C++ SpProxy::begin_col方法代码示例

inline
void
spop_mean::apply_noalias_slow
  (
        SpMat<typename T1::elem_type>& out,
  const SpProxy<T1>&                   p,
  const uword                          dim
  )
  {
  arma_extra_debug_sigprint();

  typedef typename T1::elem_type eT;
  
  const uword p_n_rows = p.get_n_rows();
  const uword p_n_cols = p.get_n_cols();

  if(dim == 0)  // find the mean in each column
    {
    arma_extra_debug_print("spop_mean::apply_noalias(): dim = 0");
    
    out.set_size((p_n_rows > 0) ? 1 : 0, p_n_cols);
    
    if( (p_n_rows == 0) || (p.get_n_nonzero() == 0) )  { return; }
    
    for(uword col = 0; col < p_n_cols; ++col)
      {
      // Do we have to use an iterator or can we use memory directly?
      if(SpProxy<T1>::must_use_iterator)
        {
        typename SpProxy<T1>::const_iterator_type it  = p.begin_col(col);
        typename SpProxy<T1>::const_iterator_type end = p.begin_col(col + 1);
        
        const uword n_zero = p_n_rows - (end.pos() - it.pos());
        
        out.at(0,col) = spop_mean::iterator_mean(it, end, n_zero, eT(0));
        }
      else
        {
        out.at(0,col) = spop_mean::direct_mean
          (
          &p.get_values()[p.get_col_ptrs()[col]],
          p.get_col_ptrs()[col + 1] - p.get_col_ptrs()[col],
          p_n_rows
          );
        }
      }
    }
  else
  if(dim == 1)  // find the mean in each row
    {
    arma_extra_debug_print("spop_mean::apply_noalias(): dim = 1");
    
    out.set_size(p_n_rows, (p_n_cols > 0) ? 1 : 0);
    
    if( (p_n_cols == 0) || (p.get_n_nonzero() == 0) )  { return; }
    
    for(uword row = 0; row < p_n_rows; ++row)
      {
      // We must use an iterator regardless of how it is stored.
      typename SpProxy<T1>::const_row_iterator_type it  = p.begin_row(row);
      typename SpProxy<T1>::const_row_iterator_type end = p.end_row(row);
      
      const uword n_zero = p_n_cols - (end.pos() - it.pos());
      
      out.at(row,0) = spop_mean::iterator_mean(it, end, n_zero, eT(0));
      }
    }
  }

inline
void
spop_var::apply_noalias
  (
        SpMat<typename T1::pod_type>& out,
  const SpProxy<T1>&                  p,
  const uword                         norm_type,
  const uword                         dim
  )
  {
  arma_extra_debug_sigprint();
  
  typedef typename T1::elem_type  in_eT;
  //typedef typename T1::pod_type  out_eT;
  
  const uword p_n_rows = p.get_n_rows();
  const uword p_n_cols = p.get_n_cols();
  
  // TODO: this is slow; rewrite based on the approach used by sparse mean()
  
  if(dim == 0)  // find variance in each column
    {
    arma_extra_debug_print("spop_var::apply_noalias(): dim = 0");
    
    out.set_size((p_n_rows > 0) ? 1 : 0, p_n_cols);
    
    if( (p_n_rows == 0) || (p.get_n_nonzero() == 0) )  { return; }
    
    for(uword col = 0; col < p_n_cols; ++col)
      {
      if(SpProxy<T1>::must_use_iterator)
        {
        // We must use an iterator; we can't access memory directly.
        typename SpProxy<T1>::const_iterator_type it  = p.begin_col(col);
        typename SpProxy<T1>::const_iterator_type end = p.begin_col(col + 1);
        
        const uword n_zero = p_n_rows - (end.pos() - it.pos());
        
        // in_eT is used just to get the specialization right (complex / noncomplex)
        out.at(0, col) = spop_var::iterator_var(it, end, n_zero, norm_type, in_eT(0));
        }
      else
        {
        // We can use direct memory access to calculate the variance.
        out.at(0, col) = spop_var::direct_var
          (
          &p.get_values()[p.get_col_ptrs()[col]],
          p.get_col_ptrs()[col + 1] - p.get_col_ptrs()[col],
          p_n_rows,
          norm_type
          );
        }
      }
    }
  else
  if(dim == 1)  // find variance in each row
    {
    arma_extra_debug_print("spop_var::apply_noalias(): dim = 1");
    
    out.set_size(p_n_rows, (p_n_cols > 0) ? 1 : 0);
    
    if( (p_n_cols == 0) || (p.get_n_nonzero() == 0) )  { return; }
    
    for(uword row = 0; row < p_n_rows; ++row)
      {
      // We have to use an iterator here regardless of whether or not we can
      // directly access memory.
      typename SpProxy<T1>::const_row_iterator_type it  = p.begin_row(row);
      typename SpProxy<T1>::const_row_iterator_type end = p.end_row(row);
      
      const uword n_zero = p_n_cols - (end.pos() - it.pos());
      
      out.at(row, 0) = spop_var::iterator_var(it, end, n_zero, norm_type, in_eT(0));
      }
    }
  }

inline
void
spop_var::apply_noalias
  (
        SpMat<typename T1::pod_type>& out_ref,
  const SpProxy<T1>&                  p,
  const uword                         norm_type,
  const uword                         dim
  )
  {
  arma_extra_debug_sigprint();

  typedef typename T1::elem_type  in_eT;
  //typedef typename T1::pod_type  out_eT;

  const uword p_n_rows = p.get_n_rows();
  const uword p_n_cols = p.get_n_cols();

  if(dim == 0)
    {
    arma_extra_debug_print("spop_var::apply(), dim = 0");

    arma_debug_check((p_n_rows == 0), "var(): given object has zero rows");

    out_ref.set_size(1, p_n_cols);

    for(uword col = 0; col < p_n_cols; ++col)
      {
      if(SpProxy<T1>::must_use_iterator == true)
        {
        // We must use an iterator; we can't access memory directly.
        typename SpProxy<T1>::const_iterator_type it  = p.begin_col(col);
        typename SpProxy<T1>::const_iterator_type end = p.begin_col(col + 1);
        
        const uword n_zero = p.get_n_rows() - (end.pos() - it.pos());
        
        // in_eT is used just to get the specialization right (complex / noncomplex)
        out_ref.at(col) = spop_var::iterator_var(it, end, n_zero, norm_type, in_eT(0));
        }
      else
        {
        // We can use direct memory access to calculate the variance.
        out_ref.at(col) = spop_var::direct_var
          (
          &p.get_values()[p.get_col_ptrs()[col]],
          p.get_col_ptrs()[col + 1] - p.get_col_ptrs()[col],
          p.get_n_rows(),
          norm_type
          );
        }
      }
    }
  else if(dim == 1)
    {
    arma_extra_debug_print("spop_var::apply_noalias(), dim = 1");
    
    arma_debug_check((p_n_cols == 0), "var(): given object has zero columns");
    
    out_ref.set_size(p_n_rows, 1);
    
    for(uword row = 0; row < p_n_rows; ++row)
      {
      // We have to use an iterator here regardless of whether or not we can
      // directly access memory.
      typename SpProxy<T1>::const_row_iterator_type it  = p.begin_row(row);
      typename SpProxy<T1>::const_row_iterator_type end = p.end_row(row);
      
      const uword n_zero = p.get_n_cols() - (end.pos() - it.pos());
      
      out_ref.at(row) = spop_var::iterator_var(it, end, n_zero, norm_type, in_eT(0));
      }
    }
  }

arma_hot
inline
void
spglue_times::apply_noalias(SpMat<eT>& c, const SpProxy<T1>& pa, const SpProxy<T2>& pb)
  {
  arma_extra_debug_sigprint();
  
  const uword x_n_rows = pa.get_n_rows();
  const uword x_n_cols = pa.get_n_cols();
  const uword y_n_rows = pb.get_n_rows();
  const uword y_n_cols = pb.get_n_cols();

  arma_debug_assert_mul_size(x_n_rows, x_n_cols, y_n_rows, y_n_cols, "matrix multiplication");

  // First we must determine the structure of the new matrix (column pointers).
  // This follows the algorithm described in 'Sparse Matrix Multiplication
  // Package (SMMP)' (R.E. Bank and C.C. Douglas, 2001).  Their description of
  // "SYMBMM" does not include anything about memory allocation.  In addition it
  // does not consider that there may be elements which space may be allocated
  // for but which evaluate to zero anyway.  So we have to modify the algorithm
  // to work that way.  For the "SYMBMM" implementation we will not determine
  // the row indices but instead just the column pointers.
  
  //SpMat<typename T1::elem_type> c(x_n_rows, y_n_cols); // Initializes col_ptrs to 0.
  c.zeros(x_n_rows, y_n_cols);
  
  //if( (pa.get_n_elem() == 0) || (pb.get_n_elem() == 0) )
  if( (pa.get_n_nonzero() == 0) || (pb.get_n_nonzero() == 0) )
    {
    return;
    }
  
  // Auxiliary storage which denotes when items have been found.
  podarray<uword> index(x_n_rows);
  index.fill(x_n_rows); // Fill with invalid links.
  
  typename SpProxy<T2>::const_iterator_type y_it  = pb.begin();
  typename SpProxy<T2>::const_iterator_type y_end = pb.end();

  // SYMBMM: calculate column pointers for resultant matrix to obtain a good
  // upper bound on the number of nonzero elements.
  uword cur_col_length = 0;
  uword last_ind = x_n_rows + 1;
  do
    {
    const uword y_it_row = y_it.row();
    
    // Look through the column that this point (*y_it) could affect.
    typename SpProxy<T1>::const_iterator_type x_it = pa.begin_col(y_it_row);
    
    while(x_it.col() == y_it_row)
      {
      // A point at x(i, j) and y(j, k) implies a point at c(i, k).
      if(index[x_it.row()] == x_n_rows)
        {
        index[x_it.row()] = last_ind;
        last_ind = x_it.row();
        ++cur_col_length;
        }

      ++x_it;
      }

    const uword old_col = y_it.col();
    ++y_it;

    // See if column incremented.
    if(old_col != y_it.col())
      {
      // Set column pointer (this is not a cumulative count; that is done later).
      access::rw(c.col_ptrs[old_col + 1]) = cur_col_length;
      cur_col_length = 0;

      // Return index markers to zero.  Use last_ind for traversal.
      while(last_ind != x_n_rows + 1)
        {
        const uword tmp = index[last_ind];
        index[last_ind] = x_n_rows;
        last_ind = tmp;
        }
      }
    }
  while(y_it != y_end);

  // Accumulate column pointers.
  for(uword i = 0; i < c.n_cols; ++i)
    {
    access::rw(c.col_ptrs[i + 1]) += c.col_ptrs[i];
    }

  // Now that we know a decent bound on the number of nonzero elements, allocate
  // the memory and fill it.
  c.mem_resize(c.col_ptrs[c.n_cols]);

  // Now the implementation of the NUMBMM algorithm.
  uword cur_pos = 0; // Current position in c matrix.
  podarray<eT> sums(x_n_rows); // Partial sums.
  sums.zeros();
  
  // setting the size of 'sorted_indices' to x_n_rows is a better-than-nothing guess;
//.........这里部分代码省略.........