text: /***********************************************************************
   * Software License Agreement (BSD License)
   *
   * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
   * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
   *
   * THE BSD LICENSE
   *
   * Redistribution and use in source and binary forms, with or without
   * modification, are permitted provided that the following conditions
   * are met:
   *
   * 1. Redistributions of source code must retain the above copyright
   *    notice, this list of conditions and the following disclaimer.
   * 2. Redistributions in binary form must reproduce the above copyright
   *    notice, this list of conditions and the following disclaimer in the
   *    documentation and/or other materials provided with the distribution.
   *
   * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
   * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
   * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
   * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
   * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
   * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
   * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
   * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
   * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
   * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
   *************************************************************************/
  
  #ifndef FLANN_HPP_
  #define FLANN_HPP_
  
  
  #include <vector>
  #include <string>
  #include <cassert>
  #include <cstdio>
  
  #include "./general.h"
  #include "./util/matrix.h"
  #include "./util/params.h"
  #include "./util/saving.h"
  
  #include "./algorithms/all_indices.h"
  
  namespace flann
  {
  
  /**
   * Sets the log level used for all flann functions
   * @param level Verbosity level
   */
  inline void log_verbosity(int level)
  {
      if (level >= 0) {
          Logger::setLevel(level);
      }
  }
  
  /**
   * (Deprecated) Index parameters for creating a saved index.
   */
  struct SavedIndexParams : public IndexParams
  {
      SavedIndexParams(std::string filename)
      {
          (*this)["algorithm"] = FLANN_INDEX_SAVED;
          (*this)["filename"] = filename;
      }
  };
  
  
  
  template<typename Distance>
  class Index
  {
  public:
      typedef typename Distance::ElementType ElementType;
      typedef typename Distance::ResultType DistanceType;
      typedef NNIndex<Distance> IndexType;
  
      Index(const IndexParams& params, Distance distance = Distance() )
          : index_params_(params)
      {
          flann_algorithm_t index_type = get_param<flann_algorithm_t>(params,"algorithm");
          loaded_ = false;
  
          Matrix<ElementType> features;
          if (index_type == FLANN_INDEX_SAVED) {
              nnIndex_ = load_saved_index(features, get_param<std::string>(params,"filename"), distance);
              loaded_ = true;
          }
          else {
          	flann_algorithm_t index_type = get_param<flann_algorithm_t>(params, "algorithm");
              nnIndex_ = create_index_by_type<Distance>(index_type, features, params, distance);
          }
      }
  
  
      Index(const Matrix<ElementType>& features, const IndexParams& params, Distance distance = Distance() )
          : index_params_(params)
      {
          flann_algorithm_t index_type = get_param<flann_algorithm_t>(params,"algorithm");
          loaded_ = false;
  
          if (index_type == FLANN_INDEX_SAVED) {
              nnIndex_ = load_saved_index(features, get_param<std::string>(params,"filename"), distance);
              loaded_ = true;
          }
          else {
          	flann_algorithm_t index_type = get_param<flann_algorithm_t>(params, "algorithm");
              nnIndex_ = create_index_by_type<Distance>(index_type, features, params, distance);
          }
      }
  
  
      Index(const Index& other) : loaded_(other.loaded_), index_params_(other.index_params_)
      {
      	nnIndex_ = other.nnIndex_->clone();
      }
  
      Index& operator=(Index other)
      {
      	this->swap(other);
      	return *this;
      }
  
      virtual ~Index()
      {
          delete nnIndex_;
      }
  
      /**
       * Builds the index.
       */
      void buildIndex()
      {
          if (!loaded_) {
              nnIndex_->buildIndex();
          }
      }
  
      void buildIndex(const Matrix<ElementType>& points)
      {
      	nnIndex_->buildIndex(points);
      }
  
      void addPoints(const Matrix<ElementType>& points, float rebuild_threshold = 2)
      {
          nnIndex_->addPoints(points, rebuild_threshold);
      }
  
      /**
       * Remove point from the index
       * @param index Index of point to be removed
       */
      void removePoint(size_t point_id)
      {
      	nnIndex_->removePoint(point_id);
      }
  
      /**
       * Returns pointer to a data point with the specified id.
       * @param point_id the id of point to retrieve
       * @return
       */
      ElementType* getPoint(size_t point_id)
      {
      	return nnIndex_->getPoint(point_id);
      }
  
      /**
       * Save index to file
       * @param filename
       */
      void save(std::string filename)
      {
          FILE* fout = fopen(filename.c_str(), "wb");
          if (fout == NULL) {
              throw FLANNException("Cannot open file");
          }
          nnIndex_->saveIndex(fout);
          fclose(fout);
      }
  
      /**
       * \returns number of features in this index.
       */
      size_t veclen() const
      {
          return nnIndex_->veclen();
      }
  
      /**
       * \returns The dimensionality of the features in this index.
       */
      size_t size() const
      {
          return nnIndex_->size();
      }
  
      /**
       * \returns The index type (kdtree, kmeans,...)
       */
      flann_algorithm_t getType() const
      {
          return nnIndex_->getType();
      }
  
      /**
       * \returns The amount of memory (in bytes) used by the index.
       */
      int usedMemory() const
      {
          return nnIndex_->usedMemory();
      }
  
  
      /**
       * \returns The index parameters
       */
      IndexParams getParameters() const
      {
          return nnIndex_->getParameters();
      }
  
      /**
       * \brief Perform k-nearest neighbor search
       * \param[in] queries The query points for which to find the nearest neighbors
       * \param[out] indices The indices of the nearest neighbors found
       * \param[out] dists Distances to the nearest neighbors found
       * \param[in] knn Number of nearest neighbors to return
       * \param[in] params Search parameters
       */
      int knnSearch(const Matrix<ElementType>& queries,
                                   Matrix<size_t>& indices,
                                   Matrix<DistanceType>& dists,
                                   size_t knn,
                             const SearchParams& params) const
      {
      	return nnIndex_->knnSearch(queries, indices, dists, knn, params);
      }
  
      /**
       *
       * @param queries
       * @param indices
       * @param dists
       * @param knn
       * @param params
       * @return
       */
      int knnSearch(const Matrix<ElementType>& queries,
                                   Matrix<int>& indices,
                                   Matrix<DistanceType>& dists,
                                   size_t knn,
                             const SearchParams& params) const
      {
      	return nnIndex_->knnSearch(queries, indices, dists, knn, params);
      }
  
      /**
       * \brief Perform k-nearest neighbor search
       * \param[in] queries The query points for which to find the nearest neighbors
       * \param[out] indices The indices of the nearest neighbors found
       * \param[out] dists Distances to the nearest neighbors found
       * \param[in] knn Number of nearest neighbors to return
       * \param[in] params Search parameters
       */
      int knnSearch(const Matrix<ElementType>& queries,
                                   std::vector< std::vector<size_t> >& indices,
                                   std::vector<std::vector<DistanceType> >& dists,
                                   size_t knn,
                             const SearchParams& params)
      {
      	return nnIndex_->knnSearch(queries, indices, dists, knn, params);
      }
  
      /**
       *
       * @param queries
       * @param indices
       * @param dists
       * @param knn
       * @param params
       * @return
       */
      int knnSearch(const Matrix<ElementType>& queries,
                                   std::vector< std::vector<int> >& indices,
                                   std::vector<std::vector<DistanceType> >& dists,
                                   size_t knn,
                             const SearchParams& params) const
      {
      	return nnIndex_->knnSearch(queries, indices, dists, knn, params);
      }
  
      /**
       * \brief Perform radius search
       * \param[in] queries The query points
       * \param[out] indices The indinces of the neighbors found within the given radius
       * \param[out] dists The distances to the nearest neighbors found
       * \param[in] radius The radius used for search
       * \param[in] params Search parameters
       * \returns Number of neighbors found
       */
      int radiusSearch(const Matrix<ElementType>& queries,
                                      Matrix<size_t>& indices,
                                      Matrix<DistanceType>& dists,
                                      float radius,
                                const SearchParams& params) const
      {
      	return nnIndex_->radiusSearch(queries, indices, dists, radius, params);
      }
  
      /**
       *
       * @param queries
       * @param indices
       * @param dists
       * @param radius
       * @param params
       * @return
       */
      int radiusSearch(const Matrix<ElementType>& queries,
                                      Matrix<int>& indices,
                                      Matrix<DistanceType>& dists,
                                      float radius,
                                const SearchParams& params) const
      {
      	return nnIndex_->radiusSearch(queries, indices, dists, radius, params);
      }
  
      /**
       * \brief Perform radius search
       * \param[in] queries The query points
       * \param[out] indices The indinces of the neighbors found within the given radius
       * \param[out] dists The distances to the nearest neighbors found
       * \param[in] radius The radius used for search
       * \param[in] params Search parameters
       * \returns Number of neighbors found
       */
      int radiusSearch(const Matrix<ElementType>& queries,
                                      std::vector< std::vector<size_t> >& indices,
                                      std::vector<std::vector<DistanceType> >& dists,
                                      float radius,
                                const SearchParams& params) const
      {
      	return nnIndex_->radiusSearch(queries, indices, dists, radius, params);
      }
  
      /**
       *
       * @param queries
       * @param indices
       * @param dists
       * @param radius
       * @param params
       * @return
       */
      int radiusSearch(const Matrix<ElementType>& queries,
                                      std::vector< std::vector<int> >& indices,
                                      std::vector<std::vector<DistanceType> >& dists,
                                      float radius,
                                const SearchParams& params) const
      {
      	return nnIndex_->radiusSearch(queries, indices, dists, radius, params);
      }
  
  private:
      IndexType* load_saved_index(const Matrix<ElementType>& dataset, const std::string& filename, Distance distance)
      {
          FILE* fin = fopen(filename.c_str(), "rb");
          if (fin == NULL) {
              return NULL;
          }
          IndexHeader header = load_header(fin);
          if (header.data_type != flann_datatype_value<ElementType>::value) {
              throw FLANNException("Datatype of saved index is different than of the one to be created.");
          }
  
          IndexParams params;
          params["algorithm"] = header.index_type;
          IndexType* nnIndex = create_index_by_type<Distance>(header.index_type, dataset, params, distance);
          rewind(fin);
          nnIndex->loadIndex(fin);
          fclose(fin);
  
          return nnIndex;
      }
  
      void swap( Index& other)
      {
      	std::swap(nnIndex_, other.nnIndex_);
      	std::swap(loaded_, other.loaded_);
      	std::swap(index_params_, other.index_params_);
      }
  
  private:
      /** Pointer to actual index class */
      IndexType* nnIndex_;
      /** Indices if the index was loaded from a file */
      bool loaded_;
      /** Parameters passed to the index */
      IndexParams index_params_;
  };
  
  
  
  
  
  /**
   * Performs a hierarchical clustering of the points passed as argument and then takes a cut in the
   * the clustering tree to return a flat clustering.
   * @param[in] points Points to be clustered
   * @param centers The computed cluster centres. Matrix should be preallocated and centers.rows is the
   *  number of clusters requested.
   * @param params Clustering parameters (The same as for flann::KMeansIndex)
   * @param d Distance to be used for clustering (eg: flann::L2)
   * @return number of clusters computed (can be different than clusters.rows and is the highest number
   * of the form (branching-1)*K+1 smaller than clusters.rows).
   */
  template <typename Distance>
  int hierarchicalClustering(const Matrix<typename Distance::ElementType>& points, Matrix<typename Distance::ResultType>& centers,
                             const KMeansIndexParams& params, Distance d = Distance())
  {
      KMeansIndex<Distance> kmeans(points, params, d);
      kmeans.buildIndex();
  
      int clusterNum = kmeans.getClusterCenters(centers);
      return clusterNum;
  }
  
  }
  #endif /* FLANN_HPP_ */
  