C++ SparseMatrixsc::resize方法代码示例

void MathUtilities::extractLowerTriangularMatrix( const SparseMatrixsc& A, SparseMatrixsc& B )
{
  std::vector< Eigen::Triplet<scalar> > triplets;
  for( int col = 0; col < A.outerSize(); ++col )
  {
    for( SparseMatrixsc::InnerIterator it( A, col ); it; ++it )
    {
      if( col > it.row() ) { continue; }
      triplets.push_back( Eigen::Triplet<scalar>( it.row(), col, it.value() ) );
    }
  }
  B.resize( A.rows(), A.cols() );
  B.setFromTriplets( triplets.begin(), triplets.end() );
  B.makeCompressed();
}

void MathUtilities::convertDenseToSparse( const bool filter_zeros, const MatrixXXsc& dense_matrix, SparseMatrixsc& sparse_matrix )
{
  std::vector<Eigen::Triplet<scalar>> triplets;
  for( int row = 0; row < dense_matrix.rows(); ++row )
  {
    for( int col = 0; col < dense_matrix.cols(); ++col )
    {
      if( dense_matrix( row, col ) != 0.0 || !filter_zeros )
      {
        triplets.emplace_back( Eigen::Triplet<scalar>{ row, col, dense_matrix( row, col ) } );
      }
    }
  }
  sparse_matrix.resize( dense_matrix.rows(), dense_matrix.cols() );
  sparse_matrix.setFromTriplets( std::begin( triplets ), std::end( triplets ) );
  sparse_matrix.makeCompressed();
}

// TODO: Use utility class here
void MathUtilities::deserialize( SparseMatrixsc& A, std::istream& stm )
{
  assert( stm.good() );

  VectorXi col_ptr;
  VectorXi row_ind;
  VectorXs val;

  // Read the number of rows in the matrix
  int rows{ -1 };
  stm.read((char*)&rows,sizeof(int));
  assert( rows >= 0 );
  // Read the number of columns in the matrix
  int cols{ -1 };
  stm.read((char*)&cols,sizeof(int));
  assert( cols >= 0 );
  // Read the column pointer array
  col_ptr.resize(cols+1);
  stm.read((char*)col_ptr.data(),col_ptr.size()*sizeof(int));
  // Read the number of nonzeros in the array
  int nnz{ -1 };
  stm.read((char*)&nnz,sizeof(int));
  assert( nnz >= 0 );
  // Read the row-index array
  row_ind.resize(nnz);
  stm.read((char*)row_ind.data(),row_ind.size()*sizeof(int));
  // Read the value array
  val.resize(nnz);
  stm.read((char*)val.data(),val.size()*sizeof(scalar));

  A.resize(rows,cols);
  A.reserve(nnz);

  for( int col = 0; col < cols; ++col )
  {
    A.startVec(col);
    for( int curel = col_ptr(col); curel < col_ptr(col+1); ++curel )
    {
      int row = row_ind(curel);
      scalar curval = val(curel);
      A.insertBack(row,col) = curval;
    }
  }
  A.finalize();
}

// TODO: Despecialize from smooth
void FrictionOperator::formGeneralizedSmoothFrictionBasis( const unsigned ndofs, const unsigned ncons, const VectorXs& q, const std::vector<std::unique_ptr<Constraint>>& K, const MatrixXXsc& bases, SparseMatrixsc& D )
{
  assert( ncons == K.size() );

  const unsigned nambientdims{ static_cast<unsigned>( bases.rows() ) };
  const unsigned nsamples{ nambientdims - 1 };

  D.resize( ndofs, nsamples * ncons );

  auto itr = K.cbegin();
  {
    VectorXi column_nonzeros( D.cols() );
    for( unsigned collision_number = 0; collision_number < ncons; ++collision_number )
    {
      for( unsigned sample_number = 0; sample_number < nsamples; ++sample_number )
      {
        assert( nsamples * collision_number + sample_number < column_nonzeros.size() );
        column_nonzeros( nsamples * collision_number + sample_number ) = (*itr)->frictionStencilSize();
      }
      ++itr;
    }
    assert( ( column_nonzeros.array() > 0 ).all() );
    assert( itr == K.cend() );
    D.reserve( column_nonzeros );
  }

  itr = K.cbegin();
  for( unsigned collision_number = 0; collision_number < ncons; ++collision_number )
  {
    for( unsigned sample_number = 0; sample_number < nsamples; ++sample_number )
    {
      const unsigned current_column{ nsamples * collision_number + sample_number };
      const VectorXs current_sample{ bases.col( nambientdims * collision_number + sample_number + 1 ) };
      assert( fabs( current_sample.dot( bases.col( nambientdims * collision_number ) ) ) <= 1.0e-6 );
      (*itr)->computeGeneralizedFrictionGivenTangentSample( q, current_sample, current_column, D );
    }
    ++itr;
  }
  assert( itr == K.cend() );

  D.prune( []( const Eigen::Index& row, const Eigen::Index& col, const scalar& value ) { return value != 0.0; } );
  assert( D.innerNonZeroPtr() == nullptr );
}

// TODO: Pull the outerIndexPtr arithmetic into a helper function
void MathUtilities::extractColumns( const SparseMatrixsc& A0, const std::vector<unsigned>& cols, SparseMatrixsc& A1 )
{
  const unsigned ncols_to_extract{ static_cast<unsigned>( cols.size() ) };

  assert( ncols_to_extract <= static_cast<unsigned>( A0.cols() ) );
  #ifndef NDEBUG
  for( unsigned i = 0; i < ncols_to_extract; ++i )
  {
    assert( cols[i] < unsigned( A0.cols() ) );
  }
  #endif
    
  // Compute the number of nonzeros in each column of the new matrix
  VectorXi column_nonzeros{ ncols_to_extract };
  for( unsigned i = 0; i < ncols_to_extract; ++i )
  {
    column_nonzeros( i ) = A0.outerIndexPtr()[cols[i]+1] - A0.outerIndexPtr()[cols[i]];
  }

  // Resize A1 and reserve space
  A1.resize( A0.rows(), ncols_to_extract );
  A1.reserve( column_nonzeros );
  // Copy the data over, column by column
  for( unsigned cur_col = 0; cur_col < ncols_to_extract; ++cur_col )
  {
    for( SparseMatrixsc::InnerIterator it( A0, cols[ cur_col ] ); it; ++it )
    {
      A1.insert( it.row(), cur_col ) = it.value();
    }
  }
  
  A1.makeCompressed();
  
  #ifndef NDEBUG
  for( int i = 0 ; i < A1.cols(); ++i )
  {
    assert( ( A1.outerIndexPtr()[i+1] - A1.outerIndexPtr()[i] ) == column_nonzeros( i ) );
  }
  #endif
}