MatrixPathFollower

Value Members

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

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

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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

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6. final def asInstanceOf[T0]: T0

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7. def clone(): AnyRef

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8. def createMatrixRow(source: Int, target: Int, feature_list: Seq[(Int, Double)]): MatrixRow

9. def createPathMatrix(path_type: BaseEdgeSequencePathType, connectivity_matrices: Map[Int, CSCMatrix[Double]]): CSCMatrix[Double]

10. val edges_to_remove: Map[Int, Seq[(Int, Int)]]

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

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12. def equals(arg0: Any): Boolean

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13. def execute(): Unit

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14. def finalize(): Unit

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

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16. def getFeatureMatrix(sources: Set[Int], allowed_targets: Set[Int]): FeatureMatrix

17. def getFeatureMatrix(pairs: Seq[(Int, Int)]): FeatureMatrix

18. def getFeatureMatrix(sources: Set[Integer], allowed_targets: Set[Integer]): FeatureMatrix

    This gets a complete feature matrix row for every target connected by any selected path to the given source nodes.

    This gets a complete feature matrix row for every target connected by any selected path to the given source nodes. Then we do a vector-matrix multiplication for each source, and keep all targets in the resultant vector that are also in allowed_targets.

19. def getFeatureMatrix(pairs: List[Pair[Integer, Integer]]): FeatureMatrix

    If you know which (source, target) pairs you want to compute features for, this is a faster way to go about it.

    If you know which (source, target) pairs you want to compute features for, this is a faster way to go about it. However, when creating the initial feature matrix, and when making blanket predictions for a source (as is my typical prediction method in my KB inference experiments), you need to actually look at _all_ of the targets connected to by any path type. That's what the next method is for.

20. def getFeatureMatrix(): FeatureMatrix

    Definition Classes

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21. def getFeatureMatrixRow(path_matrices: Map[PathType, CSCMatrix[Double]], source: Int, target: Int): MatrixRow

22. def getPathMatrices(): Map[PathType, CSCMatrix[Double]]

23. def getRangeFromFilename(filename: String): (Int, Int)

24. val graph: Graph

25. def hashCode(): Int

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26. final def isInstanceOf[T0]: Boolean

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27. val matrixDir: String

28. val maxFanOut: Int

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

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30. def normalizeMatrix(matrix: CSCMatrix[Double]): CSCMatrix[Double]

31. val normalizeWalkProbabilities: Boolean

32. final def notify(): Unit

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33. final def notifyAll(): Unit

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34. val positive_source_targets: Set[(Int, Int)]

35. def readMatricesFromFile(filename: String, relations: Set[Int]): Map[Int, CSCMatrix[Double]]

36. def removeEdgesAndBuild(builder: Builder[Int], relation: Int): CSCMatrix[Int]

37. def separateRelationsByFile(relations: Set[Int], filenames: Set[String]): Map[String, Set[Int]]

38. def shutDown(): Unit

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39. val source_nodes: Set[Int]

40. val sources_matrix: CSCMatrix[Double]

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

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42. def toString(): String

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43. def usesGraphChi(): Boolean

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44. final def wait(): Unit

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

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

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