MatrixBlockSparseCols.h

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/* +------------------------------------------------------------------------+
   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          http://www.mrpt.org/                          |
   |                                                                        |
   | Copyright (c) 2005-2018, Individual contributors, see AUTHORS file     |
   | See: http://www.mrpt.org/Authors - All rights reserved.                |
   | Released under BSD License. See details in http://www.mrpt.org/License |
   +------------------------------------------------------------------------+ */
 
#ifndef _mrpt_math_MatrixBlockSparseCols_H
#define _mrpt_math_MatrixBlockSparseCols_H
 
#include <mrpt/containers/map_as_vector.h>
#include <mrpt/math/CMatrixTemplateNumeric.h>  // For mrpt::math::CMatrixDouble
 
namespace mrpt::math
{
/** A templated column-indexed efficient storage of block-sparse Jacobian or
 * Hessian matrices, together with other arbitrary information.
  *  Columns are stored in a non-associative container, but the contents of each
 * column are kept within an std::map<> indexed by row.
  *  All submatrix blocks have the same size, which allows dense storage of them
 * in fixed-size matrices, avoiding costly memory allocations.
  *
  * \tparam NROWS Rows in each elementary matrix.
  * \tparam NCOLS Cols in each elementary matrix.
  * \tparam INFO  Type of the extra data fields within each block
  * \tparam HAS_REMAP Is true, an inverse mapping between column indices and
 * "user IDs" is kept.
  * \tparam INDEX_REMAP_MAP_IMPL Ignore if HAS_REMAP=false. Defaults to
 * "mrpt::utils::map_as_vector<size_t,size_t>" for amortized O(1). Can be set to
 * "std::map<size_t,size_t>" in very sparse systems to save memory at the cost
 * of a O(log N) access time when using the remap indices.
  *
  * \ingroup mrpt_math_grp
  */
template <typename Scalar, int NROWS, int NCOLS, typename INFO, bool HAS_REMAP,
          typename INDEX_REMAP_MAP_IMPL =
              mrpt::utils::map_as_vector<size_t, size_t>>
struct MatrixBlockSparseCols
{
    using matrix_t = Eigen::Matrix<Scalar, NROWS, NCOLS>;
    using symbolic_t = INFO;
 
    struct TEntry
    {
        /** Numeric matrix */
        matrix_t num;
        /** Extra symbolic info */
        symbolic_t sym;
    };
 
    /** Each compressed sparse column */
    using col_t = mrpt::aligned_std_map<size_t, TEntry>;
 
   private:
    /** -> cols[i]: i'th column.
      * -> Each column is a map [row] -> TEntry
      */
    std::deque<col_t> cols;
    /** "remapped index" is the index of some global variable, interpreted by
     * the external user of this class. */
    // map<size_t,size_t> col_inverse_remapped_indices;
    mrpt::utils::map_as_vector<size_t, size_t> col_inverse_remapped_indices;
    std::vector<size_t> col_remapped_indices;
 
   public:
    inline MatrixBlockSparseCols() : cols(0) {}
    inline col_t& getCol(const size_t idx) { return cols[idx]; }
    inline const col_t& getCol(const size_t idx) const { return cols[idx]; }
    inline const mrpt::utils::map_as_vector<size_t, size_t>&
        getColInverseRemappedIndices() const
    {
        if (!HAS_REMAP) assert(false);
        return col_inverse_remapped_indices;
    }
    inline const std::vector<size_t>& getColRemappedIndices() const
    {
        if (!HAS_REMAP) assert(false);
        return col_remapped_indices;
    }
 
    /** Append one column, returning a ref to the new col_t data */
    inline col_t& appendCol(const size_t remapIndex)
    {
        const size_t idx = cols.size();
        cols.push_back(col_t());
 
        if (HAS_REMAP)
        {
            col_remapped_indices.resize(idx + 1);
            col_remapped_indices[idx] = remapIndex;
 
            col_inverse_remapped_indices[remapIndex] = idx;
        }
 
        return *cols.rbegin();
    }
 
    /** Change the number of columns (keep old contents) */
    inline void setColCount(const size_t nCols) { cols.resize(nCols); }
    /** Get current number of cols. \sa findCurrentNumberOfRows */
    inline size_t cols() const { return cols.size(); }
    /** Clear all the entries in each column (do not change the number of
     * columns, though!) \sa getColCount */
    inline void clearColEntries()
    {
        for (size_t i = 0; i < cols.size(); i++) cols[i].clear();
    }
 
    /** Clear all the entries in each column (do not change the number of
     * columns, though!) \sa getColCount */
    inline void clearAll()
    {
        cols.clear();
        if (HAS_REMAP)
        {
            col_remapped_indices.clear();
            col_inverse_remapped_indices.clear();
        }
    }
 
    /** Builds a dense representation of the matrix and saves to a text file. */
    void saveToTextFileAsDense(
        const std::string& filename, const bool force_symmetry = false,
        const bool is_col_compressed = true) const
    {
        mrpt::math::CMatrixDouble D;
        getAsDense(D, force_symmetry, is_col_compressed);
        return D.saveToTextFile(filename);
    }
 
    /** Builds a dense representation of the matrix and saves to a text file.
      * \param is_col_compressed true: interpret this object as compressed by
     * cols; false: compressed by rows
      */
    void getAsDense(
        mrpt::math::CMatrixDouble& D, const bool force_symmetry = false,
        const bool is_col_compressed = true) const
    {
        const size_t nCols = cols.size();
        const size_t nRows = findCurrentNumberOfRows();
 
        if (is_col_compressed)
            D.setSize(nRows * NROWS, nCols * NCOLS);
        else
            D.setSize(nCols * NROWS, nRows * NCOLS);
 
        for (size_t j = 0; j < nCols; j++)
        {
            for (typename col_t::const_iterator itRow = cols[j].begin();
                 itRow != cols[j].end(); ++itRow)
            {
                const size_t row = itRow->first;
                const size_t row_idx =
                    is_col_compressed ? row * NROWS : j * NROWS;
                const size_t col_idx =
                    is_col_compressed ? j * NCOLS : row * NCOLS;
                D.block(row_idx, col_idx, NROWS, NCOLS) = itRow->second.num;
                if (force_symmetry && row_idx != col_idx)
                    D.block(col_idx, row_idx, NCOLS, NROWS) =
                        itRow->second.num.transpose();
            }
        }
    }
 
    /** Goes over all the columns and keep the largest column length. \sa
     * cols() */
    size_t findCurrentNumberOfRows() const
    {
        size_t nRows = 0;
        const size_t nCols = cols.size();
        for (size_t j = 0; j < nCols; j++)
            for (typename col_t::const_iterator itRow = cols[j].begin();
                 itRow != cols[j].end(); ++itRow)
                mrpt::keep_max(nRows, itRow->first);
        return nRows +
               1;  // nRows was the max. row index, now it's the row count.
    }
 
    /** Builds a binary matrix with 1s where an elementary matrix is stored, 0s
     * elsewhere. */
    template <class MATRIX>
    void getBinaryBlocksRepresentation(MATRIX& out) const
    {
        const size_t nCols = cols.size();
        const size_t nRows = findCurrentNumberOfRows();
        out.zeros(nRows, nCols);
        for (size_t j = 0; j < nCols; j++)
            for (typename col_t::const_iterator itRow = cols[j].begin();
                 itRow != cols[j].end(); ++itRow)
            {
                const size_t row = itRow->first;
                out(row, j) = 1;
            }
    }
 
    /** Clear the current contents of this objects and replicates the sparse
     * structure and numerical values of \a o */
    void copyNumericalValuesFrom(
        const MatrixBlockSparseCols<Scalar, NROWS, NCOLS, INFO, HAS_REMAP>& o)
    {
        const size_t nC = o.cols.size();
        if (cols.size() != nC)
        {
            // Just create an empty structure with the numerical matrices:
            cols.resize(nC);
            for (size_t i = 0; i < nC; i++)
            {
                cols[i].clear();
                for (typename col_t::const_iterator it = o.cols[i].begin();
                     it != o.cols[i].end(); ++it)
                    cols[i][it->first].num = it->second.num;
            }
        }
        else
        {
            // It might be that we're overwriting an existing data structure:
            for (size_t i = 0; i < nC; i++)
            {
                // ASSERTMSG_(cols[i].size()>=o.cols[i].size(),
                // "copyNumericalValuesFrom() invoked on dissimilar structures")
                typename col_t::iterator it_dst = cols[i].begin();
                typename col_t::const_iterator it_src = o.cols[i].begin();
                while (it_src != o.cols[i].end())
                {
                    if (it_dst->first < it_src->first)
                    {
                        it_dst->second.num.setZero();
                        it_dst++;
                    }
                    else if (it_dst->first > it_src->first)
                    {
                        cols[i][it_src->first].num = it_src->second.num;
                        it_src++;
                    }
                    else
                    {
                        it_dst->second.num = it_src->second.num;
                        ++it_dst;
                        ++it_src;
                    }
                }
            }
        }
    }  // end copyNumericalValuesFrom()
 
};  // end of MatrixBlockSparseCols
 
}
#endif  //_mrpt_math_MatrixBlockSparseCols_H