A domain is the HUGIN representation of a network. More...
Public Member Functions | |
void | adapt () |
Adapts this Domain according to the evidence entered. | |
double | approximate (double epsilon) |
Remove "near-zero" probabilities from the clique probability tables. | |
bool | cgEvidenceIsPropagated () const |
Test if CG evidence has been propagated for this Domain. | |
Domain * | clone () const |
Clone a Domain object. | |
void | compile () |
Compile this Domain using the default triangulation method. | |
double | compress () |
Remove the zero entries from the clique and separator tables of the junction trees in this Domain. | |
void | computeSensitivityData (const NodeList &nodes, const std::vector< size_t > &states) |
Compute the constants of the sensitivity functions for the specified output probabilities and all CPT parameters in the network. | |
Domain (const Class *cls) | |
Construct a runtime domain from the given class. | |
Domain (const Domain *domain) | |
Construct a new domain by cloning an existing domain. | |
Domain (const std::string &filename, ParseListener *pl) | |
Construct a domain by reading a NET file description. | |
Domain (const std::string &filename, const std::string &password) | |
Construct a domain by loading the corresponding password protected Hugin Knowledge Base from file. | |
Domain (const std::string &filename) | |
Construct a domain by loading the corresponding Hugin Knowledge Base from file. | |
Domain () | |
Construct a new, empty Domain object. | |
void | enterCase (size_t index) |
Enters a case as evidence. | |
bool | equilibriumIs (Equilibrium eq) const |
Test for Equilibrium type. | |
bool | evidenceIsPropagated () const |
Test if evidence has been propagated for this Domain. | |
bool | evidenceModeIs (EvidenceMode ev) const |
Test for evidence mode. | |
void | findMAPConfigurations (const NodeList &nodes, double minProbability) |
Find all configurations of nodes with probability at least minProbability . | |
double | getAIC () const |
Computes the AIC score (Akaike's Information Criterion) of the case data. | |
double | getApproximationConstant () const |
Return the approximation constant. | |
double | getBIC () const |
Computes the BIC score (Bayesian Information Criterion) of the case data. | |
Number | getCaseCount (size_t index) const |
Return the case count associated with case index in this domain. | |
size_t | getConcurrencyLevel () const |
Get the current level of concurrency. | |
double | getConflict () const |
Return the conflict value. | |
NodeList | getDConnectedNodes (const NodeList &source, const NodeList &evidence) const |
Performs a d-separation test and returns a list of d-connected nodes. | |
NodeList | getDConnectedNodes (const NodeList &source, const NodeList &hard, const NodeList &soft) const |
Performs a d-separation test and returns a list of d-connected nodes. | |
NodeList | getDSeparatedNodes (const NodeList &source, const NodeList &evidence) const |
Performs a d-separation test and returns a list of d-separated nodes. | |
NodeList | getDSeparatedNodes (const NodeList &source, const NodeList &hard, const NodeList &soft) const |
Performs a d-separation test and returns a list of d-separated nodes. | |
NodeList | getEliminationOrder () const |
Return the triangulation order. | |
Number | getExpectedUtility () const |
Return the total expected utility associated with this Domain. | |
size_t | getGrainSize () const |
Return the current value of the grain size parameter. | |
JunctionTreeList | getJunctionTrees () const |
Return the JunctionTrees of this Domain. | |
double | getLogLikelihood () const |
Computes the log-likelihood of the case data. | |
double | getLogLikelihoodTolerance () const |
Get current setting of the log-likelihood tolerance in this domain. | |
double | getLogNormalizationConstant () const |
Get the logarithm to the normalization constant. | |
std::vector< size_t > | getMAPConfiguration (size_t index) const |
Return a MAP configuration. | |
Table * | getMarginal (const NodeList &nodes) const |
Compute the marginal distribution for the Nodes provided as argument with respect to the (imaginary) joint potential, determined by the current potentials on the junction tree (s) of this Domain. | |
size_t | getMaxNumberOfEMIterations () const |
Retrieve the current maximum number of iterations for the EM algorithm. | |
size_t | getMaxNumberOfSeparators () const |
Retrieve the current maximum number of separators allowed during triangulation. | |
double | getNormalDeviate (double mean, double variance) |
Uses the pseudo-random number generator for this Domain to sample a real number from a normal (aka Gaussian) distribution. | |
double | getNormalizationConstant () const |
Retrieve the normalization constant from the most recent propagation. | |
size_t | getNumberOfCases () const |
Return the number of cases currently allocated for this domain. | |
size_t | getNumberOfMAPConfigurations () const |
Return the number of MAP configurations. | |
double | getProbabilityOfMAPConfiguration (size_t index) const |
Return the probability of a MAP configuration. | |
NodeList | getSensitivitySet (size_t output) const |
Return the sensitivity set computed by the most recent call to Domain::computeSensitivityData. | |
NodeList | getSensitivitySet () const |
Return the sensitivity set computed by the most recent call to DiscreteChanceNode::computeSensitivityData. | |
double | getSignificanceLevel () const |
Get current setting of the significance level in this domain. | |
double | getUniformDeviate () |
Uses the pseudo-random number generator for this Domain to sample a real number from the uniform distribution over the interval [0,1). | |
bool | hasEvidenceToPropagate () const |
Test if evidence has been entered since last propagation. | |
bool | hasTablesToPropagate () const |
Test for new node tables. | |
void | initialize () |
Establish the initial values for all tables of this Domain (which must be compiled). | |
bool | isCompiled () const |
Test whether this Domain is compiled. | |
bool | isCompressed () const |
Test whether this Domain is compressed. | |
bool | isTriangulated () const |
Test whether this Domain is triangulated. | |
void | learnClassTables () |
Perform EM learning on an OOBN. | |
void | learnStructure () |
Learn the structure of a network from data. | |
void | learnTables () |
Learn the conditional probability tables for each node in this domain that has an experience table. | |
bool | likelihoodIsPropagated () const |
Test if likelihood eveidence has been propagated for this Domain. | |
size_t | newCase () |
Allocate storage within this domain to a new case. | |
void | parseCase (const std::string &filename, ParseListener *pl) |
Parses the case stored in a file with the given filename and enters the associated findings into this Domain. | |
void | parseCases (const std::string &filename, ParseListener *pl) |
Parses a set of cases, stored in a file with the given filename. | |
void | propagate (Equilibrium eq=H_EQUILIBRIUM_SUM, EvidenceMode ev=H_MODE_NORMAL) |
Establish the specified equilibrium using the evidence mode indicated for incorporation of evidence on all junction trees in this Domain. | |
void | resetInferenceEngine () |
Establish the initial state of the inference engine. | |
void | retractFindings () |
Retract (all) findings for all nodes in this Domain. | |
void | saveAsKB (const std::string &filename, const std::string &password) |
Save this Domain as a password protected Hugin Knowledge Base. | |
void | saveAsKB (const std::string &filename) |
Save this Domain as a Hugin Knowledge Base. | |
void | saveCase (const std::string &filename) |
Saves all evidence entered in this Domain in a file with the given fileName (if the file exists, it is overwritten). | |
void | saveCases (const std::string &filename, const NodeList &nodes, const std::vector< size_t > &cases, bool caseCounts, const std::string &separator, const std::string &missingData) |
Saves the specified cases entered in this Domain in a file with the given fileName. | |
void | saveCases (const std::string &filename, const NodeList &nodes, bool caseCounts, const std::string &separator, const std::string &missingData) |
Saves all cases entered in this Domain in a file with the given fileName. | |
void | saveToMemory () |
Create a copy in memory of the belief and junction tree tables of this Domain. | |
void | seedRandom (unsigned int seed) |
Seeds the random number generator. | |
void | setCaseCount (size_t index, Number count) |
Set the case count associated with case index in this domain to count . | |
void | setConcurrencyLevel (size_t level) |
Set the level of concurrency. | |
void | setGrainSize (size_t size) |
Set the grains size parameter. | |
void | setLogLikelihoodTolerance (double tolerance) |
Specify the tolerance of the log-likelihood. | |
void | setMaxNumberOfEMIterations (size_t iterations) |
Set the maximal number of iterations allowed for the EM algorithm. | |
void | setMaxNumberOfSeparators (size_t separators) |
Set the maximal number of separators allowed during triangulation. | |
void | setNumberOfCases (size_t number) |
Adjust the storage capacity for cases in this domain. | |
void | setSignificanceLevel (double significancelevel) |
Specify the Significance Level used for the structurel learning aglorithm. | |
void | simulate () |
Sample a configuration for this Domain with respect to the current distribution. | |
void | triangulate (const NodeList &order) |
Triangulates the graph of this Domain using the specified elimination order. | |
void | triangulate (TriangulationMethod tm=H_TM_BEST_GREEDY) |
Triangulate the graph of this Domain using the specified triangulation method. | |
void | uncompile () |
Uncompiles this Domain. | |
void | updatePolicies () |
Update the policy tables of this domain. | |
~Domain () throw () | |
Destruct a Domain object. |
A domain is the HUGIN representation of a network.
It is one of the principal structures in HUGIN. It must be constructed before any nodes belonging to the network.
HAPI::Domain::Domain | ( | const std::string & | filename | ) | [explicit] |
Construct a domain by loading the corresponding Hugin Knowledge Base from file.
filename | the name of the Hugin KB file |
HAPI::Domain::Domain | ( | const std::string & | filename, | |
const std::string & | password | |||
) |
Construct a domain by loading the corresponding password protected Hugin Knowledge Base from file.
If the given password does not match, an exception is thrown.
filename | the name of the Hugin KB file | |
password | the password for the file |
HAPI::Domain::Domain | ( | const std::string & | filename, | |
ParseListener * | pl | |||
) |
Construct a domain by reading a NET file description.
filename | The name of the NET file. | |
pl | Pointer to object derived from class ParseListener. |
HAPI::Domain::Domain | ( | const Domain * | domain | ) |
Construct a new domain by cloning an existing domain.
domain | The domain to be cloned. |
HAPI::Domain::Domain | ( | const Class * | cls | ) |
Construct a runtime domain from the given class.
cls | The Class from which to create the runtime domain. |
double HAPI::Domain::approximate | ( | double | epsilon | ) |
Remove "near-zero" probabilities from the clique probability tables.
For each Clique object in this domain, a value delta is computed such that the sum of all elements less than delta in the (discrete part) of the clique table is less than epsilon. These elements (less than delta) are then set to 0.
epsilon | The threshold value. Maximal probability mass to eradicate from each clique. |
bool HAPI::Domain::cgEvidenceIsPropagated | ( | ) | const |
Test if CG evidence has been propagated for this Domain.
Domain* HAPI::Domain::clone | ( | ) | const |
Clone a Domain object.
This function returns a pointer to a copy of this domain.
void HAPI::Domain::compile | ( | ) |
Compile this Domain using the default triangulation method.
If the domain is already triangulated, nothing is changed. The domain must contain at least one chance or decision node.
double HAPI::Domain::compress | ( | ) |
Remove the zero entries from the clique and separator tables of the junction trees in this Domain.
Compression can only be applied to (compiled) ordinary belief networks. Continuous nodes are allowed, but compression only applies to configurations of states of the discrete nodes.
void HAPI::Domain::computeSensitivityData | ( | const NodeList & | nodes, | |
const std::vector< size_t > & | states | |||
) |
Compute the constants of the sensitivity functions for the specified output probabilities and all CPT parameters in the network.
The output probabilities are specified using a list of nodes and a list of corresponding states.
nodes | the list of (output) nodes | |
states | a list of states of the nodes in the nodes list |
void HAPI::Domain::enterCase | ( | size_t | index | ) |
Enters a case as evidence.
A subsequent propagate operation will perform inference using the case.
index | the index of the case to enter. |
bool HAPI::Domain::equilibriumIs | ( | Equilibrium | eq | ) | const |
Test for Equilibrium type.
If the equilibrium of all junction trees of this Domain is eq, return true.
eq | Type of Equilibrium to test for. |
bool HAPI::Domain::evidenceModeIs | ( | EvidenceMode | ev | ) | const |
Test for evidence mode.
Test if the equilibrium of all junction trees of this Domain could have been obtained through a propagation using ev as the evidence incorporation mode.
ev | Type of EvidenceMode to test for. |
void HAPI::Domain::findMAPConfigurations | ( | const NodeList & | nodes, | |
double | minProbability | |||
) |
Find all configurations of nodes
with probability at least minProbability
.
This method uses a Monte Carlo algorithm to solve a generalized form of the maximum a posteriori (MAP) configuration problem: The MAP configuration problem is the problem of finding the most probable configuration of a set of nodes given evidence on some of the remaining nodes.
The results of this method are provided by Domain::getNumberOfMAPConfigurations, Domain::getMAPConfiguration, and Domain::getProbabilityOfMAPConfiguration.
nodes | a NodeList containing the DiscreteNodes for which to find configurations. | |
minProbability | configurations with a lower probability than minProbability are ignored. |
double HAPI::Domain::getApproximationConstant | ( | ) | const |
Return the approximation constant.
The number returned is based on the most recent (explicit or implicit) approximation operation. An implicit approximation takes place when you change some conditional probability tables of acompressed domain, and then perform a propagation operation. Since some (discrete) state configurations have been removed from a compressed domain, the probability mass of the remaining configurations will typically be less than 1. This probability mass is returned by getApproximationConstant ().
size_t HAPI::Domain::getConcurrencyLevel | ( | ) | const |
Get the current level of concurrency.
double HAPI::Domain::getConflict | ( | ) | const |
Return the conflict value.
The conflict value is valid for this Domain computed during the most recent propagation. If no propagation has been performed, 1 is returned.
NodeList HAPI::Domain::getDConnectedNodes | ( | const NodeList & | source, | |
const NodeList & | evidence | |||
) | const |
Performs a d-separation test and returns a list of d-connected nodes.
Assuming evidence on the specified evidence nodes, this method returns the list of nodes that are d-connected to the specified list of source nodes.
source | list of source nodes | |
evidence | list of nodes assumed to be instantiated. |
NodeList HAPI::Domain::getDConnectedNodes | ( | const NodeList & | source, | |
const NodeList & | hard, | |||
const NodeList & | soft | |||
) | const |
Performs a d-separation test and returns a list of d-connected nodes.
Assuming evidence on the specified evidence nodes, this method returns the list of nodes that are d-connected to the specified list of source nodes.
source | list of source nodes | |
hard | list of nodes assumed to be instantiated | |
soft | list of nodes assumed to have multi-state or likelihood evidence. |
NodeList HAPI::Domain::getDSeparatedNodes | ( | const NodeList & | source, | |
const NodeList & | evidence | |||
) | const |
Performs a d-separation test and returns a list of d-separated nodes.
Assuming evidence on the specified evidence nodes, this method returns the list of nodes that are d-separated to the specified list of source nodes.
source | list of source nodes | |
evidence | list of nodes assumed to be instantiated. |
NodeList HAPI::Domain::getDSeparatedNodes | ( | const NodeList & | source, | |
const NodeList & | hard, | |||
const NodeList & | soft | |||
) | const |
Performs a d-separation test and returns a list of d-separated nodes.
Assuming evidence on the specified evidence nodes, this method returns the list of nodes that are d-separated to the specified list of source nodes.
source | list of source nodes | |
hard | list of nodes assumed to be instantiated | |
soft | list of nodes assumed to have multi-state or likelihood evidence. |
NodeList HAPI::Domain::getEliminationOrder | ( | ) | const |
Return the triangulation order.
A NodeList containing a list of nodes in the order used to triangulate the network of this Domain is returned.
size_t HAPI::Domain::getGrainSize | ( | ) | const |
Return the current value of the grain size parameter.
JunctionTreeList HAPI::Domain::getJunctionTrees | ( | ) | const |
Return the JunctionTrees of this Domain.
double HAPI::Domain::getLogLikelihood | ( | ) | const |
Computes the log-likelihood of the case data.
std::vector<size_t> HAPI::Domain::getMAPConfiguration | ( | size_t | index | ) | const |
Return a MAP configuration.
This method returns the configuration identified by index
among the configurations with probability at least minProbability
— as specified in the most recent successful call to Domain::findMAPConfigurations.
The index
argument must be a nonnegative integer less than Domain::getNumberOfMAPConfigurations: 0 requests the most probable configuration, 1 the second-most probable configuration, etc.
index | identifies the configuration. |
Compute the marginal distribution for the Nodes provided as argument with respect to the (imaginary) joint potential, determined by the current potentials on the junction tree (s) of this Domain.
If nodes
contains continuous nodes, they must be last in the list. This operation is not allowed on compressed domains.
nodes | NodeList containing the Node objects over which to compute the marginal. |
double HAPI::Domain::getNormalDeviate | ( | double | mean, | |
double | variance | |||
) |
Uses the pseudo-random number generator for this Domain to sample a real number from a normal (aka Gaussian) distribution.
mean | the mean of the distribution | |
variance | the variance of the distribution |
double HAPI::Domain::getNormalizationConstant | ( | ) | const |
Retrieve the normalization constant from the most recent propagation.
For sum-propagation, the normalization constant is equal to the probability of the evidence propagated. For max-propagation, the normalization constant is the probability of the most probable configuration with the evidence incorporated.
size_t HAPI::Domain::getNumberOfMAPConfigurations | ( | ) | const |
Return the number of MAP configurations.
This method returns the number of configurations found by the most recent successful call to Domain::findMAPConfigurations.
double HAPI::Domain::getProbabilityOfMAPConfiguration | ( | size_t | index | ) | const |
Return the probability of a MAP configuration.
This method returns the probability of the configuration returned by Domain::getMAPConfiguration (index).
index | identifies the configuration. |
NodeList HAPI::Domain::getSensitivitySet | ( | size_t | output | ) | const |
Return the sensitivity set computed by the most recent call to Domain::computeSensitivityData.
If the results produced by that call have been invalidated, a usage exception is thrown.
output | identifies one of the output probabilities specified in the call to Domain::computeSensitivityData. |
NodeList HAPI::Domain::getSensitivitySet | ( | ) | const |
Return the sensitivity set computed by the most recent call to DiscreteChanceNode::computeSensitivityData.
If the results produced by that call have been invalidated, a usage exception is thrown.
bool HAPI::Domain::hasTablesToPropagate | ( | ) | const |
Test for new node tables.
Are there any nodes in this Domain having (a conditional probability or utility) table that has changed since the most recent compilation or propagation.
void HAPI::Domain::initialize | ( | ) |
Establish the initial values for all tables of this Domain (which must be compiled).
Using this method will erase all evidence previously entered.
bool HAPI::Domain::isTriangulated | ( | ) | const |
Test whether this Domain is triangulated.
Being "triangulated" means that the junction forest has been created, but not the associated tables.
void HAPI::Domain::learnClassTables | ( | ) |
Perform EM learning on an OOBN.
This requires, that the data matches the domain created from the OOBN, and not the OOBN itself.
size_t HAPI::Domain::newCase | ( | ) |
Allocate storage within this domain to a new case.
void HAPI::Domain::parseCase | ( | const std::string & | filename, | |
ParseListener * | pl | |||
) |
Parses the case stored in a file with the given filename and enters the associated findings into this Domain.
All existing evidence in the Domain is retracted before entering the case findings.
filename | the name of the file containing the case. | |
pl | the ParseListener used for handling parse errors. |
void HAPI::Domain::parseCases | ( | const std::string & | filename, | |
ParseListener * | pl | |||
) |
Parses a set of cases, stored in a file with the given filename.
The found cases are entered into the Domain.
filename | the name of the file containing the case. | |
pl | the ParseListener used for handling parse errors. |
void HAPI::Domain::propagate | ( | Equilibrium | eq = H_EQUILIBRIUM_SUM , |
|
EvidenceMode | ev = H_MODE_NORMAL | |||
) |
Establish the specified equilibrium using the evidence mode indicated for incorporation of evidence on all junction trees in this Domain.
Also, revised beliefs will be computed for all nodes.
eq | Equilibrium type. Defaults to H_EQUILIBRIUM_SUM. | |
ev | EvidenceMode type. Defaults to H_MODE_NORMAL. |
void HAPI::Domain::resetInferenceEngine | ( | ) |
Establish the initial state of the inference engine.
: sum-equilibrium with no evidence incorporated. Any propagated findings will thus be removed from the junction tree potentials, but entered findings will still be "registred" (i.e., they will be incorporated in the next propagation).
void HAPI::Domain::saveAsKB | ( | const std::string & | filename, | |
const std::string & | password | |||
) |
Save this Domain as a password protected Hugin Knowledge Base.
filename | Name of the file to save the knowledge base into. | |
password | The password for the file. |
void HAPI::Domain::saveAsKB | ( | const std::string & | filename | ) |
Save this Domain as a Hugin Knowledge Base.
filename | Name of the file to save the knowledge base into. |
void HAPI::Domain::saveCase | ( | const std::string & | filename | ) |
Saves all evidence entered in this Domain in a file with the given fileName (if the file exists, it is overwritten).
filename | the name of the file in which the case is going to be saved. |
void HAPI::Domain::saveCases | ( | const std::string & | filename, | |
const NodeList & | nodes, | |||
const std::vector< size_t > & | cases, | |||
bool | caseCounts, | |||
const std::string & | separator, | |||
const std::string & | missingData | |||
) |
Saves the specified cases entered in this Domain in a file with the given fileName.
filename | The name of the file in which the cases will be saved (if the file exists, it is overwritten). | |
nodes | A list of the nodes which are to be included in the file. | |
cases | A list of indexes of cases to be included in the file. | |
caseCounts | If true, include case counts in the data file. If false, case counts will not be included. | |
separator | The string used to separate the items in the file. | |
missingData | The string used to represent missing data. |
void HAPI::Domain::saveCases | ( | const std::string & | filename, | |
const NodeList & | nodes, | |||
bool | caseCounts, | |||
const std::string & | separator, | |||
const std::string & | missingData | |||
) |
Saves all cases entered in this Domain in a file with the given fileName.
filename | The name of the file in which the cases will be saved (if the file exists, it is overwritten). | |
nodes | A list of the nodes which are to be included in the file. | |
caseCounts | If true, include case counts in the data file. If false, case counts will not be included. | |
separator | The string used to separate the items in the file. | |
missingData | The string used to represent missing data. |
void HAPI::Domain::saveToMemory | ( | ) |
Create a copy in memory of the belief and junction tree tables of this Domain.
This operation can only be performed if the domain is compiled, the current equilibrium is "sum", and no evidence has been incorporated.
void HAPI::Domain::seedRandom | ( | unsigned int | seed | ) |
Seeds the random number generator.
The random number generator, used by HUGIN, generates a sequence of numbers which appears random, but are in fact deterministic. However, "seeding" the generator will change the starting point within the sequence.
seed | determines the starting point of the random number generator. |
void HAPI::Domain::setConcurrencyLevel | ( | size_t | level | ) |
Set the level of concurrency.
The level of concurrency specifies the maximum number of threads to create when performing a specific table operation. Setting the level of concurrency to 1 will cause all table operations to be performed sequentially. The initial parameter value is 1.
void HAPI::Domain::setGrainSize | ( | size_t | size | ) |
Set the grains size parameter.
The grain size parameter specifies a lower limit of the tasks to be performed by each thread. The size of a task is approximately equal to the number of floating-point operations needed to perform the task (e.g., the number of elements to sum when performing a marginalization task).
The initial value of the grain size parameter is 10000.
void HAPI::Domain::setLogLikelihoodTolerance | ( | double | tolerance | ) |
Specify the tolerance of the log-likelihood.
Terminate the EM learning when the relative difference between the log-likelihood of two successive iterations becomes less than tolerance.
void HAPI::Domain::setMaxNumberOfEMIterations | ( | size_t | iterations | ) |
Set the maximal number of iterations allowed for the EM algorithm.
The algorithm termnates when this number is reached or when the relative improvement becomes lower than the log-likelihood tolerance.
iterations | Maximum allowed number of iterations. |
void HAPI::Domain::setMaxNumberOfSeparators | ( | size_t | separators | ) |
Set the maximal number of separators allowed during triangulation.
separators | Maximum number of separators allowed durring triangulation. |
void HAPI::Domain::setNumberOfCases | ( | size_t | number | ) |
Adjust the storage capacity for cases in this domain.
number | Storage capacity. |
void HAPI::Domain::simulate | ( | ) |
Sample a configuration for this Domain with respect to the current distribution.
The current distribution must be in sum-equilibrium and with evidence incorporated in normal mode.
void HAPI::Domain::triangulate | ( | const NodeList & | order | ) |
Triangulates the graph of this Domain using the specified elimination order.
The elimination order must contain each chance and decision node of this Domain exactly once, and continuous nodes must appear before discrete nodes.
order | a NodeList containing the Nodes of the network in the order of elimination |
void HAPI::Domain::triangulate | ( | TriangulationMethod | tm = H_TM_BEST_GREEDY |
) |
Triangulate the graph of this Domain using the specified triangulation method.
tm | the TriangulationMethod to use. |
void HAPI::Domain::uncompile | ( | ) |
Uncompiles this Domain.
The data structures of this Domain produced by a triangulation or a compilation are deleted. Note that pointers to objects within the compiled structure (e.g., Cliques and JunctionTrees) are invalidated. Also note that many of the editing functions automatically performs an uncompile() operation. When this happens, the domain must be compiled again before it can be used for inference.
void HAPI::Domain::updatePolicies | ( | ) |
Update the policy tables of this domain.
The policies of all unmade decisions are updated. The new policies maximize the overall expected utility.