PDS/homework_1/inc/v1.hpp

/**
 * \file    v0.hpp
 * \brief
 *
 * \author
 *    Christos Choutouridis AEM:8997
 *    <cchoutou@ece.auth.gr>
 */
#ifndef V1_HPP_
#define V1_HPP_

#include <vector>
#include <algorithm>

#include "matrix.hpp"
#include "v0.hpp"
#include "config.h"

namespace v1 {

template <typename DataType, typename IndexType>
void mergeResultsWithM(mtx::Matrix<IndexType>& N1, mtx::Matrix<DataType>& D1,
                       mtx::Matrix<IndexType>& N2, mtx::Matrix<DataType>& D2,
                       size_t k, size_t m,
                       mtx::Matrix<IndexType>& N, mtx::Matrix<DataType>& D) {
   size_t numQueries = N1.rows();
   size_t maxCandidates = std::min((IndexType)m, (IndexType)(N1.columns() + N2.columns()));

   for (size_t q = 0; q < numQueries; ++q) {
      // Combine distances and neighbors
      std::vector<std::pair<DataType, IndexType>> candidates(N1.columns() + N2.columns());

      // Concatenate N1 and N2 rows
      for (size_t i = 0; i < N1.columns(); ++i) {
         candidates[i] = {D1.get(q, i), N1.get(q, i)};
      }
      for (size_t i = 0; i < N2.columns(); ++i) {
         candidates[i + N1.columns()] = {D2.get(q, i), N2.get(q, i)};
      }

      // Keep only the top-m candidates
      v0::quickselect(candidates, maxCandidates);

      // Sort the top-m candidates
      std::sort(candidates.begin(), candidates.begin() + maxCandidates);

      // If m < k, pad the remaining slots with invalid values
      for (size_t i = 0; i < k; ++i) {
         if (i < maxCandidates) {
            D.set(candidates[i].first, q, i);
            N.set(candidates[i].second, q, i);
         } else {
            D.set(std::numeric_limits<DataType>::infinity(), q, i);
            N.set(static_cast<IndexType>(-1), q, i); // Invalid index (end)
         }
      }
    }
}



template<typename MatrixD, typename MatrixI>
void knnsearch(const MatrixD& C, const MatrixD& Q, size_t idx_offset, size_t k, size_t m, MatrixI& idx, MatrixD& dst) {

   using DstType = typename MatrixD::dataType;
   using IdxType = typename MatrixI::dataType;

    if (C.rows() <= 8 || Q.rows() <= 4) {
        // Base case: Call knnsearch directly
        v0::knnsearch(C, Q, idx_offset, k, m, idx, dst);
        return;
    }

    // Divide Corpus and Query into subsets
    IdxType midC = C.rows() / 2;
    IdxType midQ = Q.rows() / 2;

    // Slice corpus and query matrixes
    MatrixD C1((DstType*)C.data(), 0,    midC, C.columns());
    MatrixD C2((DstType*)C.data(), midC, midC, C.columns());
    MatrixD Q1((DstType*)Q.data(), 0,    midQ, Q.columns());
    MatrixD Q2((DstType*)Q.data(), midQ, midQ, Q.columns());

    // Allocate temporary matrixes for all permutations
    MatrixI N1_1(midQ, k), N1_2(midQ, k), N2_1(midQ, k), N2_2(midQ, k);
    MatrixD D1_1(midQ, k), D1_2(midQ, k), D2_1(midQ, k), D2_2(midQ, k);

    // Recursive calls
    knnsearch(C1, Q1, idx_offset,        k, m, N1_1, D1_1);
    knnsearch(C2, Q1, idx_offset + midC, k, m, N1_2, D1_2);
    knnsearch(C1, Q2, idx_offset,        k, m, N2_1, D2_1);
    knnsearch(C2, Q2, idx_offset + midC, k, m, N2_2, D2_2);

    // slice output matrixes
    MatrixI N1((IdxType*)idx.data(), 0,    midQ, k);
    MatrixI N2((IdxType*)idx.data(), midQ, midQ, k);
    MatrixD D1((DstType*)dst.data(), 0,    midQ, k);
    MatrixD D2((DstType*)dst.data(), midQ, midQ, k);

    // Merge results in place
    mergeResultsWithM(N1_1, D1_1, N1_2, D1_2, k, m, N1, D1);
    mergeResultsWithM(N2_1, D2_1, N2_2, D2_2, k, m, N2, D2);

}

} // namespace v1

#endif /* V1_HPP_ */