GTSAM
4.0.2
C++ library for smoothing and mapping (SAM)
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#include <Scheduler.h>
Public Types | |
using | Values = DiscreteValues |
backwards compatibility | |
using | This = DiscreteFactorGraph |
this class | |
using | Base = FactorGraph< DiscreteFactor > |
base factor graph type | |
using | BaseEliminateable = EliminateableFactorGraph< This > |
for elimination | |
using | shared_ptr = std::shared_ptr< This > |
shared_ptr to This | |
using | Indices = KeyVector |
typedef DiscreteFactor | FactorType |
factor type | |
typedef std::shared_ptr< DiscreteFactor > | sharedFactor |
Shared pointer to a factor. | |
typedef sharedFactor | value_type |
typedef FastVector< sharedFactor >::iterator | iterator |
typedef FastVector< sharedFactor >::const_iterator | const_iterator |
typedef EliminationTraits< FactorGraphType > | EliminationTraitsType |
Typedef to the specific EliminationTraits for this graph. | |
typedef EliminationTraitsType::ConditionalType | ConditionalType |
Conditional type stored in the Bayes net produced by elimination. | |
typedef EliminationTraitsType::BayesNetType | BayesNetType |
Bayes net type produced by sequential elimination. | |
typedef EliminationTraitsType::EliminationTreeType | EliminationTreeType |
Elimination tree type that can do sequential elimination of this graph. | |
typedef EliminationTraitsType::BayesTreeType | BayesTreeType |
Bayes tree type produced by multifrontal elimination. | |
typedef EliminationTraitsType::JunctionTreeType | JunctionTreeType |
Junction tree type that can do multifrontal elimination of this graph. | |
typedef std::pair< std::shared_ptr< ConditionalType >, std::shared_ptr< _FactorType > > | EliminationResult |
typedef std::function< EliminationResult(const FactorGraphType &, const Ordering &)> | Eliminate |
The function type that does a single dense elimination step on a subgraph. | |
typedef std::optional< std::reference_wrapper< const VariableIndex > > | OptionalVariableIndex |
typedef std::optional< Ordering::OrderingType > | OptionalOrderingType |
Typedef for an optional ordering type. | |
Public Member Functions | |
Scheduler (size_t maxNrStudents) | |
virtual | ~Scheduler () |
Destructor. | |
void | addFaculty (const std::string &facultyName) |
size_t | nrFaculty () const |
void | setAvailability (const std::string &available) |
void | addSlot (const std::string &slotName) |
size_t | nrTimeSlots () const |
const std::string & | slotName (size_t s) const |
void | setSlotsAvailable (const std::vector< double > &slotsAvailable) |
void | addArea (const std::string &facultyName, const std::string &areaName) |
Scheduler (size_t maxNrStudents, const std::string &filename) | |
const DiscreteKey & | key (size_t s, std::optional< size_t > area={}) const |
void | addStudent (const std::string &studentName, const std::string &area1, const std::string &area2, const std::string &area3, const std::string &advisor) |
size_t | nrStudents () const |
current number of students | |
const std::string & | studentName (size_t i) const |
const DiscreteKey & | studentKey (size_t i) const |
const std::string & | studentArea (size_t i, size_t area) const |
void | addStudentSpecificConstraints (size_t i, std::optional< size_t > slot={}) |
void | buildGraph (size_t mutexBound=7) |
void | print (const std::string &s="Scheduler", const KeyFormatter &formatter=DefaultKeyFormatter) const override |
void | printAssignment (const DiscreteValues &assignment) const |
void | printSpecial (const DiscreteValues &assignment) const |
void | accumulateStats (const DiscreteValues &assignment, std::vector< size_t > &stats) const |
DiscreteBayesNet::shared_ptr | eliminate () const |
DiscreteValues | bestSchedule () const |
DiscreteValues | bestAssignment (const DiscreteValues &bestSchedule) const |
void | addSingleValue (const DiscreteKey &dkey, size_t value) |
Add a unary constraint, allowing only a single value. | |
void | addAllDiff (const DiscreteKey &key1, const DiscreteKey &key2) |
Add a binary AllDiff constraint. | |
void | addAllDiff (const DiscreteKeys &dkeys) |
Add a general AllDiff constraint. | |
Domains | runArcConsistency (size_t cardinality, size_t maxIterations=10) const |
bool | runArcConsistency (const VariableIndex &index, Domains *domains) const |
Run arc consistency for all variables, return true if any domain changed. | |
CSP | partiallyApply (const Domains &domains) const |
template<typename... Args> | |
void | add (Args &&... args) |
KeySet | keys () const |
DiscreteKeys | discreteKeys () const |
Return the DiscreteKeys in this factor graph. | |
DecisionTreeFactor | product () const |
double | operator() (const DiscreteValues &values) const |
DiscreteBayesNet | sumProduct (OptionalOrderingType orderingType={}) const |
Implement the sum-product algorithm. More... | |
DiscreteBayesNet | sumProduct (const Ordering &ordering) const |
Implement the sum-product algorithm. More... | |
DiscreteLookupDAG | maxProduct (OptionalOrderingType orderingType={}) const |
Implement the max-product algorithm. More... | |
DiscreteLookupDAG | maxProduct (const Ordering &ordering) const |
Implement the max-product algorithm. More... | |
DiscreteValues | optimize (OptionalOrderingType orderingType={}) const |
Find the maximum probable explanation (MPE) by doing max-product. More... | |
DiscreteValues | optimize (const Ordering &ordering) const |
Find the maximum probable explanation (MPE) by doing max-product. More... | |
std::shared_ptr< BayesNetType > | eliminateSequential (OptionalOrderingType orderingType={}, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
std::shared_ptr< BayesNetType > | eliminateSequential (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
std::shared_ptr< BayesTreeType > | eliminateMultifrontal (OptionalOrderingType orderingType={}, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
std::shared_ptr< BayesTreeType > | eliminateMultifrontal (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
std::pair< std::shared_ptr< BayesNetType >, std::shared_ptr< FactorGraphType > > | eliminatePartialSequential (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
std::pair< std::shared_ptr< BayesNetType >, std::shared_ptr< FactorGraphType > > | eliminatePartialSequential (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
std::pair< std::shared_ptr< BayesTreeType >, std::shared_ptr< FactorGraphType > > | eliminatePartialMultifrontal (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
std::pair< std::shared_ptr< BayesTreeType >, std::shared_ptr< FactorGraphType > > | eliminatePartialMultifrontal (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
std::shared_ptr< BayesNetType > | marginalMultifrontalBayesNet (const Ordering &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
std::shared_ptr< BayesNetType > | marginalMultifrontalBayesNet (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
std::shared_ptr< BayesNetType > | marginalMultifrontalBayesNet (const Ordering &variables, const Ordering &marginalizedVariableOrdering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
std::shared_ptr< BayesNetType > | marginalMultifrontalBayesNet (const KeyVector &variables, const Ordering &marginalizedVariableOrdering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
std::shared_ptr< BayesTreeType > | marginalMultifrontalBayesTree (const Ordering &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
std::shared_ptr< BayesTreeType > | marginalMultifrontalBayesTree (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
std::shared_ptr< BayesTreeType > | marginalMultifrontalBayesTree (const Ordering &variables, const Ordering &marginalizedVariableOrdering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
std::shared_ptr< BayesTreeType > | marginalMultifrontalBayesTree (const KeyVector &variables, const Ordering &marginalizedVariableOrdering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
std::shared_ptr< FactorGraphType > | marginal (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const |
Testable | |
bool | equals (const This &fg, double tol=1e-9) const |
Testable | |
bool | equals (const This &fg, double tol=1e-9) const |
Check equality up to tolerance. | |
Adding Single Factors | |
IsDerived< DERIVEDFACTOR > | add (std::shared_ptr< DERIVEDFACTOR > factor) |
add is a synonym for push_back. | |
void | reserve (size_t size) |
IsDerived< DERIVEDFACTOR > | push_back (std::shared_ptr< DERIVEDFACTOR > factor) |
Add a factor directly using a shared_ptr. | |
IsDerived< DERIVEDFACTOR > | push_back (const DERIVEDFACTOR &factor) |
IsDerived< DERIVEDFACTOR > | emplace_shared (Args &&... args) |
Emplace a shared pointer to factor of given type. | |
Adding via container | |
void | add (const FACTOR_OR_CONTAINER &factorOrContainer) |
HasDerivedElementType< CONTAINER > | push_back (const CONTAINER &container) |
HasDerivedValueType< CONTAINER > | push_back (const CONTAINER &container) |
Push back non-pointer objects in a container (factors are copied). | |
Wrapper support | |
std::string | markdown (const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DiscreteFactor::Names &names={}) const |
Render as markdown tables. More... | |
std::string | html (const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DiscreteFactor::Names &names={}) const |
Render as html tables. More... | |
Adding via iterators | |
HasDerivedElementType< ITERATOR > | push_back (ITERATOR firstFactor, ITERATOR lastFactor) |
HasDerivedValueType< ITERATOR > | push_back (ITERATOR firstFactor, ITERATOR lastFactor) |
Push back many factors with an iterator (factors are copied) | |
Specialized versions | |
std::enable_if< std::is_base_of< This, typename CLIQUE::FactorGraphType >::value >::type | push_back (const BayesTree< CLIQUE > &bayesTree) |
FactorIndices | add_factors (const CONTAINER &factors, bool useEmptySlots=false) |
Standard Interface | |
size_t | size () const |
bool | empty () const |
const sharedFactor | at (size_t i) const |
sharedFactor & | at (size_t i) |
const sharedFactor | operator[] (size_t i) const |
sharedFactor & | operator[] (size_t i) |
const_iterator | begin () const |
const_iterator | end () const |
sharedFactor | front () const |
sharedFactor | back () const |
double | error (const HybridValues &values) const |
Modifying Factor Graphs (imperative, discouraged) | |
iterator | begin () |
iterator | end () |
virtual void | resize (size_t size) |
void | remove (size_t i) |
void | replace (size_t index, sharedFactor factor) |
iterator | erase (iterator item) |
iterator | erase (iterator first, iterator last) |
Graph Display | |
void | dot (std::ostream &os, const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const |
Output to graphviz format, stream version. | |
std::string | dot (const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const |
Output to graphviz format string. | |
void | saveGraph (const std::string &filename, const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const |
output to file with graphviz format. | |
Advanced Interface | |
size_t | nrFactors () const |
KeyVector | keyVector () const |
bool | exists (size_t idx) const |
Protected Member Functions | |
bool | isEqual (const FactorGraph &other) const |
Check exact equality of the factor pointers. Useful for derived ==. | |
Protected Attributes | |
FastVector< sharedFactor > | factors_ |
Scheduler class Creates one variable for each student, and three variables for each of the student's areas, for a total of 4*nrStudents variables. The "student" variable will determine when the student takes the qual. The "area" variables determine which faculty are on his/her committee.
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inherited |
The pair of conditional and remaining factor produced by a single dense elimination step on a subgraph.
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inherited |
Typedef for an optional variable index as an argument to elimination functions It is an optional to a constant reference
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inline |
Constructor We need to know the number of students in advance for ordering keys. then add faculty, slots, areas, availability, students, in that order
gtsam::Scheduler::Scheduler | ( | size_t | maxNrStudents, |
const std::string & | filename | ||
) |
Constructor that reads in faculty, slots, availibility. Still need to add areas and students after this
void gtsam::Scheduler::accumulateStats | ( | const DiscreteValues & | assignment, |
std::vector< size_t > & | stats | ||
) | const |
Accumulate faculty stats
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inlineinherited |
Add a decision-tree factor
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inlineinherited |
Add a factor or container of factors, including STL collections, BayesTrees, etc.
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inherited |
Add new factors to a factor graph and returns a list of new factor indices, optionally finding and reusing empty factor slots.
void gtsam::Scheduler::addStudent | ( | const std::string & | studentName, |
const std::string & | area1, | ||
const std::string & | area2, | ||
const std::string & | area3, | ||
const std::string & | advisor | ||
) |
addStudent has to be called after adding slots and faculty
void gtsam::Scheduler::addStudentSpecificConstraints | ( | size_t | i, |
std::optional< size_t > | slot = {} |
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) |
Add student-specific constraints to the graph
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inlineinherited |
Get a specific factor by index (this checks array bounds and may throw an exception, as opposed to operator[] which does not).
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inlineinherited |
Get a specific factor by index (this checks array bounds and may throw an exception, as opposed to operator[] which does not).
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inlineinherited |
Get the last factor
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inlineinherited |
Iterator to beginning of factors.
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inlineinherited |
non-const STL-style begin()
DiscreteValues gtsam::Scheduler::bestAssignment | ( | const DiscreteValues & | bestSchedule | ) | const |
find the corresponding most desirable committee assignment
DiscreteValues gtsam::Scheduler::bestSchedule | ( | ) | const |
find the assignment of students to slots with most possible committees
void gtsam::Scheduler::buildGraph | ( | size_t | mutexBound = 7 | ) |
Main routine that builds factor graph
DiscreteBayesNet::shared_ptr gtsam::Scheduler::eliminate | ( | ) | const |
Eliminate, return a Bayes net
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inherited |
Do multifrontal elimination of all variables to produce a Bayes tree. If an ordering is not provided, the ordering will be computed using either COLAMD or METIS, depending on the parameter orderingType (Ordering::COLAMD or Ordering::METIS)
Example - Full Cholesky elimination in COLAMD order:
Example - Reusing an existing VariableIndex to improve performance, and using COLAMD ordering:
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inherited |
Do multifrontal elimination of all variables to produce a Bayes tree. If an ordering is not provided, the ordering will be computed using either COLAMD or METIS, depending on the parameter orderingType (Ordering::COLAMD or Ordering::METIS)
Example - Full QR elimination in specified order:
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inherited |
Do multifrontal elimination of some variables, in ordering
provided, to produce a Bayes tree and a remaining factor graph. This computes the factorization \( p(X) = p(A|B) p(B) \), where \( A = \) variables
, \( X \) is all the variables in the factor graph, and \( B = X\backslash A \).
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inherited |
Do multifrontal elimination of the given variables
in an ordering computed by COLAMD to produce a Bayes tree and a remaining factor graph. This computes the factorization \( p(X) = p(A|B) p(B) \), where \( A = \) variables
, \( X \) is all the variables in the factor graph, and \( B = X\backslash A \).
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inherited |
Do sequential elimination of some variables, in ordering
provided, to produce a Bayes net and a remaining factor graph. This computes the factorization \( p(X) = p(A|B) p(B) \), where \( A = \) variables
, \( X \) is all the variables in the factor graph, and \( B = X\backslash A \).
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inherited |
Do sequential elimination of the given variables
in an ordering computed by COLAMD to produce a Bayes net and a remaining factor graph. This computes the factorization \( p(X) = p(A|B) p(B) \), where \( A = \) variables
, \( X \) is all the variables in the factor graph, and \( B = X\backslash A \).
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inherited |
Do sequential elimination of all variables to produce a Bayes net. If an ordering is not provided, the ordering provided by COLAMD will be used.
Example - Full Cholesky elimination in COLAMD order:
Example - METIS ordering for elimination
Example - Reusing an existing VariableIndex to improve performance, and using COLAMD ordering:
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inherited |
Do sequential elimination of all variables to produce a Bayes net.
Example - Full QR elimination in specified order:
Example - Reusing an existing VariableIndex to improve performance:
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inlineinherited |
Check if the graph is empty (null factors set by remove() will cause this to return false).
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inlineinherited |
Iterator to end of factors.
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inlineinherited |
non-const STL-style end()
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Erase factor and rearrange other factors to take up the empty space
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inlineinherited |
Erase factors and rearrange other factors to take up the empty space
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inherited |
Add error for all factors.
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inlineinherited |
MATLAB interface utility: Checks whether a factor index idx exists in the graph and is a live pointer
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inlineinherited |
Get the first factor
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inherited |
Render as html tables.
keyFormatter | GTSAM-style Key formatter. |
names | optional, a map from Key to category names. |
const DiscreteKey& gtsam::Scheduler::key | ( | size_t | s, |
std::optional< size_t > | area = {} |
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) | const |
get key for student and area, 0 is time slot itself
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Return the set of variables involved in the factors (set union)
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Potentially slow function to return all keys involved, sorted, as a vector
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inherited |
Compute the marginal factor graph of the requested variables.
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inherited |
Compute the marginal of the requested variables and return the result as a Bayes net. Uses COLAMD marginalization ordering by default
variables | Determines the ordered variables whose marginal to compute, will be ordered in the returned BayesNet as specified. |
function | Optional dense elimination function. |
variableIndex | Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed. |
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inherited |
Compute the marginal of the requested variables and return the result as a Bayes net. Uses COLAMD marginalization ordering by default
variables | Determines the variables whose marginal to compute, will be ordered using COLAMD; use Ordering(variables) to specify the variable ordering. |
function | Optional dense elimination function. |
variableIndex | Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed. |
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inherited |
Compute the marginal of the requested variables and return the result as a Bayes net.
variables | Determines the ordered variables whose marginal to compute, will be ordered in the returned BayesNet as specified. |
marginalizedVariableOrdering | Ordering for the variables being marginalized out, i.e. all variables not in variables . |
function | Optional dense elimination function. |
variableIndex | Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed. |
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inherited |
Compute the marginal of the requested variables and return the result as a Bayes net.
variables | Determines the variables whose marginal to compute, will be ordered using COLAMD; use Ordering(variables) to specify the variable ordering. |
marginalizedVariableOrdering | Ordering for the variables being marginalized out, i.e. all variables not in variables . |
function | Optional dense elimination function. |
variableIndex | Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed. |
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inherited |
Compute the marginal of the requested variables and return the result as a Bayes tree. Uses COLAMD marginalization order by default
variables | Determines the ordered variables whose marginal to compute, will be ordered in the returned BayesNet as specified. |
function | Optional dense elimination function.. |
variableIndex | Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed. |
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inherited |
Compute the marginal of the requested variables and return the result as a Bayes tree. Uses COLAMD marginalization order by default
variables | Determines the variables whose marginal to compute, will be ordered using COLAMD; use Ordering(variables) to specify the variable ordering. |
function | Optional dense elimination function.. |
variableIndex | Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed. |
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inherited |
Compute the marginal of the requested variables and return the result as a Bayes tree.
variables | Determines the ordered variables whose marginal to compute, will be ordered in the returned BayesNet as specified. |
marginalizedVariableOrdering | Ordering for the variables being marginalized out, i.e. all variables not in variables . |
function | Optional dense elimination function.. |
variableIndex | Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed. |
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inherited |
Compute the marginal of the requested variables and return the result as a Bayes tree.
variables | Determines the variables whose marginal to compute, will be ordered using COLAMD; use Ordering(variables) to specify the variable ordering. |
marginalizedVariableOrdering | Ordering for the variables being marginalized out, i.e. all variables not in variables . |
function | Optional dense elimination function.. |
variableIndex | Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed. |
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inherited |
Render as markdown tables.
keyFormatter | GTSAM-style Key formatter. |
names | optional, a map from Key to category names. |
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inherited |
Implement the max-product algorithm.
orderingType | : one of COLAMD, METIS, NATURAL, CUSTOM |
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inherited |
Implement the max-product algorithm.
ordering |
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inherited |
return the number of non-null factors
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inherited |
Evaluates the factor graph given values, returns the joint probability of the factor graph given specific instantiation of values
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inlineinherited |
Get a specific factor by index (this does not check array bounds, as opposed to at() which does).
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inlineinherited |
Get a specific factor by index (this does not check array bounds, as opposed to at() which does).
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inherited |
Find the maximum probable explanation (MPE) by doing max-product.
orderingType |
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inherited |
Find the maximum probable explanation (MPE) by doing max-product.
ordering |
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overridevirtual |
Reimplemented from gtsam::DiscreteFactorGraph.
void gtsam::Scheduler::printAssignment | ( | const DiscreteValues & | assignment | ) | const |
Print readable form of assignment
void gtsam::Scheduler::printSpecial | ( | const DiscreteValues & | assignment | ) | const |
Special print for single-student case
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inherited |
return product of all factors as a single factor
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inlineinherited |
Add a factor by value, will be copy-constructed (use push_back with a shared_ptr to avoid the copy).
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inlineinherited |
Push back many factors with an iterator over shared_ptr (factors are not copied)
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inlineinherited |
Push back many factors as shared_ptr's in a container (factors are not copied)
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inlineinherited |
Push back a BayesTree as a collection of factors. NOTE: This should be hidden in derived classes in favor of a type-specialized version that calls this templated function.
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inlineinherited |
delete factor without re-arranging indexes by inserting a nullptr pointer
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inlineinherited |
replace a factor by index
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inlineinherited |
Reserve space for the specified number of factors if you know in advance how many there will be (works like FastVector::reserve).
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inlinevirtualinherited |
Directly resize the number of factors in the graph. If the new size is less than the original, factors at the end will be removed. If the new size is larger than the original, null factors will be appended.
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inherited |
return product of all factors as a single factor
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inline |
boolean std::string of nrTimeSlots * nrFaculty
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inline |
slots available, boolean
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inlineinherited |
return the number of factors (including any null factors set by remove() ).
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inherited |
Implement the sum-product algorithm.
orderingType | : one of COLAMD, METIS, NATURAL, CUSTOM |
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inherited |
Implement the sum-product algorithm.
ordering |
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protectedinherited |
concept check, makes sure FACTOR defines print and equals Collection of factors