trait Recommender extends AnyRef
The Recommender trait serves as a template for recommender algorithms.
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- 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
- final def !=(arg0: Any): Boolean
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
- 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
- final def eq(arg0: AnyRef): Boolean
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- def equals(arg0: AnyRef): Boolean
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- 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
- final def getClass(): Class[_ <: AnyRef]
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- def getStats: Array[Statistic]
Return the variables for the statistics vectors.
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- 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 typeMatrixD.- 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
- final def ne(arg0: AnyRef): Boolean
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- final def notify(): Unit
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- 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
- def toString(): String
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- 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
- 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
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