Detailed Description

This module provides methods for clustering, histograms and other statistical computations.

This module provides code to compute clusterings. Adaptors are provided that allow easy clustering of points, and configurations of models in IMP::ConfigurationSet objects among other things.

Examples:

Author(s)

Keren Lasker, Daniel Russel

Version

SVN 8931

License:

LGPL. This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.

Publications:

Daniel Russel, Keren Lasker, Ben Webb, Dina Schneidman, Javier Velázquez-Muriel, Andrej Sali, “Integrative assembly modeling using IMP”, submitted, 2010. This paper provides an overview of the key concepts in IMP and how to apply them to biological problems.
Frank Alber, Friedrich Foerster, Dmitry Korkin, Maya Topf, Andrej Sali, “Integrating diverse data for structure determination of macromolecular assemblies”, Annual Review of Biochemistry, 2008. This paper provides a review of the integrative structure determination methodology and various data sources that can be used.

Data Structures
class	ConfigurationSetRMSDMetric
class	ConfigurationSetXYZEmbedding
	Embed a configuration using the XYZ coordinates of a set of particles. More...
class	Embedding
	Map clustering data to spatial positions. More...
class	Histogram
	Histogram. More...
class	Metric
	Compute a distance between two elements to be clustered. More...
class	ParticleEmbedding
class	PartitionalClustering
	The base class for clusterings of data sets. More...
class	PartitionalClusteringWithCenter
class	VectorDEmbedding
	Simply return the coordinates of a VectorD. More...
Functions
PartitionalClusteringWithCenter *	create_bin_based_clustering (Embedding *embed, double side)
PartitionalClustering *	create_centrality_clustering (Metric *d, double far, int k)
PartitionalClustering *	create_centrality_clustering (Embedding *d, double far, int k)
PartitionalClusteringWithCenter *	create_connectivity_clustering (Embedding *embed, double dist)
PartitionalClusteringWithCenter *	create_lloyds_kmeans (Embedding *embedding, unsigned int k, unsigned int iterations)
algebra::VectorKDs	get_centroids (Embedding d, PartitionalClustering pc)
std::string	get_data_path (std::string file_name)
	Return the path to installed data for this module.
std::string	get_example_path (std::string file_name)
	Return the path to installed example data for this module.
std::string	get_module_name ()
const VersionInfo &	get_module_version_info ()
Ints	get_representatives (Embedding d, PartitionalClustering pc)

Function Documentation

PartitionalClusteringWithCenter* IMP::statistics::create_bin_based_clustering	(	Embedding *	embed,
		double	side
	)

The space is grided with bins of side size and all points that fall in the same grid bin are made part of the same cluster.

PartitionalClustering* IMP::statistics::create_centrality_clustering	(	Metric *	d,
		double	far,
		int	k
	)

Cluster by repeatedly removing edges which have lots of shortest paths passing through them. The process is terminated when there are a set number of connected components. Other termination criteria can be added if someone proposes them.

PartitionalClustering* IMP::statistics::create_centrality_clustering	(	Embedding *	d,
		double	far,
		int	k
	)

Cluster by repeatedly removing edges which have lots of shortest paths passing through them. The process is terminated when there are a set number of connected components. Other termination criteria can be added if someone proposes them.

PartitionalClusteringWithCenter* IMP::statistics::create_connectivity_clustering	(	Embedding *	embed,
		double	dist
	)

Two points, $p_i$ , $p_j$ are in the same cluster if there is a sequence of points $\left(p^{ij}_{0}\dots p^{ij}_k\right)$ such that $\forall l ||p^{ij}_l-p^{ij}_{l+1}|| < d$ .

PartitionalClusteringWithCenter* IMP::statistics::create_lloyds_kmeans	(	Embedding *	embedding,
		unsigned int	k,
		unsigned int	iterations
	)

Return a k-means clustering of all points contained in the embedding (ie [0... embedding->get_number_of_embeddings())). These points are then clustered into k clusters. More iterations takes longer but produces a better clustering.

algebra::VectorKDs IMP::statistics::get_centroids	(	Embedding *	d,
		PartitionalClustering *	pc
	)

Given a clustering and an embedding, compute the centroid for each cluster

std::string IMP::statistics::get_data_path ( std::string file_name )

Return the path to installed data for this module.

Each module has its own data directory, so be sure to use the version of this function in the correct module. To read the data file "data_library" that was placed in the data directory of module "mymodule", do something like

    std::ifstream in(IMP::mymodule::get_data_path("data_library"));

This will ensure that the code works when IMP is installed or used via the tools/imppy.sh script.

std::string IMP::statistics::get_example_path ( std::string file_name )

Return the path to installed example data for this module.

Each module has its own example directory, so be sure to use the version of this function in the correct module. For example to read the file example_protein.pdb located in the examples directory of the IMP::atom module, do

    IMP::atom::read_pdb(IMP::atom::get_example_path("example_protein.pdb", model));

This will ensure that the code works when IMP is installed or used via the tools/imppy.sh script.

Ints IMP::statistics::get_representatives	(	Embedding *	d,
		PartitionalClustering *	pc
	)

Given a clustering and an embedding, compute a representatative element for each cluster.

IMP::statistics Namespace Reference