Modifier and Type | Method and Description |
---|---|
Variable |
Variable.copy()
Copies this instance.
|
Variable |
NetworkVariableCollection.get(int index)
Gets the
Variable object at the specified index. |
Variable |
NodeVariableCollection.get(int index)
Gets the
Variable object at the specified index. |
Variable |
NetworkVariableCollection.get(String name)
Performs a case sensitive lookup.
|
Variable |
NodeVariableCollection.get(String name)
Performs a case sensitive lookup.
|
Variable |
NetworkVariableCollection.get(String name,
boolean throwIfNotFound)
Performs a case sensitive lookup.
|
Variable |
NodeVariableCollection.get(String name,
boolean throwIfNotFound)
Performs a case sensitive lookup.
|
Variable |
DecomposeOutput.getDecomposedVariable(Variable networkVariable)
Maps a variable in the original network to the equivalent variable in the decomposed network.
|
Variable |
DecomposeOutput.getOriginalVariable(Variable decomposedVariable)
Maps a variable in the decomposed network to the equivalent variable in the original network.
|
Variable |
FunctionVariableExpression.getOwner()
Gets the current owner, if assigned to a variable.
|
Variable |
QueryExpression.getOwner()
Gets the current owner, if assigned to a variable.
|
Variable |
UnrollOutput.getUnrolledVariable(Variable dbnVariable,
Integer time)
Maps between a variable in the original Dynamic Bayesian network, and the corresponding variable in the unrolled network.
|
Variable |
State.getVariable()
Gets the
Variable the state belongs to, if any. |
Variable |
StateCollection.getVariable()
Gets the
Variable this collection belongs to. |
Variable |
UnrollOutput.VariableTime.getVariable()
Gets the variable.
|
Variable |
VariableContext.getVariable()
Gets the variable.
|
Variable |
NodeVariableCollection.remove(int index)
Removes an element from the collection at the specified index.
|
Variable |
NetworkVariableCollection.set(int index,
Variable value)
Gets the
Variable object at the specified index. |
Variable |
NodeVariableCollection.set(int index,
Variable value)
Sets the
Variable object at the specified index. |
Modifier and Type | Method and Description |
---|---|
void |
NodeVariableCollection.add(int index,
Variable item)
Inserts an element into the collection at the specified index.
|
int |
Variable.compareTo(Variable other) |
boolean |
VariableContextCollection.contains(Variable variable)
Determines whether a
Variable is in the collection. |
boolean |
VariableContextCollection.contains(Variable variable,
Integer time)
Determines whether a
Variable is in the collection at the specified [time]. |
double |
CLGaussian.getCovariance(Variable continuousHeadA,
Integer timeA,
Variable continuousHeadB,
Integer timeB)
Gets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
|
double |
CLGaussian.getCovariance(Variable continuousHeadA,
Integer timeA,
Variable continuousHeadB,
Integer timeB,
State... discrete)
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
|
double |
CLGaussian.getCovariance(Variable continuousHeadA,
Integer timeA,
Variable continuousHeadB,
Integer timeB,
StateContext... discrete)
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
|
double |
CLGaussian.getCovariance(Variable continuousHeadA,
Integer timeA,
Variable continuousHeadB,
Integer timeB,
TableIterator iterator)
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
|
double |
CLGaussian.getCovariance(Variable continuousHeadA,
Variable continuousHeadB)
Gets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB].
|
double |
CLGaussian.getCovariance(Variable continuousHeadA,
Variable continuousHeadB,
State... discrete)
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
|
double |
CLGaussian.getCovariance(Variable continuousHeadA,
Variable continuousHeadB,
StateContext... discrete)
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
|
double |
CLGaussian.getCovariance(Variable continuousHeadA,
Variable continuousHeadB,
TableIterator iterator)
Gets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
|
UnrollOutput.VariableTime |
UnrollOutput.getDbnVariable(Variable unrolledVariable)
Maps from a variable in the unrolled network to the corresponding variable in the original Dynamic Bayesian network.
|
Variable |
DecomposeOutput.getDecomposedVariable(Variable networkVariable)
Maps a variable in the original network to the equivalent variable in the decomposed network.
|
double |
CLGaussian.getMean(Variable continuousHead)
Gets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
|
double |
CLGaussian.getMean(Variable continuousHead,
Integer time)
Gets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable and time.
|
double |
CLGaussian.getMean(Variable continuousHead,
Integer time,
State... discrete)
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
|
double |
CLGaussian.getMean(Variable continuousHead,
Integer time,
StateContext... discrete)
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
|
double |
CLGaussian.getMean(Variable continuousHead,
Integer time,
TableIterator iterator)
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
|
double |
CLGaussian.getMean(Variable continuousHead,
State... discrete)
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
|
double |
CLGaussian.getMean(Variable continuousHead,
StateContext... discrete)
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
|
double |
CLGaussian.getMean(Variable continuousHead,
TableIterator iterator)
Gets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
|
Variable |
DecomposeOutput.getOriginalVariable(Variable decomposedVariable)
Maps a variable in the decomposed network to the equivalent variable in the original network.
|
Variable |
UnrollOutput.getUnrolledVariable(Variable dbnVariable,
Integer time)
Maps between a variable in the original Dynamic Bayesian network, and the corresponding variable in the unrolled network.
|
double |
CLGaussian.getVariance(Variable continuousHead)
Gets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
|
double |
CLGaussian.getVariance(Variable continuousHead,
Integer time)
Gets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
|
double |
CLGaussian.getVariance(Variable continuousHead,
Integer time,
State... discrete)
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
|
double |
CLGaussian.getVariance(Variable continuousHead,
Integer time,
StateContext... discrete)
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
|
double |
CLGaussian.getVariance(Variable continuousHead,
Integer time,
TableIterator iterator)
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
|
double |
CLGaussian.getVariance(Variable continuousHead,
State... discrete)
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
|
double |
CLGaussian.getVariance(Variable continuousHead,
StateContext... discrete)
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
|
double |
CLGaussian.getVariance(Variable continuousHead,
TableIterator iterator)
Gets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
|
double |
CLGaussian.getWeight(Variable continuousHead,
Integer timeHead,
Variable continuousTail,
Integer timeTail)
Gets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
|
double |
CLGaussian.getWeight(Variable continuousHead,
Integer timeHead,
Variable continuousTail,
Integer timeTail,
State... discrete)
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
|
double |
CLGaussian.getWeight(Variable continuousHead,
Integer timeHead,
Variable continuousTail,
Integer timeTail,
StateContext... discrete)
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
|
double |
CLGaussian.getWeight(Variable continuousHead,
Integer timeHead,
Variable continuousTail,
Integer timeTail,
TableIterator iterator)
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
|
double |
CLGaussian.getWeight(Variable continuousHead,
Variable continuousTail)
Gets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
|
double |
CLGaussian.getWeight(Variable continuousHead,
Variable continuousTail,
State... discrete)
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
|
double |
CLGaussian.getWeight(Variable continuousHead,
Variable continuousTail,
StateContext... discrete)
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
|
double |
CLGaussian.getWeight(Variable continuousHead,
Variable continuousTail,
TableIterator iterator)
Gets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
|
int |
VariableContextCollection.indexOf(Variable item)
Determines the index of a specific
Variable in the collection. |
int |
VariableContextCollection.indexOf(Variable variable,
Integer time)
Determines the index of a specific
Variable in the collection at the specified [time]. |
CLGaussian |
CLGaussian.instantiate(Variable variable,
double value)
Calculates the distribution which results from instantiating a particular variable.
|
CLGaussian |
CLGaussian.instantiate(Variable variable,
double value,
Integer time)
Calculates the distribution which results from instantiating a particular variable at a specified time.
|
CLGaussian |
CLGaussian.instantiateHead(Variable variable,
double value,
Integer time)
Calculates the distribution which results from instantiating a particular continuous head variable at a specified time.
|
CLGaussian |
CLGaussian.instantiateHead(Variable variable,
double value,
Integer time,
double[] logPdf)
Calculates the distribution which results from instantiating a particular continuous head variable at a specified time.
|
boolean |
NodeVariableCollection.remove(Variable item)
Removes the
Variable from the collection. |
Variable |
NetworkVariableCollection.set(int index,
Variable value)
Gets the
Variable object at the specified index. |
Variable |
NodeVariableCollection.set(int index,
Variable value)
Sets the
Variable object at the specified index. |
void |
CLGaussian.setCovariance(Variable continuousHeadA,
Integer timeA,
Variable continuousHeadB,
Integer timeB,
double value)
Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB]
|
void |
CLGaussian.setCovariance(Variable continuousHeadA,
Integer timeA,
Variable continuousHeadB,
Integer timeB,
double value,
State... discrete)
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
|
void |
CLGaussian.setCovariance(Variable continuousHeadA,
Integer timeA,
Variable continuousHeadB,
Integer timeB,
double value,
StateContext... discrete)
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
|
void |
CLGaussian.setCovariance(Variable continuousHeadA,
Integer timeA,
Variable continuousHeadB,
Integer timeB,
double value,
TableIterator iterator)
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
|
void |
CLGaussian.setCovariance(Variable continuousHeadA,
Variable continuousHeadB,
double value)
Sets the covariance of a Gaussian distribution with no discrete variables between [continuousHeadA] and [continuousHeadB]
|
void |
CLGaussian.setCovariance(Variable continuousHeadA,
Variable continuousHeadB,
double value,
State... discrete)
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
|
void |
CLGaussian.setCovariance(Variable continuousHeadA,
Variable continuousHeadB,
double value,
StateContext... discrete)
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
|
void |
CLGaussian.setCovariance(Variable continuousHeadA,
Variable continuousHeadB,
double value,
TableIterator iterator)
Sets the covariance of the Gaussian distribution between [continuousHeadA] and [continuousHeadB] for a particular discrete combination (mixture).
|
void |
CLGaussian.setMean(Variable continuousHead,
double value)
Sets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
|
void |
CLGaussian.setMean(Variable continuousHead,
double value,
State... discrete)
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
|
void |
CLGaussian.setMean(Variable continuousHead,
double value,
StateContext... discrete)
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
|
void |
CLGaussian.setMean(Variable continuousHead,
double value,
TableIterator iterator)
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
|
void |
CLGaussian.setMean(Variable continuousHead,
Integer time,
double value)
Sets the mean value of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
|
void |
CLGaussian.setMean(Variable continuousHead,
Integer time,
double value,
State... discrete)
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
|
void |
CLGaussian.setMean(Variable continuousHead,
Integer time,
double value,
StateContext... discrete)
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the [discrete] combination.
|
void |
CLGaussian.setMean(Variable continuousHead,
Integer time,
double value,
TableIterator iterator)
Sets the mean value of the Gaussian distribution for the specified [continuousHead] variable for the discrete combination.
|
void |
CLGaussian.setVariance(Variable continuousHead,
double value)
Sets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
|
void |
CLGaussian.setVariance(Variable continuousHead,
double value,
State... discrete)
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
|
void |
CLGaussian.setVariance(Variable continuousHead,
double value,
StateContext... discrete)
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
|
void |
CLGaussian.setVariance(Variable continuousHead,
double value,
TableIterator iterator)
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
|
void |
CLGaussian.setVariance(Variable continuousHead,
Integer time,
double value)
Sets the variance of a Gaussian distribution with no discrete variables for the specified [continuousHead] variable.
|
void |
CLGaussian.setVariance(Variable continuousHead,
Integer time,
double value,
State... discrete)
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
|
void |
CLGaussian.setVariance(Variable continuousHead,
Integer time,
double value,
StateContext... discrete)
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
|
void |
CLGaussian.setVariance(Variable continuousHead,
Integer time,
double value,
TableIterator iterator)
Sets the variance of the Gaussian distribution for the specified [continuousHead] variable for a particular discrete combination (mixture).
|
void |
CLGaussian.setWeight(Variable continuousHead,
Integer timeHead,
Variable continuousTail,
Integer timeTail,
double value)
Sets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
|
void |
CLGaussian.setWeight(Variable continuousHead,
Integer timeHead,
Variable continuousTail,
Integer timeTail,
double value,
State... discrete)
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
|
void |
CLGaussian.setWeight(Variable continuousHead,
Integer timeHead,
Variable continuousTail,
Integer timeTail,
double value,
StateContext... discrete)
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
|
void |
CLGaussian.setWeight(Variable continuousHead,
Integer timeHead,
Variable continuousTail,
Integer timeTail,
double value,
TableIterator iterator)
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
|
void |
CLGaussian.setWeight(Variable continuousHead,
Variable continuousTail,
double value)
Sets the weight/regression coefficient of a Gaussian distribution with no discrete variables between the [continuousTail] and [continuousHead].
|
void |
CLGaussian.setWeight(Variable continuousHead,
Variable continuousTail,
double value,
State... discrete)
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
|
void |
CLGaussian.setWeight(Variable continuousHead,
Variable continuousTail,
double value,
StateContext... discrete)
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
|
void |
CLGaussian.setWeight(Variable continuousHead,
Variable continuousTail,
double value,
TableIterator iterator)
Sets the weight/regression coefficient of the Gaussian distribution between the [continuousTail] and [continuousHead] for a particular discrete combination (mixture).
|
void |
NetworkMonitor.statesCollectionChange(Variable variable,
int index,
State add,
State remove,
CollectionAction action,
boolean complete)
For internal use.
|
void |
NetworkMonitor.variableCollectionChange(int index,
Variable add,
Variable remove,
CollectionAction action,
boolean complete)
For internal use.
|
Modifier and Type | Method and Description |
---|---|
boolean |
VariableContextCollection.containsAll(List<Variable> items)
Determines whether all [items] are matched in the collection.
|
boolean |
VariableContextCollection.containsAll(List<Variable> items,
List<Integer> times)
Determines whether all [items] are matched in the collection.
|
boolean |
VariableContextCollection.containsAny(List<Variable> items,
List<Integer> times)
Determines whether any [items] are matched in the collection.
|
Constructor and Description |
---|
CLGaussian(Variable variable)
Initializes a new instance of the
CLGaussian class with a single variable. |
CLGaussian(Variable[] variables)
Initializes a new instance of the
CLGaussian class with the specified variables. |
CLGaussian(Variable variable,
Integer time)
Initializes a new instance of the
CLGaussian class with a single variable at the specified time. |
Node(String name,
Variable... variables)
Initializes a new instance of the
Node class with a specified name and a number of variables. |
Node(Variable variable)
|
Table(Variable... variables)
Initializes a new instance of the
Table class with the specified variables. |
Table(Variable variable)
|
Table(Variable variable,
Integer time)
|
TableAccessor(Table table,
Variable[] order)
Initializes a new instance of the
TableAccessor class, allowing random access to [table] with a specified [order] for the variables. |
TableAccessor(Table table,
Variable[] order,
Integer[] times)
Initializes a new instance of the
TableAccessor class, allowing random access to [table] with a specified [order] for the variables at specified times. |
TableIterator(Table table,
Variable[] order)
Initializes a new instance of the
TableIterator class, allowing sequential access to [table] with a specified [order] for the variables. |
TableIterator(Table table,
Variable[] order,
Integer[] times)
Initializes a new instance of the
TableIterator class, allowing sequential access to [table] with a specified [order] for the variables at specified times. |
VariableContext(Variable variable)
Initializes a new instance of the
VariableContext class. |
VariableContext(Variable variable,
HeadTail headTail)
Initializes a new instance of the
VariableContext class. |
VariableContext(Variable variable,
Integer time)
Initializes a new instance of the
VariableContext class. |
VariableContext(Variable variable,
Integer time,
HeadTail headTail)
Initializes a new instance of the
VariableContext class. |
Constructor and Description |
---|
CLGaussian(List<Variable> variables,
Integer time)
Initializes a new instance of the
CLGaussian class with the specified variables at a particular time. |
CLGaussian(List<Variable> variables,
Integer time,
HeadTail headTail)
Initializes a new instance of the
CLGaussian class with the specified variables. |
Node(String name,
List<Variable> variables)
Initializes a new instance of the
Node class with a specified name and a number of variables. |
Table(List<Variable> variables,
Integer time)
Initializes a new instance of the
Table class with the specified variables, at an optional time. |
Table(List<Variable> variables,
Integer time,
HeadTail headTail)
Initializes a new instance of the
Table class with the specified variables, at an optional time. |
TableAccessor(Table table,
List<Variable> order,
List<Integer> times)
Initializes a new instance of the
TableAccessor class, allowing random access to [table] with a specified [order] for the variables at specified times. |
TableIterator(Table table,
List<Variable> order,
List<Integer> times)
Initializes a new instance of the
TableIterator class, allowing sequential access to [table] with a specified [order] for the variables at specified times. |
VariableMap(VariableContextCollection sortedVariables,
List<Variable> order,
List<Integer> times)
Initializes a new instance of the
VariableMap class. |
Modifier and Type | Method and Description |
---|---|
Variable |
AutoInsightVariableOutput.getVariable()
Gets the test variable.
|
Modifier and Type | Method and Description |
---|---|
static ImpactOutput |
Impact.calculate(Network network,
Variable hypothesisVariable,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactOptions options)
Analyzes the impact of sets of evidence on a hypothesis state and its variable.
|
static ImpactOutput |
Impact.calculate(Network network,
Variable hypothesisVariable,
State hypothesisState,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactOptions options)
Analyzes the impact of sets of evidence on a hypothesis state and its variable.
|
static AutoInsightOutput[] |
AutoInsight.calculate(Variable continuousTarget,
List<Interval<Double>> targetIntervals,
List<Variable> testVariables,
Evidence evidence,
AutoInsightOptions options)
Uses comparison queries to automatically derive insight about a target variable from a trained network.
|
static ValueOfInformationOutput |
ValueOfInformation.calculate(Variable hypothesis,
List<Variable> testVariables,
Evidence evidence,
InferenceFactory factory,
ValueOfInformationOptions options)
Calculates value of information, which can be used to determine which variables are most likely to reduce the uncertainty of a particular variable.
|
static ClusterCountOutput |
ClusterCount.detect(Network network,
Variable cluster,
List<Integer> clusterCounts,
ClusterCountActions actions,
ClusterCountOptions options)
Determine the number of clusters (discrete states of a latent variable) using cross validation.
|
Modifier and Type | Method and Description |
---|---|
static ImpactOutput |
Impact.calculate(Network network,
Distribution hypothesisQuery,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactOptions options)
Analyzes the impact of sets of evidence on the resulting probability distribution of a hypothesis variable.
|
static ImpactOutput |
Impact.calculate(Network network,
Distribution hypothesisQuery,
StateContext[] hypothesisCombination,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactOptions options)
Analyzes the impact of sets of evidence on a hypothesis query and discrete combination of that hypothesis query.
|
static LogLikelihoodAnalysisOutput |
LogLikelihoodAnalysis.calculate(Network network,
Evidence evidence,
List<Variable> evidenceToAnalyse,
LogLikelihoodAnalysisOptions options)
Analyzes the log-likelihood based on subsets of evidence.
|
static ImpactOutput |
Impact.calculate(Network network,
Variable hypothesisVariable,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactOptions options)
Analyzes the impact of sets of evidence on a hypothesis state and its variable.
|
static ImpactOutput |
Impact.calculate(Network network,
Variable hypothesisVariable,
State hypothesisState,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactOptions options)
Analyzes the impact of sets of evidence on a hypothesis state and its variable.
|
static AutoInsightOutput |
AutoInsight.calculate(State target,
List<Variable> testVariables,
Evidence evidence,
AutoInsightOptions options)
Uses comparison queries to automatically derive insight about a target variable from a trained network.
|
static AutoInsightOutput |
AutoInsight.calculate(State target,
List<Variable> testVariables,
InferenceFactory factory)
Uses comparison queries to automatically derive insight about a target variable from a trained network.
|
static AutoInsightOutput |
AutoInsight.calculate(State target,
List<Variable> testVariables,
InferenceFactory factory,
Evidence evidence)
Uses comparison queries to automatically derive insight about a target variable from a trained network.
|
static AutoInsightOutput[] |
AutoInsight.calculate(Variable continuousTarget,
List<Interval<Double>> targetIntervals,
List<Variable> testVariables,
Evidence evidence,
AutoInsightOptions options)
Uses comparison queries to automatically derive insight about a target variable from a trained network.
|
static ValueOfInformationOutput |
ValueOfInformation.calculate(Variable hypothesis,
List<Variable> testVariables,
Evidence evidence,
InferenceFactory factory,
ValueOfInformationOptions options)
Calculates value of information, which can be used to determine which variables are most likely to reduce the uncertainty of a particular variable.
|
static ImpactHypothesisOutput |
Impact.calculateStreamed(Network network,
Distribution hypothesisQuery,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactAction outputItem,
ImpactOptions options)
Analyzes the impact of sets of evidence on the resulting probability distribution of a hypothesis variable.
|
static ImpactHypothesisOutput |
Impact.calculateStreamed(Network network,
Distribution hypothesisQuery,
StateContext[] hypothesisState,
Evidence evidence,
List<Variable> evidenceToAnalyse,
ImpactAction outputItem,
ImpactOptions options)
Analyzes the impact of sets of evidence on a hypothesis query and discrete combination of that hypothesis query.
|
static LogLikelihoodAnalysisBaselineOutput |
LogLikelihoodAnalysis.calculateStreamed(Network network,
Evidence evidence,
List<Variable> evidenceToAnalyse,
LogLikelihoodAnalysisAction outputItem,
LogLikelihoodAnalysisOptions options)
Analyzes the log-likelihood based on subsets of evidence.
|
static void |
Combinations.enumerate(List<Variable> variables,
CombinationAction combinationAction,
CombinationOptions options)
Enumerates the state combinations for a set of variables.
|
Constructor and Description |
---|
AssociationPair(Variable x,
Variable y)
Initializes a new instance of the
AssociationPair class with individual variables. |
Modifier and Type | Method and Description |
---|---|
Variable |
EffectsAnalysisOutput.getOutcome()
Gets the outome (target) variable on which effects are being measured.
|
Variable |
EffectsAnalysisOutput.getTreatment()
Gets the treatment variable which is being varied.
|
Variable |
EffectsAnalysisOutputItem.getTreatmentVariable()
Gets the treatment variable used to measure the causal effect on the treatment.
|
Modifier and Type | Method and Description |
---|---|
static EffectsAnalysisOutput |
EffectsAnalysis.calculate(Variable treatment,
Variable outcome,
CausalEffectKind effect,
Evidence fixedEvidence,
InferenceFactory factory,
EffectsAnalysisOptions options)
Calculate the causal effect on a target, varying for different treatment values.
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Modifier and Type | Method and Description |
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static void |
Abduction.update(Evidence evidence,
List<Variable> abductionEvidenceVariables,
List<Variable> characteristicVariables,
AbductionOptions options)
Performs abduction which is one of the steps in 'counterfactual analysis'.
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static void |
Abduction.update(Evidence evidence,
List<Variable> abductionEvidenceVariables,
List<Variable> characteristicVariables,
AbductionOptions options)
Performs abduction which is one of the steps in 'counterfactual analysis'.
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Modifier and Type | Method and Description |
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Variable |
VariableReference.getVariable()
Gets the variable.
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Modifier and Type | Method and Description |
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VariableReference |
VariableReference.copy(Variable newVariable)
Creates a copy of this instance, but based on a different variable.
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Constructor and Description |
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VariableReference(Variable variable,
ColumnValueType columnValueType,
String column)
Initializes a new instance of the
VariableReference class. |
VariableReference(Variable variable,
ColumnValueType columnValueType,
String column,
StateNotFoundAction stateNotFoundAction)
Initializes a new instance of the
VariableReference class. |
VariableReference(Variable variable,
ColumnValueType columnValueType,
String column,
StateNotFoundAction stateNotFoundAction,
EmptyStringAction emptyStringAction)
Initializes a new instance of the
VariableReference class. |
VariableReference(Variable variable,
ColumnValueType columnValueType,
String column,
StateNotFoundAction stateNotFoundAction,
EmptyStringAction emptyStringAction,
String interventionColumn)
Initializes a new instance of the
VariableReference class. |
Modifier and Type | Method and Description |
---|---|
Variable |
VariableInfo.getVariable()
Gets the generated
Variable . |
Modifier and Type | Method and Description |
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Variable |
ExcludedVariables.get(int index) |
Variable |
ExcludedVariables.remove(int index) |
Variable |
ExcludedVariables.set(int index,
Variable item) |
Modifier and Type | Method and Description |
---|---|
void |
ExcludedVariables.add(int index,
Variable element) |
Variable |
ExcludedVariables.set(int index,
Variable item) |
Modifier and Type | Method and Description |
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Variable |
QueryFunctionOutput.getVariable()
The function variable to evaluate.
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Modifier and Type | Method and Description |
---|---|
void |
DefaultEvidence.clear(Variable variable)
Clears any evidence on a variable.
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void |
Evidence.clear(Variable variable)
Clears evidence on a variable.
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void |
DefaultEvidence.clear(Variable variable,
Integer time)
Clears evidence on a variable at the specified time.
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void |
Evidence.clear(Variable variable,
Integer time)
Clears evidence on a variable at the specified time.
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void |
DefaultEvidence.copy(Evidence evidence,
Variable variable)
Replaces the current evidence for an individual variable, with that from another
Evidence instance. |
void |
Evidence.copy(Evidence evidence,
Variable variable)
Replaces the current evidence for an individual variable, with that from another
Evidence instance. |
void |
DefaultEvidence.copy(Evidence evidence,
Variable variable,
Integer time)
Replaces the current evidence for an individual variable at a specific time, with that from another
Evidence instance. |
void |
Evidence.copy(Evidence evidence,
Variable variable,
Integer time)
Replaces the current evidence for an individual variable at a specific time, with that from another
Evidence instance. |
Double |
DefaultEvidence.get(Variable variable)
Gets the hard evidence for a discrete variable or continuous variable, or returns null if the
EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT . |
Double |
Evidence.get(Variable variable)
Gets the hard evidence for a discrete variable or continuous variable, or returns null if the
EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT . |
void |
DefaultEvidence.get(Variable variable,
Double[] destination,
int destinationStart,
int startTime,
int count)
Gets the evidence for a temporal variable.
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void |
Evidence.get(Variable variable,
Double[] destination,
int destinationStart,
int startTime,
int count)
Gets the evidence for a temporal variable.
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Double |
DefaultEvidence.get(Variable variable,
Integer time)
Gets the evidence for a discrete variable at the specified time.
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Double |
Evidence.get(Variable variable,
Integer time)
Gets the evidence for a discrete variable at the specified time.
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EvidenceType |
DefaultEvidence.getEvidenceType(Variable variable)
Returns the type of evidence currently set for a variable (if any).
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EvidenceType |
Evidence.getEvidenceType(Variable variable)
Returns the type of evidence currently set for a variable (if any).
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EvidenceType |
DefaultEvidence.getEvidenceType(Variable variable,
Integer time)
Returns the type of evidence currently set for a variable at a given time.
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EvidenceType |
Evidence.getEvidenceType(Variable variable,
Integer time)
Returns the type of evidence currently set for a variable at a given time.
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EvidenceTypes |
DefaultEvidence.getEvidenceTypes(Variable variable)
Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
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EvidenceTypes |
Evidence.getEvidenceTypes(Variable variable)
Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
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EvidenceTypes |
DefaultEvidence.getEvidenceTypes(Variable variable,
Integer time)
Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
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EvidenceTypes |
Evidence.getEvidenceTypes(Variable variable,
Integer time)
Gets the type of evidence (if any) and whether or not it is an intervention (do-operator).
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Integer |
DefaultEvidence.getMaxTime(Variable variable)
Gets the maximum time containing evidence for a variable.
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Integer |
Evidence.getMaxTime(Variable variable)
Gets the maximum time containing evidence for a variable.
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Integer |
DefaultEvidence.getState(Variable variable)
Gets the hard evidence state for a particular variable, or returns null if the
EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT . |
Integer |
Evidence.getState(Variable variable)
Gets the hard evidence state for a particular variable, or returns null if the
EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT . |
Integer |
DefaultEvidence.getState(Variable variable,
Integer time)
Gets the hard evidence state for a particular variable, or returns null if the
EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT . |
Integer |
Evidence.getState(Variable variable,
Integer time)
Gets the hard evidence state for a particular variable, or returns null if the
EvidenceType equals EvidenceType.NONE or EvidenceType.SOFT . |
void |
DefaultEvidence.getStates(Variable variable,
double[] buffer)
Fills out a buffer containing the soft evidence for a particular variable.
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void |
Evidence.getStates(Variable variable,
double[] buffer)
Fills out a buffer containing the soft evidence for a particular variable.
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void |
DefaultEvidence.getStates(Variable variable,
double[] buffer,
Integer time)
Fills out a buffer containing the soft evidence for a particular variable at a specified time.
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void |
Evidence.getStates(Variable variable,
double[] buffer,
Integer time)
Fills out a buffer containing the soft evidence for a particular variable at a specified time.
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void |
DefaultEvidence.getVariables(Variable[] buffer)
Fills out a buffer with all variables that have either hard or soft evidence.
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void |
Evidence.getVariables(Variable[] buffer)
Fills out a buffer with all variables that have either hard or soft evidence.
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void |
DefaultEvidence.set(Variable variable,
Double value)
Sets a variable to a particular value (hard evidence).
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void |
Evidence.set(Variable variable,
Double value)
Sets a variable to a particular value (hard evidence).
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void |
DefaultEvidence.set(Variable variable,
Double[] source,
int sourceStart,
int startTime,
int count)
Sets temporal evidence on a variable.
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void |
Evidence.set(Variable variable,
Double[] source,
int sourceStart,
int startTime,
int count)
Sets temporal evidence on a variable.
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void |
DefaultEvidence.set(Variable variable,
Double value,
Integer time)
Sets evidence on a variable at a specified time.
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void |
Evidence.set(Variable variable,
Double value,
Integer time)
Sets evidence on a variable at a specified time.
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void |
DefaultEvidence.set(Variable variable,
Double value,
Integer time,
InterventionType interventionType)
Sets evidence on the variable, in the form of an intervention (do-operator).
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void |
Evidence.set(Variable variable,
Double value,
Integer time,
InterventionType interventionType)
Sets evidence on the variable, in the form of an intervention (do-operator).
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void |
DefaultEvidence.setState(Variable variable,
Integer state)
Sets a discrete variable to a particular state (hard evidence).
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void |
Evidence.setState(Variable variable,
Integer state)
Sets a discrete variable to a particular state (hard evidence).
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void |
DefaultEvidence.setState(Variable variable,
Integer state,
Integer time)
Sets a discrete variable to a particular state (hard evidence), specifiying a time if the state belongs to a variable whose node is temporal.
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void |
Evidence.setState(Variable variable,
Integer state,
Integer time)
Sets a discrete variable to a particular state (hard evidence), specifiying a time if the state belongs to a variable whose node is temporal.
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void |
DefaultEvidence.setStates(Variable variable,
double[] values)
Sets soft evidence for a particular discrete variable.
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void |
Evidence.setStates(Variable variable,
double[] values)
Sets soft evidence for a particular discrete variable.
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void |
DefaultEvidence.setStates(Variable variable,
double[] values,
Integer time)
Sets soft evidence for a particular discrete variable at a specified time.
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void |
Evidence.setStates(Variable variable,
double[] values,
Integer time)
Sets soft evidence for a particular discrete variable at a specified time.
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Constructor and Description |
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QueryFunctionOutput(Variable variable)
Initializes a new instance of the
com.bayesserver.QueryFunctionOutput class. |
Modifier and Type | Method and Description |
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Variable |
FeatureSelectionTest.getTarget()
Gets the variable that was the target of the feature selection test.
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Variable |
FeatureSelectionTest.getVariable()
Gets the variable which was tested to see if it is likely to be a feature of the
FeatureSelectionTest.getTarget() variable. |
Modifier and Type | Method and Description |
---|---|
static FeatureSelectionOutput |
FeatureSelection.detect(List<Variable> variables,
EvidenceReaderCommand evidenceReaderCommand,
Variable target,
FeatureSelectionOptions options)
Determines which variables are likely to be good features (predictors) of a target variable.
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Modifier and Type | Method and Description |
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static FeatureSelectionOutput |
FeatureSelection.detect(List<Variable> variables,
EvidenceReaderCommand evidenceReaderCommand,
Variable target,
FeatureSelectionOptions options)
Determines which variables are likely to be good features (predictors) of a target variable.
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Modifier and Type | Method and Description |
---|---|
Variable |
DesignVariable.getVariable()
Gets the variable these options refer to.
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Variable |
Objective.getVariable()
Gets the variable being optimized.
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Constructor and Description |
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DesignVariable(Variable variable,
Double lowerBound,
Double upperBound,
boolean allowMissing)
Initializes a new instance of the
com.bayesserver.optization.DesignVariable class, automatically generating the necessary design states. |
DesignVariable(Variable variable,
Double lowerBound,
Double upperBound,
boolean allowMissing,
InterventionType interventionType)
Initializes a new instance of the
DesignVariable class, automatically generating the necessary design states. |
DesignVariable(Variable variable,
List<DesignState> designStates,
boolean allowMissing)
Initializes a new instance of the
DesignVariable class. |
DesignVariable(Variable variable,
List<DesignState> designStates,
DesignEvidenceKind evidenceKind,
boolean allowMissing,
InterventionType interventionType)
Initializes a new instance of the
DesignVariable class. |
Objective(Variable variable,
ObjectiveKind kind)
Initializes a new instance of the
com.bayesserver.optimization.objective. class. |
Objective(Variable variable,
ObjectiveKind kind,
Double value)
Initializes a new instance of the
com.bayesserver.optimization.objective. class. |
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