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dataFileName
- Static variable in class prea.main.
Prea
The name of data file used for test.
DataMap
<
Key
extends java.lang.Comparable<
Key
>,
Val
> - Class in
prea.data.structure
This is a class implementing HashMap-based data map.
DataMap()
- Constructor for class prea.data.structure.
DataMap
Basic constructor without specifying the capacity.
DataMap(int)
- Constructor for class prea.data.structure.
DataMap
A constructor specifying the capacity.
DataSplitManager
- Class in
prea.data.splitter
This class implements data split functions, which are common in individual model selection methods.
DataSplitManager(SparseMatrix, int, int)
- Constructor for class prea.data.splitter.
DataSplitManager
Construct a data set manager.
defaultValue
- Variable in class prea.recommender.memory.
MemoryBasedRecommender
The default voting value, if used.
defaultVote
- Variable in class prea.recommender.memory.
MemoryBasedRecommender
Indicating whether to use default vote value.
DenseMatrix
- Class in
prea.data.structure
This class implements dense matrix.
DenseMatrix(int, int)
- Constructor for class prea.data.structure.
DenseMatrix
Construct an empty dense matrix, with a given size.
DenseMatrix(Matrix)
- Constructor for class prea.data.structure.
DenseMatrix
Construct an empty dense matrix, with data copied from UJMP matrix.
DenseVector
- Class in
prea.data.structure
This class implements dense vector with array-based implementation.
DenseVector()
- Constructor for class prea.data.structure.
DenseVector
Construct an empty dense vector, with capacity 0.
DenseVector(int)
- Constructor for class prea.data.structure.
DenseVector
Construct a new dense vector with size n.
DenseVector(Matrix)
- Constructor for class prea.data.structure.
DenseVector
Construct a new dense vector, having same data with the given UJMP matrix.
diagonal()
- Method in class prea.data.structure.
DenseMatrix
Return items in the diagonal in vector form.
diagonal()
- Method in class prea.data.structure.
SparseMatrix
Return items in the diagonal in vector form.
diffMatrix
- Variable in class prea.recommender.etc.
SlopeOne
Prepared difference matrix
Distance
- Class in
prea.util
This is a class implementing various distance measures of two vectors.
Distance()
- Constructor for class prea.util.
Distance
distanceKendall(int[], double[], int[], double[], int)
- Static method in class prea.util.
Distance
Return the Kendall's Tau distance for two rankings.
distanceKendallParsed(int[], double[], int[], double[], int)
- Static method in class prea.util.
Distance
Return intermediate Kendall's Tau distance for two rankings parsed by prb
distanceNDCG(int[], double[], int[], double[])
- Static method in class prea.util.
Distance
Return NDCG score for a ranked list given the scores and relevance of items in the list.
distanceOneToAllTest(int, int, SparseVector[], int, int[])
- Method in class prea.recommender.etc.
RankBased
Compute the distance between the testing user with the testing item of every possible ratings and all the other training users with training items.
distanceOneToAllTrain(int, double[], int, int[])
- Method in class prea.recommender.etc.
RankBased
Compute the distance between the testing user and all the other training users with training items.
distancePairWise(DenseMatrix, DenseMatrix)
- Method in class prea.recommender.etc.
NonlinearPMF
Compute the squared Euclidean distance between the row vectors of one matrix and the row vectors of another matrix.
distancePairWise(DenseMatrix, DenseVector)
- Method in class prea.recommender.etc.
NonlinearPMF
Compute the squared euclidean distance between the row vectors of one matrix and a vector.
distanceSpearman(int[], double[], int[], double[], int)
- Static method in class prea.util.
Distance
Return Spearman distance for two rankings.
distanceSpearmanParsed(int[], double[], int[], double[], int)
- Static method in class prea.util.
Distance
Return the Spearman distance for two rankings parsed by probability
Distribution
- Class in
prea.util
This class implements several statistical distributions.
Distribution()
- Constructor for class prea.util.
Distribution
divideFolds(int)
- Method in class prea.data.splitter.
KfoldCrossValidation
Divide the original rating matrix into k-fold.
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