cart-elc

visitor.cpp (6384B)

---

 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
 //
 // 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/.
 
 #include "main.h"
 
 template<typename MatrixType> void matrixVisitor(const MatrixType& p)
 {
   typedef typename MatrixType::Scalar Scalar;
 
   Index rows = p.rows();
   Index cols = p.cols();
 
   // construct a random matrix where all coefficients are different
   MatrixType m;
   m = MatrixType::Random(rows, cols);
   for(Index i = 0; i < m.size(); i++)
     for(Index i2 = 0; i2 < i; i2++)
       while(m(i) == m(i2)) // yes, ==
         m(i) = internal::random<Scalar>();
   
   Scalar minc = Scalar(1000), maxc = Scalar(-1000);
   Index minrow=0,mincol=0,maxrow=0,maxcol=0;
   for(Index j = 0; j < cols; j++)
   for(Index i = 0; i < rows; i++)
   {
     if(m(i,j) < minc)
     {
       minc = m(i,j);
       minrow = i;
       mincol = j;
     }
     if(m(i,j) > maxc)
     {
       maxc = m(i,j);
       maxrow = i;
       maxcol = j;
     }
   }
   Index eigen_minrow, eigen_mincol, eigen_maxrow, eigen_maxcol;
   Scalar eigen_minc, eigen_maxc;
   eigen_minc = m.minCoeff(&eigen_minrow,&eigen_mincol);
   eigen_maxc = m.maxCoeff(&eigen_maxrow,&eigen_maxcol);
   VERIFY(minrow == eigen_minrow);
   VERIFY(maxrow == eigen_maxrow);
   VERIFY(mincol == eigen_mincol);
   VERIFY(maxcol == eigen_maxcol);
   VERIFY_IS_APPROX(minc, eigen_minc);
   VERIFY_IS_APPROX(maxc, eigen_maxc);
   VERIFY_IS_APPROX(minc, m.minCoeff());
   VERIFY_IS_APPROX(maxc, m.maxCoeff());
 
   eigen_maxc = (m.adjoint()*m).maxCoeff(&eigen_maxrow,&eigen_maxcol);
   Index maxrow2=0,maxcol2=0;
   eigen_maxc = (m.adjoint()*m).eval().maxCoeff(&maxrow2,&maxcol2);
   VERIFY(maxrow2 == eigen_maxrow);
   VERIFY(maxcol2 == eigen_maxcol);
 
   if (!NumTraits<Scalar>::IsInteger && m.size() > 2) {
     // Test NaN propagation by replacing an element with NaN.
     bool stop = false;
     for (Index j = 0; j < cols && !stop; ++j) {
       for (Index i = 0; i < rows && !stop; ++i) {
         if (!(j == mincol && i == minrow) &&
             !(j == maxcol && i == maxrow)) {
           m(i,j) = NumTraits<Scalar>::quiet_NaN();
           stop = true;
           break;
         }
       }
     }
 
     eigen_minc = m.template minCoeff<PropagateNumbers>(&eigen_minrow, &eigen_mincol);
     eigen_maxc = m.template maxCoeff<PropagateNumbers>(&eigen_maxrow, &eigen_maxcol);
     VERIFY(minrow == eigen_minrow);
     VERIFY(maxrow == eigen_maxrow);
     VERIFY(mincol == eigen_mincol);
     VERIFY(maxcol == eigen_maxcol);
     VERIFY_IS_APPROX(minc, eigen_minc);
     VERIFY_IS_APPROX(maxc, eigen_maxc);
     VERIFY_IS_APPROX(minc, m.template minCoeff<PropagateNumbers>());
     VERIFY_IS_APPROX(maxc, m.template maxCoeff<PropagateNumbers>());
 
     eigen_minc = m.template minCoeff<PropagateNaN>(&eigen_minrow, &eigen_mincol);
     eigen_maxc = m.template maxCoeff<PropagateNaN>(&eigen_maxrow, &eigen_maxcol);
     VERIFY(minrow != eigen_minrow || mincol != eigen_mincol);
     VERIFY(maxrow != eigen_maxrow || maxcol != eigen_maxcol);
     VERIFY((numext::isnan)(eigen_minc));
     VERIFY((numext::isnan)(eigen_maxc));
   }
 
 }
 
 template<typename VectorType> void vectorVisitor(const VectorType& w)
 {
   typedef typename VectorType::Scalar Scalar;
 
   Index size = w.size();
 
   // construct a random vector where all coefficients are different
   VectorType v;
   v = VectorType::Random(size);
   for(Index i = 0; i < size; i++)
     for(Index i2 = 0; i2 < i; i2++)
       while(v(i) == v(i2)) // yes, ==
         v(i) = internal::random<Scalar>();
   
   Scalar minc = v(0), maxc = v(0);
   Index minidx=0, maxidx=0;
   for(Index i = 0; i < size; i++)
   {
     if(v(i) < minc)
     {
       minc = v(i);
       minidx = i;
     }
     if(v(i) > maxc)
     {
       maxc = v(i);
       maxidx = i;
     }
   }
   Index eigen_minidx, eigen_maxidx;
   Scalar eigen_minc, eigen_maxc;
   eigen_minc = v.minCoeff(&eigen_minidx);
   eigen_maxc = v.maxCoeff(&eigen_maxidx);
   VERIFY(minidx == eigen_minidx);
   VERIFY(maxidx == eigen_maxidx);
   VERIFY_IS_APPROX(minc, eigen_minc);
   VERIFY_IS_APPROX(maxc, eigen_maxc);
   VERIFY_IS_APPROX(minc, v.minCoeff());
   VERIFY_IS_APPROX(maxc, v.maxCoeff());
   
   Index idx0 = internal::random<Index>(0,size-1);
   Index idx1 = eigen_minidx;
   Index idx2 = eigen_maxidx;
   VectorType v1(v), v2(v);
   v1(idx0) = v1(idx1);
   v2(idx0) = v2(idx2);
   v1.minCoeff(&eigen_minidx);
   v2.maxCoeff(&eigen_maxidx);
   VERIFY(eigen_minidx == (std::min)(idx0,idx1));
   VERIFY(eigen_maxidx == (std::min)(idx0,idx2));
 
   if (!NumTraits<Scalar>::IsInteger && size > 2) {
     // Test NaN propagation by replacing an element with NaN.
     for (Index i = 0; i < size; ++i) {
       if (i != minidx && i != maxidx) {
         v(i) = NumTraits<Scalar>::quiet_NaN();
         break;
       }
     }
     eigen_minc = v.template minCoeff<PropagateNumbers>(&eigen_minidx);
     eigen_maxc = v.template maxCoeff<PropagateNumbers>(&eigen_maxidx);
     VERIFY(minidx == eigen_minidx);
     VERIFY(maxidx == eigen_maxidx);
     VERIFY_IS_APPROX(minc, eigen_minc);
     VERIFY_IS_APPROX(maxc, eigen_maxc);
     VERIFY_IS_APPROX(minc, v.template minCoeff<PropagateNumbers>());
     VERIFY_IS_APPROX(maxc, v.template maxCoeff<PropagateNumbers>());
 
     eigen_minc = v.template minCoeff<PropagateNaN>(&eigen_minidx);
     eigen_maxc = v.template maxCoeff<PropagateNaN>(&eigen_maxidx);
     VERIFY(minidx != eigen_minidx);
     VERIFY(maxidx != eigen_maxidx);
     VERIFY((numext::isnan)(eigen_minc));
     VERIFY((numext::isnan)(eigen_maxc));
   }
 }
 
 EIGEN_DECLARE_TEST(visitor)
 {
   for(int i = 0; i < g_repeat; i++) {
     CALL_SUBTEST_1( matrixVisitor(Matrix<float, 1, 1>()) );
     CALL_SUBTEST_2( matrixVisitor(Matrix2f()) );
     CALL_SUBTEST_3( matrixVisitor(Matrix4d()) );
     CALL_SUBTEST_4( matrixVisitor(MatrixXd(8, 12)) );
     CALL_SUBTEST_5( matrixVisitor(Matrix<double,Dynamic,Dynamic,RowMajor>(20, 20)) );
     CALL_SUBTEST_6( matrixVisitor(MatrixXi(8, 12)) );
   }
   for(int i = 0; i < g_repeat; i++) {
     CALL_SUBTEST_7( vectorVisitor(Vector4f()) );
     CALL_SUBTEST_7( vectorVisitor(Matrix<int,12,1>()) );
     CALL_SUBTEST_8( vectorVisitor(VectorXd(10)) );
     CALL_SUBTEST_9( vectorVisitor(RowVectorXd(10)) );
     CALL_SUBTEST_10( vectorVisitor(VectorXf(33)) );
   }
 }