density_clustering.hpp File Reference

Density-based clustering. More...

#include <vector>
#include <map>
#include <set>
#include <array>
#include <utility>
#include <string>
#include <boost/program_options.hpp>
#include "tools.hpp"

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Classes
struct	Clustering::Density::BoxGrid

Namespaces
	Clustering
	general namespace for clustering package
	Clustering::Density
	namespace for density-based clustering functions

Typedefs
using	Clustering::Density::FreeEnergy = std::pair< std::size_t, float >
	matches frame id to free energy
using	Clustering::Density::Neighbor = Clustering::Tools::Neighbor
	matches neighbor's frame id to distance
using	Clustering::Density::Neighborhood = Clustering::Tools::Neighborhood
	map frame id to neighbors
using	Clustering::Density::Box = std::array< int, 2 >
	encodes 2D box for box-assisted search algorithm

Functions
constexpr Box	Clustering::Density::neighbor_box (const Box center, const int i_neighbor)
BoxGrid	Clustering::Density::compute_box_grid (const float *coords, const std::size_t n_rows, const std::size_t n_cols, const float radius)
bool	Clustering::Density::is_valid_box (const Box box, const BoxGrid &grid)
std::vector< std::size_t >	Clustering::Density::calculate_populations (const float *coords, const std::size_t n_rows, const std::size_t n_cols, const float radius)
	calculate population of n-dimensional hypersphere per frame for one fixed radius.
std::map< float, std::vector< std::size_t > >	Clustering::Density::calculate_populations (const float *coords, const std::size_t n_rows, const std::size_t n_cols, std::vector< float > radii)
std::vector< float >	Clustering::Density::calculate_free_energies (const std::vector< std::size_t > &pops)
std::vector< FreeEnergy >	Clustering::Density::sorted_free_energies (const std::vector< float > &fe)
std::tuple< Neighborhood, Neighborhood >	Clustering::Density::nearest_neighbors (const float *coords, const std::size_t n_rows, const std::size_t n_cols, const std::vector< float > &free_energy)
void	Clustering::Density::screening_log (const double sigma2, const std::size_t first_frame_above_threshold, const std::vector< FreeEnergy > &fe_sorted)
	log output for screening steps
std::tuple< std::vector< std::size_t >, std::size_t, double, std::vector< FreeEnergy >, std::set< std::size_t >, std::size_t >	Clustering::Density::prepare_initial_clustering (const std::vector< float > &free_energy, const Neighborhood &nh, const float free_energy_threshold, const std::size_t n_rows, const std::vector< std::size_t > initial_clusters)
std::vector< std::size_t >	Clustering::Density::normalized_cluster_names (std::size_t first_frame_above_threshold, std::vector< std::size_t > clustering, std::vector< FreeEnergy > &fe_sorted)
	return clustered trajectory with new, distinct cluster names.
bool	Clustering::Density::lump_initial_clusters (const std::set< std::size_t > &local_nh, std::size_t &distinct_name, std::vector< std::size_t > &clustering, const std::vector< FreeEnergy > &fe_sorted, std::size_t first_frame_above_threshold)
	lump clusters based on distance threshold in screening process
std::set< std::size_t >	Clustering::Density::high_density_neighborhood (const float *coords, const std::size_t n_cols, const std::vector< FreeEnergy > &sorted_fe, const std::size_t i_frame, const std::size_t limit, const float max_dist)
double	Clustering::Density::compute_sigma2 (const Neighborhood &nh)
bool	Clustering::Density::compare2DVector (const std::pair< std::size_t, std::size_t > &p1, const std::pair< std::size_t, std::size_t > &p2)
	compare two two dimensional pairs by their second entry
bool	Clustering::Density::has2digits (float val)
	check if float has at maximum two digits
std::vector< std::size_t >	Clustering::Density::sorted_cluster_names (std::vector< std::size_t > clustering)
	sorts the cluster by decreasing population and renames them from 1..N
std::vector< std::size_t >	Clustering::Density::assign_low_density_frames (const std::vector< std::size_t > &initial_clustering, const Neighborhood &nh_high_dens, const std::vector< float > &free_energy)
void	Clustering::Density::main (boost::program_options::variables_map args)
	user interface and controlling function for density-based geometric clustering. More...

Variables
constexpr int	Clustering::Density::BOX_DIFF [9][2]
const int	Clustering::Density::N_NEIGHBOR_BOXES = 9
	number of neigbor boxes in 2D grid (including center box).

Detailed Description

Density-based clustering.

See also

Clustering::Noise

Definition in file density_clustering.hpp.

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Class Documentation

Clustering::Density::BoxGrid

struct Clustering::Density::BoxGrid

the full grid constructed for boxed-assisted nearest neighbor search with fixed distance criterion.

Definition at line 85 of file density_clustering.hpp.

Class Members
vector< Box >	assigned_box	matching frame id to the frame's assigned box
map< Box, vector< int > >	boxes	the boxes with a list of assigned frame ids
vector< int >	n_boxes	total number of boxes