1c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// This file is part of Eigen, a lightweight C++ template library
2c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// for linear algebra. Eigen itself is part of the KDE project.
3c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath//
4c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath// Copyright (C) 2008 Daniel Gomez Ferro <dgomezferro@gmail.com>
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#include "sparse.h"
11c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
12c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathtemplate<typename Scalar> void sparse_vector(int rows, int cols)
13c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
14c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  double densityMat = std::max(8./(rows*cols), 0.01);
15c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  double densityVec = std::max(8./float(rows), 0.1);
16c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
17c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef Matrix<Scalar,Dynamic,1> DenseVector;
18c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef SparseVector<Scalar> SparseVectorType;
19c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  typedef SparseMatrix<Scalar> SparseMatrixType;
20c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Scalar eps = 1e-6;
21c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
22c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  SparseMatrixType m1(rows,cols);
23c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  SparseVectorType v1(rows), v2(rows), v3(rows);
24c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  DenseMatrix refM1 = DenseMatrix::Zero(rows, cols);
25c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  DenseVector refV1 = DenseVector::Random(rows),
26c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    refV2 = DenseVector::Random(rows),
27c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    refV3 = DenseVector::Random(rows);
28c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
29c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  std::vector<int> zerocoords, nonzerocoords;
30c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  initSparse<Scalar>(densityVec, refV1, v1, &zerocoords, &nonzerocoords);
31c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  initSparse<Scalar>(densityMat, refM1, m1);
32c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
33c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  initSparse<Scalar>(densityVec, refV2, v2);
34c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  initSparse<Scalar>(densityVec, refV3, v3);
35c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
36c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  Scalar s1 = ei_random<Scalar>();
37c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
38c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  // test coeff and coeffRef
39c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  for (unsigned int i=0; i<zerocoords.size(); ++i)
40c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
41c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY_IS_MUCH_SMALLER_THAN( v1.coeff(zerocoords[i]), eps );
42c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    //VERIFY_RAISES_ASSERT( v1.coeffRef(zerocoords[i]) = 5 );
43c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
44c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  {
45c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    VERIFY(int(nonzerocoords.size()) == v1.nonZeros());
46c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    int j=0;
47c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    for (typename SparseVectorType::InnerIterator it(v1); it; ++it,++j)
48c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    {
49c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      VERIFY(nonzerocoords[j]==it.index());
50c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      VERIFY(it.value()==v1.coeff(it.index()));
51c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath      VERIFY(it.value()==refV1.coeff(it.index()));
52c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    }
53c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
54c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(v1, refV1);
55c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
56c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  v1.coeffRef(nonzerocoords[0]) = Scalar(5);
57c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  refV1.coeffRef(nonzerocoords[0]) = Scalar(5);
58c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(v1, refV1);
59c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
60c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(v1+v2, refV1+refV2);
61c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(v1+v2+v3, refV1+refV2+refV3);
62c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
63c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(v1*s1-v2, refV1*s1-refV2);
64c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
65c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(v1*=s1, refV1*=s1);
66c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(v1/=s1, refV1/=s1);
67c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
68c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(v1+=v2, refV1+=refV2);
69c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(v1-=v2, refV1-=refV2);
70c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
71c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(v1.eigen2_dot(v2), refV1.eigen2_dot(refV2));
72c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  VERIFY_IS_APPROX(v1.eigen2_dot(refV2), refV1.eigen2_dot(refV2));
73c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
74c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
75c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
76c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamathvoid test_eigen2_sparse_vector()
77c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath{
78c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  for(int i = 0; i < g_repeat; i++) {
79c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1( sparse_vector<double>(8, 8) );
80c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_2( sparse_vector<std::complex<double> >(16, 16) );
81c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath    CALL_SUBTEST_1( sparse_vector<double>(299, 535) );
82c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath  }
83c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath}
84c981c48f5bc9aefeffc0bcb0cc3934c2fae179ddNarayan Kamath
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