c

com.cra.figaro.algorithm.decision

ProbQueryVariableEliminationDecision

class ProbQueryVariableEliminationDecision[T, U] extends OneTimeProbQuery with ProbabilisticVariableEliminationDecision with DecisionAlgorithm[T, U]

Decision VariableElimination algorithm that computes the expected utility of decision elements using the default elimination order.

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Inherited
  1. ProbQueryVariableEliminationDecision
  2. DecisionAlgorithm
  3. ProbabilisticVariableEliminationDecision
  4. VariableElimination
  5. FactoredAlgorithm
  6. OneTimeProbQuery
  7. OneTime
  8. ProbQueryAlgorithm
  9. BaseProbQueryAlgorithm
  10. Algorithm
  11. AnyRef
  12. Any
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Visibility
  1. Public
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Instance Constructors

  1. new ProbQueryVariableEliminationDecision(universe: Universe, utilityNodes: List[Element[_]], target: Element[_])(showTiming: Boolean, dependentUniverses: List[(Universe, List[NamedEvidence[_]])], dependentAlgorithm: (Universe, List[NamedEvidence[_]]) ⇒ () ⇒ Double)

Type Members

  1. class NotATargetException [T] extends AlgorithmException
    Definition Classes
    BaseProbQueryAlgorithm

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. val active: Boolean
    Attributes
    protected
    Definition Classes
    Algorithm
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def check[T](target: Element[T]): Unit
    Attributes
    protected
    Definition Classes
    BaseProbQueryAlgorithm
  7. def cleanUp(): Unit

    Called when the algorithm is killed.

    Called when the algorithm is killed. By default, does nothing. Can be overridden.

    Definition Classes
    Algorithm
  8. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. val comparator: Option[((Double, Double), (Double, Double)) ⇒ Boolean]

    Some variable elimination algorithms, such as computing the most probable explanation, record values of variables as they are eliminated.

    Some variable elimination algorithms, such as computing the most probable explanation, record values of variables as they are eliminated. Such values are stored in a factor that maps values of the other variables to a value of the eliminated variable. This factor is produced by finding the value of the variable that "maximizes" the entry associated with the value in the product factor resulting from eliminating this variable, for some maximization function. The recordingFunction determines which of two entries is greater according to the maximization function. It returns true iff the second entry is greater. The recording function is an option so that variable elimination algorithms that do not use it can ignore it.

    Definition Classes
    VariableElimination
  10. def computeDistribution[T](target: Element[T]): Stream[(Double, T)]

    Returns distribution of the target, ignoring utilities.

    Returns distribution of the target, ignoring utilities.

    Definition Classes
    ProbQueryVariableEliminationDecisionBaseProbQueryAlgorithm
  11. def computeExpectation[T](target: Element[T], function: (T) ⇒ Double): Double

    Returns expectation of the target, ignoring utilities

    Returns expectation of the target, ignoring utilities

    Definition Classes
    ProbQueryVariableEliminationDecisionBaseProbQueryAlgorithm
  12. def computeProbability[T](target: Element[T], predicate: (T) ⇒ Boolean): Double

    Return an estimate of the probability of the predicate under the marginal probability distribution of the target.

    Return an estimate of the probability of the predicate under the marginal probability distribution of the target.

    Definition Classes
    BaseProbQueryAlgorithm
  13. def computeProjection[T](target: Element[T]): List[(T, Double)]
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    BaseProbQueryAlgorithm
  14. def computeUtility(): Map[(T, U), DecisionSample]

    Returns the computed utility of all parent/decision tuple values.

    Returns the computed utility of all parent/decision tuple values. For VE, these are not samples but the actual computed expected utility for all combinations of the parent and decision.

    Definition Classes
    ProbQueryVariableEliminationDecisionDecisionAlgorithm
  15. val debug: Boolean

    By default, implementations that inherit this trait have no debug information.

    By default, implementations that inherit this trait have no debug information. Override this if you want a debugging option.

    Definition Classes
    VariableElimination
  16. val dependentAlgorithm: (Universe, List[NamedEvidence[_]]) ⇒ () ⇒ Double
  17. val dependentUniverses: List[(Universe, List[NamedEvidence[_]])]
  18. def distribution[T](target: Element[T]): Stream[(Double, T)]

    Return an estimate of the marginal probability distribution over the target that lists each element with its probability.

    Return an estimate of the marginal probability distribution over the target that lists each element with its probability. The result is a lazy stream. It is up to the algorithm how the stream is ordered. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.

    Definition Classes
    BaseProbQueryAlgorithm
  19. def doDistribution[T](target: Element[T]): Stream[(Double, T)]
    Attributes
    protected
    Definition Classes
    OneTimeProbQueryBaseProbQueryAlgorithm
  20. def doElimination(allFactors: List[Factor[(Double, Double)]], targetVariables: Seq[Variable[_]]): Unit
    Attributes
    protected
    Definition Classes
    VariableElimination
  21. def doExpectation[T](target: Element[T], function: (T) ⇒ Double): Double
    Attributes
    protected
    Definition Classes
    OneTimeProbQueryBaseProbQueryAlgorithm
  22. def doKill(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    OneTimeAlgorithm
  23. def doProbability[T](target: Element[T], predicate: (T) ⇒ Boolean): Double
    Attributes
    protected
    Definition Classes
    OneTimeProbQueryBaseProbQueryAlgorithm
  24. def doProjection[T](target: Element[T]): List[(T, Double)]
    Attributes
    protected
    Definition Classes
    OneTimeProbQueryBaseProbQueryAlgorithm
  25. def doResume(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    OneTimeAlgorithm
  26. def doStart(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    OneTimeAlgorithm
  27. def doStop(): Unit
    Attributes
    protected[com.cra.figaro.algorithm]
    Definition Classes
    OneTimeAlgorithm
  28. def eliminateInOrder(order: List[Variable[_]], factors: MultiSet[Factor[(Double, Double)]], map: FactorMap[(Double, Double)]): MultiSet[Factor[(Double, Double)]]
    Attributes
    protected
    Definition Classes
    VariableElimination
  29. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  30. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  31. def expectation[T](target: Element[T])(function: (T) ⇒ Double, c: Any = DummyImplicit): Double

    Return an estimate of the expectation of the function under the marginal probability distribution of the target.

    Return an estimate of the expectation of the function under the marginal probability distribution of the target. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.

    Definition Classes
    BaseProbQueryAlgorithm
  32. def expectation[T](target: Element[T], function: (T) ⇒ Double): Double

    Return an estimate of the expectation of the function under the marginal probability distribution of the target.

    Return an estimate of the expectation of the function under the marginal probability distribution of the target. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.

    Definition Classes
    BaseProbQueryAlgorithm
  33. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  34. def finish(factorsAfterElimination: MultiSet[Factor[(Double, Double)]], eliminationOrder: List[Variable[_]]): Unit

    All implementation of variable elimination must specify what to do after variables have been eliminated.

    All implementation of variable elimination must specify what to do after variables have been eliminated.

    Definition Classes
    ProbQueryVariableEliminationDecisionVariableElimination
  35. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  36. def getFactors(neededElements: List[Element[_]], targetElements: List[Element[_]], upper: Boolean = false): List[Factor[(Double, Double)]]

    Create the factors for decision factors.

    Create the factors for decision factors. Each factor is hardcoded as a tuple of (Double, Double), where the first value is the probability and the second is the utility.

    Definition Classes
    ProbabilisticVariableEliminationDecisionFactoredAlgorithm
  37. def getNeededElements(starterElements: List[Element[_]], depth: Int, parameterized: Boolean = false): (List[Element[_]], Boolean)

    Get the elements that are needed by the query target variables and the evidence variables.

    Get the elements that are needed by the query target variables and the evidence variables. Also compute the values of those variables to the given depth. Only get factors for elements that are actually used by the target variables. This is more efficient. Also, it avoids problems when values of unused elements have not been computed.

    In addition to getting all the needed elements, it determines if any of the conditioned, constrained, or dependent universe parent elements has * in its range. If any of these elements has * in its range, the lower and upper bounds of factors will be different, so we need to compute both. If they don't, we don't need to compute bounds.

    Definition Classes
    FactoredAlgorithm
  38. def getUtility(p: T, d: U): DecisionSample

    Get the total utility and weight for a specific value of a parent and decision.

    Get the total utility and weight for a specific value of a parent and decision.

    Definition Classes
    DecisionAlgorithm
  39. def getUtility(): Map[(T, U), DecisionSample]

    Get the total utility and weight for all sampled values of the parent and decision.

    Get the total utility and weight for all sampled values of the parent and decision.

    Definition Classes
    DecisionAlgorithm
  40. def getUtilityNodes: List[Element[_]]

    Retrieve utility nodes in the model

    Retrieve utility nodes in the model

    Definition Classes
    ProbQueryVariableEliminationDecisionProbabilisticVariableEliminationDecision
  41. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  42. def initialFactorMap(factors: Traversable[Factor[(Double, Double)]]): FactorMap[(Double, Double)]
    Attributes
    protected
    Definition Classes
    VariableElimination
  43. def initialize(): Unit

    Called when the algorithm is started before running any steps.

    Called when the algorithm is started before running any steps. By default, does nothing. Can be overridden.

    Definition Classes
    Algorithm
  44. def isActive: Boolean
    Definition Classes
    Algorithm
  45. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  46. def kill(): Unit

    Kill the algorithm so that it is inactive.

    Kill the algorithm so that it is inactive. It will no longer be able to provide answers.Throws AlgorithmInactiveException if the algorithm is not active.

    Definition Classes
    Algorithm
  47. def makeUtilFactor(e: Element[_]): Factor[(Double, Double)]

    Makes a utility factor an element designated as a utility.

    Makes a utility factor an element designated as a utility. This is factor of a tuple (Double, Double) where the first value is 1.0 and the second is a possible utility of the element.

    Definition Classes
    ProbabilisticVariableEliminationDecision
  48. def mean(target: Element[Double]): Double

    Return the mean of the probability density function for the given continuous element.

    Return the mean of the probability density function for the given continuous element.

    Definition Classes
    BaseProbQueryAlgorithm
  49. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  50. final def notify(): Unit
    Definition Classes
    AnyRef
  51. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  52. def posteriorElement[T](target: Element[T], universe: Universe = Universe.universe): Element[T]

    Return an element representing the posterior probability distribution of the given element.

    Return an element representing the posterior probability distribution of the given element.

    Definition Classes
    ProbQueryAlgorithm
  53. def probability[T](target: Element[T], value: T): Double

    Return an estimate of the probability that the target produces the value.

    Return an estimate of the probability that the target produces the value. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.

    Definition Classes
    BaseProbQueryAlgorithm
  54. def probability[T](target: Element[T])(predicate: (T) ⇒ Boolean, c: Any = DummyImplicit): Double

    Return an estimate of the probability of the predicate under the marginal probability distribution of the target.

    Return an estimate of the probability of the predicate under the marginal probability distribution of the target. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.

    Definition Classes
    BaseProbQueryAlgorithm
  55. def probability[T](target: Element[T], predicate: (T) ⇒ Boolean): Double

    Return an estimate of the probability of the predicate under the marginal probability distribution of the target.

    Return an estimate of the probability of the predicate under the marginal probability distribution of the target. Throws NotATargetException if called on a target that is not in the list of targets of the algorithm. Throws AlgorithmInactiveException if the algorithm is inactive.

    Definition Classes
    BaseProbQueryAlgorithm
  56. lazy val queryTargets: List[Element[_$6]] forSome {type _$6}
  57. val recordingFactors: List[Factor[_]]
    Attributes
    protected
    Definition Classes
    VariableElimination
  58. def resume(): Unit

    Resume the computation of the algorithm, if it has been stopped.

    Resume the computation of the algorithm, if it has been stopped. Throws AlgorithmInactiveException if the algorithm is not active.

    Definition Classes
    Algorithm
  59. def run(): Unit

    Run the algorithm, performing its computation to completion.

    Run the algorithm, performing its computation to completion.

    Definition Classes
    VariableEliminationOneTime
  60. val semiring: SumProductUtilitySemiring

    Semiring for Decisions uses a sum-product-utility semiring.

    Semiring for Decisions uses a sum-product-utility semiring.

    Definition Classes
    ProbabilisticVariableEliminationDecisionFactoredAlgorithm
  61. def setPolicy(e: Decision[T, U]): Unit

    Sets the policy for the given decision.

    Sets the policy for the given decision. This will get the computed utility of the algorithm and call setPolicy on the decision. Note there is no error checking here, so the decision in the argument must match the target decision in the algorithm.

    Definition Classes
    DecisionAlgorithm
  62. val showTiming: Boolean
  63. def start(): Unit

    Start the algorithm and make it active.

    Start the algorithm and make it active. After it returns, the algorithm must be ready to provide answers. Throws AlgorithmActiveException if the algorithm is already active.

    Definition Classes
    Algorithm
  64. def starterElements: List[Element[_]]

    Elements towards which queries are directed.

    Elements towards which queries are directed. By default, these are the target elements. This is overridden by DecisionVariableElimination, where it also includes utility variables.

    Definition Classes
    ProbabilisticVariableEliminationDecisionVariableElimination
  65. def stop(): Unit

    Stop the algorithm from computing.

    Stop the algorithm from computing. The algorithm is still ready to provide answers after it returns. Throws AlgorithmInactiveException if the algorithm is not active.

    Definition Classes
    Algorithm
  66. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  67. val targetElements: List[Element[_ >: _$10 with _$6]] forSome {type _$10, type _$6}

    The variable elimination eliminates all variables except on all decision nodes and their parents.

    The variable elimination eliminates all variables except on all decision nodes and their parents. Thus the target elements is both the decision element and the parent element.

    Definition Classes
    ProbQueryVariableEliminationDecisionVariableElimination
  68. val targetFactors: Map[Element[_], Factor[(Double, Double)]]
    Attributes
    protected[com.cra.figaro]
    Definition Classes
    VariableElimination
  69. def toString(): String
    Definition Classes
    AnyRef → Any
  70. val universe: Universe
  71. def variance(target: Element[Double]): Double

    Return the variance of the probability density function for the given continuous element.

    Return the variance of the probability density function for the given continuous element.

    Definition Classes
    BaseProbQueryAlgorithm
  72. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  73. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  74. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from DecisionAlgorithm[T, U]

Inherited from VariableElimination[(Double, Double)]

Inherited from FactoredAlgorithm[(Double, Double)]

Inherited from OneTimeProbQuery

Inherited from OneTime

Inherited from ProbQueryAlgorithm

Inherited from Algorithm

Inherited from AnyRef

Inherited from Any

Ungrouped