Quelle sparse_vector.cpp Sprache: C

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// 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 "sparse.h"

template<typename Scalar,typename StorageIndex> void sparse_vector(int rows, int cols)
{
  double densityMat = (std::max)(8./(rows*cols), 0.01);
  double densityVec = (std::max)(8./(rows), 0.1);
  typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
  typedef Matrix<Scalar,Dynamic,1> DenseVector;
  typedef SparseVector<Scalar,0,StorageIndex> SparseVectorType;
  typedef SparseMatrix<Scalar,0,StorageIndex> SparseMatrixType;
  Scalar eps = 1e-6;

  SparseMatrixType m1(rows,rows);
  SparseVectorType v1(rows), v2(rows), v3(rows);
  DenseMatrix refM1 = DenseMatrix::Zero(rows, rows);
  DenseVector refV1 = DenseVector::Random(rows),
              refV2 = DenseVector::Random(rows),
              refV3 = DenseVector::Random(rows);

  std::vector<int> zerocoords, nonzerocoords;
  initSparse<Scalar>(densityVec, refV1, v1, &zerocoords, &nonzerocoords);
  initSparse<Scalar>(densityMat, refM1, m1);

  initSparse<Scalar>(densityVec, refV2, v2);
  initSparse<Scalar>(densityVec, refV3, v3);

  Scalar s1 = internal::random<Scalar>();

  // test coeff and coeffRef
  for (unsigned int i=0; i<zerocoords.size(); ++i)
  {
    VERIFY_IS_MUCH_SMALLER_THAN( v1.coeff(zerocoords[i]), eps );
    //VERIFY_RAISES_ASSERT( v1.coeffRef(zerocoords[i]) = 5 );
  }
  {
    VERIFY(int(nonzerocoords.size()) == v1.nonZeros());
    int j=0;
    for (typename SparseVectorType::InnerIterator it(v1); it; ++it,++j)
    {
      VERIFY(nonzerocoords[j]==it.index());
      VERIFY(it.value()==v1.coeff(it.index()));
      VERIFY(it.value()==refV1.coeff(it.index()));
    }
  }
  VERIFY_IS_APPROX(v1, refV1);

  // test coeffRef with reallocation
  {
    SparseVectorType v4(rows);
    DenseVector v5 = DenseVector::Zero(rows);
    for(int k=0; k<rows; ++k)
    {
      int i = internal::random<int>(0,rows-1);
      Scalar v = internal::random<Scalar>();
      v4.coeffRef(i) += v;
      v5.coeffRef(i) += v;
    }
    VERIFY_IS_APPROX(v4,v5);
  }

  v1.coeffRef(nonzerocoords[0]) = Scalar(5);
  refV1.coeffRef(nonzerocoords[0]) = Scalar(5);
  VERIFY_IS_APPROX(v1, refV1);

  VERIFY_IS_APPROX(v1+v2, refV1+refV2);
  VERIFY_IS_APPROX(v1+v2+v3, refV1+refV2+refV3);

  VERIFY_IS_APPROX(v1*s1-v2, refV1*s1-refV2);

  VERIFY_IS_APPROX(v1*=s1, refV1*=s1);
  VERIFY_IS_APPROX(v1/=s1, refV1/=s1);

  VERIFY_IS_APPROX(v1+=v2, refV1+=refV2);
  VERIFY_IS_APPROX(v1-=v2, refV1-=refV2);

  VERIFY_IS_APPROX(v1.dot(v2), refV1.dot(refV2));
  VERIFY_IS_APPROX(v1.dot(refV2), refV1.dot(refV2));

  VERIFY_IS_APPROX(m1*v2, refM1*refV2);
  VERIFY_IS_APPROX(v1.dot(m1*v2), refV1.dot(refM1*refV2));
  {
    int i = internal::random<int>(0,rows-1);
    VERIFY_IS_APPROX(v1.dot(m1.col(i)), refV1.dot(refM1.col(i)));
  }

  VERIFY_IS_APPROX(v1.squaredNorm(), refV1.squaredNorm());

  VERIFY_IS_APPROX(v1.blueNorm(), refV1.blueNorm());

  // test aliasing
  VERIFY_IS_APPROX((v1 = -v1), (refV1 = -refV1));
  VERIFY_IS_APPROX((v1 = v1.transpose()), (refV1 = refV1.transpose().eval()));
  VERIFY_IS_APPROX((v1 += -v1), (refV1 += -refV1));

  // sparse matrix to sparse vector
  SparseMatrixType mv1;
  VERIFY_IS_APPROX((mv1=v1),v1);
  VERIFY_IS_APPROX(mv1,(v1=mv1));
  VERIFY_IS_APPROX(mv1,(v1=mv1.transpose()));

  // check copy to dense vector with transpose
  refV3.resize(0);
  VERIFY_IS_APPROX(refV3 = v1.transpose(),v1.toDense());
  VERIFY_IS_APPROX(DenseVector(v1),v1.toDense());

  // test conservative resize
  {
    std::vector<StorageIndex> inc;
    if(rows > 3)
      inc.push_back(-3);
    inc.push_back(0);
    inc.push_back(3);
    inc.push_back(1);
    inc.push_back(10);

    for(std::size_t i = 0; i< inc.size(); i++) {
      StorageIndex incRows = inc[i];
      SparseVectorType vec1(rows);
      DenseVector refVec1 = DenseVector::Zero(rows);
      initSparse<Scalar>(densityVec, refVec1, vec1);

      vec1.conservativeResize(rows+incRows);
      refVec1.conservativeResize(rows+incRows);
      if (incRows > 0) refVec1.tail(incRows).setZero();

      VERIFY_IS_APPROX(vec1, refVec1);

      // Insert new values
      if (incRows > 0)
        vec1.insert(vec1.rows()-1) = refVec1(refVec1.rows()-1) = 1;

      VERIFY_IS_APPROX(vec1, refVec1);
    }
  }

}

EIGEN_DECLARE_TEST(sparse_vector)
{
  for(int i = 0; i < g_repeat; i++) {
    int r = Eigen::internal::random<int>(1,500), c = Eigen::internal::random<int>(1,500);
    if(Eigen::internal::random<int>(0,4) == 0) {
      r = c; // check square matrices in 25% of tries
    }
    EIGEN_UNUSED_VARIABLE(r+c);

    CALL_SUBTEST_1(( sparse_vector<double,int>(8, 8) ));
    CALL_SUBTEST_2(( sparse_vector<std::complex<double>, int>(r, c) ));
    CALL_SUBTEST_1(( sparse_vector<double,long int>(r, c) ));
    CALL_SUBTEST_1(( sparse_vector<double,short>(r, c) ));
  }
}

quality79%

¤ Dauer der Verarbeitung: 0.13 Sekunden (vorverarbeitet) ¤

Wurzel

Suchen

Beweissystem der NASA

Beweissystem Isabelle

NIST Cobol Testsuite

Cephes Mathematical Library

Wiener Entwicklungsmethode

Haftungshinweis

Die Informationen auf dieser Webseite wurden nach bestem Wissen sorgfältig zusammengestellt. Es wird jedoch weder Vollständigkeit, noch Richtigkeit, noch Qualität der bereit gestellten Informationen zugesichert.

Bemerkung:

Die farbliche Syntaxdarstellung ist noch experimentell.