コード例 #1
0
ファイル: kfn_main.cpp プロジェクト: caomw/mlpack
int main(int argc, char *argv[])
{
  // Give CLI the command line parameters the user passed in.
  CLI::ParseCommandLine(argc, argv);

  if (CLI::GetParam<int>("seed") != 0)
    math::RandomSeed((size_t) CLI::GetParam<int>("seed"));
  else
    math::RandomSeed((size_t) std::time(NULL));

  // A user cannot specify both reference data and a model.
  if (CLI::HasParam("reference_file") && CLI::HasParam("input_model_file"))
    Log::Fatal << "Only one of --reference_file (-r) or --input_model_file (-m)"
        << " may be specified!" << endl;

  // A user must specify one of them...
  if (!CLI::HasParam("reference_file") && !CLI::HasParam("input_model_file"))
    Log::Fatal << "No model specified (--input_model_file) and no reference "
        << "data specified (--reference_file)!  One must be provided." << endl;

  if (CLI::HasParam("input_model_file"))
  {
    // Notify the user of parameters that will be ignored.
    if (CLI::HasParam("tree_type"))
      Log::Warn << "--tree_type (-t) will be ignored because --input_model_file"
          << " is specified." << endl;
    if (CLI::HasParam("leaf_size"))
      Log::Warn << "--leaf_size (-l) will be ignored because --input_model_file"
          << " is specified." << endl;
    if (CLI::HasParam("random_basis"))
      Log::Warn << "--random_basis (-R) will be ignored because "
          << "--input_model_file is specified." << endl;
    if (CLI::HasParam("naive"))
      Log::Warn << "--naive (-N) will be ignored because --input_model_file is "
          << "specified." << endl;
  }

  // The user should give something to do...
  if (!CLI::HasParam("k") && !CLI::HasParam("output_model_file"))
    Log::Warn << "Neither -k nor --output_model_file are specified, so no "
        << "results from this program will be saved!" << endl;

  // If the user specifies k but no output files, they should be warned.
  if (CLI::HasParam("k") &&
      !(CLI::HasParam("neighbors_file") || CLI::HasParam("distances_file")))
    Log::Warn << "Neither --neighbors_file nor --distances_file is specified, "
        << "so the furthest neighbor search results will not be saved!" << endl;

  // If the user specifies output files but no k, they should be warned.
  if ((CLI::HasParam("neighbors_file") || CLI::HasParam("distances_file")) &&
      !CLI::HasParam("k"))
    Log::Warn << "An output file for furthest neighbor search is given ("
        << "--neighbors_file or --distances_file), but furthest neighbor search"
        << " is not being performed because k (--k) is not specified!  No "
        << "results will be saved." << endl;

  // Sanity check on leaf size.
  const int lsInt = CLI::GetParam<int>("leaf_size");
  if (lsInt < 1)
    Log::Fatal << "Invalid leaf size: " << lsInt << ".  Must be greater than 0."
        << endl;

  // Sanity check on epsilon.
  double epsilon = CLI::GetParam<double>("epsilon");
  if (epsilon < 0 || epsilon >= 1)
    Log::Fatal << "Invalid epsilon: " << epsilon << ".  Must be in the range "
        << "[0,1)." << endl;

  // Sanity check on percentage.
  const double percentage = CLI::GetParam<double>("percentage");
  if (percentage <= 0 || percentage > 1)
    Log::Fatal << "Invalid percentage: " << percentage << ".  Must be in the "
        << "range (0,1] (decimal form)." << endl;

  if (CLI::HasParam("percentage") && CLI::HasParam("epsilon"))
    Log::Fatal << "Cannot provide both epsilon and percentage." << endl;

  if (CLI::HasParam("percentage"))
    epsilon = 1 - percentage;

  // We either have to load the reference data, or we have to load the model.
  NSModel<FurthestNeighborSort> kfn;
  const bool naive = CLI::HasParam("naive");
  const bool singleMode = CLI::HasParam("single_mode");
  if (CLI::HasParam("reference_file"))
  {
    // Get all the parameters.
    const string referenceFile = CLI::GetParam<string>("reference_file");
    const string treeType = CLI::GetParam<string>("tree_type");
    const bool randomBasis = CLI::HasParam("random_basis");

    KFNModel::TreeTypes tree = KFNModel::KD_TREE;
    if (treeType == "kd")
      tree = KFNModel::KD_TREE;
    else if (treeType == "cover")
      tree = KFNModel::COVER_TREE;
    else if (treeType == "r")
      tree = KFNModel::R_TREE;
    else if (treeType == "r-star")
      tree = KFNModel::R_STAR_TREE;
    else if (treeType == "ball")
      tree = KFNModel::BALL_TREE;
    else if (treeType == "x")
      tree = KFNModel::X_TREE;
    else
      Log::Fatal << "Unknown tree type '" << treeType << "'; valid choices are "
          << "'kd', 'cover', 'r', 'r-star', 'x' and 'ball'." << endl;

    kfn.TreeType() = tree;
    kfn.RandomBasis() = randomBasis;

    arma::mat referenceSet;
    data::Load(referenceFile, referenceSet, true);

    Log::Info << "Loaded reference data from '" << referenceFile << "' ("
        << referenceSet.n_rows << "x" << referenceSet.n_cols << ")." << endl;

    kfn.BuildModel(std::move(referenceSet), size_t(lsInt), naive, singleMode,
        epsilon);
  }
  else
  {
    // Load the model from file.
    const string inputModelFile = CLI::GetParam<string>("input_model_file");
    data::Load(inputModelFile, "kfn_model", kfn, true); // Fatal on failure.

    Log::Info << "Loaded kFN model from '" << inputModelFile << "' (trained on "
        << kfn.Dataset().n_rows << "x" << kfn.Dataset().n_cols << " dataset)."
        << endl;

    // Adjust singleMode and naive if necessary.
    kfn.SingleMode() = CLI::HasParam("single_mode");
    kfn.Naive() = CLI::HasParam("naive");
    kfn.LeafSize() = size_t(lsInt);
    kfn.Epsilon() = epsilon;
  }

  // Perform search, if desired.
  if (CLI::HasParam("k"))
  {
    const string queryFile = CLI::GetParam<string>("query_file");
    const size_t k = (size_t) CLI::GetParam<int>("k");

    arma::mat queryData;
    if (queryFile != "")
    {
      data::Load(queryFile, queryData, true);
      Log::Info << "Loaded query data from '" << queryFile << "' ("
          << queryData.n_rows << "x" << queryData.n_cols << ")." << endl;
    }

    // Sanity check on k value: must be greater than 0, must be less than the
    // number of reference points.  Since it is unsigned, we only test the upper
    // bound.
    if (k > kfn.Dataset().n_cols)
    {
      Log::Fatal << "Invalid k: " << k << "; must be greater than 0 and less "
          << "than or equal to the number of reference points ("
          << kfn.Dataset().n_cols << ")." << endl;
    }

    // Naive mode overrides single mode.
    if (singleMode && naive)
      Log::Warn << "--single_mode ignored because --naive is present." << endl;

    // Now run the search.
    arma::Mat<size_t> neighbors;
    arma::mat distances;

    if (CLI::HasParam("query_file"))
      kfn.Search(std::move(queryData), k, neighbors, distances);
    else
      kfn.Search(k, neighbors, distances);
    Log::Info << "Search complete." << endl;

    // Save output, if desired.
    if (CLI::HasParam("neighbors_file"))
      data::Save(CLI::GetParam<string>("neighbors_file"), neighbors);
    if (CLI::HasParam("distances_file"))
      data::Save(CLI::GetParam<string>("distances_file"), distances);
  }

  if (CLI::HasParam("output_model_file"))
  {
    const string outputModelFile = CLI::GetParam<string>("output_model_File");
    data::Save(outputModelFile, "kfn_model", kfn);
  }
}
コード例 #2
0
ファイル: kfn_main.cpp プロジェクト: MarcosPividori/mlpack
int main(int argc, char *argv[])
{
  // Give CLI the command line parameters the user passed in.
  CLI::ParseCommandLine(argc, argv);

  if (CLI::GetParam<int>("seed") != 0)
    math::RandomSeed((size_t) CLI::GetParam<int>("seed"));
  else
    math::RandomSeed((size_t) std::time(NULL));

  // A user cannot specify both reference data and a model.
  if (CLI::HasParam("reference_file") && CLI::HasParam("input_model_file"))
    Log::Fatal << "Only one of --reference_file (-r) or --input_model_file (-m)"
        << " may be specified!" << endl;

  // A user must specify one of them...
  if (!CLI::HasParam("reference_file") && !CLI::HasParam("input_model_file"))
    Log::Fatal << "No model specified (--input_model_file) and no reference "
        << "data specified (--reference_file)!  One must be provided." << endl;

  if (CLI::HasParam("input_model_file"))
  {
    // Notify the user of parameters that will be ignored.
    if (CLI::HasParam("tree_type"))
      Log::Warn << "--tree_type (-t) will be ignored because --input_model_file"
          << " is specified." << endl;
    if (CLI::HasParam("random_basis"))
      Log::Warn << "--random_basis (-R) will be ignored because "
          << "--input_model_file is specified." << endl;
    // Notify the user of parameters that will be only be considered for query
    // tree.
    if (CLI::HasParam("leaf_size"))
      Log::Warn << "--leaf_size (-l) will only be considered for the query "
          "tree, because --input_model_file is specified." << endl;
  }

  // The user should give something to do...
  if (!CLI::HasParam("k") && !CLI::HasParam("output_model_file"))
    Log::Warn << "Neither -k nor --output_model_file are specified, so no "
        << "results from this program will be saved!" << endl;

  // If the user specifies k but no output files, they should be warned.
  if (CLI::HasParam("k") &&
      !(CLI::HasParam("neighbors_file") || CLI::HasParam("distances_file")))
    Log::Warn << "Neither --neighbors_file nor --distances_file is specified, "
        << "so the furthest neighbor search results will not be saved!" << endl;

  // If the user specifies output files but no k, they should be warned.
  if ((CLI::HasParam("neighbors_file") || CLI::HasParam("distances_file")) &&
      !CLI::HasParam("k"))
    Log::Warn << "An output file for furthest neighbor search is given ("
        << "--neighbors_file or --distances_file), but furthest neighbor search"
        << " is not being performed because k (--k) is not specified!  No "
        << "results will be saved." << endl;

  if (!CLI::HasParam("k") && CLI::HasParam("true_neighbors_file"))
    Log::Warn << "--true_neighbors_file (-T) ignored because no search is being"
        << " performed (--k is not specified)." << endl;

  if (!CLI::HasParam("k") && CLI::HasParam("true_distances_file"))
    Log::Warn << "--true_distances_file (-D) ignored because no search is being"
        << " performed (--k is not specified)." << endl;

  // Sanity check on leaf size.
  const int lsInt = CLI::GetParam<int>("leaf_size");
  if (lsInt < 1)
    Log::Fatal << "Invalid leaf size: " << lsInt << ".  Must be greater than 0."
        << endl;

  // Sanity check on epsilon.
  double epsilon = CLI::GetParam<double>("epsilon");
  if (epsilon < 0 || epsilon >= 1)
    Log::Fatal << "Invalid epsilon: " << epsilon << ".  Must be in the range "
        << "[0,1)." << endl;

  // Sanity check on percentage.
  const double percentage = CLI::GetParam<double>("percentage");
  if (percentage <= 0 || percentage > 1)
    Log::Fatal << "Invalid percentage: " << percentage << ".  Must be in the "
        << "range (0,1] (decimal form)." << endl;

  if (CLI::HasParam("percentage") && CLI::HasParam("epsilon"))
    Log::Fatal << "Cannot provide both epsilon and percentage." << endl;

  if (CLI::HasParam("percentage"))
    epsilon = 1 - percentage;

  // We either have to load the reference data, or we have to load the model.
  NSModel<FurthestNeighborSort> kfn;

  const string algorithm = CLI::GetParam<string>("algorithm");
  NeighborSearchMode searchMode = DUAL_TREE_MODE;

  if (algorithm == "naive")
    searchMode = NAIVE_MODE;
  else if (algorithm == "single_tree")
    searchMode = SINGLE_TREE_MODE;
  else if (algorithm == "dual_tree")
    searchMode = DUAL_TREE_MODE;
  else if (algorithm == "greedy")
    searchMode = GREEDY_SINGLE_TREE_MODE;
  else
    Log::Fatal << "Unknown neighbor search algorithm '" << algorithm << "'; "
        << "valid choices are 'naive', 'single_tree', 'dual_tree' and 'greedy'."
        << endl;

  if (CLI::HasParam("single_mode"))
  {
    searchMode = SINGLE_TREE_MODE;

    Log::Warn << "--single_mode is deprecated.  Will be removed in mlpack "
        "3.0.0. Use '--algorithm single_tree' instead." << endl;

    if (CLI::HasParam("algorithm") && algorithm != "single_tree")
      Log::Fatal << "Contradiction between options --algorithm " << algorithm <<
          " and --single_mode." << endl;
  }

  if (CLI::HasParam("naive"))
  {
    searchMode = NAIVE_MODE;

    Log::Warn << "--naive is deprecated.  Will be removed in mlpack 3.0.0. Use "
        "'--algorithm naive' instead." << endl;

    if (CLI::HasParam("algorithm") && algorithm != "naive")
      Log::Fatal << "Contradiction between options --algorithm " << algorithm <<
          " and --naive." << endl;

    if (CLI::HasParam("single_mode"))
      Log::Warn << "--single_mode ignored because --naive is present." << endl;
  }

  if (CLI::HasParam("reference_file"))
  {
    // Get all the parameters.
    const string referenceFile = CLI::GetParam<string>("reference_file");
    const string treeType = CLI::GetParam<string>("tree_type");
    const bool randomBasis = CLI::HasParam("random_basis");

    KFNModel::TreeTypes tree = KFNModel::KD_TREE;
    if (treeType == "kd")
      tree = KFNModel::KD_TREE;
    else if (treeType == "cover")
      tree = KFNModel::COVER_TREE;
    else if (treeType == "r")
      tree = KFNModel::R_TREE;
    else if (treeType == "r-star")
      tree = KFNModel::R_STAR_TREE;
    else if (treeType == "ball")
      tree = KFNModel::BALL_TREE;
    else if (treeType == "x")
      tree = KFNModel::X_TREE;
    else if (treeType == "hilbert-r")
      tree = KFNModel::HILBERT_R_TREE;
    else if (treeType == "r-plus")
      tree = KFNModel::R_PLUS_TREE;
    else if (treeType == "r-plus-plus")
      tree = KFNModel::R_PLUS_PLUS_TREE;
    else if (treeType == "vp")
      tree = KFNModel::VP_TREE;
    else if (treeType == "rp")
      tree = KFNModel::RP_TREE;
    else if (treeType == "max-rp")
      tree = KFNModel::MAX_RP_TREE;
    else if (treeType == "ub")
      tree = KFNModel::UB_TREE;
    else
      Log::Fatal << "Unknown tree type '" << treeType << "'; valid choices are "
          << "'kd', 'vp', 'rp', 'max-rp', 'ub', 'cover', 'r', 'r-star', 'x', "
          << "'ball', 'hilbert-r', 'r-plus' and 'r-plus-plus'." << endl;

    kfn.TreeType() = tree;
    kfn.RandomBasis() = randomBasis;

    arma::mat referenceSet;
    data::Load(referenceFile, referenceSet, true);

    Log::Info << "Loaded reference data from '" << referenceFile << "' ("
        << referenceSet.n_rows << "x" << referenceSet.n_cols << ")." << endl;

    kfn.BuildModel(std::move(referenceSet), size_t(lsInt), searchMode, epsilon);
  }
  else
  {
    // Load the model from file.
    const string inputModelFile = CLI::GetParam<string>("input_model_file");
    data::Load(inputModelFile, "kfn_model", kfn, true); // Fatal on failure.

    // Adjust search mode.
    kfn.SetSearchMode(searchMode);
    kfn.Epsilon() = epsilon;

    // If leaf_size wasn't provided, let's consider the current value in the
    // loaded model.  Else, update it (only considered when building the query
    // tree).
    if (CLI::HasParam("leaf_size"))
      kfn.LeafSize() = size_t(lsInt);

    Log::Info << "Loaded kFN model from '" << inputModelFile << "' (trained on "
        << kfn.Dataset().n_rows << "x" << kfn.Dataset().n_cols << " dataset)."
        << endl;
  }

  // Perform search, if desired.
  if (CLI::HasParam("k"))
  {
    const string queryFile = CLI::GetParam<string>("query_file");
    const size_t k = (size_t) CLI::GetParam<int>("k");

    arma::mat queryData;
    if (queryFile != "")
    {
      data::Load(queryFile, queryData, true);
      Log::Info << "Loaded query data from '" << queryFile << "' ("
          << queryData.n_rows << "x" << queryData.n_cols << ")." << endl;
    }

    // Sanity check on k value: must be greater than 0, must be less than the
    // number of reference points.  Since it is unsigned, we only test the upper
    // bound.
    if (k > kfn.Dataset().n_cols)
    {
      Log::Fatal << "Invalid k: " << k << "; must be greater than 0 and less "
          << "than or equal to the number of reference points ("
          << kfn.Dataset().n_cols << ")." << endl;
    }

    // Now run the search.
    arma::Mat<size_t> neighbors;
    arma::mat distances;

    if (CLI::HasParam("query_file"))
      kfn.Search(std::move(queryData), k, neighbors, distances);
    else
      kfn.Search(k, neighbors, distances);
    Log::Info << "Search complete." << endl;

    // Save output, if desired.
    if (CLI::HasParam("neighbors_file"))
      data::Save(CLI::GetParam<string>("neighbors_file"), neighbors);
    if (CLI::HasParam("distances_file"))
      data::Save(CLI::GetParam<string>("distances_file"), distances);

    // Calculate the effective error, if desired.
    if (CLI::HasParam("true_distances_file"))
    {
      if (kfn.Epsilon() == 0)
        Log::Warn << "--true_distances_file (-D) specified, but the search is "
            << "exact, so there is no need to calculate the error!" << endl;

      const string trueDistancesFile = CLI::GetParam<string>(
          "true_distances_file");
      arma::mat trueDistances;
      data::Load(trueDistancesFile, trueDistances, true);

      if (trueDistances.n_rows != distances.n_rows ||
          trueDistances.n_cols != distances.n_cols)
        Log::Fatal << "The true distances file must have the same number of "
            << "values than the set of distances being queried!" << endl;

      Log::Info << "Effective error: " << KFN::EffectiveError(distances,
          trueDistances) << endl;
    }

    // Calculate the recall, if desired.
    if (CLI::HasParam("true_neighbors_file"))
    {
      if (kfn.Epsilon() == 0)
        Log::Warn << "--true_neighbors_file (-T) specified, but the search is "
            << "exact, so there is no need to calculate the recall!" << endl;

      const string trueNeighborsFile = CLI::GetParam<string>(
          "true_neighbors_file");
      arma::Mat<size_t> trueNeighbors;
      data::Load(trueNeighborsFile, trueNeighbors, true);

      if (trueNeighbors.n_rows != neighbors.n_rows ||
          trueNeighbors.n_cols != neighbors.n_cols)
        Log::Fatal << "The true neighbors file must have the same number of "
            << "values than the set of neighbors being queried!" << endl;

      Log::Info << "Recall: " << KFN::Recall(neighbors, trueNeighbors) << endl;
    }
  }

  if (CLI::HasParam("output_model_file"))
  {
    const string outputModelFile = CLI::GetParam<string>("output_model_file");
    data::Save(outputModelFile, "kfn_model", kfn);
  }
}
コード例 #3
0
ファイル: kfn_main.cpp プロジェクト: dasayan05/mlpack
static void mlpackMain()
{
  if (CLI::GetParam<int>("seed") != 0)
    math::RandomSeed((size_t) CLI::GetParam<int>("seed"));
  else
    math::RandomSeed((size_t) std::time(NULL));

  // A user cannot specify both reference data and a model.
  RequireOnlyOnePassed({ "reference", "input_model" }, true);

  ReportIgnoredParam({{ "input_model", true }}, "tree_type");
  ReportIgnoredParam({{ "input_model", true }}, "random_basis");

  // Notify the user of parameters that will be only be considered for query
  // tree.
  if (CLI::HasParam("input_model") && CLI::HasParam("leaf_size"))
  {
    Log::Warn << PRINT_PARAM_STRING("leaf_size") << " will only be considered"
        << " for the query tree, because "
        << PRINT_PARAM_STRING("input_model") << " is specified." << endl;
  }

  // The user should give something to do...
  RequireAtLeastOnePassed({ "k", "output_model" }, false,
      "no results will be saved");

  // If the user specifies k but no output files, they should be warned.
  if (CLI::HasParam("k"))
  {
    RequireAtLeastOnePassed({ "neighbors", "distances" }, false,
        "furthest neighbor search results will not be saved");
  }

  // If the user specifies output files but no k, they should be warned.
  ReportIgnoredParam({{ "k", false }}, "neighbors");
  ReportIgnoredParam({{ "k", false }}, "distances");
  ReportIgnoredParam({{ "k", false }}, "true_neighbors");
  ReportIgnoredParam({{ "k", false }}, "true_distances");
  ReportIgnoredParam({{ "k", false }}, "query");

  // Sanity check on leaf size.
  RequireParamValue<int>("leaf_size", [](int x) { return x > 0; },
      true, "leaf size must be positive");
  const int lsInt = CLI::GetParam<int>("leaf_size");

  // Sanity check on epsilon.
  double epsilon = CLI::GetParam<double>("epsilon");
  RequireParamValue<double>("epsilon", [](double x) { return x >= 0.0; }, true,
      "epsilon must be positive");

  // Sanity check on percentage.
  const double percentage = CLI::GetParam<double>("percentage");
  RequireParamValue<double>("percentage",
      [](double x) { return x > 0.0 && x <= 1.0; }, true,
      "percentage must be in the range (0, 1]");

  ReportIgnoredParam({{ "epsilon", true }}, "percentage");

  if (CLI::HasParam("percentage"))
    epsilon = 1 - percentage;

  // We either have to load the reference data, or we have to load the model.
  NSModel<FurthestNeighborSort>* kfn;

  const string algorithm = CLI::GetParam<string>("algorithm");
  RequireParamInSet<string>("algorithm", { "naive", "single_tree", "dual_tree",
      "greedy" }, true, "unknown neighbor search algorithm");
  NeighborSearchMode searchMode = DUAL_TREE_MODE;

  if (algorithm == "naive")
    searchMode = NAIVE_MODE;
  else if (algorithm == "single_tree")
    searchMode = SINGLE_TREE_MODE;
  else if (algorithm == "dual_tree")
    searchMode = DUAL_TREE_MODE;
  else if (algorithm == "greedy")
    searchMode = GREEDY_SINGLE_TREE_MODE;

  if (CLI::HasParam("reference"))
  {
    kfn = new KFNModel();

    // Get all the parameters.
    RequireParamInSet<string>("tree_type", { "kd", "cover", "r", "r-star",
        "ball", "x", "hilbert-r", "r-plus", "r-plus-plus", "vp", "rp", "max-rp",
        "ub", "oct" }, true, "unknown tree type");
    const string treeType = CLI::GetParam<string>("tree_type");
    const bool randomBasis = CLI::HasParam("random_basis");

    KFNModel::TreeTypes tree = KFNModel::KD_TREE;
    if (treeType == "kd")
      tree = KFNModel::KD_TREE;
    else if (treeType == "cover")
      tree = KFNModel::COVER_TREE;
    else if (treeType == "r")
      tree = KFNModel::R_TREE;
    else if (treeType == "r-star")
      tree = KFNModel::R_STAR_TREE;
    else if (treeType == "ball")
      tree = KFNModel::BALL_TREE;
    else if (treeType == "x")
      tree = KFNModel::X_TREE;
    else if (treeType == "hilbert-r")
      tree = KFNModel::HILBERT_R_TREE;
    else if (treeType == "r-plus")
      tree = KFNModel::R_PLUS_TREE;
    else if (treeType == "r-plus-plus")
      tree = KFNModel::R_PLUS_PLUS_TREE;
    else if (treeType == "vp")
      tree = KFNModel::VP_TREE;
    else if (treeType == "rp")
      tree = KFNModel::RP_TREE;
    else if (treeType == "max-rp")
      tree = KFNModel::MAX_RP_TREE;
    else if (treeType == "ub")
      tree = KFNModel::UB_TREE;
    else if (treeType == "oct")
      tree = KFNModel::OCTREE;

    kfn->TreeType() = tree;
    kfn->RandomBasis() = randomBasis;

    arma::mat referenceSet = std::move(CLI::GetParam<arma::mat>("reference"));

    Log::Info << "Using reference data from '"
        << CLI::GetPrintableParam<arma::mat>("reference") << "' ("
        << referenceSet.n_rows << "x" << referenceSet.n_cols << ")." << endl;

    kfn->BuildModel(std::move(referenceSet), size_t(lsInt), searchMode,
        epsilon);
  }
  else
  {
    // Load the model from file.
    kfn = CLI::GetParam<KFNModel*>("input_model");

    // Adjust search mode.
    kfn->SearchMode() = searchMode;
    kfn->Epsilon() = epsilon;

    // If leaf_size wasn't provided, let's consider the current value in the
    // loaded model.  Else, update it (only considered when building the query
    // tree).
    if (CLI::HasParam("leaf_size"))
      kfn->LeafSize() = size_t(lsInt);

    Log::Info << "Using kFN model from '"
        << CLI::GetPrintableParam<KFNModel*>("input_model") << "' (trained on "
        << kfn->Dataset().n_rows << "x" << kfn->Dataset().n_cols
        << " dataset)." << endl;
  }

  // Perform search, if desired.
  if (CLI::HasParam("k"))
  {
    const size_t k = (size_t) CLI::GetParam<int>("k");

    arma::mat queryData;
    if (CLI::HasParam("query"))
    {
      queryData = std::move(CLI::GetParam<arma::mat>("query"));
      Log::Info << "Using query data from '"
          << CLI::GetPrintableParam<arma::mat>("query") << "' ("
          << queryData.n_rows << "x" << queryData.n_cols << ")." << endl;
    }

    // Sanity check on k value: must be greater than 0, must be less than or
    // equal to the number of reference points.  Since it is unsigned,
    // we only test the upper bound.
    if (k > kfn->Dataset().n_cols)
    {
      Log::Fatal << "Invalid k: " << k << "; must be greater than 0 and less "
          << "than or equal to the number of reference points ("
          << kfn->Dataset().n_cols << ")." << endl;
    }

    // Sanity check on k value: must not be equal to the number of reference
    // points when query data has not been provided.
    if (!CLI::HasParam("query") && k == kfn->Dataset().n_cols)
    {
      Log::Fatal << "Invalid k: " << k << "; must be less than the number of "
          << "reference points (" << kfn->Dataset().n_cols << ") "
          << "if query data has not been provided." << endl;
    }

    // Now run the search.
    arma::Mat<size_t> neighbors;
    arma::mat distances;

    if (CLI::HasParam("query"))
      kfn->Search(std::move(queryData), k, neighbors, distances);
    else
      kfn->Search(k, neighbors, distances);
    Log::Info << "Search complete." << endl;

    // Save output.
    CLI::GetParam<arma::Mat<size_t>>("neighbors") = std::move(neighbors);
    CLI::GetParam<arma::mat>("distances") = std::move(distances);

    // Calculate the effective error, if desired.
    if (CLI::HasParam("true_distances"))
    {
      if (kfn->Epsilon() == 0)
        Log::Warn << PRINT_PARAM_STRING("true_distances") << " specified, but "
            << "the search is exact, so there is no need to calculate the "
            << "error!" << endl;

      arma::mat trueDistances =
          std::move(CLI::GetParam<arma::mat>("true_distances"));

      if (trueDistances.n_rows != distances.n_rows ||
          trueDistances.n_cols != distances.n_cols)
        Log::Fatal << "The true distances file must have the same number of "
            << "values than the set of distances being queried!" << endl;

      Log::Info << "Effective error: " << KFN::EffectiveError(distances,
          trueDistances) << endl;
    }

    // Calculate the recall, if desired.
    if (CLI::HasParam("true_neighbors"))
    {
      if (kfn->Epsilon() == 0)
        Log::Warn << PRINT_PARAM_STRING("true_neighbors") << " specified, but "
            << "the search is exact, so there is no need to calculate the "
            << "recall!" << endl;

      arma::Mat<size_t> trueNeighbors =
          std::move(CLI::GetParam<arma::Mat<size_t>>("true_neighbors"));

      if (trueNeighbors.n_rows != neighbors.n_rows ||
          trueNeighbors.n_cols != neighbors.n_cols)
        Log::Fatal << "The true neighbors file must have the same number of "
            << "values than the set of neighbors being queried!" << endl;

      Log::Info << "Recall: " << KFN::Recall(neighbors, trueNeighbors) << endl;
    }
  }

  CLI::GetParam<KFNModel*>("output_model") = kfn;
}