ground/gcs/src/libs/eigen/test/redux.cpp

   1 // This file is part of Eigen, a lightweight C++ template library
   2 // for linear algebra.
   3 //
   4 // Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
   5 // Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
   6 //
   7 // This Source Code Form is subject to the terms of the Mozilla
   8 // Public License v. 2.0. If a copy of the MPL was not distributed
   9 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
  10
  11 #define TEST_ENABLE_TEMPORARY_TRACKING
  12
  13 #include "main.h"
  14
  15 template<typename MatrixType> void matrixRedux(const MatrixType& m)
  16 {
  17   typedef typename MatrixType::Index Index;
  18   typedef typename MatrixType::Scalar Scalar;
  19   typedef typename MatrixType::RealScalar RealScalar;
  20
  21   Index rows = m.rows();
  22   Index cols = m.cols();
  23
  24   MatrixType m1 = MatrixType::Random(rows, cols);
  25
  26   // The entries of m1 are uniformly distributed in [0,1], so m1.prod() is very small. This may lead to test
  27   // failures if we underflow into denormals. Thus, we scale so that entries are close to 1.
  28   MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + RealScalar(0.2) * m1;
  29
  30   VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1));
  31   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
  32   Scalar s(0), p(1), minc(numext::real(m1.coeff(0))), maxc(numext::real(m1.coeff(0)));
  33   for(int j = 0; j < cols; j++)
  34   for(int i = 0; i < rows; i++)
  35   {
  36     s += m1(i,j);
  37     p *= m1_for_prod(i,j);
  38     minc = (std::min)(numext::real(minc), numext::real(m1(i,j)));
  39     maxc = (std::max)(numext::real(maxc), numext::real(m1(i,j)));
  40   }
  41   const Scalar mean = s/Scalar(RealScalar(rows*cols));
  42
  43   VERIFY_IS_APPROX(m1.sum(), s);
  44   VERIFY_IS_APPROX(m1.mean(), mean);
  45   VERIFY_IS_APPROX(m1_for_prod.prod(), p);
  46   VERIFY_IS_APPROX(m1.real().minCoeff(), numext::real(minc));
  47   VERIFY_IS_APPROX(m1.real().maxCoeff(), numext::real(maxc));
  48
  49   // test slice vectorization assuming assign is ok
  50   Index r0 = internal::random<Index>(0,rows-1);
  51   Index c0 = internal::random<Index>(0,cols-1);
  52   Index r1 = internal::random<Index>(r0+1,rows)-r0;
  53   Index c1 = internal::random<Index>(c0+1,cols)-c0;
  54   VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).sum(), m1.block(r0,c0,r1,c1).eval().sum());
  55   VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).mean(), m1.block(r0,c0,r1,c1).eval().mean());
  56   VERIFY_IS_APPROX(m1_for_prod.block(r0,c0,r1,c1).prod(), m1_for_prod.block(r0,c0,r1,c1).eval().prod());
  57   VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().minCoeff(), m1.block(r0,c0,r1,c1).real().eval().minCoeff());
  58   VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().maxCoeff(), m1.block(r0,c0,r1,c1).real().eval().maxCoeff());
  59
  60   // regression for bug 1090
  61   const int R1 = MatrixType::RowsAtCompileTime>=2 ? MatrixType::RowsAtCompileTime/2 : 6;
  62   const int C1 = MatrixType::ColsAtCompileTime>=2 ? MatrixType::ColsAtCompileTime/2 : 6;
  63   if(R1<=rows-r0 && C1<=cols-c0)
  64   {
  65     VERIFY_IS_APPROX( (m1.template block<R1,C1>(r0,c0).sum()), m1.block(r0,c0,R1,C1).sum() );
  66   }
  67
  68   // test empty objects
  69   VERIFY_IS_APPROX(m1.block(r0,c0,0,0).sum(),   Scalar(0));
  70   VERIFY_IS_APPROX(m1.block(r0,c0,0,0).prod(),  Scalar(1));
  71
  72   // test nesting complex expression
  73   VERIFY_EVALUATION_COUNT( (m1.matrix()*m1.matrix().transpose()).sum(), (MatrixType::IsVectorAtCompileTime && MatrixType::SizeAtCompileTime!=1 ? 0 : 1) );
  74   Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> m2(rows,rows);
  75   m2.setRandom();
  76   VERIFY_EVALUATION_COUNT( ((m1.matrix()*m1.matrix().transpose())+m2).sum(),(MatrixType::IsVectorAtCompileTime && MatrixType::SizeAtCompileTime!=1 ? 0 : 1));
  77 }
  78
  79 template<typename VectorType> void vectorRedux(const VectorType& w)
  80 {
  81   using std::abs;
  82   typedef typename VectorType::Index Index;
  83   typedef typename VectorType::Scalar Scalar;
  84   typedef typename NumTraits<Scalar>::Real RealScalar;
  85   Index size = w.size();
  86
  87   VectorType v = VectorType::Random(size);
  88   VectorType v_for_prod = VectorType::Ones(size) + Scalar(0.2) * v; // see comment above declaration of m1_for_prod
  89
  90   for(int i = 1; i < size; i++)
  91   {
  92     Scalar s(0), p(1);
  93     RealScalar minc(numext::real(v.coeff(0))), maxc(numext::real(v.coeff(0)));
  94     for(int j = 0; j < i; j++)
  95     {
  96       s += v[j];
  97       p *= v_for_prod[j];
  98       minc = (std::min)(minc, numext::real(v[j]));
  99       maxc = (std::max)(maxc, numext::real(v[j]));
 100     }
 101     VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.head(i).sum()), Scalar(1));
 102     VERIFY_IS_APPROX(p, v_for_prod.head(i).prod());
 103     VERIFY_IS_APPROX(minc, v.real().head(i).minCoeff());
 104     VERIFY_IS_APPROX(maxc, v.real().head(i).maxCoeff());
 105   }
 106
 107   for(int i = 0; i < size-1; i++)
 108   {
 109     Scalar s(0), p(1);
 110     RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
 111     for(int j = i; j < size; j++)
 112     {
 113       s += v[j];
 114       p *= v_for_prod[j];
 115       minc = (std::min)(minc, numext::real(v[j]));
 116       maxc = (std::max)(maxc, numext::real(v[j]));
 117     }
 118     VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.tail(size-i).sum()), Scalar(1));
 119     VERIFY_IS_APPROX(p, v_for_prod.tail(size-i).prod());
 120     VERIFY_IS_APPROX(minc, v.real().tail(size-i).minCoeff());
 121     VERIFY_IS_APPROX(maxc, v.real().tail(size-i).maxCoeff());
 122   }
 123
 124   for(int i = 0; i < size/2; i++)
 125   {
 126     Scalar s(0), p(1);
 127     RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
 128     for(int j = i; j < size-i; j++)
 129     {
 130       s += v[j];
 131       p *= v_for_prod[j];
 132       minc = (std::min)(minc, numext::real(v[j]));
 133       maxc = (std::max)(maxc, numext::real(v[j]));
 134     }
 135     VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.segment(i, size-2*i).sum()), Scalar(1));
 136     VERIFY_IS_APPROX(p, v_for_prod.segment(i, size-2*i).prod());
 137     VERIFY_IS_APPROX(minc, v.real().segment(i, size-2*i).minCoeff());
 138     VERIFY_IS_APPROX(maxc, v.real().segment(i, size-2*i).maxCoeff());
 139   }
 140
 141   // test empty objects
 142   VERIFY_IS_APPROX(v.head(0).sum(),   Scalar(0));
 143   VERIFY_IS_APPROX(v.tail(0).prod(),  Scalar(1));
 144   VERIFY_RAISES_ASSERT(v.head(0).mean());
 145   VERIFY_RAISES_ASSERT(v.head(0).minCoeff());
 146   VERIFY_RAISES_ASSERT(v.head(0).maxCoeff());
 147 }
 148
 149 void test_redux()
 150 {
 151   // the max size cannot be too large, otherwise reduxion operations obviously generate large errors.
 152   int maxsize = (std::min)(100,EIGEN_TEST_MAX_SIZE);
 153   TEST_SET_BUT_UNUSED_VARIABLE(maxsize);
 154   for(int i = 0; i < g_repeat; i++) {
 155     CALL_SUBTEST_1( matrixRedux(Matrix<float, 1, 1>()) );
 156     CALL_SUBTEST_1( matrixRedux(Array<float, 1, 1>()) );
 157     CALL_SUBTEST_2( matrixRedux(Matrix2f()) );
 158     CALL_SUBTEST_2( matrixRedux(Array2f()) );
 159     CALL_SUBTEST_2( matrixRedux(Array22f()) );
 160     CALL_SUBTEST_3( matrixRedux(Matrix4d()) );
 161     CALL_SUBTEST_3( matrixRedux(Array4d()) );
 162     CALL_SUBTEST_3( matrixRedux(Array44d()) );
 163     CALL_SUBTEST_4( matrixRedux(MatrixXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
 164     CALL_SUBTEST_4( matrixRedux(ArrayXXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
 165     CALL_SUBTEST_5( matrixRedux(MatrixXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
 166     CALL_SUBTEST_5( matrixRedux(ArrayXXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
 167     CALL_SUBTEST_6( matrixRedux(MatrixXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
 168     CALL_SUBTEST_6( matrixRedux(ArrayXXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
 169   }
 170   for(int i = 0; i < g_repeat; i++) {
 171     CALL_SUBTEST_7( vectorRedux(Vector4f()) );
 172     CALL_SUBTEST_7( vectorRedux(Array4f()) );
 173     CALL_SUBTEST_5( vectorRedux(VectorXd(internal::random<int>(1,maxsize))) );
 174     CALL_SUBTEST_5( vectorRedux(ArrayXd(internal::random<int>(1,maxsize))) );
 175     CALL_SUBTEST_8( vectorRedux(VectorXf(internal::random<int>(1,maxsize))) );
 176     CALL_SUBTEST_8( vectorRedux(ArrayXf(internal::random<int>(1,maxsize))) );
 177   }
 178 }