DerivationTools

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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6. def computeAllInputs(derivations: List[Derivation]): Set[Fact[Any]]

   

   Computes the Set of all unique Facts used by Derivations.

   Computes the Set of all unique Facts used by Derivations.

   derivations

   all available Derivations

   returns

   a Set containing all unique input Facts

7. def computeAllOutputs(derivations: List[Derivation]): Set[Fact[Any]]

   

   Computes the Set of all unique Facts produced by Derivations.

   Computes the Set of all unique Facts produced by Derivations. It also enforces uniqueness between Derivations, since we do not allow multiple Derivations to produce the same Fact.

   derivations

   all available Derivations

   returns

   a Set containing all unique output Facts

8. def constructGraph(derivations: List[Derivation]): Set[Node]

   

   Constructs a Dependency graph for the provided list of Derivations.

   Constructs a Dependency graph for the provided list of Derivations. Each Derivation will yield a Node describing its output and other Nodes requiring its output.

   derivations

   a List of all possible Derivations

   returns

   a Set of Nodes which reference their dependent Nodes

9. final def eq(arg0: AnyRef): Boolean

   

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10. def equals(arg0: Any): Boolean

    

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11. def finalize(): Unit

    

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12. final def getClass(): Class[_]

    

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13. def hashCode(): Int

    

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14. final def isInstanceOf[T0]: Boolean

    

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15. def levelSorter(originalNodes: Set[Node]): Levels

    

    Determines the order in which Facts should be calculated, based on their inputs.

    Determines the order in which Facts should be calculated, based on their inputs. It takes a Derivation-graph as constructed by FactEngine.constructGraph and orders them in levels. Each level contains Derivation-nodes for which the inputs will have been determined in an earlier level. Following these levels while running a derivation cycle will guarantee correct causality between individual Derivations.

    originalNodes

    the Set of Nodes describing the dependency graph between Derivations

    returns

    a List of levels that produce facts required for subsequent levels. Each level is a list of Nodes that can be evaluated once all previous levels have been evaluated

16. final def ne(arg0: AnyRef): Boolean

    

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17. final def notify(): Unit

    

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18. final def notifyAll(): Unit

    

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19. final def synchronized[T0](arg0: ⇒ T0): T0

    

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20. def toString(): String

    

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21. final def wait(): Unit

    

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22. final def wait(arg0: Long, arg1: Int): Unit

    

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23. final def wait(arg0: Long): Unit

    

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