/*! * Copyright (c) 2021 by Contributors * \file graph/transform/knn.h * \brief k-nearest-neighbor (KNN) implementation */ #ifndef DGL_GRAPH_TRANSFORM_KNN_H_ #define DGL_GRAPH_TRANSFORM_KNN_H_ #include #include namespace dgl { namespace transform { /*! * \brief For each point in each segment in \a query_points, find \a k nearest * points in the same segment in \a data_points. \a data_offsets and \a query_offsets * determine the start index of each segment in \a data_points and \a query_points. * * \param data_points dataset points. * \param data_offsets offsets of point index in \a data_points. * \param query_points query points. * \param query_offsets offsets of point index in \a query_points. * \param k the number of nearest points. * \param result output array. A 2D tensor indicating the index * relation between \a query_points and \a data_points. * \param algorithm algorithm used to compute the k-nearest neighbors. */ template void KNN(const NDArray& data_points, const IdArray& data_offsets, const NDArray& query_points, const IdArray& query_offsets, const int k, IdArray result, const std::string& algorithm); /*! * \brief For each input point, find \a k approximate nearest points in the same * segment using NN-descent algorithm. * * \param points input points. * \param offsets offsets of point index. * \param result output array. A 2D tensor indicating the index relation between points. * \param k the number of nearest points. * \param num_iters The maximum number of NN-descent iterations to perform. * \param num_candidates The maximum number of candidates to be considered during one iteration. * \param delta A value controls the early abort. */ template void NNDescent(const NDArray& points, const IdArray& offsets, IdArray result, const int k, const int num_iters, const int num_candidates, const double delta); } // namespace transform } // namespace dgl #endif // DGL_GRAPH_TRANSFORM_KNN_H_