// Ceres Solver - A fast non-linear least squares minimizer

// Copyright 2015 Google Inc. All rights reserved.

// http://ceres-solver.org/

//

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// Author: sameeragarwal@google.com (Sameer Agarwal)

#include "ceres/compressed_col_sparse_matrix_utils.h"

#include <vector>

#include <algorithm>

#include "ceres/internal/port.h"

#include "glog/logging.h"

namespace ceres {

namespace internal {

using std::vector;

void CompressedColumnScalarMatrixToBlockMatrix(

    const int* scalar_rows,

    const int* scalar_cols,

    const vector<int>& row_blocks,

    const vector<int>& col_blocks,

    vector<int>* block_rows,

    vector<int>* block_cols) {

  CHECK_NOTNULL(block_rows)->clear();

  CHECK_NOTNULL(block_cols)->clear();

  const int num_row_blocks = row_blocks.size();

  const int num_col_blocks = col_blocks.size();

  vector<int> row_block_starts(num_row_blocks);

  for (int i = 0, cursor = 0; i < num_row_blocks; ++i) {

    row_block_starts[i] = cursor;

    cursor += row_blocks[i];

  }

  // This loop extracts the block sparsity of the scalar sparse matrix

  // It does so by iterating over the columns, but only considering

  // the columns corresponding to the first element of each column

  // block. Within each column, the inner loop iterates over the rows,

  // and detects the presence of a row block by checking for the

  // presence of a non-zero entry corresponding to its first element.

  block_cols->push_back(0);

  int c = 0;

  for (int col_block = 0; col_block < num_col_blocks; ++col_block) {

    int column_size = 0;

    for (int idx = scalar_cols[c]; idx < scalar_cols[c + 1]; ++idx) {

      vector<int>::const_iterator it =

          std::lower_bound(row_block_starts.begin(),

                           row_block_starts.end(),

                           scalar_rows[idx]);

      // Since we are using lower_bound, it will return the row id

      // where the row block starts. For everything but the first row

      // of the block, where these values will be the same, we can

      // skip, as we only need the first row to detect the presence of

      // the block.

      //

      // For rows all but the first row in the last row block,

      // lower_bound will return row_block_starts.end(), but those can

      // be skipped like the rows in other row blocks too.

      if (it == row_block_starts.end() || *it != scalar_rows[idx]) {

        continue;

      }

      block_rows->push_back(it - row_block_starts.begin());

      ++column_size;

    }

    block_cols->push_back(block_cols->back() + column_size);

    c += col_blocks[col_block];

  }

}

void BlockOrderingToScalarOrdering(const vector<int>& blocks,

                                   const vector<int>& block_ordering,

                                   vector<int>* scalar_ordering) {

  CHECK_EQ(blocks.size(), block_ordering.size());

  const int num_blocks = blocks.size();

  // block_starts = [0, block1, block1 + block2 ..]

  vector<int> block_starts(num_blocks);

  for (int i = 0, cursor = 0; i < num_blocks ; ++i) {

    block_starts[i] = cursor;

    cursor += blocks[i];

  }

  scalar_ordering->resize(block_starts.back() + blocks.back());

  int cursor = 0;

  for (int i = 0; i < num_blocks; ++i) {

    const int block_id = block_ordering[i];

    const int block_size = blocks[block_id];

    int block_position = block_starts[block_id];

    for (int j = 0; j < block_size; ++j) {

      (*scalar_ordering)[cursor++] = block_position++;

    }

  }

}

}  // namespace internal

}  // namespace ceres