols).sum(), Scalar(float(rows*cols))); // the float() here to shut up excessive MSVC warning about int->complex conversion being lossy
   Scalar s(0), p(1), minc(numext::real(m1.coeff(0))), maxc(numext::real(m1.coeff(0)));
   for(int j = 0; j < cols; j++)
   for(int i = 0; i < rows; i++)
   {
     s += m1(i,j);
     p *= m1_for_prod(i,j);
     minc = (std::min)(numext::real(minc), numext::real(m1(i,j)));
     maxc = (std::max)(numext::real(maxc), numext::real(m1(i,j)));
   }
   const Scalar mean = s/Scalar(RealScalar(rows*cols));

   VERIFY_IS_APPROX(m1.sum(), s);
   VERIFY_IS_APPROX(m1.mean(), mean);
   VERIFY_IS_APPROX(m1_for_prod.prod(), p);
   VERIFY_IS_APPROX(m1.real().minCoeff(), numext::real(minc));
   VERIFY_IS_APPROX(m1.real().maxCoeff(), numext::real(maxc));

   // test that partial reduction works if nested expressions is forced to evaluate early
   VERIFY_IS_APPROX((m1.matrix() * m1.matrix().transpose())       .cwiseProduct(m2.matrix()).rowwise().sum().sum(),
                    (m1.matrix() * m1.matrix().transpose()).eval().cwiseProduct(m2.matrix()).rowwise().sum().sum());

   // test slice vectorization assuming assign is ok
   Index r0 = internal::random<Index>(0,rows-1);
   Index c0 = internal::random<Index>(0,cols-1);
   Index r1 = internal::random<Index>(r0+1,rows)-r0;
   Index c1 = internal::random<Index>(c0+1,cols)-c0;
   VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).sum(), m1.block(r0,c0,r1,c1).eval().sum());
   VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).mean(), m1.block(r0,c0,r1,c1).eval().mean());
   VERIFY_IS_APPROX(m1_for_prod.block(r0,c0,r1,c1).prod(), m1_for_prod.block(r0,c0,r1,c1).eval().prod());
   VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().minCoeff(), m1.block(r0,c0,r1,c1).real().eval().minCoeff());
   VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().maxCoeff(), m1.block(r0,c0,r1,c1).real().eval().maxCoeff());

   // regression for bug 1090
   const int R1 = MatrixType::RowsAtCompileTime>=2 ? MatrixType::RowsAtCompileTime/2 : 6;
   const int C1 = MatrixType::ColsAtCompileTime>=2 ? MatrixType::ColsAtCompileTime/2 : 6;
   if(R1<=rows-r0 && C1<=cols-c0)
   {
     VERIFY_IS_APPROX( (m1.template block<R1,C1>(r0,c0).sum()), m1.block(r0,c0,R1,C1).sum() );
   }

   // test empty objects
   VERIFY_IS_APPROX(m1.block(r0,c0,0,0).sum(),   Scalar(0));
   VERIFY_IS_APPROX(m1.block(r0,c0,0,0).prod(),  Scalar(1));

   // test nesting complex expression
   VERIFY_EVALUATION_COUNT( (m1.matrix()*m1.matrix().transpose()).sum(), (MatrixType::IsVectorAtCompileTime && MatrixType::SizeAtCompileTime!=1 ? 0 : 1) );
   VERIFY_EVALUATION_COUNT( ((m1.matrix()*m1.matrix().transpose())+m2).sum(),(MatrixType::IsVectorAtCompileTime && MatrixType::SizeAtCompileTime!=1 ? 0 : 1));
 }

 template<typename VectorType> void vectorRedux(const VectorType& w)
 {
   using std::abs;
   typedef typename VectorType::Scalar Scalar;
   typedef typename NumTraits<Scalar>::Real RealScalar;
   Index size = w.size();

   VectorType v = VectorType::Random(size);
   VectorType v_for_prod = VectorType::Ones(size) + Scalar(0.2) * v; // see comment above declaration of m1_for_prod

   for(int i = 1; i < size; i++)
   {
     Scalar s(0), p(1);
     RealScalar minc(numext::real(v.coeff(0))), maxc(numext::real(v.coeff(0)));
     for(int j = 0; j < i; j++)
     {
       s += v[j];
       p *= v_for_prod[j];
       minc = (std::min)(minc, numext::real(v[j]));
       maxc = (std::max)(maxc, numext::real(v[j]));
     }
     VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.head(i).sum()), Scalar(1));
     VERIFY_IS_APPROX(p, v_for_prod.head(i).prod());
     VERIFY_IS_APPROX(minc, v.real().head(i).minCoeff());
     VERIFY_IS_APPROX(maxc, v.real().head(i).maxCoeff());
   }

   for(int i = 0; i < size-1; i++)
   {
     Scalar s(0), p(1);
     RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
     for(int j = i; j < size; j++)
     {
       s += v[j];
       p *= v_for_prod[j];
       minc = (std::min)(minc, numext::real(v[j]));
       maxc = (std::max)(maxc, numext::real(v[j]));
     }
     VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.tail(size-i).sum()), Scalar(1));
     VERIFY_IS_APPROX(p, v_for_prod.tail(size-i).prod());
     VERIFY_IS_APPROX(minc, v.real().tail(size-i).minCoeff());
     VERIFY_IS_APPROX(maxc, v.real().tail(size-i).maxCoeff());
   }

   for(int i = 0; i < size/2; i++)
   {
     Scalar s(0), p(1);
     RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
     for(int j = i; j < size-i; j++)
     {
       s += v[j];
       p *= v_for_prod[j];
       minc = (std::min)(minc, numext::real(v[j]));
       maxc = (std::max)(maxc, numext::real(v[j]));
     }
     VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.segment(i, size-2*i).sum()), Scalar(1));
     VERIFY_IS_APPROX(p, v_for_prod.segment(i, size-2*i).prod());
     VERIFY_IS_APPROX(minc, v.real().segment(i, size-2*i).minCoeff());
     VERIFY_IS_APPROX(maxc, v.real().segment(i, size-2*i).maxCoeff());
   }

   // test empty objects
   VERIFY_IS_APPROX(v.head(0).sum(),   Scalar(0));
   VERIFY_IS_APPROX(v.tail(0).prod(),  Scalar(1));
   VERIFY_RAISES_ASSERT(v.head(0).mean());
   VERIFY_RAISES_ASSERT(v.head(0).minCoeff());
   VERIFY_RAISES_ASSERT(v.head(0).maxCoeff());
 }

 EIGEN_DECLARE_TEST(redux)
 {
   // the max size cannot be too large, otherwise reduxion operations obviously generate large errors.
   int maxsize = (std::min)(100,EIGEN_TEST_MAX_SIZE);
   TEST_SET_BUT_UNUSED_VARIABLE(maxsize);
   for(int i = 0; i < g_repeat; i++) {
     CALL_SUBTEST_1( matrixRedux(Matrix<float, 1, 1>()) );
     CALL_SUBTEST_1( matrixRedux(Array<float, 1, 1>()) );
     CALL_SUBTEST_2( matrixRedux(Matrix2f()) );
     CALL_SUBTEST_2( matrixRedux(Array2f()) );
     CALL_SUBTEST_2( matrixRedux(Array22f()) );
     CALL_SUBTEST_3( matrixRedux(Matrix4d()) );
     CALL_SUBTEST_3( matrixRedux(Array4d()) );
     CALL_SUBTEST_3( matrixRedux(Array44d()) );
     CALL_SUBTEST_4( matrixRedux(MatrixXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
     CALL_SUBTEST_4( matrixRedux(ArrayXXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
     CALL_SUBTEST_5( matrixRedux(MatrixXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
     CALL_SUBTEST_5( matrixRedux(ArrayXXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
     CALL_SUBTEST_6( matrixRedux(MatrixXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
     CALL_SUBTEST_6( matrixRedux(ArrayXXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
   }
   for(int i = 0; i < g_repeat; i++) {
     CALL_SUBTEST_7( vectorRedux(Vector4f()) );
     CALL_SUBTEST_7( vectorRedux(Array4f()) );
     CALL_SUBTEST_5( vectorRedux(VectorXd(internal::random<int>(1,maxsize))) );
     CALL_SUBTEST_5( vectorRedux(ArrayXd(internal::random<int>(1,maxsize))) );
     CALL_SUBTEST_8( vectorRedux(VectorXf(internal::random<int>(1,maxsize))) );
     CALL_SUBTEST_8( vectorRedux(ArrayXf(internal::random<int>(1,maxsize))) );
   }
 }
	// This file is part of Eigen, a lightweight C++ template library
	// for linear algebra.
	//
	// Copyright (C) 2008 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_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
	// ^^ see bug 1449

	#include "main.h"

	template<typename MatrixType> void matrixRedux(const MatrixType& m)
	{
	typedef typename MatrixType::Scalar Scalar;
	typedef typename MatrixType::RealScalar RealScalar;

	Index rows = m.rows();
	Index cols = m.cols();

	MatrixType m1 = MatrixType::Random(rows, cols);

	// The entries of m1 are uniformly distributed in [0,1], so m1.prod() is very small. This may lead to test
	// failures if we underflow into denormals. Thus, we scale so that entries are close to 1.
	MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + RealScalar(0.2) * m1;

	Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> m2(rows,rows);
	m2.setRandom();

	VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1));
	VERIFY_IS_APPROX(MatrixType::Ones(rows, cols).sum(), Scalar(float(rows*cols))); // the float() here to shut up excessive MSVC warning about int->complex conversion being lossy
	Scalar s(0), p(1), minc(numext::real(m1.coeff(0))), maxc(numext::real(m1.coeff(0)));
	for(int j = 0; j < cols; j++)
	for(int i = 0; i < rows; i++)
	{
	s += m1(i,j);
	p *= m1_for_prod(i,j);
	minc = (std::min)(numext::real(minc), numext::real(m1(i,j)));
	maxc = (std::max)(numext::real(maxc), numext::real(m1(i,j)));
	}
	const Scalar mean = s/Scalar(RealScalar(rows*cols));

	VERIFY_IS_APPROX(m1.sum(), s);
	VERIFY_IS_APPROX(m1.mean(), mean);
	VERIFY_IS_APPROX(m1_for_prod.prod(), p);
	VERIFY_IS_APPROX(m1.real().minCoeff(), numext::real(minc));
	VERIFY_IS_APPROX(m1.real().maxCoeff(), numext::real(maxc));

	// test that partial reduction works if nested expressions is forced to evaluate early
	VERIFY_IS_APPROX((m1.matrix() * m1.matrix().transpose()) .cwiseProduct(m2.matrix()).rowwise().sum().sum(),
	(m1.matrix() * m1.matrix().transpose()).eval().cwiseProduct(m2.matrix()).rowwise().sum().sum());

	// test slice vectorization assuming assign is ok
	Index r0 = internal::random<Index>(0,rows-1);
	Index c0 = internal::random<Index>(0,cols-1);
	Index r1 = internal::random<Index>(r0+1,rows)-r0;
	Index c1 = internal::random<Index>(c0+1,cols)-c0;
	VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).sum(), m1.block(r0,c0,r1,c1).eval().sum());
	VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).mean(), m1.block(r0,c0,r1,c1).eval().mean());
	VERIFY_IS_APPROX(m1_for_prod.block(r0,c0,r1,c1).prod(), m1_for_prod.block(r0,c0,r1,c1).eval().prod());
	VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().minCoeff(), m1.block(r0,c0,r1,c1).real().eval().minCoeff());
	VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().maxCoeff(), m1.block(r0,c0,r1,c1).real().eval().maxCoeff());

	// regression for bug 1090
	const int R1 = MatrixType::RowsAtCompileTime>=2 ? MatrixType::RowsAtCompileTime/2 : 6;
	const int C1 = MatrixType::ColsAtCompileTime>=2 ? MatrixType::ColsAtCompileTime/2 : 6;
	if(R1<=rows-r0 && C1<=cols-c0)
	{
	VERIFY_IS_APPROX( (m1.template block<R1,C1>(r0,c0).sum()), m1.block(r0,c0,R1,C1).sum() );
	}

	// test empty objects
	VERIFY_IS_APPROX(m1.block(r0,c0,0,0).sum(), Scalar(0));
	VERIFY_IS_APPROX(m1.block(r0,c0,0,0).prod(), Scalar(1));

	// test nesting complex expression
	VERIFY_EVALUATION_COUNT( (m1.matrix()*m1.matrix().transpose()).sum(), (MatrixType::IsVectorAtCompileTime && MatrixType::SizeAtCompileTime!=1 ? 0 : 1) );
	VERIFY_EVALUATION_COUNT( ((m1.matrix()*m1.matrix().transpose())+m2).sum(),(MatrixType::IsVectorAtCompileTime && MatrixType::SizeAtCompileTime!=1 ? 0 : 1));
	}

	template<typename VectorType> void vectorRedux(const VectorType& w)
	{
	using std::abs;
	typedef typename VectorType::Scalar Scalar;
	typedef typename NumTraits<Scalar>::Real RealScalar;
	Index size = w.size();

	VectorType v = VectorType::Random(size);
	VectorType v_for_prod = VectorType::Ones(size) + Scalar(0.2) * v; // see comment above declaration of m1_for_prod

	for(int i = 1; i < size; i++)
	{
	Scalar s(0), p(1);
	RealScalar minc(numext::real(v.coeff(0))), maxc(numext::real(v.coeff(0)));
	for(int j = 0; j < i; j++)
	{
	s += v[j];
	p *= v_for_prod[j];
	minc = (std::min)(minc, numext::real(v[j]));
	maxc = (std::max)(maxc, numext::real(v[j]));
	}
	VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.head(i).sum()), Scalar(1));
	VERIFY_IS_APPROX(p, v_for_prod.head(i).prod());
	VERIFY_IS_APPROX(minc, v.real().head(i).minCoeff());
	VERIFY_IS_APPROX(maxc, v.real().head(i).maxCoeff());
	}

	for(int i = 0; i < size-1; i++)
	{
	Scalar s(0), p(1);
	RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
	for(int j = i; j < size; j++)
	{
	s += v[j];
	p *= v_for_prod[j];
	minc = (std::min)(minc, numext::real(v[j]));
	maxc = (std::max)(maxc, numext::real(v[j]));
	}
	VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.tail(size-i).sum()), Scalar(1));
	VERIFY_IS_APPROX(p, v_for_prod.tail(size-i).prod());
	VERIFY_IS_APPROX(minc, v.real().tail(size-i).minCoeff());
	VERIFY_IS_APPROX(maxc, v.real().tail(size-i).maxCoeff());
	}

	for(int i = 0; i < size/2; i++)
	{
	Scalar s(0), p(1);
	RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
	for(int j = i; j < size-i; j++)
	{
	s += v[j];
	p *= v_for_prod[j];
	minc = (std::min)(minc, numext::real(v[j]));
	maxc = (std::max)(maxc, numext::real(v[j]));
	}
	VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.segment(i, size-2*i).sum()), Scalar(1));
	VERIFY_IS_APPROX(p, v_for_prod.segment(i, size-2*i).prod());
	VERIFY_IS_APPROX(minc, v.real().segment(i, size-2*i).minCoeff());
	VERIFY_IS_APPROX(maxc, v.real().segment(i, size-2*i).maxCoeff());
	}

	// test empty objects
	VERIFY_IS_APPROX(v.head(0).sum(), Scalar(0));
	VERIFY_IS_APPROX(v.tail(0).prod(), Scalar(1));
	VERIFY_RAISES_ASSERT(v.head(0).mean());
	VERIFY_RAISES_ASSERT(v.head(0).minCoeff());
	VERIFY_RAISES_ASSERT(v.head(0).maxCoeff());
	}

	EIGEN_DECLARE_TEST(redux)
	{
	// the max size cannot be too large, otherwise reduxion operations obviously generate large errors.
	int maxsize = (std::min)(100,EIGEN_TEST_MAX_SIZE);
	TEST_SET_BUT_UNUSED_VARIABLE(maxsize);
	for(int i = 0; i < g_repeat; i++) {
	CALL_SUBTEST_1( matrixRedux(Matrix<float, 1, 1>()) );
	CALL_SUBTEST_1( matrixRedux(Array<float, 1, 1>()) );
	CALL_SUBTEST_2( matrixRedux(Matrix2f()) );
	CALL_SUBTEST_2( matrixRedux(Array2f()) );
	CALL_SUBTEST_2( matrixRedux(Array22f()) );
	CALL_SUBTEST_3( matrixRedux(Matrix4d()) );
	CALL_SUBTEST_3( matrixRedux(Array4d()) );
	CALL_SUBTEST_3( matrixRedux(Array44d()) );
	CALL_SUBTEST_4( matrixRedux(MatrixXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
	CALL_SUBTEST_4( matrixRedux(ArrayXXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
	CALL_SUBTEST_5( matrixRedux(MatrixXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
	CALL_SUBTEST_5( matrixRedux(ArrayXXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
	CALL_SUBTEST_6( matrixRedux(MatrixXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
	CALL_SUBTEST_6( matrixRedux(ArrayXXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
	}
	for(int i = 0; i < g_repeat; i++) {
	CALL_SUBTEST_7( vectorRedux(Vector4f()) );
	CALL_SUBTEST_7( vectorRedux(Array4f()) );
	CALL_SUBTEST_5( vectorRedux(VectorXd(internal::random<int>(1,maxsize))) );
	CALL_SUBTEST_5( vectorRedux(ArrayXd(internal::random<int>(1,maxsize))) );
	CALL_SUBTEST_8( vectorRedux(VectorXf(internal::random<int>(1,maxsize))) );
	CALL_SUBTEST_8( vectorRedux(ArrayXf(internal::random<int>(1,maxsize))) );
	}
	}