1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library
2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra.
3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2008-2010 Gael Guennebaud <g.gael@free.fr>
5c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
6c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This Source Code Form is subject to the terms of the Mozilla
7c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Public License v. 2.0. If a copy of the MPL was not distributed
8c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
10c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// import basic and product tests for deprectaed DynamicSparseMatrix
12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#define EIGEN_NO_DEPRECATED_WARNING
13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "sparse_basic.cpp"
14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include "sparse_product.cpp"
15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath#include <Eigen/SparseExtra>
16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename SetterType,typename DenseType, typename Scalar, int Options>
18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathbool test_random_setter(SparseMatrix<Scalar,Options>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords)
19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    sm.setZero();
22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    SetterType w(sm);
23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    std::vector<Vector2i> remaining = nonzeroCoords;
24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    while(!remaining.empty())
25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      int i = internal::random<int>(0,static_cast<int>(remaining.size())-1);
27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      w(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y());
28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      remaining[i] = remaining.back();
29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      remaining.pop_back();
30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  return sm.isApprox(ref);
33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename SetterType,typename DenseType, typename T>
36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathbool test_random_setter(DynamicSparseMatrix<T>& sm, const DenseType& ref, const std::vector<Vector2i>& nonzeroCoords)
37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  sm.setZero();
39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  std::vector<Vector2i> remaining = nonzeroCoords;
40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  while(!remaining.empty())
41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    int i = internal::random<int>(0,static_cast<int>(remaining.size())-1);
43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    sm.coeffRef(remaining[i].x(),remaining[i].y()) = ref.coeff(remaining[i].x(),remaining[i].y());
44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    remaining[i] = remaining.back();
45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    remaining.pop_back();
46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  return sm.isApprox(ref);
48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename SparseMatrixType> void sparse_extra(const SparseMatrixType& ref)
51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename SparseMatrixType::Index Index;
53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  const Index rows = ref.rows();
54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  const Index cols = ref.cols();
55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef typename SparseMatrixType::Scalar Scalar;
56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  enum { Flags = SparseMatrixType::Flags };
57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  double density = (std::max)(8./(rows*cols), 0.01);
59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar,Dynamic,1> DenseVector;
61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Scalar eps = 1e-6;
62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  SparseMatrixType m(rows, cols);
64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  DenseMatrix refMat = DenseMatrix::Zero(rows, cols);
65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  DenseVector vec1 = DenseVector::Random(rows);
66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  std::vector<Vector2i> zeroCoords;
68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  std::vector<Vector2i> nonzeroCoords;
69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  initSparse<Scalar>(density, refMat, m, 0, &zeroCoords, &nonzeroCoords);
70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  if (zeroCoords.size()==0 || nonzeroCoords.size()==0)
72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    return;
73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // test coeff and coeffRef
75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  for (int i=0; i<(int)zeroCoords.size(); ++i)
76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_MUCH_SMALLER_THAN( m.coeff(zeroCoords[i].x(),zeroCoords[i].y()), eps );
78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    if(internal::is_same<SparseMatrixType,SparseMatrix<Scalar,Flags> >::value)
79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      VERIFY_RAISES_ASSERT( m.coeffRef(zeroCoords[0].x(),zeroCoords[0].y()) = 5 );
80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m, refMat);
82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  m.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  refMat.coeffRef(nonzeroCoords[0].x(), nonzeroCoords[0].y()) = Scalar(5);
85c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
86c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(m, refMat);
87c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
88c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // random setter
89c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//   {
90c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//     m.setZero();
91c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//     VERIFY_IS_NOT_APPROX(m, refMat);
92c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//     SparseSetter<SparseMatrixType, RandomAccessPattern> w(m);
93c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//     std::vector<Vector2i> remaining = nonzeroCoords;
94c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//     while(!remaining.empty())
95c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//     {
96c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//       int i = internal::random<int>(0,remaining.size()-1);
97c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//       w->coeffRef(remaining[i].x(),remaining[i].y()) = refMat.coeff(remaining[i].x(),remaining[i].y());
98c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//       remaining[i] = remaining.back();
99c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//       remaining.pop_back();
100c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//     }
101c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//   }
102c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//   VERIFY_IS_APPROX(m, refMat);
103c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
104c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdMapTraits> >(m,refMat,nonzeroCoords) ));
105c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    #ifdef EIGEN_UNORDERED_MAP_SUPPORT
106c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, StdUnorderedMapTraits> >(m,refMat,nonzeroCoords) ));
107c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    #endif
108c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    #ifdef _DENSE_HASH_MAP_H_
109c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleDenseHashMapTraits> >(m,refMat,nonzeroCoords) ));
110c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    #endif
111c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    #ifdef _SPARSE_HASH_MAP_H_
112c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY(( test_random_setter<RandomSetter<SparseMatrixType, GoogleSparseHashMapTraits> >(m,refMat,nonzeroCoords) ));
113c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    #endif
114c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
115c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
116c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // test RandomSetter
117c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  /*{
118c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    SparseMatrixType m1(rows,cols), m2(rows,cols);
119c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
120c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    initSparse<Scalar>(density, refM1, m1);
121c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
122c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      Eigen::RandomSetter<SparseMatrixType > setter(m2);
123c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      for (int j=0; j<m1.outerSize(); ++j)
124c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath        for (typename SparseMatrixType::InnerIterator i(m1,j); i; ++i)
125c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath          setter(i.index(), j) = i.value();
126c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
127c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_APPROX(m1, m2);
128c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }*/
129c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
130c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
131c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
132c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
133c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_sparse_extra()
134c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
135c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  for(int i = 0; i < g_repeat; i++) {
136c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    int s = Eigen::internal::random<int>(1,50);
137c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1( sparse_extra(SparseMatrix<double>(8, 8)) );
138c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_2( sparse_extra(SparseMatrix<std::complex<double> >(s, s)) );
139c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1( sparse_extra(SparseMatrix<double>(s, s)) );
140c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
141c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_3( sparse_extra(DynamicSparseMatrix<double>(s, s)) );
142c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//    CALL_SUBTEST_3(( sparse_basic(DynamicSparseMatrix<double>(s, s)) ));
143c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//    CALL_SUBTEST_3(( sparse_basic(DynamicSparseMatrix<double,ColMajor,long int>(s, s)) ));
144c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
145c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_3( (sparse_product<DynamicSparseMatrix<float, ColMajor> >()) );
146c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_3( (sparse_product<DynamicSparseMatrix<float, RowMajor> >()) );
147c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
148c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
149