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diff --git a/test/eigen2/eigen2_sparse_basic.cpp b/test/eigen2/eigen2_sparse_basic.cpp
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+++ b/test/eigen2/eigen2_sparse_basic.cpp
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+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra. Eigen itself is part of the KDE project.
+//
+// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@gmail.com>
+//
+// Eigen is free software; you can redistribute it and/or
+// modify it under the terms of the GNU Lesser General Public
+// License as published by the Free Software Foundation; either
+// version 3 of the License, or (at your option) any later version.
+//
+// Alternatively, you can redistribute it and/or
+// modify it under the terms of the GNU General Public License as
+// published by the Free Software Foundation; either version 2 of
+// the License, or (at your option) any later version.
+//
+// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
+// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU Lesser General Public
+// License and a copy of the GNU General Public License along with
+// Eigen. If not, see <http://www.gnu.org/licenses/>.
+
+#include "sparse.h"
+
+template<typename SetterType,typename DenseType, typename Scalar, int Options>
+bool test_random_setter(SparseMatrix<Scalar,Options>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords)
+{
+  typedef SparseMatrix<Scalar,Options> SparseType;
+  {
+    sm.setZero();
+    SetterType w(sm);
+    std::vector<Vector2i> remaining = nonzeroCoords;
+    while(!remaining.empty())
+    {
+      int i = ei_random<int>(0,remaining.size()-1);
+      w(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y());
+      remaining[i] = remaining.back();
+      remaining.pop_back();
+    }
+  }
+  return sm.isApprox(ref);
+}
+
+template<typename SetterType,typename DenseType, typename T>
+bool test_random_setter(DynamicSparseMatrix<T>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords)
+{
+  sm.setZero();
+  std::vector<Vector2i> remaining = nonzeroCoords;
+  while(!remaining.empty())
+  {
+    int i = ei_random<int>(0,remaining.size()-1);
+    sm.coeffRef(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y());
+    remaining[i] = remaining.back();
+    remaining.pop_back();
+  }
+  return sm.isApprox(ref);
+}
+
+template<typename SparseMatrixType> void sparse_basic(const SparseMatrixType& ref)
+{
+  const int rows = ref.rows();
+  const int cols = ref.cols();
+  typedef typename SparseMatrixType::Scalar Scalar;
+  enum { Flags = SparseMatrixType::Flags };
+  
+  double density = std::max(8./(rows*cols), 0.01);
+  typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
+  typedef Matrix<Scalar,Dynamic,1> DenseVector;
+  Scalar eps = 1e-6;
+
+  SparseMatrixType m(rows, cols);
+  DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
+  DenseVector vec1 = DenseVector::Random(rows);
+  Scalar s1 = ei_random<Scalar>();
+
+  std::vector<Vector2i> zeroCoords;
+  std::vector<Vector2i> nonzeroCoords;
+  initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
+  
+  if (zeroCoords.size()==0 || nonzeroCoords.size()==0)
+    return;
+
+  // test coeff and coeffRef
+  for (int i=0; i<(int)zeroCoords.size(); ++i)
+  {
+    VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps );
+    if(ei_is_same_type<SparseMatrixType,SparseMatrix<Scalar,Flags> >::ret)
+      VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[0].x(),zeroCoords[0].y()) = 5 );
+  }
+  VERIFY_IS_APPROX(m, refMat);
+
+  m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
+  refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
+
+  VERIFY_IS_APPROX(m, refMat);
+  /*
+  // test InnerIterators and Block expressions
+  for (int t=0; t<10; ++t)
+  {
+    int j = ei_random<int>(0,cols-1);
+    int i = ei_random<int>(0,rows-1);
+    int w = ei_random<int>(1,cols-j-1);
+    int h = ei_random<int>(1,rows-i-1);
+
+//     VERIFY_IS_APPROX(m.block(i,j,h,w), refMat.block(i,j,h,w));
+    for(int c=0; c<w; c++)
+    {
+      VERIFY_IS_APPROX(m.block(i,j,h,w).col(c), refMat.block(i,j,h,w).col(c));
+      for(int r=0; r<h; r++)
+      {
+//         VERIFY_IS_APPROX(m.block(i,j,h,w).col(c).coeff(r), refMat.block(i,j,h,w).col(c).coeff(r));
+      }
+    }
+//     for(int r=0; r<h; r++)
+//     {
+//       VERIFY_IS_APPROX(m.block(i,j,h,w).row(r), refMat.block(i,j,h,w).row(r));
+//       for(int c=0; c<w; c++)
+//       {
+//         VERIFY_IS_APPROX(m.block(i,j,h,w).row(r).coeff(c), refMat.block(i,j,h,w).row(r).coeff(c));
+//       }
+//     }
+  }
+
+  for(int c=0; c<cols; c++)
+  {
+    VERIFY_IS_APPROX(m.col(c) + m.col(c), (m + m).col(c));
+    VERIFY_IS_APPROX(m.col(c) + m.col(c), refMat.col(c) + refMat.col(c));
+  }
+
+  for(int r=0; r<rows; r++)
+  {
+    VERIFY_IS_APPROX(m.row(r) + m.row(r), (m + m).row(r));
+    VERIFY_IS_APPROX(m.row(r) + m.row(r), refMat.row(r) + refMat.row(r));
+  }
+  */
+
+  // test SparseSetters
+  // coherent setter
+  // TODO extend the MatrixSetter
+//   {
+//     m.setZero();
+//     VERIFY_IS_NOT_APPROX(m, refMat);
+//     SparseSetter<SparseMatrixType, FullyCoherentAccessPattern> w(m);
+//     for (int i=0; i<nonzeroCoords.size(); ++i)
+//     {
+//       w->coeffRef(nonzeroCoords[i].x(),nonzeroCoords[i].y()) = refMat.coeff(nonzeroCoords[i].x(),nonzeroCoords[i].y());
+//     }
+//   }
+//   VERIFY_IS_APPROX(m, refMat);
+
+  // random setter
+//   {
+//     m.setZero();
+//     VERIFY_IS_NOT_APPROX(m, refMat);
+//     SparseSetter<SparseMatrixType, RandomAccessPattern> w(m);
+//     std::vector<Vector2i> remaining = nonzeroCoords;
+//     while(!remaining.empty())
+//     {
+//       int i = ei_random<int>(0,remaining.size()-1);
+//       w->coeffRef(remaining[i].x(),remaining[i].y()) = refMat.coeff(remaining[i].x(),remaining[i].y());
+//       remaining[i] = remaining.back();
+//       remaining.pop_back();
+//     }
+//   }
+//   VERIFY_IS_APPROX(m, refMat);
+
+    VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdMapTraits> >(m,refMat,nonzeroCoords) ));
+    #ifdef EIGEN_UNORDERED_MAP_SUPPORT
+    VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdUnorderedMapTraits> >(m,refMat,nonzeroCoords) ));
+    #endif
+    #ifdef _DENSE_HASH_MAP_H_
+    VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleDenseHashMapTraits> >(m,refMat,nonzeroCoords) ));
+    #endif
+    #ifdef _SPARSE_HASH_MAP_H_
+    VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleSparseHashMapTraits> >(m,refMat,nonzeroCoords) ));
+    #endif
+
+    // test fillrand
+    {
+      DenseMatrix m1(rows,cols);
+      m1.setZero();
+      SparseMatrixType m2(rows,cols);
+      m2.startFill();
+      for (int j=0; j<cols; ++j)
+      {
+        for (int k=0; k<rows/2; ++k)
+        {
+          int i = ei_random<int>(0,rows-1);
+          if (m1.coeff(i,j)==Scalar(0))
+            m2.fillrand(i,j) = m1(i,j) = ei_random<Scalar>();
+        }
+      }
+      m2.endFill();
+      VERIFY_IS_APPROX(m2,m1);
+    }
+  
+  // test RandomSetter
+  /*{
+    SparseMatrixType m1(rows,cols), m2(rows,cols);
+    DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
+    initSparse<Scalar>(density, refM1, m1);
+    {
+      Eigen::RandomSetter<SparseMatrixType > setter(m2);
+      for (int j=0; j<m1.outerSize(); ++j)
+        for (typename SparseMatrixType::InnerIterator i(m1,j); i; ++i)
+          setter(i.index(), j) = i.value();
+    }
+    VERIFY_IS_APPROX(m1, m2);
+  }*/
+//   std::cerr << m.transpose() << "\n\n"  << refMat.transpose() << "\n\n";
+//   VERIFY_IS_APPROX(m, refMat);
+
+  // test basic computations
+  {
+    DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
+    DenseMatrix refM2 = DenseMatrix::Zero(rows, rows);
+    DenseMatrix refM3 = DenseMatrix::Zero(rows, rows);
+    DenseMatrix refM4 = DenseMatrix::Zero(rows, rows);
+    SparseMatrixType m1(rows, rows);
+    SparseMatrixType m2(rows, rows);
+    SparseMatrixType m3(rows, rows);
+    SparseMatrixType m4(rows, rows);
+    initSparse<Scalar>(density, refM1, m1);
+    initSparse<Scalar>(density, refM2, m2);
+    initSparse<Scalar>(density, refM3, m3);
+    initSparse<Scalar>(density, refM4, m4);
+
+    VERIFY_IS_APPROX(m1+m2, refM1+refM2);
+    VERIFY_IS_APPROX(m1+m2+m3, refM1+refM2+refM3);
+    VERIFY_IS_APPROX(m3.cwise()*(m1+m2), refM3.cwise()*(refM1+refM2));
+    VERIFY_IS_APPROX(m1*s1-m2, refM1*s1-refM2);
+
+    VERIFY_IS_APPROX(m1*=s1, refM1*=s1);
+    VERIFY_IS_APPROX(m1/=s1, refM1/=s1);
+    
+    VERIFY_IS_APPROX(m1+=m2, refM1+=refM2);
+    VERIFY_IS_APPROX(m1-=m2, refM1-=refM2);
+    
+    VERIFY_IS_APPROX(m1.col(0).dot(refM2.row(0)), refM1.col(0).dot(refM2.row(0)));
+    
+    refM4.setRandom();
+    // sparse cwise* dense
+    VERIFY_IS_APPROX(m3.cwise()*refM4, refM3.cwise()*refM4);
+//     VERIFY_IS_APPROX(m3.cwise()/refM4, refM3.cwise()/refM4);
+  }
+
+  // test innerVector()
+  {
+    DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
+    SparseMatrixType m2(rows, rows);
+    initSparse<Scalar>(density, refMat2, m2);
+    int j0 = ei_random(0,rows-1);
+    int j1 = ei_random(0,rows-1);
+    VERIFY_IS_APPROX(m2.innerVector(j0), refMat2.col(j0));
+    VERIFY_IS_APPROX(m2.innerVector(j0)+m2.innerVector(j1), refMat2.col(j0)+refMat2.col(j1));
+    //m2.innerVector(j0) = 2*m2.innerVector(j1);
+    //refMat2.col(j0) = 2*refMat2.col(j1);
+    //VERIFY_IS_APPROX(m2, refMat2);
+  }
+  
+  // test innerVectors()
+  {
+    DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
+    SparseMatrixType m2(rows, rows);
+    initSparse<Scalar>(density, refMat2, m2);
+    int j0 = ei_random(0,rows-2);
+    int j1 = ei_random(0,rows-2);
+    int n0 = ei_random<int>(1,rows-std::max(j0,j1));
+    VERIFY_IS_APPROX(m2.innerVectors(j0,n0), refMat2.block(0,j0,rows,n0));
+    VERIFY_IS_APPROX(m2.innerVectors(j0,n0)+m2.innerVectors(j1,n0),
+                     refMat2.block(0,j0,rows,n0)+refMat2.block(0,j1,rows,n0));
+    //m2.innerVectors(j0,n0) = m2.innerVectors(j0,n0) + m2.innerVectors(j1,n0);
+    //refMat2.block(0,j0,rows,n0) = refMat2.block(0,j0,rows,n0) + refMat2.block(0,j1,rows,n0);
+  }
+
+  // test transpose
+  {
+    DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
+    SparseMatrixType m2(rows, rows);
+    initSparse<Scalar>(density, refMat2, m2);
+    VERIFY_IS_APPROX(m2.transpose().eval(), refMat2.transpose().eval());
+    VERIFY_IS_APPROX(m2.transpose(), refMat2.transpose());
+  }
+  
+  // test prune
+  {
+    SparseMatrixType m2(rows, rows);
+    DenseMatrix refM2(rows, rows);
+    refM2.setZero();
+    int countFalseNonZero = 0;
+    int countTrueNonZero = 0;
+    m2.startFill();
+    for (int j=0; j<m2.outerSize(); ++j)
+      for (int i=0; i<m2.innerSize(); ++i)
+      {
+        float x = ei_random<float>(0,1);
+        if (x<0.1)
+        {
+          // do nothing
+        }
+        else if (x<0.5)
+        {
+          countFalseNonZero++;
+          m2.fill(i,j) = Scalar(0);
+        }
+        else
+        {
+          countTrueNonZero++;
+          m2.fill(i,j) = refM2(i,j) = Scalar(1);
+        }
+      }
+    m2.endFill();
+    VERIFY(countFalseNonZero+countTrueNonZero == m2.nonZeros());
+    VERIFY_IS_APPROX(m2, refM2);
+    m2.prune(1);
+    VERIFY(countTrueNonZero==m2.nonZeros());
+    VERIFY_IS_APPROX(m2, refM2);
+  }
+}
+
+void test_eigen2_sparse_basic()
+{
+  for(int i = 0; i < g_repeat; i++) {
+    CALL_SUBTEST_1( sparse_basic(SparseMatrix<double>(8, 8)) );
+    CALL_SUBTEST_2( sparse_basic(SparseMatrix<std::complex<double> >(16, 16)) );
+    CALL_SUBTEST_1( sparse_basic(SparseMatrix<double>(33, 33)) );
+    
+    CALL_SUBTEST_3( sparse_basic(DynamicSparseMatrix<double>(8, 8)) );
+  }
+}