AnnoyPairSCollection

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. def asAnnoy(metric: AnnoyMetric, dim: Int, nTrees: Int): SCollection[AnnoyUri]

   

   Write the key-value pairs of this SCollection as an Annoy file to a temporary location, building the trees in the index according to the parameters provided.

   Write the key-value pairs of this SCollection as an Annoy file to a temporary location, building the trees in the index according to the parameters provided.

   nTrees

   Number of trees to build. More trees means more precision & bigger indices. If nTrees is set to -1, the trees will automatically be built in such a way that they will take at most 2x the memory of the vectors.

   returns

   A singleton SCollection containing the AnnoyUri of the saved files

5. def asAnnoy(path: String, metric: AnnoyMetric, dim: Int, nTrees: Int): SCollection[AnnoyUri]

   

   Write the key-value pairs of this SCollection as an Annoy file to a specific location, building the trees in the index according to the parameters provided.

   Write the key-value pairs of this SCollection as an Annoy file to a specific location, building the trees in the index according to the parameters provided.

   path

   Can be either a local file or a GCS location e.g. gs://<bucket>/<path>

   metric

   One of Angular (cosine distance) or Euclidean

   dim

   Number of dimensions in vectors

   nTrees

   Number of trees to build. More trees means more precision & bigger indices. If nTrees is set to -1, the trees will automatically be built in such a way that they will take at most 2x the memory of the vectors.

   returns

   A singleton SCollection containing the AnnoyUri of the saved files

6. def asAnnoySideInput(metric: AnnoyMetric, dim: Int, nTrees: Int): SideInput[AnnoyReader]

   

   Write the key-value pairs of this SCollection as an Annoy file to a temporary location, building the trees in the index according to the parameters provided, then load the trees as a side input.

   Write the key-value pairs of this SCollection as an Annoy file to a temporary location, building the trees in the index according to the parameters provided, then load the trees as a side input.

   metric

   One of Angular (cosine distance) or Euclidean

   dim

   Number of dimensions in vectors

   nTrees

   Number of trees to build. More trees means more precision & bigger indices. If nTrees is set to -1, the trees will automatically be built in such a way that they will take at most 2x the memory of the vectors.

   returns

   SideInput[AnnoyReader]

7. final def asInstanceOf[T0]: T0

   

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8. def clone(): AnyRef

   

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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. final def ne(arg0: AnyRef): Boolean

    

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16. final def notify(): Unit

    

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17. final def notifyAll(): Unit

    

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18. val self: SCollection[(Int, Array[Float])]

    

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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