Package

com.github.jelmerk.knn

scalalike

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package scalalike

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Type Members

  1. type DistanceFunction[TVector, TDistance] = (TVector, TVector) ⇒ TDistance

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    Calculates the distance between 2 vectors

  2. trait Index[TId, TVector, TItem <: Item[TId, TVector], TDistance] extends Iterable[TItem] with Serializable

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    K-nearest neighbors search index.

    K-nearest neighbors search index.

    TId

    Type of the external identifier of an item

    TVector

    Type of the vector to perform distance calculation on

    TItem

    Type of items stored in the index

    TDistance

    Type of distance between items (expect any numeric type: float, double, int, ..).

    See also

    See https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm for more information.

  3. type Item[TId, TVector] = knn.Item[TId, TVector]

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    Item that can be indexed

  4. type ObjectSerializer[T] = knn.ObjectSerializer[T]

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    Serializes objects

  5. type ProgressListener = (Int, Int) ⇒ Unit

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    Called periodically to report on progress during indexing.

    Called periodically to report on progress during indexing. The first argument is the number of records processed. The second argument is the total number of record to index as part of this operation.

  6. type SearchResult[TItem, TDistance] = knn.SearchResult[TItem, TDistance]

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    Result of a nearest neighbour search.

  7. type SparseVector[TVector] = knn.SparseVector[TVector]

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    A sparse vector represented by an index array and a value array.

Value Members

  1. object SearchResult

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  2. object SparseVector

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  3. package bruteforce

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  4. val doubleBrayCurtisDistance: DistanceFunction[Array[Double], Double]

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    Calculates the bray curtis distance.

  5. val doubleCanberraDistance: DistanceFunction[Array[Double], Double]

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    Calculates the canberra distance.

  6. val doubleCorrelationDistance: DistanceFunction[Array[Double], Double]

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    Calculates the bray correlation distance.

  7. val doubleCosineDistance: DistanceFunction[Array[Double], Double]

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    Calculates the cosine distance.

  8. val doubleEuclideanDistance: DistanceFunction[Array[Double], Double]

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    Calculates the euclidean distance.

  9. val doubleInnerProduct: DistanceFunction[Array[Double], Double]

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    Calculates the inner product.

  10. val doubleManhattanDistance: DistanceFunction[Array[Double], Double]

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    Calculates the manhattan distance.

  11. val doubleSparseVectorInnerProduct: DistanceFunction[SparseVector[Array[Double]], Double]

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    Calculates the inner product.

  12. val floatBrayCurtisDistance: DistanceFunction[Array[Float], Float]

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    Calculates the bray curtis distance.

  13. val floatCanberraDistance: DistanceFunction[Array[Float], Float]

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    Calculates the canberra distance.

  14. val floatCorrelationDistance: DistanceFunction[Array[Float], Float]

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    Calculates the correlation distance.

  15. val floatCosineDistance: DistanceFunction[Array[Float], Float]

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    Calculates the cosine distance.

  16. val floatEuclideanDistance: DistanceFunction[Array[Float], Float]

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    Calculates the euclidean distance.

  17. val floatInnerProduct: DistanceFunction[Array[Float], Float]

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    Calculates the inner product.

  18. val floatManhattanDistance: DistanceFunction[Array[Float], Float]

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    Calculates the manhattan distance.

  19. val floatSparseVectorInnerProduct: DistanceFunction[SparseVector[Array[Float]], Float]

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    Calculates the inner product.

  20. package hnsw

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  21. package metrics

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