Divide-and-conquer inferential optimization in discrete space. More...

Detailed Description

Divide-and-conquer inferential optimization in discrete space.

Traditional optimizers require the data be encoded as a single (or a small number of) scoring function and then move the particles with a variety of random and downhill steps to try to find configurations of the particles which minimize the scoring function used. These sampling processes, in practice, provide no guarantees about whether the true minimal solutions. Since they depend on a scoring function, the set of solutions they do find are sensitive to the relative weights of the terms coming from different types of data, which can be difficult to choose correctly. Finally, the choice of which type of moves to take an when can make a large difference.

How Domino is different

The Domino sampler provides a wider variety of ways in which the data about the system can be encoded as restrictions on the allowed configurations and then enumerates all configurations that meet the restrictions, given sufficient memory/time to represent/store them all. Specifically, in a Domino encoding of a problem, each particle has a discrete set of states that it can be assigned. Data that concerns only a single particle, such as that about a particles absolute location/orientation and can be encoded directly this sampling space. Other data is encoded as filters, which determine whether a particular assignment of discrete states to a subset of the particles is acceptable or not. Most simply, a filter can be a scoring function term (a Restraint) along with an associated maximum value that term is allowed to take. Other filters include ones based on a maximum score for a set of scoring function terms, that eliminate all except one of assignments that are invariant under the permutation of particles which are deemed to be equivalent (eg to only find unique volumes occupied by proteins when sequence information is not used), that eliminate assignments where multiple variables are to the same state (eg to provide cheap excluded volume). There is no need to establish weighting between different types of experimental data.

Why Domino works

Domino can efficiently perform the enumeration by subdividing the particles into possibly overlapping subsets. The set of satisfying assignments for each subset is enumerated independently and then the subsets are repeatedly merged pairwise until only the set of all particles is remaining. A merge step involves first enumerating all consistent assignments of the two subsets being merged (that is, those assignments which agree on the assignment of any shared particles). Then, each such assignment is checked for consistency with all the filters which additionally constrain the newly created subset of particles. Since after each merge only the satisfying assignments for the currently subset are kept, and any satisfying assignment for a merged subset can be generated from satisfying assignments of subsets it was created by merging, at the end, exactly the set of satisfying assignments of the full particles set is generated.

Considerations for choosing a merge tree

Each filters is used to prune the assignments as early in the process as possible, that is, as soon as all the needed particles are first found in the same subset. In general, the earlier in the subset enumeration and merging filters can be applied, the more efficient the enumeration process. We have found that a depth first traversal of the junction tree {ref something} on a sparse subset of the interactions implied by connectivity based restraints (those derived from protein structures, proteomics, cross linking etc) is a good starting point for the merge tree. The junction tree ensures that the set of particles used by each filter occurs together in at least one of the leaf sets. The junction tree also ensures that each particle only is found in a connected subtree, helping limit the creation of assignments which will later be eliminated due to incompatibility during later merges. For further efficiency, each node of the junction tree -derived merge tree is broken down into a linear tree of subsets each containing a single particle. The particles are ordered heuristically, attempting to merge the most constrained particles first.

Domino as spatial filtering

An alternate way to think about the way Domino works is to imagine eliminating parts of the space of all possible assignments based on the filters. Initially we have a n dimensional grid (n is the number of particles), which has m (the number of states for each particles, assuming, for simplicity, they all have the same number) voxels along each dimension. Each n^m voxel in the grid is a possible assignment. Applying filters to a subset of size k involves eliminating an m-k-dimensional line of voxels (orthogonal to the k-dimension subspace of the subset, passing through the eliminated assignment of in the subset space). Each time two subsets of size k are merged, a never seen before 2k-dimensional subspace of solutions is made available that can have filters applied to it. Each such state eliminated by a filter again eliminates a m-2k-dimensional line.

Rules of Thumb

There are some good general rules of thumb for converting data into a problem suitable for a Domino sampler. First, data is only useful in the sampling process in so far as it can be used to accept or discard a configuration of a subset of the particles unconditionally. That is, simply scoring a configuration is not useful one must be able to say that a certain configuration is not worth considering. The restrictions can be relaxed in later runs to find configurations which are less good, so there is nothing to be worried about from having the filters be fine up front.

Pieces of data which concern only a single particle should be used to determine the discrete set of conformations allowed for that particle. This way, no computation has to be done during sampling for those terms.

Other data can be incorporated into the sampling process either as restraints with accompanying maximum scores (or sets of restraints with a maximum score), or more directly as filters. The latter can be more efficient, but requires more thought and so the former should be the default.

The set of data which involve only a few particles each (pair scores, connectivity etc), should be encoded as restraints and used to build the interaction graph (which is then used to build the junction tree and the merge tree). Other data that involves many particles interacting at once or all pairs of particles (eg dope scoring terms, when one can not show that most pairs of particles are never close to one another), can still be used, they just should not be used for the interaction graph.

The idea of a Table is used in several places in the module. A Table, here, is an object which, for example, knows how to create a an object which can filter the subset states of a particular subset. That is, a SubsetFilterTable knows how to create a SubsetFilter (via the SubsetFilterTable::get_subset_filter() ) which can answer queries about whether a particular SubsetState is OK (via the SubsetFilter::get_is_ok() method). This makes it easy to perform preprocessing to make the SubsetFilter::get_is_ok() calls faster. The table idea is usedf with ParticleStatesTable, SubsetStatesTable and SubsetFilterTable.

Info

Author(s): Daniel Russel, Keren Lasker

Maintainer: benmwebb

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:

See main IMP papers list.

Classes
class	Assignment
	Store a configuration of a subset. More...

class	AssignmentContainer

class	AssignmentsTable

class	BranchAndBoundAssignmentsTable

class	BranchAndBoundSampler
	Sample best solutions using BranchAndBound. More...

class	CappedAssignmentContainer

class	CompoundStates

class	DependencyScoreState
	Add a dependency to the dependency graph. More...

class	DiscreteSampler
	A base class for discrete samplers in Domino2. More...

class	DisjointSetsSubsetFilterTable
	A base class. More...

class	DominoSampler
	Sample best solutions using Domino. More...

class	EqualitySubsetFilterTable
	Do not allow two particles to be in the same state. More...

class	EquivalenceAndExclusionSubsetFilterTable
	Define sets of equivalent and exclusive particles. More...

class	EquivalenceSubsetFilterTable
	Define sets of equivalent particles. More...

class	ExclusionSubsetFilterTable
	Do not allow two particles to be in the same state. More...

class	HeapAssignmentContainer

class	IndexStates

class	ListAssignmentContainer

class	ListAssignmentsTable

class	ListSubsetFilterTable
	Maintain an explicit list of what states each particle is allowed to have. More...

class	MinimumRestraintScoreSubsetFilterTable
	Filter a configuration of the subset using the kernel::Model thresholds. More...

class	NestedRigidBodyStates

class	Order
	Store a persistent ordering for a subset based on the list. More...

class	PackedAssignmentContainer

class	PairListSubsetFilterTable

class	ParticlesAdaptor

class	ParticleStates

class	ParticleStatesTable

class	PermutationStates

class	ProbabilisticSubsetFilterTable

class	RangeViewAssignmentContainer

class	ReadAssignmentContainer

class	ReadHDF5AssignmentContainer

class	RecursiveAssignmentsTable

class	RecursiveStates

class	RestraintCache

class	RestraintScoreSubsetFilterTable
	Filter a configuration of the subset using the kernel::Model thresholds. More...

class	RigidBodyStates

class	SampleAssignmentContainer

class	SimpleAssignmentsTable

class	Slice
	Store a subset of a subset or assignment. More...

class	Subset
	Represent a subset of the particles being optimized. More...

class	SubsetFilter

class	SubsetFilterTable

class	WriteAssignmentContainer

class	WriteHDF5AssignmentContainer

class	XYZStates

Typedefs
typedef IMP::base::Vector < IMP::base::Pointer < AssignmentContainer > >	AssignmentContainers

typedef IMP::base::Vector < IMP::base::WeakPointer < AssignmentContainer > >	AssignmentContainersTemp

typedef IMP::base::Vector < Assignment >	Assignments

typedef IMP::base::Vector < IMP::base::Pointer < AssignmentsTable > >	AssignmentsTables

typedef IMP::base::Vector < IMP::base::WeakPointer < AssignmentsTable > >	AssignmentsTablesTemp

typedef IMP::base::Vector < IMP::base::Pointer < BranchAndBoundSampler > >	BranchAndBoundSamplers

typedef IMP::base::Vector < IMP::base::WeakPointer < BranchAndBoundSampler > >	BranchAndBoundSamplersTemp

typedef IMP::base::Vector < IMP::base::Pointer < DiscreteSampler > >	DiscreteSamplers

typedef IMP::base::Vector < IMP::base::WeakPointer < DiscreteSampler > >	DiscreteSamplersTemp

typedef IMP::base::Vector < IMP::base::Pointer < DominoSampler > >	DominoSamplers

typedef IMP::base::Vector < IMP::base::WeakPointer < DominoSampler > >	DominoSamplersTemp

typedef IMP::base::Vector < IMP::base::Pointer < EqualitySubsetFilterTable > >	EqualitySubsetFilterTables

typedef IMP::base::Vector < IMP::base::WeakPointer < EqualitySubsetFilterTable > >	EqualitySubsetFilterTablesTemp

typedef IMP::base::Vector < IMP::base::Pointer < EquivalenceAndExclusionSubsetFilterTable > >	EquivalenceAndExclusionSubsetFilterTables

typedef IMP::base::Vector < IMP::base::WeakPointer < EquivalenceAndExclusionSubsetFilterTable > >	EquivalenceAndExclusionSubsetFilterTablesTemp

typedef IMP::base::Vector < IMP::base::Pointer < EquivalenceSubsetFilterTable > >	EquivalenceSubsetFilterTables

typedef IMP::base::Vector < IMP::base::WeakPointer < EquivalenceSubsetFilterTable > >	EquivalenceSubsetFilterTablesTemp

typedef IMP::base::Vector < IMP::base::Pointer < ExclusionSubsetFilterTable > >	ExclusionSubsetFilterTables

typedef IMP::base::Vector < IMP::base::WeakPointer < ExclusionSubsetFilterTable > >	ExclusionSubsetFilterTablesTemp

typedef boost::graph	InteractionGraph

typedef InteractionGraph::EdgeNameMap	InteractionGraphConstEdgeName

typedef InteractionGraph::VertexNameMap	InteractionGraphConstVertexName

typedef InteractionGraph::edge_descriptor	InteractionGraphEdge

typedef boost::graph_traits < InteractionGraph >	InteractionGraphTraits

typedef InteractionGraph::vertex_descriptor	InteractionGraphVertex

typedef IMP::base::Vector < IMP::base::Pointer < ListSubsetFilterTable > >	ListSubsetFilterTables

typedef IMP::base::Vector < IMP::base::WeakPointer < ListSubsetFilterTable > >	ListSubsetFilterTablesTemp

typedef IMP::base::Vector < IMP::base::Pointer < MinimumRestraintScoreSubsetFilterTable > >	MinimumRestraintScoreSubsetFilterTables

typedef IMP::base::Vector < IMP::base::WeakPointer < MinimumRestraintScoreSubsetFilterTable > >	MinimumRestraintScoreSubsetFilterTablesTemp

typedef IMP::base::Vector< Order >	Orders

typedef IMP::base::Vector < IMP::base::Pointer < PairListSubsetFilterTable > >	PairListSubsetFilterTables

typedef IMP::base::Vector < IMP::base::WeakPointer < PairListSubsetFilterTable > >	PairListSubsetFilterTablesTemp

typedef IMP::base::Vector < IMP::base::Pointer < ParticleStates > >	ParticleStatesList

typedef IMP::base::Vector < IMP::base::WeakPointer < ParticleStates > >	ParticleStatesListTemp

typedef IMP::base::Vector < IMP::base::Pointer < ParticleStatesTable > >	ParticleStatesTables

typedef IMP::base::Vector < IMP::base::WeakPointer < ParticleStatesTable > >	ParticleStatesTablesTemp

typedef IMP::base::Vector < IMP::base::Pointer < RestraintScoreSubsetFilterTable > >	RestraintScoreSubsetFilterTables

typedef IMP::base::Vector < IMP::base::WeakPointer < RestraintScoreSubsetFilterTable > >	RestraintScoreSubsetFilterTablesTemp

typedef IMP::base::Vector< Slice >	Slices

typedef IMP::base::Vector < IMP::base::Pointer < SubsetFilter > >	SubsetFilters

typedef IMP::base::Vector < IMP::base::WeakPointer < SubsetFilter > >	SubsetFiltersTemp

typedef IMP::base::Vector < IMP::base::Pointer < SubsetFilterTable > >	SubsetFilterTables

typedef IMP::base::Vector < IMP::base::WeakPointer < SubsetFilterTable > >	SubsetFilterTablesTemp

typedef boost::graph	SubsetGraph

typedef SubsetGraph::EdgeNameMap	SubsetGraphConstEdgeName

typedef SubsetGraph::VertexNameMap	SubsetGraphConstVertexName

typedef SubsetGraph::edge_descriptor	SubsetGraphEdge

typedef boost::graph_traits < SubsetGraph >	SubsetGraphTraits

typedef SubsetGraph::vertex_descriptor	SubsetGraphVertex

typedef IMP::base::Vector< Subset >	Subsets

Functions
Subset	get_difference (const Subset &a, const Subset &b)

double	get_distance_if_smaller_than (const Subset &s, const Assignment &a, const Assignment &b, ParticleStatesTable *pst, const statistics::Metrics &metrics, double max)

algebra::VectorKD	get_embedding (const Subset &s, const Assignment &a, ParticleStatesTable *pst)
	Return an embedding for an assignment. More...

Ints	get_index (const kernel::ParticlesTemp &particles, const Subset &subset, const Subsets &excluded)

Subset	get_intersection (const Subset &a, const Subset &b)

SubsetGraph	get_junction_tree (const InteractionGraph &ig)

Assignment	get_nearest_assignment (const Subset &s, const algebra::VectorKD &embedding, ParticleStatesTable *pst)
	Return the nearest assignment from an embedding. More...

kernel::ParticlesTemp	get_order (const Subset &s, const SubsetFilterTables &sft)

Ints	get_partial_index (const kernel::ParticlesTemp &particles, const Subset &subset, const Subsets &excluded)

kernel::ParticlePairsTemp	get_possible_interactions (const kernel::ParticlesTemp &ps, double max_distance, ParticleStatesTable *pst)

SubsetGraph	get_restraint_graph (ScoringFunctionAdaptor rs, const ParticleStatesTable *pst)

Slice	get_slice (Subset outer, Subset inner, const Subsets &excluded)

Assignments	get_state_clusters (const Subset &subset, const Assignments &states, ParticleStatesTable *pst, double resolution)
	Return subset of the passed states which are representative. More...

Subsets	get_subsets (const SubsetGraph &g)
	Gets all of the Subsets of a SubsetGraph. More...

Subset	get_union (const Subset &a, const Subset &b)

void	load_leaf_assignments (const Subset &subset, AssignmentsTable at, AssignmentContainer ac)
	Fill in assignments for a leaf. More...

void	load_merged_assignments (const Subset &first_subset, AssignmentContainer first, const Subset &second_subset, AssignmentContainer second, const SubsetFilterTablesTemp &filters, AssignmentContainer *ret)
	Fill in assignments for an internal node. More...

void	load_merged_assignments_random_order (const Subset &first_subset, AssignmentContainer first, const Subset &second_subset, AssignmentContainer second, const SubsetFilterTablesTemp &filters, unsigned int maximum_tries, AssignmentContainer *ret)
	Sample from the merged assignments. More...

void	swap (Assignment &a, Assignment &b)

void	swap (Slice &a, Slice &b)

void	swap (Order &a, Order &b)

void	swap (Subset &a, Subset &b)

Standard module functions
All `IMP` modules have a set of standard functions to help get information about the module and about files associated with the module.
std::string	get_module_version ()

std::string	get_module_name ()

std::string	get_data_path (std::string file_name)
	Return the full path to installed data. More...

std::string	get_example_path (std::string file_name)
	Return the path to installed example data for this module. More...

typedef boost::graph	MergeTree

typedef MergeTree::VertexNameMap	MergeTreeConstVertexName

typedef MergeTree::EdgeNameMap	MergeTreeConstEdgeName

typedef boost::graph_traits < MergeTree >	MergeTreeTraits

typedef MergeTree::vertex_descriptor	MergeTreeVertex

typedef MergeTree::edge_descriptor	MergeTreeEdge

void	show_as_graphviz (const SubsetGraph &name, base::TextOutput out)

SubsetGraphVertexIndex	get_vertex_index (const SubsetGraph &g)

InteractionGraph	get_interaction_graph (ScoringFunctionAdaptor rs, const ParticleStatesTable *pst)

InteractionGraph	get_interaction_graph (ScoringFunctionAdaptor rs, const kernel::ParticlesTemp &pst)

display::Geometries	get_interaction_graph_geometry (const InteractionGraph &ig)

display::Geometries	get_subset_graph_geometry (const SubsetGraph &ig)

MergeTree	get_merge_tree (ScoringFunctionAdaptor input, const ParticleStatesTable *pst)

MergeTree	get_merge_tree (const SubsetGraph &junction_tree)

MergeTree	get_balanced_merge_tree (const SubsetGraph &junction_tree)

bool	get_is_merge_tree (const MergeTree &tree, Subset all, bool verbose=true)

Read and write merge trees
Merge trees can be written to and read from a text stream. As always, this requires a canonical particles list whose order is the same for the reading and writing.
void	write_merge_tree (const MergeTree &tree, const kernel::ParticlesTemp &ps, std::ostream &out)

MergeTree	read_merge_tree (std::istream &input, const kernel::ParticlesTemp &ps)

Debug tools
We provide a number of different functions for helping optimize and understand domino-based sampling. These functions are expose part of the implementation and are liable to change without notice.
void	load_particle_states (const Subset &s, const Assignment &ss, const ParticleStatesTable *pst)

kernel::RestraintsTemp	get_restraints (const Subset &s, const ParticleStatesTable pst, const DependencyGraph &dg, kernel::RestraintSet rs)

Typedef Documentation

typedef IMP::base::Vector<IMP::base::Pointer< AssignmentContainer > > IMP::domino::AssignmentContainers

Store a set of objects.

Definition at line 58 of file assignment_containers.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< AssignmentContainer > > IMP::domino::AssignmentContainersTemp

Pass a set of objects.

See Also: AssignmentContainer

Definition at line 58 of file assignment_containers.h.

typedef IMP::base::Vector< Assignment > IMP::domino::Assignments

Pass or store a set of Assignment .

Definition at line 44 of file Assignment.h.

typedef IMP::base::Vector<IMP::base::Pointer< AssignmentsTable > > IMP::domino::AssignmentsTables

Store a set of objects.

Definition at line 44 of file assignment_tables.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< AssignmentsTable > > IMP::domino::AssignmentsTablesTemp

Pass a set of objects.

See Also: AssignmentsTable

Definition at line 44 of file assignment_tables.h.

typedef IMP::base::Vector<IMP::base::Pointer< BranchAndBoundSampler > > IMP::domino::BranchAndBoundSamplers

Store a set of objects.

Definition at line 38 of file BranchAndBoundSampler.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< BranchAndBoundSampler > > IMP::domino::BranchAndBoundSamplersTemp

Pass a set of objects.

See Also: BranchAndBoundSampler

Definition at line 38 of file BranchAndBoundSampler.h.

typedef IMP::base::Vector<IMP::base::Pointer< DiscreteSampler > > IMP::domino::DiscreteSamplers

Store a set of objects.

Definition at line 112 of file DiscreteSampler.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< DiscreteSampler > > IMP::domino::DiscreteSamplersTemp

Pass a set of objects.

See Also: DiscreteSampler

Definition at line 112 of file DiscreteSampler.h.

typedef IMP::base::Vector<IMP::base::Pointer< DominoSampler > > IMP::domino::DominoSamplers

Store a set of objects.

Definition at line 122 of file DominoSampler.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< DominoSampler > > IMP::domino::DominoSamplersTemp

Pass a set of objects.

See Also: DominoSampler

Definition at line 122 of file DominoSampler.h.

typedef IMP::base::Vector<IMP::base::Pointer< EqualitySubsetFilterTable > > IMP::domino::EqualitySubsetFilterTables

Store a set of objects.

Definition at line 228 of file subset_filters.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< EqualitySubsetFilterTable > > IMP::domino::EqualitySubsetFilterTablesTemp

Pass a set of objects.

See Also: EqualitySubsetFilterTable

Definition at line 228 of file subset_filters.h.

typedef IMP::base::Vector<IMP::base::Pointer< EquivalenceAndExclusionSubsetFilterTable > > IMP::domino::EquivalenceAndExclusionSubsetFilterTables

Store a set of objects.

Definition at line 245 of file subset_filters.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< EquivalenceAndExclusionSubsetFilterTable > > IMP::domino::EquivalenceAndExclusionSubsetFilterTablesTemp

Pass a set of objects.

See Also: EquivalenceAndExclusionSubsetFilterTable

Definition at line 245 of file subset_filters.h.

typedef IMP::base::Vector<IMP::base::Pointer< EquivalenceSubsetFilterTable > > IMP::domino::EquivalenceSubsetFilterTables

Store a set of objects.

Definition at line 238 of file subset_filters.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< EquivalenceSubsetFilterTable > > IMP::domino::EquivalenceSubsetFilterTablesTemp

Pass a set of objects.

See Also: EquivalenceSubsetFilterTable

Definition at line 238 of file subset_filters.h.

typedef IMP::base::Vector<IMP::base::Pointer< ExclusionSubsetFilterTable > > IMP::domino::ExclusionSubsetFilterTables

Store a set of objects.

Definition at line 219 of file subset_filters.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< ExclusionSubsetFilterTable > > IMP::domino::ExclusionSubsetFilterTablesTemp

Pass a set of objects.

See Also: ExclusionSubsetFilterTable

Definition at line 219 of file subset_filters.h.

typedef boost::graph IMP::domino::InteractionGraph

An undirected graph with one vertex per particle of interest. Two particles are connected by an edge if a kernel::Restraint or ScoreState creates an interaction between the two particles.

See Graphs in IMP for more information.See Graphs in IMP for more information.

Definition at line 33 of file subset_graphs.h.

typedef IMP::base::Vector<IMP::base::Pointer< ListSubsetFilterTable > > IMP::domino::ListSubsetFilterTables

Store a set of objects.

Definition at line 289 of file subset_filters.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< ListSubsetFilterTable > > IMP::domino::ListSubsetFilterTablesTemp

Pass a set of objects.

See Also: ListSubsetFilterTable

Definition at line 289 of file subset_filters.h.

typedef boost::graph IMP::domino::MergeTree

A directed graph on subsets of vertices. Each vertex is named with a Subset.See Graphs in IMP for more information.

Definition at line 117 of file subset_graphs.h.

typedef IMP::base::Vector<IMP::base::Pointer< MinimumRestraintScoreSubsetFilterTable > > IMP::domino::MinimumRestraintScoreSubsetFilterTables

Store a set of objects.

Definition at line 154 of file subset_filters.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< MinimumRestraintScoreSubsetFilterTable > > IMP::domino::MinimumRestraintScoreSubsetFilterTablesTemp

Pass a set of objects.

See Also: MinimumRestraintScoreSubsetFilterTable

Definition at line 154 of file subset_filters.h.

typedef IMP::base::Vector< Order > IMP::domino::Orders

Pass or store a set of Order .

Definition at line 67 of file Order.h.

typedef IMP::base::Vector<IMP::base::Pointer< PairListSubsetFilterTable > > IMP::domino::PairListSubsetFilterTables

Store a set of objects.

Definition at line 315 of file subset_filters.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< PairListSubsetFilterTable > > IMP::domino::PairListSubsetFilterTablesTemp

Pass a set of objects.

See Also: PairListSubsetFilterTable

Definition at line 315 of file subset_filters.h.

typedef IMP::base::Vector<IMP::base::Pointer< ParticleStates > > IMP::domino::ParticleStatesList

Store a set of objects.

Definition at line 64 of file particle_states.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< ParticleStates > > IMP::domino::ParticleStatesListTemp

Pass a set of objects.

See Also: ParticleStates

Definition at line 64 of file particle_states.h.

typedef IMP::base::Vector<IMP::base::Pointer< ParticleStatesTable > > IMP::domino::ParticleStatesTables

Store a set of objects.

Definition at line 114 of file particle_states.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< ParticleStatesTable > > IMP::domino::ParticleStatesTablesTemp

Pass a set of objects.

See Also: ParticleStatesTable

Definition at line 114 of file particle_states.h.

typedef IMP::base::Vector<IMP::base::Pointer< RestraintScoreSubsetFilterTable > > IMP::domino::RestraintScoreSubsetFilterTables

Store a set of objects.

Definition at line 123 of file subset_filters.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< RestraintScoreSubsetFilterTable > > IMP::domino::RestraintScoreSubsetFilterTablesTemp

Pass a set of objects.

See Also: RestraintScoreSubsetFilterTable

Definition at line 123 of file subset_filters.h.

typedef IMP::base::Vector< Slice > IMP::domino::Slices

Pass or store a set of Slice .

Definition at line 60 of file Slice.h.

typedef IMP::base::Vector<IMP::base::Pointer< SubsetFilter > > IMP::domino::SubsetFilters

Store a set of objects.

Definition at line 65 of file subset_filters.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< SubsetFilter > > IMP::domino::SubsetFiltersTemp

Pass a set of objects.

See Also: SubsetFilter

Definition at line 65 of file subset_filters.h.

typedef IMP::base::Vector<IMP::base::Pointer< SubsetFilterTable > > IMP::domino::SubsetFilterTables

Store a set of objects.

Definition at line 341 of file subset_filters.h.

typedef IMP::base::Vector<IMP::base::WeakPointer< SubsetFilterTable > > IMP::domino::SubsetFilterTablesTemp

Pass a set of objects.

See Also: SubsetFilterTable

Definition at line 341 of file subset_filters.h.

typedef boost::graph IMP::domino::SubsetGraph

An undirected graph on subsets of vertices. Each vertex is named with a Subset.See Graphs in IMP for more information.

Definition at line 23 of file subset_graphs.h.

typedef IMP::base::Vector< Subset > IMP::domino::Subsets

Pass or store a set of Subset .

Definition at line 78 of file Subset.h.

Function Documentation

MergeTree IMP::domino::get_balanced_merge_tree ( const SubsetGraph & junction_tree )

See Also: get_merge_tree(kernel::RestraintSet*,const ParticleStatesTable*)

Compute the merge tree from a junction tree, attempting to keep it fairly balanced.

Note: the merge tree is not necessarily deterministic (you can get different answers if you run it different times with the same inputs).

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

Return the full path to installed data.

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 setup_environment.sh script.

double IMP::domino::get_distance_if_smaller_than	(	const Subset &	s,
		const Assignment &	a,
		const Assignment &	b,
		ParticleStatesTable *	pst,
		const statistics::Metrics &	metrics,
		double	max
	)

Return a distance between two assignments if they are less than a threshold. The distance returned is the l2 norm on the distances between each state as given by the corresponding metric. If no metric is passed, then the l2 norm on the embedding is used.

Warning: get_distance_if_close has not been stabilized and is likely to change without notice.

Definition at line 113 of file domino/utility.h.

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algebra::VectorKD IMP::domino::get_embedding	(	const Subset &	s,
		const Assignment &	a,
		ParticleStatesTable *	pst
	)

Return an embedding for an assignment.

std::string IMP::domino::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 setup_environment.sh script.

Ints IMP::domino::get_index	(	const kernel::ParticlesTemp &	particles,
		const Subset &	subset,
		const Subsets &	excluded
	)

If the passed particles are all contained in the Subset and are not contained any of the Subsets in excluded, then return a a list of indices given the location of each passed particle in the passed subset. That is

particles[i]==subset[returned[i]];

Otherwise return an empty list.

This function is designed to be used for implementing SubsetFilterTable classes.

InteractionGraph IMP::domino::get_interaction_graph	(	ScoringFunctionAdaptor	rs,
		const ParticleStatesTable *	pst
	)

Compute the interaction graph of the restraints and the specified particles. The dependency graph in the model is traversed to determine how the passed particles relate to the actual particles read as input by the model. For example, if particles contains a rigid body, then a restraint which uses a member of the rigid body will have an edge from the rigid body particle.

Note: You may want to create an OptimizeRestraints object before calling this function.; These functions are here to aid in debugging of optimization protocols that use Domino. As a result, its signature and functionality may change without notice.

display::Geometries IMP::domino::get_interaction_graph_geometry ( const InteractionGraph & ig )

Assuming that all the particles have Cartesian coordinates, output edges corresponding to the edges in the interaction graph. The edges are named by the restraint which induces them.

SubsetGraph IMP::domino::get_junction_tree ( const InteractionGraph & ig )

Compute the exact junction tree for an interaction graph. The resulting graph has the junction tree properties

it is a tree
for any two vertices whose subsets both contain a vertex, that vertex is contained in all subsets along the path connecting those two vertices.

Note: the junction tree is not necessarily deterministic (you can get different answers if you run it different times with the same inputs).

MergeTree IMP::domino::get_merge_tree	(	ScoringFunctionAdaptor	input,
		const ParticleStatesTable *	pst
	)

See Also: get_merge_tree(const SubsetGraph&)

Compute the merge tree for a given restraint set and particle states table. An OptimizeRestraints object is created to improve the InteractionGraph used internally.

MergeTree IMP::domino::get_merge_tree ( const SubsetGraph & junction_tree )

See Also: get_merge_tree(kernel::RestraintSet*,const ParticleStatesTable*)

Compute the merge tree from a junction tree.

Note: the merge tree is not necessarily deterministic (you can get different answers if you run it different times with the same inputs).

Assignment IMP::domino::get_nearest_assignment	(	const Subset &	s,
		const algebra::VectorKD &	embedding,
		ParticleStatesTable *	pst
	)

Return the nearest assignment from an embedding.

kernel::ParticlesTemp IMP::domino::get_order	(	const Subset &	s,
		const SubsetFilterTables &	sft
	)

Return the order computed for the particles in the subset to be used for enumeration. This function is there in order to expose internal functionality for easier testing and should not be depended upon.

Ints IMP::domino::get_partial_index	(	const kernel::ParticlesTemp &	particles,
		const Subset &	subset,
		const Subsets &	excluded
	)

All of the passed particles are not contained in an ofthe Subsets in excluded, then return a a list of indices given the location of each passed particle in the passed subset or -1 if it is missing.

This function is designed to be used for implementing SubsetFilterTable classes.

kernel::ParticlePairsTemp IMP::domino::get_possible_interactions	(	const kernel::ParticlesTemp &	ps,
		double	max_distance,
		ParticleStatesTable *	pst
	)

Return the list of interactions implied by the passed balls given the allowed positions specified by the ParticleStatesTable.

SubsetGraph IMP::domino::get_restraint_graph	(	ScoringFunctionAdaptor	rs,
		const ParticleStatesTable *	pst
	)

The restraint graph is formed by having one node per restraint and an edge connecting two restraints if they share input particles. The associated Subsets are the set of input particles for the restraint, projected onto ps.

Note: The graph for the optimized restraints (after temporary application of OptimizeContainers and OptimizeRestraints) is returned.

kernel::RestraintsTemp IMP::domino::get_restraints	(	const Subset &	s,
		const ParticleStatesTable *	pst,
		const DependencyGraph &	dg,
		kernel::RestraintSet *	rs
	)

Return a list of all restraints from rs that do not depend on any particle in pst->get_particles() that is not in s The dependency graph is passed for efficiency.

Slice IMP::domino::get_slice	(	Subset	outer,
		Subset	inner,
		const Subsets &	excluded
	)

Return a slice for the inner subset if it is not contained in any of the excluded subsets, otherwise return the empty slice.

Definition at line 66 of file Slice.h.

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Assignments IMP::domino::get_state_clusters	(	const Subset &	subset,
		const Assignments &	states,
		ParticleStatesTable *	pst,
		double	resolution
	)

Return subset of the passed states which are representative.

The states for each particle is clustered and then the passed states are reduced to only be on the centers of that clustering.

The XYZ coordinates are used for clustering, there is not currently a nice mechanism to vary this, but it should be figured out. Probably by subclasses statistics::Embedding to statistics::ParticleEmbedding which has a method which takes a particle.

display::Geometries IMP::domino::get_subset_graph_geometry ( const SubsetGraph & ig )

Display the subsets of a subset graph, superimposed on the 3D coordinates of the particles.

Subsets IMP::domino::get_subsets ( const SubsetGraph & g )

Gets all of the Subsets of a SubsetGraph.

void IMP::domino::load_leaf_assignments	(	const Subset &	subset,
		AssignmentsTable *	at,
		AssignmentContainer *	ac
	)

Fill in assignments for a leaf.

void IMP::domino::load_merged_assignments	(	const Subset &	first_subset,
		AssignmentContainer *	first,
		const Subset &	second_subset,
		AssignmentContainer *	second,
		const SubsetFilterTablesTemp &	filters,
		AssignmentContainer *	ret
	)

Fill in assignments for an internal node.

The passed assignments, the ordering for the children is that of the node indexes for the children.

void IMP::domino::load_merged_assignments_random_order	(	const Subset &	first_subset,
		AssignmentContainer *	first,
		const Subset &	second_subset,
		AssignmentContainer *	second,
		const SubsetFilterTablesTemp &	filters,
		unsigned int	maximum_tries,
		AssignmentContainer *	ret
	)

Sample from the merged assignments.

The passed assignments, the ordering for the children is that of the node indexes for the children.

It will try maximum_tries times to merge an assignment from the first set with a randomly chosen assignment from the second. Note that duplicate solutions may be present in the output and the output will never be guaranteed to be complete.

Warning: load_sampled_merged_assignments has not been stabilized and is likely to change without notice.

void IMP::domino::load_particle_states	(	const Subset &	s,
		const Assignment &	ss,
		const ParticleStatesTable *	pst
	)

Load the appropriate state for each particle in a Subset.

Detailed Description

How Domino is different

Why Domino works

Considerations for choosing a merge tree

Domino as spatial filtering

Rules of Thumb

Info

Classes

Typedefs

Functions

Standard module functions

Read and write merge trees

Debug tools

Typedef Documentation

Function Documentation