pearson_search.hpp

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#ifndef MLPACK_METHODS_CF_PEARSON_SEARCH_HPP
#define MLPACK_METHODS_CF_PEARSON_SEARCH_HPP

#include <mlpack/prereqs.hpp>
#include <mlpack/methods/neighbor_search/neighbor_search.hpp>

namespace mlpack {
namespace cf {

class PearsonSearch
{
 public:
  PearsonSearch(const arma::mat& referenceSet)
  {
    // Normalize all vectors in referenceSet.
    // For each vector x, first subtract mean(x) from each element in x.
    // Then normalize the vector to unit length.
    arma::mat normalizedSet(arma::size(referenceSet));
    normalizedSet = arma::normalise(
        referenceSet.each_row() - arma::mean(referenceSet));

    neighborSearch.Train(std::move(normalizedSet));
  }

  void Search(const arma::mat& query, const size_t k,
              arma::Mat<size_t>& neighbors, arma::mat& similarities)
  {
    // Normalize all vectors in query.
    // For each vector x, first subtract mean(x) from each element in x.
    // Then normalize the vector to unit length.
    arma::mat normalizedQuery;
    normalizedQuery = arma::normalise(query.each_row() - arma::mean(query));

    neighborSearch.Search(normalizedQuery, k, neighbors, similarities);

    // Resulting similarities from Search() are Euclidean distance.
    // For normalized vectors a and b, pearson(a, b) = 1 - dis(a, b) ^ 2 / 2,
    // where dis(a, b) is Euclidean distance.
    // Furthermore, we restrict the range of similarity to be [0, 1]:
    // similarities = (pearson(a,b) + 1) / 2.0. As a result we have the
    // following formula.
    similarities = 1 - arma::pow(similarities, 2) / 4.0;
  }

 private:
  neighbor::KNN neighborSearch;
};

} // namespace cf
} // namespace mlpack

#endif