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trait Recommender extends AnyRef

The Recommender trait serves as a template for recommender algorithms.

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AnyRef, Any
Known Subclasses
ItemBasedRecommender, ModelBasedRecommender, RecommenderTest, UserBasedRecommender
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Abstract Value Members

  1. abstract def rate(i: Int, j: Int): Double

    Return the final rating for a given '(i, j)' cell, e.g., (user, item).

    Return the final rating for a given '(i, j)' cell, e.g., (user, item).

    i

    the ith row, e.g., user

    j

    the jth column, e.g., item

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
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  2. final def ##(): Int
    Definition Classes
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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
    Attributes
    protected[lang]
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    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  6. def crossValidate(tester: MatrixD): Unit

    Phase 2: Cross validate the final predictions against the test dataset.

    Phase 2: Cross validate the final predictions against the test dataset.

    tester

    testing data matrix

  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
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  8. def equals(arg0: Any): Boolean
    Definition Classes
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  9. def error_metrics(input: MatrixI): Unit

    Phase 1: Print MAE and RMSE metrics based on the final predictions for the test dataset.

    Phase 1: Print MAE and RMSE metrics based on the final predictions for the test dataset.

    input

    the test portion of the original 4-column input matrix

  10. final def getClass(): Class[_]
    Definition Classes
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    Annotations
    @native() @HotSpotIntrinsicCandidate()
  11. def getStats: Array[Statistic]

    Return the variables for the statistics vectors.

  12. def hashCode(): Int
    Definition Classes
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    @native() @HotSpotIntrinsicCandidate()
  13. final def isInstanceOf[T0]: Boolean
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  14. def makeRatings(input: MatrixI, m: Int, n: Int): MatrixD

    Convert an original 4-column 'input' integer matrix (i, j, value, timestamp) into a two-dimensional 'ratings' double matrix with 'm' rows and 'n' columns.

    Convert an original 4-column 'input' integer matrix (i, j, value, timestamp) into a two-dimensional 'ratings' double matrix with 'm' rows and 'n' columns. The 'input' matrix has type MatrixI, while the 'ratings' matrix has type MatrixD.

    input

    the original 4-column input data matrix containing ratings, e.g., from a file

    m

    the number of rows for the ratings matrix

    n

    the number of columns for the ratings matrix

  15. final def ne(arg0: AnyRef): Boolean
    Definition Classes
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  16. final def notify(): Unit
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    @native() @HotSpotIntrinsicCandidate()
  17. final def notifyAll(): Unit
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    @native() @HotSpotIntrinsicCandidate()
  18. final def synchronized[T0](arg0: ⇒ T0): T0
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  19. def test(istart: Int, iend: Int, input: MatrixI): Unit

    Phase 3: Test the accuracy of the predictions and add it to the statistics vector.

    Phase 3: Test the accuracy of the predictions and add it to the statistics vector.

    istart

    the start point

    iend

    the end point

    input

    the original 4-column input matrix

  20. def toString(): String
    Definition Classes
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  21. def topk(x: VectorD, k: Int): VectorI

    Return the indices of the 'k' largest values in vector 'x'.

    Return the indices of the 'k' largest values in vector 'x'. FIX - replace with more efficient top-k algorithm

    x

    the input vector

    k

    the number of values to be returned

  22. def topk2(x: VectorD, k: Int): Array[Int]

    Return the indices of the 'k' largest values in vector 'x'.

    Return the indices of the 'k' largest values in vector 'x'.

    x

    the input vector

    k

    the number of values to be returned

  23. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  24. final def wait(arg0: Long): Unit
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    @throws( ... ) @native()
  25. final def wait(): Unit
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    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
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    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

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