C++ GridStorage::dim方法代码示例

  /**
   * Descends on all dimensions beside dim_sweep. Class functor for dim_sweep
   * Boundaries are not regarded
   *
   * @param source a DataVector containing the source coefficients of the grid points
   * @param result a DataVector containing the result coefficients of the grid points
   * @param dim_sweep the dimension in which the functor is executed
   */
  void sweep1D(DataVector& source, DataVector& result, size_t dim_sweep) {
    // generate a list of all dimension (-dim_sweep)
    // from dimension recursion unrolling
    std::vector<size_t> dim_list;

    for (size_t i = 0; i < storage->dim(); i++) {
      if (i != dim_sweep) {
        dim_list.push_back(i);
      }
    }

    grid_iterator index(storage);

    sweep_rec(source, result, index, dim_list, storage->dim() - 1, dim_sweep);
  }

  /**
   * Recursive traversal of the "tree" of basis functions for evaluation, used in operator().
   * For a given evaluation point \f$x\f$, it stores tuples (std::pair) of
   * \f$(i,\phi_i(x))\f$ in the result vector for all basis functions that are non-zero.
   *
   * @param basis a sparse grid basis
   * @param point evaluation point within the domain
   * @param current_dim the dimension currently looked at (recursion parameter)
   * @param value the value of the evaluation of the current basis function up to (excluding) dimension current_dim (product of the evaluations of the one-dimensional ones)
   * @param working iterator working on the GridStorage of the basis
   * @param source array of indices for each dimension (identifying the indices of the current grid point)
   * @param alpha the spars grid's ansatzfunctions coefficients
   * @param result reference to a float_t into which the result should be stored
   */
  void rec(BASIS& basis, const DataVector& point, size_t current_dim,
           float_t value, GridStorage::grid_iterator& working,
           GridStorage::index_type::index_type* source, const DataVector& alpha,
           float_t& result) {
    typedef GridStorage::index_type::level_type level_type;
    typedef GridStorage::index_type::index_type index_type;

    const unsigned int BITS_IN_BYTE = 8;
    // maximum possible level for the index type
    const level_type max_level = static_cast<level_type> (
                                   sizeof(index_type) * BITS_IN_BYTE - 1);
    index_type src_index = source[current_dim];

    level_type work_level = 1;

    while (true) {
      size_t seq = working.seq();

      if (storage->end(seq)) {
        break;
      } else {
        index_type work_index;
        level_type temp;

        working.get(current_dim, temp, work_index);

        float_t new_value = basis.eval(work_level, work_index,
                                       point[current_dim]);
        new_value *= value;

        if (current_dim == storage->dim() - 1) {
          result += (alpha[seq] * new_value);
        } else {
          rec(basis, point, current_dim + 1, new_value,
              working, source, alpha, result);
        }
      }

      if (working.hint()) {
        break;
      }

      // this decides in which direction we should descend by evaluating
      // the corresponding bit
      // the bits are coded from left to right starting with level 1
      // being in position max_level
      bool right = (src_index & (1 << (max_level - work_level))) > 0;
      ++work_level;

      if (right) {
        working.rightChild(current_dim);
      } else {
        working.leftChild(current_dim);
      }
    }

    working.resetToLevelOne(current_dim);
  }

int main() {
  // create a two-dimensional piecewise bilinear grid
  size_t dim = 2;
  Grid* grid = Grid::createLinearGrid(dim);
  GridStorage* gridStorage = grid->getStorage();
  std::cout << "dimensionality:                   " << gridStorage->dim() << std::endl;

  // create regular grid, level 3
  size_t level = 3;
  GridGenerator* gridGen = grid->createGridGenerator();
  gridGen->regular(level);
  std::cout << "number of initial grid points:    " << gridStorage->size() << std::endl;

  // create coefficient vector
  DataVector alpha(gridStorage->size());
  alpha.setAll(0.0);
  std::cout << "length of alpha vector:           " << alpha.getSize() << std::endl;

  GridIndex* gp;

  // refine adaptively 5 times
  for (int step = 0; step < 5; step++) {
    // set function values in alpha
    for (size_t i = 0; i < gridStorage->size(); i++) {
      gp = gridStorage->get(i);
      alpha[i] = f(gp->getCoord(0), gp->getCoord(1));
    }

    // hierarchize
    SGPP::op_factory::createOperationHierarchisation(*grid)->doHierarchisation(
      alpha);

    // refine a single grid point each time
    gridGen->refine(new SurplusRefinementFunctor(&alpha, 1));
    std::cout << "refinement step " << step + 1 << ", new grid size: " << alpha.getSize()
         << std::endl;

    // extend alpha vector (new entries uninitialized)
    alpha.resize(gridStorage->size());
  }

  delete grid;
}

void testHierarchisationDehierarchisation(SGPP::base::Grid* grid, size_t level,
                                          SGPP::float_t (*func)(DataVector&),
                                          SGPP::float_t tolerance = 0.0, bool naiveOp = false,
                                          bool doStretch = false) {
  GridGenerator* gridGen = grid->createGridGenerator();
  gridGen->regular(level);
  GridStorage* gridStore = grid->getStorage();
  size_t dim = gridStore->dim();
  Stretching* stretch = NULL;

  if (doStretch) stretch = gridStore->getStretching();

  DataVector node_values = DataVector(gridStore->size());
  DataVector coords = DataVector(dim);

  for (size_t n = 0; n < gridStore->size(); n++) {
    if (doStretch) {
      gridStore->get(n)->getCoordsStretching(coords, *stretch);
    } else {
      gridStore->get(n)->getCoords(coords);
    }

    node_values[n] = func(coords);
  }

  DataVector alpha = DataVector(node_values);
  OperationHierarchisation* hierarchisation =
      SGPP::op_factory::createOperationHierarchisation(*grid);
  hierarchisation->doHierarchisation(alpha);

  if (naiveOp == true) {
    OperationNaiveEval* op = SGPP::op_factory::createOperationNaiveEval(*grid);

    for (size_t n = 0; n < gridStore->size(); n++) {
      if (doStretch) {
        gridStore->get(n)->getCoordsStretching(coords, *stretch);
      } else {
        gridStore->get(n)->getCoords(coords);
      }

      SGPP::float_t eval = op->eval(alpha, coords);
      BOOST_CHECK_CLOSE(eval, node_values[n], tolerance);
    }
  } else {
    OperationEval* op = SGPP::op_factory::createOperationEval(*grid);

    for (size_t n = 0; n < gridStore->size(); n++) {
      if (doStretch) {
        gridStore->get(n)->getCoordsStretching(coords, *stretch);
      } else {
        gridStore->get(n)->getCoords(coords);
      }

      SGPP::float_t eval = op->eval(alpha, coords);
      BOOST_CHECK_CLOSE(eval, node_values[n], tolerance);
    }
  }

  DataVector node_values_back = DataVector(alpha);
  hierarchisation->doDehierarchisation(node_values_back);

  for (size_t n = 0; n < gridStore->size(); n++) {
    BOOST_CHECK_CLOSE(node_values_back[n], node_values[n], tolerance);
  }
}